CN115994800B - Advertisement data processing method, device, equipment and storage medium - Google Patents
Advertisement data processing method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention relates to the field of big data, and discloses an advertisement data processing method, device, equipment and storage medium, which are used for improving the accuracy of advertisement data feedback analysis and improving the advertisement putting effect. The method comprises the following steps: inputting account coin consumption data into a data statistics model for consumption data analysis to obtain an account coin consumption distribution curve, and calculating a consumption fluctuation period of the account coin consumption distribution curve to obtain a consumption fluctuation period set; determining a release mode according to the historical release record, and generating a period weight vector according to the consumption fluctuation period set and the release mode; vector encoding is carried out on account recovery data, recovery encoding vectors are generated, and the periodic weight vectors and the recovery encoding vectors are input into an account detection model to carry out account feedback abnormality detection, so that account feedback abnormality detection results are obtained; and carrying out delivery scheme analysis and optimization on the original delivery scheme according to the account return anomaly detection result, and generating an optimized delivery scheme.
Description
Technical Field
The present invention relates to the field of big data, and in particular, to a method, an apparatus, a device, and a storage medium for processing advertisement data.
Background
After the advertisement platform end is entrusted by an advertiser to put in the internet advertisement, the mode of triggering the advertisement is that a user clicks the advertisement material on the advertisement landing page, and the advertisement platform end jumps to the advertiser APP in a mode of downloading the APP or starting the APP. After triggering the advertisement landing page, the user can enter the APP of the advertiser after leaving the advertisement landing page, so that the advertisement platform end cannot timely track whether the current advertisement is converted or not, namely the acquisition of the advertisement return data is difficult.
The existing scheme is that offline statistics is carried out through an advertiser channel, namely, an advertiser firstly carries out offline recovery on advertisement delivery data and then carries out statistics on the advertisement delivery data, and feedback analysis of the existing scheme cannot carry out real-time recovery on the advertisement delivery data generated by user behaviors, so that abnormal feedback processing is not timely, and further, the delivery effect is poor.
Disclosure of Invention
The invention provides an advertisement data processing method, an advertisement data processing device, advertisement data processing equipment and a storage medium, which are used for improving the accuracy of advertisement data feedback analysis and improving the advertisement putting effect.
The first aspect of the present invention provides an advertisement data processing method, which includes:
acquiring account coin consumption data and historical delivery records of a target account from a preset advertisement delivery port;
Extracting the throwing characteristics of the account coin consumption data and the historical throwing record, generating an original throwing scheme of the target account, and inquiring account recovery data of the target account according to the historical throwing record;
inputting the account coin consumption data into a preset data statistics model for consumption data analysis to obtain an account coin consumption distribution curve, and performing consumption fluctuation period calculation on the account coin consumption distribution curve to obtain a consumption fluctuation period set corresponding to the target account;
determining a release mode of each consumption fluctuation period in the consumption fluctuation period set according to the historical release record, and generating a period weight vector according to the consumption fluctuation period set and the release mode, wherein the release mode comprises the following steps: a default mode and a put mode;
vector encoding is carried out on the account recovery data to generate a recovery encoding vector, the periodic weight vector and the recovery encoding vector are input into a preset account detection model to carry out account feedback abnormality detection, and an account feedback abnormality detection result is obtained;
and optimizing the return path of the target account according to the account return anomaly detection result to obtain a target return path, and analyzing and optimizing the original delivery scheme according to the target return path to generate an optimized delivery scheme.
With reference to the first aspect, in a first implementation manner of the first aspect of the present invention, the extracting the release characteristics of the account coin consumption data and the historical release record, generating an original release scheme of the target account, and querying account recovery data of the target account according to the historical release record includes:
analyzing the historical release record to obtain a release period and release operation frequency;
calculating the throwing budget data of the target account for the account coin consumption data;
generating an original delivery scheme of the target account according to the delivery time period, the delivery operation frequency and the delivery budget data;
and inquiring account recovery data of the target account according to the historical release record.
With reference to the first aspect, in a second implementation manner of the first aspect of the present invention, the inputting the account currency consumption data into a preset data statistics model to perform consumption data analysis to obtain an account currency consumption distribution curve, and performing consumption fluctuation period calculation on the account currency consumption distribution curve to obtain a consumption fluctuation period set corresponding to the target account, where the obtaining includes:
Acquiring a time stamp of the account coin consumption data, and inputting the account coin consumption data into a preset data statistics model;
performing discrete processing on the consumption data of the target account to obtain discrete distribution of the consumption data;
fitting a distribution curve to the discrete distribution of the consumption data according to the time stamp by the data statistical model to obtain an account coin consumption distribution curve;
performing curve inflection point and peak value analysis on the account coin consumption distribution curve to obtain curve inflection point data and curve peak value data;
calling a preset fluctuation period function, and dividing consumption fluctuation periods of the curve inflection point data and the curve peak value data to obtain a plurality of initial fluctuation periods corresponding to the target account;
and carrying out standardization processing on the plurality of initial fluctuation periods to obtain a consumption fluctuation period set.
With reference to the first aspect, in a third implementation manner of the first aspect of the present invention, the determining, according to the historical delivery record, a delivery mode of each initial fluctuation period in the consumption fluctuation period set, and generating a period weight vector according to the consumption fluctuation period set and the delivery mode, where the delivery mode includes: default mode and put mode, including:
Determining a release mode of each initial fluctuation period in the consumption fluctuation period set according to the historical release record, wherein the release mode comprises the following steps: a default mode and a put mode;
setting weight data corresponding to the initial fluctuation period according to the putting mode of each initial fluctuation period;
and constructing a period weight vector corresponding to the consumption fluctuation period set according to the weight data of the initial fluctuation period.
With reference to the first aspect, in a fourth implementation manner of the first aspect of the present invention, the performing vector encoding on the account recovery data to generate a recovery encoding vector, inputting the periodic weight vector and the recovery encoding vector into a preset account detection model to perform account feedback anomaly detection, and obtaining an account feedback anomaly detection result includes:
vector encoding is carried out on the account recovery data to generate a recovery encoding vector, and vector fusion is carried out on the periodic weight vector and the recovery encoding vector to generate a target fusion vector;
inputting the target fusion vector into a preset account detection model, wherein the account detection model comprises: an input layer, a double-layer long-short-time memory network and an output layer;
Vector input processing is carried out on the target fusion vector through the input layer, so that a target input vector is obtained;
inputting the target input vector into the double-layer long-short time memory network, and carrying out feature extraction and integration on the target input vector through the double-layer long-short time memory network to obtain a target feature vector;
and in the output layer, carrying out account feedback abnormality prediction on the target feature vector, generating an account feedback abnormality detection result and outputting the account feedback abnormality detection result.
With reference to the first aspect, in a fifth implementation manner of the first aspect of the present invention, the optimizing the return path of the target account according to the account return anomaly detection result to obtain a target return path, and performing, according to the target return path, a delivery scheme analysis and optimization on the original delivery scheme to generate an optimized delivery scheme includes:
optimizing the return path of the target account according to the account return abnormality detection result to obtain a target return path;
information association is carried out on the target return path and the original delivery scheme;
performing delivery scheme analysis and optimization on the original delivery scheme to generate an optimized delivery scheme, wherein the optimized delivery scheme comprises the following steps: setting a delivery scene, setting a budget, setting a delivery date, and setting a delivery period.
With reference to the first aspect, in a sixth implementation manner of the first aspect of the present invention, the advertisement data processing method further includes:
obtaining the conversion rate of the target account, and generating a conversion rate evaluation score according to the conversion rate;
and generating a delivery quality evaluation value of the target account according to the conversion rate evaluation score and a preset weight.
A second aspect of the present invention provides an advertisement data processing apparatus, comprising:
the acquisition module is used for acquiring account coin consumption data and historical delivery records of the target account from a preset advertisement delivery port;
the inquiring module is used for extracting the throwing characteristics of the account coin consumption data and the historical throwing record, generating an original throwing scheme of the target account, and inquiring account recovery data of the target account according to the historical throwing record;
the analysis module is used for inputting the account coin consumption data into a preset data statistics model to perform consumption data analysis to obtain an account coin consumption distribution curve, and performing consumption fluctuation period calculation on the account coin consumption distribution curve to obtain a consumption fluctuation period set corresponding to the target account;
The processing module is used for determining a release mode of each consumption fluctuation period in the consumption fluctuation period set according to the historical release record and generating a period weight vector according to the consumption fluctuation period set and the release mode, wherein the release mode comprises the following steps: a default mode and a put mode;
the detection module is used for carrying out vector coding on the account recovery data to generate a recovery coding vector, inputting the periodic weight vector and the recovery coding vector into a preset account detection model to carry out account feedback abnormality detection, and obtaining an account feedback abnormality detection result;
the generation module is used for optimizing the return path of the target account according to the account return abnormality detection result to obtain a target return path, and carrying out delivery scheme analysis and optimization on the original delivery scheme according to the target return path to generate an optimized delivery scheme.
With reference to the second aspect, in a first implementation manner of the second aspect of the present invention, the query module is specifically configured to:
analyzing the historical release record to obtain a release period and release operation frequency;
calculating the throwing budget data of the target account for the account coin consumption data;
Generating an original delivery scheme of the target account according to the delivery time period, the delivery operation frequency and the delivery budget data;
and inquiring account recovery data of the target account according to the historical release record.
With reference to the second aspect, in a second implementation manner of the second aspect of the present invention, the analysis module is specifically configured to:
acquiring a time stamp of the account coin consumption data, and inputting the account coin consumption data into a preset data statistics model;
performing discrete processing on the consumption data of the target account to obtain discrete distribution of the consumption data;
fitting a distribution curve to the discrete distribution of the consumption data according to the time stamp by the data statistical model to obtain an account coin consumption distribution curve;
performing curve inflection point and peak value analysis on the account coin consumption distribution curve to obtain curve inflection point data and curve peak value data;
calling a preset fluctuation period function, and dividing consumption fluctuation periods of the curve inflection point data and the curve peak value data to obtain a plurality of initial fluctuation periods corresponding to the target account;
and carrying out standardization processing on the plurality of initial fluctuation periods to obtain a consumption fluctuation period set.
With reference to the second aspect, in a third implementation manner of the second aspect of the present invention, the processing module is specifically configured to:
determining a release mode of each initial fluctuation period in the consumption fluctuation period set according to the historical release record, wherein the release mode comprises the following steps: a default mode and a put mode;
setting weight data corresponding to the initial fluctuation period according to the putting mode of each initial fluctuation period;
and constructing a period weight vector corresponding to the consumption fluctuation period set according to the weight data of the initial fluctuation period.
With reference to the second aspect, in a fourth implementation manner of the second aspect of the present invention, the detection module is specifically configured to:
vector encoding is carried out on the account recovery data to generate a recovery encoding vector, and vector fusion is carried out on the periodic weight vector and the recovery encoding vector to generate a target fusion vector;
inputting the target fusion vector into a preset account detection model, wherein the account detection model comprises: an input layer, a double-layer long-short-time memory network and an output layer;
vector input processing is carried out on the target fusion vector through the input layer, so that a target input vector is obtained;
Inputting the target input vector into the double-layer long-short time memory network, and carrying out feature extraction and integration on the target input vector through the double-layer long-short time memory network to obtain a target feature vector;
and in the output layer, carrying out account feedback abnormality prediction on the target feature vector, generating an account feedback abnormality detection result and outputting the account feedback abnormality detection result.
With reference to the second aspect, in a fifth implementation manner of the second aspect of the present invention, the generating module is specifically configured to:
optimizing the return path of the target account according to the account return abnormality detection result to obtain a target return path;
information association is carried out on the target return path and the original delivery scheme;
performing delivery scheme analysis and optimization on the original delivery scheme to generate an optimized delivery scheme, wherein the optimized delivery scheme comprises the following steps: setting a delivery scene, setting a budget, setting a delivery date, and setting a delivery period.
With reference to the second aspect, in a sixth implementation manner of the second aspect of the present invention, the advertisement data processing apparatus further includes:
the evaluation module is used for obtaining the conversion rate of the target account and generating a conversion rate evaluation score according to the conversion rate; and generating a delivery quality evaluation value of the target account according to the conversion rate evaluation score and a preset weight.
A third aspect of the present invention provides an advertisement data processing apparatus comprising: a memory and at least one processor, the memory having instructions stored therein; the at least one processor invokes the instructions in the memory to cause the advertising data processing apparatus to perform the advertising data processing method described above.
A fourth aspect of the present invention provides a computer-readable storage medium having instructions stored therein which, when run on a computer, cause the computer to perform the advertising data processing method described above.
According to the technical scheme provided by the invention, account coin consumption data are input into a data statistics model for consumption data analysis to obtain an account coin consumption distribution curve, and consumption fluctuation period calculation is performed on the account coin consumption distribution curve to obtain a consumption fluctuation period set; determining a release mode according to the historical release record, and generating a period weight vector according to the consumption fluctuation period set and the release mode; vector encoding is carried out on account recovery data, recovery encoding vectors are generated, and the periodic weight vectors and the recovery encoding vectors are input into an account detection model to carry out account feedback abnormality detection, so that account feedback abnormality detection results are obtained; according to the method, the account coin consumption data and the historical delivery record are subjected to data feedback anomaly analysis through the account detection model, the accuracy of advertisement data feedback analysis is improved, and then the original delivery scheme is subjected to optimization suggestion, so that the advertisement delivery effect is improved.
Drawings
FIG. 1 is a schematic diagram of an embodiment of an advertisement data processing method according to an embodiment of the present invention;
FIG. 2 is a flow chart of consumption fluctuation period calculation in an embodiment of the present invention;
FIG. 3 is a flow chart of generating a periodic weight vector in an embodiment of the present invention;
FIG. 4 is a flowchart of account return anomaly detection in an embodiment of the present invention;
FIG. 5 is a schematic diagram of an embodiment of an advertisement data processing apparatus according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of another embodiment of an advertisement data processing apparatus according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of an embodiment of an advertisement data processing apparatus according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides an advertisement data processing method, device, equipment and storage medium, which are used for improving the accuracy of advertisement data feedback analysis and improving the advertisement putting effect. The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
For ease of understanding, a specific flow of an embodiment of the present invention is described below with reference to fig. 1, where an embodiment of an advertisement data processing method in an embodiment of the present invention includes:
s101, acquiring account coin consumption data and historical delivery records of a target account from a preset advertisement delivery port;
it will be appreciated that the execution subject of the present invention may be an advertisement data processing apparatus, and may also be a terminal or a server, which is not limited herein. The embodiment of the invention is described by taking a server as an execution main body as an example.
Specifically, account coin consumption data and historical delivery records of a target account are obtained from a preset advertisement delivery port, wherein a server obtains the historical delivery records of the advertisement delivery port in a preset time range of the target account, and meanwhile, the account coin consumption data of the target account is determined according to the historical delivery records.
S102, extracting the throwing characteristics of the account coin consumption data and the historical throwing record, generating an original throwing scheme of the target account, and inquiring account recovery data of the target account according to the historical throwing record;
specifically, the server performs release feature extraction on the account coin consumption data and the historical release record, generates an original release scheme of the target account, and inquires account recovery data of the target account according to the historical release record, wherein the server performs data field analysis on the account coin consumption data and the historical release record, determines corresponding field feature data, further performs release feature extraction on the account coin consumption data and the historical release record through the field feature data, generates the original release scheme of the target account, and inquires account recovery data of the target account according to the historical release record.
S103, inputting account coin consumption data into a preset data statistics model for consumption data analysis to obtain an account coin consumption distribution curve, and performing consumption fluctuation period calculation on the account coin consumption distribution curve to obtain a consumption fluctuation period set corresponding to a target account;
the server inputs the account coin consumption data into a preset data statistics model to perform consumption data analysis, specifically, performs timestamp analysis on the account coin consumption data through the data statistics model, performs discretization processing on the account coin consumption data according to the timestamp data after determining the timestamp data, determines the discrete distribution of the consumption data, further generates an account coin consumption distribution curve according to the discrete distribution of the consumption data, and further performs consumption fluctuation period calculation on the account coin consumption distribution curve to obtain a consumption fluctuation period set corresponding to a target account.
S104, determining a release mode of each consumption fluctuation period in the consumption fluctuation period set according to the historical release record, and generating a period weight vector according to the consumption fluctuation period set and the release mode, wherein the release mode comprises the following steps: a default mode and a put mode;
specifically, the historical delivery record is subjected to data traversal to obtain data information corresponding to the historical delivery record, the delivery mode of each consumption fluctuation period is further determined through the data information, then weight data matching is carried out according to the delivery mode of each consumption fluctuation period and the consumption fluctuation period set, weight data of an initial fluctuation period are determined, and finally vector conversion is carried out on the weight data to obtain a period weight vector.
S105, vector encoding is carried out on account recovery data, recovery encoding vectors are generated, and the periodic weight vectors and the recovery encoding vectors are input into a preset account detection model to carry out account feedback abnormality detection, so that account feedback abnormality detection results are obtained;
specifically, vector encoding is performed on account recovery data to generate a recovery encoding vector, the periodic weight vector and the recovery encoding vector are input into a preset account detection model to perform account feedback abnormality detection, and an account feedback abnormality detection result is obtained.
S106, optimizing the return path of the target account according to the account return anomaly detection result to obtain a target return path, and analyzing and optimizing the original delivery scheme according to the target return path to generate an optimized delivery scheme.
Specifically, the return path of the target account is optimized according to the account return anomaly detection result to obtain a target return path, wherein when the return path is optimized, when judging whether the return path is a congestion return path, a congestion mark is added in return path data corresponding to the congestion return path to form return path data, the return path of the target account is further optimized through the return path data to obtain the target return path, and finally, the server analyzes and optimizes the delivery scheme of the original delivery scheme according to the target return path to generate an optimized delivery scheme.
In the embodiment of the invention, account coin consumption data are input into a data statistics model for consumption data analysis to obtain an account coin consumption distribution curve, and consumption fluctuation period calculation is performed on the account coin consumption distribution curve to obtain a consumption fluctuation period set; determining a release mode according to the historical release record, and generating a period weight vector according to the consumption fluctuation period set and the release mode; vector encoding is carried out on account recovery data, recovery encoding vectors are generated, and the periodic weight vectors and the recovery encoding vectors are input into an account detection model to carry out account feedback abnormality detection, so that account feedback abnormality detection results are obtained; according to the method, the account coin consumption data and the historical delivery record are subjected to data feedback anomaly analysis through the account detection model, the accuracy of advertisement data feedback analysis is improved, and then the original delivery scheme is subjected to optimization suggestion, so that the advertisement delivery effect is improved.
In a specific embodiment, the process of executing step S102 may specifically include the following steps:
(1) Analyzing the historical release record to obtain a release period and release operation frequency;
(2) Calculating the release budget data of the target account for the account coin consumption data;
(3) Generating an original delivery scheme of the target account according to the delivery time period, the delivery operation frequency and the delivery budget data;
(4) And inquiring account recovery data of the target account according to the historical release record.
Specifically, the server analyzes the historical release record to obtain a release period and release operation frequency, wherein the server determines physical identification of an extension field according to the number of original columns in the historical release record and the number of other added columns before adding columns, determines data offset of the extension field according to the sum of the data length of the original field and the sum of the data length of other extension fields before the extension field, analyzes according to the physical identification and the data offset of the extension field to obtain the release period and release operation frequency, and calculates release budget data of a target account for account coin consumption data; according to the throwing period, the throwing operation frequency and the throwing budget data, an original throwing scheme of the target account is generated, and according to the historical throwing record, account recovery data of the target account are inquired.
In a specific embodiment, as shown in fig. 2, the process of performing step S103 may specifically include the following steps:
S201, acquiring a time stamp of the account coin consumption data, and inputting the account coin consumption data into a preset data statistical model;
s202, performing discrete processing on consumption data of a target account to obtain discrete distribution of the consumption data;
s203, fitting a distribution curve of discrete distribution of consumption data according to a timestamp through a data statistics model to obtain an account coin consumption distribution curve;
s204, performing curve inflection point and peak value analysis on the account coin consumption distribution curve to obtain curve inflection point data and curve peak value data;
s205, calling a preset fluctuation period function, and carrying out consumption fluctuation period division on curve inflection point data and curve peak value data to obtain a plurality of initial fluctuation periods corresponding to a target account;
s206, performing standardization processing on a plurality of initial fluctuation periods to obtain a consumption fluctuation period set.
Specifically, the server acquires a timestamp of account coin consumption data, inputs the account coin consumption data into a preset data statistics model, carries out consumption data discretization processing on a target account to obtain consumption data discretization distribution, wherein consumption data discretization indexes are collected to form a data set, normalization processing is carried out on the data set, a data processing matrix is established, feature vectors of the data processing matrix are extracted, classification dimensions are determined, iterative processing is carried out on the data processing matrix until the credibility of each classification dimension is greater than a set threshold, the data is divided into classification dimensions with the largest coincidence degree, the consumption data is restored into consumption data discretization indexes, further, the consumption data discretization distribution is obtained, a distribution curve fitting is carried out on the consumption data discretization distribution according to the timestamps through the data statistics model, the accumulated frequency of the account coin consumption distribution is obtained, a fitting curve equation of the accumulated frequency of the timestamp distribution is established, and parameters of the fitting curve equation are obtained to obtain frequency fitting values of the timestamp distribution; according to the fitting curve equation, an inverse function of the fitting curve equation of the cumulative frequency is established, according to the inverse function of the fitting curve equation of the cumulative frequency and parameters of the fitting curve equation, distribution curve fitting is carried out on discrete distribution of consumption data to obtain account coin consumption distribution curves, curve inflection points and peak value analysis are carried out on the account coin consumption distribution curves to obtain curve inflection point data and curve peak value data, a preset fluctuation periodic function is called, consumption fluctuation periodic division is carried out on the curve inflection point data and the curve peak value data to obtain a plurality of initial fluctuation periods corresponding to a target account, and standardized processing is carried out on the plurality of initial fluctuation periods to obtain a consumption fluctuation periodic set.
In a specific embodiment, as shown in fig. 3, the process of executing step S104 may specifically include the following steps:
s301, determining a release mode of each initial fluctuation period in the consumption fluctuation period set according to the historical release record, wherein the release mode comprises the following steps: a default mode and a put mode;
s302, setting weight data corresponding to the initial fluctuation period according to the release mode of each initial fluctuation period;
s303, constructing a period weight vector corresponding to the consumption fluctuation period set according to the weight data of the initial fluctuation period.
Specifically, the server determines a delivery mode of each initial fluctuation period in the consumption fluctuation period set according to the historical delivery record, wherein the delivery mode comprises: the method comprises the steps of setting weight data corresponding to initial fluctuation periods according to a default mode and a discharge mode, setting a list of possible discharge modes according to each initial fluctuation period, calculating weights according to each mode in the list, setting the weight data corresponding to the initial fluctuation period, and further constructing a period weight vector corresponding to a consumption fluctuation period set according to the weight data of the initial fluctuation period by a server.
In a specific embodiment, as shown in fig. 4, the process of executing step S104 may specifically include the following steps:
s401, vector encoding is carried out on account recovery data to generate a recovery encoding vector, and vector fusion is carried out on the periodic weight vector and the recovery encoding vector to generate a target fusion vector;
s402, inputting a target fusion vector into a preset account detection model, wherein the account detection model comprises: an input layer, a double-layer long-short-time memory network and an output layer;
s403, vector input processing is carried out on the target fusion vector through an input layer, and a target input vector is obtained;
s404, inputting the target input vector into a double-layer long-short-time memory network, and carrying out feature extraction and integration on the target input vector through the double-layer long-short-time memory network to obtain a target feature vector;
s405, in the output layer, account feedback abnormality prediction is carried out on the target feature vector, and an account feedback abnormality detection result is generated and output.
Specifically, the server performs vector encoding on account recovery data to generate a recovery encoding vector, performs vector fusion on the periodic weight vector and the recovery encoding vector to generate a target fusion vector, and inputs the target fusion vector into a preset account detection model, wherein the account detection model comprises: the method comprises the steps of carrying out vector input processing on a target fusion vector through an input layer, obtaining a target input vector, inputting the target input vector into the double-layer long-short-time memory network, carrying out feature extraction and integration on the target input vector through the double-layer long-short-time memory network, and obtaining a target feature vector, wherein the server carries out vector feature extraction on the target input vector when carrying out feature extraction and integration on the target input vector, obtains a vector integration identifier of the target input vector according to the target fusion vector, finally, carrying out feature integration on the target input vector according to the vector integration identifier, and finally, carrying out account feedback abnormality prediction on the target feature vector in the output layer by the server, and generating an account feedback abnormality detection result and outputting the account feedback abnormality detection result.
In a specific embodiment, the process of executing step S105 may specifically include the following steps:
(1) Optimizing the return path of the target account according to the account return abnormality detection result to obtain a target return path;
(2) Information association is carried out on the target return path and the original delivery scheme;
(3) Carrying out delivery scheme analysis and optimization on the original delivery scheme to generate an optimized delivery scheme, wherein the optimized delivery scheme comprises the following steps: setting a delivery scene, setting a budget, setting a delivery date, and setting a delivery period.
Specifically, the return path of the target account is optimized according to the account return anomaly detection result to obtain a target return path, wherein the return path of the target account is optimized according to the account return anomaly detection result to obtain the target return path, when the return path is optimized, if the return path is judged to be a congestion return path, a congestion identification is added in return path data corresponding to the congestion return path to form return path data, the return path of the target account is further optimized through the return path data to obtain the target return path, finally, the target return path and the original delivery scheme are subjected to information association, and then the delivery scheme analysis and optimization are performed on the original delivery scheme to generate an optimized delivery scheme, wherein the optimized delivery scheme comprises: setting a delivery scene, setting a budget, setting a delivery date, and setting a delivery period.
In a specific embodiment, the process of executing step S106 may specifically include the following steps:
(1) Obtaining the conversion rate of the target account, and generating a conversion rate evaluation score according to the conversion rate;
(2) And generating a throwing quality evaluation value of the target account according to the conversion rate evaluation score and the preset weight.
Specifically, the conversion rate of the target account is obtained, and a conversion rate evaluation score is generated according to the conversion rate, wherein the server performs conversion rate numerical analysis according to the conversion rate, further determines a conversion rate numerical range, further performs score conversion rule matching according to the conversion rate numerical range, determines a corresponding score conversion rule, finally, generates the conversion rate evaluation score according to the data conversion rule and the conversion rate, and finally generates a delivery quality evaluation value of the target account according to the conversion rate evaluation score and preset weight.
The advertisement data processing method in the embodiment of the present invention is described above, and the advertisement data processing apparatus in the embodiment of the present invention is described below, referring to fig. 5, where an embodiment of the advertisement data processing apparatus in the embodiment of the present invention includes:
the acquisition module 501 is configured to acquire account coin consumption data and a history delivery record of a target account from a preset advertisement delivery port;
The query module 502 is configured to perform a feature extraction on the account currency consumption data and the historical placement record, generate an original placement scheme of the target account, and query account recovery data of the target account according to the historical placement record;
the analysis module 503 is configured to input the account currency consumption data into a preset data statistics model to perform consumption data analysis, obtain an account currency consumption distribution curve, and perform consumption fluctuation period calculation on the account currency consumption distribution curve, so as to obtain a consumption fluctuation period set corresponding to the target account;
a processing module 504, configured to determine a delivery mode of each consumption fluctuation period in the consumption fluctuation period set according to the historical delivery record, and generate a period weight vector according to the consumption fluctuation period set and the delivery mode, where the delivery mode includes: a default mode and a put mode;
the detection module 505 is configured to perform vector encoding on the account recovery data, generate a recovery encoding vector, input the periodic weight vector and the recovery encoding vector into a preset account detection model, and perform account feedback anomaly detection to obtain an account feedback anomaly detection result;
The generating module 506 is configured to optimize a return path of the target account according to the account return anomaly detection result, obtain a target return path, and analyze and optimize the delivery scheme of the original delivery scheme according to the target return path, so as to generate an optimized delivery scheme.
Through the cooperation of the components, the account coin consumption data is input into a data statistics model for consumption data analysis to obtain an account coin consumption distribution curve, and consumption fluctuation period calculation is carried out on the account coin consumption distribution curve to obtain a consumption fluctuation period set; determining a release mode according to the historical release record, and generating a period weight vector according to the consumption fluctuation period set and the release mode; vector encoding is carried out on account recovery data, recovery encoding vectors are generated, and the periodic weight vectors and the recovery encoding vectors are input into an account detection model to carry out account feedback abnormality detection, so that account feedback abnormality detection results are obtained; according to the method, the account coin consumption data and the historical delivery record are subjected to data feedback anomaly analysis through the account detection model, the accuracy of advertisement data feedback analysis is improved, and then the original delivery scheme is subjected to optimization suggestion, so that the advertisement delivery effect is improved.
Referring to fig. 6, another embodiment of the advertisement data processing apparatus according to the present invention includes:
the acquisition module 501 is configured to acquire account coin consumption data and a history delivery record of a target account from a preset advertisement delivery port;
the query module 502 is configured to perform a feature extraction on the account currency consumption data and the historical placement record, generate an original placement scheme of the target account, and query account recovery data of the target account according to the historical placement record;
the analysis module 503 is configured to input the account currency consumption data into a preset data statistics model to perform consumption data analysis, obtain an account currency consumption distribution curve, and perform consumption fluctuation period calculation on the account currency consumption distribution curve, so as to obtain a consumption fluctuation period set corresponding to the target account;
a processing module 504, configured to determine a delivery mode of each consumption fluctuation period in the consumption fluctuation period set according to the historical delivery record, and generate a period weight vector according to the consumption fluctuation period set and the delivery mode, where the delivery mode includes: a default mode and a put mode;
The detection module 505 is configured to perform vector encoding on the account recovery data, generate a recovery encoding vector, input the periodic weight vector and the recovery encoding vector into a preset account detection model, and perform account feedback anomaly detection to obtain an account feedback anomaly detection result;
the generating module 506 is configured to optimize a return path of the target account according to the account return anomaly detection result, obtain a target return path, and analyze and optimize the delivery scheme of the original delivery scheme according to the target return path, so as to generate an optimized delivery scheme.
Optionally, the query module 502 is specifically configured to:
analyzing the historical release record to obtain a release period and release operation frequency;
calculating the throwing budget data of the target account for the account coin consumption data;
generating an original delivery scheme of the target account according to the delivery time period, the delivery operation frequency and the delivery budget data;
and inquiring account recovery data of the target account according to the historical release record.
Optionally, the analysis module 503 is specifically configured to:
acquiring a time stamp of the account coin consumption data, and inputting the account coin consumption data into a preset data statistics model;
Performing discrete processing on the consumption data of the target account to obtain discrete distribution of the consumption data;
fitting a distribution curve to the discrete distribution of the consumption data according to the time stamp by the data statistical model to obtain an account coin consumption distribution curve;
performing curve inflection point and peak value analysis on the account coin consumption distribution curve to obtain curve inflection point data and curve peak value data;
calling a preset fluctuation period function, and dividing consumption fluctuation periods of the curve inflection point data and the curve peak value data to obtain a plurality of initial fluctuation periods corresponding to the target account;
and carrying out standardization processing on the plurality of initial fluctuation periods to obtain a consumption fluctuation period set.
Optionally, the processing module 504 is specifically configured to:
determining a release mode of each initial fluctuation period in the consumption fluctuation period set according to the historical release record, wherein the release mode comprises the following steps: a default mode and a put mode;
setting weight data corresponding to the initial fluctuation period according to the putting mode of each initial fluctuation period;
and constructing a period weight vector corresponding to the consumption fluctuation period set according to the weight data of the initial fluctuation period.
Optionally, the detection module 505 is specifically configured to:
vector encoding is carried out on the account recovery data to generate a recovery encoding vector, and vector fusion is carried out on the periodic weight vector and the recovery encoding vector to generate a target fusion vector;
inputting the target fusion vector into a preset account detection model, wherein the account detection model comprises: an input layer, a double-layer long-short-time memory network and an output layer;
vector input processing is carried out on the target fusion vector through the input layer, so that a target input vector is obtained;
inputting the target input vector into the double-layer long-short time memory network, and carrying out feature extraction and integration on the target input vector through the double-layer long-short time memory network to obtain a target feature vector;
and in the output layer, carrying out account feedback abnormality prediction on the target feature vector, generating an account feedback abnormality detection result and outputting the account feedback abnormality detection result.
Optionally, the generating module 506 is specifically configured to:
optimizing the return path of the target account according to the account return abnormality detection result to obtain a target return path;
information association is carried out on the target return path and the original delivery scheme;
Performing delivery scheme analysis and optimization on the original delivery scheme to generate an optimized delivery scheme, wherein the optimized delivery scheme comprises the following steps: setting a delivery scene, setting a budget, setting a delivery date, and setting a delivery period.
Optionally, the advertisement data processing device further includes:
the evaluation module 507 is configured to obtain a conversion rate of the target account, and generate a conversion rate evaluation score according to the conversion rate; and generating a delivery quality evaluation value of the target account according to the conversion rate evaluation score and a preset weight.
In the embodiment of the invention, account coin consumption data are input into a data statistics model for consumption data analysis to obtain an account coin consumption distribution curve, and consumption fluctuation period calculation is performed on the account coin consumption distribution curve to obtain a consumption fluctuation period set; determining a release mode according to the historical release record, and generating a period weight vector according to the consumption fluctuation period set and the release mode; vector encoding is carried out on account recovery data, recovery encoding vectors are generated, and the periodic weight vectors and the recovery encoding vectors are input into an account detection model to carry out account feedback abnormality detection, so that account feedback abnormality detection results are obtained; according to the method, the account coin consumption data and the historical delivery record are subjected to data feedback anomaly analysis through the account detection model, the accuracy of advertisement data feedback analysis is improved, and then the original delivery scheme is subjected to optimization suggestion, so that the advertisement delivery effect is improved.
The advertisement data processing apparatus in the embodiment of the present invention is described in detail above in fig. 5 and 6 from the point of view of modularized functional entities, and the advertisement data processing device in the embodiment of the present invention is described in detail below from the point of view of hardware processing.
Fig. 7 is a schematic diagram of an advertisement data processing device according to an embodiment of the present invention, where the advertisement data processing device 600 may have a relatively large difference due to different configurations or performances, and may include one or more processors (central processing units, CPU) 610 (e.g., one or more processors) and a memory 620, and one or more storage media 630 (e.g., one or more mass storage devices) storing application programs 633 or data 632. Wherein the memory 620 and the storage medium 630 may be transitory or persistent storage. The program stored on the storage medium 630 may include one or more modules (not shown), each of which may include a series of instruction operations in the advertisement data processing apparatus 600. Still further, the processor 610 may be configured to communicate with the storage medium 630 and execute a series of instruction operations in the storage medium 630 on the advertisement data processing device 600.
The advertising data processing apparatus 600 may also include one or more power supplies 640, one or more wired or wireless network interfaces 650, one or more input/output interfaces 660, and/or one or more operating systems 631, such as Windows Serve, mac OS X, unix, linux, freeBSD, and the like. It will be appreciated by those skilled in the art that the advertising data processing apparatus structure shown in fig. 7 is not limiting of the advertising data processing apparatus, and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
The present invention also provides an advertisement data processing apparatus including a memory and a processor, the memory storing computer readable instructions which, when executed by the processor, cause the processor to perform the steps of the advertisement data processing method in the above embodiments.
The present invention also provides a computer readable storage medium, which may be a non-volatile computer readable storage medium, or a volatile computer readable storage medium, having stored therein instructions that, when executed on a computer, cause the computer to perform the steps of the advertisement data processing method.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random acceS memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. An advertisement data processing method, characterized in that the advertisement data processing method comprises:
acquiring account coin consumption data and historical delivery records of a target account from a preset advertisement delivery port;
extracting the throwing characteristics of the account coin consumption data and the historical throwing record, generating an original throwing scheme of the target account, and inquiring account recovery data of the target account according to the historical throwing record;
inputting the account coin consumption data into a preset data statistics model for consumption data analysis to obtain an account coin consumption distribution curve, and performing consumption fluctuation period calculation on the account coin consumption distribution curve to obtain a consumption fluctuation period set corresponding to the target account;
Determining a release mode of each consumption fluctuation period in the consumption fluctuation period set according to the historical release record, and generating a period weight vector according to the consumption fluctuation period set and the release mode, wherein the release mode comprises the following steps: a default mode and a put mode;
vector encoding is carried out on the account recovery data to generate a recovery encoding vector, the periodic weight vector and the recovery encoding vector are input into a preset account detection model to carry out account feedback abnormality detection, and an account feedback abnormality detection result is obtained;
and optimizing the return path of the target account according to the account return anomaly detection result to obtain a target return path, and analyzing and optimizing the original delivery scheme according to the target return path to generate an optimized delivery scheme.
2. The advertisement data processing method according to claim 1, wherein the performing the impression feature extraction on the account coin consumption data and the historical impression record, generating an original impression scheme of the target account, and querying account recycling data of the target account according to the historical impression record, comprises:
Analyzing the historical release record to obtain a release period and release operation frequency;
calculating the throwing budget data of the target account for the account coin consumption data;
generating an original delivery scheme of the target account according to the delivery time period, the delivery operation frequency and the delivery budget data;
and inquiring account recovery data of the target account according to the historical release record.
3. The advertisement data processing method according to claim 1, wherein the inputting the account coin consumption data into a preset data statistics model for consumption data analysis to obtain an account coin consumption distribution curve, and performing consumption fluctuation period calculation on the account coin consumption distribution curve to obtain a consumption fluctuation period set corresponding to the target account, includes:
acquiring a time stamp of the account coin consumption data, and inputting the account coin consumption data into a preset data statistics model;
performing discrete processing on the consumption data of the target account to obtain discrete distribution of the consumption data;
fitting a distribution curve to the discrete distribution of the consumption data according to the time stamp by the data statistical model to obtain an account coin consumption distribution curve;
Performing curve inflection point and peak value analysis on the account coin consumption distribution curve to obtain curve inflection point data and curve peak value data;
calling a preset fluctuation period function, and dividing consumption fluctuation periods of the curve inflection point data and the curve peak value data to obtain a plurality of initial fluctuation periods corresponding to the target account;
and carrying out standardization processing on the plurality of initial fluctuation periods to obtain a consumption fluctuation period set.
4. The advertising data processing method as claimed in claim 3, wherein the determining a delivery pattern of each initial fluctuation period in the consumption fluctuation period set according to the historical delivery record, and generating a period weight vector according to the consumption fluctuation period set and the delivery pattern, wherein the delivery pattern includes: default mode and put mode, including:
determining a release mode of each initial fluctuation period in the consumption fluctuation period set according to the historical release record, wherein the release mode comprises the following steps: a default mode and a put mode;
setting weight data corresponding to the initial fluctuation period according to the putting mode of each initial fluctuation period;
And constructing a period weight vector corresponding to the consumption fluctuation period set according to the weight data of the initial fluctuation period.
5. The advertisement data processing method according to claim 1, wherein the vector encoding the account recycle data to generate a recycle encoding vector, inputting the periodic weight vector and the recycle encoding vector into a preset account detection model to perform account feedback anomaly detection, and obtaining an account feedback anomaly detection result includes:
vector encoding is carried out on the account recovery data to generate a recovery encoding vector, and vector fusion is carried out on the periodic weight vector and the recovery encoding vector to generate a target fusion vector;
inputting the target fusion vector into a preset account detection model, wherein the account detection model comprises: an input layer, a double-layer long-short-time memory network and an output layer;
vector input processing is carried out on the target fusion vector through the input layer, so that a target input vector is obtained;
inputting the target input vector into the double-layer long-short time memory network, and carrying out feature extraction and integration on the target input vector through the double-layer long-short time memory network to obtain a target feature vector;
And in the output layer, carrying out account feedback abnormality prediction on the target feature vector, generating an account feedback abnormality detection result and outputting the account feedback abnormality detection result.
6. The advertisement data processing method according to claim 1, wherein optimizing the return path of the target account according to the account return anomaly detection result to obtain a target return path, and performing a delivery scheme analysis and optimization on the original delivery scheme according to the target return path to generate an optimized delivery scheme includes:
optimizing the return path of the target account according to the account return abnormality detection result to obtain a target return path;
information association is carried out on the target return path and the original delivery scheme;
performing delivery scheme analysis and optimization on the original delivery scheme to generate an optimized delivery scheme, wherein the optimized delivery scheme comprises the following steps: setting a delivery scene, setting a budget, setting a delivery date, and setting a delivery period.
7. The advertising data processing method according to claim 1, wherein the advertising data processing method further comprises:
obtaining the conversion rate of the target account, and generating a conversion rate evaluation score according to the conversion rate;
And generating a delivery quality evaluation value of the target account according to the conversion rate evaluation score and a preset weight.
8. An advertisement data processing apparatus, characterized in that the advertisement data processing apparatus comprises:
the acquisition module is used for acquiring account coin consumption data and historical delivery records of the target account from a preset advertisement delivery port;
the inquiring module is used for extracting the throwing characteristics of the account coin consumption data and the historical throwing record, generating an original throwing scheme of the target account, and inquiring account recovery data of the target account according to the historical throwing record;
the analysis module is used for inputting the account coin consumption data into a preset data statistics model to perform consumption data analysis to obtain an account coin consumption distribution curve, and performing consumption fluctuation period calculation on the account coin consumption distribution curve to obtain a consumption fluctuation period set corresponding to the target account;
the processing module is used for determining a release mode of each consumption fluctuation period in the consumption fluctuation period set according to the historical release record and generating a period weight vector according to the consumption fluctuation period set and the release mode, wherein the release mode comprises the following steps: a default mode and a put mode;
The detection module is used for carrying out vector coding on the account recovery data to generate a recovery coding vector, inputting the periodic weight vector and the recovery coding vector into a preset account detection model to carry out account feedback abnormality detection, and obtaining an account feedback abnormality detection result;
the generation module is used for optimizing the return path of the target account according to the account return abnormality detection result to obtain a target return path, and carrying out delivery scheme analysis and optimization on the original delivery scheme according to the target return path to generate an optimized delivery scheme.
9. An advertisement data processing apparatus, characterized in that the advertisement data processing apparatus comprises: a memory and at least one processor, the memory having instructions stored therein;
the at least one processor invoking the instructions in the memory to cause the advertisement data processing apparatus to perform the advertisement data processing method of any of claims 1-7.
10. A computer readable storage medium having instructions stored thereon, which when executed by a processor, implement the advertisement data processing method of any of claims 1-7.
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| WO2022127543A1 (en) * | 2020-12-17 | 2022-06-23 | 百果园技术(新加坡)有限公司 | Advertisement information processing method and apparatus, device and storage medium |
| CN114841748A (en) * | 2022-05-13 | 2022-08-02 | 北京奇艺世纪科技有限公司 | Advertisement putting method and device, electronic equipment and storage medium |
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| WO2022127543A1 (en) * | 2020-12-17 | 2022-06-23 | 百果园技术(新加坡)有限公司 | Advertisement information processing method and apparatus, device and storage medium |
| CN115545737A (en) * | 2021-06-30 | 2022-12-30 | 腾讯科技（深圳）有限公司 | Advertisement putting strategy calibration method and device, storage medium and electronic equipment |
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