Digital advertisement putting method based on big data

Abstract

The invention relates to the technical field of digital marketing, in particular to a digital advertisement putting method based on big data, and the method comprises the following steps: initializing an advertisement model, importing advertisement request time sequence data, carrying out time sequence analysis, adjusting an enqueue rule and a processing rate, calculating request distribution in a differentiated time period, and generating model parameter configuration. According to the invention, through a queuing theory and time sequence data analysis, advertisement time sequence and processing efficiency optimization, time sequence analysis and parameter adjustment, advertisement timeliness is enhanced, user activity peak synchronization is ensured, user participation degree and advertisement effect are improved, and recalculation of service time and estimation of waiting time support real-time monitoring of service efficiency. Accurate matching of advertisement resources and user requirements is achieved, real-time adjustment of strategies and simulation testing rapidly adapt to market changes, the resource utilization efficiency and ROI are improved, and precision and economic benefits of advertisement putting are remarkably improved through fine management and data driving optimization.

Description

Technical Field

[0001] The present invention relates to the field of digital marketing technology, and in particular to a digital advertising delivery method based on big data. Background Art

[0002] The field of digital marketing technology covers strategies and methods for promoting products and services using digital technology, network platforms and big data analysis. The field is aimed at interacting with Internet users through electronic devices to optimize the efficiency and effectiveness of advertising content. Technical means include search engine optimization (SEO), content marketing, social media marketing, email marketing, affiliate marketing and programmatic advertising purchases. Digital marketing also uses user behavior data to personalize and accurately target advertisements. By analyzing users' online behavior and preferences, advertisers can more effectively achieve their market goals and enhance interaction and engagement with consumers.

[0003] Among them, digital advertising methods refer to advertising activities implemented on digital platforms, which display brand or product information to the target audience through various online channels. Its main purpose is to attract potential customers, enhance brand awareness and promote sales. This method relies on Internet technology, including search engine advertising, social media advertising, mobile advertising and video advertising, etc. It can formulate personalized advertising strategies based on users' Internet usage habits and preferences and insights obtained through data analysis, thereby improving advertising conversion rates and ROI (return on investment).

[0004] Although existing technologies are widely used in various online platforms and tools, common problems include insufficient timeliness and accuracy of advertising delivery, and the inability to fully achieve the optimal match between advertising content and user behavior. For example, traditional digital advertising methods rely on fixed advertising delivery rules and simple behavioral goals, which leads to inefficient use of advertising resources during non-peak periods and insufficient resources during peak periods. Existing technologies fail to fully utilize real-time data for dynamic adjustments and lack the ability to provide immediate feedback and adjustment on the effectiveness of advertising delivery, which is particularly unfavorable in a dynamic and changing market environment, resulting in a decline in advertising ROI and a reduction in user experience, and an inability to effectively attract and maintain potential customers. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and propose a digital advertising method based on big data.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme, a digital advertising delivery method based on big data, comprising the following steps: S1: Initialize the advertising model, import the advertising request time series data, perform time series analysis, adjust the queue entry rules and processing rate, calculate the request distribution in the differentiated time period, and generate the model parameter configuration; S2: Calculate the arrival rate and service rate using the model parameter configuration, extract the timestamp and duration of each advertisement request, perform cluster analysis, adjust the time parameters, and generate a timing parameter adjustment plan; S3: Apply the timing parameter adjustment scheme, recalculate the service time, use a simplified model to estimate the waiting time, analyze the difference between the estimated result and the real-time data, make fine adjustments, and generate performance tuning results; S4: According to the performance tuning result, real-time adjustment of advertising resources is performed, advertising resource allocation and cache management are reconfigured, a new loading strategy is implemented, and a strategy implementation blueprint is generated; S5: adopt the strategy implementation blueprint, design simulation tests, capture advertising effect data during the test, conduct effect analysis, identify advertising delivery patterns, and generate advertising delivery optimization results; S6: Based on the advertising delivery optimization results, review the advertising content and user interaction, identify key advertising elements and viewing peaks, adjust the copy and visual elements, and synchronously adjust the delivery time to generate an optimal advertising delivery plan.

[0007] As a further solution of the present invention, the model parameter configuration includes an advertisement request time series, a queue processing rate, and an advertisement display time period; the timing parameter adjustment scheme includes an advertisement arrival rate adjustment, an advertisement service rate optimization, and an advertisement display time synchronization; the performance tuning results include a reduction in waiting time, an optimization of service time, and efficiency optimization indicators; the strategy implementation blueprint includes an advertisement loading speed optimization, an improvement in response speed, and resource reconfiguration parameters; the advertisement delivery optimization results include an advertisement effect improvement indicator, an advertisement delivery effect comparison, and an advertisement strategy adjustment feedback; the optimal advertisement delivery scheme includes a copy optimization parameter, a visual element adjustment scheme, and a delivery time adjustment strategy.

[0008] As a further solution of the present invention, the steps of initializing the advertising model, importing the advertising request time series data, performing time series analysis, adjusting the queue entry rules and processing rate, calculating the request distribution in the differentiated time periods, and generating the model parameter configuration are specifically as follows: S101: Initialize the advertising model, import the advertising request time series data, perform data screening operations, remove duplicate records, screen invalid and incomplete data items, perform timestamp standardization on the remaining data, and obtain a sorted data set; S102: According to the sorted data set, apply the moving average algorithm to analyze the request frequency in each time window, calculate the average request interval time in each window, identify the peak request period, make preliminary adjustments to the model's queuing rules and processing rate parameters based on the information, and obtain request distribution characteristics; S103: Optimize the advertisement model parameters through the request distribution characteristics, adjust the queuing rules of advertisement requests, adjust the processing rate and process the request load, and build the model parameter configuration.

[0009] As a further solution of the present invention, the formula of the moving average algorithm is as follows:

[0010] Calculate the average request interval time for each time window, where Represents the average request interval time, Representative The sum of the request intervals in the time windows, Representative The weight coefficient of the time window, Represents the number of time windows.

[0011] As a further solution of the present invention, the steps of calculating the arrival rate and service rate by using the model parameter configuration, extracting the timestamp and duration of each advertisement request, performing cluster analysis, adjusting the time parameters, and generating a timing parameter adjustment solution are as follows: S201: According to the model parameter configuration, filter the advertisement request records including timestamps and durations from the logs of the advertisement server, extract the data for unified storage, and generate a time data set; S202: Based on the time data set, grouping technology is used to divide the timestamp and duration into differentiated categories, and independent statistics are performed for each category to determine the activity distribution of the advertisement request, label the grouped data, and generate a clustering result set; S203: Using the label information in the clustering result set, adjust the time parameters of the advertisement display, and optimize the utilization efficiency of the advertisement resources by comparing the peak activity times of multiple categories, matching the advertisement publishing time and the user active time, and generating a timing parameter adjustment plan.

[0012] As a further solution of the present invention, the steps of applying the timing parameter adjustment solution, recalculating the service time, estimating the waiting time using a simplified model, analyzing the difference between the estimation result and the real-time data, and performing fine-tuning to generate the performance tuning result are as follows: S301: recalculating the service time according to the timing parameter adjustment scheme, estimating the preliminary waiting time using the baseline model, inputting the original service time data into the model, adjusting the input parameters of the time series, performing initialization calculation and predicting the waiting time, and obtaining the waiting time estimation data; S302: using the waiting time estimation data, performing a difference comparison between the real-time data and the predicted data, including calculating the deviation between the predicted value and the real-time observed value, adjusting key parameters in the model according to the deviation result, and generating an adjusted parameter solution; S303: Use the adjusted parameter solution to recalculate the model and optimize the service efficiency, compare the service time and processing rate of the model before and after the adjustment, conduct efficiency evaluation, and obtain performance tuning results.

[0013] As a further solution of the present invention, according to the performance tuning results, real-time adjustment of advertising resources is performed, advertising resource allocation and cache management are reconfigured, and a new loading strategy is implemented. The steps of generating a strategy implementation blueprint are specifically as follows: S401: Analyze the performance tuning results, identify areas where advertising resources are inefficiently utilized in differentiated time periods, adjust the advertising resource allocation strategy based on the data, optimize coverage and response time, and generate a resource reconfiguration plan; S402: using the resource reconfiguration scheme to adjust the cache management rules of the advertisement, including updating the preloading conditions of the advertisement content and setting the cache priority of the differentiated advertisement types, monitoring the impact of the adjustment on the advertisement loading speed and user response, and generating a cache management optimization result; S403: Based on the cache management optimization result, refine the adjustment strategy and match the peak period of user activities, including adjusting the loading strategy and time window of the demand advertisements, continuously monitoring the advertisement display efficiency and adjusting it in real time, and generating a strategy implementation blueprint.

[0014] As a further solution of the present invention, the steps of adopting the strategy implementation blueprint, designing simulation tests, capturing advertising effect data during the test, performing effect analysis, identifying advertising delivery patterns, and generating advertising delivery optimization results are specifically as follows: S501: According to the strategy implementation blueprint, key parameters of the simulation test are set, and advertising delivery tests are conducted according to the set advertising frequency and target audience. Differentiated advertising display forms are used to conduct multiple rounds of tests, and advertising effect data in each round of testing is captured, including user clicks and advertising interaction time, to obtain simulation test data records; S502: Based on the simulation test data records, a linear regression algorithm is applied to classify the data of the differentiated advertising formats, perform group analysis of advertising effects, analyze the performance of advertising delivery in turn, identify the effect differences of the differentiated advertising formats by calculating key indicators such as click volume and interaction time, and generate advertising effect analysis results; S503: Based on the advertising effect analysis results, identify poorly performing advertising formats, fine-tune advertising strategies by adjusting advertising content, display order, and delivery time, retest advertising delivery models, capture new advertising effect data, and generate advertising delivery optimization results.

[0015] As a further solution of the present invention, the formula of the linear regression algorithm is as follows:

[0016] Calculate the advertising effect and get the advertising efficiency value ,in, Represents the number of clicks, Represents the interaction time. Represents the number of interactions, Represents the total duration of the test period. represents the frequency of interaction per unit time, is the intercept of the regression model, , and They are the weight coefficients of click volume, interaction duration and interaction frequency per unit time respectively.

[0017] As a further solution of the present invention, the steps of reviewing the advertising content and user interaction, identifying key advertising elements and viewing peaks, adjusting the copy and visual elements, and synchronously adjusting the delivery time to generate the optimal advertising delivery plan according to the advertising delivery optimization results are specifically as follows: S601: Based on the advertising delivery optimization result, user interaction data in the advertisement is captured, and the time period with high user activity is identified by recording the number of clicks and comments of users on the advertisement, and the interaction data of the advertisement elements in the peak period is recorded to obtain the interaction record of the advertisement elements; S602: Based on the interactive records of the advertising elements, the copy and visual elements are screened, the layout and content of the elements are adjusted, the advertising delivery period is adjusted by matching the user's active time period, the advertising effect test is performed within the time period, and the adjustment effect is evaluated by capturing user feedback and interactive data during the test period to obtain an advertising test optimization plan; S603: Apply the advertising test optimization plan to adjust the advertising strategy, unify the advertising content and delivery time on the differentiated platforms, and generate the optimal advertising delivery plan through cross-platform data collection and analysis.

[0018] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by introducing the queuing theory framework and advertising request time series data analysis, the timing accuracy and processing efficiency of advertising delivery are optimized, and by performing timing analysis and parameter adjustment on the advertising request data, the timeliness of advertising delivery is enhanced to ensure that the advertising display is synchronized with the peak of user activities, thereby improving user participation and advertising effect. By recalculating the service time and estimating the waiting time, the service efficiency is allowed to be monitored and adjusted in real time, thereby more accurately matching advertising resources with user needs. Through real-time adjustment of strategies and simulation testing, it is possible to quickly respond to market changes, improve the utilization efficiency and ROI of advertising resources, and significantly improve the accuracy and economic benefits of advertising delivery through refined management and data-driven optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the main steps of the present invention; Figure 2 It is a schematic diagram of the refinement of S1 of the present invention; Figure 3 It is a schematic diagram of the refinement of S2 of the present invention; Figure 4 It is a schematic diagram of the refinement of S3 of the present invention; Figure 5 It is a schematic diagram of the refinement of S4 of the present invention; Figure 6 It is a detailed schematic diagram of S5 of the present invention; Figure 7 It is a detailed schematic diagram of S6 of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0021] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined. Example

[0022] See also Figure 1The present invention provides a technical solution, a digital advertising delivery method based on big data, comprising the following steps: S1: Initialize the advertising model, use the basic framework of queuing theory, import the time series data of advertising requests, perform time series analysis on the data, adjust the queue entry rules and processing rate, calculate the request distribution in differentiated time periods, adjust the model parameters, and generate the model parameter configuration; S2: Calculate the arrival rate and service rate using the model parameter configuration. For each ad request, extract the timestamp and duration, perform cluster analysis on the data, adjust the timing parameters based on the clustering results, synchronize ad display with user activity peaks, and generate a timing parameter adjustment plan. S3: Apply the timing parameter adjustment plan to recalculate the service time, use a simplified model to estimate the waiting time, analyze the difference between the estimated result and the real-time data, make parameter fine-tuning, compare the service efficiency before and after the adjustment, evaluate the adjustment effect, and generate performance tuning results; S4: Based on the performance tuning results, make real-time adjustments to advertising resources, reconfigure advertising resource allocation and cache management, implement new loading strategies, monitor advertising loading and response speed in real time, adjust strategies based on monitoring data, and generate a strategy implementation blueprint; S5: Adopt the strategy implementation blueprint, design simulation tests, capture advertising effect data during the test, analyze the data, identify advertising delivery patterns, fine-tune the advertising strategy based on the analysis results, optimize advertising delivery, and generate advertising delivery optimization results; S6: Based on the results of ad delivery optimization, review ad content and user interactions, identify key ad elements and viewing peaks, adjust copy and visual elements, synchronously adjust delivery time to match user activities, conduct small-scale tests and collect feedback, and generate the optimal ad delivery plan.

[0023] Model parameter configuration includes ad request time series, queue processing rate, and ad display time period. Timing parameter adjustment plans include ad reach rate adjustment, ad service rate optimization, and ad display time synchronization. Performance tuning results include waiting time reduction, service time optimization, and efficiency optimization indicators. The strategy implementation blueprint includes ad loading speed optimization, response speed improvement, and resource reconfiguration parameters. Ad delivery optimization results include ad effect improvement indicators, ad delivery effect comparison, and ad strategy adjustment feedback. The optimal ad delivery plan includes copy optimization parameters, visual element adjustment plan, and delivery time adjustment strategy.

[0024] Specifically, if Figure 2 As shown in the figure, the steps to initialize the advertising model, import the advertising request time series data, perform time series analysis, adjust the queue rules and processing rate, calculate the request distribution in the differentiated time period, and generate the model parameter configuration are as follows: S101: Initialize the advertising model, import the advertising request time series data, perform data screening operations, remove duplicate records, screen invalid and incomplete data items, and perform timestamp standardization on the remaining data to obtain the sorted data set. The execution process is as follows; Initialize the advertising model, import the advertising request time series data, pre-process the input advertising request time series data, and use a specific algorithm to identify and remove duplicate records in the data during the data screening stage to ensure the uniqueness of the data. Further screen the data to exclude invalid and incomplete data items. For example, the integrity of each record will be checked to ensure that all necessary fields are filled in, and records with missing key information will be deleted. After completing the steps, the remaining data will be timestamped and standardized, which involves converting all date and time information into a unified format and time zone for subsequent data processing and analysis. After the timestamps are standardized, the system will sort the data to ensure that they are correctly arranged in chronological order, thereby facilitating subsequent data processing operations. Detailed logs of data processing are recorded, including the number and reasons for data removal, as well as the results of data standardization, for problem tracking and subsequent audits. The smooth execution of the steps is the key foundation for establishing an accurate advertising model, laying a solid foundation for the next step of data analysis and model building, and obtaining a sorted data set.

[0025] S102: Based on the sorted data set, apply the moving average algorithm to analyze the request frequency in each time window, calculate the average request interval time in each window, identify the peak request period, and make preliminary adjustments to the model's queuing rules and processing rate parameters based on the information. The execution process of obtaining the request distribution characteristics is as follows; The formula for the moving average algorithm is as follows:

[0026] Calculate the average request interval time for each time window, where Represents the average request interval time, Representative The sum of the request intervals in the time windows, Representative The weight coefficient of the time window, Represents the number of time windows.

[0027] Detailed explanation of the formula and the process of formula calculation and derivation: Indicates the calculated average request interval in each time window, which is a key indicator for evaluating request frequency and adjusting queue rules. For the The sum of the request intervals within a time window, which is obtained by actually collecting and time-stamping historical log data; It is The weight coefficient of each time window. This coefficient is set based on the reference that the data of the most recent time window is more important than the data of the distant past. It is usually defined by an exponential decay model. For example, the weight is proportional to the inverse of the time from the time window to the present, ensuring that the recent data has a greater impact on the average value. is the total number of time windows, determined by the duration and granularity of data collection; There are three time windows, and the sum of the request intervals in each window is They are 100 seconds, 150 seconds and 200 seconds respectively, and the corresponding weights are Set to 0.5, 1, and 1.5. The weight setting is based on the distance from the current time. The closer the time window, the higher the weight. The value of is 3; Compute the product of each weight term:

[0028] Find the sum of the weights:

[0029] Calculated according to the formula :

[0030] The results show that after referring to the time window weight, the weighted average request interval is 166.67 seconds, which means that under the current settings, the average interval between each request is 166.67 seconds. This value reflects the combined impact of recent and long-term data, which helps to more accurately identify peak request periods and adjust the model's queuing rules and processing rate parameters, thereby optimizing the overall request processing performance.

[0031] S103: Optimize the advertisement model parameters through the request distribution characteristics, adjust the queue rules of advertisement requests, adjust the processing rate and process the request load, and construct the execution flow of the model parameter configuration as follows; Based on the request distribution characteristics, the advertising model parameters are optimized and the queue entry rules of advertising requests are adjusted. First, the distribution characteristics of advertising requests in the collated data set are analyzed. Through multi-dimensional analysis of the time distribution, frequency and type of advertising requests, high-frequency request periods and inefficient request types are identified. Based on the analysis results, the model parameters are optimized. For example, the time window for advertising delivery is adjusted to match the high user activity period, increase the exposure opportunities of advertisements and the click-through rate of users. According to the specific content of the request, the relevance and attractiveness of the advertising content are optimized to improve user participation and satisfaction. The queue entry rules of advertising requests are adjusted and priority queues are set to ensure that high-value advertising requests can be processed first to maximize advertising revenue. To adjust the processing rate, the system adopts a dynamic adjustment strategy to automatically adjust the processing rate according to the real-time system load and request volume to ensure that it will not be overloaded in the face of sudden large traffic, and save resources at low load. For the management of request load, the system implements load balancing technology to reasonably allocate requests to differentiated processing nodes to avoid overloading of a single node and build model parameter configuration.

[0032] Specifically, if Figure 3 As shown, the steps of using model parameter configuration to calculate the arrival rate and service rate, extracting the timestamp and duration of each advertising request, performing cluster analysis, adjusting the time parameters, and generating a timing parameter adjustment plan are as follows: S201: According to the model parameter configuration, the advertisement request records including timestamp and duration are filtered from the log of the advertisement server, and the data is extracted for unified storage. The execution process of generating the time data set is as follows; According to the model parameter configuration, we first filter the ad request records containing timestamps and durations from the ad server logs. During the screening process, regular expressions are used to match the corresponding log formats to ensure the accuracy and completeness of the acquired data. Then, the data is extracted for unified storage. The storage involved includes data formatting, cleaning, and conversion to ensure the consistency and availability of the data set. The extracted data includes the specific timestamp of the ad request, the specific duration of the request, the associated user information, and the category of the ad. The data will be stored in a pre-set database, using efficient database storage technologies, such as distributed storage systems, to ensure fast data reading and writing and high reliability, and generate a time data set.

[0033] S202: Based on the time data set, the timestamp and duration are divided into differentiated categories by using grouping technology, and independent statistics are performed for each category to determine the activity distribution of the advertising request, and labels are assigned to the grouped data to generate the clustering result set. The execution process is as follows; Independent statistics are performed for each category according to the formula:

[0034] Calculate the activity distribution of ad requests, where Represents the percentage of advertising activity. Represents duration, Represents the total number of records; Set ad request logging per category to , the sum of the duration is The total number of records is , then the advertising activity of each category It can be calculated by the above formula. Assuming that there are 500 ad request records of a certain category, the sum of the duration is 10,000 seconds, and the total number of records is 1,000, the ad activity is calculated as ; This result shows that ad requests in this category are very active and the frequency of ad delivery should be increased accordingly.

[0035] S203: Using the label information in the clustering result set, adjust the time parameters of the advertisement display, and optimize the utilization efficiency of the advertisement resources by comparing the peak activity time of multiple categories, matching the advertisement release time and the user active time. The execution process of generating the timing parameter adjustment plan is as follows; By using the label information in the clustering result set and comparing the peak activity times of multiple categories, the time parameters of ad display are adjusted. The label information in the clustering result set includes the category label of each ad request and its corresponding activity. The information is classified and clustered through data analysis tools. The data analysis tools used include K-means clustering algorithm or hierarchical clustering algorithm to ensure that the label of each category is correct. For each category, the peak activity time of its ad request is calculated. This calculation is achieved by counting the frequency of ad requests in each time period, and then matching the ad release time according to the user's active time. This process involves complex data comparison and time series analysis, and finally generates a timing parameter adjustment plan to optimize the efficiency of ad resource utilization.

[0036] Specifically, if Figure 4 As shown in the figure, the steps to apply the timing parameter adjustment plan, recalculate the service time, use the simplified model to estimate the waiting time, analyze the difference between the estimated results and the real-time data, make fine adjustments, and generate the performance tuning results are as follows: S301: recalculate the service time according to the time series parameter adjustment plan, use the baseline model to estimate the preliminary waiting time, input the original service time data into the model, adjust the input parameters of the time series, perform initialization calculation and predict the waiting time, and the execution process of obtaining the waiting time estimation data is as follows; The baseline model is used to estimate the initial waiting time according to the formula:

[0037] Calculate wait time estimates where: represents the estimated waiting time, represents the initial service time, and are model parameters; The initial service time is based on the baseline estimate of the model. Assuming the average time per request is 15 minutes (900 seconds), the model parameters and Represent the time adjustment coefficient and constant deviation respectively. and seconds, and estimate the waiting time, which is calculated as Second; This indicates that by adjusting the parameters, the estimated waiting time increases slightly, reflecting the adaptability of the model after referring to the actual operational impact.

[0038] S302: Using the waiting time estimation data, the difference between the real-time data and the predicted data is compared, including calculating the deviation between the predicted value and the real-time observed value, and adjusting the key parameters in the model according to the deviation result. The execution process of generating the adjusted parameter scheme is as follows; The waiting time estimation data is used to compare the differences between real-time data and predicted data. During the calculation process, the difference between the actual waiting time and the predicted waiting time of each service is first calculated, and the size of the deviation is quantified by the standard deviation or the sum of squared errors. This calculation not only involves simple numerical subtraction, but also includes an overall performance evaluation of the data, such as statistical analysis of deviations. According to the obtained deviation results, key parameters in the model are adjusted, such as adjusting the sliding window size or weight parameters in the time series model, to ensure that the accuracy of the model prediction is more consistent with the actual data, and generate an adjusted parameter scheme. The parameter adjustment is based on the analysis of the systematic deviation between the actual observed data and the predicted data, aiming to reduce future prediction errors and improve the practicality and accuracy of the model.

[0039] S303: Use the adjusted parameter solution to recalculate the model and optimize the service efficiency, compare the service time and processing rate of the model before and after the adjustment, conduct efficiency evaluation, and obtain the performance tuning result. The execution process is as follows; Use the adjusted parameter scheme to recalculate the model and optimize service efficiency. Recalculate the model through data analysis software or customized scripts, including entering new parameter values ​​and monitoring resource consumption during model runtime, such as CPU and memory usage. Compare the service time and processing rate of the model before and after adjustment. This involves data aggregation, comparison and visualization. The process not only focuses on improving the model calculation speed, but also focuses on the overall improvement of service quality. Efficiency is evaluated and performance tuning results are obtained. The results are obtained by comparing the model's response time and processing capacity. The optimization results show that the model can process requests faster after adjusting the parameters, thereby improving service efficiency and customer satisfaction.

[0040] Specifically, if Figure 5 As shown in the figure, according to the performance tuning results, the advertising resources are adjusted in real time, the advertising resource allocation and cache management are reconfigured, and a new loading strategy is implemented. The specific steps of generating a strategy implementation blueprint are as follows: S401: Analyze the performance tuning results, identify the areas where the advertising resources are inefficiently used in the differentiated time periods, adjust the allocation strategy of the advertising resources according to the data, optimize the coverage and response time, and generate the execution process of the resource reconfiguration plan as follows; Analyze the performance tuning results and identify areas with low utilization efficiency of advertising resources in differentiated time periods. This analysis uses data mining techniques such as cluster analysis to identify changes in advertising display efficiency in differentiated time periods. Through the data, a multi-dimensional efficiency chart is generated to show the advertising response and coverage in differentiated time periods. According to the analysis results, the advertising resource allocation strategy is adjusted, including reallocating the time and area of ​​advertising display to improve resource utilization in inefficient areas, optimize coverage and response time, and finally generate a resource reconfiguration plan. This plan is driven by real-time data to ensure that advertising resource allocation is better aligned with market demand and user behavior.

[0041] S402: Adopting a resource reconfiguration solution, adjusting the cache management rules of advertisements, including updating the preloading conditions of advertisement content and setting cache priorities of differentiated advertisement types, monitoring the impact of the adjustment on advertisement loading speed and user response, and generating the cache management optimization results. The execution process is as follows; Adjust the cache management rules for ads according to the formula:

[0042] Calculate the cache management optimization results, where: Represents the adjusted cache capacity, Represents the original cache capacity, represents the adjustment factor based on user response time; Refer to Cache Management Optimization, Original Cache Capacity Set to 100 units, adjust the coefficient According to the average user response time increased by 20%, that is ; Calculate the new cache capacity as units, indicating an increase in adjusted cache capacity to meet higher data requests and improved user experience.

[0043] S403: Based on the cache management optimization results, refine the adjustment strategy and match the user activity peak period, including adjusting the loading strategy and time window of the demand advertisement, continuously monitoring the advertisement display efficiency and adjusting it in real time, and generating the execution process of the strategy implementation blueprint as follows; Based on the cache management optimization results, refine the adjustment strategy and match the peak period of user activity. The adjustment strategy includes analyzing user activity data, identifying high-demand ads, and reconfiguring the ad loading strategy. The adjustment strategy uses data analysis to determine the specific time period of user peak period, and then sets the time window for ad loading to ensure that the necessary ad support is provided during the user's most active period. Continuously monitor the ad display efficiency and adjust it in real time. The monitoring technologies involved include real-time data flow analysis and response time tracking to ensure the continuous optimization and adjustment of the advertising strategy, and finally generate a strategy implementation blueprint, which details the implementation steps and expected effects of various strategy adjustments to ensure the effective use and optimization of advertising resources.

[0044] Specifically, if Figure 6 As shown in the figure, the steps of adopting the strategy implementation blueprint, designing simulation tests, capturing advertising effect data during the test, conducting effect analysis, identifying advertising delivery patterns, and generating advertising delivery optimization results are as follows: S501: According to the strategy implementation blueprint, set the key parameters of the simulation test, conduct advertising delivery tests according to the set advertising frequency and target audience, use differentiated advertising display forms to conduct multiple rounds of tests, capture the advertising effect data in each round of testing, including user clicks and advertising interaction time, and obtain the execution process of the simulation test data record as follows; According to the strategy implementation blueprint, set the key parameters of the simulation test. According to the data characteristics obtained from the previous analysis, set the key parameters of the simulation test, including the frequency of advertising display, that is, the number of advertising displays per hour and day, and the definition of the target audience, such as age, geographic location and interest preferences, to ensure that the parameters accurately correspond to the actual needs of the target market. According to the set advertising frequency and target audience, conduct advertising delivery tests. The system uses differentiated advertising display forms, such as static images, dynamic videos or interactive ads, to test the effects of differentiated forms in differentiated target groups. Conduct multiple rounds of testing, each round targeting one advertising form or target group, to ensure the multidimensionality of the test and the reliability of the results. During the test, the system records each advertising display and user interaction through advanced data capture tools, and captures advertising effect data in each round of testing, including user clicks and advertising interaction duration. Through the data, the system can monitor the performance of the advertisement in real time, and conduct preliminary analysis based on the real-time data to obtain simulation test data records. The records not only assist in verifying which advertising forms are most effective, but also guide the next optimization direction.

[0045] S502: Based on the simulated test data records, the linear regression algorithm is applied to classify the data of the differentiated advertising forms, perform group analysis of the advertising effects, analyze the performance of the advertising delivery in turn, and identify the effect differences of the differentiated advertising forms by calculating the key indicators of click volume and interaction time. The execution process of generating the advertising effect analysis results is as follows; The formula for the linear regression algorithm is as follows:

[0046] Calculate the advertising effect and get the advertising efficiency value ,in, Represents the number of clicks, Represents the interaction time. Represents the number of interactions, Represents the total duration of the test period. represents the frequency of interaction per unit time, is the intercept of the regression model, , and They are the weight coefficients of click volume, interaction duration and interaction frequency per unit time respectively.

[0047] Detailed explanation of the formula and the process of formula calculation and derivation: This formula is used to calculate the advertising effect, where Represents the advertising efficiency value; Indicates the number of clicks, which is obtained by monitoring the total number of clicks on a specific advertisement within a certain period of time. For example, the cumulative number of clicks on an advertisement in a week is set to 1200; Indicates the interaction time, that is, the total time that users interact with ads. It is calculated by recording the interaction time of all users on the ad through the data collection system. The total user interaction time for the same ad within one week is set to 3600 seconds. It represents the number of interactions, which is obtained by counting the interaction events of a specific ad. The number of interactions in a week is set to 300; Indicates the total duration of the test period in seconds, such as 604800 seconds (7 days) for a week. Weight coefficient are the intercept and the weight of each factor on the advertising effect. According to historical data analysis and linear regression model fitting, we set , , , ,The setting of coefficients is based on the statistical analysis of actual advertising operation data, reflecting the contribution of differentiation factors to advertising effects; Substitute the specific values ​​into the formula for calculation:

[0048] The result shows that, given the number of clicks, interaction duration, and interaction frequency, the efficiency value of the ad is 10.100744. This value reflects the comprehensive effect of the ad in attracting clicks and maintaining user interaction. The high efficiency value of the ad indicates that the ad performed well during the test period and effectively promoted user interaction and participation.

[0049] S503: Based on the advertising effect analysis results, identify the advertising forms with poor performance, fine-tune the advertising strategy by adjusting the advertising content, display order and delivery time, retest the advertising delivery mode, capture new advertising effect data, and generate the execution process of advertising delivery optimization results as follows; Based on the results of advertising effectiveness analysis, identify the advertising forms with poor performance. Based on the results of simulation tests, analyze to find out the advertising forms that perform poorly in terms of user interaction and click volume. Through a detailed data analysis module, conduct in-depth analysis of the collected advertising effectiveness data to identify the common characteristics and potential causes of the advertising forms with poor performance. According to the analysis results, adjust the advertising content, such as changing the visual elements or information content of the advertisement to better attract the attention of the target audience, adjust the display order to ensure that more popular advertising forms can be displayed to users more frequently, and adjust the delivery time to deliver the advertisements during the time when users are most active to increase the effective contact rate of the advertisements. After fine-tuning the advertising strategy, retest the advertising delivery mode. This retest not only verifies the effect of the previous adjustment, but also captures the improved effect of the advertisement through new data capture. The new test results will be recorded and analyzed in detail by the system to generate advertising delivery optimization results. The results provide a scientific basis for future advertising strategies and ensure the maximum return on advertising investment.

[0050] Specifically, if Figure 7 As shown in the figure, based on the optimization results of advertising delivery, the steps of reviewing advertising content and user interaction, identifying key advertising elements and viewing peaks, adjusting copy and visual elements, and synchronously adjusting delivery time to generate the optimal advertising delivery plan are as follows: S601: Based on the optimization result of advertisement delivery, user interaction data in advertisements is captured. By recording the number of clicks and comments of users on advertisements, time periods with high user activity are identified. Interaction data of advertisement elements during peak time periods are recorded. The execution process of obtaining interaction records of advertisement elements is as follows; Identify the time period when users are most active, according to the formula:

[0051] Calculate the ad element interaction record, where: Represents the time period when users are most active. Represents the number of clicks, Represents the number of comments. Represents the total time period; Referring to monitoring users’ clicks and comments on ads, if 100 clicks and 50 comments are recorded within an hour, and the total time period is set to 1 hour (60 minutes), the time period with high user activity is calculated as ; The results show that during this hour, there were an average of 2.5 interactions per minute, reflecting a period of high user activity.

[0052] S602: Based on the interactive records of advertising elements, the copy and visual elements are screened, the layout and content of the elements are adjusted, the advertising delivery period is adjusted by matching the user's active time period, the advertising effect test is conducted within the time period, and the adjustment effect is evaluated by capturing user feedback and interactive data during the test period. The execution process of obtaining the advertising test optimization plan is as follows; Based on the interaction records of advertising elements, screen the copy and visual elements, and adjust the layout and content of the elements. The process includes analyzing the interaction data, determining which copy and visual elements are most attractive to users, adjusting the content and visual layout of the advertisement to suit user preferences through data-driven methods, adjusting the advertising delivery time period by matching the user's active time period, implementing advertising effect testing within the time period, capturing user feedback and interaction data during the test to evaluate the adjustment effect, and obtaining an advertising test optimization plan, which is dynamically adjusted according to the user's specific response to ensure the optimal match between advertising content and delivery time.

[0053] S603: Apply the advertising test optimization plan to adjust the advertising strategy, unify the advertising content and delivery time on the differentiated platforms, and generate the optimal advertising delivery plan through cross-platform data collection and analysis. The execution process is as follows; The application advertising test optimization plan is used to adjust the advertising strategy, unify the advertising content and delivery time on differentiated platforms, including cross-platform data collection and analysis. The advertising delivery data of differentiated platforms are collected through a unified interface, and the data is analyzed to discover the performance differences between differentiated platforms. According to the analysis results, the advertising content and delivery strategy are adjusted to adapt to the specific needs and user behavior patterns of each platform, and the optimal advertising delivery plan is generated. This plan aims to improve the overall effectiveness and ROI of advertising through fine-tuning.

[0054] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A digital advertising method based on big data, characterized in that: The following steps are involved: Initialize the advertising model, import the advertising request time series data, perform time series analysis, adjust the queue rules and processing rate, calculate the request distribution in differentiated time periods, and generate model parameter configuration; Utilizing the model parameter configuration, calculating the arrival rate and service rate, extracting the timestamp and duration of each advertisement request, performing cluster analysis, adjusting the time parameters, and generating a timing parameter adjustment plan; Apply the timing parameter adjustment scheme, recalculate the service time, use a simplified model to estimate the waiting time, analyze the difference between the estimated result and the real-time data, make fine adjustments, and generate performance tuning results; According to the performance tuning results, real-time adjustment of advertising resources is performed, advertising resource allocation and cache management are reconfigured, new loading strategies are implemented, and a strategy implementation blueprint is generated; Adopt the strategy implementation blueprint, design simulation tests, capture advertising performance data during the test, conduct performance analysis, identify advertising delivery patterns, and generate advertising delivery optimization results; Based on the advertising optimization results, review the advertising content and user interaction, identify key advertising elements and viewing peaks, adjust the copy and visual elements, and synchronously adjust the delivery time to generate the optimal advertising delivery plan.

2. The method for placing digital advertisements based on big data according to claim 1, characterized in that: The model parameter configuration includes the advertising request time series, queue processing rate, and advertising display time period; the timing parameter adjustment plan includes advertising arrival rate adjustment, advertising service rate optimization, and advertising display time synchronization; the performance tuning results include waiting time reduction, service time optimization, and efficiency optimization indicators; the strategy implementation blueprint includes advertising loading speed optimization, response speed improvement, and resource reconfiguration parameters; the advertising delivery optimization results include advertising effect improvement indicators, advertising delivery effect comparison, and advertising strategy adjustment feedback; the optimal advertising delivery plan includes copy optimization parameters, visual element adjustment plan, and delivery time adjustment strategy.

3. The method for placing digital advertisements based on big data according to claim 1, characterized in that: The steps to initialize the advertising model, import the advertising request time series data, perform time series analysis, adjust the queue rules and processing rate, calculate the request distribution in differentiated time periods, and generate model parameter configuration are as follows: Initialize the advertising model, import the advertising request time series data, perform data filtering operations, remove duplicate records, filter invalid and incomplete data items, and perform timestamp standardization on the remaining data to obtain the sorted data set; According to the collated data set, a moving average algorithm is applied to analyze the request frequency in each time window, calculate the average request interval time in each window, identify the peak request period, make preliminary adjustments to the model's queuing rules and processing rate parameters based on the information, and obtain the request distribution characteristics; Through the request distribution characteristics, the advertising model parameters are optimized, the queuing rules of the advertising requests are adjusted, the processing rate is adjusted and the request load is processed, and the model parameter configuration is constructed.

4. The method for placing digital advertisements based on big data according to claim 3, characterized in that: The formula of the moving average algorithm is as follows: Calculate the average request interval time for each time window, where Represents the average request interval time, Representative The sum of the request intervals in the time windows, Representative The weight coefficient of the time window, Represents the number of time windows.

5. The method for placing digital advertisements based on big data according to claim 1, characterized in that: The steps of using the model parameter configuration to calculate the arrival rate and service rate, extracting the timestamp and duration of each advertising request, performing cluster analysis, adjusting the time parameters, and generating a timing parameter adjustment plan are as follows: According to the model parameter configuration, the advertisement request records including timestamps and durations are filtered from the logs of the advertisement server, and the data are extracted for unified storage to generate a time data set; Based on the time data set, a grouping technology is used to divide the timestamp and duration into differentiated categories, and independent statistics are performed on each category to determine the activity distribution of the advertising requests, and labels are assigned to the grouped data to generate a clustering result set; The label information in the clustering result set is used to adjust the time parameters of the advertisement display. By comparing the peak activity times of multiple categories, the advertisement publishing time and the user active time are matched, the utilization efficiency of the advertisement resources is optimized, and a timing parameter adjustment plan is generated.

6. The method for placing digital advertisements based on big data according to claim 1, characterized in that: The steps of applying the timing parameter adjustment scheme, recalculating the service time, estimating the waiting time using a simplified model, analyzing the difference between the estimated result and the real-time data, and making fine adjustments to generate the performance tuning results are as follows: Recalculate the service time according to the timing parameter adjustment plan, use the baseline model to estimate the preliminary waiting time, input the original service time data into the model, adjust the input parameters of the time series, perform initialization calculation and predict the waiting time, and obtain the waiting time estimation data; Using the waiting time estimation data, performing a difference comparison between the real-time data and the predicted data, including calculating the deviation between the predicted value and the real-time observed value, adjusting key parameters in the model according to the deviation result, and generating an adjusted parameter scheme; The adjusted parameter scheme is used to recalculate the model and optimize the service efficiency. The service time and processing rate of the model before and after the adjustment are compared to evaluate the efficiency and obtain the performance tuning result.

7. The method for placing digital advertisements based on big data according to claim 1, characterized in that: According to the performance tuning results, the advertising resources are adjusted in real time, the advertising resource allocation and cache management are reconfigured, and a new loading strategy is implemented. The specific steps of generating a strategy implementation blueprint are as follows: Analyze the performance tuning results, identify areas where advertising resources are inefficiently utilized in differentiated time periods, adjust the advertising resource allocation strategy based on the data, optimize coverage and response time, and generate a resource reconfiguration plan; Adopting the resource reconfiguration scheme, adjusting the cache management rules of advertisements, including updating the preloading conditions of advertisement content and setting the cache priority of differentiated advertisement types, monitoring the impact of the adjustment on advertisement loading speed and user response, and generating cache management optimization results; Based on the cache management optimization results, refine the adjustment strategy and match the peak period of user activity, including adjusting the loading strategy and time window of demand ads, continuously monitoring the ad display efficiency and adjusting it in real time, and generating a strategy implementation blueprint.

8. The method for placing digital advertisements based on big data according to claim 1, characterized in that: The specific steps of adopting the strategy implementation blueprint, designing simulation tests, capturing advertising effect data during the test, conducting effect analysis, identifying advertising delivery patterns, and generating advertising delivery optimization results are as follows: According to the strategy implementation blueprint, set the key parameters of the simulation test, conduct advertising delivery tests according to the set advertising frequency and target audience, use differentiated advertising display forms to conduct multiple rounds of testing, capture the advertising effect data in each round of testing, including user clicks and advertising interaction time, and obtain simulation test data records; Based on the simulated test data records, a linear regression algorithm is applied to classify and process the data of the differentiated advertising formats, perform group analysis of advertising effects, analyze the performance of advertising delivery in turn, and identify the effect differences of differentiated advertising formats by calculating key indicators such as click volume and interaction time, and generate advertising effect analysis results; Based on the advertising effectiveness analysis results, identify poorly performing advertising formats, fine-tune advertising strategies by adjusting advertising content, display order, and delivery time, retest advertising delivery models, capture new advertising effectiveness data, and generate advertising delivery optimization results.

9. The method for placing digital advertisements based on big data according to claim 8, characterized in that: The formula for the linear regression algorithm is as follows: Calculate the advertising effect and get the advertising efficiency value ,in, Represents the number of clicks, Represents the interaction time. Represents the number of interactions, Represents the total duration of the test period. Indicates the frequency of interaction per unit time. is the intercept of the regression model, , and They are the weight coefficients of click volume, interaction duration and interaction frequency per unit time respectively.

10. The method for placing digital advertisements based on big data according to claim 1, characterized in that: According to the advertising delivery optimization results, the steps of reviewing the advertising content and user interaction, identifying key advertising elements and viewing peaks, adjusting the copy and visual elements, and synchronously adjusting the delivery time to generate the optimal advertising delivery plan are as follows: Based on the advertising delivery optimization results, user interaction data in the advertisement is captured, and by recording the number of clicks and comments of users on the advertisement, time periods with high user activity are identified, and interaction data of the advertising elements during peak hours is recorded to obtain interaction records of the advertising elements; Based on the interactive records of the advertising elements, the copy and visual elements are screened, the layout and content of the elements are adjusted, the advertising delivery period is adjusted by matching the user's active time period, and the advertising effect test is conducted within the time period. The adjustment effect is evaluated by capturing user feedback and interactive data during the test period, and an advertising test optimization plan is obtained; Apply the advertising test optimization plan to adjust the advertising strategy, unify the advertising content and delivery time on differentiated platforms, and generate the optimal advertising delivery plan through cross-platform data collection and analysis.

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