Data processing method, system, storage medium and program product

Abstract

Embodiments of the present application provide a data processing method, system, storage medium and program product. The method comprises: a server acquires original data of a target object in a workshop according to a collection frequency requirement of the original data, using a collection mode corresponding to the collection frequency requirement, the response delay of different collection modes being different. According to the original data, motion trajectory data of a target twin object in a digital twin workshop of the workshop is generated, the target twin object corresponding to the target object. The motion trajectory data is sent to at least one client, so that the client displays the motion change of the target twin object rendered based on the motion trajectory data in the digital twin workshop. The method of the present application can improve the real-time synchronization of digital twinning in the manufacturing scene and the flexibility of user access.

Description

Technical Field

[0001] This application relates to the field of digital twin technology, and in particular to a data processing method, system, storage medium, and program product. Background Technology

[0002] Traditional manufacturing is characterized by its widely distributed production bases, large-scale factories, numerous production lines, and complex equipment. During the manufacturing process, workshops often operate in a "black box" state, lacking interconnectivity between physical and information spaces. This results in insufficient intelligence, initiative, predictability, transparency, and global optimization capabilities in workshop information, failing to meet the demands of intelligent manufacturing. In recent years, the emergence and rapid development of digital twin technology has offered opportunities to improve the intelligence and efficiency of manufacturing. However, current digital twin technologies used in manufacturing scenarios suffer from poor real-time synchronization.

[0003] Therefore, improving the real-time synchronization of digital twins in manufacturing scenarios is an urgent problem to be solved. Summary of the Invention

[0004] The data processing methods, systems, storage media, and program products provided in this application are used to improve the real-time synchronization of digital twins in manufacturing scenarios.

[0005] In a first aspect, embodiments of this application provide a data processing method applied to a server, comprising:

[0006] Based on the required collection frequency of raw data of the target objects in the workshop, the raw data is obtained by adopting a collection method corresponding to the required collection frequency. Different collection methods have different response delays.

[0007] Based on the original data, motion trajectory data of the target twin object in the digital twin workshop of the workshop is generated, wherein the target twin object corresponds to the target object;

[0008] The motion trajectory data is sent to at least one client so that the client can display the motion changes of the target twin object rendered based on the motion trajectory data in the digital twin workshop.

[0009] Optionally, the data collection method includes subscription-based collection and periodic collection. The step of acquiring the raw data using a collection method corresponding to the required collection frequency of the target object's raw data in the workshop includes:

[0010] When the required collection frequency of the raw data is greater than or equal to the first frequency, the raw data is obtained by subscription-based collection.

[0011] When the required acquisition frequency of the original data is less than the second frequency, the original data is acquired by the fixed-period acquisition, wherein the second frequency is less than or equal to the first frequency.

[0012] Optional, also includes:

[0013] Based on the original data, generate the data analysis results of the target twin object;

[0014] The data analysis results are sent to the client so that the client can display the data analysis results in the data panel.

[0015] Optional, also includes:

[0016] Store at least one of the following: the raw data, the motion trajectory data, and the data analysis results.

[0017] Optional, also includes:

[0018] Upon receiving a backtracking request from the client, the system sends target historical data corresponding to the requested target time period to the client according to the request content of the backtracking request. The target historical data includes at least one of the original data, the motion trajectory data, and the data analysis results.

[0019] Optionally, the target historical data is stored in a relational database.

[0020] Optionally, sending the motion trajectory data to at least one client includes:

[0021] The motion trajectory data is broadcast to at least one of the clients.

[0022] Optionally, the method further includes:

[0023] Obtain the equipment structure information, production process information, and technological procedure information of the workshop;

[0024] A virtual model of the digital twin workshop is constructed based on the equipment structure information;

[0025] Based on the production process information and the technological process information, the behavior restrictions of each twin object in the virtual model and the association rules between the twin objects are configured to generate the digital twin workshop.

[0026] Optionally, constructing the virtual model of the digital twin workshop based on the equipment structure information includes:

[0027] Construct a geometric model file of the digital twin workshop based on the equipment structure information;

[0028] The geometric model file is subjected to lightweight processing to generate a geometric model file to be rendered. The lightweight processing includes at least one of component reduction, mesh simplification, and occlusion culling.

[0029] The geometric model file is rendered and optimized to obtain the virtual model of the digital twin workshop.

[0030] Secondly, embodiments of this application provide a data processing method applied to a client, comprising:

[0031] In response to the user's backtracking operation, a backtracking request is sent to the server, the backtracking request including the target time period to be backtracked;

[0032] Receive target historical data corresponding to the target time period sent by the server, wherein the target historical data includes at least one of raw data, motion trajectory data, and data analysis results;

[0033] Generate and output backtracking results based on the target's historical data.

[0034] Thirdly, embodiments of this application provide a data processing system, the system comprising: a server and at least one client; the server being connected to a workshop and at least one of the clients respectively; the server being used to execute the method as described in any one of the first aspects; and the client being used to execute the method as described in any one of the second aspects.

[0035] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement various possible implementations of the first or second aspect described above.

[0036] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements various possible implementations of the first or second aspect described above.

[0037] The data processing method, system, storage medium, and program product provided in this application embodiment flexibly select appropriate acquisition methods with different response delays to acquire raw data based on the different raw data acquisition frequency requirements of the target objects in the workshop. Then, based on the acquired raw data, motion trajectory data of the corresponding target twin object in the digital twin workshop is accurately generated, and this motion trajectory data is sent to at least one client. This allows the client to render and display the motion changes of the target twin object in the digital twin workshop based on this data. Through this targeted acquisition, accurate generation, and multi-terminal synchronous display method, the digital twin workshop can ensure that it can reflect the real-time dynamic changes of the actual workshop in a timely and accurate manner, thereby improving the synchronization between the digital twin workshop and the actual workshop. Furthermore, the system architecture of the server and client enables multi-user access to the digital twin workshop, improving access flexibility. Attached Figure Description

[0038] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0039] Figure 1 This is a schematic diagram of the structure of a data processing system provided in an embodiment of this application;

[0040] Figure 2 A flowchart illustrating another data processing method provided in an embodiment of this application;

[0041] Figure 3 A flowchart illustrating another data processing method provided in an embodiment of this application;

[0042] Figure 4 A flowchart illustrating another data processing method provided in an embodiment of this application;

[0043] Figure 5 A flowchart illustrating another data processing method provided in an embodiment of this application;

[0044] Figure 6 This is a schematic diagram of another data processing system provided in an embodiment of this application;

[0045] Figure 7 This is a schematic diagram of a data interaction scenario provided in an embodiment of this application.

[0046] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0047] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0048] In today's manufacturing sector, with the deepening of the intelligent manufacturing concept, improving the intelligence, visualization, and collaboration of the production process has become key for enterprises to increase production efficiency, reduce costs, and enhance competitiveness. Digital twin technology, as an emerging advanced technology, has brought new changes and development opportunities to the manufacturing process.

[0049] The core of 3D visualization technology based on digital twins lies in constructing a virtual digital twin model that highly maps to the actual physical workshop. This model not only encompasses various physical elements in the physical workshop, such as production equipment, materials, and personnel, but also includes the logical relationships and operational rules between these elements. By deploying numerous sensors and data acquisition devices in the physical workshop, various data such as equipment operating parameters, production progress, and material flow are acquired in real time. This data is then transmitted to the digital twin model, which is updated and optimized in real time using advanced data processing and analysis algorithms. This allows the digital twin model to dynamically and accurately reflect the actual operating status of the physical workshop. Furthermore, 3D visualization technology can present the digital twin model to users in an intuitive and vivid 3D graphical interface, enabling users to observe the production process in the workshop as if they were there. This allows for remote monitoring, fault diagnosis, and production scheduling, significantly improving the efficiency and flexibility of production management.

[0050] However, despite the enormous application potential of digital twin-based 3D visualization technology in the manufacturing field, there are still many shortcomings that need to be addressed in its practical application.

[0051] First, existing digital twin technologies suffer from poor real-time synchronization between the virtual and physical workshops when constructing a digital twin model that includes all elements of the physical workshop and updating it using collected data. Because production activities in the physical workshop are constantly changing, factors such as the frequency of data acquisition, transmission delays, and model update algorithms can all cause the digital twin model to fail to reflect the actual state of the physical workshop in a timely and accurate manner, thus affecting the accuracy and timeliness of production decisions.

[0052] Secondly, existing digital twin technologies typically use a workshop twin model as a single client, which limits remote access for multiple users. In actual production scenarios, it is often necessary for personnel from multiple departments and positions to simultaneously monitor and manage the workshop's production status, such as production managers, equipment maintenance personnel, and quality control personnel. The single-client model cannot meet the needs of multi-user remote collaborative work, hinders information sharing and communication, and reduces the collaborative efficiency of production management.

[0053] In summary, existing digital twin-based 3D visualization technologies suffer from problems such as poor real-time synchronization and limited user access flexibility when applied in the manufacturing process.

[0054] In view of this, this application provides a data processing method based on a server-side and client-side system architecture. The server flexibly selects appropriate acquisition methods with varying response latency to obtain raw data based on the different raw data acquisition frequency requirements of the target objects in the workshop. Then, based on the acquired raw data, it accurately generates motion trajectory data of the corresponding target twin object in the digital twin workshop and sends this motion trajectory data to the clients. This allows each client to render and display the motion changes of the target twin object in the digital twin workshop based on this data. Through this targeted acquisition, accurate generation, and multi-terminal synchronous display method, it ensures that the digital twin workshop can reflect the real-time dynamic changes of the actual workshop in a timely and accurate manner, thereby improving the synchronization between the digital twin workshop and the actual workshop. Furthermore, the server-side and client-side system architecture enhances the flexibility of user access.

[0055] First, the application scenarios of this application will be introduced.

[0056] Figure 1 This is a schematic diagram of the structure of a data processing system provided in an embodiment of this application. Figure 1 As shown, the system includes: a server and at least one client. The server is connected to both the workshop and at least one client.

[0057] The workshop mentioned here refers to a physical workshop in the production and manufacturing environment, such as an automobile manufacturing workshop, an electronic chip production workshop, or a machining workshop. The workshop includes a variety of production equipment, and the production and manufacturing process is completed through the coordinated operation of various production equipment.

[0058] The server can be a high-performance industrial server, which has powerful computing and storage capabilities, enabling it to quickly process large amounts of data from the workshop and store and manage the data to support the stable operation of the entire data processing system. Alternatively, the server can be a data processing platform deployed in the cloud, which, with the elastic expansion and distributed computing advantages of cloud computing, can flexibly adjust computing resources according to actual needs to meet the data processing requirements of workshops of different sizes.

[0059] The client can be a software client, a web client, or similar. The client provides users with an interface and functional access points for interacting with the data processing system. Users can conveniently access and process workshop-related data by accessing the client. If the client is a software client, it can be installed on the user's electronic device, such as a computer, tablet, mobile phone, or smart wearable device. If the client is a web client, users can access the corresponding website and log in to use the client.

[0060] It should be understood that the number of clients included in the data processing system can be determined according to actual needs, and this application does not impose any restrictions on this.

[0061] The server is used to execute the corresponding method steps in the subsequent data processing methods. The client is used to execute the corresponding method steps in the subsequent data processing methods. The specific method steps to be executed by both can be found in the following method embodiments, and will not be elaborated here.

[0062] For example, taking a data processing system developed based on Unity and the Mirror network framework as an example, the server and client can achieve twin state synchronization using the Mirror network framework as the core, and achieve cross-platform communication through the WebSocket protocol. Specifically, the data processing system can be built through the following steps:

[0063] First, based on the constructed workshop twin model, import the Mirror plugin into the Unity development environment. The Mirror plugin is a powerful tool in Unity for implementing network synchronization functions, which simplifies the complexity of network programming and facilitates the construction of real-time systems with multi-user interaction.

[0064] Next, the imported Mirror plugin was configured accordingly. Synchronization components were added to the relevant objects in the workshop twin model. These components are responsible for encapsulating and marking the model's state information (such as position, rotation, scaling, attribute changes, etc.) for transmission and synchronization over the network. Simultaneously, WebSocket was explicitly selected as the communication protocol in the settings. The WebSocket protocol features full-duplex communication, enabling the establishment of a persistent connection between the client and server, achieving real-time bidirectional data transmission. This effectively addresses the shortcomings of the traditional HTTP protocol in terms of real-time performance, providing a reliable guarantee for cross-platform communication.

[0065] After completing the plugin setup, Unity's build functionality is used to export the project as a real-time monitoring twin EXE server and a WebGL client. The EXE server possesses powerful computing and processing capabilities, enabling it to receive data from the workshop in real time, update and maintain the twin model, and send the updated status information to various clients via the network. The WebGL client offers cross-platform compatibility, running in various mainstream browsers without requiring users to install complex additional software, thus lowering the barrier to entry.

[0066] Finally, a WebGL client is embedded in the front end of the visualization system. Using technologies such as HTML and JavaScript, the WebGL client is integrated into the web interface of the visualization system, making it a part of the system.

[0067] When a user accesses the site, the WebGL client automatically connects to the real-time monitoring twin server via the WebSocket protocol. Once the connection is established, the client sends a request to the server to obtain the initial state information of the twin model and renders and displays it locally. Simultaneously, the client continuously listens for state update data sent from the server and adjusts the local model's display state in real time based on this data, achieving synchronization between data and actions.

[0068] In this way, multiple users can access the visualization system simultaneously, and the state of the twin model seen by each user is real-time and consistent, thus enabling simultaneous access and collaborative work by multiple users.

[0069] Optionally, the data processing system can be developed based on a web front-end and back-end framework. The back-end (i.e., the server-side) can be developed using the C#.NET framework and Unity. Leveraging the powerful cross-platform development capabilities and rich class library resources of the C#.NET framework, it can efficiently integrate functions such as visual panel data management, real-time monitoring twin server, and historical data management.

[0070] In terms of visual panel data management, the backend can collect, organize, and classify relevant data of various equipment and objects in the workshop, store them in the database, and provide operation interfaces such as data query, update, and deletion to ensure the accuracy and integrity of the data.

[0071] For the real-time monitoring twin server, the backend can receive raw data from the workshop in real time, generate motion trajectory data and data analysis results of the target twin object based on the data, and push this data to the frontend (i.e., the client) in a timely manner. In terms of historical data management, the backend can store and back up historical data for a long time, while providing data retrieval and statistical analysis functions so that users can review and analyze historical data.

[0072] The front-end can be developed using the Vue framework, which is characterized by its simplicity, ease of use, responsive design, and component-based development, enabling the rapid creation of visual data dashboards. The front-end can access real-time and historical data by calling data interfaces provided by the back-end, and then display this data in intuitive charts, reports, and graphs on the visual data dashboard, allowing users to clearly understand the workshop's operational status and various indicators.

[0073] This data processing system features real-time status display, historical data playback, scene roaming, human-computer interaction, fault alarms, and AR support. In terms of real-time status display, the front end can display the status information of various equipment and objects in the workshop in real time, such as equipment operating status, temperature, pressure, and other parameters. The historical data playback function allows users to select different time periods to view historical data and movement within that time period. The scene roaming function allows users to freely move their viewpoint within the virtual environment of the digital twin workshop, observing the layout and equipment operation from all angles. The human-computer interaction function allows users to interact with the digital twin workshop through the front-end interface, such as remotely controlling equipment and adjusting parameters. The fault alarm function can promptly send alarm information to users when abnormal conditions occur in the workshop equipment, reminding them to take appropriate measures. The AR support function combines the virtual information of the digital twin workshop with the real-world scene, providing users with a more intuitive and immersive experience. Through this front-end and back-end collaborative development approach, this data processing system provides users with a feature-rich and user-friendly visualization platform, meeting the diverse needs of workshop production management and decision-making.

[0074] The following is based on Figure 1Taking the data processing system shown as an example, the technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems will be described in detail through specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0075] Figure 2 This is a flowchart illustrating a data processing method provided in an embodiment of this application. Figure 2 As shown, the method may include:

[0076] S201. The server obtains the raw data by adopting the collection method corresponding to the collection frequency requirement of the target object in the workshop.

[0077] The response delay varies depending on the acquisition method.

[0078] In a real-world workshop production environment, the target objects can be various production equipment within the workshop. Different target objects, due to differences in their functions and operating characteristics, have varying requirements for the frequency of raw data collection. For example, some high-speed operating production equipment may require a higher data collection frequency to monitor its operating status in real time; while relatively stable storage equipment may require a lower frequency. The determination of the required collection frequency can be based on factors such as the target object's performance parameters, production task requirements, and historical data. Raw data can include, for example, workshop production data, process data, equipment data, and personnel data.

[0079] Optionally, data collection methods can include subscription-based collection and periodic collection, each with different response latency characteristics. Subscription-based collection is an event-driven method that only collects data when specific changes occur in the raw data. This method typically has a short response latency and can promptly capture data changes, but the uncertainty of event triggering may lead to unstable data collection frequency. Periodic collection, on the other hand, collects data according to pre-set time intervals, resulting in a relatively stable collection frequency, but the response latency may be affected by the period length.

[0080] For example, for raw data in the workshop that requires frequent collection, if it is necessary to obtain minute changes in the raw data in a timely manner, a subscription-based collection method can be used. Data can be collected only when specific conditions are triggered (such as when the data change exceeds a certain threshold), thereby improving data real-time performance and reducing bandwidth consumption. Conversely, for raw data in the workshop that requires less frequent collection, such as configuration parameters, static data, and routine monitoring data, a periodic collection method can be used, collecting data at regular intervals to reduce resource consumption.

[0081] Alternatively, for a CNC machine tool in the workshop, when machining complex parts, it is necessary to monitor parameters such as tool wear and spindle speed in real time to ensure machining quality. In this case, the required frequency of raw data acquisition for these parameters is relatively high, which may require a periodic acquisition method, collecting data at short intervals to ensure timeliness and accuracy. For lighting equipment in the workshop, its operating status is relatively stable, and the required frequency of raw data acquisition is lower. A subscription-based acquisition method can be used, collecting data only when the on / off status of the lighting equipment changes.

[0082] Optionally, the server may include an in-memory database. The collected raw data can be stored in this database for later extraction to generate other data (such as motion trajectory data or data analysis results, as mentioned later). Expired raw data can be periodically cleaned up in the in-memory database, or the database can be adaptively cleaned based on remaining storage capacity and the space occupancy rate of stored data to ensure its storage functionality. This in-memory database can further accelerate the efficiency of reading and writing raw data on the server, thereby improving the synchronization between digital twin workshops.

[0083] S202. The server generates motion trajectory data of the target twin object in the digital twin workshop based on the original data.

[0084] Among them, the target twin object corresponds to the target object.

[0085] A digital twin workshop is a digital mapping of an actual workshop, and the target twin object is the virtual counterpart of the target object in the digital twin workshop. After obtaining the raw data of the target object, the server needs to process and analyze this data to generate the motion trajectory data of the target twin object.

[0086] The raw data typically contains various information such as the target object's position, velocity, and acceleration. The server can integrate and process this information, combining it with the coordinate system and geometric model of the digital twin workshop, to calculate the target twin object's trajectory within the digital twin workshop. For example, for a transport vehicle in the workshop, the raw data might contain information such as the vehicle's real-time position coordinates, speed, and direction. Based on this information, the server can simulate the vehicle's trajectory in the digital twin workshop, ensuring that the target twin object accurately reflects the actual vehicle's movement.

[0087] Specifically, the server can use interpolation algorithms, filtering algorithms, and other techniques to process the raw data to improve its accuracy and reliability. Simultaneously, it can combine physical models and dynamic principles to predict and simulate the motion of the target object, thereby generating more precise motion trajectory data.

[0088] S203. The server sends motion trajectory data to at least one client.

[0089] Correspondingly, the client receives motion trajectory data sent by the server.

[0090] The server can broadcast motion trajectory data to at least one client. Alternatively, the server can send motion trajectory data to a specified client upon request.

[0091] In practical applications, multiple clients may need to obtain the motion trajectory data of the target twin object in the digital twin workshop. For example, workshop managers can use the client to monitor the operating status of production equipment in real time, and production workers can use the client to understand the location and driving status of transport vehicles. The server can send the motion trajectory data to the appropriate client according to the client's permissions and needs.

[0092] Broadcasting is a common method of transmission, allowing the server to simultaneously send motion trajectory data to all connected clients, ensuring that all clients receive the latest motion trajectory information in a timely manner. This method is suitable for scenarios where all clients need to synchronize data updates.

[0093] S204. The client displays the motion changes of the target twin object generated based on motion trajectory data in the digital twin workshop.

[0094] After receiving motion trajectory data from the server, the client needs to render this data to display the motion changes of the target twin object in the digital twin workshop. Rendering is the process of transforming abstract motion trajectory data into visual graphics and animations. The client stores a pre-built digital twin workshop.

[0095] The client can use a 3D rendering engine to generate a 3D model of the target twin object based on motion trajectory data and the geometric model of the digital twin workshop, and then display it in the digital twin workshop. Simultaneously, the client can also update the position, posture, and motion state of the target twin object in real time based on the motion trajectory data, allowing users to intuitively observe the motion changes of the target twin object.

[0096] For example, the client can use animation to demonstrate the driving process of a transport vehicle in a digital twin workshop, including actions such as starting, accelerating, turning, and stopping. Users can zoom, rotate, and translate the digital twin workshop through the client's interactive interface to observe the movement and changes of the target twin object from different angles.

[0097] The method provided in this application embodiment flexibly selects appropriate acquisition methods with varying response delays to obtain raw data based on the different raw data acquisition frequency requirements of the target objects in the workshop. Then, based on the acquired raw data, it accurately generates motion trajectory data of the corresponding target twin object in the digital twin workshop and sends this motion trajectory data to at least one client. This allows the client to render and display the motion changes of the target twin object in the digital twin workshop based on this data. Through this targeted acquisition, accurate generation, and multi-terminal synchronous display method, it ensures that the digital twin workshop can reflect the real-time dynamic changes of the actual workshop in a timely and accurate manner, thereby improving the synchronization between the digital twin workshop and the actual workshop. Furthermore, the system architecture of the server and client enables multi-user access to the digital twin workshop, improving access flexibility.

[0098] The following section, taking subscription-based collection and periodic collection as examples, details how the server in step S201 acquires raw data according to the collection frequency requirements of the target objects in the workshop, using a collection method corresponding to the collection frequency requirements. Step S201 can specifically include the following two scenarios:

[0099] Scenario 1: When the required frequency of raw data collection is greater than or equal to the first frequency, a subscription-based collection method is used to obtain the raw data.

[0100] When the required frequency of raw data collection is high, subscription-based collection can promptly capture changes in the target object's data. The first frequency is a pre-set threshold used to differentiate the appropriate collection methods for different collection frequency requirements. This first frequency can be determined based on actual needs, and this application does not impose any restrictions on it.

[0101] For example, for a precision testing device in a workshop, even minor changes in its testing parameters can affect product quality assessment when testing high-precision products. Therefore, a high frequency of raw data collection for these testing parameters is required. When the required raw data collection frequency for this device is greater than or equal to a primary frequency, the server adopts a subscription-based collection method. The server sets a series of event subscription rules for this device. For example, when specific changes occur in the testing parameters such as temperature, pressure, and precision values, a data collection operation is immediately triggered. This ensures rapid collection when there are significant data changes, obtaining the latest data that plays a crucial role in production decisions. Although the collection frequency may be unstable due to the uncertainty of event triggering, it ensures the timeliness and effectiveness of the data, meeting the needs of high-precision data monitoring.

[0102] Scenario 2: When the required frequency of raw data acquisition is less than the second frequency, raw data is acquired by periodic acquisition.

[0103] The second frequency is less than the first frequency.

[0104] When the required frequency of raw data acquisition is relatively low, periodic acquisition can provide stable and regular data collection. The second frequency is also a pre-set threshold used to define the applicable range of periodic acquisition.

[0105] For example, some basic auxiliary facilities in the workshop, such as the ventilation and lighting systems, operate relatively stably and do not require constant monitoring of their data changes. When the required frequency of raw data collection for these facilities is less than the second frequency, the server adopts a fixed-period data collection method. The server will set an appropriate collection cycle based on the required collection frequency, such as collecting data on the ventilation system's wind speed and air volume, as well as the lighting system's on / off status and energy consumption data, at longer time intervals, such as every half hour or hour. This ensures that the necessary data is obtained to understand the basic operating status of the facilities without increasing the system load due to overly frequent data collection. Furthermore, the stable collection frequency facilitates subsequent systematic analysis and processing of the data.

[0106] For example, Table 1 provides a description of a data collection method and applicable scenarios provided in this application. The server can use the method shown in Table 1 below to collect raw data.

[0107] Table 1

[0108]

[0109] The response latency varies across different data collection methods. Subscription-based collection has a lower response latency than periodic collection, making it more suitable for collecting raw data with a high collection frequency. Periodic collection, on the other hand, has a higher response latency than subscription-based collection, making it more suitable for collecting raw data with a lower collection frequency. Table 1 further categorizes subscription-based and periodic collection into different levels to meet more detailed data collection needs. For example, subscription-based collection can be divided into fast subscription mode and normal subscription mode, while periodic collection can be divided into normal polling and slow polling.

[0110] As shown in Table 1, high-frequency change data has a higher acquisition frequency requirement, such as control commands. Process parameter acquisition frequency requirements are lower than high-frequency change data, but still belong to the category of data with higher acquisition frequency requirements, such as temperature and pressure data. Conventional monitoring data has a lower acquisition frequency requirement, such as equipment runtime, cumulative motor start-up count, and equipment vibration amplitude. Configuration parameter / static data acquisition frequency requirements are lower than conventional monitoring data acquisition frequency requirements, and longer acquisition cycles can be used, with longer response delays. Configuration parameters can include, for example, the equipment's rated power, maximum operating speed, sampling accuracy settings, and operating mode parameters.

[0111] Figure 3 This is a flowchart illustrating another data processing method provided in an embodiment of this application. Figure 3 As shown, the method may further include:

[0112] S301. The server generates data analysis results for the target twin object based on the original data.

[0113] After obtaining the raw data of the target object, the server can not only generate motion trajectory data for the target twin object, but also perform in-depth analysis on the raw data to generate data analysis results for the target twin object. These data analysis results can include information such as the target object's operational status assessment, fault prediction, and performance optimization suggestions.

[0114] For example, for a production machine in a workshop, the server can analyze parameters such as temperature, pressure, and vibration from its raw data. By establishing a data analysis model, it can assess whether the machine's operating status is normal. If a parameter is found to be outside the normal range, the server can predict potential malfunctions and provide corresponding maintenance suggestions. Simultaneously, the server can also perform performance optimization analysis based on historical and real-time data, proposing suggestions to improve production efficiency and quality.

[0115] S302, The server sends the data analysis results to the client.

[0116] Correspondingly, the client receives the data analysis results sent by the server.

[0117] After the server generates the data analysis results of the target twin object, it needs to send these results to the client so that the client user can understand the operation status of the target object and related analysis information in a timely manner.

[0118] The server can send the data analysis results to the client in the same way as it sends the motion trajectory data, such as by broadcasting or sending upon client request. After receiving the data analysis results, the client can display them on a data panel or other interface for user viewing and analysis. Optionally, the server can send the data analysis results simultaneously with the motion trajectory data, or it can send only the data analysis results upon client request.

[0119] For example, the client can display data analysis results in the form of charts and reports, such as equipment operating status curves and failure probability distribution maps. Users can further query and analyze the data analysis results through the client's interactive interface to make informed decisions.

[0120] S303: The client displays the data analysis results in the data panel.

[0121] After receiving the data analysis results from the server, the client can display them in a data panel. The data panel is a visual interface provided by the client to display various data and analysis results.

[0122] The client can use different display methods depending on the type and content of the data analysis results. For example, the results of equipment operation status assessment can be displayed as indicator lights, with green indicating normal and red indicating abnormal; the results of fault prediction can be displayed as a list showing the possible fault types and probabilities; and the performance optimization suggestions can be displayed as text descriptions, etc.

[0123] Meanwhile, the client also provides interactive functions such as data filtering, sorting, and exporting, making it convenient for users to further process and utilize the data analysis results. For example, users can filter data analysis results for a specific time period according to their needs, or export the analysis results to a file for more in-depth research and analysis.

[0124] The method provided in this application embodiment analyzes the raw data on the server side to generate data analysis results of the target twin object, and sends them to the client. The client displays these results in the data panel so that the client user can understand the operation status of the target object and related analysis information in a timely manner, thereby providing strong support for production management and decision-making in the workshop.

[0125] Furthermore, existing digital twin visualization systems for smart workshops primarily focus on real-time monitoring of equipment operation status and production processes within the workshop, failing to provide retrospective access to historical data on the workshop's operational status. During production, troubleshooting, production optimization, and quality traceability often require referencing historical data. The lack of effective retrospective access to historical data significantly hinders these tasks and prevents companies from extracting valuable information from historical data to guide future production activities.

[0126] Therefore, the workshop backtracking function can also be implemented in the following ways. Figure 4 This is a flowchart illustrating another data processing method provided in an embodiment of this application. Figure 4 As shown, the method may further include:

[0127] S401. In response to the user's rollback operation, the client sends a rollback request to the server.

[0128] Correspondingly, the server receives the backtracking request sent by the client.

[0129] The backtracking request includes the target time period to be backtracked.

[0130] In practical applications, users may need to view historical data and analysis results of a target object over a specific time period for troubleshooting, production tracing, and other operations. The client can provide an interface or function for backtracking operations. When a user performs a backtracking operation, the client generates a backtracking request based on the target time period selected by the user and sends it to the server.

[0131] For example, after discovering a malfunction in a piece of equipment in the workshop, a user might want to view the equipment's operating status and related data for a period of time prior to the malfunction. The user can select the target time period to be traced back on the client interface, such as one day or one week before the malfunction. The client will then include this target time period information in the traceback request and send it to the server.

[0132] S402. The server sends the target historical data corresponding to the target time period requested to the client based on the content of the backtracking request.

[0133] Correspondingly, the client receives the target historical data corresponding to the target time period sent by the server.

[0134] The target historical data includes at least one of the following: raw data, motion trajectory data, and data analysis results.

[0135] On the server side, after collecting raw data, it can persist the raw data. Similarly, after generating motion trajectory data and / or data analysis results based on the raw data, these can also be persisted. During storage, the timestamps corresponding to each data point can be stored synchronously. This way, even if the server subsequently cleans up the raw data in the in-memory database, it will not affect the persisted raw data, motion trajectory data, data analysis results, and other historical data, thus providing the corresponding target historical data for backtracking requests.

[0136] The server can store at least one of the following in a relational database: raw data, motion trajectory data, and data analysis results. This enables persistent storage and allows for quick retrieval of historical data corresponding to a target time period using timestamps. The relational database could be, for example, SQL Server or MySQL.

[0137] In this step, the server can determine the target time period and workshop information to be traced based on the content of the traceback request. Then, based on the target time period and workshop information, the server extracts target historical data that matches the request content from the relational database, including at least one of the following: original data of the corresponding time period and workshop, motion trajectory data, and data analysis results, and sends it to the client.

[0138] S403. The client generates and outputs backtracking results based on the target's historical data.

[0139] If the target historical data includes motion trajectory data, a dynamic retrospective scene of the digital twin workshop can be generated based on the motion trajectory data, and then the dynamic retrospective scene can be displayed.

[0140] Specifically, if the target's historical data includes motion trajectory data, the client can use this data to render and generate dynamic retrospective scenes in the digital twin workshop. Based on the location and time information in the motion trajectory data, the client can recreate the movement of the target twin object within a target time period in the digital twin workshop. For example, the client can use animation to display the driving trajectory of a transport vehicle over a past period, including actions such as starting, driving, turning, and stopping. After generating the dynamic retrospective scene, the client can display it on the interface for users to view and analyze. Users can interact with the dynamic retrospective scene through the client's interface, such as pausing, playing, fast-forwarding, and slowing down, to observe the movement changes of the target twin object in more detail.

[0141] If the target historical data also includes data analysis results, the data analysis results corresponding to the target time period can be extracted from the target historical data, and then the data analysis results of the retrospective can be output. For example, they can be displayed on the data panel in the retrospective interface, or they can be output to other data processing devices (such as electronic devices of other staff for display, or printing devices for printing output, etc.).

[0142] The method provided in this application embodiment sends a backtracking request to the server in response to the user's backtracking operation. The server sends the target historical data according to the request, and the client generates and outputs the backtracking result based on the target historical data. This allows users to easily view the historical data and movement of the target object in a certain period of time, thereby providing strong support for fault diagnosis, production traceability, etc.

[0143] The following section describes the method for constructing a digital twin workshop as described in the aforementioned method embodiments. Figure 5 This is a flowchart illustrating another data processing method provided in an embodiment of this application. Figure 5 As shown, this method, when applied to the server side, may also include:

[0144] S501. Obtain information on the equipment structure, production process, and technological procedures in the workshop.

[0145] The equipment structure information describes the physical structure and components of various equipment within the workshop, such as the equipment's dimensions, shape, and installation location. This information can be obtained through equipment design drawings, 3D models, or on-site measurements. For example, for a CNC machine tool, the equipment structure information may include the dimensions and relative positions of components such as the machine tool's spindle, tool post, and worktable.

[0146] Production process information describes the production process of products within the workshop, including raw material input, processing, assembly, and finished product output. Production process information can be obtained through production plans, process flow documents, or on-site observation. For example, in an electronics production workshop, production process information may include the sequence and time requirements for steps such as circuit board soldering, component assembly, and product testing.

[0147] Process information describes the processes and operating methods used in each production stage, such as machining processes and assembly processes. Process information can be obtained through process documents, operating procedures, or the experience of technical personnel. For example, in a machining workshop for mechanical parts, process information may include the parameters and operating requirements for machining processes such as turning, milling, and grinding.

[0148] The server can obtain equipment structure information, production process information, and technological process information obtained in advance through manual analysis of the workshop, such as by extracting them from relevant platforms or databases. Alternatively, the server can automatically collect equipment structure information, production process information, and technological process information from the workshop, such as by collecting process data, planning data, and quality data from workshop management systems, manufacturing execution systems (MES), enterprise resource planning (ERP), etc., and directly connect with the equipment in the workshop to determine equipment structure information, production process information, etc.

[0149] S502. Construct a virtual model of the digital twin workshop based on the equipment structure information.

[0150] After obtaining the equipment structure information of the workshop, the server needs to build a virtual model of the digital twin workshop based on this information. The virtual model is the foundation of the digital twin workshop, and it can accurately reflect the physical structure and layout of the actual workshop.

[0151] In one possible implementation, a virtual model of the digital twin workshop can be directly constructed based on the equipment's structural information. The server can use 3D modeling software to create 3D models of the equipment based on its size, shape, and installation location, and then combine these models to form the virtual model of the digital twin workshop. Alternatively, the virtual model can be constructed manually using 3D software, using standard format model files provided by the manufacturer, or by combining 3D point cloud reconstruction models.

[0152] In another possible implementation, a virtual model of the digital twin workshop can be constructed through the following sub-steps:

[0153] S5021. Construct a geometric model file for the digital twin workshop based on the equipment structure information.

[0154] The server can use 3D modeling tools to construct geometric model files for the digital twin workshop based on the equipment structure information. The geometric model files describe the geometric shapes and spatial relationships of various equipment and objects within the workshop.

[0155] For example, the server can use CAD software to create a 3D geometric model of the equipment based on its design drawings and on-site measurement data. These equipment models are then imported into the modeling environment of the digital twin workshop, where the geometric model file of the entire digital twin workshop is constructed based on the equipment's installation location and layout.

[0156] S5022. Perform lightweight processing on the geometric model file to generate the geometric model file to be rendered.

[0157] The lightweighting process includes at least one of the following: component reduction, mesh simplification, and occlusion removal.

[0158] Since geometric model files may contain a large amount of detail and data, they can be lightweighted to improve the rendering efficiency and performance of virtual models.

[0159] Component reduction refers to removing parts or details from the geometric model that have a minor impact on the overall effect, such as tiny screws or washers. Mesh simplification involves reducing the number of triangular meshes in the geometric model by merging adjacent meshes or reducing mesh precision to decrease the model's data size. Occlusion culling removes parts occluded by other objects, retaining only the visible parts of the model, thereby reducing unnecessary rendering calculations.

[0160] For example, for a complex industrial equipment model, the server can remove small components that do not affect the overall structure and function by component reduction; reduce the number of triangles in the model to a reasonable range by mesh simplification; and remove parts obscured by other equipment by occlusion culling, generating a geometric model file to be rendered. This process significantly reduces the data size of the geometric model file to be rendered, which in turn significantly reduces the consumption of computing resources and improves rendering speed during subsequent rendering operations, enabling the virtual model of the digital twin workshop to be displayed more smoothly on the client side.

[0161] For example, lightweight processing can be achieved by using software such as Pixyz Studio to perform operations such as component reduction, mesh simplification, multi-level detail model construction, and occlusion culling.

[0162] S5023. Render and optimize the geometric model file to obtain the virtual model of the digital twin workshop.

[0163] After receiving the geometric model file to be rendered, the server needs to optimize its rendering to obtain a high-quality virtual model for the digital twin workshop. Rendering optimization can be performed on multiple aspects, including lighting effects, material textures, and shadow processing.

[0164] Regarding lighting effects, the server can add appropriate light sources to the virtual model based on the actual lighting conditions of the workshop. For example, if the actual workshop uses ceiling lights, then light sources can be added to the corresponding locations in the virtual model, and parameters such as the intensity, color, and illumination range of the light source can be set. By adjusting the lighting effects, the virtual model can be made more realistic, allowing users to have a more intuitive experience when viewing the digital twin workshop.

[0165] Material texture processing is also a crucial aspect of rendering optimization. The server can assign appropriate material textures to different devices and objects in the virtual model. For metal equipment, metallic textures can be added to make it look more realistic; for plastic parts, plastic texture effects can be added. The selection and setting of material textures should be based on the material characteristics of objects in the actual workshop to ensure visual consistency between the virtual model and the actual workshop.

[0166] Shadow processing enhances the three-dimensionality and realism of virtual models. The server can calculate and add shadow effects based on the position of the light source and the geometry of the object. For example, when a light source shines on a device, it casts a shadow on the ground or other objects. The server can simulate this shadow effect to make the virtual model more lifelike.

[0167] Through the above rendering optimization operations, the server can obtain a high-quality virtual model of the digital twin workshop. This virtual model not only accurately reflects the physical structure and layout of the actual workshop, but also has good visual effects, providing a solid foundation for subsequent simulations and analyses in the digital twin workshop.

[0168] For example, the lightweighted collection model file can be imported into Unity for rendering and optimization (such as modifying and adjusting the relative positions between models, adding model materials and textures, adding ambient lighting, etc.) to obtain the physical geometry model of the digital twin workshop.

[0169] S503. Based on production process information and technological process information, configure the behavioral constraints of each twin object in the virtual model and the association rules between twin objects to generate a digital twin workshop.

[0170] After constructing the virtual model of the digital twin workshop, the server needs to further configure each twin object in the virtual model based on production process information and technological procedures. For example, this process can be based on Unity's geometric model, attaching scripts to the model to construct behavior and rule models, thereby generating a complete digital twin workshop.

[0171] The server can set behavioral restrictions for each twin object based on process information. Specifically, the server can define parameters, construct equipment association rules, and trigger rules for workshop equipment. For example, in a machining workshop, for a robot twin object, the server can define the movement direction, joint speed and acceleration, and joint limits of each joint based on the robot's operating process and actual operating requirements. When the robot performs a task, the movement of each joint has specific directional ranges, speed and acceleration limits, as well as the joint's extreme positions. The server will set the specific values ​​and ranges of these parameters for the twin object in the virtual model to ensure that it does not exceed the actual operating range during simulation, just as the movement of an actual robot in physical space is limited by its own mechanical structure and control program. At the same time, for twin objects of other equipment such as machine tools, the server can also set corresponding behavioral restrictions based on their processing technology and operating procedures, such as upper and lower limits for parameters like the machine tool spindle speed and feed rate, to ensure that the equipment operation in the virtual model conforms to the actual situation.

[0172] The association rules between twin objects can be determined based on production process information. The server can attach scripts to the physical geometry model to implement these association rules. For example, in a machining scenario, there is a close collaborative relationship between robots and machine tools. The server can construct association operation rules between the robot and the machine tool. When the machine tool completes a certain machining operation, it triggers a specific signal. Upon receiving this signal, the robot executes corresponding actions according to a preset program, such as picking up the machined parts and placing them in a designated position, or changing the cutting tools for the machine tool. In the virtual model, the server can set these association rules through scripts, enabling the robot twin object and the machine tool twin object to accurately simulate this collaborative process. Another example is in an electronics manufacturing workshop, where the soldering process of circuit boards must be completed before the component assembly process can begin. The server can set association rules between circuit board twin objects and component twin objects in the virtual model, meaning that only after the circuit board twin object completes the soldering process can the component twin object begin the assembly process. Through the setting of these association rules, the actual workshop production process can be accurately simulated, allowing the digital twin workshop to realistically reflect the actual production process.

[0173] After configuring the behavioral constraints and association rules for each twin object on the server side, a complete digital twin workshop can be generated. This digital twin workshop not only has a similar physical structure and layout to the actual workshop, but also simulates the actual workshop's production process based on behavior and rule models using additional scripts, providing strong support for workshop production management, fault prediction, and performance optimization. Through precise configuration of the behaviors and association rules of each twin object in the virtual model, the digital twin workshop can highly replicate the operation of the actual workshop in a virtual environment, providing relevant personnel with more realistic and accurate production simulation and decision-making basis.

[0174] The method provided in this application acquires equipment structure information, production process information, and technological procedure information of a workshop through a server, constructs a virtual model of a digital twin workshop, and configures behavioral constraints and association rules for the twin object based on the production process and technological procedure information to generate a complete digital twin workshop. Then, based on the different raw data collection frequency requirements of the target objects in the workshop, an appropriate collection method is flexibly selected to acquire raw data, generating motion trajectory data and data analysis results of the target twin object. This data is sent to the client, which can display the motion changes and data analysis results of the target twin object in the digital twin workshop. It can also provide historical data retrospective results based on user retrospective operations. Through this comprehensive and systematic approach, a high degree of synchronization and accurate mapping between the digital twin workshop and the actual workshop is achieved, providing effective support for intelligent management and decision-making in the workshop, improving production efficiency and quality, and reducing production costs and risks.

[0175] For example, Figure 6 This is a schematic diagram of another data processing system provided in an embodiment of this application. Figure 6 As shown, the data processing system includes a digital twin visualization system integration module, a model optimization module, a geometric model, a behavioral model, a rule model, an in-memory database, a relational database, and a data acquisition system.

[0176] The server-side architecture includes an in-memory database, a relational database, a data acquisition system, a model optimization module, geometric models, behavioral models, and rule models. The data acquisition system is responsible for collecting various types of data from field devices such as Programmable Logic Controllers (PLCs), robots, Automated Guided Vehicles (AGVs), and industrial cameras, as well as from business systems such as MES, ERP, and Supervisory Control and Data Acquisition (SCADA). The in-memory database is used for rapid storage and access to some of the collected data, meeting the system's high-speed data processing requirements. The relational database primarily performs structured storage and management of the data, facilitating subsequent backtracking operations. The geometric model, behavioral model, and rule model are continuously optimized and improved by the model optimization module to more accurately simulate physical entities. The digital twin visualization system integration module integrates these optimized models and processed data, ultimately providing clients with various functional services such as real-time status viewing, scene roaming, fault alarms, historical backtracking, human-computer interaction, and AR support.

[0177] The methods and technical effects of the data processing system described above are the same as those in the aforementioned method embodiments, and will not be repeated here.

[0178] For example, Figure 7 This is a schematic diagram illustrating a data interaction scenario provided in an embodiment of this application. Figure 7 As shown, the server collects raw data of target objects within the workshop from field devices and stores it in an in-memory database, while simultaneously persisting the raw data to a relational database. Then, based on the raw data in the in-memory database, it generates motion trajectory data (generated via a digital twin server) and / or data analysis results (generated via a data server), and sends the motion trajectory data and / or data analysis results to the client to achieve online real-time monitoring of the workshop (i.e., the client renders and generates a dynamic digital twin workshop scene synchronized with the workshop). Simultaneously, the generated motion trajectory data (or motion data) and data analysis results can also be persistently stored in the relational database. This allows for subsequent backtracking operations, where the backtracking server retrieves corresponding historical data (including raw data, motion trajectory data, and data analysis results) from the relational database and pushes it to the client for historical backtracking display.

[0179] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0180] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0181] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0182] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0183] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0184] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0185] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0186] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0187] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0188] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A data processing method, characterized in that, Applied to the server side, including: Based on the required collection frequency of raw data of the target objects in the workshop, the raw data is obtained by adopting a collection method corresponding to the required collection frequency. Different collection methods have different response delays. Based on the original data, motion trajectory data of the target twin object in the digital twin workshop of the workshop is generated, wherein the target twin object corresponds to the target object; The motion trajectory data is sent to at least one client so that the client can display the motion changes of the target twin object rendered based on the motion trajectory data in the digital twin workshop.

2. The method according to claim 1, characterized in that, The data collection methods include subscription-based collection and periodic collection. The step of acquiring the raw data using a collection method corresponding to the required collection frequency of the target object's raw data in the workshop includes: When the required collection frequency of the raw data is greater than or equal to the first frequency, the raw data is obtained by subscription-based collection. When the required acquisition frequency of the original data is less than the second frequency, the original data is acquired by the fixed-period acquisition, wherein the second frequency is less than or equal to the first frequency.

3. The method according to claim 1, characterized in that, Also includes: Based on the original data, generate the data analysis results of the target twin object; The data analysis results are sent to the client so that the client can display the data analysis results in the data panel.

4. The method according to claim 3, characterized in that, Also includes: Store at least one of the following: the raw data, the motion trajectory data, and the data analysis results.

5. The method according to claim 4, characterized in that, Also includes: Upon receiving a backtracking request from the client, the system sends target historical data corresponding to the requested target time period to the client according to the request content of the backtracking request. The target historical data includes at least one of the original data, the motion trajectory data, and the data analysis results.

6. The method according to claim 5, characterized in that, The target historical data is stored in a relational database.

7. The method according to claim 1, characterized in that, Sending the motion trajectory data to at least one client includes: The motion trajectory data is broadcast to at least one of the clients.

8. The method according to any one of claims 1-7, characterized in that, The method further includes: Obtain the equipment structure information, production process information, and technological procedure information of the workshop; A virtual model of the digital twin workshop is constructed based on the equipment structure information; Based on the production process information and the technological process information, the behavior restrictions of each twin object in the virtual model and the association rules between the twin objects are configured to generate the digital twin workshop.

9. The method according to claim 8, characterized in that, The step of constructing a virtual model of the digital twin workshop based on the equipment structure information includes: Construct a geometric model file of the digital twin workshop based on the equipment structure information; The geometric model file is subjected to lightweight processing to generate a geometric model file to be rendered. The lightweight processing includes at least one of component reduction, mesh simplification, and occlusion culling. The geometric model file is rendered and optimized to obtain the virtual model of the digital twin workshop.

10. A data processing method, characterized in that, Applied to the client side, including: In response to the user's backtracking operation, a backtracking request is sent to the server, the backtracking request including the target time period to be backtracked; Receive target historical data corresponding to the target time period sent by the server, wherein the target historical data includes at least one of raw data, motion trajectory data, and data analysis results; Generate and output backtracking results based on the target's historical data.

11. The method according to claim 10, characterized in that, The target historical data includes the motion trajectory data, and the step of generating and outputting backtracking results based on the target historical data includes: A dynamic retrospective scene of the digital twin workshop is generated based on the motion trajectory data; This displays the dynamic backtracking scenario.

12. A data processing system, characterized in that, The system includes: a server and at least one client; The server is connected to the workshop and at least one of the clients. The server is used to perform the method as described in any one of claims 1-9; The client is used to perform the method as described in any one of claims 10-11.

13. The system according to claim 12, characterized in that, The server-side includes: an in-memory database and a relational database; The in-memory database is used to store the original data of the target objects in the workshop; The relational database is used to store at least one of the following: the original data, the motion trajectory data of the target twin object in the digital twin workshop of the workshop, and the data analysis results of the original data, wherein the target twin object corresponds to the target object.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-11.

15. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-11.

Images