An advertisement resource intelligent matching and transaction management method and system

Abstract

The application discloses an advertisement resource intelligent matching and transaction management method and system, collects putting demand data and media resource data, constructs an advertisement resource portrait database through cross-platform audience overlap correlation analysis; identifies a bidding interval based on the portrait database and generates a bidding correction coefficient through traffic quality analysis to form a dynamic bidding matching interval; determines a dominant advertisement form through directional matching of the bidding matching interval, executes material fatigue reach performance mapping to obtain a reach effect attenuation coefficient, evaluates a putting effect response level to generate graded resource putting parameters; performs bidding delay analysis and overtime rate response identification based on the graded resource putting parameters to form an advertisement putting execution sequence; and couples the resource putting result with the advertisement putting execution sequence to construct a budget time-sharing putting strategy and output an advertisement resource transaction instruction, so that cross-platform audience accurate portrait and invalid traffic dynamic correction are realized.

Description

Technical Field

[0001] This invention relates to the field of digital advertising technology, and in particular to a method and system for intelligent matching and transaction management of advertising resources. Background Technology

[0002] In the internet advertising service sector, programmatic advertising relies on the real-time matching of advertisers' needs and media traffic supply. The same audience exists on different platforms with independent account systems, making it difficult for advertisers to determine the actual overlap of audiences across platforms, leading to persistent issues of duplicate exposure. Traffic inflation and click fraud continuously distort the exposure and interaction data reported by the platform, making it difficult to correct discrepancies between bidding strategies and the true value of traffic in a timely manner, and causing budget losses that are difficult to control.

[0003] As ad creatives are continuously deployed, audience acceptance gradually declines, and the degree of performance degradation varies significantly across different audience behavior stages. Existing methods lack a refined analysis of the correlation between creative fatigue and abnormal performance at each stage, making it difficult to generate differentiated campaign adjustment strategies for different degradation states. Current budget allocation mechanisms largely rely on static rules, failing to dynamically coordinate budget and traffic allocation based on real-time changes in ad placement response status and traffic supply and demand. This easily leads to mismatches, such as budgets being exhausted prematurely during prime time slots and continued consumption during inefficient time slots. Summary of the Invention

[0004] This invention discloses an intelligent matching and transaction management method and system for advertising resources. It aims to construct an advertising resource profile library through cross-platform audience overlap analysis, generate a bid correction coefficient to form a dynamic bidding matching range by combining traffic quality detection, generate hierarchical resource delivery parameters through material fatigue mapping, and finally form an advertising delivery execution sequence through bidding delay analysis. The sequence is then coupled with the resource delivery results to output advertising resource transaction instructions, thereby realizing intelligent management of the entire advertising resource matching and transaction chain.

[0005] The first aspect of this invention proposes a method for intelligent matching and transaction management of advertising resources, comprising the following steps:

[0006] Collect advertising demand data and media resource data, and conduct cross-platform audience overlap correlation analysis on the advertising demand data and the media resource data to construct an advertising resource profile library;

[0007] The advertising resource profile database identifies demand and resource bidding ranges. Based on the advertising demand data and the media resource data, traffic quality analysis is performed to identify invalid traffic and generate a bid correction coefficient. The bid correction coefficient is used to configure traffic supply and demand in the demand and resource bidding ranges to form a dynamic bidding matching range.

[0008] Resource targeting matching is performed on the dynamic bidding matching interval to form resource delivery results. Based on the resource delivery results, the dominant advertising format is determined. Material fatigue reach performance mapping is performed on the dominant advertising format to obtain the reach effect decay coefficient. Based on the reach effect decay coefficient, the delivery effect response level is evaluated to generate graded resource delivery parameters.

[0009] The bidding delay analysis is performed on the tiered resource placement parameters to determine the priority ad placements. The switching interval of the priority ad placements is detected. The bidding timeout rate response characteristics are obtained through the switching interval to adjust the ad placement priority and form an ad placement execution sequence.

[0010] The resource allocation results are coupled with the advertising execution sequence to construct a budget-based time-sharing strategy, and advertising resource transaction instructions are output based on the budget-based time-sharing strategy.

[0011] A second aspect of this invention provides an intelligent matching and transaction management system for advertising resources, comprising:

[0012] The data acquisition module is used to collect advertising demand data and media resource data, and to perform cross-platform audience overlap correlation analysis on the advertising demand data and the media resource data to build an advertising resource profile library.

[0013] The interval identification module is used to identify the demand and resource bidding interval through the advertising resource profile library, perform traffic quality analysis based on the placement demand data and the media resource data to identify invalid traffic and generate a bid correction coefficient, and implement traffic supply and demand configuration for the demand and resource bidding interval through the bid correction coefficient to form a dynamic bidding matching interval.

[0014] The resource allocation module is used to perform targeted resource matching on the dynamic bidding matching interval to form resource delivery results, determine the dominant advertising format based on the resource delivery results, perform material fatigue reach performance mapping on the dominant advertising format to obtain reach effect attenuation coefficient, and evaluate the delivery effect response level based on the reach effect attenuation coefficient to generate graded resource delivery parameters.

[0015] The ad delivery scheduling module is used to perform bidding delay analysis on the tiered resource delivery parameters to determine the priority ad slots, detect the switching interval of the priority ad slots, and adjust the ad slot priority to form an ad delivery execution sequence by obtaining the bidding timeout rate response characteristics through the switching interval.

[0016] The strategy execution module is used to couple the resource delivery results with the advertising delivery execution sequence to construct a budget time-sharing delivery strategy, and output advertising resource transaction instructions based on the budget time-sharing delivery strategy.

[0017] The beneficial effects of this invention are reflected in the following points: 1. By using cross-platform audience identity mapping and overlap correlation analysis technology, the same audience group scattered under different platform account systems is associated and integrated to construct an advertising resource profile library, realizing the accurate quantification of the cross-platform value of audience resources; the advertising resource profile library drives the accurate identification of demand and resource bidding range, and through the distribution density analysis of quality mutation points, the erosion of the bidding range by invalid traffic is transformed into a quantifiable bid correction signal, enabling the dynamic bidding matching range to be adjusted in real time according to changes in traffic quality, thereby improving the effective utilization rate of the budget. 2. By extracting the reach effect decay coefficient through the mapping relationship between material fatigue and reach performance, a direct correlation is established between the frequency of material reuse and the decline in audience interest. Combined with the abnormal detection of conversion funnel backflow and attribution path verification, different causes such as low data credibility, integrity warning and cross-channel collaborative benefits are distinguished, realizing the generation of graded resource placement parameters based on the actual effect decay state, and improving the response accuracy of the placement strategy to the material fatigue problem. 3. By combining the evaluation of bidding latency equilibrium with the identification of timeout rate value reversal window, the ad availability determination covers both response stability and competitive environment dimensions, enabling dynamic and accurate selection of priority ad slots; by identifying budget exhaustion risk nodes, the system drives the coordinated adjustment of advertiser budget allocation ratio and media traffic allocation ratio, and constructs a budget time-sharing strategy with matching strength as the core indicator, solving the mismatch between budget supply and traffic supply on the time axis and improving the overall scheduling efficiency of advertising resource transactions. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating an intelligent matching and transaction management method for advertising resources according to the present invention.

[0019] Figure 2 This is a structural block diagram of an intelligent matching and transaction management system for advertising resources according to the present invention. Detailed Implementation

[0020] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0021] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0022] The technical solutions of the embodiments of this application will be described below.

[0023] like Figure 1 As shown, this embodiment of the invention provides a method for intelligent matching and transaction management of advertising resources, including the following steps S11-S15:

[0024] Step S11: Collect ad placement demand data and media resource data, and conduct cross-platform audience overlap correlation analysis on ad placement demand data and media resource data to build an advertising resource profile library.

[0025] Specifically, data on ad placement requirements and media resources are collected. Ad placement requirements data is extracted from advertisers' task requests and covers three attributes: target audience targeting conditions, budget range, and creative type. Targeting conditions are refined to the level of audience interest tags and geographic granularity. Advertisers aiming for brand exposure typically submit ad placement requirements with a broader age range, while advertisers aiming for conversion rates narrow their targeting to audience tags with recent clear purchase intentions. Media resource data is collected from the traffic supply side of various platforms, reflecting the exposure level of ad placements, audience composition distribution, and historical transaction price range. Platforms where content browsing is the primary activity differ significantly from platforms where active search is the primary activity in terms of audience reach and interaction depth described by their media resource data. Both the demand data and media resource data are collected in fixed time windows. When the number of new entries in the demand data exceeds the threshold within the time window, incremental supplementary collection is triggered to ensure the timeliness of the two types of data. Media resource data collected from cross-platforms are aligned according to a unified field specification when they are aggregated to eliminate differences in field naming and units between different platforms.

[0026] In some embodiments, the step of constructing an advertising resource profile library by performing cross-platform audience overlap correlation analysis on the ad placement demand data and the media resource data includes: extracting audience feature parameters by performing audience interest decay cycle extraction based on the ad placement demand data and the media resource data; performing cross-platform audience identity mapping recognition based on the audience feature parameters to obtain identity mapping identifiers; performing audience overlap correlation analysis through the identity mapping identifiers to form overlap feature parameters; and constructing an advertising resource profile library through the overlap feature parameters.

[0027] Audience characteristic parameters are obtained by extracting the audience interest decay cycle based on ad placement demand data and media resource data. The interest decay cycle reflects the time span from the audience's first exposure to specific advertising content to the waning of interest. The rate of decay is jointly determined by the behavioral retention rate in the media resource data and the historical interaction records in the ad placement demand data. For ads with promotional activities as their content, the audience retention rate in the media resource data declines rapidly as the activity deadline approaches, corresponding to a steep slope in the interaction intensity sequence of that audience group in the ad placement demand data, indicating a short interest decay cycle. For ads with brand concept as their content, the audience interaction intensity remains relatively stable over a longer period, with a later decay inflection point. The interaction intensity sequence corresponding to each audience targeting tag in the ad placement demand data serves as the main input for decay analysis. The steeper the slope of the decline in interaction intensity over time, the higher the value of the decay rate component in the audience characteristic parameters. The decay rate component of the audience characteristic parameters shows significant differences among the ad placement types described by different media resource data. The interest tag distribution of each audience targeting tag in the ad placement demand data is extracted and used as the tag distribution attribute of the audience characteristic parameters, and the audience size of the corresponding audience group in the media resource data is extracted and used as the audience size component of the audience characteristic parameters. The audience return rate of ad slots in media resource data is time-series aligned with the interaction intensity sequence of ad demand data. After alignment, the decay inflection point of each audience group is extracted. After the inflection point, the interaction intensity of that group in the ad demand data continues to decline. The audience interest decay cycle is determined by the difference between the decay inflection point and the first touch time, and is recorded as the decay cycle component as an audience characteristic parameter.

[0028] Cross-platform audience identity mapping is performed based on audience feature parameters to obtain identity mapping identifiers. The core challenge of cross-platform identity mapping lies in the fact that different platforms use their own independent user identification systems for the same audience. The tag distribution attributes in the audience feature parameters serve as the medium for cross-platform association. Although the same audience may have different accounts on content platforms and transaction platforms, their interest tags and behavioral time periods show a high degree of consistency in the two sets of audience feature parameters. During mapping, the feature vector similarity is high, and they can be identified as different platform accounts of the same individual. The decay period component and the audience size component in the audience feature parameters jointly constitute the feature vector for cross-platform comparison. Cross-platform audience records with feature vector similarity exceeding a set threshold are identified as different platform accounts of the same audience. If the threshold is too loose, different audiences will be incorrectly merged; if the threshold is too tight, cross-platform accounts of the same true audience will be missed. When mapping audiences with fewer tag dimensions in the audience feature parameters, the feature vector discrimination is low, and generalized tags exist extensively on multiple platforms. The audience feature parameters of different individuals are very likely to generate high similarity. A low-confidence label is added to the mapping results of such audience feature parameters, and the confidence score of the identity mapping identifier is correspondingly reduced in this case. The identity mapping identifier is composed of a cross-platform account set and a corresponding confidence score. The confidence score is determined by the similarity of feature vectors and the consistency of cross-platform account behavior. Behavioral consistency is determined by comparing the interaction time patterns of accounts on each platform. When the active periods of two platform accounts highly overlap, the confidence score of the identity mapping identifier is significantly higher than that of accounts with misaligned active periods. The identity mapping identifier suspected of being mistakenly merged has its contribution weight reduced in the overlap correlation analysis.

[0029] Overlapping characteristic parameters are generated through audience overlap correlation analysis using identity mapping identifiers. Audience overlap is quantified by the ratio of the intersection size of audience sets across different platforms to their respective totals. Cross-platform account combinations with high confidence in identity mapping identifiers are prioritized for inclusion in overlap statistics, while low-confidence records in identity mapping identifiers are included in the statistics with reduced weights. The reduction coefficient is equal to the corresponding confidence score to avoid excessive interference from mistakenly merged accounts in the overlap results. Audience groups with more accounts covered by identity mapping identifiers have more sufficient sample sizes in the statistics. The overlap of each cross-platform account combination in identity mapping identifiers is statistically analyzed in layers according to the type of ad placement and the dimension of audience tags. For ad formats with video content as the carrier, the content consumption behavior of the audience is continuous across different platforms. The density of cross-platform account combinations in this form of ad is higher, and the overlap rate is generally higher than that of ad formats with static display as the carrier. The latter's audience has a weaker motivation to migrate browsing behavior between platforms. After stratification, the overlap rate of a layer is calculated as the ratio of the number of overlapping audiences within that layer to the total number of audiences within that layer. Audience groups with highly vertical interest tags tend to have more fixed platform usage habits and less cross-platform migration behavior. Consequently, they have fewer cross-platform account combinations in their corresponding identity mappings, resulting in a significantly lower overlap rate compared to audience groups with more generalized interest tags. The overlap feature parameters consist of three attributes: the overlap rate sequence for each layer, the absolute value of the overlap size, and the weighted average of the overlap confidence. Groups with a large variance in the overlap rate sequence indicate a highly uneven distribution of cross-platform audiences, requiring differentiated targeting of advertising resources.

[0030] An ad resource profile library is constructed using overlapping feature parameters. The library uses combinations of ad placements and audience segments as basic profile units. The overlap rate sequence at each layer of the overlapping feature parameters directly maps to the cross-platform audience penetration rate metric of the corresponding profile unit. Audience groups with larger absolute values ​​of overlap scale in the overlapping feature parameters correspond to high-value profile units in the ad resource profile library; audience groups with smaller absolute values ​​of overlap scale correspond to scarce audience profiles. The ad resource profile library adds a premium coefficient to the bidding reference value of scarce audience profiles to reflect their scarcity attribute. During the construction of the ad resource profile library, profile units with a confidence-weighted mean in the overlapping feature parameters below a threshold are labeled with uncertainty. Profile units with uncertainty labels have a lower priority for direct adoption in bidding matching. Audience groups with large variance in the overlap rate sequence in the overlapping feature parameters are split into multiple sub-profile units in the ad resource profile library. Each sub-profile unit corresponds to a specific segment with a high overlap rate. The number of profile units in the ad resource profile library expands synchronously with the number of audience groups covered by the overlapping feature parameters, and the corresponding profile units are updated synchronously when the overlapping feature parameters are updated.

[0031] Step S12: Identify demand and resource bidding range through the advertising resource profile library, perform traffic quality analysis based on the placement demand data and media resource data to identify invalid traffic and generate bid correction coefficients, and implement traffic supply and demand configuration for demand and resource bidding ranges through bid correction coefficients to form dynamic bidding matching ranges.

[0032] Specifically, the demand and resource bidding ranges are identified through the advertising resource profile database. The demand and resource bidding ranges describe the price boundaries of the same audience profile unit from both the supply and demand sides. The bidding range on the demand side is constrained by the advertiser's budget ceiling and historical bidding habits, while the bidding range on the resource side is determined by the historical average transaction price of the ad slot and the current competition intensity. The overlap between the two ranges represents the potential price space for a transaction of that profile unit. High-value profile units in the advertising resource profile database typically have wider demand and resource bidding ranges. Many advertisers are pursuing these audiences, leading to fierce bidding competition. To ensure exposure, advertisers have to raise their bid ceilings, which in turn raises the lower limit of the resource bidding range on the media side. Conversely, the resource bidding range for scarce audience profiles exhibits a price premium due to limited supply. The premium coefficient added to scarce audience profiles in the advertising resource profile database directly affects the upward adjustment of the lower limit of the resource bidding range. Overall, the demand and resource bidding ranges are relatively high but limited in width. In the advertising resource profile library, the demand-side bidding records and resource-side transaction records of each profile unit are merged into intervals. The upper and lower boundaries of the initial bidding interval are the upper limit of the demand-side bidding and the lowest historical transaction price of the resource side, respectively. Abnormal bids exceeding this range are removed before merging to exclude the stretching of the boundaries by occasional extreme values. Profile units with added uncertainty labels in the advertising resource profile library have reduced priority for direct adoption during interval merging. The sub-profile units split in the advertising resource profile library independently perform demand and resource bidding interval identification.

[0033] In some embodiments, the step of performing traffic quality analysis based on the ad placement demand data and the media resource data to identify invalid traffic and generate a bid correction coefficient includes: performing click-through rate (CTR) benchmark identification on the ad placement demand data and the media resource data to obtain a CTR status identifier sequence; extracting CTR decay feature parameters from the CTR status identifier sequence to obtain a CTR offset; performing invalid traffic feature mapping on the CTR offset to identify quality mutation points; and generating a bid correction coefficient based on the distribution density of the quality mutation points.

[0034] Click-through rate (CTR) benchmark identification is performed on both ad placement demand data and media resource data to obtain CTR status identifier sequences. The CTR benchmark is determined by the CTR distribution range of each ad placement within its historical normal operating cycle under a specific audience group. The benchmark reference value on the demand side is extracted from the historical conversion records of the corresponding targeting tags in the ad placement demand data, while the benchmark reference value on the resource side is extracted from the historical CTR distribution of similar ad placements in the media resource data. The benchmark values ​​from both sides are merged after taking the 5th to 95th percentile of their respective distributions to form the joint CTR benchmark for that ad placement-audience combination. During merging, the intersection of the two intervals is taken to exclude the stretching of the benchmark boundary by extreme distributions on one side. In the ad placement demand data, ad demands with strong promotional information generally have a higher audience CTR benchmark than ad demands with brand image content. If the CTR records of both types of demands are mixed into the same benchmark, normal promotional clicks may be misjudged as abnormally high clicks. Therefore, ad placement demand data needs to be categorized by ad content type during the benchmark establishment phase to avoid such misjudgments. The click-through rate (CTR) time series of each ad placement in the media resource data is compared with the interaction density sequence of the corresponding targeted audience in the ad placement demand data, time by time. When the CTR of the current time period falls within the joint CTR benchmark range, the corresponding time period in the CTR status identifier sequence is marked as within the benchmark state; when it falls outside the range, the corresponding time period in the CTR status identifier sequence is marked as outside the benchmark state. Time periods in the media resource data where exposure increases sharply but the CTR does not increase synchronously are also marked as outside the benchmark in the CTR status identifier sequence. The CTR status identifier sequence is composed of the time-by-time status identifiers of each ad placement-audience combination. The proportion of time periods outside the benchmark state in the CTR status identifier sequence directly reflects the frequency of abnormal traffic quality for that ad placement within the statistical period.

[0035] For example, the step of extracting the click-rate decay feature parameters of the click-rate status identifier sequence to obtain the click-rate offset includes: obtaining click interval regularity feature points in the click-rate status identifier sequence; detecting machine behavior fluctuation features based on the click interval regularity feature points to generate click-rate decay feature parameters; performing regular offset positioning through the click-rate decay feature parameters to generate an offset candidate set; and generating the click-rate offset based on the offset candidate set and the click-rate decay feature parameters.

[0036] This process involves identifying regularity features in click intervals within the click-through rate (CTR) status sequence. These regularity features capture the unusual patterns in the time intervals between adjacent clicks. Normal audience clicks are driven by subjective intent, resulting in random distributions of click intervals over time. In contrast, automated click manipulation triggers clicks at fixed or near-fixed intervals, leading to statistically significant periodicity in the click interval sequence. Click interval data from outside the baseline state period within the CTR status sequence are prioritized for regularity detection. Autocorrelation analysis is performed on the click interval sequence. When the autocorrelation coefficient shows a significant peak at a specific lag order, that interval length is considered a potential candidate for regularity. When multiple lag orders show significant peaks, they are arranged in descending order to form a candidate set. Click intervals within the baseline state period within the CTR status sequence serve as a normal reference. If a particular ad placement also shows a significant autocorrelation peak within the baseline period, it indicates strong behavioral regularity among the ad placement's audience. Subsequent threshold adjustments are necessary to avoid misclassifying normal behavior as automated click manipulation. This differentiated thresholding ensures that the accuracy of identifying regularity features in click intervals remains stable across different audience behavior patterns. The regularity feature points of click intervals are determined by several lag orders with the most significant peak values ​​in autocorrelation detection. The shorter the interval length corresponding to the regularity feature points of click intervals, the higher the frequency of click boosting.

[0037] Click-through rate (CTR) decay feature parameters are generated based on the detection of machine behavior fluctuation characteristics using click interval regularity feature points. Machine behavior fluctuation characteristics do not simply refer to fixed-interval brushing, but also encompass the variable-frequency behavior of brushing sources actively switching trigger rhythms at different times to evade detection. This type of behavior manifests in click interval regularity feature points as multiple interval lengths alternating, with each individual interval feature point having a short duration but a consistently high overall feature point density. After extracting the interval sequence corresponding to the click interval regularity feature points, the transition patterns between adjacent regular segments are analyzed. Smooth transition patterns indicate a gradual adjustment in the brushing rhythm, while abrupt transition patterns indicate a switch in the trigger mechanism by the brushing source. These two transition patterns leave different characteristics in the fluctuation intensity component of the CTR decay feature parameters. Gradual adjustment corresponds to a continuous fluctuation intensity sequence, while abrupt switching corresponds to a discrete spike fluctuation sequence. The higher the concentration of the interval length distribution of the click interval regularity feature points, the more singular the machine behavior, and the higher the value of the regularity intensity component in the CTR decay feature parameters. When the interval length distribution is dispersed, the machine behavior exhibits a multi-strategy hybrid characteristic, with the regularity intensity component decreasing but the fluctuation frequency component increasing in the CTR decay feature parameters. The click-through rate decay characteristic parameter consists of three components: regularity intensity component, fluctuation frequency component, and decay slope. A high regularity intensity component indicates that the click-through rate boosting mode is fixed, while a high fluctuation frequency component indicates that the click-through rate boosting strategy is frequently switched.

[0038] A candidate set of offsets is generated by regular offset localization using click-through rate (CTR) decay feature parameters. Regular offset localization is categorized into positive and negative offsets. Positive offsets correspond to an abnormal increase in CTR above the baseline, while negative offsets correspond to an abnormal decrease in CTR below the baseline. Machine click fraud typically produces positive offsets, while exposure inflation (artificially increasing exposure without corresponding valid clicks) produces negative offsets. These two types of offsets leave distinct characteristics on different components of the CTR decay feature parameters, requiring independent detection for each direction. Periods with high regularity intensity in the CTR decay feature parameters are prioritized for positive offset localization. Within this range, locations with highly concentrated click intervals are extracted as positive offset candidates, with temporal accuracy down to the minute level to ensure the candidate set covers the specific time and location of click fraud. Periods with a persistently large negative decay slope in the CTR decay feature parameters are prioritized for negative offset localization. A sudden steepening of the slope within a short period indicates a sudden suppression of the CTR at that moment. When the density of negative offset candidates is higher than that of positive offset candidates, it suggests that the intensity of malicious budget consumption exceeds that of machine click fraud. The offset candidate set is formed by merging positive offset candidates and negative offset candidates. During merging, the direction marks of the two types of candidates are retained for subsequent processing. Candidates with densely overlapping time positions in the offset candidate set are grouped into the same candidate time density concentration segment. The time period with high candidate time density in the offset candidate set corresponds to the offset window with a longer duration of abnormal behavior.

[0039] Click-through rate (CTR) offsets are generated based on an offset candidate set and CTR decay feature parameters. The offset candidate set determines the time and location of CTR anomalies, while the CTR decay feature parameters provide information on the intensity of abnormal behavior at each candidate location. Their complementary relationship lies in the fact that the candidate set addresses the problem of locating when the offset occurs, while the decay feature parameters address the problem of quantifying the degree of offset. The original offset is obtained by subtracting the measured CTR from the baseline CTR at each candidate time point in the offset candidate set. The formula for calculating the corrected offset is D_c = D_r × S_r, where D_c is the corrected offset, D_r is the original offset (the difference between the measured CTR and the baseline CTR), and S_r is the regularity intensity component in the CTR decay feature parameters, ranging from [0,2] and obtained by linear mapping after normalization of the autocorrelation peak intensity. When S_r is greater than 1, the regularity intensity is high, amplifying the original offset; when S_r is less than 1, the regularity intensity is low, and the correction amplitude is correspondingly narrowed to reduce the interference of random noise on the offset. Candidate moments marked as positive offsets in the offset candidate set typically have positive correction offsets; the larger the positive value, the more the click-through rate (CTR) exceeds the baseline at that moment. Candidate moments with negative offsets have negative correction offsets; the larger the absolute value, the more severe the click-through rate suppression. When merging the correction offsets of both directions into the final CTR offset, the sign is preserved to distinguish the offset direction. The CTR offset is composed of the correction offsets of each candidate moment arranged in chronological order. CTR offsets corresponding to regions with high fluctuation frequency components in the CTR decay characteristic parameters typically exhibit an oscillating pattern of fluctuating highs and lows, while CTR offsets corresponding to regions with low fluctuation frequency components exhibit a stable, abnormal pattern of continuous unidirectional deviation.

[0040] Click-through rate (CTR) offsets are used to identify quality mutation points through invalid traffic feature mapping. A quality mutation point marks the time when traffic quality jumps from an acceptable state to an abnormal state. The mutation is determined by the magnitude of the CTR offset change between adjacent time periods exceeding a set mutation threshold. A consistently low CTR alone does not constitute a mutation; only a significant jump in the offset within a short period is considered a mutation event. Consecutive periods in the CTR offset that are consistently below the baseline without any significant jump usually correspond to natural inefficiency due to insufficient attractiveness of the ad placement itself, rather than invalid traffic behaviors such as bot clicks or bidding fraud. A sudden, large negative jump in the CTR offset at a specific moment indicates a structural change in the traffic source before and after that moment, with invalid traffic concentrating at that time. A sliding window scan is performed on the time-series of click-through rate offsets. The window step size is set to 50% of the window width to ensure overlap between adjacent windows and to avoid missing abrupt changes when they fall at the window edge. When the difference between the average offset within a window and the average offset of the preceding window exceeds a threshold, the starting time of that window is identified as a quality abrupt change. When the offsets of multiple attribute fields trigger a change at the same time, it is marked as a joint abrupt change quality abrupt change. The risk weight of joint abrupt change quality abrupt changes is higher than that of single-field quality abrupt changes. The density of quality abrupt changes on the time axis directly reflects the concentration of invalid traffic impacts.

[0041] The bid adjustment coefficient is generated based on the distribution density of quality mutation points. The adjustment direction of the bid adjustment coefficient is inversely related to the distribution density of quality mutation points. The higher the density, the more frequently invalid traffic impacts the ad slot, and the bid adjustment coefficient is adjusted downward accordingly to reduce the bid amplitude during periods of high invalid traffic. The distribution density of quality mutation points is determined by the number of mutation events within a statistical unit time window. Joint mutation quality mutation points are converted into high-weight density contributions according to the number of fields involved, while single-field quality mutation points are counted as standard weights. The peak of the density distribution curve corresponds to the period when invalid traffic impact is most concentrated, and the bid adjustment coefficient is adjusted downward the most during this period. The formula for calculating the bid adjustment factor is M = M_base - (M_base - M_min) × D / D_max, where M is the bid adjustment factor, M_base is the base value of the bid adjustment factor, M_min is the lower limit of the bid adjustment factor determined by the historical lowest effective bid rate, D is the weighted distribution density of quality mutation points within the current unit time window, and D_max is the upper density limit corresponding to the high-risk threshold. When D is zero, M equals M_base; when D reaches D_max, M is reduced to M_min. For ad positions with low density of quality mutation points and regular mutation intervals, the bid adjustment factor is precisely reduced in the corresponding time period, while maintaining a normal bid level in other time periods, achieving refined bid control on a time-by-time basis.

[0042] By adjusting the bid adjustment coefficient, a dynamic bidding matching range is formed by allocating traffic supply and demand between the demand and resource bidding ranges. The essence of traffic supply and demand allocation is to rebalance the intersection between the demand and resource bidding ranges after adjusting the bid adjustment coefficient. Ad slots with a low bid adjustment coefficient mean that the upper limit of the demand-side bid is compressed, narrowing the price space that originally overlapped with the demand and resource bidding ranges. Some price points acceptable to both supply and demand are therefore removed from the matching candidates, and the dynamic bidding matching range shrinks accordingly. For ad slots with high price elasticity on the resource side within the demand and resource bidding range, even if the bid adjustment coefficient is lowered and the demand-side bid is below the original lower limit on the resource side, media resources may still be willing to lower their floor price to accept matching during periods of ample traffic supply. In such cases, the dynamic bidding matching range does not narrow synchronously with the decrease in the bid adjustment coefficient, but rather dynamically adjusts based on the adjusted demand bid as the new lower boundary of the range. The period when the bid adjustment coefficient is at the benchmark value indicates that the traffic quality is normal. The overlapping part of the demand and resource bidding range is directly converted into the dynamic bidding matching range without additional compression. When the bid adjustment coefficient is lower than the benchmark, the upper boundary of the dynamic bidding matching range is compressed downward. The compression range is positively correlated with the degree to which the bid adjustment coefficient deviates from the benchmark. The price elasticity of the resource side is estimated by the ratio of the standard deviation of historical transaction prices to the mean. Ad slots with higher elasticity have a relatively smaller narrowing range of dynamic bidding matching range when the bid adjustment coefficient is lowered.

[0043] Step S13: Perform resource-targeted matching on the dynamic bidding matching range to form resource delivery results. Based on the resource delivery results, determine the dominant advertising format. Perform material fatigue reach performance mapping on the dominant advertising format to obtain the reach effect decay coefficient. Based on the reach effect decay coefficient, evaluate the delivery effect response level and generate graded resource delivery parameters.

[0044] Specifically, resource-targeted matching is performed on the dynamic bidding matching interval to form the resource delivery result. Resource-targeted matching is based on the price distribution of each ad slot within the dynamic bidding matching interval, prioritizing the matching of price nodes where the bids from both supply and demand sides are most concentrated. The higher the bidding density at a price node, the greater the probability of a match. Ad slots with wider dynamic bidding matching intervals within the same audience group have more available price nodes, providing greater flexibility in price adjustments when multiple ad slots compete for the same demand. Ad slots whose dynamic bidding matching intervals are compressed to an extremely narrow level by invalid traffic have fewer matchable price nodes, resulting in significantly lower actual delivery volume compared to other ad slots within the same group. If an ad slot's dynamic bidding matching interval continues to narrow during high-traffic periods while the intervals of other ad slots in the same group remain normal, the final resource delivery result for that ad slot will be systematically lower. After the matching is completed, the winning bid price, impressions, and targeted audience group for each ad slot constitute the core fields of the resource delivery result. Requests with bids higher than the upper boundary of the dynamic bidding matching interval are judged as abnormally high prices in the current matching round and their matching is delayed to prevent abnormal bids from disrupting the price center of the normal resource delivery result. The deviation between the actual transaction price of each ad slot and the central value of the dynamic bidding matching range in the resource allocation results reflects the stability of the bidding process. A persistently large deviation indicates a significant disagreement between the bidding behavior of the supply and demand sides for that ad slot.

[0045] The dominant ad format is determined based on resource allocation results. Ad formats are categorized by type tags in the resource allocation results. Common formats include news feeds, banner ads, and video overlays. The difference in exposure and frequency of each format in the resource allocation results is the basis for determining format dominance. Only formats that occupy the absolute majority of exposure within a single time window and maintain a leading position across multiple consecutive windows can be identified as dominant ad formats. Ad demand driven by recent behavioral data typically concentrates on news feed formats in the resource allocation results because the dynamic content of news feeds better matches the browsing habits of high-intent audiences. Ad demand aimed at brand awareness concentrates on video overlay formats in the resource allocation results because video formats, with their longer single-exposure duration, deliver brand information more completely. The differences in format distribution across different demand types serve as a stratified reference for identifying dominant ad formats. The exposure of each format type is counted independently within its respective group to prevent the mixing of exposures across demand types from leading to biases in the identification of dominant formats. The determination of a dominant advertising format requires that its advantage be consistently maintained within a continuous statistical period. This prevents a format from being mistakenly identified as the dominant format due to a single round of bidding where a high bid occasionally results in a large amount of exposure. Occasional leadership is categorized as fluctuation and does not trigger a switch in dominant format. The degree of audience overlap among different formats in the resource allocation results is also included in the dominance determination criteria. If multiple formats reach highly overlapping audience groups, the dominant advertising format is determined by the format with higher exposure efficiency. Exposure efficiency is measured by the number of effective exposures obtained per unit budget expenditure. Format combinations with high audience overlap in the resource allocation results are prevented from being simultaneously listed as independent dominant formats. Audience overlap is determined by comparing the ratio of the intersection size of the targeted audience groups of each format to their respective totals. Format combinations with an intersection ratio exceeding a threshold are judged as having highly overlapping audiences. Only the format with the highest exposure efficiency is retained as the dominant advertising format for highly overlapping formats.

[0046] A fatigue-based reach performance mapping was performed on the dominant ad format to obtain a reach effect decay coefficient. Creative fatigue measures the degree to which audience interest decreases after repeated exposure to the same creative. The mapping process aligns the frequency of creative reuse in the dominant ad format with the corresponding interaction rate curve. The starting point where the interaction rate declines following the increase in reuse frequency is defined as the fatigue trigger node. After this node, the incremental interaction from each additional exposure of the creative continuously diminishes, indicating that the audience's perception of the creative's novelty has been largely exhausted. When the same audience group is exposed to the creative of the dominant ad format fewer times, the interaction rate usually remains at a high level. As cumulative exposure increases, the interaction rate enters a plateau period and then declines. The length of the plateau period varies significantly depending on the quality of the creative. Creatives with high creative diversity have a longer duration of audience acceptance and a wider plateau period. Highly homogeneous creatives experience rapid decay after the audience's first exposure, and reach performance deteriorates significantly after a short period of exposure accumulation. The reach effect decay coefficient is determined by the slope of the interaction rate decline after the fatigue trigger node and the current creative reuse frequency. The formula for calculating the reach effect decay coefficient is C_d = k_f × F_c, where C_d is the reach effect decay coefficient, k_f is the absolute value of the interaction rate decline slope, and F_c is the current creative reuse frequency. When the creative reuse frequency F_c has not reached the frequency threshold corresponding to the fatigue trigger node, the interaction rate has not yet entered the decline stage, k_f approaches zero, and C_d remains at a low level. The steeper the decline slope and the higher the reuse frequency, the greater the reach effect decay coefficient. When the creative rotation cycle of the dominant ad format is lower than the frequency threshold corresponding to the audience fatigue trigger node, the reach effect decay coefficient remains at a low level. When the creative rotation frequency is much lower than this threshold, the reach effect decay coefficient rises rapidly. The difference between the two constitutes the core basis for adjusting the creative strategy. When the reach effect decay coefficient exceeds the set threshold, it is determined that the current creative has entered the deep fatigue range. The duration of C_d above this threshold and the extent of the excess jointly determine the starting triggering time of the conversion funnel level statistics.

[0047] In some embodiments, the step of evaluating the campaign performance response level based on the reach effect decay coefficient and generating tiered resource delivery parameters includes: performing conversion funnel hierarchy statistics on the reach effect decay coefficient to obtain a funnel hierarchy change sequence; performing funnel backflow anomaly detection based on the funnel hierarchy change sequence to identify backflow segments where the number of lower-level audiences reaches exceeds that of upper-level audiences; performing attribution path verification through the backflow segments to form a campaign performance response level; and assigning tiered weights to the campaign performance response level to generate tiered resource delivery parameters.

[0048] The outreach attenuation coefficient was statistically analyzed across the conversion funnel to obtain the funnel level change sequence. The conversion funnel is divided into four levels based on audience behavior paths: exposure, click, landing page view, and final conversion. The conversion rate between each level is calculated independently rather than relying on the overall conversion rate. The basis for this layered approach is that even with the same overall conversion rate, the conversion status of each level within a campaign may vary significantly. The impact of the outreach attenuation coefficient on different parts of the funnel inherently differs, and simply looking at the overall conversion rate cannot pinpoint where the effect loss is concentrated. When the outreach attenuation coefficient is high, its impact usually first appears in the middle of the funnel. Exposure remains the same, but the conversion from click to landing page view declines first, reflecting that although the audience still receives the ad content, their willingness to actively explore has diminished. When the outreach attenuation coefficient is low, the conversion rate of each level is generally stable. The morphology of the funnel level change sequence differs significantly under different attenuation levels. The turnover rates of each level are arranged according to the deployment cycle to form their own time series. Multiple time series side by side constitute a funnel-level change sequence. A sudden drop in the turnover rate of a certain level is presented in the form of a single-line numerical abrupt change in the funnel-level change sequence. Comparing the change amplitude with that of adjacent levels can determine which two levels the effect attenuation is concentrated between. The time series change of the reach effect attenuation coefficient is correlated with the change direction of the turnover rate of each level in the funnel-level change sequence. When the change directions are consistent, it is determined that the decline of that level is driven by material fatigue. When they are divergent, it indicates that there are interfering factors unrelated to the reach effect attenuation coefficient in that level. When the slope direction of the turnover rate time series of each level in the funnel-level change sequence is consistent, it indicates that the effect change is an overall trend. When the slope direction reverses between two levels, it indicates that the effect attenuation is concentrated between that pair of levels.

[0049] Based on the funnel-level change sequence, anomaly detection is performed to identify backflow segments where the number of lower-level audiences exceeds that of upper-level audiences. The abnormal nature of backflow stems from the principle of audience quantity conservation in the conversion funnel, meaning that the number of audiences in each lower level should theoretically not exceed the number of audiences flowing in from the directly upper level. When the number of audiences arriving at a certain level in the funnel-level change sequence consistently exceeds the number of audiences flowing out from the directly upper level, the extra conversion volume in the lower level cannot be explained by the normal audience path, inevitably indicating attribution contamination or data collection anomalies. The ratio of the number of audiences arriving at each level in the funnel-level change sequence to the number of audiences in the directly upper level is calculated. A ratio less than or equal to 1 is considered normal. A ratio greater than 1, indicating that the number of lower-level audiences exceeds that of upper-level audiences, triggers a backflow determination. The degree of backflow is quantified by the magnitude of the ratio exceeding 1. When the ratio is greater than 1 at multiple consecutive time points, it expands into a continuous backflow segment. A single, occasional exceedance does not constitute a backflow segment and is only classified as noise. The duration of a backflow segment reflects the stability of the attribution problem. Short-term backflow segments usually correspond to temporary statistical deviations caused by sudden data transmission delays. Backflow segments lasting longer than a complete campaign cycle indicate a systematic path mismatch in the attribution system. When an ad placement consistently experiences a backflow where the number of click-through viewers is lower than the number of landing page visitors, it usually means that traffic directly accessing the landing page is being incorrectly attributed to the conversion path of that ad placement. The level pairs in the funnel hierarchy change sequence that exhibit backflow are labeled as the hierarchical features of that backflow segment. When multiple level pairs exhibit backflow segments simultaneously, it indicates a structural problem in the overall funnel attribution system. The size of a backflow segment is quantified by the product of its duration and its severity. The severity of the backflow is the peak value of the ratio of the number of visitors reaching two adjacent levels exceeding 1. Larger backflow segments are prioritized in attribution path verification. When multiple backflow segments occur concurrently, they are entered into the verification queue in descending order of size.

[0050] Attribution path verification is implemented through reverse flow segments to determine the campaign performance response level. Attribution path verification extracts all conversion events within the corresponding time range of the reverse flow segment. The source tracing link of each conversion event is matched and compared with the standard attribution path of the current ad placement. The standard attribution path requires that the trigger node of the conversion event can be traced back to the exposure or click behavior of this ad placement. Conversion events with broken source links or pointing to other ad placements are judged as attribution misalignments. The higher the proportion of attribution misalignment in the reverse flow segment, the greater the reverse flow stems from data attribution mixing rather than actual audience behavior anomalies. Reverse flow segments with high attribution misalignment rates are judged as having low data credibility; adjusting bidding strategies based on data of this level will result in directional deviations. In cases where the attribution path is complete but the lower layer is still higher than the upper layer in a reverse flow segment, the causes are divided into two categories: When the upper layer data collection is incomplete, leading to an underestimated conversion rate, it corresponds to a data integrity warning level; when the lower layer benefits from cross-channel synergy, it corresponds to a cross-channel synergy benefit level. The hierarchical characteristics of the reverse flow segment and the attribution path verification conclusion jointly determine the distinction between the two types of causes. The campaign performance response level is divided into four categories: low data credibility, data integrity warning, cross-channel synergy benefit, and normal campaign performance. When a reverse flow segment does not exist or the attribution path is complete and the conversion is healthy, it is judged as a normal campaign performance level. When multiple reverse flow segments overlap within the same campaign period, the highest risk level is taken. The evaluation conclusion of the campaign performance response level is marked by the corresponding ad placement identifier and the campaign period.

[0051] The tiered resource allocation parameters are generated by assigning weights to the campaign performance response level. When the campaign performance response level is normal, the tiered resource allocation parameters maintain the current ad placement's bid request allocation ratio and audience targeting parameters unchanged, and each ad placement continues to execute according to the existing matching strategy. When the level rises to a data integrity warning, the confidence weight of the conversion layer data within that period is reduced according to the proportion of missing data. Bidding adjustments are based solely on exposure and click layer metrics with complete data, avoiding incorrect bidding direction judgments based on incomplete data. When the campaign performance response level is low data credibility, the tiered resource allocation parameters implement a step-by-step reduction in the bid request allocation ratio for that ad placement. The reduction increases with the duration of the low data credibility state. If an ad placement maintains a low data credibility campaign performance response level for an extended period, the tiered resource allocation parameters reduce the allocation ratio for that ad placement to a safe lower limit until the attribution path verification result turns positive and gradually recovers. The tiered resource allocation parameters for cross-channel collaborative revenue levels are tilted towards the channel combination that contributes the strongest synergistic effect, rather than being adjusted individually for each ad placement. The tiered resource allocation parameters are indexed by the response level of the campaign performance. When the level changes, the parameter configuration of the corresponding ad placement is adjusted accordingly, while the parameter configuration remains stable when the level does not change.

[0052] Step S14: Perform bidding delay analysis on the tiered resource placement parameters to determine the priority ad placements, detect the switching interval of the priority ad placements, obtain the bidding timeout rate response characteristics through the switching interval, adjust the ad placement priority, and form an ad placement execution sequence.

[0053] In some embodiments, the step of performing bidding latency analysis on the tiered resource delivery parameters to determine the priority ad slots includes: performing bidding latency distribution detection based on the tiered resource delivery parameters to obtain a latency gradient sequence; performing latency uniformity evaluation based on the latency gradient sequence to obtain the ad slot latency balance; performing anomaly detection based on the ad slot latency balance to identify high-latency ad slots; and adjusting the bidding latency configuration of the high-latency ad slots to determine the priority ad slots.

[0054] Based on the tiered resource delivery parameters, the auction latency distribution is detected to obtain the latency gradient sequence. Auction latency is the actual time it takes for an ad placement to receive a bid request and return a result. The number of bid requests and the performance response level of each ad placement are extracted from the tiered resource delivery parameters as the sampling basis for latency detection. Ad placements with higher performance response levels in the tiered resource delivery parameters usually receive a higher volume of bid requests, and their auction latency distribution generally has a longer right tail. A large number of requests are concentrated in the low latency range, but a small number of requests have extremely high latency. This long-tail distribution has a much greater drag on the overall auction response quality than the average latency can reflect. The detection sampled the total bidding request time for each ad placement within the statistical period. The quantiles of the latency gradient sequence, from P10 to P99, were extracted gradient-wise. Each quantile in the latency gradient sequence was independently represented. The difference between P50 and P99 in the latency gradient sequence reflected the long tail of the distribution; a larger difference indicated a higher proportion of high-latency requests. In one ad placement's latency gradient sequence, P50 was only 30 milliseconds while P99 reached 400 milliseconds, meaning that most requests responded quickly, but a small number of extreme latency requests dragged down overall availability. Ad placements in the high-risk performance response level of the tiered resource delivery parameters were prioritized for latency distribution detection. These ad placements faced uncertainty in performance; if latency was also abnormal, the combination of these two problems would further reduce their actual usability. Ad placements with both latency and performance anomalies received the highest level of demotion in subsequent configuration adjustments. The change in the quantile difference between adjacent ad placements in the latency gradient sequence reflected the degree of difference in response speed between ad placements at the same level.

[0055] Latency uniformity is assessed based on latency gradient sequences to obtain the latency balance of ad placements. The focus of uniformity assessment is not on the absolute time consumption at a single moment, but on whether the time distribution pattern of the same ad placement remains consistent across statistical periods. Consistent patterns indicate predictable ad placement response behavior, while frequent changes suggest performance is affected by uncertainties in external traffic or service status. The periodic fluctuation of the P50 quantile in the latency gradient sequence measures the stability of normal request processing capacity, while the periodic fluctuation of the P99 quantile measures performance consistency under extreme load scenarios. P99 has a higher weight because a sudden deterioration in P99 directly causes bidding requests to fail to respond before the deadline. The ad placement latency balance U is determined by the formula U=max(0, 1-(w_50×σ_P50 / μ_P50+w_99×σ_P99 / μ_P99)), based on the latency gradient sequence. Here, σ_P50 and σ_P99 are the standard deviations of the corresponding quantiles across periods in the latency gradient sequence, μ_P50 and μ_P99 are the means of the corresponding quantiles, and w_50 and w_99 are weighting coefficients of 0.3 and 0.7 respectively. When the sum of the weighted coefficients of variation exceeds 1, U is truncated to 0. The value of U ranges from 0 to 1; a higher U indicates a more stable latency distribution across periods. For an ad placement where P50 remains stable for a long period but P99 experiences a sharp increase every few periods, the latency balance of this ad placement is still at a low level. The stability of P50 cannot mask the potential problems with P99. In the U-value calculation, the σ_P99 term dominates in lowering the score of ad placements with periodically deteriorating P99.

[0056] High-latency ad slots are identified through anomaly detection based on ad slot latency balance. Anomaly detection uses the median latency balance of ad slots within the historical normal operating cycle of the same type of ad slot as the baseline. If the current ad slot's latency balance deviates from the baseline by more than a set threshold and the absolute P99 latency exceeds the bidding response deadline, the ad slot is identified as a high-latency ad slot. Only ad slots with low latency balance but whose absolute latency does not exceed the deadline are placed in a warning observation state and are not directly marked as high-latency. High-latency ad slots are divided into severe and moderate tiers based on the severity of the anomaly. Ad slots with an absolute P99 latency exceeding the response deadline by more than 50% are classified as severe, and those exceeding 20% ​​but less than 50% are classified as moderate. Severe ad slots are directly suspended from participating in the current round of bidding request allocation, while moderate ad slots have their allocation ratio reduced but are not completely withdrawn. This difference in handling between the two tiers reflects refined management of different degrees of latency anomalies, avoiding a blanket shutdown that would drastically reduce the number of available ad slots. The results of ad slot latency balance determination are time-sensitive. If an ad slot experiences a sudden drop in latency balance during peak traffic hours, it will be marked as a high-latency ad slot. Once the balance returns to normal after the peak ends, the high-latency label will be removed accordingly. There are significant differences in the removal conditions between continuous insufficient service capacity and temporary load deterioration. The former can only be removed after the balance is stably higher than the baseline and the quota will be gradually restored according to the proportion of the medium-level. The latter will be automatically removed and the original quota will be directly restored after two consecutive normal cycles.

[0057] For high-latency ad slots, the bidding latency configuration is adjusted to prioritize ad slots. The bidding latency configuration adjustment reduces the allocation ratio of bidding requests for high-latency ad slots tiered by level. The allocation ratio for severely lagging ad slots is reduced to zero, and the allocation ratio for moderately lagging ad slots is reduced proportionally according to the degree of imbalance. The released quota is transferred to ad slots with high latency balance and normal performance response levels in their respective resource delivery parameters. If the capacity of the ad slots receiving the transferred quota is insufficient, they are replenished sequentially in descending order of balance until all released quota is allocated. Ad slots that simultaneously meet the conditions of latency stability and reliable performance receive a higher share after quota redistribution, becoming the core source of priority ad slots. The priority increase of these ad slots depends on overall availability rather than absolute bidding levels; the dual achievement of latency balance and performance response levels ensures a high response success rate during periods of high bidding request volume. When the proportion of high-latency ad slots is too high, resulting in the total number of available ad slots being lower than the minimum number required to maintain audience coverage, the bidding latency configuration seeks a balance between latency optimization and coverage breadth. Some medium-latency ad slots re-enter the priority ad slot candidate after the latency returns to normal during low-traffic periods. The restoration of candidate eligibility is conditional on the balance of two consecutive statistical periods. The composition of priority ad slots is dynamically adjusted according to the changes in the latency balance of ad slots.

[0058] The system monitors the switching interval of priority ad placements. Switching events for priority ad placements are collected based on real-time status changes in the priority ad placement sequence. Priority ad placements are not fixed in actual campaigns. A switch occurs when a priority ad placement exits the current schedule due to traffic fluctuations, changes in bidding conditions, or audience group switching. The time span between two consecutive switching events is the switching interval, which directly reflects the stability of the priority ad placement sequence. A long switching interval indicates that the priority ad placement is consistently stable, allowing advertisers to continuously accumulate audience reach on that placement. A short switching interval means that each switch interrupts the campaign and results in a cold start for the new ad placement, significantly negatively impacting the lower levels of the conversion funnel. If an ad placement experiences more than ten switches in a single day, its mid-funnel turnover rate is typically significantly lower than ad placements with lower switching frequencies. This is because audiences have a poorer perception of continuity when ad content repeatedly changes its exposure entry point. Switching trigger sources are divided into active switching and passive switching. The interval of passive switching is randomly distributed due to external traffic interference. Ad slots with a high proportion of passive switching have a greater dispersion in their switching interval. When identifying inverted windows, a higher competition density drop is required to eliminate random fluctuation interference.

[0059] In some embodiments, the step of adjusting ad slot priority to form an ad delivery execution sequence by obtaining the bidding timeout rate response characteristics through the switching interval includes: obtaining the bidding timeout rate statistical distribution during the switching interval; identifying the timeout value reversal window in high-timeout rate ad slots based on the bidding timeout rate statistical distribution and obtaining the reversal window timing identifier; performing competitive sparsity verification through the reversal window timing identifier to form an ad slot availability determination result; and adjusting ad slot priority to form an ad delivery execution sequence based on the ad slot availability determination result.

[0060] Obtain the statistical distribution of bidding timeout rate over the switching interval. Bidding timeout rate refers to the proportion of ad placements where bidding requests fail to respond within the deadline within a unit time window. The switching interval provides a time coordinate system for timeout rate analysis. The bidding timeout rates within each switching interval segment are aggregated to form a time series of timeout rates based on the switching interval. Ad placements with shorter switching intervals have denser time series data points, allowing for timely capture of rapidly changing timeout rate trends. Ad placements with longer switching intervals have sparser data points, resulting in more representative timeout rates for single segments but with a lag in responding to rapid traffic changes. These two scenarios contrast in the time series density of the bidding timeout rate statistical distribution. The statistical distribution of bidding timeout rates is composed of the mean, variance, and skewness of the time series of timeout rates. Ad placements with high variance experience drastic fluctuations in timeout rates across different segments, primarily due to the accumulation of random timeouts triggered by frequent passive switching. This makes it difficult to directly distinguish between genuine competition waning and random fluctuations when the timeout rate drops sharply. Ad placements with high variance require a higher competition density and a simultaneous decrease in the rate during the subsequent reversal window identification phase to confirm an effective reversal. Ad placements with a mean timeout rate exceeding a threshold are marked as high-timeout-rate ad placements. High-timeout-rate ad placements are not directly equivalent to low-value ad placements. Some of these ad placements exhibit a regular window of competition waning and response recovery after a period of concentrated timeouts. A left-sloping distribution skew indicates that timeout events occur in a few concentrated periods rather than being evenly distributed. This skewness is a precursor signal for identifying value reversal windows.

[0061] Based on the statistical distribution of bidding timeout rates, the value reversal window for high-timeout ad placements is identified, and its timing identifier is obtained. The value reversal window refers to the time period after a concentrated period of timeouts for high-timeout ad placements, during which competition intensity drops sharply and response becomes smooth again. This is usually caused by other advertisers withdrawing from bidding due to timeout issues. The number of competitors bidding for this ad placement decreases significantly in a short period, and the probability of winning a bid is actually higher than during normal periods. The inflection point where the average timeout rate in the bidding timeout rate statistical distribution suddenly drops after a period of sustained high levels marks the beginning of the value reversal window. The greater the drop after the inflection point, the more thorough the competition retreat. For ad placements with high variance in the bidding timeout rate statistical distribution, it is necessary to confirm that the decrease in competition density exceeds the decrease in the timeout rate to determine a valid reversal. Not all decreases in timeout rates constitute a value reversal window. Normal traffic troughs also lead to a decrease in timeout rates. The distinction lies in the change in the number of bidders for the ad slot during the same period. Only when competition density and timeout rate decrease simultaneously is a reversal considered. In the statistical distribution of bidding timeout rates, timeout events for ad slots with a left-skewed distribution are concentrated in a few time periods. The reversal window identification uses the end point of the concentrated timeout period locked by the skewed features as the starting reference. A decrease in timeout rate caused by simple traffic contraction corresponds to a proportional decrease in competition density, rather than a sudden drop. If the timeout rate of an ad slot decreases by 40% while the number of bidders decreases by more than 60%, the asymmetry in the decrease strongly suggests that there is a competitive ebb during this period, rather than simply a decrease in traffic. The reversal window time sequence identifier consists of two pieces of information: the estimated duration of the reversal window and the reversal magnitude. The larger the reversal magnitude, the more significant the drop from a high timeout state to a normal bidding state for the ad slot. The set of time windows covered by the reversal window time sequence identifier is the output of all value reversal windows identified in this step, which can be retrieved window by window for subsequent competition sparsity verification.

[0062] For example, the step of performing competitive sparsity verification using the inverted window time sequence identifier to form an ad slot availability determination result includes: performing bid win rate quantification on the inverted window time sequence identifier to obtain a win rate quantification value; dividing the win rate quantification value into threshold intervals to determine the win rate response level; performing time sequence stability analysis using the win rate response level to form a win rate stability feature; and performing availability confidence assessment on the win rate stability feature and the win rate quantification value to form an ad slot availability determination result.

[0063] Win rate quantification is performed on the time sequence identifier of the inverted window to obtain a quantified win rate value. Win rate quantification is a quantitative representation of the competition sparsity within the inverted window. When competition subsides, the number of bidders decreases, corresponding to a significant increase in competition sparsity. The surge in win rate is a direct reflection of this increased competition sparsity. Win rate quantification retrieves historical bid win / loss records for each time window locked by the inverted window time sequence identifier. The win rate is defined as the ratio of the number of bids that successfully gained exposure within that window to the total number of bids. The windows covered by the inverted window time sequence identifier come from high-timeout-rate ad slots. During normal periods, these ad slots have low win rates due to intense competition. The subsidence of competition within the inverted window causes a surge in the win rate compared to the historical average. The quantified win rate value captures this surge. The larger the surge, the more significant the competition sparsity. Windows with larger inversion amplitudes typically have larger win rate surges. The inversion amplitude can be used as a reference weight for win rate quantification. Windows with low inversion amplitudes have relatively limited room for improvement in win rate even if the timeout rate decreases. The win rate quantification is composed of two factors: absolute win rate W_abs and relative win rate W_rel, where W_rel = W_abs / W_baseline. W_baseline represents the historical baseline win rate for the ad placement. A W_rel greater than 1 indicates that the probability of bidding within the reversal window is higher than the historical normal level. Only by combining these two metrics can the absolute level and relative improvement of the win rate be reflected simultaneously. Referring only to W_abs ignores the differences in historical baselines across different ad placements, and referring only to W_rel ignores situations where the relative improvement is meaningless when the absolute win rate is too low. In the reversal window time series identifier, the sample size for bidding within windows with short estimated durations is limited. When the sample size is insufficient, a confidence interval is added to the win rate quantification. When the sample size is below the lower threshold, the win rate quantification is marked as low confidence. Low-confidence win rate quantification uses conservative estimates instead of actual measured point estimates in subsequent threshold divisions to prevent small sample noise from distorting the win rate response level division results.

[0064] Win rate response levels are determined by dividing threshold intervals based on quantified win rate values. Threshold interval division divides the value space of quantified win rate into several intervals. The interval boundaries are determined by the joint distribution of absolute win rate W_abs and relative win rate W_rel. Relying solely on W_abs ignores the differences in historical benchmarks for ad placements. Two ad placements with the same absolute win rate but significantly different historical benchmarks have fundamentally different current bidding states. Only by combining W_rel can the interval boundaries truly reflect the meaning of the reversal effect. Win rate response levels are divided into three tiers from high to low: strong reversal, weak reversal, and no reversal. Strong reversal requires W_abs to be higher than the historical average and W_rel to exceed 1.5. Weak reversal requires W_rel to be between 1.0 and 1.5, but W_abs to be below the historical high. Windows with W_rel below 1.0 are judged as no reversal, indicating that the decline in competition is insufficient to produce substantial improvement in win rate. For example, if an ad placement has a W_rel of only 0.9 and a low W_abs, even if the timeout rate decreases within this window, the actual improvement in bidding revenue is extremely limited, and it is placed in the no-reversal tier without triggering priority upgrades. Samples marked as low confidence in the win rate quantification value are uniformly classified as weak reversal or no reversal when dividing the threshold range, and are not included in the strong reversal judgment, in order to avoid the overestimation of the level caused by small sample noise. The division of win rate response level is independently evaluated between different reversal windows in the same ad position, and historical strong reversal windows do not have an anchoring effect on the level judgment of the current window.

[0065] Win rate stability features are formed through temporal stability analysis using win rate response levels. This analysis observes the consistency of the win rate response levels for the same ad placement across multiple historical reversal windows. High consistency indicates repeatable performance during the reversal window, while frequent jumps between strong and non-reversal levels indicate highly unstable reversal quality; even a good current window level cannot support a high-confidence availability assessment. Ad placements with multiple consecutive historical windows showing strong reversals in the win rate response level time series are marked as highly stable. Reversal windows for highly stable ad placements can be reused regularly as reliable, low-cost exposure periods. Ad placements with mixed levels in historical windows are marked as low stable; even if their current level is high, they need to be downweighted in availability confidence assessments. In addition to grade consistency, the win rate stability feature also includes the variance of the duration of strong reversals. Ad placements with large duration variance have blurred reversal window boundaries; exiting early wastes opportunities, while prolonged retention risks increased costs due to renewed competition. Ad placements with small duration variance have clear reversal window boundaries; the duration of historical strong reversal windows for a particular ad placement is concentrated between 20 and 30 minutes, with minimal variance, providing a more robust basis for availability assessment. The win rate stability feature combines grade consistency rating and duration variance; ad placements with high grade consistency and small variance have a higher weighting for the historical regularity of their reversal window performance.

[0066] A usability confidence assessment is performed on the win rate stability feature and the win rate quantification value to determine the ad placement's usability. The usability confidence assessment cross-validates the historical regularity described by the win rate stability feature with the current window's measured performance described by the win rate quantification value. If the historical regularity is strong but the current measured win rate is low, it indicates that the current window is affected by irregular factors, and the usability confidence score is conservatively estimated by lowering the weight of the historical regularity. If the historical regularity is weak but the current win rate quantification value is abnormally high, the probability of a high measured value being an isolated incident is relatively high, and the usability confidence score should also not be too high. When the two pieces of evidence diverge, the assessment leans towards a conservative approach. For ad placements with highly stable win rate features and win rate quantification values ​​in a strong reversal range, the two pieces of evidence corroborate each other, and the usability confidence assessment result is assigned a high confidence score, resulting in the ad placement being determined as highly available. When the win rate stability feature is low stability or the win rate quantification value is marked as low confidence, at least one of the two pieces of evidence is uncertain. The lower one is used to determine the availability confidence. The ad slot availability judgment result is assigned a medium availability rating. When both are abnormal, it is directly judged as low availability. The bidding request quota of medium availability ad slots remains unchanged in the current cycle without active adjustment. However, when the capacity of high availability ad slots is insufficient, they can be used as a supplementary source to fill quota gaps in descending order of balance. The ad slot availability judgment result is updated synchronously with the update cycle of the reversal window time sequence identifier. Expired judgment results are automatically downgraded to a conservative rating when no new reversal window is triggered. The validity period of the high availability rating is set to 1.2 times the estimated duration of the corresponding reversal window. If the validity period expires and no new window confirmation is obtained, it will be downgraded.

[0067] The ad slot priority is adjusted based on the ad slot availability assessment to form the ad delivery execution sequence. Ad slots with high availability receive a priority boost during the priority adjustment, resulting in a higher bid win rate during the inversion window compared to normal periods. This priority boost allows them to receive a larger share of bidding requests in the ad delivery execution sequence. Ad slots with low availability do not trigger a priority boost even if their timeout rate improves briefly during certain switching intervals; their position in the ad delivery execution sequence remains low and they do not participate in active allocation. Ad slots with medium availability maintain their current position in the ad delivery execution sequence without active adjustment. They are only added in descending order of balance when the number of high-availability ad slots is insufficient to cover the total number of bidding requests in the current round. The ad delivery execution sequence arranges all ad slots after priority adjustment according to their comprehensive score from highest to lowest. The comprehensive score is calculated as Score = w_a × A_level + w_e × E_level + w_u × U, where A_level is the normalized score of the ad slot's availability rating, E_level is the normalized score of the response level of the tiered resource delivery parameters, U is the ad slot's latency balance, and w_a, w_e, and w_u are the three weights with a sum of 1. w_a has a weight of 0.5, w_e has a weight of 0.3, and w_u has a weight of 0.2. Availability has the highest weight because it directly determines whether bidding requests can be responded to within the time limit. Ad slots whose comprehensive scores cross the boundary of adjacent tiers trigger an update of the ad delivery execution sequence position. The update frequency is consistent with the detection cycle of the switching interval. After all ad slots are re-ranked according to the latest score, the ad delivery execution sequence is completed and output.

[0068] Step S15: Couple the resource delivery results with the advertising delivery execution sequence to construct a budget-based time-sharing delivery strategy, and output advertising resource transaction instructions based on the budget-based time-sharing delivery strategy.

[0069] In some embodiments, the step of coupling the resource delivery results with the advertising delivery execution sequence to construct a budget time-sharing delivery strategy includes: identifying budget exhaustion risk nodes and obtaining concurrent delivery identifiers based on the resource delivery results; performing budget consumption analysis on the advertising delivery execution sequence based on the concurrent delivery identifiers to determine the advertiser budget allocation ratio and the media traffic allocation ratio; performing dynamic matching analysis on the advertiser budget allocation ratio and the media traffic allocation ratio to form a budget traffic allocation scheme; and constructing a budget time-sharing delivery strategy using the budget traffic allocation scheme.

[0070] Based on resource allocation results, the system identifies budget exhaustion risk nodes and obtains concurrent delivery identifiers. A budget exhaustion risk node is the point in time where the contradiction between the budget consumption rate and the remaining available budget accumulates to a critical state within the campaign period. The occurrence of a risk node does not depend on the total budget size, but rather on whether the ratio of the consumption rate to the remaining budget increases sharply within a short period. In the resource allocation results, the transaction price and exposure level of each ad slot jointly determine the budget consumption rate per unit time. The consumption rate sequence shows a significant acceleration during high-competition periods; the hourly consumption rate of a certain ad slot during peak traffic hours is several times that during off-peak hours. If the budget allocation does not differentiate and limit the rate during this period, the budget is very likely to be exhausted prematurely during peak hours, leading to ad slots being discontinued in subsequent periods. When the consumption rate in the resource allocation results is higher than the safety line for several consecutive periods and the remaining budget is lower than the estimated available time threshold at the current rate, this moment is identified as a budget exhaustion risk node. When multiple advertisers' resource allocation results simultaneously trigger risk nodes in the same period, they are marked with concurrent delivery identifiers. Concurrent delivery identifiers indicate that the periods when multiple advertisers face budget exhaustion risks highly overlap, and the concentrated exhaustion risk has a linked impact on the traffic supply and demand relationship on the media resource side. Concurrent delivery is categorized by the number of advertisers triggering risk nodes. When the number exceeds a threshold, the concurrent delivery status is upgraded to high concurrency. In high concurrency, the audience groups covered by the concurrent delivery status are sorted in descending order of competition intensity and entered into the budget consumption analysis range. Advertisers with no risk nodes in the resource delivery results are assigned a low-risk label to their corresponding concurrent delivery status.

[0071] Based on concurrent delivery identifiers, the budget consumption analysis of the ad execution sequence determines the advertiser budget allocation ratio and media traffic allocation ratio. The budget consumption analysis focuses on the high-risk periods covered by the concurrent delivery identifiers. Within these periods, the bidding request volume and estimated transaction price of each ad placement in the ad execution sequence are jointly extrapolated. The extrapolation results reflect the budget consumption trajectory of each advertiser during this period if the current execution sequence remains unchanged. The advertiser budget allocation ratio describes how the budget of each advertiser marked by the concurrent delivery identifier should be redistributed among the ad placements during high-concurrency periods. Ad placements with higher overall scores in the ad execution sequence receive a higher allocation ratio, while ad placements with lower scores have their allocation ratio reduced under high-concurrency conditions covered by the concurrent delivery identifiers. The reduction rate is positively correlated with the degree to which their scores deviate from the mean, preventing low-quality ad placements from consuming a large amount of budget during highly competitive periods without effective conversion. The media traffic allocation ratio describes the proportion of traffic supply that should be allocated to each media ad slot during the high-concurrency period covered by the concurrent delivery indicator. In the ad delivery execution sequence, the availability judgment result indicates that the traffic allocation ratio of ad slots with high availability is increased, while the traffic allocation ratio of ad slots with low availability is reduced. The adjustment of the media traffic allocation ratio causes traffic to concentrate on ad slots with strong responsiveness and high bidding quality. The linkage between the advertiser budget allocation ratio and the media traffic allocation ratio lies in the fact that both constrain the bidding matching volume in the same period. The advertiser budget allocation ratio is output in the form of an allocation weight matrix of each advertiser × each ad slot, covering all high-risk periods marked by the concurrent delivery indicator; the media traffic allocation ratio is output in the form of a traffic weight sequence of each ad slot × each period. Both outputs serve as inputs for dynamic matching analysis.

[0072] A budget and traffic allocation plan is formed through dynamic matching analysis of advertiser budget allocation ratios and media traffic allocation ratios. The core of dynamic matching analysis lies in identifying structural mismatches between the two ratio distributions. Mismatches manifest as a timeline where periods of concentrated budgets and periods of abundant traffic are misaligned. High-budget allocation periods coincide with low-quality traffic supply, while low-budget periods correspond to the windows with the most abundant traffic supply. The higher the degree of mismatch, the lower the overall resource utilization efficiency of the campaign. Dynamic matching uses the product of the advertiser budget allocation ratio and the corresponding media traffic allocation ratio for each period as the matching strength index M_t for that period, M_t = B_t × F_t, where B_t is the advertiser budget allocation ratio for that period and F_t is the corresponding media traffic allocation ratio. A higher M_t indicates that both budget and traffic supply are abundant during that period, representing a high-quality window for bidding matching. The distribution of matching strength M_t on the time axis reveals the resource coordination efficiency of each period within the campaign cycle. Periods with consistently low M_t indicate the most severe mismatch between budget and traffic. Dynamic matching analysis extrapolates and adjusts the plan for these low periods, shifting the budget allocation ratio towards periods with ample traffic supply. Simultaneously, by adjusting the distribution of bidding requests across different time periods and the bidding strategy, it indirectly guides the media traffic allocation ratio towards periods with ample budget. After multiple rounds of iterative adjustments, the M_t distribution tends to be more uniform, and the degree of mismatch is significantly reduced. The budget and traffic allocation plan is composed of the adjusted advertiser budget allocation ratio and the media traffic allocation ratio for each period. The improvement of the mean M_t for each period compared to before the adjustment serves as an evaluation indicator of the plan's quality. When the improvement falls below a set threshold, the plan is re-iterated. Periods in the budget and traffic allocation plan where M_t is consistently below the mean are marked with an inefficient window. The budget and traffic allocation plan outputs a complete plan consisting of the adjusted advertiser budget allocation ratio, the media traffic allocation ratio, and the inefficient window markings for each period.

[0073] The budget allocation scheme is used to construct a time-based budget delivery strategy. This strategy is based on the matching intensity distribution across different time periods within the budget allocation scheme. The delivery cycle is divided into three categories—high-efficiency, medium-efficiency, and low-efficiency—according to the M_t level in the budget allocation scheme. The bidding aggression, bid cap, and frequency control parameters are set separately for each category. High-efficiency periods in the budget allocation scheme have high matching intensity, suitable for concentrated budget spending, with the bid cap set at the upper quartile of the demand and resource bidding range. Low-efficiency periods in the budget allocation scheme have bids tightened to the lower quartile to avoid excessive budget consumption during mismatch windows. The time periods marked as low-efficiency windows in the budget allocation scheme are subject to a budget consumption rate cap constraint in the time-based budget delivery strategy. This rate cap is determined by the deviation of the product of the advertiser's budget allocation ratio and the media traffic allocation ratio for that time period from the mean. The larger the deviation, the stronger the constraint, preventing multiple advertisers from concentrating their spending during low-efficiency windows and causing subsequent high-efficiency periods to be interrupted. In the time-sharing budget delivery strategy, the bidding parameters for each time period are dynamically determined based on M_t in the corresponding time period of the budget traffic allocation scheme. For time periods with high M_t, the upper limit of the bid approaches the upper quartile of the demand and resource bidding range. For time periods with medium M_t, the bid is set according to the average level of the range. When M_t in the budget traffic allocation scheme is updated, the bidding parameters for the corresponding time period of the time-sharing budget delivery strategy are adjusted synchronously. The time-sharing budget delivery strategy consists of a complete strategy output composed of the bidding parameter matrix and rate constraint set for three types of time periods: high efficiency, medium efficiency, and low efficiency.

[0074] The ad resource transaction instruction is output based on the budget-time delivery strategy. The ad resource transaction instruction is the final output of the budget-time delivery strategy, transformed into an executable bidding action. The instruction includes three elements: target ad placement identifier, current time-segment bidding parameters, and trigger conditions. The bidding parameters are jointly determined by the bid ceiling for the corresponding time period in the budget-time delivery strategy and the current central value of the dynamic bidding matching interval; the smaller of the two values ​​is taken to ensure that the bid does not exceed the reasonable bidding space. In the budget-time delivery strategy, the bidding parameters of ad resource transaction instructions corresponding to high-efficiency time periods are close to the ceiling, while the bidding parameters for instructions corresponding to low-efficiency time periods are tightened to the lower quartile of the interval. This time-segment difference in bidding parameters causes ad resource transaction instructions to automatically exhibit a high-low rhythm across different windows. The trigger condition field of the ad resource transaction instruction sets the effective time window and audience matching conditions for the instruction. If the trigger conditions are not met, the instruction is suspended and waiting, without initiating a bidding request; during the suspension period, no budget quota is consumed. After the ad resource transaction instructions are issued, the bidding success rate and actual consumption rate of each instruction are monitored in real time. When the consumption rate deviates from the estimated value of the budget time-sharing strategy by more than the tolerance range, the instruction parameters are automatically corrected. The correction direction is to bring the consumption rate back to the estimated range. The bid parameters of the corresponding concurrent delivery period in the ad resource transaction instructions are subject to additional hard upper limit constraints to ensure that even if the bid is automatically increased during the high-concurrency period, it will not exceed the smoothing protection boundary. The ad resource transaction instructions are updated synchronously with the refresh cycle of the budget time-sharing strategy.

[0075] To implement the above-described method embodiments, a method for intelligent matching and transaction management of advertising resources is proposed to achieve the corresponding functions and technical effects. See also... Figure 2 , Figure 2 This paper illustrates a structural block diagram of an advertising resource intelligent matching and transaction management system S20 provided in an embodiment of this application, including:

[0076] Data acquisition module 201 is used to collect ad placement demand data and media resource data, and to perform cross-platform audience overlap correlation analysis on the ad placement demand data and the media resource data to build an advertising resource profile library;

[0077] The interval identification module 202 is used to identify the demand and resource bidding interval through the advertising resource profile library, perform traffic quality analysis based on the placement demand data and the media resource data to identify invalid traffic and generate a bid correction coefficient, and implement traffic supply and demand configuration for the demand and resource bidding interval through the bid correction coefficient to form a dynamic bidding matching interval.

[0078] Resource allocation module 203 is used to perform resource-oriented matching on the dynamic bidding matching interval to form resource delivery results, determine the dominant advertising format based on the resource delivery results, perform material fatigue reach performance mapping on the dominant advertising format to obtain reach effect decay coefficient, and evaluate the delivery effect response level based on the reach effect decay coefficient to generate graded resource delivery parameters.

[0079] The ad delivery scheduling module 204 is used to perform bidding delay analysis on the hierarchical resource delivery parameters to determine the priority ad slots, detect the switching interval of the priority ad slots, and adjust the ad slot priority to form an ad delivery execution sequence by obtaining the bidding timeout rate response characteristics through the switching interval.

[0080] The strategy execution module 205 is used to couple the resource delivery results with the advertising delivery execution sequence to construct a budget time-sharing delivery strategy, and output advertising resource transaction instructions based on the budget time-sharing delivery strategy.

[0081] The aforementioned intelligent matching and transaction management system S20 for advertising resources can implement one of the intelligent matching and transaction management methods for advertising resources described in the above method embodiments. The options in the above method embodiments are also applicable to this embodiment, and will not be detailed here. The remaining content of this application embodiment can be referred to the content of the above method embodiments, and will not be repeated in this embodiment.

[0082] The purpose of the above embodiments is to reproduce and derive the technical solution of the present invention by way of example, and to fully describe the technical solution, purpose and effect of the present invention. The purpose is to enable the public to have a more thorough and comprehensive understanding of the disclosure of the present invention, and not to limit the scope of protection of the present invention.

Claims

1. A method for intelligent matching and transaction management of advertising resources, characterized in that, include: Collect advertising demand data and media resource data, and conduct cross-platform audience overlap correlation analysis on the advertising demand data and the media resource data to construct an advertising resource profile library; The advertising resource profile database identifies demand and resource bidding ranges. Based on the advertising demand data and the media resource data, traffic quality analysis is performed to identify invalid traffic and generate a bid correction coefficient. The bid correction coefficient is used to configure traffic supply and demand in the demand and resource bidding ranges to form a dynamic bidding matching range. Resource targeting matching is performed on the dynamic bidding matching interval to form resource delivery results. Based on the resource delivery results, the dominant advertising format is determined. Material fatigue reach performance mapping is performed on the dominant advertising format to obtain the reach effect decay coefficient. Based on the reach effect decay coefficient, the delivery effect response level is evaluated to generate graded resource delivery parameters. The bidding delay analysis is performed on the tiered resource placement parameters to determine the priority ad placements. The switching interval of the priority ad placements is detected. The bidding timeout rate response characteristics are obtained through the switching interval to adjust the ad placement priority and form an ad placement execution sequence. The resource allocation results are coupled with the advertising execution sequence to construct a budget-based time-sharing strategy, and advertising resource transaction instructions are output based on the budget-based time-sharing strategy.

2. The method according to claim 1, characterized in that, The step of constructing an advertising resource profile library by performing cross-platform audience overlap correlation analysis on the ad placement demand data and the media resource data includes: Based on the aforementioned ad demand data and media resource data, audience interest decay cycle is extracted to obtain audience characteristic parameters. Based on the aforementioned audience characteristic parameters, perform cross-platform audience identity mapping recognition to obtain identity mapping identifiers; The overlap feature parameters are generated by performing audience overlap correlation analysis using the identity mapping identifiers. An advertising resource profile library is constructed using the overlapping feature parameters.

3. The method according to claim 1, characterized in that, The step of analyzing traffic quality based on the demand data and media resource data to identify invalid traffic and generate bid correction coefficients includes: The click-through rate (CTR) benchmark identification is performed on the aforementioned ad placement demand data and the aforementioned media resource data to obtain a CTR status identifier sequence; Extract the click-rate decay feature parameters from the click-rate status identifier sequence to obtain the click-rate offset; The click-through rate offset is used to identify quality abrupt changes by performing invalid traffic feature mapping. The bid correction coefficient is generated based on the distribution density of the quality mutation points.

4. The method according to claim 1, characterized in that, The process of generating tiered resource delivery parameters based on the reach effect attenuation coefficient to evaluate the response level of the delivery effect includes: Perform conversion funnel level statistics on the reach effect attenuation coefficient to obtain the funnel level change sequence; Based on the funnel-level change sequence, funnel backflow anomaly detection is performed to identify backflow sections where the number of lower-level audiences exceeds that of upper-level audiences. The attribution path verification is performed in the aforementioned countercurrent section to form the response level of the delivery effect; The response level of the delivery effect is assigned a graded weight to generate graded resource delivery parameters.

5. The method according to claim 1, characterized in that, The step of performing bidding delay analysis on the tiered resource allocation parameters to determine the priority ad slots includes: Based on the tiered resource allocation parameters, perform bidding delay distribution detection to obtain the delay gradient sequence; The delay uniformity of the ad slots is obtained by evaluating the delay gradient sequence. Anomaly detection is performed using the ad slot latency balance to identify high-latency ad slots; Adjust the bidding delay configuration for the high-latency ad slots to determine the ad slots to be prioritized for placement.

6. The method according to claim 1, characterized in that, The step of adjusting ad placement priority based on the bidding timeout rate response characteristics obtained through the switching interval to form an ad delivery execution sequence includes: Obtain the statistical distribution of the bidding timeout rate for the switching interval; Based on the statistical distribution of bidding timeout rates, identify the value reversal window after timeout in high-timeout-rate ad slots and obtain the time sequence identifier of the reversal window. The ad slot availability determination result is formed by performing competitive sparsity verification using the inverted window timing identifier. Based on the ad slot availability determination results, the ad slot priority is adjusted to form an ad delivery execution sequence.

7. The method according to claim 1, characterized in that, The step of coupling the resource allocation results with the ad delivery execution sequence to construct a budget-based time-sharing strategy includes: Based on the resource allocation results, identify budget exhaustion risk nodes and obtain concurrent allocation identifiers; Based on the concurrent delivery identifier, the budget consumption analysis of the ad delivery execution sequence is performed to determine the advertiser's budget allocation ratio and the media traffic allocation ratio; A budget and traffic allocation scheme is formed by dynamically matching and analyzing the advertiser budget allocation ratio and the media traffic allocation ratio. The budget traffic allocation scheme is used to construct a budget time-sharing delivery strategy.

8. The method according to claim 3, characterized in that, The step of extracting the click-rate decay feature parameters of the click-rate status identifier sequence to obtain the click-rate offset includes: Obtain the regularity feature points of the click interval in the click rate status identifier sequence; Click rate decay feature parameters are generated based on the machine behavior fluctuation features detected by the regularity feature points of the click interval. The click-rate decay feature parameters are used to generate a candidate set of offsets by performing regular offset positioning. A click-rate offset is generated based on the offset candidate set and the click-rate decay feature parameters.

9. The method according to claim 6, characterized in that, The process of determining ad slot availability through competitive sparsity verification using the inverted window timing identifier includes: Perform bid win rate quantization on the inverted window timing identifier to obtain the win rate quantization value; The win rate response level is determined by dividing the threshold interval based on the quantified win rate value. Win rate stability characteristics are formed by performing time-series stability analysis on the win rate response levels. The availability confidence assessment is performed on the win rate stability feature and the win rate quantification value to form the ad placement availability determination result.

10. An intelligent matching and transaction management system for advertising resources, characterized in that, include: The data acquisition module is used to collect advertising demand data and media resource data, and to perform cross-platform audience overlap correlation analysis on the advertising demand data and the media resource data to build an advertising resource profile library. The interval identification module is used to identify the demand and resource bidding interval through the advertising resource profile library, perform traffic quality analysis based on the placement demand data and the media resource data to identify invalid traffic and generate a bid correction coefficient, and implement traffic supply and demand configuration for the demand and resource bidding interval through the bid correction coefficient to form a dynamic bidding matching interval. The resource allocation module is used to perform targeted resource matching on the dynamic bidding matching interval to form resource delivery results, determine the dominant advertising format based on the resource delivery results, perform material fatigue reach performance mapping on the dominant advertising format to obtain reach effect attenuation coefficient, and evaluate the delivery effect response level based on the reach effect attenuation coefficient to generate graded resource delivery parameters. The ad delivery scheduling module is used to perform bidding delay analysis on the tiered resource delivery parameters to determine the priority ad slots, detect the switching interval of the priority ad slots, and adjust the ad slot priority to form an ad delivery execution sequence by obtaining the bidding timeout rate response characteristics through the switching interval. The strategy execution module is used to couple the resource delivery results with the advertising delivery execution sequence to construct a budget time-sharing delivery strategy, and output advertising resource transaction instructions based on the budget time-sharing delivery strategy.

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