Data processing method and device based on digital avatar and storage medium

Abstract

The invention discloses a data processing method and device based on digital clones and a storage medium, and relates to the field of computer science and technology. The method comprises the following steps: acquiring multi-source data in a target scene; data influencing the digital copies are screened out from the multi-source data, the screened data influencing the digital copies serve as target information, the target information is received through the digital copies, and the digital copies are associated with the multiple systems and the multiple devices in the target scene through different target interfaces; and when it is detected that task information exists in the target information, determining a task level of the task information, and adjusting a task allocation strategy and a resource allocation strategy of the digital avatar based on the task level and the resource information. According to the method and the device, the technical problems of data synchronization delay and unsmooth function coordination in the digital duplicate integration process in the prior art are solved.

Description

Technical Field

[0001] This application relates to the fields of computer science and technology, and more specifically, to a data processing method, apparatus, and storage medium based on digital clones. Background Technology

[0002] In the wave of digital transformation, digital avatar technology is increasingly demonstrating its unique value in simulating real-world entities and scenarios, and is widely used in various fields such as smart manufacturing, smart cities, and remote collaboration. However, existing technological frameworks face significant challenges when integrating digital avatars with physical systems, with data synchronization delays and functional inconsistencies being particularly prominent. Data synchronization delays are not only reflected in the low efficiency of multi-source data acquisition and processing, but also in the latency and inconsistencies commonly encountered in data transmission. This directly weakens the real-time performance and accuracy of digital avatars, reduces system stability, and impacts user experience. Functional inconsistencies stem from insufficient standardization of interfaces between digital avatars and different systems (such as IoT devices and AI platforms), leading to low interaction efficiency during functional integration and making it difficult to meet the high requirements of cross-scenario, multi-task collaboration.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This application provides a data processing method, apparatus, and storage medium based on digital clones, to at least solve the technical problems of data synchronization delay and poor functional coordination encountered in the integration process of digital clones in the prior art.

[0005] According to one aspect of this application, a data processing method based on digital clones is provided, comprising: acquiring multi-source data in a target scenario; filtering out data that affects the digital clone from the multi-source data, using the filtered data that affects the digital clone as target information, and receiving the target information through the digital clone, wherein the digital clone is interconnected with multiple systems and multiple devices in the target scenario through different target interfaces; when task information is detected in the target information, determining the task level of the task information, and adjusting the task allocation strategy and resource allocation strategy of the digital clone based on the task level and resource information, wherein the resource information is used to characterize the load information of the systems and devices associated with the digital clone.

[0006] Optionally, data that affects the digital clone is filtered from multi-source data, and the filtered data that affects the digital clone is used as target information. The target information is then received through the digital clone. This includes: receiving multi-source data through a message queue, wherein the message queue is used for asynchronous data transmission; reading multi-source data from the message queue and filtering data that affects the digital clone from the multi-source data, and using the filtered data that affects the digital clone as target information; storing the target information through distributed storage and receiving the target information through the digital clone.

[0007] Optionally, in the process of receiving target information through the digital clone, the method further includes: compressing the target information to obtain first information; performing data verification and data validation on the first information, wherein the data verification is used to check whether there are any abnormalities in the data during transmission, and the data validation is used to verify whether the data conforms to the expected format; if the first information fails the data verification and / or data validation, triggering a retransmission mechanism or a repair mechanism, wherein the retransmission mechanism is used to indicate the reacquisition of multi-source data, and the repair mechanism is used to repair the data that failed in the first information; if the first information passes the data verification and data validation, the passed first information is transmitted to the digital clone.

[0008] Optionally, the task allocation strategy and resource allocation strategy of the digital clone can be adjusted based on the task level and resource information, including: adjusting the task allocation strategy of the digital clone based on the task level and resource information of the task information; and adjusting the resource allocation strategy of the digital clone based on the task level, resource information and task requirements of the task information.

[0009] Optionally, the method further includes: setting target rules, wherein the target rules are used to guide the action strategies that the digital clone should take under different conditions; and adjusting the response strategy of the digital clone according to the target rules and multi-source data.

[0010] Optionally, the method further includes: collecting user operation information in the target application, wherein the target application is used to represent the application of the digital clone embedded in the target scenario; identifying user pattern information based on user operation information, wherein the pattern information includes at least user preference information, demand information, and habit information; and adjusting the page layout and interaction logic of the target application based on the pattern information and user feedback information.

[0011] Optionally, the method further includes: obtaining device information of the target device where the target application is located, wherein the device information includes at least the screen resolution and pixel density of the target device; and adjusting the display layout of the digital clone in the target application according to the device information.

[0012] According to another aspect of this application, a data processing device based on a digital clone is also provided, comprising: an acquisition unit for acquiring multi-source data in a target scenario; a first determination unit for filtering out data that affects the digital clone from the multi-source data, using the filtered data that affects the digital clone as target information, and receiving the target information through the digital clone, wherein the digital clone is interconnected with multiple systems and multiple devices in the target scenario through different target interfaces; and a second determination unit for determining the task level of the task information when task information is detected in the target information, and adjusting the task allocation strategy and resource allocation strategy of the digital clone based on the task level and resource information, wherein the resource information is used to characterize the load information of the systems and devices associated with the digital clone.

[0013] According to another aspect of this application, a computer-readable storage medium is also provided, which includes a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the above-described data processing method based on digital clone.

[0014] According to another aspect of this application, an electronic device is also provided, including one or more processors and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors perform a data processing method based on digital clones.

[0015] This application first acquires multi-source data from the target scenario, then filters out data that impacts the digital clone, using this impactful data as target information. The digital clone receives this target information, which is interconnected with multiple systems and devices in the target scenario through different target interfaces. When task information is detected within the target information, its task level is determined, and the task allocation and resource allocation strategies of the digital clone are adjusted based on the task level and resource information. The resource information characterizes the load information of the systems and devices associated with the digital clone. By employing efficient synchronization and dynamic resource allocation, the application ensures real-time data synchronization and efficient functional coordination between the digital clone and the systems and devices in the target scenario. This improves the real-time responsiveness, collaboration efficiency, and user experience of the digital clone application, thereby solving the technical problems of data synchronization delays and poor functional coordination encountered during the integration process of digital clones in existing technologies. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0017] Figure 1 This is a flowchart of an optional data processing method based on digital clone according to an embodiment of this application;

[0018] Figure 2 This is a schematic diagram of an optional data processing method based on digital clone according to an embodiment of this application. Detailed Implementation

[0019] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0020] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0021] It should be noted that the information collected in this application (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) are information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of this data all comply with relevant laws, regulations, and standards, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding access points are provided for users to choose to authorize or refuse. For example, interfaces are set up between this system and relevant users or organizations, providing users with corresponding access points to choose to agree to or refuse automated decision-making results; if the user chooses to refuse, the process proceeds to the expert decision-making stage.

[0022] According to an embodiment of this application, a method embodiment for data processing based on digital clones is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0023] It should be noted that the intelligent processing system can serve as the execution subject of the data processing method based on digital cloning in the embodiments of this application. It is understood that the data processing method based on digital cloning provided in the embodiments of this application can also be executed by other systems or devices, and the embodiments of this application do not specifically limit this.

[0024] Figure 1 This is a flowchart of an optional data processing method based on digital cloning according to an embodiment of this application, such as... Figure 1 As shown, the method includes the following steps:

[0025] Step S101: Obtain multi-source data for the target scenario.

[0026] Optionally, the target scenario refers to the specific context in which the digital avatar interacts with the actual environment or system, such as a manufacturing production line, a smart home environment, or a remote education platform. The scenario defines the scope and type of data collection.

[0027] Optionally, multi-source data includes real-time data from different systems, devices, or sensors, such as device status data, environmental monitoring data, and user behavior data, which provide comprehensive information support for the digital clone.

[0028] Optionally, the intelligent processing system establishes a stable communication connection between the digital clone and the existing system or platform in the target scene, monitors data changes in real time, and triggers a synchronization operation immediately once the data is updated. For example, a listening mechanism can be set up in the database to ensure that the digital clone can obtain the latest data in a timely manner and reduce latency.

[0029] Optionally, the intelligent processing system employs real-time data synchronization and efficient synchronization algorithms to ensure that the digital clone can acquire and update data in a timely manner, reducing data latency and inconsistency issues.

[0030] Optionally, the system can acquire real-time multi-source data of the target scene through various means (such as API interfaces, sensor readings, database queries, etc.), and aggregate this data to provide raw information for subsequent processing steps.

[0031] Optionally, after acquiring multi-source data, the system will perform denoising, completion, and fusion processing on the collected multi-source data to ensure data accuracy and consistency. Outlier data points are identified and removed using statistical and machine learning methods. A weighted fusion algorithm is then used to fuse the data, and the weights are dynamically adjusted based on the reliability of the data sources.

[0032] Step S102: Select data that affects the digital clone from the multi-source data, use the selected data that affects the digital clone as target information, and receive the target information through the digital clone.

[0033] In step S102, the digital clone is interconnected with multiple systems and devices in the target scenario through different target interfaces.

[0034] Optionally, an efficient synchronization algorithm can be used to filter data that affects the digital clone from multi-source data. This algorithm is mainly used to filter key information that directly affects the digital clone from a large amount of multi-source data. The algorithm may combine data filtering, feature extraction, pattern recognition and other technologies to ensure efficient data processing and accurate matching.

[0035] Optionally, when synchronizing large amounts of data, the efficient synchronization algorithm can quickly filter out key data for synchronization, while using data compression technology to reduce the amount of data transmitted and improve synchronization efficiency. In addition, the algorithm verifies and validates the data during the synchronization process to ensure the integrity and accuracy of the data.

[0036] Optionally, the target information, after being processed by an efficient synchronization algorithm, consists of information that can influence the decisions and actions of the digital avatar. For example, when a digital avatar is deployed in an industrial manufacturing scenario, the target information may include the operating status of production equipment, raw material inventory status, and changes in work plans.

[0037] Optionally, the target interface refers to a standardized API interface specification that allows the digital clone to deeply integrate with different types of existing systems or platforms. By calling these interfaces, the digital clone can perform operations such as data querying, function invocation, and event notification, achieving seamless functional integration and interaction. This ensures that the digital clone can correctly interpret and respond to different system signals and service requests.

[0038] Optionally, the interaction between the digital avatar and multiple systems and devices is achieved through target interfaces, ensuring bidirectional data flow and close cooperation between systems.

[0039] Optionally, the intelligent processing system also includes a plug-in mechanism: a plug-in management module is developed to handle the installation, uninstallation, loading, and updating of plug-ins. Users can easily add and manage various functional plug-ins, such as voice recognition plug-ins and image processing plug-ins, through configuration files or a management interface, according to their own needs, enhancing the scalability and adaptability of the digital avatar application.

[0040] Optionally, through API interfaces and plugin mechanisms, the digital clone can seamlessly connect and interact with the functions of existing systems or platforms, enhancing the scalability and adaptability of the functions.

[0041] Optionally, after collecting multi-source data, the intelligent processing system uses a first algorithm to analyze and filter the data to determine the target information. This target information is then transmitted to the digital avatar. Upon receiving the information, the digital avatar can adjust itself or perform specific tasks based on this key data, enhancing its adaptability and functionality in the target scenario.

[0042] Step S103: When the target information includes task information, determine the task level of the task information, and adjust the task allocation strategy and resource allocation strategy of the digital clone based on the task level and resource information.

[0043] In step S103, resource information is used to characterize the load information of the systems and devices associated with the digital clone.

[0044] Optionally, task information: information used to characterize the target information that needs to be executed by the digital clone.

[0045] Optionally, task level: This is a priority assessment of task information, which determines the execution order and resource allocation of tasks. Higher-level tasks often require higher priority and more resource support.

[0046] Optionally, resource information: characterizes the current resource status of systems and devices associated with the digital clone, including computing resources, network bandwidth, storage capacity, etc. Monitoring resource information helps to allocate tasks rationally and avoid resource overload.

[0047] Optionally, when the target information includes task information, the intelligent processing system will further evaluate the priority of these tasks. This evaluation may be based on criteria such as task urgency, resource consumption, or impact on the overall system. Subsequently, based on the task priority and real-time monitored resource information, the intelligent processing system adjusts the task allocation and resource allocation strategies of the digital avatar to ensure that high-priority tasks are executed first, while optimizing resource allocation to support parallel processing of multiple tasks and avoiding waste or bottlenecks caused by excessive resource concentration.

[0048] Optionally, the above steps, through efficient data collection, filtering, and intelligent task management, ensure the digital avatar's real-time perception, decision-making, and execution capabilities in complex scenarios. Collaborative work with the target interface enables close interaction between the digital avatar and its environment. Furthermore, the hierarchical processing of task information, combined with dynamic adjustment of resource information, further optimizes the digital avatar's task execution efficiency and resource utilization efficiency, enhancing its response speed and collaborative capabilities in the target scenario. The implementation of this series of steps not only overcomes the problems of data synchronization delays and poor functional coordination in existing digital avatar integration methods but also significantly improves the user experience, laying a solid technical foundation for the in-depth application of digital avatar technology in fields such as intelligent manufacturing, smart cities, and remote collaboration.

[0049] As described in steps S101 to S103, this application first acquires multi-source data from the target scenario, then filters out data that affects the digital clone from the multi-source data, using this filtered data as target information. The digital clone receives this target information, which is interconnected with multiple systems and devices in the target scenario through different target interfaces. When task information is detected in the target information, the task level is determined, and the task allocation strategy and resource allocation strategy of the digital clone are adjusted based on the task level and resource information. The resource information represents the load information of the systems and devices associated with the digital clone. By employing efficient synchronization and dynamic resource allocation, the application ensures real-time data synchronization and efficient functional coordination between the digital clone and the systems and devices in the target scenario. This improves the real-time response capability, collaboration efficiency, and user experience of the digital clone application, thereby solving the technical problems of data synchronization delay and poor functional coordination encountered in the integration process of digital clones in existing technologies.

[0050] In one optional embodiment, the intelligent processing system receives multi-source data through a message queue, wherein the message queue is used for asynchronous data transmission. Then, it reads the multi-source data from the message queue, filters out the data that affects the digital clone from the multi-source data, uses the filtered data that affects the digital clone as target information, stores the target information through distributed storage, and receives the target information through the digital clone.

[0051] Optionally, a message queue is a data structure used for asynchronous data transmission that can store messages until they are processed, allowing data production and consumption to be separated in time, thus improving the flexibility and efficiency of system processing.

[0052] Optionally, the intelligent processing system first establishes stable connections with multiple external systems, including sensors, databases, and third-party API services of the physical system. To achieve real-time and efficient asynchronous data transmission, this embodiment employs message queue technology as middleware for data transmission. A message queue is a first-in, first-out (FIFO) data structure that can store information from different data sources until it is processed or requested. When a data source generates new data, the intelligent processing system pushes it to the message queue; when the intelligent processing system is ready to process the data, it pulls the data from the queue, achieving asynchronous data transmission and processing.

[0053] Optionally, after reading multi-source data from the message queue, the intelligent processing system applies a high-efficiency synchronization algorithm to process the data. This algorithm is designed to accurately identify "target information" relevant to the current application scenario from massive amounts of data. For example, in a scenario where digital avatars are used for industrial manufacturing monitoring, target information may include key indicators such as machine operating status, production line efficiency, and inventory levels. The processed "target information" is stored in a distributed storage system to ensure high availability and multiple copies for redundancy, preventing data loss and improving access speed.

[0054] Optionally, the target information stored in distributed storage is further transmitted to the client of the digital clone. The digital clone can update and respond intelligently in real time based on the received target information. For example, the digital clone may display a real-time status synchronized with the physical system, or automatically activate maintenance mode upon receiving a fault alarm to notify technicians for handling.

[0055] Optionally, the intelligent processing system may use distributed storage technology (such as blockchain or distributed database) to store data and achieve real-time data synchronization through message queues.

[0056] As shown above, message queue technology ensures asynchronous and efficient data transmission, avoiding bottlenecks in data processing. The introduction of efficient synchronization algorithms optimizes the data filtering and analysis process, enabling intelligent processing systems to quickly and accurately determine target information. Distributed storage guarantees data reliability and response speed, allowing the digital avatar to update and respond promptly upon receiving target information. These combined technologies achieve efficient, stable, and intelligent data synchronization and processing between the digital avatar and multi-source systems, significantly improving the performance and user experience of the digital avatar in practical applications.

[0057] In one optional embodiment, the intelligent processing system compresses the target information to obtain first information, and performs data verification and data validation on the first information. The data verification is used to check whether there are any abnormalities in the data during transmission, and the data validation is used to verify whether the data conforms to the expected format. If the first information fails the data verification and / or data validation, a retransmission mechanism or a repair mechanism is triggered. The retransmission mechanism is used to indicate the reacquisition of multi-source data, and the repair mechanism is used to repair the data that failed the first information. If the first information passes the data verification and data validation, the passed first information is transmitted to the digital clone.

[0058] Optionally, this embodiment provides an optimized application method based on digital avatars, which particularly emphasizes the data processing flow in the real-time data synchronization mechanism. In specific implementation, the intelligent processing system is responsible for compressing the target information to reduce data transmission volume and improve data synchronization efficiency. The target information originates from multiple data sources, including but not limited to sensor data from physical systems, database updates, and data returned from real-time communication interfaces.

[0059] Optionally, data compression processing: The intelligent processing system compresses the collected multi-source data using compression algorithms to generate smaller first-information data. This step aims to reduce the time and bandwidth required for data transmission and improve the speed of data synchronization.

[0060] Optionally, data verification and validation: The compressed initial information is further verified and validated. Data verification uses algorithms such as CRC (Cyclic Redundancy Check) to ensure that the data has not been damaged or tampered with during transmission. Data validation checks whether the data conforms to the expected format and standards, such as JSON, XML, etc., to ensure that the digital clone can correctly parse and use the data.

[0061] Optionally, an exception handling mechanism: If the initial information fails data validation or verification, the intelligent processing system will trigger an exception handling procedure. The retransmission mechanism is activated, and the system re-obtains multi-source data from the data source to ensure data integrity and timeliness. The repair mechanism then repairs the data that failed validation, such as supplementing missing fields or correcting format errors, to ensure that the initial information meets the reception standards.

[0062] Optionally, once the initial information passes data verification and validation, the intelligent processing system will transmit it to the digital clone in real time via a stable communication connection, enabling real-time data updates. The digital clone will then adjust its behavior accordingly to maintain synchronization with the physical system.

[0063] Optionally, the intelligent processing system monitors the data synchronization status in real time and automatically triggers a retransmission or repair mechanism when an anomaly is detected.

[0064] As described above, the intelligent processing system effectively improves the data synchronization efficiency and accuracy in digital avatar applications through a series of processes such as data compression, verification, and validation. The introduction of anomaly handling mechanisms (retransmission and repair mechanisms) further ensures the stability and quality of data synchronization. Even in complex or unstable network environments, it ensures that the data received by the digital avatar is complete and accurate, thereby achieving more intelligent and efficient operational responses and user experiences in various application scenarios. The overall effect is a significant reduction in data transmission latency, an improvement in data synchronization accuracy, and a more synchronized and stable operational performance between the digital avatar and the physical system.

[0065] In one optional embodiment, the intelligent processing system adjusts the task allocation strategy of the digital clone based on the task level and resource information of the task information, and adjusts the resource allocation strategy of the digital clone based on the task level, resource information and task requirements of the task information.

[0066] Optionally, the intelligent processing system first collects detailed information on all tasks to be processed, including the nature of the task, its urgency, and the type and quantity of resources required. Accurate collection of this information is the foundation for subsequent intelligent task and resource management. The system then classifies each collected task according to preset rules and algorithms. The criteria for task classification may include task priority, deadline, and criticality to the business. Higher-level tasks will receive priority processing and resource allocation.

[0067] Optionally, the intelligent processing system monitors the resource status within the system in real time, including CPU utilization, memory usage, network bandwidth, and storage capacity. This information is crucial for dynamic resource allocation. Based on task priority and current resource information, the intelligent processing system employs optimization algorithms (such as dynamic task scheduling algorithms based on reinforcement learning) to adjust the task allocation strategy. High-priority tasks will be preferentially allocated to nodes with sufficient resources to ensure timely completion.

[0068] Optionally, based on the resource requirements of the task and the resource status within the system, the intelligent processing system intelligently adjusts the allocation of resources used by the digital clone through a resource prediction model and an adaptive allocation strategy. For example, when the system detects that a task has a high demand for CPU computing power, it will prioritize scheduling idle CPU resources to ensure the efficient execution of the task.

[0069] Optionally, the system's intelligent processing not only occurs at the start of a task, but also adjusts task allocation and resource allocation strategies in real time based on task execution and resource usage status through a continuous dynamic feedback mechanism to adapt to the ever-changing environment.

[0070] Optionally, the intelligent processing system can coordinate the interaction between the digital clone and different systems through task scheduling algorithms and resource allocation strategies. Specifically, the system can dynamically optimize the task allocation strategy based on greedy algorithms or genetic algorithms, and dynamically adjust the resource allocation ratio according to the system load and task requirements.

[0071] As can be seen from the above, through the implementation methods described, the digital avatar can intelligently adjust task allocation and resource allocation strategies based on task level and resource information in complex and ever-changing environments, significantly improving task execution efficiency, resource utilization efficiency, and user experience. Through adaptive reinforcement learning algorithms, the system can not only respond to changes in task requirements in real time but also predict and adapt to future needs, thereby achieving long-term efficient system operation and providing users with a more intelligent, efficient, and personalized service experience.

[0072] In one alternative embodiment, the intelligent processing system sets target rules, which guide the action strategies that the digital clone should take under different conditions, and adjusts the response strategies of the digital clone according to the target rules and multi-source data.

[0073] Optionally, the intelligent processing system first sets a series of target rules based on the application scenario. These rules may involve, but are not limited to, aspects such as user experience optimization, efficient resource utilization, and data synchronization accuracy, to guide the digital clone's action strategies under different conditions.

[0074] Optionally, in digital avatar technology, intelligent interaction logic is key to improving user experience and system efficiency. Building an intelligent processing system based on rule engines and machine learning algorithms, capable of adapting to different scenarios and flexibly responding to changes in multi-source data, is the cornerstone for achieving efficient and intelligent interaction in digital avatars. Specifically:

[0075] One of the core components of an intelligent processing system is the rule engine. Built upon fuzzy logic or neural networks, it handles uncertainties and formulates action strategies (target rules) applicable to various conditions. Upon system startup, the rule engine loads a pre-set set of target rules. These rules cover aspects such as user experience optimization, data synchronization accuracy, and efficient resource utilization, forming the foundation for guiding the digital avatar's actions. Subsequently, based on the target rules and multi-source data in the current context, the rule engine dynamically generates or adjusts the digital avatar's response strategy, enabling intelligent interaction.

[0076] Optionally, the intelligent processing system constructs intelligent interaction rules (target rules) based on fuzzy logic or neural networks through a rule engine. It also constructs intelligent interaction logic based on the rule engine and machine learning algorithms, enhancing the system's flexibility and adaptability.

[0077] As shown above, the response strategy of digital avatars can be dynamically adjusted based on target rules and real-time multi-source data. The implementation of this technical solution makes the behavior of digital avatars more intelligent under different conditions, significantly improving the system's flexibility and adaptability, effectively enhancing the user experience, and ensuring efficient resource utilization.

[0078] In one optional embodiment, the intelligent processing system collects user operation information in a target application, wherein the target application is used to represent the application of the digital clone embedded in the target scene. Then, based on the user operation information, the system identifies the user's pattern information, wherein the pattern information includes at least the user's preference information, demand information, and habit information. Then, based on the pattern information and the user's feedback information, the system adjusts the page layout and interaction logic of the target application.

[0079] Optionally, the intelligent processing system deploys a data acquisition module within the target application to record user actions in real time, such as clicks, swipes, keyword searches, and dwell time. The system utilizes time-series analysis and clustering algorithms to extract pattern information from the collected user action data, including user preferences, needs, and habits. For example, by analyzing frequently accessed functional modules, user preferences can be identified; by observing the order and frequency of user actions, user needs and habits can be understood.

[0080] Optionally, based on the identified pattern information and the user's real-time feedback, the intelligent processing system generates personalized adjustment strategies. This includes adjusting the layout of page elements and changing the interaction flow to better suit user behavior and ease of operation.

[0081] Optionally, the system adjusts the page layout and interaction logic according to the strategy, while continuously collecting user feedback to optimize the adjustment strategy in an iterative manner, ensuring that each adjustment moves towards improving the user experience.

[0082] Optionally, based on user operation information, the system can not only dynamically adjust the user interface of the digital clone, but also adjust the function recommendations of the digital clone. Specifically, the system can generate personalized function recommendations based on collaborative filtering and deep learning.

[0083] Optionally, the system can collect user behavior data, such as click frequency, dwell time, and operation path, and use data analysis algorithms to mine and analyze the data to identify user needs and pain points. This allows for targeted optimization of the interaction process and interface design, such as adjusting menu layout and simplifying operation steps, thereby improving user satisfaction and ease of use.

[0084] As described above, through the specific implementation methods, the intelligent processing system can accurately capture user preferences, needs, and habits based on user operation information within the target application, thereby dynamically adjusting the application's page layout and interaction logic to achieve a highly personalized user experience. Specific technical effects are as follows: By continuously learning user operation patterns, the intelligent processing system can dynamically adjust the target application's interface and interaction. For example, it can adjust the entry positions of frequently used functions based on user habits, improving the convenience and smoothness of operation. The system can quickly respond to user feedback, adjusting the page layout and interaction logic in real time, reducing user waiting time and improving user satisfaction. By providing a user experience that better suits individual needs and habits, the intelligent processing system helps increase user dependence and satisfaction with the target application, thereby enhancing user stickiness and activity.

[0085] In one optional embodiment, the intelligent processing system obtains device information of the target device where the target application is located, wherein the device information includes at least the screen resolution and pixel density of the target device, and then adjusts the display layout of the digital clone in the target application according to the device information.

[0086] Optionally, when the target application launches on the target device, the intelligent processing system will automatically send a request to the device's operating system to obtain basic device information, including but not limited to screen resolution and pixel density. The system analyzes the obtained device information to understand the display characteristics of the target device. For example, a high-resolution device may support more detailed graphics display, while high pixel density indicates higher screen clarity.

[0087] Optionally, based on device information, the intelligent processing system generates display layout rules for specific devices. These rules cover element size, position arrangement, font adjustments, etc., to adapt to the display characteristics of the target device. The system calculates the optimal layout parameters for the digital clone in the target application based on screen resolution and pixel density, ensuring appropriate display size and clarity across different devices. The intelligent processing system applies the calculated layout parameters to adjust the display layout of the digital clone in the target application in real time. For example, for high-resolution devices, it may increase the detail of elements; for low-pixel-density devices, it will appropriately enlarge elements to ensure visibility.

[0088] Optionally, the system has dynamic adaptability, which can continuously monitor changes in device information and automatically adjust the display layout according to the latest information. At the same time, it collects user feedback on the display effect for subsequent layout rule optimization.

[0089] Optionally, the adaptive adjustment algorithm integrated into the client application of the digital clone will automatically adjust the display layout and size of the digital clone according to parameters such as the device's screen resolution and pixel density, so that it can have a good visual effect on different devices.

[0090] As described above, the intelligent processing system can intelligently adjust the display layout of the digital clone in the target application based on the target device's screen resolution and pixel density, ensuring optimal display performance on any device. This adaptive layout adjustment based on device information not only solves the problem of poor display performance of traditional fixed layouts on different devices, but also significantly improves user satisfaction and experience with the digital clone application.

[0091] In one optional embodiment, this embodiment includes the following three core modules: A real-time data synchronization module: Through multi-source data acquisition, cleaning, and fusion, combined with distributed storage and synchronization mechanisms, it ensures real-time data updates and consistency between the digital avatar and the actual system. A function coordination module: Based on dynamic task allocation and multi-system collaborative optimization strategies, it achieves functional coordination and intelligent interaction between the digital avatar and different systems. A user experience optimization module: Combining user behavior analysis and personalized recommendation algorithms, it dynamically adjusts the digital avatar's user interface and interaction logic to improve the user experience.

[0092] As the above demonstrates, by introducing real-time data synchronization mechanisms, intelligent function coordination mechanisms, and user experience optimization strategies, efficient integration and collaboration between digital avatars and actual systems can be achieved. This significantly improves the integration efficiency, system stability, and collaborative capabilities of digital avatar applications, providing users with a more intelligent, efficient, and intuitive operating experience, demonstrating significant technological value and application prospects. Improved integration efficiency means reduced time and costs for system integration and maintenance. The stable operation and excellent user experience of digital avatars help enhance their application value across various industries, bringing more business opportunities and economic benefits to relevant enterprises. For example, in internal enterprise applications, it can reduce training costs, improve work efficiency, and lower operating costs. Simultaneously, the above implementation methods promote the widespread application of digital avatar technology, bringing more convenience to people's lives and work. For instance, in fields such as distance education, telemedicine, and customer service, it provides a more efficient and higher-quality digital service experience, helping to improve the digitalization level and quality of life in society, while also promoting the development of related industries and creating more employment opportunities and economic benefits for society.

[0093] This application also provides a data processing device based on digital cloning. It should be noted that this data processing device based on digital cloning can be used to execute the data processing method based on digital cloning provided in this application. The following describes the data processing device based on digital cloning provided in this application.

[0094] According to an embodiment of this application, an apparatus for implementing the above-described data processing method based on digital avatars is also provided. Figure 2 This is a schematic diagram of an optional data processing device based on digital cloning according to an embodiment of this application, such as... Figure 2 As shown, the device includes: an acquisition unit 201, used to acquire multi-source data in a target scene; a first determination unit 202, used to filter out data that affects the digital clone from the multi-source data, use the filtered data that affects the digital clone as target information, and receive the target information through the digital clone, wherein a first algorithm is used to filter out data that affects the digital clone from the multi-source data, and the digital clone is interconnected with multiple systems and multiple devices in the target scene through different target interfaces; and a second determination unit 203, used to determine the task level of the task information when task information is detected in the target information, and adjust the task allocation strategy and resource allocation strategy of the digital clone based on the task level and resource information, wherein the resource information is used to characterize the load information of the systems and devices associated with the digital clone.

[0095] Optionally, the first determining unit 202 includes: a first receiving subunit, a first filtering subunit, and a first storage subunit. The first receiving subunit is used to receive multi-source data via a message queue, wherein the message queue is used for asynchronous data transmission; the first filtering subunit is used to read multi-source data from the message queue and filter out data that affects the digital clone, using the filtered data as target information; the first storage subunit is used to store the target information via distributed storage and receive the target information through the digital clone.

[0096] Optionally, the data processing device based on the digital clone further includes: a first processing unit, a first verification unit, a first triggering unit, and a first transmission unit. The first processing unit is used to compress the target information to obtain first information; the first verification unit is used to perform data verification and data validation on the first information, wherein data verification is used to check whether there are any anomalies in the data during transmission, and data validation is used to verify whether the data conforms to the expected format; the first triggering unit is used to trigger a retransmission mechanism or a repair mechanism if the first information fails data verification and / or data validation, wherein the retransmission mechanism is used to indicate the reacquisition of multi-source data, and the repair mechanism is used to repair the data that failed in the first information; the first transmission unit is used to transmit the passed first information to the digital clone if the first information passes data verification and data validation.

[0097] Optionally, the second determining unit 203 includes: a first adjustment subunit and a second adjustment subunit. The first adjustment subunit is used to adjust the task allocation strategy of the digital clone based on the task level and resource information of the task information; the second adjustment subunit is used to adjust the resource allocation strategy of the digital clone based on the task level, resource information, and task requirements of the task information.

[0098] Optionally, the data processing device based on digital clones further includes: a first setting unit and a first adjustment unit. The first setting unit is used to set target rules, which guide the action strategies that the digital clones should take under different conditions; the first adjustment unit is used to adjust the response strategies of the digital clones according to the target rules and multi-source data.

[0099] Optionally, the data processing device based on the digital clone further includes: a first acquisition unit, a first identification unit, and a second adjustment unit. The first acquisition unit is used to acquire user operation information within a target application, wherein the target application represents the application of the digital clone embedded in the target scenario; the first identification unit is used to identify user pattern information based on the user operation information, wherein the pattern information includes at least user preference information, demand information, and habit information; the second adjustment unit is used to adjust the page layout and interaction logic of the target application based on the pattern information and user feedback information.

[0100] Optionally, the data processing device based on the digital clone further includes: a first acquisition unit and a third adjustment unit. The first acquisition unit is used to acquire device information of the target device where the target application resides, wherein the device information includes at least the screen resolution and pixel density of the target device; the third adjustment unit is used to adjust the display layout of the digital clone in the target application according to the device information.

[0101] According to another aspect of this application, a computer-readable storage medium is also provided, which includes a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the above-described data processing method based on digital clone.

[0102] According to another aspect of this application, an electronic device is also provided, including one or more processors and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors perform a data processing method based on digital clones.

[0103] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0104] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0105] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0106] 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 units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0107] Furthermore, the functional units in the various embodiments of this application 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. The integrated unit can be implemented in hardware or as a software functional unit.

[0108] If the integrated unit 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 application, in essence, or the part that contributes to the prior art, or all or 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 described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0109] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A data processing method based on digital avatars, characterized in that, include: Acquire multi-source data in the target scenario; Data that affects the digital clone is filtered from the multi-source data. The filtered data that affects the digital clone is used as target information, and the target information is received through the digital clone. The digital clone is interconnected with multiple systems and multiple devices in the target scenario through different target interfaces. When task information is detected in the target information, the task level of the task information is determined, and the task allocation strategy and resource allocation strategy of the digital clone are adjusted based on the task level and resource information, wherein the resource information is used to characterize the load information of the system and device associated with the digital clone.

2. The data processing method based on digital cloning according to claim 1, characterized in that, The process involves filtering data from the multi-source data that has an impact on the digital clone, using this filtered data as target information, and receiving the target information through the digital clone, including: The multi-source data is received via a message queue, wherein the message queue is used for asynchronous data transmission; Read the multi-source data from the message queue, filter out the data that affects the digital clone from the multi-source data, and use the filtered data that affects the digital clone as target information; The target information is stored in a distributed storage manner, and the target information is received through the digital clone.

3. The data processing method based on digital cloning according to claim 1, characterized in that, In the process of receiving the target information through the digital clone, the method further includes: The target information is compressed to obtain the first information; The first information is subjected to data verification and data validation, wherein the data verification is used to check whether there are any abnormalities in the data during transmission, and the data validation is used to verify whether the data conforms to the expected format; If the first information fails the data verification and / or the data validation, a retransmission mechanism or a repair mechanism is triggered, wherein the retransmission mechanism is used to indicate the reacquisition of the multi-source data, and the repair mechanism is used to repair the data that failed the first information. If the first information passes the data verification and the data validation, the passed first information is transmitted to the digital clone.

4. The data processing method based on digital cloning according to claim 1, characterized in that, Adjusting the task allocation strategy and resource allocation strategy of the digital clone based on the task level and resource information includes: Based on the task level and resource information, the task allocation strategy of the digital clone is adjusted. Based on the task level, the resource information, and the task requirements of the task information, adjust the resource allocation strategy of the digital clone.

5. The data processing method based on digital cloning according to claim 1, characterized in that, The method further includes: Set target rules, wherein the target rules are used to guide the action strategies that the digital clone should take under different conditions; The response strategy of the digital clone is adjusted according to the target rules and the multi-source data.

6. The data processing method based on digital cloning according to claim 1, characterized in that, The method further includes: Collect user operation information in a target application, wherein the target application is used to represent a digital clone application embedded in the target scenario; Based on the user operation information, the user's pattern information is identified, wherein the pattern information includes at least the user's preference information, demand information, and habit information; Based on the pattern information and the user's feedback, the page layout and interaction logic of the target application are adjusted.

7. The data processing method based on digital cloning according to claim 6, characterized in that, The method further includes: Obtain device information of the target device where the target application is located, wherein the device information includes at least the screen resolution and pixel density of the target device; Adjust the display layout of the digital clone in the target application based on the device information.

8. A data processing device based on digital clone, characterized in that, include: The acquisition unit is used to acquire multi-source data in the target scenario. The first determining unit is used to filter out data that affects the digital clone from the multi-source data, take the filtered data that affects the digital clone as target information, and receive the target information through the digital clone. The digital clone is interconnected with multiple systems and multiple devices in the target scenario through different target interfaces. The second determining unit is used to determine the task level of the task information when the target information contains task information, and to adjust the task allocation strategy and resource allocation strategy of the digital clone based on the task level and resource information, wherein the resource information is used to characterize the load information of the system and device associated with the digital clone.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the computer program is executed, the device on which the computer-readable storage medium is located performs the data processing method based on digital clone as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, It includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to perform the data processing method based on digital clones as described in any one of claims 1 to 7.

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