Automatic production line operation state intelligent monitoring platform system and monitoring method thereof

Abstract

The invention discloses an automatic production line operation state intelligent monitoring platform system and a monitoring method thereof, and belongs to the technical field of production line intelligent monitoring. The automatic production line operation state intelligent monitoring platform system comprises a data acquisition unit, a data processing storage unit and an information display unit; integrating into a json format; after the data processing storage unit receives the data, the data are stored in a relational database MySql, a time sequence database TD engine and a cache database Redis through a statistical analysis sub-module; the information display unit adopts a front-end and rear-end separation design, and directly displays production line operation information in the forms of standard evaluation, trend analysis and log management through ten modules such as production line overview and equipment overview. According to the method, the existing production line does not need to be transformed on a large scale, comprehensive data acquisition, efficient processing and visual display can be realized, data support is provided for production line process planning and productivity decision, and the digital and intelligent level of production line management is effectively improved.

Description

Technical Field

[0001] This application belongs to the field of intelligent monitoring technology for production lines, and specifically relates to an intelligent monitoring platform system for the operation status of automated production lines and its monitoring method. Background Technology

[0002] As modern manufacturing transforms towards digitalization, intelligence, and greening, higher demands are being placed on production line monitoring systems, especially in the machinery manufacturing industry. Real-time monitoring data from production lines plays a crucial supporting role in enterprise process planning and capacity decisions. Currently, the industry faces three core challenges: First, many existing automated devices in factories lack network communication capabilities, and the diverse communication protocols and lack of a unified format among different devices require significant time and manpower to develop and verify communication programs, severely hindering the intelligentization process. Second, traditional monitoring relies on manual inspections and simple sensors, resulting in incomplete monitoring, delayed responses, and weak data analysis capabilities, failing to meet the efficient and precise management needs of modern industry. Third, existing intelligent monitoring technologies require large-scale modifications to production lines and equipment, leading to high costs and production disruptions. Furthermore, the real-time performance and accuracy of data processing are insufficient, making it difficult to cope with the surge in data volume in large-scale production environments, potentially impacting production decisions.

[0003] From the perspective of the current state of industry development, foreign countries are in a leading position in this field. For example, the German Trumpf Group can detect equipment anomalies in the early stages of failure through remote condition monitoring and AI algorithms. Siemens' monitoring solutions based on industrial edge and AI have been widely used in many industries and have achieved predictive maintenance. Although some domestic companies have achieved data collection and remote monitoring through the Internet of Things, there is still a gap with foreign countries in terms of in-depth data analysis and intelligent decision-making. Related AI monitoring research in universities and research institutions is mostly in the laboratory or small-scale application stage and has not yet been widely popularized. Summary of the Invention

[0004] The purpose of this application is to provide an intelligent monitoring platform system and monitoring method for the operation status of automated production lines. This addresses the problems mentioned in the background art, such as the lack of network communication capabilities and diverse protocols in existing automated equipment within factories, the reliance on manual inspections and simple sensors leading to incomplete monitoring and weak data analysis, and the high cost of upgrading existing intelligent monitoring technologies while also ensuring sufficient real-time performance and accuracy of data processing.

[0005] To achieve the above objectives, this application adopts the following technical solution: Firstly, an intelligent monitoring platform system for the operation status of an automated production line is provided, including a data acquisition unit, a data processing and storage unit, and an information display unit; The data acquisition unit is used to communicate with production line equipment, collect production line information, equipment information, equipment operating status information, equipment capacity data, alarm information, program information and processing parameter information, and classify and statistically analyze the collected information according to data format, integrate it into JSON data format and send it to the data processing and storage unit. The data processing and storage unit is connected to the data acquisition unit and is used to receive information transmitted by the data acquisition unit. After statistical analysis of the information, it is stored in a preset database in modules. The preset database includes a relational database MySQL, a time-series database TDengine, and a cache database Redis. The information display unit adopts a front-end and back-end separation design and is connected to the data processing and storage unit. It is used to extract data from the data processing and storage unit, and display production line operation information in the form of standard evaluation, trend analysis and log management through real-time data processing monitoring and historical data statistics. The information display unit includes a production line overview module, an equipment overview module, a status statistics module, an efficiency analysis module, a quality analysis module, a capacity analysis module, an alarm management module, a program management module, a parameter monitoring module and a report statistics module.

[0006] In one possible implementation, the data acquisition unit acquires data by calling the built-in function library of the production line equipment, acquiring data through a PLC, and acquiring data through sensors. When the data acquisition unit integrates information, it classifies and statistically analyzes the collected information according to data format and integrates it into JSON data format.

[0007] In one possible implementation, the relational database MySQL is used to store equipment information, equipment uptime statistics, production line and equipment capacity data, and alarm information statistics. The time-series database TDengine is used to store equipment status data, running program data, machining parameter data, current alarm data, and shaft information data; The cached database Redis is used to store login verification information.

[0008] In one possible implementation, the production line overview module is used to display overall production line operation data, including production line equipment information, equipment utilization rate information, production line efficiency, production line capacity, and production line processing quality. The production line equipment information includes the total number of production line equipment, the proportion of machine tools / robots / auxiliary equipment, and the proportion of equipment operating status. The equipment operating status includes shutdown, processing, standby, and offline. The production line efficiency includes overall production line efficiency, production line utilization rate, time utilization rate, performance utilization rate, and failure loss rate. The production line capacity includes today's capacity, yesterday's capacity, and the capacity trend over the past seven days; the production line processing quality includes the quality loss rate and the pass rate.

[0009] In one possible implementation, the device overview module includes a device basic information and device list submodule and a device real-time overview submodule; The equipment basic information and equipment list sub-modules are used to display all equipment on the production line in a list format, allowing users to click on the equipment to view the corresponding real-time information. The real-time equipment overview submodule is used to display the operating information of a specified device, including basic device information, device status, processing parameters, daily device operation status, shaft information, program information, tool information, and alarm information; Among them, robot equipment does not display shaft type information and tool information, and auxiliary equipment does not display machining parameters, shaft type information, program information and tool information.

[0010] In one possible implementation, the alarm management module includes an alarm statistics submodule and an alarm log submodule; The alarm statistics submodule is used to display the alarm duration, alarm frequency, and daily alarm information change trend of the device within a specified time. The alarm log submodule is used to display the alarm history information of the device within a specified date, including alarm date, device number, device name, alarm number, alarm content, start time, end time, and alarm duration.

[0011] In one possible implementation, the program management module includes a program transmission submodule and a program transmission log submodule; the program transmission submodule is used to manage the uploading and downloading of machine tool processing programs through the data acquisition underlying interface; the program transmission log submodule is used to record the program transmission status of production line equipment and generate logs.

[0012] In one possible implementation, the parameter monitoring module is used to receive user subscription instructions for important parameters during equipment processing, and to draw a line graph of parameter changes based on the subscription instructions; The report statistics module is used to generate today's production report based on production line capacity and operating efficiency indicators, and supports report download.

[0013] Secondly, a method for intelligent monitoring of the operating status of an automated production line is provided, applied to the intelligent monitoring platform system for the operating status of the automated production line described in the first aspect, comprising the following steps: S1: The data acquisition unit collects the operating status information, equipment capacity data, alarm information, program information and processing parameter information of the production line equipment using a preset acquisition method; S2: The data acquisition unit classifies and statistically analyzes the collected information according to the data format, integrates it into JSON data format, and then sends it to the data processing and storage unit. S3: The data processing and storage unit sends a data request to the data acquisition unit, receives and parses JSON format data, and stores the parsed data into a preset database in modules; S4: The information display unit interacts with the data processing and storage unit through the HTTP interface, extracts data from the preset database and performs statistical calculations to obtain the front-end display data, which is displayed on the device operation terminal in the form of standard evaluation, trend analysis and log management.

[0014] In one possible implementation, the preset acquisition method in step S1 includes calling the function library built into the production line equipment, acquiring data through a PLC, and acquiring data through a sensor. The preset databases mentioned in step S3 include the relational database MySQL, the time-series database TDengine, and the cache database Redis. Different types of data are stored in different databases.

[0015] Compared with the prior art, this application has the following beneficial effects: This application provides an intelligent monitoring platform system for the operation status of an automated production line. By setting up a data acquisition unit, a data processing and storage unit, and an information display unit, it achieves comprehensive collection, scientific processing, storage, and intuitive display of multi-dimensional information from the production line. This solves the problems of incomplete information collection, scattered data processing, and limited display formats in traditional monitoring. It provides complete data support for enterprise managers to grasp the production line operation status in real time and promptly identify production problems, which helps to improve the level of precision in production line management and thus ensures the stable and efficient operation of the production line.

[0016] In one possible implementation, the data acquisition unit combines multiple acquisition methods to adapt to different types and interface configurations of production line equipment, breaking the limitations of a single acquisition method and ensuring the comprehensiveness and compatibility of production line information acquisition. At the same time, the information is classified and integrated into JSON data format, which not only unifies the data transmission standard and facilitates the data processing and storage unit to quickly parse the data, but also reduces redundant information in the data transmission process, improves data transmission efficiency, and lays an efficient data foundation for subsequent data processing.

[0017] In one possible implementation, data is stored in three databases—MySQL, TDengine, and Redis—based on the storage needs and usage scenarios of different data types, achieving efficient data categorization and storage. MySQL is suitable for storing structured statistical data, facilitating multi-condition queries and data correlation analysis; TDengine is optimized for storing and querying time-series data, efficiently handling high-frequency changing real-time data and meeting the needs of historical data tracing; Redis, with its high-speed read and write capabilities, quickly responds to login verification requests, improving the system's interactive experience. The synergy of these three databases ensures the rationality, security, and efficiency of data storage throughout the entire system.

[0018] In one possible implementation, the production line overview module centrally displays key data of the overall production line operation in a variety of visual formats, covering multiple dimensions such as equipment, utilization rate, efficiency, capacity, and quality. This not only allows managers to grasp the overall operation status of the production line at a glance and quickly identify the advantages and disadvantages in the operation of the production line, but also provides intuitive data basis for the overall optimization decision of the production line, which helps to improve the efficiency of production line management and the accuracy of decision-making.

[0019] An intelligent monitoring method for automated production line operation status is proposed. This method achieves end-to-end management of production line data from collection, integration, storage to display through standardized steps. The collection stage adapts to different equipment types to ensure data comprehensiveness, integration into JSON format ensures data transmission consistency, modular storage ensures efficient data management, and multi-format display ensures intuitive data viewing. The entire process is interconnected, solving the problems of chaotic data flow and poor connection between links in traditional monitoring methods. It realizes the automation and intelligence of production line operation status monitoring, providing complete methodological support for enterprises to grasp production dynamics in real time, respond quickly to production problems, and make scientific production decisions, which helps to improve the overall production efficiency and management level of the production line. Attached Figure Description

[0020] Figure 1 This application provides a structural framework for an intelligent monitoring platform system for the operation status of an automated production line. Figure 2 A unit diagram of an intelligent monitoring platform system for the operation status of an automated production line provided in this application; Figure 3 A flowchart of an intelligent monitoring method for the operating status of an automated production line provided in this application. Detailed Implementation

[0021] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0022] In the description of this application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicating the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0023] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "a plurality of" means two or more, unless otherwise explicitly defined. The specific embodiments of this application will be further described in detail below with reference to the accompanying drawings.

[0024] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection, an electrical connection, or a communication connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0025] In this application, unless otherwise expressly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature being directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature being directly above or diagonally above the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0026] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] like Figure 1 , Figure 2 and Figure 3 As shown, this application discloses an intelligent monitoring platform system for the operation status of an automated production line. The intelligent monitoring platform system for the operation status of an automated production line may include a data acquisition unit, a data processing and storage unit, and an information display unit.

[0028] This data acquisition unit is used to communicate with production line equipment, collect production line information, equipment information, equipment operating status information, equipment capacity data, alarm information, program information and processing parameter information, and classify and statistically analyze the collected information according to data format, integrate it into JSON data format and send it to the data processing and storage unit.

[0029] Specifically, the data acquisition unit adopts a communication interface compatible with machine tools, industrial robots, and auxiliary equipment in the workshop. Optionally, for machine tools of different brands, data interaction can be achieved by calling their built-in Focas library functions. At the same time, the PLC device is used to collect the robot's running signals and the working status signals of the auxiliary equipment. Furthermore, by adding sensors such as temperature and speed, processing parameter information is collected. Ultimately, comprehensive collection of production line information, equipment information, equipment operating status information, equipment capacity data, alarm information, program information, and processing parameter information of 20 devices in the production line is achieved. After the collected information is classified and organized, it is integrated into JSON data format and sent to the data processing and storage unit in the form of data packets.

[0030] The data processing and storage unit is connected to the data acquisition unit and is used to receive information transmitted by the data acquisition unit. After statistical analysis of the information, it is stored in a preset database in modules. The preset database includes a relational database MySQL, a time-series database TDengine, and a cache database Redis.

[0031] Specifically, after receiving the data packets transmitted by the data acquisition unit, the data processing and storage unit first cleans the data, removing invalid and abnormal data. Then, it analyzes data such as equipment capacity and operating time using a preset statistical algorithm. Subsequently, it stores different types of data in a preset database in modules. Basic equipment information such as equipment model and serial number, as well as daily equipment operating time statistics, total production line capacity, alarm count statistics, etc., are stored in the relational database MySQL.

[0032] The system stores high-frequency changing data such as the real-time status of the equipment, the real-time instructions of the running program, the dynamic changes of the processing parameters, the real-time status of the current alarm, and the real-time coordinates of shaft-type equipment into the time-series database TDengine.

[0033] Login verification information, such as user login account, encrypted password, and login token, is stored in a cached database, Redis, to quickly respond to login verification requests.

[0034] This information display unit adopts a front-end and back-end separation design and is connected to the data processing and storage unit. It is used to extract data from the data processing and storage unit, and display production line operation information in the form of standard evaluation, trend analysis and log management through real-time data processing monitoring and historical data statistics. This information display unit includes a production line overview module, an equipment overview module, a status statistics module, an efficiency analysis module, a quality analysis module, a capacity analysis module, an alarm management module, a program management module, a parameter monitoring module and a report statistics module.

[0035] Specifically, this information display unit adopts a front-end and back-end separation design pattern, using Vue as the front-end development framework and ASP.NET as the back-end development framework. The front-end extracts data from the data processing and storage unit by calling the back-end API interface.

[0036] In practical applications, when workshop managers need to view the overall status of the production line, they can view relevant data through the production line overview module; when they need to understand the details of a single piece of equipment, they can query through the equipment overview module; at the same time, through the other 8 modules such as status statistics and efficiency analysis, various information about the operation of the production line can be intuitively displayed in the form of data tables, line graphs, and log lists, meeting the information viewing needs in different management scenarios.

[0037] In this embodiment, by setting up a data acquisition unit, a data processing and storage unit, and an information display unit, comprehensive collection, scientific processing and storage, and intuitive display of multi-dimensional information of the production line are achieved. This solves the problems of incomplete information collection, scattered data processing, and single display format in traditional monitoring. It provides complete data support for enterprise managers to grasp the production line operation status in real time and discover production problems in a timely manner, which helps to improve the level of precision in production line management and thus ensure the stable and efficient operation of the production line.

[0038] In one possible implementation, the data acquisition unit may acquire data by calling the built-in function library of the production line equipment, acquiring data through a PLC, or acquiring data through sensors. When integrating information, the data acquisition unit classifies and statistically analyzes the acquired information according to its data format and integrates it into a JSON data format.

[0039] Specifically, the data acquisition unit employs differentiated acquisition methods for different types of production line equipment. For FANUC series machine tools in the workshop, it directly reads data such as machining parameters and program information stored internally by calling their built-in Focas library functions.

[0040] For ABB industrial robots responsible for workpiece handling, since their operating status data is controlled by a PLC, the data acquisition unit establishes a communication connection with the PLC that comes with the robot and uses the Modbus protocol to collect relevant data.

[0041] For auxiliary equipment that does not have its own data output interface, proximity sensors and laser displacement sensors are further installed in key parts of the equipment. The physical signals are converted into electrical signals by the sensors and then transmitted to the data acquisition unit.

[0042] During the information integration phase, the data acquisition unit first classifies and statistically analyzes the collected information according to its data format. For example, it categorizes integer data, floating-point data, and string data separately. Then, through a preset data conversion program, it integrates all the classified information into JSON data format and adds a timestamp to each JSON data packet to ensure the timeliness and traceability of the data. Finally, it sends the integrated JSON data to the data processing and storage unit.

[0043] In this embodiment, the data acquisition unit combines multiple acquisition methods to adapt to production line equipment of different types and interface configurations, breaking the limitations of a single acquisition method and ensuring the comprehensiveness and compatibility of production line information acquisition. At the same time, the information is classified and integrated into JSON data format, which not only unifies the data transmission standard and facilitates the data processing and storage unit to quickly parse the data, but also reduces redundant information in the data transmission process, improves data transmission efficiency, and lays an efficient data foundation for subsequent data processing.

[0044] In one possible implementation, the relational database MySQL is used to store equipment information, equipment uptime statistics, production line and equipment capacity data, and alarm information statistics.

[0045] In the database deployment of this monitoring platform system, the relational database MySQL specifically creates multiple data tables for classifying and storing data: The "Device Information Table" stores basic information such as device number, device name, device type, IP address, port number, and system type, with each record corresponding to one device.

[0046] The "Equipment Operation Time Statistics Table" stores the cumulative duration of each piece of equipment under different operating conditions on a daily basis. For example, equipment 101 had a processing time of 8 hours and a standby time of 1.5 hours on May 20, 2024.

[0047] The "Capacity Data Table" stores the total daily capacity of the production line and the daily capacity of a single piece of equipment, including fields such as date, production line number, equipment number, actual capacity, and theoretical capacity.

[0048] The "Alarm Information Statistics Table" compiles weekly statistics on the number of alarms, total alarm duration, and main alarm types for each device, facilitating management personnel in analyzing the frequency of equipment failures.

[0049] The time-series database TDengine is used to store equipment status data, running program data, machining parameter data, current alarm data, and shaft information data.

[0050] Specifically, the time-series database TDengine creates different data collection points based on device type and data collection frequency. For example, for machine tools, it is set to collect data such as device status, spindle speed, spindle load, and axis coordinates every 1 second, and collect the name and program segment number of the currently executing program every 5 seconds. When the device alarms, it collects the alarm number, alarm content, and other current alarm data in real time, and stores these high-frequency changing data in a time series, supporting quick querying of historical data within a certain time range. For robot devices, it mainly collects device status data such as joint speed and working mode, as well as running program data, which are also stored in a time series.

[0051] This cached database, Redis, is used to store login verification information.

[0052] The Redis cache database stores user login information, including user account, encrypted password, and temporary login token generated after successful login. When a user attempts to log in to the system, the system first queries the Redis database to quickly verify the correctness of the user account and password and the validity of the login token, without accessing the MySQL database, which greatly improves login verification speed and reduces database access pressure.

[0053] In this embodiment, data is stored in three databases—MySQL, TDengine, and Redis—according to the storage requirements and usage scenarios of different data types, achieving efficient data classification and storage. MySQL is suitable for storing structured statistical data, facilitating multi-condition queries and data correlation analysis; TDengine is optimized for storing and querying time-series data, efficiently handling high-frequency changing real-time data and meeting the needs of historical data tracing; Redis, with its high-speed read and write capabilities, quickly responds to login verification requests, improving the system's interactive experience. The synergistic effect of these three databases ensures the rationality, security, and efficiency of data storage throughout the entire system.

[0054] In one possible implementation, the production line overview module is used to display overall production line operation data, including production line equipment information, equipment utilization rate information, production line efficiency, production line capacity, and production line processing quality.

[0055] The production line equipment information includes the total number of production line equipment, the proportion of machine tools / robots / auxiliary equipment, and the proportion of equipment operating status. The equipment operating status includes shutdown, processing, standby, and offline.

[0056] Within the information display unit of this monitoring platform, the production line overview module presents data in the form of a visual dashboard. In the production line equipment information display area, the total number of production line equipment is displayed intuitively as 50 units. A pie chart shows the percentage of equipment types, including 25 machine tools, 15 robots, and 10 auxiliary machines. Another pie chart shows the current equipment operating status percentage, such as 20 units in processing mode, 15 in standby mode, 10 in shutdown mode, and 5 in offline mode. Management personnel can quickly understand the overall distribution and operating status of the equipment.

[0057] The equipment utilization rate information area calculates the daily utilization rate of each piece of equipment according to the utilization rate calculation formula, and displays the five pieces of equipment with the highest utilization rates in a bar chart. For example, equipment A has a utilization rate of 92%, equipment B has a utilization rate of 88%, equipment C has a utilization rate of 85%, equipment D has a utilization rate of 82%, and equipment E has a utilization rate of 80%. At the same time, the equipment number and type of each piece of equipment are marked to facilitate the identification of high-efficiency operating equipment.

[0058] The production line efficiency includes overall production line efficiency, production line utilization rate, time utilization rate, performance utilization rate, and failure loss rate; the production line capacity includes today's capacity, yesterday's capacity, and the capacity change trend of the previous seven days; the production line processing quality includes quality loss rate and pass rate.

[0059] Optionally, the production line efficiency display area presents various efficiency indicators in the form of a numerical list, including an overall production line efficiency of 85%, a production line utilization rate of 88%, a time utilization rate of 90%, a performance utilization rate of 95%, and a failure loss rate of 5%. Each indicator is accompanied by a brief explanation of the calculation formula to help managers understand the meaning of the indicators.

[0060] The production line capacity display area shows today's capacity of 1200 units and yesterday's capacity of 1150 units using large number cards. It also displays the capacity change trend of the past seven days using line graphs, such as 1000 units on May 14, 1050 units on May 15, 1100 units on May 16, 1120 units on May 17, 1150 units on May 18, 1180 units on May 19, and 1200 units on May 20. The theoretical value of the daily capacity is also marked on the line graph, which makes it easy to compare the difference between the actual capacity and the theoretical capacity.

[0061] The processing quality display area of ​​this production line is presented in a combination of numbers and pie charts. The quality loss rate is 3% and the pass rate is 97%. In the pie chart, the green part represents the percentage of pass products and the red part represents the percentage of non-pass products. Clicking on the area will show the specific types of non-pass products and their corresponding equipment numbers for the day, providing direction for quality improvement.

[0062] In this embodiment, the production line overview module centrally displays key data of the overall operation of the production line in a variety of visual formats, covering multiple dimensions such as equipment, utilization rate, efficiency, capacity, and quality. This not only allows managers to grasp the overall operation status of the production line at a glance and quickly identify the advantages and disadvantages in the operation of the production line, but also provides intuitive data basis for the overall optimization decision of the production line, which helps to improve the efficiency of production line management and the accuracy of decision-making.

[0063] In one possible implementation, the real-time equipment overview submodule is used to display the operating information of a specified device, including basic device information, device status, processing parameters, daily device operation status, shaft information, program information, tool information, and alarm information.

[0064] Among them, robot equipment does not display shaft type information and tool information, and auxiliary equipment does not display machining parameters, shaft type information, program information and tool information.

[0065] The Equipment Overview module's Equipment Basic Information and Equipment List sub-modules display information on all 50 pieces of equipment in the production line in a paginated table format. The table columns include equipment number, equipment name, equipment type, IP address, port number, system type, and current operating status. Users can quickly filter equipment by equipment number, equipment name, or equipment type using the search box above the table. For example, entering "FANUC" will filter all FANUC series machine tools. Clicking on a piece of equipment in a row of the table will automatically redirect the system to that equipment's real-time overview page.

[0066] In the machine tool equipment section of the equipment real-time overview submodule, such as the equipment 101 display page, basic equipment information is first displayed: Device name "FANUC-01", device number "101", device type "CNC lathe", IP address "192.168.1.101", port number "502", system type "FANUC 0i-MF".

[0067] The equipment status area is displayed with indicator lights of different colors. A green indicator light indicates "connected", "operating status" shows "processing", "control mode" is "automatic" and "alarm status" is "no alarm".

[0068] The machining parameters area displays in real time the spindle speed (3000 r / min), spindle load (45%), spindle temperature (32℃), spindle ratio (100%), feed ratio (90%), and feed speed (800 mm / min). The data is refreshed every second.

[0069] The daily equipment operation status is displayed in a pie chart, showing a processing time of 6 hours, a standby time of 1.5 hours, and a shutdown time of 0.5 hours. The list also shows the specific start and end times for each status.

[0070] The axis information area displays real-time data for the X, Y, and Z axes. The X-axis temperature is 30℃, the load is 35%, and the coordinate is "X100.5". The Y-axis temperature is 29℃, the load is 32%, and the coordinate is "Y80.3". The Z-axis temperature is 31℃, the load is 38%, and the coordinate is "Z50.2".

[0071] The program information area displays the main program name "PROG-001", program segment number "N120", current program line "G01 X100Y80 F800", number of workpieces processed today "120", and cumulative number of workpieces processed "15000".

[0072] The tool information area displays the currently used tool number "T01", tool compensation number "H01", and tool compensation value "0.02mm".

[0073] The alarm information area displays "No current alarms," ​​and you can also view the historical alarm records for the day.

[0074] For robot equipment, such as equipment number 201 and equipment name "ABB-01", the real-time overview page does not display axis information and tool information, but other information is displayed normally. The basic equipment information includes equipment name "ABB-01", equipment number "201", equipment type "industrial robot", IP address "192.168.1.201", port number "503", and system type "ABBIRC5".

[0075] The equipment status display shows "Connected", "Operating Status: Moving", "Control Mode: Automatic", and "No Alarm".

[0076] The processing parameters area is not displayed; the daily operation status pie chart shows "In Progress" duration of 7 hours and "Standby" duration of 1 hour; the program information shows the currently executing program "MOVE-PROG-01" and the number of workpieces moved today "300"; the alarm information shows "No current alarm".

[0077] For auxiliary equipment, such as equipment number 301 and equipment name "marking machine-01", its real-time overview page only displays basic equipment information, equipment status, daily equipment operation status and alarm information. The basic equipment information includes equipment name "marking machine-01", equipment number "301", equipment type "auxiliary", IP address "192.168.1.301", port number "504", and system type "custom".

[0078] The device status displays "Connected", "Running Status: Marking", and "No Alarm".

[0079] The pie chart showing the daily operation status indicates that the "marking in progress" time lasts for 5 hours and the "standby" time lasts for 3 hours.

[0080] The alarm message displays "No current alarms," ​​and the machining parameters, shaft information, program information, and tool information areas are not displayed.

[0081] In this embodiment, the equipment overview module, through the division of labor among sub-modules, not only achieves a centralized list display of all equipment on the production line, making it convenient for users to quickly locate target equipment, but also differentiates the display of operating information according to the characteristics of different types of equipment, avoiding interference from irrelevant information, and allowing users to accurately obtain key real-time data of specified equipment. This not only facilitates equipment maintenance personnel to promptly detect equipment anomalies, but also provides detailed data support for equipment fault diagnosis and operating parameter optimization, helping to improve the operation and maintenance efficiency and operational stability of individual equipment.

[0082] In one possible implementation, the alarm management module includes an alarm statistics submodule and an alarm log submodule.

[0083] This alarm statistics submodule is used to display the alarm duration, number of alarms, and daily alarm information change trends of the device within a specified time period.

[0084] Specifically, in the alarm statistics submodule of the alarm management module, users can set a specified time range through the time selector. For example, if "May 1, 2024 - May 20, 2024" is selected, the system will collect alarm data of all equipment on the production line within that time period.

[0085] In the alarm duration and frequency statistics area, the total alarm duration and total number of alarms for each device are displayed in a table format. For example, device 101 has a total alarm duration of 120 minutes and a total number of alarms of 8, device 201 has a total alarm duration of 60 minutes and a total number of alarms of 5, and device 301 has a total alarm duration of 30 minutes and a total number of alarms of 3. The table can also be sorted in ascending / descending order by total alarm duration or total number of alarms, making it easy to identify devices with high alarm frequency and long fault duration.

[0086] The daily alarm information trend area is displayed as a line graph, showing the total number of alarms and the total alarm duration for each day within a specified time period. For example, on May 1st, there were 10 alarms and a total duration of 150 minutes; on May 2nd, there were 8 alarms and a total duration of 120 minutes; and so on, on May 20th, there were 5 alarms and a total duration of 80 minutes. The line graph can also be set to display the daily alarm trend by equipment type, such as 3 machine tool alarms, 1 robot alarm, and 1 auxiliary machine alarm on May 20th, helping users analyze alarm trends and the alarm distribution of different types of equipment.

[0087] This alarm log submodule is used to display the alarm history information of the device within a specified date, including alarm date, device number, device name, alarm number, alarm content, start time, end time, and alarm duration.

[0088] Specifically, in this alarm log submodule, after the user sets a specified date, such as "May 20, 2024", the system displays all alarm records within that date in a paginated list format. The list columns include alarm date (2024-05-20), device number (101), device name (FANUC-01), alarm number (ALM-102), alarm content (spindle temperature too high, exceeding 40℃), start time (10:00:00), end time (10:05:30), and alarm duration (5 minutes and 30 seconds). Users can filter alarm records by device number, alarm number, or alarm content keywords, such as "temperature," using the search box. Clicking on a specific alarm record also allows users to view the device operating parameters at the time the alarm occurred, providing auxiliary information for analyzing the cause of the alarm.

[0089] In this embodiment, the alarm management module intuitively presents the overall alarm situation and trend within a specified time period through the alarm statistics submodule, helping managers to grasp the overall pattern of production line equipment failures. The alarm log submodule records detailed information for each alarm, facilitating the tracing of alarm history and accurate location of faulty equipment and causes. The combination of the two satisfies both the macro-statistical analysis needs for alarm information and the micro-detail query needs, providing strong support for equipment failure prevention, rapid troubleshooting and resolution, and helping to reduce the impact of equipment failures on production and improve the stability of production line operation.

[0090] In one possible implementation, the program management module includes a program transmission submodule and a program transmission log submodule. The program transmission submodule is used to manage the uploading and downloading of machine tool processing programs through the data acquisition interface. The program transmission submodule is also used to record the program transmission status of the production line equipment and generate a log.

[0091] Specifically, in the program transfer submodule of the program management module, the user first selects the target machine tool on the front-end interface, such as device 101, FANUC-01. The system automatically establishes a communication connection with the machine tool's data acquisition interface based on the Focas protocol. When a machining program needs to be uploaded, the user clicks the "Upload" button, selects a machining program file with the ".nc" extension in the local file system, such as "PROG-002.nc". The system performs format verification on the file. After successful verification, an upload progress bar is displayed. Upon completion, a "Upload Successful" message pops up, and the program list within the machine tool is updated synchronously. When a machining program needs to be downloaded from the machine tool, the user selects an existing program within the machine tool on the system interface, such as "PROG-001.nc", clicks the "Download" button, selects the local storage path, and the system reads the program data from the machine tool's data acquisition interface and downloads it to the local machine. Batch upload / download operations are also supported, allowing users to select multiple program files or multiple machine tools of the same type to complete the program transfer at once.

[0092] In this program transfer log submodule, the system automatically records detailed information for each program transfer, displaying it in a paginated list. The list columns include transfer time (2024-05-20 14:30:00), transfer type (upload / download), target device number (101), target device name (FANUC-01), program name (e.g., PROG-002.nc), program size, transfer status, operator, and reason for failure. Users can quickly search for desired program transfer records using the time range selector, transfer type filter, and device number search box. Clicking on a record also allows users to view a brief preview of the program's content, ensuring the transferred program matches expectations.

[0093] In this embodiment, the program transmission submodule enables convenient uploading and downloading of machine tool processing programs through the data acquisition underlying interface, supporting batch operations. Compared with the traditional method of manually transferring programs via USB flash drive, this significantly improves program transmission efficiency and reduces human error. The program transmission log submodule fully records the program transmission process, facilitating the tracing of historical program transmission records. When program anomalies occur, it is possible to quickly investigate whether the problem is caused by program transmission issues, ensuring the standardization and traceability of processing program management, and helping to improve the accuracy and production efficiency of machine tool processing.

[0094] In one possible implementation, the parameter monitoring module receives user subscription instructions for important parameters during equipment processing and plots line graphs of parameter changes based on these instructions. The report statistics module generates daily production reports on production line capacity and operational efficiency indicators, and supports report downloading.

[0095] Specifically, in this parameter monitoring module, the user first selects the device to be monitored on the front-end interface, such as device 101, FANUC-01. Then, in the processing parameter list of that device, the user selects the important parameters to be subscribed to, sets the data collection frequency and monitoring duration, and clicks "Confirm Subscription." The system then begins collecting data for the subscribed parameters at the set frequency. In the parameter display area, the parameter change trend is plotted in real time in the form of a line graph, where the X-axis represents time and the Y-axis represents the parameter value. Different parameters are distinguished by different colored lines. The normal threshold range of the parameter is also marked on the line graph. When the parameter value exceeds the threshold range, the corresponding part of the line automatically turns yellow or red, and a prompt message pops up to remind the user to pay attention to the parameter anomaly.

[0096] In this report statistics module, the system automatically calculates the daily production line capacity and operational efficiency indicators at 23:59, generating a "Today's Production Report." The report includes basic production line information, production line capacity data, and operational efficiency data, along with statistical explanations. The report supports multiple download formats; users can click the "Download" button to choose between Excel, PDF, and Word formats. Downloaded reports retain all data formats and charts, facilitating archiving, sharing, or use in production meetings by management.

[0097] In this embodiment, the parameter monitoring module allows users to customize and subscribe to important processing parameters of the equipment, and displays parameter changes in real time using line graphs with early warnings of anomalies. This helps operators to promptly detect parameter fluctuations and anomalies during equipment processing, preventing product quality issues or equipment damage caused by parameter abnormalities. The report statistics module automatically generates today's production report and supports downloading in multiple formats, saving managers the tedious work of manually compiling data and creating reports, improving report production efficiency. At the same time, the standardized report format ensures the standardization and accuracy of data statistics, providing standardized data document support for production summaries, performance evaluations, and subsequent production planning.

[0098] A method for intelligent monitoring of the operating status of an automated production line, applied to the aforementioned intelligent monitoring platform system for the operating status of an automated production line, includes the following steps: In a monitoring scenario of an automated automotive parts production workshop, the intelligent monitoring method for the operation status of this automated production line is applied. The specific steps are as follows: S1: The data acquisition unit collects the operating status information, equipment capacity data, alarm information, program information and processing parameter information of the production line equipment using a preset acquisition method.

[0099] Optionally, the data acquisition unit may use a preset acquisition method to acquire data.

[0100] For the 20 CNC lathes in the workshop, operating status information, program information, and machining parameter information are collected by calling their built-in Focas library functions; for the 10 KUKA robots responsible for loading and unloading workpieces, communication is established with the PLCs that come with the robots, and the Profinet protocol is used to collect their operating status information and equipment capacity data; for the 5 workpiece inspection auxiliary machines, pressure sensors and vision sensors are added to collect their operating status information, alarm information, and equipment capacity data. All data collection actions are performed in real time at a frequency of 1 second / time.

[0101] S2: The data acquisition unit classifies and statistically analyzes the collected information according to its data format, integrates it into JSON data format, and then sends it to the data processing and storage unit.

[0102] Specifically, the data acquisition unit processes and transmits the acquired information.

[0103] First, the data is categorized and statistically analyzed according to its format, with integer data, floating-point data, and string data being classified separately. Then, using a built-in data conversion algorithm, all the categorized information is integrated into a unified JSON data format, and a timestamp accurate to milliseconds is added to each JSON data packet. Subsequently, the JSON data packets are sent to the data processing and storage unit in real time via the TCP / IP protocol.

[0104] S3: The data processing and storage unit sends a data request to the data acquisition unit, receives and parses JSON format data, and stores the parsed data in modules to the preset database.

[0105] Specifically, this data processing and storage unit receives, parses, and stores data.

[0106] The data processing and storage unit sends data requests to the data acquisition unit every 500ms to ensure timely receipt of JSON data. Upon receiving the data, it extracts data fields using a preset JSON parsing algorithm and performs data validity verification. After successful verification, different types of data are stored in preset databases in modules: basic information such as equipment number and equipment type, as well as daily production capacity and alarm count statistics, are stored in a MySQL database; real-time variable data such as spindle speed and operating status are stored in a TDengine database; and user information required for subsequent login verification is pre-stored in a Redis database.

[0107] S4: The information display unit interacts with the data processing and storage unit through the HTTP interface, extracts data from the preset database and performs statistical calculations to obtain the front-end display data, which is displayed on the device operation terminal in the form of standard evaluation, trend analysis and log management.

[0108] Optionally, this information display unit can extract, process, and display data.

[0109] The front end of this information display unit sends data requests to the back end of the data processing and storage unit via an HTTP interface according to user operation requirements. The back end extracts the corresponding data from MySQL and TDengine databases, and further processes the data through statistical algorithms. After obtaining the data to be displayed on the front end, it returns the data to the front end via an HTTP response. The front end finally displays the data in the form of data tables, line charts, and log lists on the monitoring screens in the workshop, the computer clients of managers, mobile apps, and other equipment operation terminals for different roles to view.

[0110] In this embodiment, the intelligent monitoring method achieves end-to-end management of production line data from collection, integration, storage to display through standardized steps. The collection stage adapts to different equipment types to ensure data comprehensiveness, integration into JSON format ensures data transmission consistency, modular storage ensures efficient data management, and multi-format display ensures intuitive data viewing. The entire process is interconnected, solving the problems of chaotic data flow and poor connection between links in traditional monitoring methods. It realizes the automation and intelligence of production line operation status monitoring, providing complete methodological support for enterprises to grasp production dynamics in real time, respond quickly to production problems, and make scientific production decisions, which helps to improve the overall production efficiency and management level of the production line.

[0111] In one possible implementation, the preset acquisition method in step S1 includes calling the function library built into the production line equipment, acquiring data through a PLC, and acquiring data through a sensor.

[0112] Optionally, in step S1, in the monitoring scenario of an automated production line for aerospace components, the preset data acquisition method is specifically applied as follows: For the five high-end CNC milling machines in the production line, since they have their own complete function library, the machining parameters, program information and running status information of the milling machines can be directly collected by calling the function library provided by Siemens. For the eight collaborative robots used for parts assembly, their operation control depends on the PLC. Therefore, the S7-1200 PLC connected to the robot uses the MPI protocol to collect the robot's running status, equipment capacity data and alarm information.

[0113] For the three high-precision measuring instruments used for component size inspection, laser displacement sensors are installed on the key moving parts of the measuring instruments, and vision sensors are installed at the inspection station. The operating status, inspection data, and alarm information of the measuring instruments are converted into electrical signals and transmitted to the data acquisition unit. The three acquisition methods are carried out in parallel to ensure that information acquisition covers all key equipment on the production line.

[0114] In step S3, the preset database includes the relational database MySQL, the time-series database TDengine, and the cache database Redis, with different types of data stored in different databases.

[0115] Specifically, in step S3, the application and data storage allocation of the preset database are as follows: In the relational database MySQL, a "Basic Equipment Information Table" is created to store static data such as equipment number, equipment model, manufacturer, and installation date; a "Capacity Statistics Table" stores statistical data such as daily total production line capacity, single equipment capacity, and capacity compliance rate; and an "Alarm Statistics Table" stores summary data such as weekly equipment alarm count, main alarm types, and fault handling time.

[0116] In the time-series database TDengine, data acquisition points are created according to equipment type. For CNC milling machines, high-frequency real-time data such as spindle torque, cutting depth, and coordinates of each axis are collected every 200ms. For collaborative robots, real-time data such as joint angles and running speed are collected every 500ms. For measuring instruments, real-time data such as detection data and running status are collected every 1 second. All data are stored in timestamp order. In the cache-type database Redis, login information of production line managers, system operation permission configuration, and temporary cache data of real-time alarms are stored.

[0117] When storing data, static device information and statistical data are stored in MySQL, high-frequency real-time data is stored in TDengine, and login and permission information and temporary alarm data are stored in Redis, thus achieving accurate classification and storage of data.

[0118] In this embodiment, the combination of multiple preset data acquisition methods in step S1 can adapt to equipment with different interface configurations and functional types in the production line. Whether it is high-end equipment with its own function library, robot controlled by PLC, or old equipment without data interface, effective data acquisition can be achieved, ensuring the comprehensiveness and compatibility of data acquisition. In step S3, different types of data are stored in different preset databases, making full use of the characteristics of each database. This ensures both the efficiency and rationality of data storage and improves the speed of subsequent data query and processing, providing key support for the efficient operation of the entire monitoring method and helping to improve the real-time performance of production line monitoring and the efficiency of data processing.

[0119] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them; although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications to the technical solutions described in the foregoing embodiments, or equivalent substitutions for some or all of the technical features, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. An intelligent monitoring platform system for automated production line operation status, characterized in that, The data acquisition unit, the data processing and storage unit, and the information display unit are included. The data acquisition unit is used for communicating with the production line equipment, collecting production line information, equipment information, equipment running state information, equipment productivity data, alarm information, program information and processing parameter information, and sending the collected information to the data processing and storage unit after classification and statistics according to the data format and integration into a json data format. The data processing and storage unit is connected with the data acquisition unit, used for receiving the information transmitted by the data acquisition unit, and storing the information in the preset database after statistics and analysis, the preset database including a relational database MySql, a time series database TDengine and a cache database Redis. The information display unit is designed with front-end and back-end separation, connected with the data processing and storage unit, used for extracting data from the data processing and storage unit, and displaying production line running information in the form of standard evaluation, trend analysis and log management through real-time processing data monitoring and historical data statistics, the information display unit including a production line overview module, an equipment overview module, a state statistics module, an efficiency analysis module, a quality analysis module, a productivity analysis module, an alarm management module, a program management module, a parameter monitoring module and a report statistics module.

2. The automated line operational status intelligent monitoring platform system of claim 1, wherein, The data acquisition unit includes calling a function library of the production line equipment, collecting through a PLC and collecting through a sensor. When the data acquisition unit integrates information, the collected information is classified and counted according to the data format, and is integrated into a json data format.

3. The automated line operational status intelligent monitoring platform system of claim 1, wherein, The relational database MySql is used for storing equipment information, equipment running time statistical data, production line and equipment productivity data and alarm information statistical data. The time series database TDengine is used for storing equipment state data, running program data, processing parameter data, current alarm data and shaft information data. The cache database Redis is used for storing login verification information.

4. The automated line operational state intelligent monitoring platform system of claim 1, wherein, The production line overview module is used for displaying production line overall running data, including production line equipment information, equipment utilization rate information, production line efficiency, production line productivity and production line processing quality. The production line equipment information includes the total number of production line equipment, the proportion of machine tools / robots / accessory equipment and the proportion of equipment running state, the equipment running state including shutdown, processing, standby and offline. The production line efficiency includes production line comprehensive efficiency, production line utilization rate, time utilization rate, performance utilization rate and failure loss rate. The production line productivity includes today's productivity, yesterday's productivity and the change trend of the previous seven days' productivity; the production line processing quality includes quality loss rate and qualified product rate.

5. The automated line operational status intelligent monitoring platform system of claim 1, wherein, The equipment overview module includes an equipment basic information and equipment list submodule and an equipment real-time overview submodule. The equipment basic information and equipment list submodule is used for displaying all equipment of the production line in a list form, supporting users to click on the equipment to view the corresponding real-time information. The equipment real-time overview submodule is used for displaying the running information of a specified equipment, including equipment basic information, equipment state, processing parameters, daily equipment running condition, shaft information, program information, tool information and alarm information. Among them, the robot equipment does not show the shaft information and the tool information, and the auxiliary equipment does not show the machining parameter, the shaft information, the program information and the tool information.

6. The automated line operational state intelligent monitoring platform system of claim 1, wherein, The alarm management module comprises an alarm statistics submodule and an alarm log submodule. The alarm statistics submodule is used to show the alarm time length, the alarm times and the daily alarm information change trend of the equipment within a specified time. The alarm log submodule is used to show the alarm historical information of the equipment within a specified date, including the alarm date, the equipment number, the equipment name, the alarm number, the alarm content, the start time, the end time and the alarm time length.

7. The automated line operational state intelligent monitoring platform system of claim 1, wherein, The program management module comprises a program transmission submodule and a program transmission log submodule; the program transmission submodule is used to upload and download the management of the machine tool machining program through the data acquisition bottom interface; and the program transmission log submodule is used to record the program transmission situation of the production line equipment and generate a log.

8. The automated line operational state intelligent monitoring platform system of claim 1, wherein, The parameter monitoring module is used to receive the subscription instruction of the user to the important parameters in the equipment machining process, and draw a parameter change broken line according to the subscription instruction. The report statistics module is used to generate a today's production report for the production line production capacity and operation efficiency index, and support the report download.

9. An intelligent monitoring method for the running state of an automated production line, characterized in that, The application is applied to the intelligent monitoring platform system of the automatic production line running state as claimed in any one of claims 1-8, comprising the following steps: S1: through the data acquisition unit, the running state information, the equipment production capacity data, the alarm information, the program information and the machining parameter information of the production line equipment are collected by using a preset collection mode; S2: the data acquisition unit classifies and counts the collected information according to the data format, integrates the information into a json data format and sends the information to the data processing and storage unit; S3: the data processing and storage unit sends a data request to the data acquisition unit, receives the json format data and analyzes the data, stores the analyzed data into a preset database, and sends the data to the data processing and storage unit; S4: the information display unit interacts with the data processing and storage unit through an Http interface, extracts data from the preset database and performs statistical calculation, obtains front-end display data, and displays the data in the form of standard evaluation, trend analysis and log management on the equipment operation terminal.

10. The automated line operational state intelligent monitoring method of claim 9, wherein, The preset collection mode in step S1 comprises calling a function library of the production line equipment, collecting through a PLC and collecting through a sensor; The preset database in step S3 comprises a relational database MySql, a time sequence database TDengine and a cache database Redis, and different types of data are stored in different databases.

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