Decision-making method and system for digital avatar crossing time light backtracking data optimization

Abstract

The invention provides a method and a system for optimizing decision of digital avatar time backtracking data, and the method comprises the following steps: continuously detecting an original table of a production system, and updating a corresponding data table in a data image library after detecting that an original record of the original table changes; obtaining a target crossing time point; reproducing the target system state of the production system at the target crossing time point based on the data table; obtaining interaction operation information; and simulating user behaviors and forming a traversal log in combination with the interactive operation information. According to the invention, the data of the production system can be fully utilized, system backtracking is realized based on the recorded data of the production system, and simulation is carried out on the basis of backtracking to obtain the traversal log, so that a user can conveniently determine the influence brought by different operations, and the user is helped to make a decision.

Description

Technical Field

[0001] The present invention relates to the field of data management and virtual reality technology, and in particular to a method and system for optimizing decision-making by a digital avatar traveling through time and tracing back data. Background Art

[0002] In modern society, various production systems, such as enterprise resource planning systems, customer relationship management systems, student information management systems, and medical information systems, have become important management tools for enterprises and institutions. These systems record a large amount of data information, which is of great significance for analysis and decision-making.

[0003] However, existing technologies have shortcomings in utilizing this data information: (1) Existing technologies lack an effective method to trace back and reproduce historical data. This makes it difficult for users to obtain accurate data support when they need to trace back past operations or states. (2) Data consistency issues: When tracing back data, data consistency issues are often encountered. Existing systems find it difficult to ensure data consistency and integrity when reproducing historical states, resulting in unreliable analysis results. (3) Poor interactive experience: Existing systems lack an intuitive interactive interface when tracing back historical data, resulting in poor user experience. Users cannot conveniently select a specific time point for tracing back, nor can they perform interactive operations in the tracing back state. (4) Lack of behavioral simulation: Existing systems can usually only reproduce data states, but cannot simulate user behaviors and decisions at specific points in time. This limits the application of the system in education and training, customer service and other fields. (5) Low storage and retrieval efficiency: Existing systems are inefficient when storing and retrieving large amounts of historical data. Optimization of data storage space and efficient data retrieval technology urgently need to be improved.

[0004] Therefore, existing technologies cannot process data information of production systems well, and it is difficult to meet the needs of optimizing decisions based on data information. Summary of the Invention

[0005] The first purpose of the present invention is to provide a method for a digital avatar to travel through time and backtrack data to optimize decision-making, which can improve the utilization of data information and assist in optimizing decision-making.

[0006] The second object of the present invention is to provide a digital time travel system that realizes the above-mentioned digital avatar's time travel and backtracking data optimization decision-making.

[0007] In order to achieve the above-mentioned first purpose, the present invention provides a method for a digital avatar to travel through time and trace back data to optimize decisions, which includes the following steps: continuously detecting the original table of the production system, and updating the corresponding data table in the data image library after detecting that the original records of the original table have changed; obtaining the target travel time point; reproducing the target system status of the production system at the target travel time point based on the data table; obtaining interactive operation information; simulating user behavior and forming a travel log in combination with the interactive operation information.

[0008] As can be seen from the above scheme, the present invention detects and stores data on each production system change in a data table, thereby reproducing the target system state corresponding to the target traversal time point based on the data table. Within this target system state, user interactions are combined to simulate user behavior to obtain different system states. The present invention can fully utilize production system data, perform system backtracking based on recorded production system data, and perform simulation based on this backtracking to generate a traversal log, making it easier for users to determine the impact of different operations and assist in decision-making.

[0009] A further solution is to simulate user behavior and combine interactive operation information to form a travel log, where the simulated user includes using machine learning and a rule engine to generate the user's behavior and decision.

[0010] This shows that a better effect of simulating user behavior can be achieved.

[0011] A further solution is to initialize the digital avatar before obtaining the target crossing point, and one digital avatar corresponds to one user or one thing.

[0012] This shows that by setting up different digital avatars, the effect of system backtracking can be improved.

[0013] A further solution is to reproduce the target system state of the production system at the target crossing time point based on the data table, obtain interactive operation information, simulate user behavior and combine the interactive operation information to form a crossing log, including the use of transaction management and consistency check mechanisms for processing.

[0014] This shows that the consistency and integrity of data can be guaranteed and the reliability of analysis can be improved.

[0015] A further solution is that the data image library stores multiple data tables, one data table corresponds to one original table, and one data table includes several change records. Each change record includes: image library change record identifier, original record identifier, record start time, record end time, operation type and original table data; after detecting that the original record of the original table has changed, a change record is added and / or updated according to the change of the original record.

[0016] This shows that setting up data tables can improve the efficiency of utilizing data in the original tables of the production system.

[0017] A further solution is that after detecting that the original record of the original table has changed, when adding and / or updating the change record according to the change of the original record, it includes: analyzing the change record involving the deletion operation of the original record and checking the transaction log according to a preset period.

[0018] This shows that the accuracy of the change record of the data table can be improved, thereby improving the effect of backtracking.

[0019] A further solution is to reproduce the target system state of the production system at the target crossing time point based on the data table, including: retrieving and determining the target data frame combination in the data image library, the target data frame combination including change records whose record start time is less than the target time point, and change records whose record end time is greater than the target crossing time point or is empty; based on the target data frame combination, reproduce the target system interface and target data content.

[0020] This shows that backtracking through the change records of the selected target time point can improve the backtracking effect.

[0021] A further solution is that the operation types include adding, updating and deleting, and the changes of the original records of the original table include adding, updating and deleting.

[0022] A further solution is that when it is detected that the original record is updated or deleted, the change record with the end record time corresponding to the original record is updated first, and then another change record corresponding to the original record is added; when it is detected that the original record is added, a change record with the original record identifier corresponding to the original record is added according to the original record.

[0023] It can be seen that each change record corresponds to a data change in the data table.

[0024] In order to achieve the second purpose mentioned above, the present invention provides a digital time travel system that realizes the above-mentioned method of digital avatar time travel and backtracking data optimization decision-making, which includes: a production system, a data recording module, a data image library, a digital avatar, a digital time machine module, and a digital avatar time travel module; the data recording module is respectively connected to the production system and the data image library, the digital time machine module is respectively connected to the production system, the data image library and the digital avatar time travel module, and the digital avatar time travel module is connected to the data image library. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a system framework diagram of an embodiment of the digital avatar crossing system of the present invention.

[0026] Figure 2It is a flow chart of the method for the digital avatar to travel through time and backtrack data to optimize decision-making.

[0027] The present invention will be further described below with reference to the accompanying drawings and embodiments. DETAILED DESCRIPTION

[0028] The present invention's method for enabling digital avatars to travel back in time and optimize decision-making by traversing data back in time records data changes in production systems, simulating the behavior and status of users or objects in the digital world. This enables data backtracking and historical reconstruction, providing support for management and decision-making in various fields. The present invention also provides a digital time-travel system that implements the above method.

[0029] Digital avatar crossing system embodiment:

[0030] See also Figure 1 The digital avatar travel system of this embodiment includes a production system 11, a data recording module 12, a data image library 13, a digital avatar 14, a digital time machine module 15, and a digital avatar travel module 16.

[0031] The data recording module 12 is connected to the production system 11 and the data image library 13. The digital time machine module 15 is connected to the production system 11, the data image library 13, and the digital avatar travel module 16. The digital avatar travel module 16 is connected to the data image library 13. Data transmission is achieved through the connection between each module.

[0032] The production system 11 is the basis for the digital avatar 14 to travel through the system. This embodiment realizes the digital avatar's travel based on recording the data changes of the production system 11.

[0033] Optionally, the production system 11 can be one of the following systems: an enterprise resource planning system (ERP), a customer relationship management system (CRM), a student information management system (SIS), or a healthcare information system (HIS). An ERP system is used to manage various aspects of a company, such as finance, supply chain, and human resources. A CRM system is used to record and manage customer information and interaction history. A student information management system is used to record student status, grades, attendance, and other information. A healthcare information system is used to manage patient medical records, diagnosis, treatment, and other information.

[0034] Optionally, the production system 11 needs to be coupled to the data recording module 12 through specific technical means, which can be any of the following: (1) API integration: Provide standardized API interfaces for the production system 11, through which the data recording module 12 can capture and record data changes. (2) Database triggers: Set triggers in the database of the production system 11. When data changes, the triggers automatically record these changes and send them to the data recording module 12. (3) Log file parsing: Use the log files generated by the production system 11 to parse the data changes and record them in the data recording module 12. (4) Data synchronization tool: Use data synchronization tools (such as ETL tools) to regularly synchronize data in the production system 11 to the data recording module 12. (5) Middleware: Introduce middleware between the production system 11 and the data recording module 12 to capture and transmit data changes.

[0035] The data recording module 12 is used to capture and record every data change in the production system 11, and specifically includes a shutter unit, a change tracking unit, and a lifecycle management unit.

[0036] The shutter unit is used to set the capture frequency and conditions for data recording of the production system 11. It can be set through a configuration file or a graphical interface, supporting different precisions such as seconds and minutes.

[0037] The change tracking unit is used to monitor the creation, modification, and deletion operations of data in the production system 11. This can be achieved by using database triggers, log files, API calls, or data synchronization based on data creation time and update time.

[0038] The lifecycle management unit records the entire lifecycle of a change record. Timestamps and version control technologies ensure that each change record can be accurately tracked. Whenever data in the production system 11 changes, a recorded image frame is generated. The time at which the change is recorded is the birth time of the new record and the death time of the old record. Each recorded image frame corresponds to a change record in the data image library 13.

[0039] The data image library 13 is used to store the data changes captured and recorded by the data recording module 12 to ensure the integrity and security of the data. The data image library 13 includes a data format expansion unit, an efficient storage unit, and a data retrieval unit.

[0040] The data format extension unit is used to add extended attributes such as timestamp, operation type, etc. on the basis of retaining the original data format of the production system 11.

[0041] Efficient storage units utilize compression and deduplication technologies to optimize storage space. For example, they use distributed file systems and databases (such as Hadoop and NoSQL), combined with compression algorithms (such as Gzip and Snappy) and deduplication algorithms (such as hash tables and Bloom filters).

[0042] The data retrieval unit provides fast data retrieval and playback capabilities and supports multi-dimensional queries. For example, it utilizes indexing technologies (such as B-trees and inverted indexes) and query optimizers to ensure efficient data retrieval.

[0043] The data image library 13 stores multiple data tables, each corresponding to a raw table in the production system 11. A raw table represents data stored in the production system 11. A raw table includes multiple raw records, each representing the data of a record in the raw table. Each raw record has a unique raw record identifier.

[0044] The data table includes multiple change records, each of which is provided with the same original fields as the corresponding original table, stores the same content as the original table in the original fields, and adds the following fields: image library change record identifier, original record identifier, record start time, record end time and operation type. The image library change record identifier is unique and is used to identify each change record in the data image library 13. The original record identifier is used to identify which specific original record in the original table the change record corresponds to. The record start time is used to record the time when the original record is created or changed in the production system. The record end time is used to record the time when the original record is changed or deleted in the production system, and is initially empty. The original record in the record start time and record end time is the original record corresponding to the original record identifier in the change record.

[0045] The digital avatar 14 is used to represent the virtual embodiment of a user or object in the real world in the digital world, and includes an identity mapping unit, a behavior simulation unit, and an interactive interface unit.

[0046] The identity mapping unit is used to map real-world identity information to the digital world. For example, identity mapping is achieved through unique identifiers (such as UU ID, user ID, etc.), ensuring that each user or object has a unique corresponding virtual avatar in the digital world.

[0047] The behavior simulation unit simulates user behavior and decision-making at different time points. For example, it uses machine learning and rule engines to generate user behavior and decision-making at different time points based on historical data and behavior patterns.

[0048] In this embodiment, machine learning and rule engines are used, based on historical data and behavior patterns, and the following steps are included: (1) Feature extraction: Extract and construct features, including user operation frequency, operation type, time interval, business context, etc. (2) Behavior pattern analysis: ① Cluster analysis: Use clustering algorithms (such as K-means, DBSCAN, etc.) to cluster user behaviors and identify different types of user behavior patterns. ② Association rule mining: Use association rule mining algorithms (such as April, FP-Growth, etc.) to discover the association relationship and frequent patterns between user behaviors. (3) Behavior prediction model training: ① Model selection: Select a suitable machine learning model according to the specific application scenario, such as decision tree, random forest, support vector machine, neural network, etc. ② Model training: Use historical data to train the model and optimize the model parameters so that it can accurately predict user behavior and decision-making. ③ Model evaluation: Evaluate the performance of the model through methods such as cross-validation, confusion matrix, ROC curve, etc. to ensure the accuracy and robustness of the model. (4) Rule engine configuration: ① Rule definition: Define the rules in the rule engine according to business logic and operation specifications. For example, some operations can only be performed under certain conditions, and some operations require specific approval processes. ② Rule management: Use rule engines (such as Droo ls, Jess, etc.) to manage and execute rules to constrain and guide user behavior. (5) Behavior simulation and decision generation: ① Behavior simulation: Based on the trained behavior prediction model and rule engine, simulate the user's behavior at a specific time point. For example, predict the type and specific operation that the user may perform at a certain time point. ② Decision generation: Use the rule engine to verify and adjust the simulated behavior and generate a decision that conforms to the business logic. For example, if an operation requires an approval process, the rule engine will automatically generate the corresponding approval decision. (6) Interactive interface and visualization: ① Interactive interface: Provide an interface for users to interact with the system. Users can view the simulated behavior and decisions through the interface and perform further operations and verifications. ② Visualization: Use visualization tools (such as charts, graphs, etc.) to display the simulated behavior and decisions to help users understand and analyze the simulation results. Through the above steps, the simulation of user behavior and decision-making can be achieved, so that the digital avatar traversal system can more realistically reproduce the system status and user interaction scenario at a specific time point.

[0049] The interactive interface unit is used to provide an interface for users to interact with the digital world and supports visual operations. For example, it uses various forms such as web interfaces, mobile applications, or virtual reality devices (such as AR / VR glasses) to provide an intuitive interactive experience.

[0050] The digital time machine module 15 is used to realize data backtracking and historical reproduction, and includes a production system mirror unit, a time backtracking unit, and a data consistency unit.

[0051] The production system mirror unit is used to generate a mirror system of the production system 11 at a specific time point, for example, by using snapshot technology (such as database snapshot, file system snapshot, etc.) to generate a mirror system of the system at a specific time point.

[0052] The time rewind unit allows users to select any point in time and rewind to the past state. For example, through a timeline interface or a time selector, users can select the time point to rewind.

[0053] The data consistency unit uses transaction management and consistency checking mechanisms to ensure data consistency and integrity during the backtracking process. When backtracking and replaying historical data, the data consistency unit ensures that all parts of the data are logically consistent. Specifically, the data consistency unit ensures data integrity, data accuracy, transaction consistency, and time consistency.

[0054] The digital avatar travel module 16 is used to travel through time and generate a travel log, including a time selection unit, a history reproduction unit and an interactive operation unit.

[0055] The time selection unit provides an interface for users to select a time point for backtracking, for example, through a graphical interface or a command line interface, providing an intuitive time selection function.

[0056] The history reproducing unit reconstructs the system state at a specific time point using the data image library 13 according to the selected time point, and reproduces the system interface and data content at that time.

[0057] The interactive operation unit allows users to perform interactive operations at the time of the retrospective, observing the impact of different operations on the mirror system. It supports users to simulate operations at the time of the retrospective, observe the impact of different operations on the mirror system, and record these operations for further analysis.

[0058] It should be noted that the digital time machine module 15 and the digital avatar travel module 16 do not need to completely replicate all the functions of the production system 11, but they need to implement some key functions to support user interaction at the time of retrospection. These key functions include basic operations such as data query, data modification, data addition, and data deletion.

[0059] An embodiment of a method for a digital avatar to travel through time and backtrack data to optimize decision-making:

[0060] This embodiment is implemented based on the computer program executed by the above-mentioned digital avatar traversal system, see Figure 2 , specifically including the following steps:

[0061] S1: Continuously monitor the original table of the production system. When changes are detected in the original records of the original table, the corresponding data table in the data image library is updated.

[0062] This embodiment is described by taking the addition, update, and deletion of the first original record as an example.

[0063] When the data recording module detects the addition of a first original record to the original table, it adds a first change record based on the first original record in the data table corresponding to the original table. Specifically, the first record start time of the newly added first change record is the time when the first original record was added to the original table, the first record end time of the first change record is null, the original record identifier of the first change record is the original record identifier of the first original record, and the first operation type of the first change record is new. The first change record also stores the same data content as the newly added first original record via a set first original field.

[0064] Therefore, when a first original record is added to the original table, a first change record corresponding to the first original record is added to the data table corresponding to the original table, and the situation of the newly added data in the original table is recorded through the first change record.

[0065] When the first original record in the original table is updated for the first time, the data recording module detects that the first original record in the original table has been updated. At this time, it first determines the change record corresponding to the first original record in the data table (this is the first change record), and then adds a change record (this is the second change record) to record the situation that the first original record has been updated for the first time.

[0066] Specifically, first, the first record end time of the first change record is set to the time when the first original record was first updated, and then a second change record is added. The second record start time of the second change record is the update time of the first original record, the second record end time of the second change record is blank, the second original record identifier of the second change record is the same as the original record identifier of the first original record, and the second operation type of the second change record is update. The second change record also stores the same data content as the first original record after the first update through the set second original field.

[0067] When the first original record in the original table is updated for the second time, the data recording module detects that the first original record in the original table has been updated. It first determines the change record corresponding to the first original record in the data table (this is the second change record), and then adds a change record (this is the third change record) to record the situation after the first original record is updated for the second time.

[0068] Specifically, first, the second record end time of the second change record is set to the time when the first original record was updated for the second time, and then a third change record is added. The third record start time of the third change record is the update time of the second original record, the third record end time of the third change record is blank, the third original record identifier of the third change record is the same as the original record identifier of the first original record, and the third operation type of the third change record is update. The third change record also stores the same content as the first original record after the second update through the set third original field.

[0069] When the first original record in the original table is deleted (including physical deletion and soft deletion), the data recording module detects that the first original record in the original table is deleted, first determines the change record corresponding to the first original record in the data table (this is the third change record), and then adds a new change record (this is the fourth change record) to record the deletion of the first original record.

[0070] Specifically, first, the third record end time of the third change record is set to the time when the first original record was deleted, and then a fourth change record is added. The fourth record start time and fourth record end time of the fourth change record are both the time when the first original record was deleted. The fourth original record identifier of the fourth change record is the same as the original record identifier of the first original record, and the fourth operation type of the fourth change record is deletion. The fourth change record also saves the same content as the current first original record (i.e., the second original record after the second update) through the set fourth original field.

[0071] Similarly, the specific method for adding, updating or modifying other original records in the original table is the same as the first original record mentioned above.

[0072] It should be noted that the above-mentioned "first", "second", "third", "fourth" and "fifth" are only used to distinguish things of the same kind and do not have the meaning of order of precedence.

[0073] Optionally, during the data table update process, to avoid data loss caused by simultaneous additions and deletions between two captures (i.e., when the data recording module records data changes in adjacent time periods), data objects involved in physical deletion operations can be analyzed and verified against transaction logs at a preset interval to supplement the data. Simultaneously, the actual deletion time of the change record is obtained and the end time of the newly added record is corrected.

[0074] Therefore, for each change in the original record in the original table of the production system, the data recording module records each change in the original record in the data image library by identifying and adding a new change record, thereby supporting the reproduction of the target system status in subsequent steps.

[0075] S2: Initialize the digital avatar.

[0076] Create a corresponding digital avatar for each user and / or thing to ensure that it has a unique identity (such as UU ID, user ID, etc.) in the digital world (i.e., the target system state to be traced back).

[0077] S3: Get the target crossing time point.

[0078] On the interface for selecting a time point to go back to, the user enters a time point as the target time point for travel. The digital twin travel system uses the time point entered by the user (e.g., 12:00 on January 1, 2022) as the target time point for travel.

[0079] S4: Reproduce the target system state of the production system at the target crossing time point based on the data table.

[0080] The digital twin transit system searches the image library to determine the target data frame combination based on the target transit time. The target data frame combination includes change records with a start time before the target transit time, as well as change records with a record end time after the target transit time or a null value (in which case the null value indicates that the change record is still valid at the target transit time).

[0081] Then, the target data frame combination is used to reproduce the target system state of the target mirror system of the production system at the target crossing time point, where the target system state includes the target system interface and target data content.

[0082] S5: Obtain interactive operation information.

[0083] Interaction information refers to the operations performed by users in the interface provided by the target system state, including deletion, addition, and modification. The digital time travel system obtains interaction information to support users' simulated operations at the target time travel point.

[0084] The impact of different user operations on the target mirror system may include changes in data status, business processes, and system performance. For example, deleting a record may affect associated business processes, adding a record may trigger new business processes, and modifying a record may affect the decision-making and analysis results of the target mirror system.

[0085] S6: Simulate user behavior and combine interactive operation information to form a traversal log.

[0086] In the above steps S4, S5, and S6, in order to ensure the consistency and integrity of the data, transaction management and consistency check mechanisms are adopted to prevent data conflicts and errors.

[0087] Combining the interactive operation information obtained above, the digital travel system simulates different user behaviors and decisions based on historical data and behavioral patterns to alter the target mirror system's state after the target travel time. The altered state of the target mirror system is recorded as a travel log. The travel log includes the time point, operation type, and operation content. Users can review and analyze the travel log to understand the specific process of historical replay and the impact of the operation on the target mirror system.

[0088] Optionally, when the production system is an enterprise resource planning system or a customer relationship management system, it can be used to analyze decisions and optimize management strategies by looking back historical data; when the production system is a student information management system, it can simulate the student learning process, evaluate teaching effectiveness, and assist in formulating personalized teaching plans.

[0089] In summary, the present invention can make full use of the data of the production system, realize system backtracking based on the recorded data of the production system, and simulate to obtain the traversal log based on the backtracking, so as to facilitate users to determine the impact of different operations and help users make decisions.

Claims

1. A method for optimizing decision-making by using a digital avatar to travel through time and trace back data, characterized in that: The following steps are involved: Continuously detect the original table of the production system, and after detecting that the original record of the original table has changed, update the corresponding data table in the data image library; Get the target crossing time point; Reproducing the target system state of the production system at the target crossing time point based on the data table; Get interactive operation information; The user behavior is simulated and combined with the interactive operation information to form a traversal log.

2. The method for optimizing decision-making by backtracking data through time by a digital avatar as claimed in any one of claims 1, characterized in that: When simulating user behavior and combining the interactive operation information to form the traversal log, simulating the user includes using machine learning and a rule engine to generate the user's behavior and decision.

3. The method for optimizing decision-making by backtracking data through time by a digital avatar as claimed in any one of claims 1, characterized in that: Before acquiring the target crossing point, the digital twin is initialized, and one digital twin corresponds to one user or one thing.

4. The method for optimizing decision-making by backtracking data through time by a digital avatar as claimed in any one of claims 1, characterized in that: The process of reproducing the target system state of the production system at the target crossing time point based on the data table, acquiring the interactive operation information, simulating user behavior and combining the interactive operation information to form the crossing log includes adopting transaction management and consistency check mechanisms for processing.

5. The method for optimizing decision-making by backtracking data through time for a digital avatar according to any one of claims 1 to 4, characterized in that: The data image library stores a plurality of data tables, one data table corresponds to one original table, one data table includes a plurality of change records, each change record includes: image library change record identifier, original record identifier, record start time, record end time, operation type and data of the original table; the original record identifier is used to uniquely identify the original record in the original table; After detecting that the original record of the original table has changed, the change record is added and / or updated according to the change of the original record.

6. The method for optimizing decision-making by backtracking data through time by a digital avatar as claimed in any one of claim 5, characterized in that: After detecting that the original record of the original table has changed, adding and / or updating the change record according to the change of the original record includes: According to the preset period, the change records involving the deletion operation of the original record are analyzed and the transaction log is verified.

7. The method for optimizing decision-making by backtracking data through time by a digital avatar as claimed in any one of claim 5, characterized in that: When reproducing the target system state of the production system at the target crossing time point based on the data table, it includes: Retrieving and determining a target data frame combination in the data image library, the target data frame combination includes a change record whose recording start time is less than the target time point, and a change record whose recording end time is greater than the target crossing time point or is empty; The target system interface and the target data content are reproduced based on the target data frame combination.

8. The method for optimizing decision-making by backtracking data through time by a digital avatar as claimed in claim 5, characterized in that: The operation types include adding, updating and deleting, and the changes of the original records of the original table include adding, updating and deleting.

9. The method for optimizing decision-making by backtracking data through time by a digital avatar as claimed in claim 8, characterized in that: When it is detected that the original record is updated or deleted, firstly update the change record with the end record time corresponding to the original record being empty, and then add another change record corresponding to the original record; When it is detected that the original record is newly added, a new change record having the original record identifier corresponding to the original record is added according to the original record.

10. A digital time travel system that implements the method for optimizing decision-making of digital avatar time travel backtracking data as described in any one of claims 1 to 9, characterized in that: include: Production system, data recording module, data image library, digital clone, digital time machine module, digital clone crossing module; The data recording module is respectively connected to the production system and the data image library, the digital time machine module is respectively connected to the production system, the data image library and the digital avatar crossing module, and the digital avatar crossing module is connected to the data image library.

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