Cross-region advertisement putting analysis and decision-making method and system based on artificial intelligence

Abstract

The invention relates to the technical field of computers, and discloses a cross-region advertisement putting analysis and decision method and system based on artificial intelligence, and the method comprises the steps: responding to a cross-region advertisement putting instruction, carrying out the analysis of the cross-region advertisement putting instruction, and determining a target advertisement putting region in the cross-region advertisement putting instruction; obtaining multi-dimensional data of the target advertisement putting area; performing semantic analysis and code conversion on the multi-dimensional data, and constructing a structured regional data matrix; feature extraction is performed on the regional data matrix based on a preset deep learning algorithm, and potential features related to the advertisement putting effect are mined; and inputting the potential features into an advertisement putting decision model to obtain an advertisement putting strategy output by the advertisement putting decision model for the target advertisement putting area. According to the method, the problems that the dynamic change and potential trend of the market cannot be accurately predicted and the advertisement putting strategy is lack of accuracy are solved, and the accuracy and predictability of cross-region advertisement putting analysis decision are improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to an artificial intelligence-based cross-regional advertising analysis and decision-making method and system. Background Art

[0002] In today's commercial advertising field, advertising analysis and decision-making play a vital role in the marketing effect and resource utilization efficiency of enterprises. Traditional advertising analysis and decision-making methods mainly rely on historical data statistical analysis and simple market research.

[0003] The method based on historical data statistical analysis usually collects advertising data from different regions in the past period of time, calculates the average advertising effect indicators of each region based on these data, and formulates future advertising strategies based on this. However, this method only reflects the past situation and cannot accurately predict the dynamic changes and potential trends of the market, making it difficult to detect and adjust the advertising strategy in advance, which can easily lead to companies missing market opportunities or wasting resources in ineffective advertising. Simple market research methods focus on understanding the basic characteristics, consumption habits and attitudes of consumers in different regions through questionnaires, interviews, etc. However, on the one hand, the number of samples in market research is often limited, and it is difficult to fully represent the consumer group in the entire region. Especially in the case of a large geographical area and a large population, sample bias may lead to misjudgment of market conditions. On the other hand, the information obtained from market research is mostly qualitative data, which lacks accuracy when converted into quantitative advertising strategies. Summary of the invention

[0004] The present invention provides an artificial intelligence-based cross-regional advertising analysis and decision-making method and system thereof, which are used to solve the problem of being unable to accurately predict the dynamic changes and potential trends of the market and the lack of precision in advertising delivery strategies, and to improve the accuracy and predictability of cross-regional advertising analysis and decision-making.

[0005] In a first aspect, the present invention provides a cross-regional advertising analysis and decision-making method based on artificial intelligence, comprising:

[0006] In response to a cross-region advertisement delivery instruction, the cross-region advertisement delivery instruction is parsed to determine a target advertisement delivery area in the cross-region advertisement delivery instruction;

[0007] Acquire multi-dimensional data of the target advertising delivery area; the multi-dimensional data includes historical advertising delivery data, regional population data, regional economic data, regional cultural data, regional media data and real-time market dynamics data;

[0008] Performing semantic analysis and encoding conversion on the multi-dimensional data to construct a structured regional data matrix;

[0009] Based on a preset deep learning network, feature extraction is performed on the regional data matrix to mine potential features related to the advertising effect; the potential features include regional consumption potential features, regional cultural preference features, regional media communication features and market trend features;

[0010] The potential features are input into an advertising placement decision model, and the advertising placement decision model outputs an advertising placement strategy for the target advertising placement area; the advertising placement strategy includes advertising creative adjustment suggestions, delivery channel selection, delivery time planning and delivery budget allocation; the advertising placement decision model is trained based on sample features and their corresponding advertising placement strategy label results.

[0011] In a second aspect, the present invention further provides an artificial intelligence-based cross-regional advertising delivery analysis and decision-making system, which is applied to the artificial intelligence-based cross-regional advertising delivery analysis and decision-making method as described in the first aspect, and the artificial intelligence-based cross-regional advertising delivery analysis and decision-making system includes:

[0012] A parsing module, used to respond to a cross-region advertisement delivery instruction, parse the cross-region advertisement delivery instruction, and determine a target advertisement delivery area in the cross-region advertisement delivery instruction;

[0013] An acquisition module, used to acquire multi-dimensional data of the target advertising delivery area; the multi-dimensional data includes historical advertising delivery data, regional population data, regional economic data, regional cultural data, regional media data and real-time market dynamics data;

[0014] A data processing module, used for performing semantic analysis and encoding conversion on the multi-dimensional data to construct a structured regional data matrix;

[0015] A feature extraction module is used to extract features from the regional data matrix based on a preset deep learning network, and to mine potential features related to the advertising effect; the potential features include regional consumption potential features, regional cultural preference features, regional media communication features, and market trend features;

[0016] The analysis and decision-making module is used to input the potential features into the advertising delivery decision model, and obtain the advertising delivery strategy output by the advertising delivery decision model for the target advertising delivery area; the advertising delivery strategy includes advertising creative adjustment suggestions, delivery channel selection, delivery time planning and delivery budget allocation; the advertising delivery decision model is trained based on sample features and their corresponding advertising delivery strategy label results.

[0017] In a third aspect, the present invention further provides an electronic device, comprising: a memory for storing a computer software program; a processor for reading and executing the computer software program, thereby implementing the cross-regional advertising analysis and decision-making method based on artificial intelligence as described in the first aspect above.

[0018] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium, wherein a computer software program is stored in the storage medium. When the computer software program is executed by a processor, the cross-regional advertising delivery analysis and decision-making method based on artificial intelligence as described in the first aspect above is implemented.

[0019] In a fifth aspect, the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the cross-regional advertising delivery analysis and decision-making method based on artificial intelligence as described in the first aspect above.

[0020] The cross-regional advertising analysis and decision-making method based on artificial intelligence provided by the present invention, on the one hand, covers historical advertising data, regional population data, regional economic data, regional cultural data, regional media data and real-time market dynamic data by collecting multi-dimensional data from different regions, so that the advertising strategy can be adjusted quickly, greatly improving the accuracy of cross-regional advertising analysis and decision-making. Feature extraction is performed using a preset deep learning algorithm to dig out potential features related to the advertising effect. These potential features are difficult to discover by traditional analysis methods, thereby providing a basis for early layout of advertising. Combined with the advertising decision-making model of artificial intelligence, it can accurately predict the dynamic changes and potential trends of the market, so that the delivery strategy can be detected and adjusted in advance, providing strong support for the formulation of accurate advertising strategies, and improving the predictability of cross-regional advertising analysis and decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flowchart of the cross-regional advertising delivery analysis and decision-making method based on artificial intelligence provided by the present invention;

[0022] Figure 2 It is a structural diagram of the cross-regional advertising delivery analysis and decision-making system based on artificial intelligence provided by the present invention;

[0023] Figure 3 A schematic diagram of an electronic device according to an embodiment of the present invention;

[0024] Figure 4 A schematic diagram of an embodiment of a computer-readable storage medium provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0026] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0027] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described in the present invention as "for example" is not necessarily to be construed as being preferred or advantageous over other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0028] Optional, see Figure 1 , Figure 1 The figure is a flow chart of the cross-regional advertising delivery analysis and decision-making method based on artificial intelligence provided by the present invention. The cross-regional advertising delivery analysis and decision-making method based on artificial intelligence provided by the present invention is applied to the delivery analysis and decision-making system. Therefore, the cross-regional advertising delivery analysis and decision-making method based on artificial intelligence includes the following steps:

[0029] Step 10, responding to the cross-region advertisement delivery instruction, parsing the cross-region advertisement delivery instruction, and determining the target advertisement delivery area in the cross-region advertisement delivery instruction.

[0030] Optionally, when a user needs to perform advertising delivery analysis on a certain area, he needs to send an instruction to the delivery analysis decision system, where the instruction carries the area where the advertising delivery analysis is to be performed, where the area can be a local area or a cross-regional area. The area in the embodiment of the present invention is a cross-regional area.

[0031] Therefore, the delivery analysis and decision system responds to the cross-regional advertising delivery instruction, parses the cross-regional advertising delivery instruction, and determines the target advertising delivery area in the cross-regional advertising delivery instruction. Specifically, the cross-regional advertising delivery instruction is parsed, and the instruction text is segmented and analyzed according to lexical and syntactic rules. In one embodiment, the instruction text is represented as Text, and the instruction text representation Text is matched with words through the set word library W to find the keyword set K related to the region = {k1, k2, k n}. Further, for each keyword, calculate its regional matching value F match (k i ), the specific calculation formula is as follows:

[0032]

[0033] Among them, C ij Indicates keyword k i and the jth known region identifier L in the word library W j The association weight can be calculated by cosine similarity, and the value range is between 0 and 1; S ij Indicates keyword k i The jth known region identifier L in the vocabulary W j The frequency statistics of occurrence in the relevant corpus. By calculating F match (k i ) for all keywords k i Then select F match The regions with the largest sum of values ​​are taken as target advertising delivery areas, where the target advertising delivery area can be expressed as R = {r1, r2, ..., r p}.

[0034] Step 20: Obtain multi-dimensional data of the target advertisement delivery area.

[0035] Furthermore, the delivery analysis and decision-making system obtains multi-dimensional data of the target advertising delivery area, wherein the multi-dimensional data includes historical advertising delivery data, regional population data, regional economic data, regional cultural data, regional media data and real-time market dynamics data. Among them, regional population data includes population size, age structure, gender ratio, etc. in different regions, regional economic data includes information such as per capita income, consumption level, industrial structure, etc., and regional cultural data includes local consumers' values, aesthetic concepts, consumption customs, etc. Regional media data includes the coverage of various media in different regions, audience characteristics and communication effects. Real-time market dynamics data includes capturing market trends, such as new actions of competitors and sudden changes in consumer demand.

[0036] In one embodiment, the acquisition of historical advertisement delivery data:i The corresponding advertising platform database is connected. Optionally, the advertising information recorded in the database is A ij (including exposure, clicks, conversion rate and other indicators), with the time dimension of t, integrated into the historical advertising data vector

[0037]

[0038] Where Q represents the number of entries related to advertising information recorded in the database, ω ij It is a weight coefficient set according to the advertisement type, delivery target, etc., and is used to measure the performance of different advertisement delivery records. ij The total contribution to the overall historical data is 1.

[0039] Acquisition of regional population data: Obtain the data of each target advertising area from the authoritative demographic department interface or professional data supplier i Population-related data, such as total population Age distribution (a represents age range), gender ratio etc., integrated into the regional population data vector V pop (r i ):

[0040]

[0041] Acquisition of regional economic data: Collect data from each target advertising area i GDPE GDP (r i ), per capita income E income (r i ), industrial structure proportion E struct (r i ,s)(s represents different industry categories) and other economic indicators, integrated into the regional economic data vector V eco (r i ):

[0042] V eco (r i )=[E GDP (r i ),E income (r i ),E struct (r i ,s),...].

[0043] Acquisition of regional cultural data: Through the mining of multi-source data such as reports from regional cultural research institutions and the popularity of regional topics on social media, statistics are collected on the target advertising areas.i The frequency of occurrence of cultural elements, such as cultural activity type C act (r i ,c)(c represents different cultural activities), cultural tradition preference C pref (r i ,t)(t represents different traditional elements), forming a regional cultural data vector V cult (r i ):

[0044] V cult (r i )=[C act (r i ,c1),C act (r i ,c2),C pref (r i ,t1),C pref (r i ,t2),...].

[0045] Acquisition of regional media data: Analyze each target advertising area i The coverage of various media (such as TV stations, newspapers, websites, social media platforms, etc.) in the country cov (r i ,m)(m represents different media), user activity M act (r i ,m), forming a regional media data vector V media (r i ):

[0046] V media (r i )=[M cov (r i ,m1),M cov (r i ,m2),M act (r i ,m1),M act (r i ,m2)),...].

[0047] Acquisition of real-time market dynamic data: by real-time monitoring of market-related information platforms, e-commerce platform transaction data, etc., obtain the target advertising area i Such as the current popular product category S prod (r i ,p)(p represents different products), price fluctuation coefficient S price (r i ,g)(g represents different commodity categories), integrated into real-time market dynamic data vector V market (ri ):

[0048] V market (r i )=[S prod (r i ,p1),S prod (r i ,p2),S price (r i ,g1),S price (r i ,g2),...].

[0049] Step 30, perform semantic analysis and encoding conversion on the multi-dimensional data to construct a structured regional data matrix.

[0050] Furthermore, the placement analysis and decision-making system performs semantic analysis and encoding conversion on the multi-dimensional data. For the semantic analysis process: semantic annotation is performed on the data vector of each dimension. For example, for the historical advertising placement data vector Each element in the advertisement is labeled with its meaning according to the semantic definition related to the advertising effect, such as the high exposure element is labeled as "high spreadability", etc. Therefore, the historical advertising data vector can be Each element in is transformed into a semantic label set L x = {l x1 ,l x2 ,...}, the specific conversion formula is as follows:

[0051]

[0052] Among them, x represents the historical advertising data vector Each element in F represents the total number of semantic feature judgment functions; k (x) is the kth predefined semantic feature judgment function (such as judging whether the exposure is higher than a certain threshold, etc.), α k is the corresponding weight coefficient.

[0053] For the encoding conversion process: the delivery analysis and decision-making system converts the semantically annotated data elements into numerical codes E code (l ij ), and mapped to specific numerical ranges according to different semantic labels, such as the "high spread" label corresponds to the numerical range [0.8,1], "medium spread" corresponds to [0.4,0.8), etc. After semantic analysis and encoding conversion of each element of all dimensional data vectors, the target advertising delivery area r i Arrange the encoded data into a structured regional data matrix ; Indicates the data row separation for different regions.

[0054] Step 40: extract features from the regional data matrix based on a preset deep learning network to mine potential features related to the advertising effect.

[0055] Furthermore, the advertising analysis and decision-making system extracts features from the regional data matrix through an embedded preset deep learning network, and mines out potential features related to the effectiveness of advertising, among which the potential features include regional consumption potential characteristics, regional cultural preference characteristics, regional media communication characteristics and market trend characteristics.

[0056] Among them, the regional consumption potential feature can analyze the subtle differences in the potential consumption capacity and consumption tendencies of consumers in different regions. The regional cultural preference feature can analyze the potential connection between local cultural elements and advertising elements, such as the differences in the acceptance of certain colors, patterns, and language styles in specific regions. The regional media communication feature can analyze the differences in the communication effects of different media in different regions, such as the active time distribution of social media users in different regions, and the differences in regional user stickiness of different media platforms. The market trend feature can analyze the development direction of the market, such as the development speed and potential of an emerging industry in different regions.

[0057] In one embodiment, the delivery analysis and decision-making system inputs the regional data matrix into a preset deep learning network, and determines the contribution coefficient of each data feature of each data type in the regional data matrix to the advertising delivery effect through convolution feature operation. The specific convolution feature operation formula is as follows:

[0058]

[0059] Among them, A abt (z) represents the contribution coefficient of each data feature of each data type to the advertising effect, and z represents the characteristic value of each data feature of each data type.

[0060] Furthermore, the delivery analysis and decision system ranks the contribution coefficient of each data feature in each data type to the advertising delivery effect in descending order, and extracts the target data features whose contribution coefficients are ranked in the front preset positions in each data type, wherein the front preset positions are such as the front 10, that is, the data features whose contribution coefficients are ranked in the front 10 in each data type are determined as the target data features of each data type.

[0061] Furthermore, the delivery analysis and decision-making system classifies the target data features of each data type according to preset types, wherein the preset types of the embodiment of the present invention include regional consumption potential, regional cultural preference, regional media communication and market trend. Therefore, according to the types of regional consumption potential, regional cultural preference, regional media communication and market trend, the target data features of each data type are classified to obtain regional consumption potential features, regional cultural preference features, regional media communication features and market trend features.

[0062] Step 50, input the potential features into the advertisement placement decision model, and obtain the advertisement placement decision model outputs an advertisement placement strategy for the target advertisement placement area.

[0063] Furthermore, the delivery analysis and decision system inputs the potential features into the embedded advertising delivery decision model, and outputs the advertising delivery strategy for the target advertising delivery area through the advertising delivery decision model, wherein the advertising delivery decision model is obtained by training a preset neural network based on the sample features and their corresponding advertising delivery strategy label results, and the advertising delivery strategy includes advertising creative adjustment suggestions, delivery channel selection, delivery time planning and delivery budget allocation.

[0064] In one embodiment, the advertising placement decision model includes a feature processing layer, an input layer, multiple hidden layers, an output layer, and a strategy prediction layer. Therefore, the input is the potential feature vector F of the potential feature output previously mined. out , whose dimension is q. In the embodiment of the present invention, the potential features include regional consumption potential features, regional cultural preference features, regional media communication features and market trend features. Therefore, the dimension is q=4. The input layer of the advertising decision model directly receives the feature vector F out , and pass it to the subsequent hidden layers.

[0065] In order to better focus on the part of potential features that is more relevant to the advertising delivery strategy, the embodiment of the present invention introduces an attention mechanism, which is specifically analyzed as follows: through a preset linear transformation matrix W att1 , the dimension is q*d, d is the preset intermediate dimension, for example d=8, for the input potential feature vector F out Transform and obtain the transformed eigenvector H att1 =F out *W att1 .

[0066] Further, calculate the transformed eigenvector H att1 Each element h i , i=1,2,3,4 attention weights S att (h i ), the specific calculation formula is as follows:

[0067]

[0068] Furthermore, for the attention weight S att (h i ) performs softmax normalization to obtain the attention weight vector A att1 =[a1,a2,...,a q ],in:

[0069]

[0070] Furthermore, the attention weight A att1 =[a1,a2,...,a q ] is applied to the input latent feature vector F out On the top, we get the weighted feature representation F att , where the weighted feature representation F att It can be expressed as:

[0071] F att =[a1*F out *(1),a2*F out (2),...,a q *F out (q)];

[0072] Optionally, the hidden layer of the advertising placement decision model in the embodiment of the present invention is composed of multiple fully connected layers, which are used to further extract features and build a mapping relationship with the advertising placement strategy. The first hidden layer in the advertising placement decision model has h1 neurons, such as h1 = 32, and the weight matrix (dimension is q*h1), and the weighted feature representation F is obtained through the first hidden layer att Processing is performed to obtain the output of the first hidden layer Among them, the output of the first hidden layer It can be expressed as:

[0073]

[0074] in, represents the output of the kth neuron in the first hidden layer, Indicates the initialization function, F att (i) represents the weighted feature representation F att The i-th element in Represents the weight matrix of the first hidden layer The weight element with index i,k in , represents the bias term of the kth neuron in the first hidden layer.

[0075]

[0076] Optionally, the output of the first hidden layer is input to the subsequent hidden layer in the advertising decision model, and so on. For example, the second hidden layer has h2 neurons, such as h2 = 16, and the weight matrix Bias and the weight matrix in the first hidden layer Bias Similarly, we get the output of the last hidden layer in the advertising decision model.

[0077] Optionally, the number of nodes in the output layer of the advertising delivery decision model corresponds to each dimension of the advertising delivery strategy. The advertising delivery strategy in the embodiment of the present invention includes advertising creative adjustment suggestions, delivery channel selection, delivery time planning and delivery budget allocation, a total of 4 dimensions, and the output layer has 4 nodes.

[0078] The connection weight W of the output layer out The dimension is h last *4 matrix (h last is the number of neurons in the last hidden layer), the bias term b out is a vector of length 4, so the initialization and output layer output S = [S creat ,S channel ,S time ,S budget ], which correspond to the predicted probabilities or predicted probability distributions of advertising creative adjustment suggestions, delivery channel selection, delivery time planning, and delivery budget allocation.

[0079] Furthermore, based on the probability interval of the predicted probabilities of the advertising creative adjustment suggestions, delivery channel selection, delivery time planning and delivery budget allocation, or by sorting the probability values ​​in the predicted probability distribution, the advertising creative adjustment suggestions, delivery channel selection, delivery time planning and delivery budget allocation for the target advertising delivery area are determined.

[0080] In the output layer, the predicted probability of the ad creative adjustment suggestion is calculated as follows:

[0081]

[0082] Among them, S creat represents the predicted probability of the creative adjustment suggestion; last (j) represents the jth output of the last hidden layer, W out (j,0) represents the weight matrix W of the last hidden layer out The weight element with index j in b out(0) represents the bias term of the first neuron of the last hidden layer. Therefore, according to the probability interval of the predicted probability of the advertising creative adjustment suggestion, the corresponding advertising creative adjustment suggestion is matched.

[0083] Among them, the predicted probability distribution S of the delivery channel selection channel The calculation formula is as follows:

[0084]

[0085] Among them, the predicted probability distribution of delivery channel selection includes the probability values ​​corresponding to channels such as social media platforms, TV advertising, and outdoor advertising. The final selected delivery channel is determined by sorting the probability values.

[0086] Among them, the predicted probability distribution S of the delivery time planning time The calculation formula is as follows:

[0087]

[0088] Among them, the predicted probability distribution of the delivery time planning includes the delivery weights or heat values ​​of different time periods (hours, days, weeks, etc.), and specific delivery time plans are formulated based on these values.

[0089] Among them, the predicted probability of budget allocation S budget The calculation formula is as follows:

[0090]

[0091] Therefore, according to the probability interval of the predicted probability of the delivery budget allocation, the corresponding advertising delivery budget, that is, the specific advertising delivery budget amount, is matched.

[0092] Furthermore, the loss function of the advertising decision model is:

[0093] loss total =α*loss creat +β*loss channel +γ*loss time +δ*loss budget ;

[0094] Among them, loss total Represents the loss function of the advertising decision model, loss creat Represents the loss function of the creative adjustment suggestion, loss channel Represents the loss function for channel selection, loss time Represents the loss function of the delivery time planning, loss budgetrepresents the loss function of the budget allocation, α, β, γ, δ represent the important coefficients of the preset loss function;

[0095]

[0096] Among them, C represents the number of categories of advertising creative adjustment suggestions, y creat (i) represents the true label of the ad creative adjustment suggestion, S creat (i) represents the predicted value of the advertising creative adjustment suggestion;

[0097]

[0098] Among them, T represents the dimension of delivery time planning, S time (i) represents the predicted value of the delivery time plan, y time (i) The true label representing the delivery time plan;

[0099] loss budget =|S budget -y budget |.

[0100] Among them, S budget represents the predicted value of the delivery budget allocation, y budget Indicates the true label of the delivery budget allocation.

[0101] The embodiment of the present invention collects multi-dimensional data from different regions, covering historical advertising data, regional population data, regional economic data, regional cultural data, regional media data and real-time market dynamic data, so that the advertising delivery strategy can be adjusted quickly, greatly improving the accuracy of cross-regional advertising delivery analysis and decision-making. The preset deep learning algorithm is used for feature extraction to dig out potential features related to the effect of advertising delivery. These potential features are difficult to discover with traditional analysis methods, thereby providing a basis for early layout of advertising delivery. Combined with the artificial intelligence advertising delivery decision model, it can accurately predict the dynamic changes and potential trends of the market, so that the delivery strategy can be detected and adjusted in advance, providing strong support for the formulation of accurate advertising delivery strategies, and improving the predictability of cross-regional advertising delivery analysis and decision-making.

[0102] The following is a description of the cross-regional advertising delivery analysis and decision-making system based on artificial intelligence provided by the present invention. The cross-regional advertising delivery analysis and decision-making system based on artificial intelligence described below and the cross-regional advertising delivery analysis and decision-making method based on artificial intelligence described above can be referred to each other. Figure 2 , Figure 2 A schematic diagram of the structure of a cross-regional advertising delivery analysis and decision-making system based on artificial intelligence provided by an embodiment of the present invention, wherein the cross-regional advertising delivery analysis and decision-making system based on artificial intelligence includes:

[0103] The parsing module 210 is used to respond to the cross-region advertisement delivery instruction, parse the cross-region advertisement delivery instruction, and determine the target advertisement delivery area in the cross-region advertisement delivery instruction;

[0104] An acquisition module 220 is used to acquire multi-dimensional data of a target advertising delivery area; the multi-dimensional data includes historical advertising delivery data, regional population data, regional economic data, regional cultural data, regional media data, and real-time market dynamics data;

[0105] The data processing module 230 is used to perform semantic analysis and code conversion on the multi-dimensional data to construct a structured regional data matrix;

[0106] The feature extraction module 240 is used to extract features from the regional data matrix based on a preset deep learning network, and to mine potential features related to the advertising effect; the potential features include regional consumption potential features, regional cultural preference features, regional media communication features, and market trend features;

[0107] The analysis and decision module 250 is used to input the potential features into the advertising delivery decision model, and obtain the advertising delivery decision model to output the advertising delivery strategy for the target advertising delivery area; the advertising delivery strategy includes advertising creative adjustment suggestions, delivery channel selection, delivery time planning and delivery budget allocation; the advertising delivery decision model is trained based on sample features and their corresponding advertising delivery strategy label results.

[0108] The cross-regional advertising analysis and decision-making system based on artificial intelligence provided by the present invention, on the one hand, collects multi-dimensional data from different regions, covering historical advertising data, regional population data, regional economic data, regional cultural data, regional media data and real-time market dynamics data, so that the advertising strategy can be adjusted quickly, greatly improving the accuracy of cross-regional advertising analysis and decision-making. Feature extraction is performed using a preset deep learning algorithm to dig out potential features related to the advertising effect. These potential features are difficult to discover with traditional analysis methods, thereby providing a basis for early layout of advertising. Combined with the advertising decision-making model of artificial intelligence, it can accurately predict the dynamic changes and potential trends of the market, so that the delivery strategy can be detected and adjusted in advance, providing strong support for the formulation of accurate advertising strategies, and improving the predictability of cross-regional advertising analysis and decision-making.

[0109] See also Figure 3 , Figure 3 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 3As shown, an embodiment of the present invention provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor 320. When the processor 320 executes the computer program 311, the following steps are implemented:

[0110] Responding to a cross-region advertisement delivery instruction, parsing the cross-region advertisement delivery instruction, and determining a target advertisement delivery area in the cross-region advertisement delivery instruction;

[0111] Obtain multi-dimensional data of the target advertising area; the multi-dimensional data includes historical advertising data, regional population data, regional economic data, regional cultural data, regional media data and real-time market dynamics data;

[0112] Perform semantic analysis and encoding conversion on multi-dimensional data to build a structured regional data matrix;

[0113] Based on the preset deep learning network, the regional data matrix is ​​extracted to find out the potential features related to the advertising effect; the potential features include regional consumption potential features, regional cultural preference features, regional media communication features and market trend features;

[0114] The potential features are input into the advertising placement decision model, and the advertising placement decision model outputs an advertising placement strategy for the target advertising placement area; the advertising placement strategy includes advertising creative adjustment suggestions, delivery channel selection, delivery time planning and delivery budget allocation; the advertising placement decision model is trained based on sample features and their corresponding advertising placement strategy label results.

[0115] See also Figure 4 , Figure 4 Schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. Figure 4 As shown, this embodiment provides a computer-readable storage medium 400, on which a computer program 311 is stored. When the computer program 311 is executed by a processor, the following steps are implemented:

[0116] Responding to a cross-region advertisement delivery instruction, parsing the cross-region advertisement delivery instruction, and determining a target advertisement delivery area in the cross-region advertisement delivery instruction;

[0117] Obtain multi-dimensional data of the target advertising area; the multi-dimensional data includes historical advertising data, regional population data, regional economic data, regional cultural data, regional media data and real-time market dynamics data;

[0118] Perform semantic analysis and encoding conversion on multi-dimensional data to build a structured regional data matrix;

[0119] Based on the preset deep learning network, the regional data matrix is ​​extracted to find out the potential features related to the advertising effect; the potential features include regional consumption potential features, regional cultural preference features, regional media communication features and market trend features;

[0120] The potential features are input into the advertising placement decision model, and the advertising placement decision model outputs an advertising placement strategy for the target advertising placement area; the advertising placement strategy includes advertising creative adjustment suggestions, delivery channel selection, delivery time planning and delivery budget allocation; the advertising placement decision model is trained based on sample features and their corresponding advertising placement strategy label results.

[0121] On the other hand, the present invention further provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the cross-regional advertising analysis and decision-making method based on artificial intelligence provided by the above methods, and the method includes:

[0122] Responding to a cross-region advertisement delivery instruction, parsing the cross-region advertisement delivery instruction, and determining a target advertisement delivery area in the cross-region advertisement delivery instruction;

[0123] Obtain multi-dimensional data of the target advertising area; the multi-dimensional data includes historical advertising data, regional population data, regional economic data, regional cultural data, regional media data and real-time market dynamics data;

[0124] Perform semantic analysis and encoding conversion on multi-dimensional data to build a structured regional data matrix;

[0125] Based on the preset deep learning network, the regional data matrix is ​​extracted to find out the potential features related to the advertising effect; the potential features include regional consumption potential features, regional cultural preference features, regional media communication features and market trend features;

[0126] The potential features are input into the advertising placement decision model, and the advertising placement decision model outputs an advertising placement strategy for the target advertising placement area; the advertising placement strategy includes advertising creative adjustment suggestions, delivery channel selection, delivery time planning and delivery budget allocation; the advertising placement decision model is trained based on sample features and their corresponding advertising placement strategy label results.

[0127] The system embodiment described above is merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0128] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0129] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A cross-regional advertising analysis and decision-making method based on artificial intelligence, characterized in that: include: In response to a cross-region advertisement delivery instruction, the cross-region advertisement delivery instruction is parsed to determine a target advertisement delivery area in the cross-region advertisement delivery instruction; Acquire multi-dimensional data of the target advertisement delivery area; The multi-dimensional data includes historical advertising data, regional population data, regional economic data, regional cultural data, regional media data and real-time market dynamics data; Performing semantic analysis and encoding conversion on the multi-dimensional data to construct a structured regional data matrix; Based on a preset deep learning network, feature extraction is performed on the regional data matrix to mine potential features related to the advertising effect; the potential features include regional consumption potential features, regional cultural preference features, regional media communication features and market trend features; The potential features are input into an advertising placement decision model, and the advertising placement decision model outputs an advertising placement strategy for the target advertising placement area; the advertising placement strategy includes advertising creative adjustment suggestions, delivery channel selection, delivery time planning and delivery budget allocation; the advertising placement decision model is trained based on sample features and their corresponding advertising placement strategy label results.

2. The cross-regional advertising analysis and decision-making method based on artificial intelligence according to claim 1 is characterized in that: The advertising placement decision model comprises a feature processing layer, an input layer, multiple hidden layers, an output layer and a strategy prediction layer; the step of inputting the potential features into the advertising placement decision model to obtain the advertising placement strategy output by the advertising placement decision model for the target advertising placement area comprises: Inputting the potential features into the advertising placement decision model, performing feature processing on the potential features based on the feature processing layer combined with the attention mechanism, and obtaining weighted feature representation; Inputting the weighted feature representation to the multiple hidden layers based on the input layer, and processing the weighted feature representation based on the multiple hidden layers to obtain an intermediate output; Processing the intermediate output based on the output layer to obtain the predicted probability or predicted probability distribution of the advertisement creative adjustment suggestions, delivery channel selection, delivery time planning and delivery budget allocation output by the output layer; Based on the strategy prediction layer, the predicted probability of advertising creative adjustment suggestions, delivery channel selection, delivery time planning and delivery budget allocation, or the predicted probability distribution is used to perform strategy prediction to obtain the advertising delivery strategy for the target advertising delivery area.

3. The cross-regional advertising analysis and decision-making method based on artificial intelligence according to claim 2 is characterized in that: The processing process of the feature processing layer is as follows: Through the preset linear transformation matrix W att1 , the dimension is q*d, d is the preset intermediate dimension, for the input q-dimensional potential feature vector F out Transform and obtain the transformed eigenvector H att1 =F out *W att1 ; Calculate the transformed eigenvector H att1 Each element h i ,i=1,2,...,q’s attention weight S att (h i ), the specific calculation formula is as follows: For the attention weight S att (h i ) performs softmax normalization to obtain the attention weight vector A att1 =[a1,a2,...,a q ],in: The attention weight A att1 =[a1,a2,...,a q ] is applied to the input latent feature vector F out On the top, we get the weighted feature representation F att , where the weighted feature representation F att It can be expressed as: F att =[a1*F out *(1),a2*F out (2),...,a q *F out (q)]。 4. The cross-regional advertising analysis and decision-making method based on artificial intelligence according to claim 2 is characterized in that: The processing of the multiple hidden layers is as follows: Based on a weight matrix of h1 neurons with dimension q*h1 The first hidden layer of the weighted feature representation F att Processing is performed to obtain the output of the first hidden layer It is expressed as: in, represents the output of the kth neuron in the first hidden layer, Represents the initialization function, F att (i) represents the weighted feature representation F att The i-th element in Represents the weight matrix of the first hidden layer The weight element with index i,k in , represents the bias term of the kth neuron in the first hidden layer; The output of the first hidden layer is used as the input of the second hidden layer, and the output of each hidden layer is used as the input of the next hidden layer in turn, until the output of the last hidden layer is obtained.

5. The cross-regional advertising analysis and decision-making method based on artificial intelligence according to claim 2 is characterized in that: The processing of the output layer is as follows: In the output layer, a probability prediction is made based on the output of the last hidden layer; For the predicted probability S of the advertising creative adjustment suggestion creat The calculation formula is as follows: Among them, h last represents the number of neurons in the last hidden layer; O last (j) represents the jth output of the last hidden layer, W out (j,0) represents the weight matrix W of the last hidden layer out The weight element with index j,0 in b out (0) represents the bias term of the first neuron in the last hidden layer; For the predicted probability distribution S of the delivery channel selection channel The calculation formula is as follows: Among them, W out (j,1) represents the weight matrix W of the last hidden layer out The weight element with index j,1 in b out (1) represents the bias term of the second neuron in the last hidden layer; For the predicted probability distribution S of the delivery time planning time The calculation formula is as follows: Among them, W out (j,2) represents the weight matrix W of the last hidden layer out The weight element with index j,2 in b out (2) represents the bias term of the third neuron in the last hidden layer; For the predicted probability S of budget allocation budget The calculation formula is as follows: Among them, W out (j,3) represents the weight matrix W of the last hidden layer out The weight element with index j,3 in b out (3) represents the bias term of the 4th neuron in the last hidden layer.

6. The cross-regional advertising analysis and decision-making method based on artificial intelligence according to claim 2 is characterized in that: The loss function of the advertising decision model is: loss total =α*loss creat +β*loss channel +γ*loss time +δ*loss budget ; Among them, loss total Represents the loss function of the advertising decision model, loss creat Represents the loss function of the creative adjustment suggestion, loss channel Represents the loss function for channel selection, loss time Represents the loss function of the delivery time planning, loss budget represents the loss function of the budget allocation, α, β, γ, δ represent the important coefficients of the preset loss function; Among them, C represents the number of categories of advertising creative adjustment suggestions, y creat (i) represents the true label of the ad creative adjustment suggestion, S creat (i) represents the predicted value of the advertising creative adjustment suggestion; Among them, T represents the dimension of delivery time planning, S time (i) represents the predicted value of the delivery time plan, y time (i) The true label representing the delivery time plan; loss budget =|S budget -y budget |。 Among them, S budget represents the predicted value of the delivery budget allocation, y budget Indicates the true label of the delivery budget allocation.

7. The cross-regional advertising analysis and decision-making method based on artificial intelligence according to any one of claims 1 to 6, characterized in that: The feature extraction of the regional data matrix based on the preset deep learning network to mine potential features related to the advertising effect includes: Based on the convolution feature operation in the preset deep learning network, determine the contribution coefficient of each data feature of each data type in the regional data matrix to the advertising delivery effect; Sort the contribution coefficient of each data feature in each data type to the advertising effect, and extract the target data features with the contribution coefficient ranked in the front position in each data type; The target data features in each data type are classified according to preset types to obtain the regional consumption potential features, the regional cultural preference features, the regional media communication features and the market trend features.

8. An artificial intelligence-based cross-regional advertising analysis and decision-making system, characterized in that: Applied to the cross-regional advertising delivery analysis and decision-making method based on artificial intelligence as claimed in any one of claims 1 to 7, the cross-regional advertising delivery analysis and decision-making system based on artificial intelligence comprises: A parsing module, used to respond to a cross-region advertisement delivery instruction, parse the cross-region advertisement delivery instruction, and determine a target advertisement delivery area in the cross-region advertisement delivery instruction; An acquisition module, used to acquire multi-dimensional data of the target advertising delivery area; the multi-dimensional data includes historical advertising delivery data, regional population data, regional economic data, regional cultural data, regional media data and real-time market dynamics data; A data processing module, used for performing semantic analysis and encoding conversion on the multi-dimensional data to construct a structured regional data matrix; A feature extraction module is used to extract features from the regional data matrix based on a preset deep learning network, and to mine potential features related to the advertising effect; the potential features include regional consumption potential features, regional cultural preference features, regional media communication features, and market trend features; The analysis and decision-making module is used to input the potential features into the advertising delivery decision model, and obtain the advertising delivery strategy output by the advertising delivery decision model for the target advertising delivery area; the advertising delivery strategy includes advertising creative adjustment suggestions, delivery channel selection, delivery time planning and delivery budget allocation; the advertising delivery decision model is trained based on sample features and their corresponding advertising delivery strategy label results.

9. An electronic device, comprising: Memory for storing computer software programs; A processor, used to read and execute the computer software program, characterized in that when the computer software program is executed by the processor, it implements the cross-regional advertising analysis and decision-making method based on artificial intelligence as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer software program stored therein, characterized in that: When the computer software program is executed by the processor, the cross-regional advertising delivery analysis and decision-making method based on artificial intelligence as described in any one of claims 1 to 7 is implemented.

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