Smart factory production process data visualization method, system, equipment and medium

Abstract

The present invention provides a method, system, device and medium for visualizing production process data of a smart factory, which belongs to the field of big data technology. The method includes: collecting production process data of a smart factory in real time through pre-arranged sensor components; cleaning, converting and integrating the collected production process data to form a unified data format; performing data modeling on the processed production process data and storing it in a database; using artificial intelligence algorithms to quantitatively analyze the production process data stored in the database, extract features, establish data models, and predict equipment failures and product quality through data models; using 3D digital twin technology to present the factory production process in real time based on the production process data, and display key performance indicators through visualization plug-ins; identifying production anomalies based on preset thresholds and quantitative analysis results, and issuing warning information; using decision-making tools to generate decision analysis reports.

Description

Technical Field

[0001] The present invention belongs to the field of big data technology, and more specifically, relates to a method, system, equipment and medium for visualizing production process data of a smart factory. Background Art

[0002] With the advent of the Industrial 4.0 era, traditional manufacturing is experiencing unprecedented pressure for transformation and upgrading. In this context, companies are seeking ways to improve production efficiency, reduce production costs, and ensure product quality in order to cope with increasingly fierce market competition. However, existing factory management systems are unable to cope with the high standards of intelligent manufacturing.

[0003] A series of problems in the existing factory management system have seriously restricted the intelligentization process of the manufacturing industry. First, the information island phenomenon is serious, and the data of different production links cannot be effectively integrated, resulting in a broken information chain and difficulty in achieving global optimization. This not only affects production efficiency, but also increases management costs.

[0004] Secondly, low data utilization is also a major pain point. In the manufacturing process, a large amount of data collected by the system is often idle or not fully mined, and the valuable information contained in these data cannot provide strong support for the company's decision-making. This not only wastes resources, but may also lead to wrong decisions.

[0005] Furthermore, the traditional management method relies too much on manual experience, which leads to a lengthy and delayed decision-making process. In a rapidly changing market environment, this decision-making model obviously cannot keep up with the pace, and it is difficult for companies to quickly adjust production strategies to cope with market changes.

[0006] In addition, the visualization effect of the existing system is not satisfactory. The complex production process is difficult to present in an intuitive way, which makes it difficult for managers to monitor and effectively manage the production process in real time. Summary of the invention

[0007] In view of the above problems, the purpose of the present invention is to provide a method, system, equipment and medium for visualizing production process data of a smart factory, so as to integrate multi-source heterogeneous data, establish a unified data model, and realize real-time data collection, storage, analysis and visualization.

[0008] In order to achieve the above object, the present invention is implemented through the following technical solutions: In a first aspect, an embodiment of the present application provides a method for visualizing production process data of a smart factory, comprising: The production process data of the smart factory is collected in real time through pre-arranged sensor components; the production process data includes environmental parameters, equipment operation status data, product quality parameters, and location information of materials, workpieces and equipment on the production line; Cleaning, converting and integrating the collected production process data to form a unified data format; Carry out data modeling on the processed production process data and store them in the database; Use artificial intelligence algorithms to quantitatively analyze the production process data stored in the database, extract features, and build data models to predict equipment failures and product quality through data models; Through 3D digital twin technology, the factory production process is presented in real time based on the production process data, and key performance indicators are displayed through visualization plug-ins; Identify production anomalies and issue early warning information based on preset thresholds and quantitative analysis results; Based on the quantitative analysis results, decision analysis reports are generated using decision tools.

[0009] In an optional embodiment, the real-time collection of production process data of the smart factory by pre-arranged sensor components includes: Environmental sensors are used to measure temperature, humidity, light intensity and noise parameters to monitor whether the production environment meets the requirements and provide reference for the production process; The vibration, temperature, current and pressure parameters of the equipment are collected through the equipment status sensor to predict equipment failure; Product quality sensors measure product size, weight, appearance and composition parameters; The position information of workpieces and equipment on the production line is collected in real time through positioning sensors.

[0010] In an optional implementation, the collected production process data is cleaned, converted, and integrated to form a unified data format, including: Use deletion and interpolation methods to handle missing values ​​in production process data; Use box plots to identify outliers in production process data, mark them, and delete them; Use filters to remove high-frequency noise from production process data; Classify the production process data and identify numerical data, categorical data and time series data; convert categorical data into numerical data, perform unified timestamp processing on time series data, and perform feature extraction; The classified production process data is scaled so that the values ​​are scaled to a uniform range.

[0011] In an optional embodiment, the processing of the production process data is modeled and stored in a database, including: According to the application scenarios of the data, the conceptual model, logical model and physical model of the production process data are established, and the corresponding data is written; Using a preset database to store the conceptual model, logical model and physical model; The preset database includes but is not limited to: a relational database, a NoSQL database, a time series database and a cloud database.

[0012] In an optional embodiment, the use of artificial intelligence algorithms to quantitatively analyze the production process data stored in the database, extract features, establish a data model, and use the data model to predict equipment failures and product quality, including: Extract the time characteristics and statistical characteristics of the production process data stored in the database; the time characteristics include timestamp and time series, and the statistical characteristics include mean, variance, maximum and minimum values; The extracted time features and statistical features are used as input data sets for model training; Establish a data model that reflects the real world, set entities, attributes and relationships, and convert them into a logical structure; Using a machine learning algorithm, training the data model using an input data set, and evaluating the performance of the model to generate a prediction model; Use predictive models to predict equipment failure and product quality.

[0013] In an optional embodiment, the 3D digital twin technology is used to present the factory production process in real time based on the production process data, and the key performance indicators are displayed through a visualization plug-in, including: Use 3D digital twin technology to digitally model smart projects, and use Three.js to build virtual 3D scenes similar to the real environment; Dynamically adjust the element status in the virtual 3D scene according to the production process data; the element status includes the equipment operation status and the AGV trolley path; intuitively display the element status through visual elements; Convert the time characteristics and statistical characteristics into key indicators; Use visualization plug-ins to embed key performance indicators into virtual 3D scenes in the form of charts or dashboards.

[0014] In an optional embodiment, identifying production anomalies based on preset thresholds and quantitative analysis results and issuing warning information includes: Set abnormal thresholds for each key performance indicator; If a key performance indicator reaches or exceeds an abnormal threshold, a warning message is generated based on the key indicator using a notification template; Early warning information is sent to the manager's mobile terminal via SMS, email, APP push or voice call.

[0015] In a second aspect, the embodiment of the present application further provides a smart factory production process data visualization system, including: A data acquisition module is used to collect the production process data of the smart factory in real time through pre-arranged sensor components; the production process data includes environmental parameters, equipment operation status data, product quality parameters, and location information of materials, workpieces and equipment on the production line; A data preprocessing module is used to clean, convert and integrate the collected production process data to form a unified data format; A data storage module is used to perform data modeling on the processed production process data and store it in a database; The data analysis module is used to use artificial intelligence algorithms to quantitatively analyze the production process data stored in the database, extract features, establish data models, and predict equipment failures and product quality through data models; The visualization module is used to present the factory production process in real time based on the production process data through 3D digital twin technology, and to display key performance indicators through visualization plug-ins; The early warning module is used to identify production anomalies and issue early warning information based on preset thresholds and quantitative analysis results; The decision module is used to generate a decision analysis report based on the quantitative analysis results using decision tools.

[0016] In a third aspect, an embodiment of the present application further provides an electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the program, the steps of the smart factory production process data visualization method as described in any one of the above items are implemented.

[0017] In a fourth aspect, an embodiment of the present application further provides a storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the smart factory production process data visualization method as described in any of the above items are implemented.

[0018] It can be seen from the above technical solutions that the present invention has the following advantages: This application integrates multi-source heterogeneous data and establishes a unified data model to achieve real-time data collection, storage, analysis and visualization. Using artificial intelligence technology, production data is deeply mined to discover potential patterns and provide intelligent decision-making support for managers. At the same time, through 3D digital twin technology, real-time visualization of the factory production process is achieved, allowing managers to intuitively understand the production status, identify problems in a timely manner and take measures.

[0019] This application deeply integrates modern information technology with traditional manufacturing to achieve real-time perception, prediction and optimization of the entire production process, thereby improving production efficiency, reducing costs, improving product quality and enhancing the core competitiveness of enterprises.

[0020] This application makes full use of big data technology, artificial intelligence technology, Internet of Things technology and cloud computing technology. Big data technology can process massive, high-speed and diverse data and extract valuable information from it; artificial intelligence technology, such as machine learning, is used for data analysis, model building, prediction and optimization; Internet of Things technology realizes the interconnection between devices and systems and realizes real-time data collection and transmission; cloud computing technology provides powerful computing and storage capabilities to support large-scale data processing and analysis.

[0021] This application can improve production efficiency and reduce production costs by optimizing production processes, predictive maintenance, energy optimization, etc.; through real-time monitoring and quality analysis, it can effectively improve product quality and enhance customer satisfaction. This application can adapt to changes in market demand and achieve flexible production.

[0022] The application scenarios of this application are very broad. It is not only suitable for production process monitoring, equipment maintenance, quality management, energy management, etc. in the manufacturing industry, but can also be applied to logistics tracking, warehousing management, transportation optimization, etc. in the logistics industry, as well as power system monitoring, energy forecasting, fault diagnosis, etc. in the energy industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the description will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 A flow chart of the smart factory production process data visualization method provided for this application.

[0025] Figure 2 A schematic diagram of the structure of the smart factory production process data visualization system provided for this application.

[0026] Figure 3 A schematic diagram of the structure of the electronic device provided in this application. DETAILED DESCRIPTION

[0027] In the specific steps of the method for visualizing the production process data of a smart factory, which will be described in detail below, various embodiments of the present disclosure will be described more comprehensively. The present disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of the present disclosure to the specific embodiments disclosed herein, but rather the present disclosure should be understood to cover all adjustments, equivalents and / or alternatives that fall within the spirit and scope of the various embodiments of the present disclosure.

[0028] Hereinafter, the terms "include" or "may include" used in various embodiments of the present disclosure indicate the presence of disclosed functions, operations, or elements, and do not limit the addition of one or more functions, operations, or elements. In addition, as used in various embodiments of the present disclosure, the terms "include", "have", and their cognates are intended only to indicate specific features, numbers, steps, operations, elements, components, or a combination of the foregoing, and should not be understood as first excluding the presence of one or more other features, numbers, steps, operations, elements, components, or a combination of the foregoing or the possibility of adding one or more features, numbers, steps, operations, elements, components, or a combination of the foregoing.

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] See also Figure 1 The figure is a method flow chart of a method for visualizing production process data of a smart factory in a specific embodiment, and the method includes: S1: Collect production process data of smart factories in real time through pre-arranged sensor components.

[0031] The production process data includes environmental parameters, equipment operation status data, product quality parameters, and location information of materials, workpieces and equipment on the production line.

[0032] For example, environmental sensors are used to measure environmental parameters such as temperature, humidity, illumination, noise, etc., to monitor whether the production environment meets the requirements and provide reference for the production process. Equipment status sensors are used to monitor the operating status of equipment, such as vibration, temperature, current, pressure, etc., to predict equipment failures and perform maintenance. Product quality sensors are used to measure product size, weight, appearance, composition and other parameters to detect product quality and ensure that products meet standards. Positioning sensors are used to track the location of materials, workpieces and equipment on the production line to achieve lean production and real-time tracking.

[0033] The pre-arranged sensor components send the real-time collected data to the data collector. The data collector is the bridge between the sensor and the data center, responsible for collecting sensor data and transmitting it to the data center for storage and analysis. The data collector usually includes the following components: 1) Interface module: provides multiple interfaces, such as analog input, digital input, serial port, CAN bus, etc., for connecting various types of sensors.

[0034] 2) Data processing unit: Filter, calibrate, convert and process the collected raw data to improve data quality.

[0035] 3) Storage module: Store the processed data in local storage devices such as SD card, SSD, etc.

[0036] 4) Communication module: transmits data to the data center via wired or wireless means, such as Ethernet, Wi-Fi, 4G, etc.

[0037] S2: Clean, convert and integrate the collected production process data to form a unified data format.

[0038] In this step, the raw data collected from factory equipment, sensors, etc. is converted into high-quality data that can be used for analysis and modeling. This process involves a series of operations such as data cleaning, conversion, and feature engineering, which aims to remove noise, fill missing values, unify formats, and extract effective features, providing a high-quality data foundation for subsequent data mining and machine learning.

[0039] For example, the specific process of this step is as follows: S201: Use deletion and interpolation methods to handle missing values ​​in production process data.

[0040] Specifically, the deletion method is used to delete samples containing missing values, which is suitable for situations where the proportion of missing values ​​is small. The interpolation method is used for linear interpolation, polynomial interpolation, spline interpolation, etc., which is suitable for time series data. The machine learning model is used to predict missing values, which is suitable for situations where there are many missing values ​​or there are complex relationships between data.

[0041] S201: Use a box plot to identify outliers in the production process data, mark them, and delete them.

[0042] Specifically, outliers are identified using methods such as Z-score and box plots. Clustering-based methods are used to divide data into different clusters, and points far from the cluster center are outliers. Domain knowledge-based methods are used in combination with production process knowledge to determine which data are outliers.

[0043] S203: Use a filter to remove high-frequency noise in the production process data.

[0044] Specifically, filters are used to remove high-frequency noise. Methods such as sliding average and exponential smoothing are used to smooth the data.

[0045] S204: Classify the production process data, identify numerical data, categorical data and time series data; convert the categorical data into numerical data, perform unified timestamp processing on the time series data, and perform feature extraction.

[0046] Among them, for numerical data, Min-Max Scaling and Z-score standardization methods can be used for normalization processing, and equal-width binning, equal-frequency binning, and clustering-based binning methods can be used for discretization processing. For categorical data, one-hot encoding and label encoding can be used for encoding processing.

[0047] S205: Scaling the classified production process data to scale the values ​​to a uniform range, such as between 0 and 1 or between -1 and 1, to facilitate comparison and calculation between different features.

[0048] S3: Perform data modeling on the processed production process data and store it in the database.

[0049] First, according to the application scenario of the data, establish the conceptual model, logical model and physical model of the production process data and write the corresponding data. Specifically, establish a data model that reflects the real world and clarify the entities, attributes and relationships. Convert the conceptual model into a logical model and determine the table structure, field type and constraints. Map the logical model to a specific database system and determine the physical implementation details such as indexes and partitions.

[0050] Then, the conceptual model, the logical model and the physical model are stored in a preset database; The preset database includes but is not limited to: a relational database, a NoSQL database, a time series database and a cloud database.

[0051] The following describes common factory data storage solutions, which can be combined and selected according to specific business: Relational databases are applicable to structured data, such as equipment parameters, production records, etc. The advantages are clear data organization structure, support for SQL queries, and easy management. The disadvantage is that the processing efficiency of massive unstructured data is low.

[0052] NoSQL databases are suitable for unstructured data, such as raw sensor data, log data, etc. The advantages are flexibility, strong scalability, and suitable for storing massive data. The disadvantages are high query complexity and relatively complex data consistency assurance.

[0053] The applicable scenario of time series database is time series data, such as sensor data, equipment operation status, etc. The advantages are high-performance time series query, support for data compression, and saving storage space. The disadvantage is that the function is relatively single and mainly oriented to time series data.

[0054] Cloud storage is applicable to mass data storage, backup, and disaster recovery. Its advantages are elastic expansion, high availability, and low cost. Its disadvantages are that there may be network latency issues, so it needs to be selected carefully for scenarios with high real-time requirements.

[0055] S4: Use artificial intelligence algorithms to quantitatively analyze the production process data stored in the database, extract features, establish data models, and use the data models to predict equipment failures and product quality.

[0056] In a specific implementation manner, the specific process of this step includes: S401: extracting time features and statistical features of production process data stored in a database; time features include timestamps and time series, and statistical features include mean, variance, maximum and minimum values.

[0057] S402: Using the extracted time features and statistical features as input data sets for model training.

[0058] S403: Establish a data model that reflects the real world, set entities, attributes and relationships, and convert them into a logical structure.

[0059] For example, a dimensional model is established, where the dimensions include time, product, and equipment, and the metrics include production capacity and yield. Subsequently, the conceptual model is converted into a logical model to determine the table structure, field type, and constraints. For example, a fact table is created to store production data, and a dimension table is created to store product information, equipment information, etc. Finally, the logical model is mapped to a specific database system to determine the physical implementation details such as indexes and partitions. For example, indexes are created for frequently queried fields, and data is partitioned according to the time dimension.

[0060] S404: Using a machine learning algorithm, using an input data set to train the data model, and evaluating the performance of the model to generate a prediction model.

[0061] For example, the model building and training process may include first extracting features such as the equipment's operating time and temperature change trend from the data. Then, select a suitable model according to the problem type, such as regression, classification, clustering, etc. After the model selection is confirmed, use the training data to train the model. Finally, use the test data to evaluate the performance of the model, such as accuracy, recall, etc.

[0062] S405: Use the prediction model to predict equipment failure and product quality.

[0063] S5: Through 3D digital twin technology, the factory production process is presented in real time based on the production process data, and key performance indicators are displayed through visualization plug-ins.

[0064] In the specific implementation, abstract data is converted into intuitive and easy-to-understand graphical representations to help users quickly gain insight into the information behind the data and make more informed decisions. In the factory scene, the visualization module can display the production process, equipment status, quality indicators, workshop overview, etc., providing managers with real-time and comprehensive information.

[0065] First, the smart project is digitally modeled using 3D digital twin technology, and a virtual 3D scene similar to the real environment is constructed through Three.js. Then, the element status in the virtual 3D scene is dynamically adjusted according to the production process data. The element status includes the equipment operation status and the AGV vehicle path. The element status is intuitively displayed through visual elements. At this time, the time characteristics and statistical characteristics are converted into key indicators. Finally, the key performance indicators are embedded in the virtual 3D scene in the form of charts or dashboards through visualization plug-ins.

[0066] Among them, charts and dashboards are commonly used visualization methods. Charts can show the trend, distribution, and comparison of data. For example, a line chart shows the change of production output over time, and a bar chart shows the comparison of the output of different products. The dashboard can combine multiple charts to form a comprehensive monitoring interface, which displays key performance indicators (KPIs) in real time, such as equipment operation status, product qualification rate, etc. Three.js is a powerful 3D JavaScript library that can be used to create and render 3D scenes. For example, through Three.js, we can build a virtual workshop environment and display the working path of the AGV in 3D. We can use color, size, etc. to represent the different states of the AGV. For example, the running AGV is displayed in green, and the standby AGV is displayed in gray. In addition, the movement process of the AGV can be displayed through animation effects to make the visualization more vivid.

[0067] S6: Based on the preset thresholds and quantitative analysis results, production anomalies are identified and early warning information is issued.

[0068] In a specific implementation method, first, an abnormal threshold is set for each key performance indicator; if the key performance indicator reaches or exceeds the abnormal threshold, an early warning message is generated based on the key indicator using a notification template; finally, an early warning message is sent to the manager's mobile terminal via SMS, email, APP push or voice call.

[0069] Among them, the warning can be triggered by the established warning rules. Warning rules include simple rules and complex rules. Simple rules can be set to trigger an alarm when the indicator value exceeds or falls below the set threshold, or when the indicator value shows an upward or downward trend for a period of time. Complex rules can combine multiple simple rules and establish a time series model based on historical data to predict future trends. When the predicted value exceeds the confidence interval, the alarm is triggered.

[0070] S7: Based on the quantitative analysis results, use decision-making tools to generate a decision analysis report.

[0071] In the specific implementation, by converting massive data into valuable insights, it provides managers with a basis for decision-making. By comprehensively analyzing historical data, real-time data and forecast results, and generating decision analysis reports, it can help companies discover development opportunities, optimize production processes and improve operating efficiency.

[0072] Specifically, first integrate the company's data into a unified data model to facilitate multi-dimensional analysis. Then, develop or select professional BI tools to provide data visualization, query analysis, predictive modeling and other functions. At this time, customize and generate various decision analysis reports according to the needs of the company, such as production performance analysis, cost-benefit analysis, market trend analysis, etc. Finally, integrate all decision support tools and data into a unified platform for user access and use. In addition, based on the needs and feedback of the company, continuously improve the platform's functions and performance.

[0073] In this embodiment, by integrating multi-source heterogeneous data and establishing a unified data model, real-time data collection, storage, analysis and visualization are achieved. Artificial intelligence technology is used to deeply mine production data, discover potential rules, and provide intelligent decision support for managers. At the same time, through 3D digital twin technology, real-time visualization of the factory production process is achieved, allowing managers to intuitively understand the production status, discover problems in time and take measures.

[0074] like Figure 2As shown, the following is an embodiment of the smart factory production process data visualization system provided by the embodiment of the present disclosure. The system and the smart factory production process data visualization method of the above-mentioned embodiments belong to the same inventive concept. For details not described in detail in the embodiment of the smart factory production process data visualization system, please refer to the embodiment of the above-mentioned smart factory production process data visualization method.

[0075] A smart factory production process data visualization system includes: a data acquisition module 1, a data preprocessing module 2, a data storage module 3, a data analysis module 4, a visualization module 5, an early warning module 6 and a decision module 7.

[0076] The data acquisition module 1 is used to collect the production process data of the smart factory in real time through pre-arranged sensor components; the production process data includes environmental parameters, equipment operation status data, product quality parameters, and location information of materials, workpieces and equipment on the production line.

[0077] For example, various data in the factory production process are collected in real time through sensors, PLC and other equipment, including equipment operating status, product quality data, energy consumption data, etc.

[0078] The data preprocessing module 2 is used to clean, convert and integrate the collected production process data to form a unified data format.

[0079] For example, the collected raw data is cleaned, converted, and integrated to form a unified data format, providing a basis for subsequent analysis.

[0080] The data storage module 3 is used to perform data modeling on the processed production process data and store it in a database. The pre-processed data is stored in the database to facilitate subsequent query and analysis.

[0081] The data analysis module 4 is used to use artificial intelligence algorithms to perform quantitative analysis on the production process data stored in the database, extract features, establish data models, and predict equipment failures and product quality through data models.

[0082] For example, artificial intelligence algorithms are used to conduct in-depth analysis of stored data, extract features, build models, and implement functions such as prediction, classification, and clustering.

[0083] The visualization module 5 is used to present the factory production process in real time based on the production process data through the 3D digital twin technology, and to display the key performance indicators through the visualization plug-in. For example, the factory production process is presented in real time through the 3D digital twin technology, and the key performance indicators (KPI) are displayed through charts, dashboards, etc., to achieve comprehensive monitoring of the production process.

[0084] The early warning module 6 is used to identify production anomalies and issue early warning information based on preset thresholds and quantitative analysis results.

[0085] The decision module 7 is used to generate a decision analysis report based on the quantitative analysis results using a decision tool.

[0086] For example, based on the results of data analysis, it provides managers with suggestions on optimizing production processes, improving equipment efficiency, and reducing energy consumption.

[0087] The smart factory production process data visualization system provided in this embodiment integrates advanced data collection, preprocessing, storage, analysis, visualization, early warning and decision-making modules, and uses the Internet of Things (IoT) technology, big data processing technology, artificial intelligence algorithm and 3D digital twin technology to achieve comprehensive and real-time monitoring and optimization of the production process. Specifically, the pre-arranged sensor components collect environmental parameters, equipment operation status, product quality and other data in real time; the collected data is cleaned, converted and integrated using big data processing technology to form a unified data format; artificial intelligence algorithms are used to conduct in-depth analysis of the data, establish a prediction model, and predict equipment failures and product quality; the factory production process is presented in real time with the help of 3D digital twin technology, and key performance indicators are displayed; and through preset thresholds and quantitative analysis results, production anomalies are identified in a timely manner and early warnings are issued, and finally a decision analysis report is generated to support management to make scientific decisions. This system not only improves production efficiency and product quality, reduces operating costs, but also enhances decision support capabilities, promotes safe production and environmental protection management, strengthens cross-departmental collaboration, and improves customer satisfaction and the market competitiveness of enterprises.

[0088] Figure 3 A schematic diagram of the hardware structure of an electronic device for implementing various embodiments of the present invention.

[0089] The method for visualizing the production process data of a smart factory provided in the embodiment of the present application can be applied to electronic devices. It will be appreciated by those skilled in the art that the electronic device structure involved in the embodiment of the present invention does not constitute a limitation on the electronic device, and the electronic device may include more or less components than shown, or combine certain components, or arrange different components. In an embodiment of the present invention, the electronic device includes but is not limited to a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples, and are not intended to limit the implementation of the embodiments of the present application described and / or required herein.

[0090] The electronic device may include a processor, an external memory interface, an internal memory, a universal serial bus (USB) interface, a charging management module, a power management module, a battery, a wireless communication module, an audio module, a speaker, a microphone, a sensor module, buttons, a camera, a display, and a SIM card interface, etc.

[0091] The processor may include one or more processing units, for example, the processor may include a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Among them, different processing units may be independent devices or integrated into one or more processors.

[0092] The processor can be the nerve center and command center of the electronic device. The controller can generate an operation control signal according to the instruction operation code and timing signal to complete the control of fetching and executing instructions.

[0093] A memory may also be provided in the processor for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. The memory may store instructions or data that the processor has just used or is cyclically used. If the processor needs to use the instruction or data again, it may be directly called from the memory. This avoids repeated access, reduces the waiting time of the processor, and thus improves system efficiency.

[0094] The external memory interface can be used to connect an external memory card, such as a MicroSD card, to expand the storage capacity of the electronic device. The external memory card communicates with the processor through the external memory interface to implement data storage functions. For example, files such as music and videos can be saved in the external memory card.

[0095] The internal memory can be used to store computer executable program codes, which include instructions. The processor executes various functional applications and data processing of the electronic device by running the instructions stored in the internal memory. The internal memory may include a program storage area and a data storage area. The internal memory may include a high-speed random access memory and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), etc.

[0096] The wireless communication function of an electronic device can be realized through an antenna, a wireless communication module, a modem processor, and a baseband processor.

[0097] The wireless communication module can provide wireless communication solutions for electronic devices, including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), infrared technology (IR), etc.

[0098] Electronic devices can implement audio functions, etc. through audio modules, speakers, receivers, microphones, headphone jacks, and application processors.

[0099] Electronic devices can achieve shooting functions through ISP, camera, video codec, GPU, display and application processor.

[0100] Electronic devices can achieve display functions through GPU, display screen and application processor.

[0101] The GPU is a microprocessor for image processing that connects the display screen and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. The processor may include one or more GPUs that execute program instructions to generate or change display information.

[0102] The display screen is used to display images, videos, etc. The display screen includes a display panel.

[0103] The above-mentioned electronic equipment realizes the data visualization method of the smart factory production process of this application through integrated sensor real-time data collection, data cleaning and modeling, artificial intelligence analysis, 3D digital twin visualization, abnormal warning and decision support, etc., realizes comprehensive monitoring and optimization of the production process, significantly improves production efficiency and product quality, reduces operating costs, enhances the scientificity and timeliness of decision-making, promotes safe production and environmental protection management, strengthens cross-departmental collaboration, and ultimately improves customer satisfaction and the company's market competitiveness.

[0104] The storage medium provided in the present application stores a program product that can implement a method for visualizing production process data of a smart factory.

[0105] The method for visualizing production process data of a smart factory includes: collecting production process data of the smart factory in real time through pre-arranged sensor components; the production process data includes environmental parameters, equipment operation status data, product quality parameters, and location information of materials, workpieces and equipment on the production line; cleaning, converting, and integrating the collected production process data to form a unified data format; performing data modeling on the processed production process data and storing it in a database; using artificial intelligence algorithms to quantitatively analyze the production process data stored in the database, extract features, establish a data model, and predict equipment failures and product quality through the data model; using 3D digital twin technology to present the factory production process in real time based on the production process data, and display key performance indicators through visualization plug-ins; identifying production anomalies based on preset thresholds and quantitative analysis results, and issuing early warning information; based on the quantitative analysis results, using decision tools to generate decision analysis reports.

[0106] In some possible embodiments, the smart factory production process data visualization method disclosed herein can be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps described in the above "Exemplary Method" section of this specification according to various exemplary embodiments of the present disclosure.

[0107] The storage medium of the present disclosure can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0108] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for visualizing production process data of a smart factory, characterized in that: include: Collect production process data of smart factories in real time through pre-arranged sensor components; The production process data includes environmental parameters, equipment operation status data, product quality parameters, and location information of materials, workpieces and equipment on the production line; Cleaning, converting and integrating the collected production process data to form a unified data format; Carry out data modeling on the processed production process data and store them in the database; Use artificial intelligence algorithms to quantitatively analyze the production process data stored in the database, extract features, and build data models to predict equipment failures and product quality through data models; Through 3D digital twin technology, the factory production process is presented in real time based on the production process data, and key performance indicators are displayed through visualization plug-ins; Identify production anomalies and issue early warning information based on preset thresholds and quantitative analysis results; Based on the quantitative analysis results, decision analysis reports are generated using decision tools.

2. The method for visualizing production process data of a smart factory according to claim 1, characterized in that: The real-time collection of production process data of the smart factory by pre-arranged sensor components includes: Environmental sensors are used to measure temperature, humidity, light intensity and noise parameters to monitor whether the production environment meets the requirements and provide reference for the production process; The vibration, temperature, current and pressure parameters of the equipment are collected through the equipment status sensor to predict equipment failure; Product quality sensors measure product size, weight, appearance and composition parameters; The position information of workpieces and equipment on the production line is collected in real time through positioning sensors.

3. The method for visualizing production process data of a smart factory according to claim 1, characterized in that: The collected production process data is cleaned, converted, and integrated to form a unified data format, including: Use deletion and interpolation methods to handle missing values ​​in production process data; Use box plots to identify outliers in production process data, mark them, and delete them; Use filters to remove high-frequency noise from production process data; Classify the production process data and identify numerical data, categorical data and time series data; convert categorical data into numerical data, perform unified timestamp processing on time series data, and perform feature extraction; The classified production process data is scaled so that the values ​​are scaled to a uniform range.

4. The method for visualizing production process data of a smart factory according to claim 1, characterized in that: The processing of the production process data is performed on the data model and stored in the database, including: According to the application scenarios of the data, the conceptual model, logical model and physical model of the production process data are established, and the corresponding data is written; Using a preset database to store the conceptual model, logical model and physical model; The preset database includes but is not limited to: a relational database, a NoSQL database, a time series database and a cloud database.

5. The method for visualizing production process data of a smart factory according to claim 1, characterized in that: The artificial intelligence algorithm is used to quantitatively analyze the production process data stored in the database, extract features, establish a data model, and use the data model to predict equipment failure and product quality, including: Extract the time characteristics and statistical characteristics of the production process data stored in the database; the time characteristics include timestamp and time series, and the statistical characteristics include mean, variance, maximum and minimum values; The extracted time features and statistical features are used as input data sets for model training; Establish a data model that reflects the real world, set entities, attributes and relationships, and convert them into a logical structure; Using a machine learning algorithm, training the data model using an input data set, and evaluating the performance of the model to generate a prediction model; Use predictive models to predict equipment failure and product quality.

6. The method for visualizing production process data of a smart factory according to claim 5, characterized in that: The 3D digital twin technology is used to present the factory production process in real time based on the production process data, and key performance indicators are displayed through visualization plug-ins, including: Use 3D digital twin technology to digitally model smart projects, and use Three.js to build virtual 3D scenes similar to the real environment; Dynamically adjust the element status in the virtual 3D scene according to the production process data; the element status includes the equipment operation status and the AGV trolley path; intuitively display the element status through visual elements; Convert the time characteristics and statistical characteristics into key indicators; Use visualization plug-ins to embed key performance indicators into virtual 3D scenes in the form of charts or dashboards.

7. The method for visualizing production process data of a smart factory according to claim 6, characterized in that: The method of identifying production anomalies and issuing early warning information based on preset thresholds and quantitative analysis results includes: Set abnormal thresholds for each key performance indicator; If a key performance indicator reaches or exceeds an abnormal threshold, a warning message is generated based on the key indicator using a notification template; Early warning information is sent to the manager's mobile terminal via SMS, email, APP push or voice call.

8. A smart factory production process data visualization system, characterized in that: The system adopts the smart factory production process data visualization method as claimed in any one of claims 1 to 7; The system comprises: A data acquisition module is used to collect the production process data of the smart factory in real time through pre-arranged sensor components; the production process data includes environmental parameters, equipment operation status data, product quality parameters, and location information of materials, workpieces and equipment on the production line; A data preprocessing module is used to clean, convert and integrate the collected production process data to form a unified data format; A data storage module is used to perform data modeling on the processed production process data and store it in a database; The data analysis module is used to use artificial intelligence algorithms to quantitatively analyze the production process data stored in the database, extract features, establish data models, and predict equipment failures and product quality through data models; The visualization module is used to present the factory production process in real time based on the production process data through 3D digital twin technology, and to display key performance indicators through visualization plug-ins; The early warning module is used to identify production anomalies and issue early warning information based on preset thresholds and quantitative analysis results; The decision module is used to generate a decision analysis report based on the quantitative analysis results using decision tools.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the smart factory production process data visualization method as described in any one of claims 1 to 7 are implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the smart factory production process data visualization method as described in any one of claims 1 to 7 are implemented.

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