Cross-platform digital content creator integrated operation method and device and storage medium

Abstract

The invention relates to the field of digitization, and provides a cross-platform digital content creator integrated operation method, which comprises the following steps: capturing public anchor data of a target platform in real time through a unified data acquisition interface, and creating an anchor archive library; executing conflict detection when the scheduling saving operation is triggered, and dynamically adjusting the task priority based on a preset rule; establishing association mapping with anchor archives in an anchor archive library; associating the anchor video metadata with the anchor file in real time, and generating a multi-dimensional visual analysis report; generating an initial anchor tag based on a preset tag system of the multi-dimensional visual analysis report and the trend prediction result, and recording a tag version change history; creating an anchor task template based on the visual scheduling view, and triggering reward settlement calculation after the state verification is passed; and constructing a platform adaptation layer to uniformly manage an application program interface (API) and a crawler calling rule, and triggering the timed task scheduling system to retry a task based on an incremental updating mechanism so as to repair abnormal data.

Description

Technical Field

[0001] This invention relates to the field of digitalization, and in particular to a cross-platform integrated operation method, device, and storage medium for digital content creators. Background Technology

[0002] With the explosive growth of the live streaming and short video industries, professional streamers typically need to operate simultaneously on multiple platforms to expand their influence. Due to the independent data interfaces and significantly different operating rules of each platform, streamer management needs to spend a lot of manpower on cross-platform data integration to support core operational processes such as streamer recruitment, content scheduling, performance analysis, and settlement.

[0003] Current mainstream streamer management solutions mainly rely on two models: single-platform management systems and manual dashboard tools. However, both of these solutions suffer from major drawbacks such as data silos, lack of dynamic scheduling, and non-closed-loop operation. Summary of the Invention

[0004] This application provides a cross-platform digital content creator integrated operation method, device, and storage medium, which can realize cross-platform anchor data automatic aggregation and dynamic closed-loop operation management.

[0005] On the one hand, this application provides a cross-platform integrated operation method for digital content creators, the method comprising:

[0006] By capturing publicly available anchor data from the target platform in real time through a unified data collection interface, and responding to new lead entry or lead conversion operations, an anchor profile database is created.

[0007] A visual schedule view is generated based on the anchor archive. When the schedule saving operation is triggered, conflict detection is performed and task priorities are dynamically adjusted based on preset rules.

[0008] The system uses a timed task scheduling system to drive multi-platform application programming interfaces (APIs) and crawler modules to incrementally collect metadata of broadcaster videos and establish an association mapping with broadcaster profiles in the broadcaster archive.

[0009] The system links the anchor's video metadata with the anchor's profile in real time, generates a multi-dimensional visual analysis report, and outputs trend prediction results in response to user-customized commands.

[0010] Initial anchor tags are generated based on the preset tag system of the multi-dimensional visualization analysis report and the trend prediction results, and the tag version change history is recorded after receiving manual correction instructions.

[0011] Based on the aforementioned visual scheduling view, a live streamer task template is created, and the task completion status is automatically obtained through the data acquisition interface. After the status verification is passed, the remuneration settlement calculation is triggered.

[0012] A platform adaptation layer is built to uniformly manage application programming interfaces (APIs) and crawler call rules. Based on an incremental update mechanism, the scheduled task system is triggered to retry tasks to repair abnormal data.

[0013] On the other hand, this application provides a cross-platform integrated operation device for digital content creators, the device comprising:

[0014] The module is used to capture publicly available anchor data from the target platform in real time through a unified data acquisition interface, and to create an anchor profile database in response to new lead entry or lead conversion operations.

[0015] The adjustment module is used to generate a visual schedule view based on the anchor archive, perform conflict detection when the schedule saving operation is triggered, and dynamically adjust the task priority based on preset rules.

[0016] The mapping module is used to drive the multi-platform application programming interface (API) and crawler module through a timed task scheduling system to incrementally collect the anchor video metadata and establish an association mapping with the anchor archive in the anchor archive database.

[0017] The first generation module is used to associate the anchor's video metadata with the anchor's profile in real time, generate a multi-dimensional visualization analysis report, and output trend prediction results in response to user-customized instructions.

[0018] The second generation module is used to generate initial anchor tags based on the preset tag system of the multi-dimensional visualization analysis report and the trend prediction results, and to record the tag version change history after receiving manual correction instructions;

[0019] The first triggering module is used to create a live streamer task template based on the visual scheduling view, automatically obtain the task completion status through the data acquisition interface, and trigger the remuneration settlement calculation after the status verification is passed.

[0020] The second triggering module is used to build a unified management application programming interface (API) and crawler call rules for the platform adaptation layer, and to trigger the scheduled task system to retry tasks to repair abnormal data based on the incremental update mechanism.

[0021] Thirdly, this application provides an electronic device, the device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the technical solution of the cross-platform digital content creator integrated operation method described above.

[0022] Fourthly, this application provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the technical solution of the cross-platform digital content creator integrated operation method described above.

[0023] As can be seen from the technical solution provided in this application, on the one hand, by using a unified data collection interface and a unified management application programming interface (API) through a platform adaptation layer, standardized automatic collection of public data from multiple platforms (e.g., Douyin, Kuaishou, and Bilibili) is achieved, solving the problem of fragmented multi-source data in traditional solutions and providing a unified data foundation for subsequent operations. On the other hand, based on real-time video metadata and multi-dimensional visualization analysis reports obtained through scheduling conflict detection and incremental collection, task conflicts are dynamically detected and priorities are reordered during scheduling and saving operations. This not only avoids resource waste caused by data lag but also forms an automated operational closed loop of "planning-execution-settlement" without human intervention. Thirdly, the incremental collection mechanism of anchor video metadata, combined with timed retry tasks, automatically repairs data synchronization anomalies, ensuring the real-time mapping relationship between the anchor archive, anchor video metadata, and multi-dimensional visualization analysis reports, effectively eliminating data gaps in multiple stages. In summary, the technical solution of this application can achieve automatic aggregation and dynamic closed-loop operation management of cross-platform anchor data. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart of the cross-platform digital content creator integrated operation method provided in the embodiments of this application;

[0026] Figure 2 This is a schematic diagram of the structure of the cross-platform digital content creator integrated operation device provided in the embodiments of this application;

[0027] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0028] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0029] In this specification, adjectives such as "first" and "second" are used only to distinguish one element or action from another, without necessarily requiring or implying any actual such relationship or order. Where circumstances permit, reference to an element, component, or step (etc.) should not be construed as limited to only one element, component, or step, but may include one or more of the elements, components, or steps, etc.

[0030] For ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn to actual scale.

[0031] With the explosive growth of the live streaming and short video industries, professional streamers typically need to operate simultaneously on multiple platforms to expand their influence. Due to the independent data interfaces and significantly different operational rules of each platform, streamer management requires substantial manpower for cross-platform data integration to support core operational processes such as streamer recruitment, content scheduling, performance analysis, and settlement. Currently, mainstream streamer management solutions mainly rely on two models: 1) Single-platform management systems, which only support streamer task assignment and settlement on a single platform and cannot aggregate and analyze cross-platform data; 2) Manual dashboard tools, which generate static reports by manually collecting data from various platforms, resulting in data lag and the inability to detect task conflicts in real time. However, the two solutions mentioned above have the following main drawbacks: 1) Data silos, specifically the lack of a unified cross-platform data collection mechanism, resulting in fragmented core metrics such as streamer follower count and video views, making it impossible to generate real-time aggregated analysis; 2) Lack of dynamic scheduling, specifically the disconnect between the scheduling plan and the streamer's actual live streaming status (e.g., removal from streaming platforms, sudden changes in views), making it impossible to dynamically adjust priorities to optimize resource allocation; 3) Non-closed-loop operation, specifically the separation of task assignment, performance tracking, and reward settlement, requiring repeated manual verification of data consistency, resulting in low operational efficiency.

[0032] To address the aforementioned problems in existing technologies, this application proposes a cross-platform integrated operation method for digital content creators, applicable to scenarios such as warehousing and logistics. Its flowchart is attached. Figure 1 As shown, the main steps include S101 to S107, which are detailed below:

[0033] Step S101: Capture publicly available anchor data from the target platform in real time through a unified data acquisition interface, and create an anchor profile database in response to new lead entry or lead conversion operations.

[0034] In cross-platform streamer management scenarios, streamer identities are scattered across heterogeneous platforms such as Douyin and Kuaishou. Traditional manual data entry methods suffer from inconsistent data formats across platforms (e.g., inconsistent standards for fan count statistics), leading to confusion in identity identification. To avoid data consistency issues in subsequent operations—namely, the inability to link video aggregation to content from multiple platforms belonging to the same streamer—and to prevent errors in fund allocation due to ambiguity in settlement, this application addresses this problem. It utilizes a unified data collection interface to capture publicly available streamer data from target platforms in real time, standardizing multi-platform streamer information into a unified archive. This establishes a foundational identity resolution layer for comprehensive streamer profiles, providing a unified data basis for subsequent operations. Specifically, as an embodiment of this application, in response to new lead entry or lead conversion operations, the creation of the streamer archive can involve: automatically triggering the data collection interface to capture historical fan growth rate data from the target platform in response to user-inputted streamer nickname, platform type, and contact information; and calculating preliminary evaluation indicators in real time based on the captured historical fan growth rate data, dynamically displaying these indicators on the input interface.

[0035] Step S102: Generate a visual schedule view based on the anchor archive, perform conflict detection when the schedule saving operation is triggered, and dynamically adjust the task priority based on preset rules.

[0036] In the live streaming industry, scheduling typically involves multiple conflicting factors such as platform traffic fluctuations and streamer availability. Static scheduling tools (such as calendar views) lack real-time detection mechanisms and cannot detect task overlap, such as a streamer broadcasting simultaneously in two live streaming rooms. Dynamically triggering conflict detection when saving the schedule essentially embeds a real-time decision engine at key operational nodes. Therefore, to avoid resource idleness caused by processing delays in post-processing detection (such as idle live streaming rooms during prime time), a visual schedule view can be generated based on the streamer database, and conflict detection can be performed when the schedule saving operation is triggered. In addition, dynamic priority adjustment based on preset rules creates positive feedback; that is, high-priority tasks automatically preempt resources, avoiding operational stagnation caused by inefficient negotiation. Therefore, while performing conflict detection when the schedule saving operation is triggered, task priorities can also be dynamically adjusted based on preset rules, thereby solving the timeliness and adaptive scheduling requirements of the scheduling system.

[0037] As one embodiment of this application, generating a visual schedule view based on the anchor archive, performing conflict detection and dynamically adjusting task priorities based on preset rules when the schedule saving operation is triggered can be achieved through steps S1021 to S1023, as detailed below:

[0038] Step S1021: Compare the time nodes of the current scheduled tasks in the visual scheduling view with the existing schedules in the anchor archive.

[0039] Specifically, the system can read the task start time, task end time, and associated platform ID from the current scheduled task in the visual scheduling view. At the same time, it can retrieve the existing scheduling data of the same streamer from the streamer archive, including the time interval set of all incomplete tasks and existing tasks with the same streamer ID. Then, it can compare the time nodes of the current scheduled task in the visual scheduling view with the existing schedules in the streamer archive.

[0040] Step S1022: When it is detected that the current scheduled task overlaps with the existing scheduled time in the anchor archive, generate an alarm flag with a conflict timestamp and activate the priority adjustment interface.

[0041] Specifically, overlap detection logic can be performed on each time interval, that is, the current task time interval is compared with each existing task time interval, and the intersection of the time intervals is calculated. If the event is identified as a conflict, an alarm tag data packet with a timestamp is generated. This includes defining the timestamp construction rules and the tag data structure. The former includes collecting the precise system time of the conflict occurrence and binding conflict task ID pairs. The data structure can be as follows: {Conflict type: [Complete overlap | Partial overlap], Conflict timestamp: "YYYY-MM-DD HH:MM:SS", Conflict task group: [Current task ID, Conflict task ID], Associated platform: [Douyin | Kuaishou | Bilibili]}. Finally, an interface activation command is sent to the decision module, which includes passing the conflict tag data packet to the priority adjustment engine and opening the system's automatic decision entry and conflict ignore entry.

[0042] Step S1023: Dynamically rearrange the task execution order by adjusting the interface response system command through priority adjustment.

[0043] Traditional scheduling systems can only detect conflicts but cannot provide solutions, forcing operators to manually adjust based on experience. However, by parsing the quantitative weighting logic of commercial value weights (such as advertising revenue weights) and historical performance data such as the broadcaster's playback decay rate, the core technical problem of how to scientifically allocate scarce time slot resources can be solved, thereby achieving Pareto optimality in resource allocation. Therefore, as an embodiment of this application, responding to system instructions through a priority adjustment interface to dynamically rearrange the task execution order can be as follows: parsing the task templates in the visual scheduling view to extract the commercial value weight coefficients of the target broadcasters corresponding to the broadcaster profiles; proportionally weighting and merging the commercial value weight coefficients with the historical playback decay rates of the target broadcasters corresponding to the broadcaster profiles; generating priority ranking suggestions based on the weighted merging calculation results and writing them into the operable controls of the visual scheduling view in real time. Through the above solution, executable priority rankings can be automatically generated, avoiding the subjectivity of manual decision-making. This not only ensures that high-commercial-value tasks are executed first, improving overall operational efficiency, but also allows for real-time weight adjustments based on changes in broadcaster performance, forming a closed loop in resource scheduling.

[0044] Step S103: Drive the multi-platform application programming interface (API) and crawler module through the scheduled task system to incrementally collect the anchor video metadata and establish an association mapping with the anchor archive in the anchor archive database.

[0045] In the live video streaming industry, streamers can upload hundreds of videos daily, and using full data collection would lead to a storage disaster. On one hand, incremental data collection mechanisms use intelligent version number comparison to only capture newly added / changed videos; essentially, they are difference-aware systems for massive amounts of unstructured data. On the other hand, when a video is taken down by the crawler, the metadata validity is automatically updated, preventing erroneous data from contaminating the analysis results. For example, taken-down videos might still be included in fan conversion rates. Therefore, to protect data freshness and maintain system data quality, a scheduled task system can drive multi-platform application programming interfaces (APIs) and crawler modules to incrementally collect streamer video metadata and establish a mapping between this metadata and the streamer profiles in the streamer archive. Furthermore, incremental collection of anchor video metadata can be achieved by: identifying newly added videos through version number comparison, generating a collection anomaly marker when the comparison fails; when the crawler module identifies that the video corresponding to the collection anomaly marker has been removed based on the collection anomaly marker, updating the validity identifier of the associated anchor video metadata and triggering an anchor video metadata integrity check; based on the verification result of the data integrity check, if the anchor video metadata is incomplete, the collection task is retried periodically until the verification is passed. The anchor video metadata mainly includes video ID, publication time, play count, and interaction metrics (e.g., fan and like data, etc.), etc. The processing method of the above embodiment avoids redundant content collection by intelligently identifying data changes, automatically isolates invalid content to ensure the reliability of video metadata, and maintains service stability by using partial retries instead of full updates.

[0046] Step S104: Real-time association of anchor video metadata and anchor profile to generate multi-dimensional visualization analysis reports, and output trend prediction results in response to user-customized instructions.

[0047] Analyzing single metrics in isolation, such as play counts, can lead to misjudgments of development potential (e.g., videos with inflated play counts are overestimated). By forcibly linking streamer video metadata with streamer profiles, a causal network of metrics can be constructed. That is, play counts need to be combined with fan growth rate to determine sustainability, content type needs to be matched with fan profiles to assess suitability, and user-driven trend prediction further forms a decision-making reinforcement loop, transforming data insights into operational actions, thereby avoiding the trap of "rich data but poor knowledge." Based on the above facts, the embodiments of this application can link streamer video metadata and streamer profiles in real time, generate multi-dimensional visual analysis reports, and output trend prediction results in response to user-customized commands.

[0048] Given that liveness verification and play count mutation detection can together constitute a data trust firewall, in the live streaming ecosystem where fake play counts are rampant, the collaboration of the two can solve the fundamental technical problem of how to determine the authenticity and usability of data, and can also avoid erroneous decision chains caused by data contamination. The above embodiment of anchor video metadata integrity verification can be: verifying the liveness of the video ID on the target platform to obtain the basic status code; calling the task completion status based on the abnormal alarm status and the basic status code to mark the trust level of the anchor video metadata and form a trust report.

[0049] The trend prediction results of the above embodiments can be obtained by performing trend prediction in steps S1041 to S1043 using a prediction model, as detailed below:

[0050] Step S1041: Analyze the temporal correlation between the play count curve and fan growth to identify key inflection points.

[0051] Specifically, step S1041 mainly includes data collection and preprocessing, calculating the rate of change of play counts and number of followers, and identifying key inflection points. Among these, data collection and preprocessing mainly includes obtaining play count data and follower count data of the streamer at different time points from the streamer's video metadata to form time series data, and normalizing the play count and follower count data to eliminate the influence of units. The normalization formula is as follows: In the above normalization formula, V(t) represents the number of views at time point t, F(t) represents the number of fans at time point t, and μ V and σ V Let μ represent the mean and standard deviation of the play count sequence, respectively. F and σ F Let V represent the mean and standard deviation of the follower count sequence, respectively. Calculating the rate of change in play count and follower count mainly involves: calculating the rate of change in play count between adjacent time points (first-order difference) ΔV(t) = V norm (t)-V norm (t-1), similarly calculate the rate of change of the number of fans ΔF(t)=F norm (t)-F norm (t-1); Identifying key inflection points mainly includes: defining a key inflection point as a point where the rate of change in play count and the rate of change in followers change significantly at the same time. The specific judgment condition is: if three consecutive time points satisfy: ΔV(t)*ΔV(t+1)<0 and ΔF(t)*ΔF(t+1)<0, then time point t is marked as a candidate inflection point. Further screening of inflection point candidates requires that the absolute value of the rate of change exceeds the threshold: |ΔV(t)|>θ V And |ΔF(t)|>θ F , where θ V and θ FThe thresholds for the rate of change in play count and the rate of change in followers are respectively (obtained from historical data statistics, for example, taking the upper 10 percentile of the historical rate of change). For each inflection point, record its time point t, the direction of change in play count (increase or decrease), and the direction of change in followers (increase or decrease).

[0052] Step S1042: Based on the identified inflection point, construct a prediction model by combining the content style classification in the tag system and output the model fit parameters.

[0053] Specifically, the implementation of step S1042 mainly includes feature extraction, constructing a prediction model, evaluating the prediction model, and outputting fit parameters. Among these, feature extraction mainly involves extracting the following features for each inflection point: 1) the average number of views V in the time window before the inflection point (e.g., the previous 7 days). pre and average number of fans F pre ;2) Average play count V in the time window after the inflection point (e.g., the next 7 days) post and average number of fans F post 3) The change in play count at the inflection point ΔV = V post -V pre ; and 4) the change in the number of fans at the inflection point ΔF=F post -F pre Then, combining the content style classification tags of the anchor (e.g., the categories of "funny," "beauty," and "gaming" in the tag system), each tag is used as a category feature (or one-hot encoded); the prediction model mainly includes: using a multiple linear regression model to predict the change in play count, the model formula is as follows: ΔV=β0+β1*ΔF+β2*style1+β3*style2+...+β n *style k +ε, where β0 represents the intercept term, β1 represents the coefficient of the fan variation, β2, ..., β n The coefficients representing different content style tags (each content style tag is a binary feature, 1 if it appears, 0 otherwise), style1, style2, ..., style k A binary feature representing the content style, with ε being the error term; model parameters are estimated using the least squares method; the evaluation of the prediction model and output fit parameters mainly includes: calculating the model's coefficient of determination (i.e., the fit parameter). Among them, y i This represents the change in the actual number of views for the i-th sample. This represents the change in the predicted play count for the i-th sample. This represents the average change in actual play counts across all samples, where n is the sample size; the output model includes parameter estimates and the coefficient of determination R0. 2 .

[0054] Step S1043: When the model detects a sudden change in play count that exceeds the fitting range, an automatic platform distribution suggestion report is generated.

[0055] Specifically, the implementation of step S1043 mainly includes mutation detection and generating a platform distribution suggestion report. Among them, mutation detection mainly includes: real-time monitoring of the streamer's playback data and calculating the playback change rate at the current time point (compared to the previous time point): The expected change in play count ΔV at the current time point is calculated using the constructed prediction model. pred Calculate the residual (the difference between the actual change and the predicted change): residual = |ΔV| current -ΔV pred If the residual is greater than the preset threshold δ (e.g., δ = 0.3, indicating a 30% deviation), it is determined that the sudden change in play count exceeds the fitting range. The generated platform diversion suggestion report mainly includes: analyzing the direction (increase or decrease) and magnitude of the change; obtaining the content style tags of the streamer from the streamer's archive; querying the popularity of the content style on each platform (e.g., obtaining the average play count growth rate of the content style on each platform from the preset database); selecting platforms based on the following criteria: prioritizing platforms with high content style matching and those with growth potential in the number of streamer's fans (i.e., ranking of fan growth rate); and generating a diversion suggestion report, including: a description of the detected change (time, magnitude), the matching degree of the current content style on the target platform, a list of suggested diversion platforms (sorted by priority), and the expected effect (estimated increase in play count and fan growth rate based on historical data).

[0056] The operation method described above not only automatically identifies suspicious data (such as abnormal play counts) and blocks the spread of fraud, but also provides an authentic data foundation for analysis and decision-making by cross-verifying data integrity in conjunction with platform status and service status.

[0057] Furthermore, static prediction models can quickly become ineffective due to market changes, such as adjustments to platform algorithm rules. To ensure the model's continuous evolution through a cyclical mechanism of label history → parameter correction → strategy feedback, the embodiments of this application can further optimize the prediction model. This can be achieved through steps S'1041 to S'1043, as detailed below:

[0058] Step S'1041: Periodically retrieve the change history of tag versions to match the content style evolution path.

[0059] Step S'1042: When the activity level tag is downgraded, the prediction model parameters are automatically adjusted based on the detection result of the sudden change in play count.

[0060] Specifically, step S'1042 mainly includes monitoring tag changes, obtaining playback volume mutation detection results, and correcting model parameters. Monitoring tag changes involves obtaining the streamer's activity level tag from the streamer's tags. The activity level tag is divided into multiple levels (e.g., high activity, medium activity, low activity). When the system detects a decrease in the streamer's activity level tag level (e.g., from "high activity" to "medium activity"), it triggers the model parameter correction process. Obtaining playback volume mutation detection results mainly involves querying the playback volume mutation detection results for the streamer over a recent period (e.g., the past 30 days). These results include: the mutation time point, the direction and magnitude of the mutation, and the actual and predicted playback volume values ​​at that time, etc. Correcting model parameters mainly includes: adjusting the coefficients of the prediction model (i.e., the multiple linear regression model) in the above embodiment based on the mutation detection results; and using weighted least squares to assign higher weights to samples after the mutation occurs. Specifically, a new weighted loss function can be constructed. Where D represents the set of samples after the mutation, w i The weight of the i-th sample is calculated using the time decay function: w i =exp(-α*time) elapsed ), where α is the attenuation coefficient (e.g., 0.1), time elapsed This represents the time difference (in days) between the current time and the time of the mutation; the model is retrained (using the original training data plus the new data, trained according to the weights) to obtain the corrected model parameters.

[0061] Step S'1043: Feed back the corrected model parameters to the scheduling priority calculation module and generate scheduling optimization instructions.

[0062] Specifically, the implementation of step S'1043 mainly includes steps such as updating model parameters, recalculating task priorities, and generating scheduling optimization instructions. Among these, updating model parameters involves revising the model parameters (i.e., the new regression coefficients β0', β1', ..., β2) and recalculating the task priorities. n The process involves updating the prediction model, recalculating task priorities (i.e., using the revised prediction model to calculate new expected play volume growth), and generating scheduling optimization instructions. This involves reordering all of the streamer's tasks based on the new priority scores. For each task whose priority has changed, an optimization instruction is generated, including the task ID, the priority before adjustment, the priority after adjustment, and the suggested adjustment time (such as postponement or advancement). Finally, the instruction is sent to the scheduling system to automatically adjust the task order in the visual scheduling view.

[0063] By optimizing the prediction model as described above, we can not only continuously improve the prediction accuracy based on operational feedback and transform the model output into executable scheduling instructions, but also enable the system to proactively adapt to changes in the industry environment.

[0064] Step S105: Generate initial anchor tags based on the preset tag system of multi-dimensional visualization analysis report and trend prediction results, and record the tag version change history after receiving manual correction instructions.

[0065] Considering that a static and fixed anchor tag system, such as one that is never updated after initialization, will become completely ineffective due to anchor transformation (e.g., from entertainment to e-commerce), and in order to eliminate human subjective bias and avoid strategy turmoil caused by tag fluctuations, leading to the complete failure of downstream links such as ad matching and resource allocation, this application can generate initial anchor tags based on a preset tag system of multi-dimensional visualization analysis reports and trend prediction results, and record the tag version change history after receiving a manual correction instruction. Specifically, recording the tag version change history in the above embodiment can be as follows: a timestamp version is generated synchronously when manually modifying tags; a historical version snapshot package is formed by automatically associating snapshots of the multi-dimensional visualization analysis reports at the modification time; and when a retrospective instruction is received, historical tag versions and associated analysis snapshots are loaded based on the timeline. In the above embodiment, manual tag modification requires a trigger condition, which can be formed as follows: parsing abnormal fan profile data returned by the platform adaptation layer and generating a deviation report; when the deviation report shows that the cross-platform fan overlap rate deviation exceeds a threshold, the tag correction interface is activated and the historical version snapshot package is preloaded.

[0066] Step S106: Create a live streamer task template based on the visual scheduling view, automatically obtain the task completion status through the data collection interface, and trigger the remuneration settlement calculation after the status verification is passed.

[0067] The complexity of live streaming settlement lies in the mixed billing of multiple modes. Existing manual reconciliation is prone to financial risks due to lag in status verification, such as over-settlement caused by undetected fraudulent activity. To ensure consistency between planning and execution, block fraudulent paths, and establish a financial safety valve, this application can create anchor task templates based on a visual scheduling view. The task completion status can be automatically obtained through a data acquisition interface, and the reward settlement calculation can be triggered after the status verification is passed. Furthermore, the technical integration challenge of multi-mode settlement in a single system can be solved through an architecture of dynamic mode selection → real-time data retrieval → parameter calculation. Specifically, anchor video metadata includes video views, and multi-dimensional visual analysis reports include conversion rate analysis results. Correspondingly, the reward settlement calculation method can be: dynamically selecting a fixed reward, commission, or mixed calculation mode based on the task template in the visual scheduling view; when using the commission mode, calculating dynamic revenue parameters by real-time retrieval of video views and conversion rate analysis results; and generating a list of rewards to be confirmed and marking abnormal task status items based on the dynamic revenue parameters and settlement cycle. The solutions described above can flexibly support diverse business cooperation models, ensure that remuneration is linked to actual results, and prevent settlement errors in advance by identifying abnormal statuses, thereby enhancing risk pre-control.

[0068] Considering that settlement disputes in the live streaming industry often stem from a lack of status verification, this application constructs a three-tiered defense system—freezing, tracing, and credit association—to proactively ensure fund security at the technical level. Specifically, the reward list in the above embodiment can employ the following confirmation process: for marked abnormal status items, compare the automatically collected task completion status with the manual review results; when there is a discrepancy between the task completion status and the manual review results, freeze the task settlement and activate the issue tracing process until the abnormal mark on the reward list is removed; upon unfreezing, update the settlement weight coefficient of the associated streamer's performance credit tag. It should be noted that the streamer's performance credit tag is a subset of the initial streamer tag or the version-changed tag in the aforementioned embodiment. The above confirmation process for the reward list provides a buffer mechanism for dispute resolution, not only quickly locating the problem's occurrence point but also establishing long-term constraints on streamer behavior.

[0069] Step S107: Build a platform adaptation layer to uniformly manage application programming interfaces (APIs) and crawler call rules, and trigger the scheduled task system to retry tasks based on the incremental update mechanism to repair abnormal data.

[0070] In the live streaming industry, frequent changes to interfaces between different platforms pose a persistent threat to the system. To prevent seamless switching of crawlers when APIs malfunction, avoid service interruptions, and ensure eventual data consistency, this embodiment of the application can construct a platform adaptation layer to uniformly manage application programming interfaces (APIs) and crawler call rules, and trigger a scheduled task retry system based on an incremental update mechanism to repair abnormal data.

[0071] Specifically, to address platform interface instability through dual-channel switching and error rate monitoring mechanisms, thereby forming a control loop of perception → switching → recovery → optimization, the unified management of application programming interfaces (APIs) and crawler call rules in the platform adaptation layer of the above embodiment can be achieved as follows: During the initial configuration phase, API call frequency rules and crawler backup channel on / off status are set for each platform; during operation, when the platform's interface returns a changed error code, the system automatically switches to the crawler backup channel and resets the frequency counter; after each data collection, the success rate index and data freshness are recorded, and the frequency rules are dynamically optimized; wherein, automatically switching to the crawler backup channel can be achieved by: monitoring API response error codes and timeout frequencies based on the recorded success rate index; when the error rate exceeds the standard multiple times consecutively (e.g., 5 times), the crawler backup channel is activated and a switching log is generated; based on changes in the data freshness index, the API channel is automatically restored for testing during the scheduled maintenance window. Through the perception → switching → recovery → optimization control loop of the above embodiment, not only is the robustness of the service improved, but also the strategy is dynamically adjusted according to service quality, effectively preventing local anomalies from spreading to the global level, thereby quickly isolating faults.

[0072] From the above appendix Figure 1 The example of the cross-platform integrated operation method for digital content creators demonstrates that, on the one hand, by using a unified data collection interface and building a platform adaptation layer to manage application programming interfaces (APIs), standardized and automatic collection of publicly available data from multiple platforms (e.g., Douyin, Kuaishou, and Bilibili) is achieved. This solves the problem of fragmented multi-source data in traditional solutions and provides a unified data foundation for subsequent operations. On the other hand, based on scheduling conflict detection and incremental collection of real-time video metadata and multi-dimensional visualization analysis reports, task conflicts are dynamically detected and priorities are reordered during scheduling and saving operations. This not only avoids resource waste caused by data lag but also forms an automated "plan-execution-settlement" operation loop without human intervention. Thirdly, the incremental collection mechanism of anchor video metadata, combined with timed retry tasks, automatically repairs data synchronization anomalies, ensuring the real-time mapping relationship between the anchor archive, anchor video metadata, and multi-dimensional visualization analysis reports, effectively eliminating data gaps across multiple stages. In summary, the technical solution of this application can achieve automatic aggregation and dynamic closed-loop operation management of cross-platform anchor data.

[0073] Please see the appendix Figure 2 This application provides a cross-platform integrated operation device for digital content creators. The device may include a creation module 201, an adjustment module 202, a mapping module 203, a first generation module 204, a second generation module 205, a first trigger module 206, and a second trigger module 207, as detailed below:

[0074] Create module 201 to capture publicly available anchor data from the target platform in real time through a unified data acquisition interface, and create an anchor archive in response to new lead entry or lead conversion operations.

[0075] The adjustment module 202 is used to generate a visual schedule view based on the anchor archive, perform conflict detection when the schedule saving operation is triggered, and dynamically adjust the task priority based on preset rules.

[0076] The mapping module 203 is used to drive the multi-platform application programming interface (API) and crawler module through the timed task scheduling system to incrementally collect the anchor video metadata and establish an association mapping with the anchor archive in the anchor archive database.

[0077] The first generation module 204 is used to associate the anchor's video metadata and anchor profile in real time, generate a multi-dimensional visual analysis report, and output trend prediction results in response to user-customized instructions.

[0078] The second generation module 205 is used to generate initial anchor tags based on a preset tag system of multi-dimensional visualization analysis reports and trend prediction results, and to record the tag version change history after receiving manual correction instructions.

[0079] The first trigger module 206 is used to create a live streamer task template based on a visual scheduling view, automatically obtain the task completion status through a data acquisition interface, and trigger the remuneration settlement calculation after the status verification is passed.

[0080] The second triggering module 207 is used to build a unified management application programming interface (API) and crawler call rules for the platform adaptation layer, and to trigger the scheduled task system to retry tasks to repair abnormal data based on the incremental update mechanism.

[0081] From the above appendix Figure 2As illustrated by the example of the cross-platform digital content creator integrated operation device, on the one hand, by using a unified data collection interface and building a platform adaptation layer to manage application programming interfaces (APIs), standardized automatic collection of public data from multiple platforms (e.g., Douyin, Kuaishou, and Bilibili) is achieved, solving the problem of fragmented multi-source data in traditional solutions and providing a unified data foundation for subsequent operations. On the other hand, based on scheduling conflict detection and incremental collection of real-time video metadata and multi-dimensional visualization analysis reports, task conflicts are dynamically detected and priorities are reordered during scheduling and saving operations. This not only avoids resource waste caused by data lag but also forms an automated operation loop of "planning-execution-settlement" without human intervention. Thirdly, the incremental collection mechanism of anchor video metadata, combined with timed retry tasks, automatically repairs data synchronization anomalies, ensuring the real-time mapping relationship between the anchor archive, anchor video metadata, and multi-dimensional visualization analysis reports, effectively eliminating data gaps in multiple stages. In summary, the technical solution of this application can realize automatic aggregation and dynamic closed-loop operation management of cross-platform anchor data.

[0082] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. For example... Figure 3 As shown, the electronic device 3 in this embodiment mainly includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30, such as a program for a cross-platform digital content creator integrated operation method. When the processor 30 executes the computer program 32, it implements the steps in the above-described embodiment of the cross-platform digital content creator integrated operation method, for example... Figure 1 The steps S101 to S107 are shown. Alternatively, when the processor 30 executes the computer program 32, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 2 The functions of the creation module 201, adjustment module 202, mapping module 203, first generation module 204, second generation module 205, first trigger module 206, and second trigger module 207 are shown.

[0083] For example, the computer program 32 of the cross-platform digital content creator integrated operation method mainly includes: real-time capture of publicly available anchor data from the target platform through a unified data acquisition interface; creation of an anchor archive in response to new lead entry or lead conversion operations; generation of a visual scheduling view based on the anchor archive; execution of conflict detection and dynamic adjustment of task priorities based on preset rules when the scheduling save operation is triggered; incremental collection of anchor video metadata through a timed task scheduling system driving multi-platform application programming interfaces (APIs) and crawler modules; establishment of a correlation mapping with anchor archives in the anchor archive; and real-time correlation of anchor video metadata. Based on the anchor profiles, a multi-dimensional visual analysis report is generated, and trend prediction results are output in response to user-customized instructions. Initial anchor tags are generated based on a preset tag system of the multi-dimensional visual analysis report and trend prediction results, and the tag version change history is recorded after receiving manual correction instructions. Anchor task templates are created based on a visual scheduling view, and task completion status is automatically obtained through a data acquisition interface. Remuneration settlement calculation is triggered after status verification. A platform adaptation layer is constructed to uniformly manage application programming interfaces (APIs) and crawler call rules, and the timed task scheduling system is triggered to retry tasks to repair abnormal data based on an incremental update mechanism. The computer program 32 can be divided into one or more modules / units, one or more of which are stored in memory 31 and executed by processor 30 to complete this application. One or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program 32 in the electronic device 3.For example, computer program 32 can be divided into the functions of creation module 201, adjustment module 202, mapping module 203, first generation module 204, second generation module 205, first trigger module 206, and second trigger module 207 (modules in the virtual device). The specific functions of each module are as follows: Creation module 201 is used to capture publicly available anchor data from the target platform in real time through a unified data acquisition interface, and create an anchor archive in response to new lead entry or lead conversion operations; Adjustment module 202 is used to generate a visual schedule view based on the anchor archive, perform conflict detection when the schedule saving operation is triggered, and dynamically adjust the task priority based on preset rules; Mapping module 203 is used to drive multi-platform application programming interface (API) and crawler module through a timed task scheduling system, incrementally collect anchor video metadata, and establish a mapping relationship with the anchor archive. The system includes: a mapping of anchor profiles in the anchor archive; a first generation module 204, used to associate anchor video metadata with anchor profiles in real time, generate multi-dimensional visualization analysis reports, and output trend prediction results in response to user-customized instructions; a second generation module 205, used to generate initial anchor tags based on a preset tag system of multi-dimensional visualization analysis reports and trend prediction results, and record tag version change history after receiving manual correction instructions; a first trigger module 206, used to create anchor task templates based on a visualization scheduling view, automatically obtain task completion status through a data acquisition interface, and trigger remuneration settlement calculation after status verification; and a second trigger module 207, used to build a platform adaptation layer to uniformly manage application programming interfaces (APIs) and crawler call rules, and trigger the scheduled task scheduling system to retry tasks to repair abnormal data based on an incremental update mechanism.

[0084] Electronic device 3 may include, but is not limited to, processor 30 and memory 31. Those skilled in the art will understand that... Figure 3 This is merely an example of electronic device 3 and does not constitute a limitation on electronic device 3. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device may also include input / output devices, network access devices, buses, etc.

[0085] The processor 30 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0086] The memory 31 can be an internal storage unit of the electronic device 3, such as a hard disk or RAM. The memory 31 can also be an external storage device of the electronic device 3, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory 31 can include both internal and external storage units of the electronic device 3. The memory 31 is used to store computer programs and other programs and data required by the electronic device. The memory 31 can also be used to temporarily store data that has been output or will be output.

[0087] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed. That is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments 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. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above-described device can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0088] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0089] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0090] In the embodiments provided in this application, it should be understood that the disclosed apparatus / device and method can be implemented in other ways. For example, the apparatus / device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0091] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0092] 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.

[0093] If integrated modules / units are implemented as software functional units and sold or used as independent products, they can be stored in a storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program of the cross-platform digital content creator integrated operation method can be stored in a storage medium. When the computer program is executed by a processor, it can implement the steps of the above method embodiments, namely, real-time capture of publicly available anchor data from the target platform through a unified data acquisition interface, creation of an anchor archive in response to new lead entry or lead conversion operations; generation of a visual schedule view based on the anchor archive, execution of conflict detection and dynamic adjustment of task priority based on preset rules when the schedule saving operation is triggered; and driving multi-platform application programming interfaces (APIs) and crawler modules through a timed task scheduling system. The system incrementally collects anchor video metadata and establishes a mapping between it and anchor profiles in the anchor archive. It then real-time correlates anchor video metadata with anchor profiles, generating multi-dimensional visual analysis reports and responding to user-defined commands by outputting trend prediction results. Based on the multi-dimensional visual analysis reports and trend prediction results, it generates initial anchor tags using a preset tag system and records tag version change history upon receiving manual correction commands. It creates anchor task templates based on a visual scheduling view, automatically acquires task completion status through a data collection interface, and triggers reward settlement calculations upon successful status verification. A platform adaptation layer is constructed to uniformly manage application programming interfaces (APIs) and crawler call rules, triggering the scheduled task retry system based on an incremental update mechanism to repair abnormal data. The computer program includes computer program code, which can be in source code form, object code form, executable files, or some intermediate form. Storage media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the contents of storage media can be appropriately added to or removed according to the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, storage media may not include electrical carrier signals and telecommunication signals.

[0094] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application. The specific embodiments described above further illustrate the purpose, technical solutions, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the protection scope of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this invention.

Claims

1. A cross-platform integrated operation method for digital content creators, characterized in that, The method includes: By capturing publicly available anchor data from the target platform in real time through a unified data collection interface, and responding to new lead entry or lead conversion operations, an anchor profile database is created. A visual schedule view is generated based on the anchor archive. When the schedule saving operation is triggered, conflict detection is performed and task priorities are dynamically adjusted based on preset rules. The system uses a timed task scheduling system to drive multi-platform application programming interfaces (APIs) and crawler modules to incrementally collect metadata of broadcaster videos and establish an association mapping with broadcaster profiles in the broadcaster archive. The system can link the anchor's video metadata with the anchor's profile in real time, generate a multi-dimensional visualization analysis report, and output trend prediction results in response to user-customized commands. Initial anchor tags are generated based on the preset tag system of the multi-dimensional visualization analysis report and the trend prediction results, and the tag version change history is recorded after receiving manual correction instructions. Based on the aforementioned visual scheduling view, a live streamer task template is created, and the task completion status is automatically obtained through the data acquisition interface. After the status verification is passed, the remuneration settlement calculation is triggered. A platform adaptation layer is built to uniformly manage application programming interfaces (APIs) and crawler call rules. Based on an incremental update mechanism, the scheduled task system is triggered to retry tasks to repair abnormal data.

2. The cross-platform digital content creator integrated operation method according to claim 1, characterized in that, The step of generating a visual schedule view based on the anchor archive, performing conflict detection and dynamically adjusting task priorities based on preset rules when the schedule saving operation is triggered includes: Compare the time nodes of the currently scheduled tasks in the visualized scheduling view with the existing schedules in the anchor archive; When it is detected that the current scheduled task overlaps with the existing scheduled time in the anchor archive, an alarm flag with a conflict timestamp is generated and the priority adjustment interface is activated; The priority adjustment interface responds to system commands and dynamically rearranges the task execution order.

3. The cross-platform digital content creator integrated operation method according to claim 2, characterized in that, The step of responding to system commands through the priority adjustment interface and dynamically rearranging the task execution order includes: The task templates in the visualized scheduling view are analyzed to extract the commercial value weight coefficient of the target anchor corresponding to the anchor profile; The commercial value weighting coefficient is weighted and combined proportionally with the historical playback volume decay rate of the target streamer corresponding to the streamer profile; Based on the weighted merging calculation results, priority sorting suggestions are generated and written into the operable controls of the visual scheduling view in real time.

4. The cross-platform digital content creator integrated operation method according to claim 1, characterized in that, The incremental collection of anchor video metadata includes: New videos are identified by comparing version numbers, and an abnormal acquisition marker is generated when the comparison fails. When the crawler module identifies that the video corresponding to the collection anomaly marker has been taken down based on the collection anomaly marker, it updates the validity identifier associated with the anchor video metadata and triggers the integrity verification of the anchor video metadata; Based on the verification result of the data integrity check, if the anchor video metadata is incomplete, the collection task will be retried periodically until the verification is passed.

5. The cross-platform digital content creator integrated operation method according to claim 4, characterized in that, The integrity verification of the anchor's video metadata includes: Verify the liveness of the video ID on the target platform to obtain the basic status code; Based on the abnormal alarm status and the basic status code, the task completion status is invoked to mark the credibility level of the anchor video metadata and form a credibility report.

6. The cross-platform digital content creator integrated operation method according to claim 1, characterized in that, The anchor's video metadata includes video views; the multi-dimensional visualization analysis report includes conversion rate analysis results; and the method for calculating compensation settlement includes: Dynamically select fixed compensation, commission, or hybrid calculation mode based on the task template in the visual scheduling view; When a commission-based model is adopted, the dynamic revenue parameters are calculated in real time by calling the video playback volume and conversion rate analysis results. Based on dynamic revenue parameters and settlement cycles, a list of rewards to be confirmed is generated and abnormal task status items are marked.

7. The cross-platform digital content creator integrated operation method according to claim 6, characterized in that, The label includes the streamer's performance credit label, and the confirmation process for the reward list includes: For the marked abnormal status items, compare the completion status of the automatically collected tasks with the results of manual review; When there is a discrepancy between the task completion status and the manual review result, the task settlement is frozen and the problem tracing process is activated until the abnormal mark is removed from the reward list. Upon unfreezing, the settlement weight coefficient is updated based on the streamer's performance credit tag.

8. A cross-platform integrated operation device for digital content creators, characterized in that, The device includes: The module is used to capture publicly available anchor data from the target platform in real time through a unified data acquisition interface, and to create an anchor profile database in response to new lead entry or lead conversion operations. The adjustment module is used to generate a visual schedule view based on the anchor archive, perform conflict detection when the schedule saving operation is triggered, and dynamically adjust the task priority based on preset rules. The mapping module is used to drive the multi-platform application programming interface (API) and crawler module through a timed task scheduling system to incrementally collect the anchor video metadata and establish an association mapping with the anchor archive in the anchor archive database. The first generation module is used to associate the anchor's video metadata with the anchor's profile in real time, generate a multi-dimensional visualization analysis report, and output trend prediction results in response to user-customized instructions. The second generation module is used to generate initial anchor tags based on the preset tag system of the multi-dimensional visualization analysis report and the trend prediction results, and to record the tag version change history after receiving manual correction instructions; The first triggering module is used to create a live streamer task template based on the visual scheduling view, automatically obtain the task completion status through the data acquisition interface, and trigger the remuneration settlement calculation after the status verification is passed. The second triggering module is used to build a unified management application programming interface (API) and crawler call rules for the platform adaptation layer, and to trigger the scheduled task system to retry tasks to repair abnormal data based on the incremental update mechanism.

9. An electronic device, the device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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