A method, apparatus, and equipment for dynamically adjusting advertising execution parameters.

Abstract

This application discloses a method, apparatus, and device for dynamically adjusting advertising execution parameters, comprising: acquiring current advertising business data, historical advertising business data, and system monitoring indicators in real time; analyzing the data volume changes of historical and current advertising business data, and predicting target advertising business data based on the analysis results; comparing the target advertising business data with the current advertising business data, and determining the adjustment direction and adjustment ratio of the parameters to be adjusted based on the comparison results and system monitoring indicators; and dynamically adjusting the parameters to be adjusted based on the adjustment direction and adjustment ratio, wherein the parameters to be adjusted include the concurrent number of advertising business data collection tasks and the time interval of advertising requests. This improves the flexibility in determining the concurrent number and time interval, and can adapt to the fluctuations in traffic volume on various platforms.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, apparatus, and device for dynamically adjusting advertising execution parameters. Background Technology

[0002] With the rapid development of Internet technology, the number of business systems connected to advertising platforms has increased dramatically. This requires the middleware system to obtain data from a large number of advertising platforms in real time in order to conduct accurate analysis and determine the number of concurrent ads and the time interval between ad delivery requests.

[0003] In related technologies, advertising data collected from multiple platforms over a historical period is typically analyzed to predict advertising data traffic in the future based on the analysis results. This allows for the determination of the concurrent number of advertising tasks and the time interval between advertising delivery requests. However, the method for determining the concurrent number of advertising tasks and the time interval between advertising delivery requests lacks flexibility, which may lead to data processing bottlenecks during peak data periods and resource waste during off-peak data periods. Summary of the Invention

[0004] This application provides a method, apparatus, and device for dynamically adjusting advertising execution parameters, solving the problem of inflexibility in determining the number of concurrent advertising tasks and the time interval. By predicting platform data for the next time period based on historical platform data and current advertising business data, and comparing the prediction results with the current advertising business data, the initial number of concurrent tasks and the initial concurrent time interval are dynamically and timely adjusted, improving the flexibility in determining the number of concurrent tasks and the time interval, and adapting to the fluctuations in traffic volume on various platforms.

[0005] In a first aspect, embodiments of this application provide a method for dynamically adjusting advertising execution parameters, including:

[0006] Real-time acquisition of current advertising business data, historical advertising business data, and system monitoring indicators; analysis of data volume changes in the historical and current advertising business data; and prediction of target advertising business data based on the analysis results.

[0007] The target advertising business data is compared with the current advertising business data, and the adjustment direction and adjustment ratio of the parameters to be adjusted are determined based on the comparison results and the system monitoring indicators.

[0008] The parameters to be adjusted are dynamically adjusted based on the adjustment direction and the adjustment ratio. The parameters to be adjusted include the number of concurrent data collection tasks for advertising business and the time interval of advertising requests.

[0009] Optionally, the system monitoring metrics include request success rate and response time. Determining the adjustment direction and adjustment ratio of the parameter to be adjusted based on the comparison results and the system monitoring metrics includes:

[0010] Determine whether the request success rate and response time are abnormal, identify the abnormal conditions of the request success rate and response time, and if the request success rate and response time are both normal, determine the adjustment direction and adjustment ratio of the parameter to be adjusted based on the dynamic adjustment strategy associated with the comparison results.

[0011] Optionally, determining the adjustment direction of the parameter to be adjusted based on the comparison results and the system monitoring indicators further includes:

[0012] In the event of an abnormal request success rate, a first adjustment direction corresponding to the abnormal request success rate is determined, and the first adjustment direction is matched with a second adjustment direction corresponding to the comparison result. Based on the matching result, the adjustment direction of the number of concurrent users of the advertising business data collection task and the time interval of the advertising request is determined.

[0013] In the event of an abnormal response time, a third adjustment direction corresponding to the abnormal response time is determined, and the third adjustment direction is matched with the second adjustment direction corresponding to the comparison result. Based on the matching result, the adjustment direction of the number of concurrent advertising business data collection tasks and the time interval of advertising requests is determined.

[0014] Optionally, determining the adjustment direction and adjustment ratio of the parameter to be adjusted based on the dynamic adjustment strategy associated with the comparison results includes:

[0015] If the data volume of the target advertising business data is greater than the data volume of the current advertising business data, calculate the data volume difference, and determine the adjustment direction and adjustment ratio of the parameter to be adjusted corresponding to the data volume difference according to the preset first dynamic adjustment strategy;

[0016] If the amount of data in the target advertising business is less than the amount of data in the current advertising business, the data difference is calculated, and the adjustment direction and adjustment ratio of the parameter to be adjusted corresponding to the data difference are determined according to the preset second dynamic adjustment strategy.

[0017] Optionally, the step of analyzing the data volume changes of the historical advertising business data and the current advertising business data, and predicting the target advertising business data based on the analysis results, includes:

[0018] The data volume of the historical advertising business data and the data volume of the current advertising business data are analyzed to determine the trend and rate of change of the data volume in future time periods.

[0019] Predict target advertising business data for the future period based on the trend and rate of change of the data volume.

[0020] Optionally, the step of parsing the data volume of the historical advertising business data and the data volume of the current advertising business data to determine the trend and rate of change of the data volume in future time periods includes:

[0021] Extract the first platform data corresponding to the target historical time period from the historical advertising business data, and calculate the difference between the data volume of the first platform data and the data volume of the current advertising business data to obtain the data volume difference.

[0022] Determine whether the difference in data volume is within a preset range, and determine the trend and rate of change of data volume in future periods based on the determination result.

[0023] Optionally, determining the trend and rate of change of data volume in future time periods based on the judgment result includes:

[0024] If the difference in data volume is within a preset range, the historical change trend and historical change rate of the next historical period adjacent to the target historical period will be determined as the change trend and change rate of data volume in the future period.

[0025] If the difference in data volume is not within the preset difference range, extract the data of the second platform from the most recent period in the historical platform data, calculate the difference between the data volume of the second platform data and the data volume of the current advertising business data, and determine the trend and rate of change of data volume in future periods based on the calculation result.

[0026] In a second aspect, embodiments of this application provide a dynamic adjustment device for advertising execution parameters, comprising:

[0027] The platform data acquisition module is used to acquire current advertising business data, historical advertising business data, and system monitoring metrics in real time.

[0028] The parameter parsing module is used to parse the data volume changes of the historical advertising business data and the current advertising business data;

[0029] The target platform data prediction module is used to predict target advertising business data based on the parsing results.

[0030] The data comparison module is used to compare the target advertising business data with the current advertising business data;

[0031] The adjustment strategy determination module is used to determine the adjustment direction and adjustment ratio of the parameter to be adjusted based on the comparison results and the system monitoring indicators;

[0032] A dynamic adjustment module is used to dynamically adjust the parameters to be adjusted based on the adjustment direction and the adjustment ratio. The parameters to be adjusted include the number of concurrent advertising business data collection tasks and the time interval of advertising requests.

[0033] In a third aspect, embodiments of this application provide an electronic device, the device comprising: one or more processors; and a storage device configured to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the dynamic adjustment method for advertising execution parameters described in the first aspect.

[0034] In a fourth aspect, embodiments of this application provide a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform a dynamic adjustment method for advertising execution parameters as described in the first aspect.

[0035] This application embodiment acquires current advertising business data, historical advertising business data, and system monitoring indicators in real time. It analyzes the data volume changes of both historical and current advertising business data and predicts target advertising business data based on the analysis results. The target advertising business data is compared with the current advertising business data, and the adjustment direction and proportion of the parameters to be adjusted are determined based on the comparison results and system monitoring indicators. The parameters to be adjusted are dynamically adjusted based on the adjustment direction and proportion. These parameters include the concurrency of advertising business data collection tasks and the time interval of advertising requests. This solution enables dynamic and timely adjustment of the initial concurrency and initial concurrency time interval by predicting platform data for the next period based on historical platform data and current advertising business data, and comparing the prediction results with the current advertising business data. This improves the flexibility in determining the concurrency and time interval and can adapt to fluctuations in traffic volume across different platforms. Attached Figure Description

[0036] Figure 1 This is a flowchart of a method for dynamically adjusting advertising execution parameters provided in an embodiment of this application;

[0037] Figure 2 This is a flowchart of a method for determining the adjustment direction provided in an embodiment of this application;

[0038] Figure 3 This is a flowchart of a method for predicting target advertising business data provided in an embodiment of this application;

[0039] Figure 4 This application provides a line chart showing the statistical data volume within each historical period.

[0040] Figure 5 This is a flowchart of a method for determining a change trend and a change rate provided in an embodiment of this application;

[0041] Figure 6 This is a schematic diagram of the structure of a dynamic adjustment device for advertising execution parameters provided in an embodiment of this application;

[0042] Figure 7 This is a schematic diagram of the structure of a dynamic adjustment device for advertising execution parameters provided in an embodiment of this application. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this application clearer, specific embodiments of this application will be described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely for explaining this application and not for limiting it. It should also be noted that, for ease of description, only the parts relevant to this application are shown in the drawings, not all of them. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe operations (or steps) as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but may also have additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0044] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0045] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0046] The following description, in conjunction with the accompanying drawings, details the method, apparatus, and equipment for dynamically adjusting advertising execution parameters provided in this application through specific embodiments and application scenarios.

[0047] The method for dynamically adjusting advertising execution parameters provided in this application is applicable to scenarios where multiple platforms control advertising business data, such as the management of advertising accounts by advertising agencies and the control and adjustment of advertising placement strategies by e-commerce platforms. Based on the above application scenarios, it is understood that the executing entity for each step can be a computer device. This computer device refers to any electronic device with data computing, processing, and storage capabilities, such as mobile phones, PCs (Personal Computers), tablets, and other terminal devices, or it can be a server or other similar device. This application does not limit the scope of the method.

[0048] Figure 1 This is a flowchart of a method for dynamically adjusting advertising execution parameters provided in an embodiment of this application, such as... Figure 1 As shown, it includes:

[0049] Step S101: Real-time acquisition of current advertising business data, historical advertising business data, and system monitoring indicators; analysis of data volume changes in historical and current advertising business data; and prediction of target advertising business data based on the analysis results.

[0050] The current advertising business data can refer to real-time monitoring of data traffic, account counts, and cost records from major advertising platforms. Historical advertising business data can refer to data traffic, account counts, and cost records monitored from major advertising platforms within a preset historical time period, such as historical data from the past 3 days or the past 24 hours. System monitoring metrics refer to a series of observable parameters used to quantify and measure the system's operating status, performance, resource consumption, business processing effectiveness, and health, such as resource utilization, task backlog, and business processing success rate. Target advertising business data refers to data collected by major advertising platforms within a future period or the next time period, such as data collected by major advertising platforms in the next 10 minutes or the next adjacent time period.

[0051] In one embodiment, real-time data such as data traffic, number of accounts, and number of expense entries from major advertising platforms are collected, along with historical data collected from major advertising platforms within a preset time period, and a series of observational data such as the current operating status and performance of the advertising data processing system. The data volume of current and historical advertising business data is statistically analyzed to determine the data volume of current and historical advertising business data. Based on the data volume of current and historical advertising business data, the trend of data volume change in the target advertising business data for the next time period is predicted. An average data volume change parameter for each time period is calculated based on historical advertising business data. Finally, based on the data volume, trend, and average data volume change parameter for each time period, information such as data traffic, number of accounts, and number of expense entries for the next time period is calculated.

[0052] Step S102: Compare the target advertising business data with the current advertising business data, and determine the adjustment direction and adjustment ratio of the parameters to be adjusted based on the comparison results and system monitoring indicators.

[0053] Among these, the parameters to be adjusted can refer to key variables that are dynamically adjusted based on advertising platform limitations, network status, system resource load, task backlog, and business needs, and can affect the system's processing efficiency, stability, and resource utilization. The adjustment direction can refer to the specific trend of increasing, decreasing, switching modes, or dynamically adapting the parameters based on system goals, changes in the external environment, or changes in the internal state. The adjustment ratio can refer to the proportional relationship between the new and original values ​​when adjusting the parameters, used to quantify the magnitude of the adjustment, ensuring that the adjustment process is controllable and stable, and avoiding system fluctuations due to excessive adjustments.

[0054] In one embodiment, the data volume of the target advertising business data is compared with the data volume of the current advertising business data. Based on the comparison result, it is determined whether the trend in the next period is upward or downward. The parameter to be adjusted is increased or decreased according to this trend, and the adjustment ratio associated with the parameter to be adjusted is determined. For example, if the data volume of the target advertising business data is greater than the data volume of the current advertising business data, the parameter to be adjusted is a retry mechanism parameter. Since the probability of a single request / task failure may increase when the data volume increases, the number of retries can be increased accordingly. Multiple attempts can compensate for temporary failures and reduce the situation where a task that could have succeeded is discarded due to a single failure. In this case, the adjustment direction of the parameter to be adjusted can be determined as increasing or decreasing, and the adjustment ratio associated with the number of retries, such as +10%, can be determined according to a pre-set correlation.

[0055] Step S103: Dynamically adjust the parameters to be adjusted based on the adjustment direction and the adjustment ratio. The parameters to be adjusted include the number of concurrent advertising business data collection tasks and the time interval of advertising requests.

[0056] Optionally, the parameters to be adjusted may include the number of concurrent advertising data collection tasks and the time interval between advertising requests. The number of concurrent advertising data collection tasks refers to the number of advertising data collection tasks initiated and executed simultaneously by the system to multiple advertising platforms within the same time window. The time interval between advertising requests refers to the time difference between two consecutive requests to an advertising platform and is a core parameter for controlling the request frequency.

[0057] For example, when the target advertising business data volume is greater than the current advertising business data volume, the number of concurrent advertising business data collection tasks is increased and the time interval of advertising requests is reduced. The pre-set adjustment ratios for the number of concurrent requests and the time interval are also determined. For example, the adjustment ratio for the number of concurrent requests is +20% and the adjustment ratio for the time interval is -10%. Therefore, in the next time period, the number of concurrent requests in the current time period is increased by 20% and the time interval is reduced by 10%.

[0058] This application embodiment acquires current advertising business data, historical advertising business data, and system monitoring indicators in real time. It analyzes the data volume changes of both historical and current advertising business data and predicts target advertising business data based on the analysis results. The target advertising business data is compared with the current advertising business data, and the adjustment direction and proportion of the parameters to be adjusted are determined based on the comparison results and system monitoring indicators. The parameters to be adjusted are dynamically adjusted based on the adjustment direction and proportion. These parameters include the concurrency of advertising business data collection tasks and the time interval of advertising requests. This solution enables dynamic and timely adjustment of the initial concurrency and initial concurrency time interval by predicting platform data for the next period based on historical platform data and current advertising business data, and comparing the prediction results with the current advertising business data. This improves the flexibility in determining the concurrency and time interval and can adapt to fluctuations in traffic volume across different platforms.

[0059] In one embodiment, the system monitoring metrics include request success rate and response time. Based on the comparison results and system monitoring metrics, the adjustment direction and adjustment ratio of the parameter to be adjusted are determined, including: determining whether the request success rate and response time are abnormal, determining the abnormal conditions of the request success rate and response time, and, if the request success rate and response time are normal, determining the adjustment direction and adjustment ratio of the parameter to be adjusted according to the dynamic adjustment strategy associated with the comparison results.

[0060] The request success rate refers to the proportion of requests successfully completed out of all requests initiated by the system to the target service within a certain period. The response time refers to the total time elapsed from when the system initiates a request to the target service until the system fully receives and confirms the response result. In one embodiment, the abnormality of the request success rate and response time can be determined based on preset request success rate thresholds and response time thresholds. For example, if the request success rate is less than the preset request success rate threshold, it is considered abnormal; otherwise, it is normal. Similarly, if the response time is greater than the preset response time threshold, it is considered abnormal; otherwise, it is normal. When both the request success rate and response time are determined to be normal, there is no need to consider their impact on adjusting the parameters to be adjusted. The adjustment direction and proportion of the parameters to be adjusted can be determined directly based on the comparison between the target advertising business data volume and the current advertising business data volume. If the target advertising business data volume is greater than the current advertising business data volume, the concurrency of the advertising business data collection task is increased, the time interval of advertising requests is shortened, and the preset concurrency adjustment proportion and advertising request time interval adjustment proportion are determined as the corresponding adjustment proportions. If the target advertising business data volume is less than the current advertising business data volume, then reduce the number of concurrent advertising business data collection tasks and increase the time interval of advertising requests. The adjustment ratio of the number of concurrent tasks and the adjustment ratio of the time interval are the same as the preset adjustment ratio.

[0061] This application embodiment determines abnormal conditions in request success rate and response time by judging whether they are abnormal. When both request success rate and response time are normal, the adjustment direction and proportion of the parameter to be adjusted are determined based on a dynamic adjustment strategy associated with the comparison results. In the above scheme, the impact of request success rate and response time on parameter adjustment is fully considered when determining the adjustment direction and proportion, thus improving the accuracy of the adjustment direction and proportion.

[0062] In one embodiment, when the data volume of the target advertising business data is greater than the data volume of the current advertising business data, the data volume difference is calculated, and the adjustment direction and adjustment ratio of the parameter to be adjusted corresponding to the data volume difference are determined according to a preset first dynamic adjustment strategy; when the data volume of the target advertising business data is less than the data volume of the current advertising business data, the data volume difference is calculated, and the adjustment direction and adjustment ratio of the parameter to be adjusted corresponding to the data volume difference are determined according to a preset second dynamic adjustment strategy.

[0063] Both the first and second dynamic adjustment strategies are rules or mechanisms that adaptively adjust core parameters based on real-time monitoring of system status, business indicators, and changes in the external environment to maintain the system's optimal operating state. However, the specific adjustment rules of the first and second dynamic adjustment strategies differ. In one embodiment, when the target advertising business data volume is greater than the current advertising business data volume, the data volume difference is calculated. The adjustment ratio corresponding to different data volume difference ranges in the first dynamic adjustment strategy is determined. After calculating the data volume difference, the range to which the data volume difference belongs, the corresponding adjustment ratios for concurrency and time intervals within that range, and the adjustment direction for concurrency and time intervals in the first dynamic adjustment strategy are determined. When the target advertising business data volume is less than the current advertising business data volume, the data volume difference is calculated, the target range to which the data volume difference belongs in the second dynamic adjustment strategy is determined, the corresponding adjustment ratios for concurrency and time intervals within that target range, and the adjustment direction for concurrency and time intervals in the second dynamic adjustment strategy are determined.

[0064] This application embodiment calculates the data volume difference when the target advertising data volume is greater than the current advertising data volume, and determines the adjustment direction and adjustment ratio of the parameter to be adjusted corresponding to the data volume difference according to a preset first dynamic adjustment strategy; when the target advertising data volume is less than the current advertising data volume, it calculates the data volume difference, and determines the adjustment direction and adjustment ratio of the parameter to be adjusted corresponding to the data volume difference according to a preset second dynamic adjustment strategy. In the above scheme, the adjustment ratio corresponding to the data volume difference and the adjustment direction corresponding to the comparison result can be determined according to the corresponding dynamic adjustment strategy, improving the accuracy of the adjustment direction and adjustment ratio.

[0065] Figure 2 This is a flowchart of a method for determining the adjustment direction provided in an embodiment of this application, such as... Figure 2 As shown, it includes:

[0066] Step S1021: In the case of an abnormal request success rate, determine the first adjustment direction corresponding to the abnormal request success rate, match the first adjustment direction with the second adjustment direction corresponding to the comparison result, and determine the adjustment direction of the number of concurrent advertising business data collection tasks and the time interval of advertising requests based on the matching result.

[0067] The first adjustment direction refers to adjusting the concurrency of advertising business data collection tasks and the time interval of advertising requests corresponding to the success rate of abnormal requests. The second adjustment direction refers to adjusting the concurrency of advertising business data collection tasks and the time interval of advertising requests corresponding to the comparison result between the target advertising business data volume and the current advertising business data volume.

[0068] In one embodiment, when the request success rate is abnormal, the adjustment direction of the concurrent quantity of the advertising business data collection task and the adjustment direction of the time interval of the advertising request are determined. The adjustment direction of the concurrent quantity and the adjustment direction of the time interval are matched with the adjustment direction of the concurrent quantity and the adjustment direction of the time interval corresponding to the comparison result, respectively. Based on the matching result, the parameters to be adjusted with the same adjustment direction and the parameters to be adjusted with the different adjustment directions are determined. The adjustment direction with the same adjustment direction is determined as the final adjustment direction of the corresponding parameter to be adjusted. At this time, there is no need to adjust the parameters to be adjusted with the different adjustment directions. Therefore, the final adjustment direction of the parameters to be adjusted without adjustment is the balance adjustment, that is, no adjustment.

[0069] Step S1022: In the event of an abnormal response time, determine the third adjustment direction corresponding to the abnormal response time, match the third adjustment direction with the second adjustment direction corresponding to the comparison result, and determine the adjustment direction of the number of concurrent advertising business data collection tasks and the time interval of advertising requests based on the matching result.

[0070] The third adjustment direction refers to the adjustment direction of the concurrency of the advertising business data collection task corresponding to the abnormal response time and the adjustment direction of the time interval of the advertising request. In one embodiment, in the case of abnormal response time, the adjustment direction of the concurrency of the advertising business data collection task and the adjustment direction of the time interval of the advertising request corresponding to the abnormal response time are determined. This concurrency adjustment direction and the time interval adjustment direction are then matched with the concurrency adjustment direction and the time interval adjustment direction corresponding to the comparison results. Based on the matching results, parameters to be adjusted with consistent adjustment directions and parameters to be adjusted with inconsistent adjustment directions are determined. The consistent adjustment direction is determined as the final adjustment direction of the corresponding parameter to be adjusted. At this time, there is no need to adjust the parameters to be adjusted with inconsistent adjustment directions. Therefore, the final adjustment direction of the parameters to be adjusted without adjustment is a balanced adjustment, i.e., no adjustment. For example, the third adjustment direction is to increase the time interval and increase the concurrency, while the second adjustment direction is to decrease the time interval and increase the concurrency. Thus, the adjustment directions of the concurrency are consistent, while the adjustment directions of the time interval are inconsistent. At this time, there is no need to adjust the time interval; the adjustment direction of the time interval is a balanced adjustment, and the adjustment direction of the concurrency is an increasing adjustment.

[0071] In this embodiment, when the request success rate is abnormal, a first adjustment direction corresponding to the abnormal request success rate is determined. This first adjustment direction is then matched with a second adjustment direction corresponding to the comparison result. Based on the matching result, the adjustment direction for the concurrent number of advertising business data collection tasks and the time interval of advertising requests is determined. Similarly, when the response time is abnormal, a third adjustment direction corresponding to the abnormal response time is determined. This third adjustment direction is then matched with the second adjustment direction corresponding to the comparison result. Based on the matching result, the adjustment direction for the concurrent number of advertising business data collection tasks and the time interval of advertising requests is determined. This solution considers the impact of abnormal request success rates and response times on system parameter adjustments, improving the accuracy of the adjustment direction.

[0072] Figure 3 This is a flowchart of a method for predicting target advertising business data provided in an embodiment of this application, such as... Figure 3 As shown, it includes:

[0073] Step S1011: Analyze the data volume of historical advertising business data and the data volume of current advertising business data to determine the trend and rate of change of data volume in future periods.

[0074] The trend of data volume change can refer to the continuous direction, magnitude, and pattern of change in the total amount, increment, or processing volume of data over time. This trend can include increases, decreases, or stagnation. The rate of change of data volume can refer to the magnitude of increase or decrease in data volume per unit time, which is a quantitative indicator for measuring how fast data volume changes. In one embodiment, historical advertising business data can include historical advertising business data from multiple data collection periods, such as historical data from the first period, the second period, and the third period. If the data collection period is one day, then the historical data from the first period, the second period, and the third period are respectively the historical data from the previous three days, the previous two days, and the previous day. Figure 4 This application provides a line chart illustrating the statistical data volume within various historical periods, such as... Figure 4As shown, the data volume within each preset time period in each historical cycle is statistically analyzed. The target time period within each historical cycle that is the same as the current time period is determined, as well as the next time period adjacent to the target time period. For example, if the current time is 10:00 and the time period is 10:00-10:10, then the 10:00-10:10 time period in the first cycle, the second cycle, and the third cycle are all determined as the target time period. The next time period adjacent to the target time period is the 10:10-10:20 time period in the first cycle, the second cycle, and the third cycle, respectively. The data change from the 10:00-10:10 time period to the 10:00-10:20 time period in the same cycle is calculated. Based on the data change and the duration of the time period, the data volume change rate per minute is determined. By comparing the data volume of the target time period within each historical period with the data volume of the next adjacent time period, the target change trend of the data volume in each period and the next adjacent time period is determined. It is then determined whether the change trends of each target are consistent. If they are consistent, this target change trend is taken as the change trend of the data volume in the future period. If they are inconsistent, the change trend of the same time period within the previous adjacent historical data collection period is determined as the change trend of the data volume in the future period. Figure 4 As shown, the current time period is 10:00-10:10, and the next adjacent time period is 10:00-10:20. Therefore, the data volume change trend of 10:00-10:20 in the previous day (that is, in the third cycle) is determined as the change trend of the future time period.

[0075] Step S1012: Predict target advertising business data for future periods based on the changing trend and rate of change of data volume.

[0076] For example, if the data volume trend is upward, the rate of change is 10,000 records / minute, and the current collected ad impressions are 200,000, then based on this change and trend, the data volume of the target advertising business data in the next adjacent time period can be predicted to be 300,000 records.

[0077] This application embodiment analyzes the data volume of historical and current advertising business data to determine the trend and rate of change of data volume in future periods; based on the trend and rate of change of data volume, it predicts the target advertising business data for future periods. In the above solution, by analyzing the data volume changes of historical and current advertising business data to determine the trend and rate of change, the accuracy of determining the target advertising business data is improved.

[0078] Figure 5 This is a flowchart of a method for determining the trend and rate of change provided in an embodiment of this application, such as... Figure 5 As shown, it includes:

[0079] Step S10111: Extract the first platform data corresponding to the target historical time period from the historical advertising business data, and calculate the difference between the data volume of the first platform data and the data volume of the current advertising business data to obtain the data volume difference.

[0080] Step S10112: Determine whether the difference in data volume is within the preset difference range, and determine the trend and rate of change of data volume in the future period based on the determination result.

[0081] Among them, the target historical period is the same period as the current period in the historical data collection cycle, and the first platform data is the data traffic, number of accounts, and number of fees collected from major advertising platforms within the target historical period.

[0082] In one embodiment, advertising platform data (i.e., first platform data) from the same time period as the current time period within the target historical data collection period is extracted. The difference between the data volume of the first platform data and the data volume of the current advertising business data is calculated to obtain the data volume difference. It is then determined whether this data volume difference is within a preset range. If it is, the average rate of change for the same time period within multiple historical data collection periods is determined as the rate of change for the data volume in the future time period, and the trend of change for any same time period within multiple historical data collection periods is determined as the trend of change for the data volume in the future time period. If not, the data volume of the current advertising business data is compared with the data volume collected in the same time period within each historical data collection period to determine the data volume of the target time period closest to the data volume of the current time period. The trend and rate of change of the data volume from this target time period to the next adjacent time period are then determined as the trend and rate of change for the data volume in the future time period.

[0083] This application embodiment extracts first platform data corresponding to the current time period from historical advertising business data, calculates the difference between the data volume of the first platform data and the data volume of the current advertising business data, and determines whether the data volume difference is within a preset difference range. Based on the determination result, it determines the trend and rate of change of data volume in future time periods. In the above scheme, by calculating the data volume difference and comparing it with a preset difference range, the trend and rate of change of data volume in the target historical time period for reference are determined, which improves the efficiency of determining the trend and rate of change.

[0084] In one possible embodiment, determining the trend and rate of change of data volume in future periods based on the judgment result includes: if the difference in data volume is within a preset difference range, determining the historical trend and rate of change of the next historical period adjacent to the target historical period as the trend and rate of change of data volume in future periods; if the difference in data volume is not within the preset difference range, extracting the data of the second platform from the most recent period in the historical platform data, calculating the difference between the data volume of the second platform data and the data volume of the current advertising business data, and determining the trend and rate of change of data volume in future periods based on the calculation result.

[0085] The second platform data refers to the platform data of the previous time period adjacent to the current time period. In one embodiment, if the difference in data volume is within a preset range, it can be considered that the data volume change in the same time period in each data collection cycle is small. The data volume of each time period in the historical data collection cycle is of reference significance for judging the trend of data volume change in the future time period. The change in data volume in the current cycle is similar to the change in data volume in the historical data collection cycle. Therefore, the trend of data volume change from the target historical time period to the next adjacent historical time period can be directly determined as the trend of data volume change in the future time period. The difference between the data volume of the first platform data of the target historical time period and the data volume of the platform data of the next adjacent historical time period is calculated to determine the change value of data volume in the two adjacent historical time periods. The rate of change is calculated based on the change value of data volume and the total duration of the two time periods, and the rate of change is determined as the rate of change in the future time period. If the difference in data volume is not within the preset range, it can be assumed that the data volume changes significantly within the same time period in each data collection cycle. The data volume of each time period in the historical data collection cycle is not meaningful for judging the trend of data volume change in the future period. In this case, the data of the second platform in the most recent period in the historical platform data can be extracted, and the difference between the data volume of the second platform data and the data volume of the current advertising business data can be calculated. If the difference is greater than 0, the trend of data volume change in the future period is downward. If the difference is equal to 0, the trend of data volume change in the future period is unchanged. If the difference is less than 0, the trend of data volume change in the future period is upward. The rate of change of data volume in the future period is calculated by using the data volume difference obtained above and the data collection duration of the corresponding period.

[0086] This application embodiment determines the future data volume trend and rate of change by using the historical change trend and rate of change of the next historical period adjacent to the target historical period when the data volume difference is within a preset difference range. When the data volume difference is outside the preset difference range, it extracts the most recent period's data from the historical platform data, calculates the difference between the data volume of the second platform data and the current advertising business data, and determines the future data volume trend and rate of change based on the calculation result. This solution compares the data volume difference with a preset difference range to determine whether the historical data volume trend has reference value, and directly refers to valuable data trends to determine the future data volume trend and rate of change. This improves the efficiency of determining the trend and rate of change while ensuring the accuracy of the trend and rate of change.

[0087] Figure 6 This is a schematic diagram of the structure of a dynamic adjustment device for advertising execution parameters provided in an embodiment of this application, as shown below. Figure 6 As shown, it includes:

[0088] Platform data acquisition module 21 is used to acquire current advertising business data, historical advertising business data and system monitoring indicators in real time;

[0089] Parameter parsing module 22 is used to parse the data volume changes of the historical advertising business data and the current advertising business data;

[0090] The target platform data prediction module 23 is used to predict target advertising business data based on the parsing results;

[0091] The data comparison module 24 is used to compare the target advertising business data with the current advertising business data;

[0092] The adjustment strategy determination module 25 is used to determine the adjustment direction and adjustment ratio of the parameter to be adjusted based on the comparison results and the system monitoring indicators;

[0093] The dynamic adjustment module 26 is used to dynamically adjust the parameter to be adjusted based on the adjustment direction and the adjustment ratio. The parameter to be adjusted includes the number of concurrent advertising business data collection tasks and the time interval of advertising requests.

[0094] This application embodiment acquires current advertising business data, historical advertising business data, and system monitoring indicators in real time. It analyzes the data volume changes of both historical and current advertising business data and predicts target advertising business data based on the analysis results. The target advertising business data is compared with the current advertising business data, and the adjustment direction and proportion of the parameters to be adjusted are determined based on the comparison results and system monitoring indicators. The parameters to be adjusted are dynamically adjusted based on the adjustment direction and proportion. These parameters include the concurrency of advertising business data collection tasks and the time interval of advertising requests. This solution enables dynamic and timely adjustment of the initial concurrency and initial concurrency time interval by predicting platform data for the next period based on historical platform data and current advertising business data, and comparing the prediction results with the current advertising business data. This improves the flexibility in determining the concurrency and time interval and can adapt to fluctuations in traffic volume across different platforms.

[0095] In one possible embodiment, system monitoring metrics include request success rate and response time, and the adjustment strategy determination module 25 is specifically used for:

[0096] Determine whether the request success rate and response time are abnormal, identify the abnormal conditions of the request success rate and response time, and if the request success rate and response time are both normal, determine the adjustment direction and adjustment ratio of the parameter to be adjusted based on the dynamic adjustment strategy associated with the comparison results.

[0097] In one possible embodiment, the adjustment strategy determination module 25 is further configured to:

[0098] In the event of an abnormal request success rate, a first adjustment direction corresponding to the abnormal request success rate is determined, and the first adjustment direction is matched with a second adjustment direction corresponding to the comparison result. Based on the matching result, the adjustment direction of the number of concurrent users of the advertising business data collection task and the time interval of the advertising request is determined.

[0099] In the event of an abnormal response time, a third adjustment direction corresponding to the abnormal response time is determined, and the third adjustment direction is matched with the second adjustment direction corresponding to the comparison result. Based on the matching result, the adjustment direction of the number of concurrent advertising business data collection tasks and the time interval of advertising requests is determined.

[0100] In one possible embodiment, the adjustment strategy determination module 25 is specifically used for:

[0101] If the data volume of the target advertising business data is greater than the data volume of the current advertising business data, calculate the data volume difference, and determine the adjustment direction and adjustment ratio of the parameter to be adjusted corresponding to the data volume difference according to the preset first dynamic adjustment strategy;

[0102] If the amount of data in the target advertising business is less than the amount of data in the current advertising business, the data difference is calculated, and the adjustment direction and adjustment ratio of the parameter to be adjusted corresponding to the data difference are determined according to the preset second dynamic adjustment strategy.

[0103] In one possible embodiment, the target platform data prediction module 23 is specifically used for:

[0104] The data volume of the historical advertising business data and the data volume of the current advertising business data are analyzed to determine the trend and rate of change of the data volume in future time periods.

[0105] Predict target advertising business data for the future period based on the trend and rate of change of the data volume.

[0106] In one possible embodiment, the target platform data prediction module 23 is specifically used for:

[0107] Extract the first platform data corresponding to the target historical time period from the historical advertising business data, and calculate the difference between the data volume of the first platform data and the data volume of the current advertising business data to obtain the data volume difference.

[0108] Determine whether the difference in data volume is within a preset range, and determine the trend and rate of change of data volume in future periods based on the determination result.

[0109] In one possible embodiment, the target platform data prediction module 23 is specifically used for:

[0110] If the difference in data volume is within a preset range, the historical change trend and historical change rate of the next historical period adjacent to the target historical period will be determined as the change trend and change rate of data volume in the future period.

[0111] If the difference in data volume is not within the preset difference range, extract the data of the second platform from the most recent period in the historical platform data, calculate the difference between the data volume of the second platform data and the data volume of the current advertising business data, and determine the trend and rate of change of data volume in future periods based on the calculation result.

[0112] This application also provides an electronic device, which can integrate a dynamic adjustment device for advertising execution parameters provided in this application. Figure 7 This is a schematic diagram of the structure of a dynamic adjustment device for advertising execution parameters provided in an embodiment of this application, with reference to... Figure 7The device for dynamically adjusting advertising execution parameters includes: an input device 33, an output device 34, a memory 32, and one or more processors 31; the memory 32 is used to store one or more programs; when one or more programs are executed by one or more processors 31, the one or more processors 31 implement the method for dynamically adjusting advertising execution parameters as provided in the above embodiments. The input device 33, output device 34, memory 32, and processors 31 can be connected via a bus or other means. Figure 7 Taking the example of a connection between China and Israel via a bus.

[0113] The memory 32, as a computing device readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the dynamic adjustment method for advertising execution parameters provided in any embodiment of this application. The memory 32 may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function; the data storage area may store data created based on the use of the device. Furthermore, the memory 32 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 32 may further include memory remotely located relative to the processor 31, and these remote memories can be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0114] Input device 33 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of the device. Output device 34 may include display devices such as a display screen.

[0115] The processor 31 executes various functional applications and data processing of the device by running software programs, instructions and modules stored in the memory 32, thereby realizing the above-mentioned dynamic adjustment method of advertising execution parameters.

[0116] The aforementioned dynamic adjustment device, equipment, and computer for advertising execution parameters can be used to execute the dynamic adjustment method for advertising execution parameters provided in any of the above embodiments, and have corresponding functions and beneficial effects.

[0117] This application embodiment also provides a storage medium for storing computer-executable instructions, which, when executed by a computer processor, are used to perform a dynamic adjustment method for advertising execution parameters as provided in the above embodiment. The dynamic adjustment method for advertising execution parameters includes:

[0118] Real-time acquisition of current advertising business data, historical advertising business data, and system monitoring indicators; analysis of data volume changes in the historical and current advertising business data; and prediction of target advertising business data based on the analysis results.

[0119] The target advertising business data is compared with the current advertising business data, and the adjustment direction and adjustment ratio of the parameters to be adjusted are determined based on the comparison results and the system monitoring indicators.

[0120] The parameters to be adjusted are dynamically adjusted based on the adjustment direction and the adjustment ratio. The parameters to be adjusted include the number of concurrent data collection tasks for advertising business and the time interval of advertising requests.

[0121] Storage medium – any type of memory device or storage device. The term “storage medium” is intended to include: mounting media, such as CD-ROMs, floppy disks, or magnetic tape devices; computer system memory or random access memory, such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; non-volatile memory, such as flash memory, magnetic media (e.g., hard disks or optical storage); registers or other similar types of memory elements, etc. Storage media may also include other types of memory or combinations thereof. Furthermore, storage media may reside in a first computer system in which a program is executed, or may reside in a different second computer system connected to the first computer system via a network (such as the Internet). The second computer system can provide program instructions to the first computer for execution. The term “storage medium” can include two or more storage media that may reside in different locations (e.g., in different computer systems connected via a network). Storage media may store program instructions (e.g., specifically implemented as a computer program) executable by one or more processors.

[0122] Of course, the computer-executable instructions provided in the embodiments of this application are not limited to the dynamic adjustment method of advertising execution parameters as described above, but can also perform related operations in the dynamic adjustment method of advertising execution parameters provided in any embodiment of this application.

[0123] The dynamic adjustment device, equipment, and storage medium for advertising execution parameters provided in the above embodiments can execute the dynamic adjustment method for advertising execution parameters provided in any embodiment of this application. For technical details not described in detail in the above embodiments, please refer to the dynamic adjustment method for advertising execution parameters provided in any embodiment of this application.

[0124] The above description is merely a preferred embodiment and the technical principles employed in this application. This application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions that can be made by those skilled in the art will not depart from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of this application, the scope of which is determined by the scope of the claims.

Claims

1. A method of dynamically adjusting an advertisement execution parameter, characterized by, include: The system acquires current advertising business data, historical advertising business data, and system monitoring metrics in real time. It analyzes the data volume changes of the historical advertising business data and the current advertising business data, and predicts the target advertising business data based on the analysis results. The system monitoring metrics include request success rate and response time. The target advertising business data is compared with the current advertising business data, and the adjustment direction and adjustment ratio of the parameters to be adjusted are determined based on the comparison results and the system monitoring indicators. The parameters to be adjusted are dynamically adjusted based on the adjustment direction and the adjustment ratio. The parameters to be adjusted include the number of concurrent data collection tasks for advertising business and the time interval of advertising requests. The step of determining the adjustment direction and adjustment ratio of the parameter to be adjusted based on the comparison results and the system monitoring indicators includes: determining whether the request success rate and the response time are abnormal; if the request success rate and the response time are both normal and the data volume of the target advertising business data is greater than the data volume of the current advertising business data, calculating the data volume difference, determining the adjustment ratio of the parameter to be adjusted corresponding to the data volume difference range in the preset first dynamic adjustment strategy, and the adjustment direction of the parameter to be adjusted corresponding to the data volume difference; if the request success rate and the response time are both normal and the data volume of the target advertising business data is less than the data volume of the current advertising business data, calculating the data volume difference, determining the adjustment ratio of the parameter to be adjusted corresponding to the data volume difference range in the preset second dynamic adjustment strategy, and the adjustment direction of the parameter to be adjusted corresponding to the data volume difference.

2. The method of claim 1, wherein, The step of determining the adjustment direction of the parameter to be adjusted based on the comparison results and the system monitoring indicators also includes: In the event of an abnormal request success rate, a first adjustment direction corresponding to the abnormal request success rate is determined, and the first adjustment direction is matched with a second adjustment direction corresponding to the comparison result. Based on the matching result, the adjustment direction of the concurrent number of the advertising business data collection task and the time interval of the advertising request is determined. In the event of an abnormal response time, a third adjustment direction corresponding to the abnormal response time is determined, and the third adjustment direction is matched with the second adjustment direction corresponding to the comparison result. Based on the matching result, the adjustment direction of the number of concurrent advertising business data collection tasks and the time interval of advertising requests is determined.

3. The method of claim 1, wherein, The step of analyzing the data volume changes of the historical advertising business data and the current advertising business data, and predicting the target advertising business data based on the analysis results, includes: The data volume of the historical advertising business data and the data volume of the current advertising business data are analyzed to determine the trend and rate of change of the data volume in future time periods. Predict target advertising business data for the future period based on the trend and rate of change of the data volume.

4. The method for dynamically adjusting advertising execution parameters according to claim 3, characterized in that, The step of analyzing the data volume of the historical advertising business data and the data volume of the current advertising business data to determine the trend and rate of change of the data volume in future time periods includes: Extract the first platform data corresponding to the target historical time period from the historical advertising business data, and calculate the difference between the data volume of the first platform data and the data volume of the current advertising business data to obtain the data volume difference. Determine whether the difference in data volume is within a preset range, and determine the trend and rate of change of data volume in future periods based on the determination result.

5. The method for dynamically adjusting advertising execution parameters according to claim 4, characterized in that, The determination of the trend and rate of change of data volume in future time periods based on the judgment result includes: If the difference in data volume is within a preset range, the historical change trend and historical change rate of the next historical period adjacent to the target historical period will be determined as the change trend and change rate of data volume in the future period. If the difference in data volume is not within the preset difference range, extract the data of the second platform from the most recent period in the historical platform data, calculate the difference between the data volume of the second platform data and the data volume of the current advertising business data, and determine the trend and rate of change of data volume in future periods based on the calculation result.

6. A dynamic adjustment device for advertising execution parameters, characterized in that, include: The platform data acquisition module is used to acquire current advertising business data, historical advertising business data and system monitoring indicators in real time. The system monitoring indicators include request success rate and response time. The parameter parsing module is used to parse the data volume changes of the historical advertising business data and the current advertising business data; The target platform data prediction module is used to predict target advertising business data based on the parsing results. The data comparison module is used to compare the target advertising business data with the current advertising business data; The adjustment strategy determination module is used to determine the adjustment direction of the parameter to be adjusted based on the comparison results and the system monitoring indicators. Specifically, the adjustment strategy determination module is used to determine whether the request success rate and the response time are abnormal. When the request success rate and the response time are both normal and the data volume of the target advertising business data is greater than the data volume of the current advertising business data, the module calculates the data volume difference and determines the adjustment ratio of the parameter to be adjusted corresponding to the data volume difference range in the preset first dynamic adjustment strategy, as well as the adjustment direction of the parameter to be adjusted corresponding to the data volume difference. When the request success rate and the response time are both normal and the data volume of the target advertising business data is less than the data volume of the current advertising business data, the module calculates the data volume difference and determines the adjustment ratio of the parameter to be adjusted corresponding to the data volume difference range in the preset second dynamic adjustment strategy, as well as the adjustment direction of the parameter to be adjusted corresponding to the data volume difference. A dynamic adjustment module is used to dynamically adjust the parameters to be adjusted based on the adjustment direction and the adjustment ratio. The parameters to be adjusted include the number of concurrent advertising business data collection tasks and the time interval of advertising requests.

7. An electronic device, characterized in that, The device includes: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the method for dynamically adjusting advertising execution parameters as described in any one of claims 1-5.

8. A storage medium for storing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform the dynamic adjustment method for advertising execution parameters as described in any one of claims 1-5.

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