A marketing data analysis method based on big data portrait

Abstract

The present invention discloses a marketing data analysis method based on big data portraits, which relates to the technical field of electronic digital data processing. The marketing data analysis method based on big data portraits includes the following steps: obtaining a portrait similarity coefficient, obtaining a comparison result, and adopting a portrait data collection strategy. The present invention collects video portrait data within a preset time period, obtains a portrait similarity coefficient based on the collected video portrait data, then compares the portrait similarity coefficient with a similarity threshold obtained from a preset database to obtain a comparison result, and finally adopts a portrait data collection strategy based on the comparison result, thereby achieving the effect of improving the timeliness of updating user portrait data in marketing data, and solving the problem of untimely updating of user portrait data in marketing data in the prior art.

Description

Technical Field

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

[0002] With the rapid development of big data technology, companies are able to collect vast amounts of user information, including basic attributes, behavioral data, and purchasing preferences. By building refined user profiles, companies can gain a deeper understanding of their target customer groups and implement precision marketing. This approach not only improves marketing efficiency but also reduces costs and enhances market competitiveness. Big data profiling has become a key tool for companies to formulate marketing strategies, optimize product design, and enhance user experience, providing them with unprecedented business insights and growth opportunities.

[0003] Existing marketing data analysis methods based on big data profiling primarily focus on building user profiles by collecting, processing, and analyzing big data, thereby guiding precision marketing. These methods typically utilize big data technologies to comprehensively capture static and dynamic user information, generate personalized user tags through data analysis and feature extraction, and construct user profile models based on these tags. Subsequently, based on these user profile models, companies can implement precision marketing strategies such as personalized recommendations and targeted advertising. These technologies not only improve marketing efficiency but also enhance user experience, providing companies with significant competitive advantages in the market.

[0004] For example, the patent application with the publication number CN111737338A discloses a closed-loop marketing data analysis method based on big data portraits, including: step S1: acquiring data; step S2: data processing, step S3: forming the results and storing them in the database; step S4: visualizing the stored data, reading and printing through the database; step S5: precise push, wherein the precise push is pushed through commercial advertisements. Advantage 1: The present invention reduces the physical computer storage space occupied by these data through efficient analysis of sales data, can more accurately grasp the dynamic changes of sales data, determine the production direction according to sales information, maximize the development of the functions of sales data, and manage and make decisions on the entire sales process in real time. Advantage 2: The present invention uses an e-commerce marketing system based on big data analysis to put the company's marketing, sales, customer service, production scheduling and resource management into a complete closed-loop system, enabling the company to maintain rapid development with the help of the O2O model in the fierce market competition.

[0005] For example, the invention patent announcement with announcement number: CN115168740B discloses a method and system for generating marketing tasks based on big data analysis, including: constructing a label sequence for a new game and obtaining a label sequence for released games based on big data, obtaining games of the same category as the new game based on the similarity of the label sequences; obtaining all users of each released game in the same category and the activity representation value of each user and the interaction value between users; constructing a user association graph corresponding to each released game; obtaining the degree of the node based on the node information of each node in the user association graph, performing modular calculations according to the degree of each node and performing graph clustering on the user association graph to obtain multiple node categories; obtaining the user recommendation priority of each user and the category recommendation priority of each node category; generating marketing tasks in combination with the user recommendation priority and the category recommendation priority; the marketing tasks are more applicable and have a wider promotion scope.

[0006] However, in the process of implementing the technical solutions of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the existing technology, especially in short video platforms, since users' interests and needs are constantly changing with various factors such as time, environment, and social trends, user portraits constructed based on big data portraits may not be able to promptly reflect changes in users' interests in watching videos, and there is a problem of untimely updates of user portrait data in marketing data. Summary of the Invention

[0008] The embodiment of the present application solves the problem of untimely updating of user portrait data in marketing data in the prior art by providing a marketing data analysis method based on big data portraits, thereby improving the timeliness of updating user portrait data in marketing data.

[0009] An embodiment of the present application provides a marketing data analysis method based on big data portraits, comprising the following steps: collecting video portrait data within a preset time period, and obtaining a portrait similarity coefficient based on the collected video portrait data, wherein the video portrait data includes feedback portrait data and user portrait data, and the portrait similarity coefficient is used to measure the degree of similarity between the current user portrait data and the feedback portrait data; comparing the portrait similarity coefficient with a similarity threshold obtained from a preset database to obtain a comparison result, wherein the comparison result includes whether the portrait meets the standard and whether the portrait does not meet the standard; and adopting a portrait data collection strategy based on the comparison result, wherein the portrait data collection strategy is used to improve the timeliness of the user portrait.

[0010] Furthermore, the process of obtaining the video portrait data is as follows: feedback portrait data is collected by feedback questionnaires to the users to be surveyed in the short video platform to be analyzed within a preset time period, and the obtained feedback portrait data is transmitted to a preset database for storage, wherein the feedback portrait data includes feedback video tags and feedback video duration; user portrait data of the users to be surveyed are obtained from the short video platform to be analyzed, wherein the user portrait data includes user video tags and user video duration, and the content category of the user portrait data is consistent with the content category of the feedback portrait data; the video type similarity between the feedback video tag and the user video tag is obtained through a hash algorithm, and the video duration similarity between the feedback video duration and the user video duration is obtained through a hash algorithm, wherein the video type similarity indicates the similarity between the user video tag and the feedback video tag, and the video duration similarity indicates the similarity between the user video duration and the feedback video duration.

[0011] Furthermore, the process of obtaining the portrait similarity coefficient is as follows: obtaining evaluation data from a preset database, the evaluation data including a video type evaluation threshold and a video duration evaluation threshold; numbering the users to be surveyed, and obtaining individual portrait similarity coefficients based on video type similarity and video duration similarity, and obtaining a portrait similarity coefficient based on the individual portrait similarity coefficients, wherein the individual portrait similarity coefficient represents the portrait similarity coefficient of a single user to be surveyed, and the portrait similarity coefficient is calculated using the following formula:

[0012]

[0013] Where, f represents the number of users to be investigated, f=1,2,,,F, F represents the total number of users to be investigated, VTS f VDS represents the video type similarity of the fth user to be investigated. f represents the video duration similarity of the fth user to be investigated, μ represents the video type evaluation threshold, ρ represents the video duration evaluation threshold, ISC f represents the individual portrait similarity coefficient of the fth user to be investigated, and PSC represents the portrait similarity coefficient of the user to be investigated in the current preset time period in the short video platform to be analyzed.

[0014] Furthermore, the process of obtaining the comparison result is as follows: a similarity threshold is obtained from a preset database, and the obtained similarity threshold is compared with the portrait similarity coefficient: if the portrait similarity coefficient is not less than the similarity threshold, the comparison result is that the portrait meets the standard; if the portrait similarity coefficient is less than the similarity threshold, the comparison result is that the portrait does not meet the standard.

[0015] Furthermore, the specific process of adopting the portrait data collection strategy based on the comparison result is as follows: if the comparison result is that the portrait meets the standards, the current user portrait will continue to be used; if the comparison result is that the portrait does not meet the standards, the impact of the current data collection delay on the user portrait construction will be judged based on the data collection delay index. The data collection delay includes data processing delay and data transmission delay. The data collection delay index is used to quantify the impact of data collection delay on video portrait data collection.

[0016] Furthermore, the process of obtaining the data collection delay index is as follows: obtaining data processing delay and data transmission delay through the log records of the short video platform to be analyzed, the data processing delay represents the time delay generated by the short video platform to be analyzed when processing video portrait data, and the data transmission delay represents the time delay generated by the short video platform to be analyzed in the process of transmitting video portrait data; obtaining reference data from a preset database, the reference data including average data processing delay, average data transmission delay, processing delay evaluation value and transmission delay evaluation value; obtaining an individual delay index based on the data collection delay and the reference data, and then obtaining a data collection delay index based on the individual delay index, the individual delay index represents the data collection delay index of a single user to be surveyed, and the data collection delay index is calculated using the following formula:

[0017]

[0018]

[0019] Where f represents the number of users to be investigated, f=1,2,,,F, F represents the total number of users to be investigated, CD f represents the data processing delay of the fth user to be investigated, represents the average data processing delay, α represents the processing delay evaluation value, TD f represents the data transmission delay of the fth user to be investigated, represents the average data transmission delay, β represents the transmission delay evaluation value, IDI f represents the individual delay index of the fth user to be investigated, and DCI represents the data collection delay index within a preset time period.

[0020] Furthermore, the data collection delay index is obtained, and then the data collection delay index is compared with the data collection delay index threshold obtained from a preset database to obtain a judgment result, specifically as follows: the judgment result includes normal data collection delay and abnormal data collection delay; the specific method for obtaining the judgment result is as follows: if the data collection delay index is not less than the data collection delay index threshold, the judgment result is that the data collection delay is normal; if the data collection delay index is less than the data collection delay index threshold, the judgment result is that the data collection delay is abnormal.

[0021] Furthermore, the judgment result is obtained, and then feedback on the judgment result is included. The specific process is as follows: if the judgment result is that the data collection delay is normal, a portrait update judgment is performed based on the user portrait update frequency, and the portrait update judgment is used to determine whether the user portrait update frequency has an impact on video marketing; if the judgment result is that the data collection delay is abnormal, the factor that causes the portrait similarity coefficient to not meet the video marketing requirements is the data collection delay, and a command prompt for reducing the data collection delay is sent to the preset analyst.

[0022] Furthermore, the specific steps of judging the user portrait update based on the user portrait update frequency are as follows: Step 1, obtaining the current user portrait update frequency from the log records of the short video platform to be analyzed, and obtaining the average user portrait update frequency from the preset database, the user portrait update frequency represents the frequency of the short video platform to be analyzed updating the user portrait in the current preset time period; Step 2, obtaining the update frequency evaluation value from the preset database, and obtaining the individual user portrait update frequency index based on the user portrait update frequency, the average user portrait update frequency and the update frequency evaluation value, and obtaining the user portrait update frequency index based on the individual user portrait update frequency index, the individual user portrait update frequency index represents the user portrait update frequency index of a single user to be investigated, and the user portrait update frequency index is used to quantify the degree of influence of the user portrait update frequency on video marketing; Step 3, comparing the user portrait update frequency index with the user portrait update frequency index threshold obtained from the preset database and performing update feedback;

[0023] The user portrait update frequency index is calculated using the following formula:

[0024]

[0025] Where f represents the number of users to be investigated, f=1,2,,,F, F represents the total number of users to be investigated, UF f represents the update frequency of the user profile of the fth user to be investigated, represents the average user profile update frequency, θ represents the update frequency evaluation value, IUI fIt represents the individual user portrait update frequency index of the fth user to be investigated, and UFI represents the user portrait update frequency index of the short video platform to be analyzed within the preset time period.

[0026] Furthermore, the specific process of comparing the user portrait update frequency index with the user portrait update frequency index threshold obtained from the preset database and performing update feedback is as follows: if the user portrait update frequency index is not less than the user portrait update frequency index threshold, feedback is given to the preset analyst that there is no problem with data collection delay and user portrait update frequency; if the user portrait update frequency index is less than the user portrait update frequency index threshold, a command prompt to increase the user portrait update frequency is sent to the preset analyst.

[0027] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0028] 1. By collecting video portrait data within a preset time period, and obtaining a portrait similarity coefficient based on the collected video portrait data, and then comparing the portrait similarity coefficient with the similarity threshold obtained from a preset database to obtain a comparison result, and finally adopting a portrait data collection strategy based on the comparison result, the user portrait is dynamically updated, thereby improving the timeliness of the update of user portrait data in the marketing data, and effectively solving the problem of untimely update of user portrait data in the marketing data in the existing technology.

[0029] 2. By obtaining the similarity threshold from the preset database and comparing the obtained similarity threshold with the portrait similarity coefficient, it is determined whether the current user portrait is timely, and then a portrait data collection strategy is adopted to improve the timeliness of the user portrait.

[0030] 3. By comparing the data collection delay index with the data collection delay index threshold obtained from the preset database, the judgment result is obtained, thereby determining the impact of data collection delay on user portrait construction, and then taking different methods to improve the accuracy of user portrait construction based on the judgment result. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 A flowchart of a marketing data analysis method based on big data portraits provided in an embodiment of the present application;

[0032] Figure 2 A schematic diagram of the changes in the individual portrait similarity coefficient provided in the embodiment of the present application. DETAILED DESCRIPTION

[0033] An embodiment of the present application solves the problem of untimely updating of user portrait data in marketing data in the prior art by providing a marketing data analysis method based on big data portraits. The method collects video portrait data within a preset time period, obtains a portrait similarity coefficient based on the collected video portrait data, obtains a similarity threshold from a preset database, and compares the obtained similarity threshold with the portrait similarity coefficient to obtain a comparison result. The method then obtains data processing delay and data transmission delay through the log records of the short video platform to be analyzed, obtains reference data from the preset database to obtain a data acquisition delay index, and then compares the data acquisition delay index with the data acquisition delay index threshold obtained from the preset database to obtain a judgment result. Finally, the current user portrait update frequency is obtained from the log records of the short video platform to be analyzed, and the average user portrait update frequency is obtained from the preset database to obtain a user portrait update frequency index. At the same time, the user portrait update frequency index is compared with the user portrait update frequency index threshold obtained from the preset database and update feedback is performed, thereby improving the timeliness of updating user portrait data in marketing data.

[0034] The technical solution in the embodiment of the present application is to solve the problem of untimely updating of user portrait data in the above-mentioned marketing data. The overall idea is as follows:

[0035] By collecting video portrait data within a preset time period and obtaining a portrait similarity coefficient based on the collected video portrait data, and then comparing the portrait similarity coefficient with the similarity threshold obtained from a preset database to obtain a comparison result, and finally adopting a portrait data collection strategy based on the comparison result, the timeliness of updating user portrait data in marketing data is improved.

[0036] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0037] like Figure 1As shown, it is a flowchart of a marketing data analysis method based on big data portrait provided by an embodiment of the present application, the method comprising the following steps: collecting video portrait data within a preset time period, and obtaining a portrait similarity coefficient based on the collected video portrait data, the video portrait data originating from the short video platform to be analyzed, the video portrait data comprising feedback portrait data and user portrait data, the feedback portrait data being used to reflect the actual user portrait of the videos that the user is interested in, the user portrait data being used to reflect the user portrait of the videos that the user is interested in constructed based on big data portraits, the portrait similarity coefficient being used to measure the degree of similarity between the current user portrait data and the feedback portrait data; comparing the portrait similarity coefficient with the similarity threshold obtained from a preset database to obtain a comparison result, the similarity threshold being used to describe the minimum requirements that the current user portrait data and the feedback portrait data meet, the comparison result comprising a portrait meeting the standard and a portrait failing to meet the standard; adopting a portrait data collection strategy based on the comparison result, the portrait data collection strategy being used to improve the timeliness of the user portrait.

[0038] In this embodiment, the preset time period refers to the time range for data collection and analysis. Within this time range, the short video platform to be analyzed (such as Douyin, Bilibili, Kuaishou, Xiaohongshu, etc.) will collect relevant data for constructing user portraits for processing, ensuring that the user portrait is generated based on the latest data and can reflect the user's current interests and behaviors. Among them, the portrait similarity coefficient is a metric used to measure the similarity between the current user portrait data (user interest video portrait constructed based on big data) and the feedback portrait data (portrait reflected by the video that the user is actually interested in). By calculating the portrait similarity coefficient, the short video platform to be analyzed can determine whether the current user portrait accurately reflects the user's true interests; the feedback portrait data is portrait data generated based on the user's actual behavior (such as viewing history, likes, comments, etc.), reflecting the type and characteristics of videos that the user is actually interested in. Provide a direct reflection of the user's real interests as a comparison benchmark to ensure that the construction of the user portrait is accurate; user portrait data is a predictive portrait constructed based on big data analysis, reflecting the types and features of videos that the user may be interested in, and is used to predict the user's interests and provide personalized recommendation services; through the method provided in this embodiment, the short video platform can continuously collect and update the user's portrait data within a certain period of time, monitor the accuracy of the user portrait in real time, and take corresponding measures to adjust according to the degree of match between the portrait and the user's actual interests, thereby improving the timeliness of the update of user portrait data in marketing data.

[0039] Furthermore, the process of obtaining video portrait data is as follows: feedback portrait data is collected through a portrait questionnaire fed back to the users to be surveyed in the short video platform to be analyzed within a preset time period, and the obtained feedback portrait data is transmitted to a preset database for storage, the feedback portrait data includes feedback video tags and feedback video duration, the feedback video tags indicate the type of videos of interest fed back by the users through the portrait questionnaire, the feedback video duration indicates the duration of videos of interest fed back by the users through the portrait questionnaire, and the portrait questionnaire is used to obtain the users' actual preferences for watching videos; user portrait data of the users to be surveyed is obtained from the short video platform to be analyzed, the user portrait data includes user video tags and user video duration, the user video tags indicate the type of videos of interest fed back by the short video platform to be analyzed based on the users' video The video viewing log is a label for the type of videos that the user is interested in watching added to the user portrait. The user video duration indicates the duration of the videos that the short video platform to be analyzed is interested in watching and added to the user portrait based on the user's video viewing log. The video viewing log includes viewing history, likes and comments, number of video shares, and search records. The content category of the user portrait data is consistent with that of the feedback portrait data. The hash algorithm is used to obtain the video type similarity between the feedback video label and the user video label, and the hash algorithm is used to obtain the video duration similarity between the feedback video duration and the user video duration. The video type similarity indicates the similarity between the user video label and the feedback video label, and the video duration similarity indicates the similarity between the user video duration and the feedback video duration. The hash algorithm is used to obtain the similarity of video portrait data of the same type.

[0040] In this embodiment, the process of obtaining video portrait data through user portrait questionnaires and short video platform data is explained, wherein the feedback portrait data is collected by sending a portrait questionnaire to the users to be surveyed within a preset time period, and the user feedback data includes the user's feedback on the label (type) and duration of the video of interest. For example, if a video type that the user is interested in is sports, then sports is a label. In addition, in actual applications, video labels also include beauty, life, study, news, etc.; user portrait data is extracted from the user video viewing log of the short video platform. The data includes the video labels of interest and video duration generated by the short video platform to be analyzed based on user behavior. For example, if the log shows that the user is interested in beauty short videos, then beauty is recorded as a label of the user; then the hash algorithm is used to calculate the video type similarity between the feedback video label and the user video label, and the video duration similarity between the feedback video duration and the user video duration. The hash algorithm compares the similarity between different data by converting the data into hash values. The method of this embodiment combines user feedback portrait data and platform user portrait data, uses a hash algorithm to calculate the similarity, and quantifies the degree of matching between the two; this method can provide data support for marketing optimization and user portrait analysis.

[0041] Specifically, a user portrait questionnaire containing video type labels and video duration is sent to the users to be surveyed. After the users fill out the portrait questionnaire, they submit it to the short video platform to be analyzed. The short video platform to be analyzed then performs statistical analysis to obtain feedback video labels and feedback video duration. At the same time, the short video platform to be analyzed checks the users' viewing logs, counts the types of videos watched by users, labels each type to obtain user video labels, and counts the duration of videos watched by users to obtain feedback video duration.

[0042] Specifically, a hashing algorithm converts input data into a fixed-length output. Here, it converts video tags and duration data into hash values, then compares these hash values ​​to assess similarity. The calculated similarity reflects the degree of match between the user profile data provided in the survey and the platform-generated profile data. For example, consider two video clips: Video A is tagged with ["Music," "Pop," "MTV"] and has a duration of 210 seconds, while Video B is tagged with ["Music," "Rock," "MV"] and has a duration of 205 seconds. The video tags and duration data are converted into a string and used as input to the hashing algorithm. The resulting string for Video A is "Music-Pop-MTV-210," and the string for Video B is "Music-Rock-MV-205." Next, the strings are applied to a hashing algorithm, such as SHA-256, which converts each string into a fixed-length hash value. The resulting hash values ​​for Video A and Video B are then compared. Finally, these hash values ​​are compared to assess the similarity between the two videos. In a hashing algorithm, even small differences in the input data often result in significantly different hash values. Therefore, if two hash values ​​are very similar or even identical, it indicates that the two videos are likely very close in terms of their labels and duration. In this example, the large difference in hash values ​​indicates that Video A and Video B have significant differences in their labels and duration.

[0043] Furthermore, the process of obtaining the portrait similarity coefficient is as follows: obtaining evaluation data from a preset database, the evaluation data including a video type evaluation threshold and a video duration evaluation threshold, the video type evaluation threshold indicating the minimum video type similarity within the evaluation range required for video marketing for video type similarity, and the video duration evaluation threshold indicating the minimum video duration similarity within the evaluation range required for video marketing for video duration similarity; numbering the users to be surveyed, and obtaining individual portrait similarity coefficients based on the video type similarity and the video duration similarity, and obtaining a portrait similarity coefficient based on the individual portrait similarity coefficients, the individual portrait similarity coefficient indicating the portrait similarity coefficient of a single user to be surveyed, and the portrait similarity coefficient is calculated using the following formula:

[0044]

[0045] Where, f represents the number of users to be investigated, f=1,2,,,F, F represents the total number of users to be investigated, VTS f VDS represents the video type similarity of the fth user to be investigated. f represents the video duration similarity of the fth user to be investigated, μ represents the video type evaluation threshold, ρ represents the video duration evaluation threshold, ISC f represents the individual portrait similarity coefficient of the fth user to be investigated, and PSC represents the portrait similarity coefficient of the user to be investigated in the current preset time period in the short video platform to be analyzed.

[0046] In this embodiment, the algorithm combines the evaluation data, video type similarity and video duration similarity for comprehensive analysis to obtain the individual portrait similarity coefficient, and then obtains the portrait similarity coefficient based on the individual portrait similarity coefficient; wherein, each variable is independent but jointly affects the changing trend of the individual portrait similarity coefficient, and due to the division of the evaluation data, the expression of the algorithm is divided into two cases, when the video type similarity is less than the video type evaluation threshold, or the video duration similarity is less than the video duration evaluation threshold, it is the first case, if the video type similarity is not less than the video type evaluation threshold, and the video duration similarity is not less than the video duration evaluation threshold, it is the second case, the two cases obtain the value of the individual portrait similarity coefficient by different methods, the first case obtains the individual portrait similarity coefficient according to the formula, wherein the video type similarity and the video duration similarity are positively correlated with the individual portrait similarity coefficient, as shown in the following example: Figure 2 As shown, Figure 2 This is a schematic diagram of the change in the similarity coefficient of individual user portraits. The figure shows that as the values ​​of video type similarity and video length similarity increase, the image shows a gradually rising trend and approaches the maximum value of 1, indicating that the value of the individual portrait similarity coefficient gradually increases and tends to 1; it can be concluded that the larger the values ​​of video type similarity and video length similarity, the closer the user portrait constructed by the platform is to the user's actual interest in watching videos, then the larger the value of the individual portrait similarity coefficient, the larger the value of the portrait similarity, and the more conducive it is to the video marketing of the short video platform to be analyzed. Therefore, the size of the portrait similarity coefficient is helpful to accurately and efficiently judge whether the current video marketing situation meets the requirements of video marketing, and to take video marketing measures in time to improve the timeliness of user portraits.

[0047] Specifically, assuming that the data of ten users to be investigated are analyzed, and assuming that the video type evaluation threshold is 1.5, the video length evaluation threshold is also 1.5, and the value range of video type similarity and video length similarity is between 0 and 2, then the portrait similarity coefficient data change table obtained according to the formula is shown in Table 1:

[0048] Table 1. Data change table of portrait similarity coefficient

[0049]

[0050] As can be seen from the table, as the video type similarity and video duration similarity increase, the individual portrait similarity coefficient also increases. Correspondingly, the larger the individual portrait similarity coefficient, the larger the portrait similarity coefficient. Among them, when the video type similarity is not less than the video type evaluation threshold of 1.5, and the video duration similarity is not less than the video duration evaluation threshold of 1.5, the individual portrait similarity coefficient reaches the maximum value of 1. However, if the two independent variables are not less than the corresponding thresholds at the same time, the maximum value cannot be reached. For example, the two rows of data for the user to be investigated are numbered 8 and 9. In the 8th row of data, although the video type similarity is not less than 1.5, the individual portrait similarity coefficient reaches the maximum value of 1. The video type similarity is less than the video type evaluation threshold of 1.5, but the video length similarity is less than the video length evaluation threshold of 1.5. Therefore, the individual portrait similarity coefficient cannot reach the maximum value of 1. Similarly, in the 9th row of data, the video type similarity is less than the video type evaluation threshold of 1.5, but the video length similarity is not less than the video length evaluation threshold of 1.5. Finally, the individual portrait similarity coefficient still cannot reach the maximum value of 1. It can be seen that when analyzing the portrait similarity coefficient, the individual portrait similarity coefficient is mainly analyzed. When analyzing the individual portrait similarity coefficient, the consideration of both video type similarity and video length similarity cannot be ignored.

[0051] Specifically, the video type evaluation threshold is obtained from a preset database. In a specific embodiment, the video type evaluation threshold is generally the video type similarity value corresponding to when the video conversion rate meets the standard. For example, if the video conversion rate meets the standard requirement that the video type similarity reaches 0.85, then 0.85 can be used as the video type evaluation threshold.

[0052] Specifically, the video duration evaluation threshold is obtained from a preset database. In a specific embodiment, the video duration evaluation threshold is generally the video duration similarity value corresponding to the video conversion rate meeting the standard. For example, if the video conversion rate meeting the standard requires the video type similarity to reach 0.90, then 0.90 is the video type evaluation threshold.

[0053] Furthermore, the process of obtaining the comparison result is as follows: a similarity threshold is obtained from a preset database, and the obtained similarity threshold is compared with the portrait similarity coefficient: if the portrait similarity coefficient is not less than the similarity threshold, the comparison result is that the portrait meets the standard; if the portrait similarity coefficient is less than the similarity threshold, the comparison result is that the portrait does not meet the standard.

[0054] In this embodiment, the portrait similarity coefficient represents the degree of match between the user portrait and the feedback portrait, and the coefficient is calculated for two dimensions: video type and video length. The system extracts a similarity threshold from a preset database, and then compares this threshold with the portrait similarity coefficient calculated previously: if the portrait similarity coefficient is not less than the threshold, it means that the similarity between the user portrait and the user's self-reported preferences has reached or exceeded the minimum standard, and such a portrait is considered to be "up to standard"; if the portrait similarity coefficient is less than the threshold, it means that the similarity between the user portrait and the user's self-reported preferences is lower than the standard, and such a portrait is considered to be "substandard". By comparing the calculated portrait similarity coefficient with the preset similarity threshold, the short video platform to be analyzed can determine whether the user portrait meets the standard. This judgment process helps to ensure the accuracy of the user portrait, thereby improving the accuracy of content recommendations, user experience, and the overall service quality of the platform.

[0055] Specifically, the similarity threshold is obtained from a preset database. In a specific embodiment, the similarity threshold is generally the minimum user profile similarity coefficient required by marketing based on historical data analysis in past successful marketing campaigns. For example, if a box plot is drawn based on the profile similarity coefficients in successful marketing campaigns in the past year, the similarity threshold is represented by the difference between the first quartile of the box plot and 1.5 times the interquartile range.

[0056] Furthermore, the specific process of adopting the portrait data collection strategy based on the comparison results is as follows: if the comparison result is that the portrait meets the standards, indicating that the similarity between the current user portrait and the actual user portrait meets the video marketing requirements, then the portrait data collection strategy adopted is to continue to use the current user portrait; if the comparison result is that the portrait does not meet the standards, indicating that the similarity between the current user portrait and the actual user portrait does not meet the video marketing requirements, then the portrait data collection strategy adopted is to judge the impact of the current data collection delay on the user portrait construction based on the data collection delay index. The data collection delay represents the time delay in collecting and processing user portrait data when constructing a user portrait based on big data portraits. The data collection delay includes data processing delay and data transmission delay. The data collection delay index is used to quantify the impact of data collection delay on video portrait data collection.

[0057] In this embodiment, the process of adjusting the user portrait data collection strategy according to the comparison results, especially the processing method when judging whether the current portrait meets the video marketing requirements, when the comparison result shows that the user portrait meets the standards, it means that the similarity between the current user portrait and the actual user portrait has met the requirements of video marketing. In this case, the short video platform to be analyzed believes that the current data collection and processing method is effective, so it chooses to continue using the existing user portrait without the need for additional data collection or adjustment; when the comparison result shows that the user portrait does not meet the standards, it means that there is a large inconsistency between the current user portrait and the actual user portrait, which cannot meet the requirements of video marketing. At this time, the system needs to take further measures to improve the accuracy of the portrait; by adjusting the portrait data collection strategy according to the comparison results (meeting the standards or not), the short video platform to be analyzed can ensure the accuracy of the user portrait, and ultimately improve the quality of the user portrait, thereby improving the effect of video marketing.

[0058] It is important to understand that data collection delay refers to the time delay from data collection to processing completion when building a user profile. It consists of two parts: data processing delay is the time required to process data, such as preprocessing of raw data; data transmission delay is the time required to transmit data from one point to another, such as from the user device to the server, and then to the database. The data collection delay index is an indicator used to quantify the impact of data collection delay on user profile construction. It can help evaluate whether the delay has led to inaccurate profiles.

[0059] Furthermore, the process of obtaining the data collection delay index is as follows: obtaining data processing delay and data transmission delay through the log records of the short video platform to be analyzed, the data processing delay represents the time delay generated by the short video platform to be analyzed when processing video portrait data, and the data transmission delay represents the time delay generated by the short video platform to be analyzed in the process of transmitting video portrait data. The log records are used to record the real-time data of data processing and transmission of the short video platform to be analyzed; obtaining reference data from a preset database, the reference data includes average data processing delay, average data transmission delay, processing delay evaluation value and transmission delay evaluation value, the average data processing delay represents the average time taken by the short video platform to be analyzed to process video portrait data, the average data transmission delay represents the average time taken by the short video platform to be analyzed to transmit video portrait data, the processing delay evaluation value represents the maximum value of the data processing delay allowed, and the transmission delay evaluation value represents the maximum value of the data transmission delay allowed; obtaining an individual delay index based on the data collection delay and the reference data, and then obtaining a data collection delay index based on the individual delay index, the individual delay index represents the data collection delay index of a single user to be surveyed, and the data collection delay index is calculated using the following formula:

[0060]

[0061] Where f represents the number of users to be investigated, f=1,2,,,F, F represents the total number of users to be investigated, CD f represents the data processing delay of the fth user to be investigated, represents the average data processing delay, α represents the processing delay evaluation value, TD f represents the data transmission delay of the fth user to be investigated, represents the average data transmission delay, β represents the transmission delay evaluation value, IDI f represents the individual delay index of the fth user to be investigated, and DCI represents the data collection delay index within a preset time period.

[0062] In this embodiment, the algorithm combines data collection delay and reference data for comprehensive analysis to obtain an individual delay index, and then obtains a data collection delay index based on the individual delay index. In the formula, each variable is independent but jointly affects the changing trend of the individual delay index. Due to the division of the reference data, the expression of the algorithm is divided into two cases. The first case occurs when the data processing delay is not less than the processing delay evaluation value, or the data transmission delay is not less than the transmission delay evaluation value. The second case occurs when the data processing delay is less than the processing delay evaluation value and the data transmission delay is less than the transmission delay evaluation value. The values ​​of the individual delay index are obtained by different methods in the two cases. In the first case, the individual delay index is obtained according to the formula. In the formula, the data processing delay and the data transmission delay are positively correlated with the individual delay index. As the absolute value of the difference between the data processing delay and the average data processing delay increases, and as the absolute value of the difference between the data transmission delay and the average data transmission delay increases, the individual delay index increases. In the second case, the value of the individual delay index can be directly obtained as 0. It can be concluded that reducing the data processing delay and the data transmission delay is conducive to building a more accurate user profile and formulating appropriate video marketing measures.

[0063] Specifically, the average data processing delay is represented by the average value of the data processing delay of historical data in the log record.

[0064] Specifically, the average data transmission delay is represented by the average value of the data transmission delay of historical data in the log record.

[0065] Specifically, the processing delay evaluation value is obtained from a preset database. In one specific embodiment, the processing delay evaluation value is generally the maximum limit of the concentrated distribution of data processing delays in successful marketing campaigns. Assuming that statistical analysis finds that in successful marketing campaigns, a corresponding box plot is drawn based on the data processing delays. The processing delay evaluation value is then represented by the sum of the third quartile of the box plot and 1.5 times the interquartile range.

[0066] Specifically, the transmission delay evaluation value is obtained from a preset database. In one specific embodiment, the transmission delay evaluation value is generally the maximum limit of the concentrated distribution of data transmission delays in successful marketing campaigns. Assuming that statistical analysis finds that in successful marketing campaigns, a corresponding box plot is drawn based on the data transmission delays. The transmission delay evaluation value is then represented by the sum of the third quartile of the box plot and 1.5 times the interquartile range.

[0067] Furthermore, a data collection delay index is obtained, and then the data collection delay index is compared with a data collection delay index threshold obtained from a preset database to obtain a judgment result, specifically as follows: the data collection delay index threshold is used to judge the impact of the current data collection delay on the portrait construction, and the judgment results include normal data collection delay and abnormal data collection delay; the specific method of obtaining the judgment result is as follows: if the data collection delay index is not greater than the data collection delay index threshold, the judgment result is that the data collection delay is normal; if the data collection delay index is greater than the data collection delay index threshold, the judgment result is that the data collection delay is abnormal.

[0068] In this embodiment, the impact of data collection delay on user portrait construction is determined by comparing the data collection delay index with the data collection delay index threshold, and by comparing the data collection delay index with the data collection delay index threshold, it is possible to accurately evaluate whether the current delay is within an acceptable range; this evaluation helps to promptly discover potential problems and ensure the timeliness and accuracy of data collection; then, based on clear threshold standards, the system can automatically and accurately determine whether the delay will affect user portrait construction, which reduces the interference of subjective judgment and improves the degree of automation of data processing; the process of this embodiment ensures the accuracy and timeliness of user portraits, thereby supporting more accurate video recommendations and marketing strategies.

[0069] Specifically, the data collection delay index threshold is obtained from a preset database. In one specific embodiment, the data collection delay index threshold is generally the mode of the data collection delay index in successful marketing campaigns. For example, if historical data statistical analysis shows that the mode of the data collection delay index in successful marketing campaigns is 0.95, then the data collection delay index threshold can be set to 0.95.

[0070] Furthermore, a judgment result is obtained, and then feedback on the judgment result is included. The specific process is as follows: if the judgment result is that the data collection delay is normal, indicating that the current data collection delay does not affect the data collection for portrait construction, then a portrait update judgment is performed based on the user portrait update frequency, and the portrait update judgment is used to determine whether the user portrait update frequency has an impact on video marketing; if the judgment result is that the data collection delay is abnormal, indicating that the current data collection delay affects the data collection for portrait construction, then the factor that causes the portrait similarity coefficient to not meet the video marketing requirements is the data collection delay, and feedback is given to the preset analyst, and a command prompt for reducing the data collection delay is sent to the preset analyst.

[0071] In this embodiment, it is described how to provide feedback on the results and take corresponding measures after judging whether the data collection delay is normal. By judging whether the data collection delay is normal and adjusting the user portrait update frequency according to the judgment result, it can be ensured that the portrait data is synchronized with the user behavior, thereby improving the accuracy and effectiveness of video recommendations; through the automated judgment and feedback process, the system can quickly and accurately identify and handle data collection delay problems, reduce human intervention, and improve the efficiency and reliability of data processing; through the dynamic judgment and feedback mechanism, the short video platform to be analyzed can ensure the timely update of user portraits, optimize video marketing strategies, and solve delay problems in a timely manner. This mechanism not only improves the degree of automation of data processing, but also ensures the accuracy of user portraits and the effectiveness of video marketing.

[0072] Furthermore, the specific steps for judging the update of user portraits based on the update frequency of user portraits are as follows: Step 1, obtain the current user portrait update frequency from the log records of the short video platform to be analyzed, and obtain the average user portrait update frequency from the preset database. The user portrait update frequency represents the frequency with which the short video platform to be analyzed updates the user portrait in the current preset time period, and the average user portrait update frequency represents the average value of the frequency with which the short video platform to be analyzed updates the user portrait; Step 2, obtain the update frequency evaluation value from the preset database, and obtain the individual user portrait update frequency index based on the user portrait update frequency, the average user portrait update frequency and the update frequency evaluation value, and calculate the update frequency index based on the individual user portrait. The update frequency index obtains the user portrait update frequency index. The update frequency evaluation value indicates the minimum value of the evaluation range within which the user portrait update frequency can meet the requirements of video marketing. The individual user portrait update frequency index indicates the user portrait update frequency index of a single user to be investigated. The user portrait update frequency index is used to quantify the impact of the user portrait update frequency on video marketing. Step three: compare the user portrait update frequency index with the user portrait update frequency index threshold obtained from the preset database and provide updated feedback. The user portrait update frequency index threshold is used to determine the impact of the current user portrait update frequency on video marketing. The user portrait update frequency index is calculated using the following formula:

[0073]

[0074] Where f represents the number of users to be investigated, f=1,2,,,F, F represents the total number of users to be investigated, UF f represents the update frequency of the user profile of the fth user to be investigated, represents the average user profile update frequency, θ represents the update frequency evaluation value, IUI f It represents the individual user portrait update frequency index of the fth user to be investigated, and UFI represents the user portrait update frequency index of the short video platform to be analyzed within the preset time period.

[0075] In this embodiment, the algorithm combines the user portrait update frequency, the average user portrait update frequency and the update frequency evaluation value for comprehensive analysis to obtain the individual user portrait update frequency index, and then obtains the user portrait update frequency index based on the individual user portrait update frequency index; wherein, the user portrait update frequency independently affects the changing trend of the individual user portrait update frequency index, and due to the division of the update frequency evaluation value, the expression of the algorithm is divided into two cases, when the user portrait update frequency is less than the update frequency evaluation value, it is the first case, and when the user portrait update frequency is not less than the update frequency evaluation value, it is the second case. The two cases derive the individual user portrait update frequency index through different methods. The value of the frequency index. In the first case, the individual user portrait update frequency index is obtained according to the formula, where the user portrait update frequency is negatively correlated with the individual user portrait update frequency index. As the absolute value of the difference between the user portrait update frequency and the average user portrait update frequency increases, the individual user portrait update frequency index decreases. In the second case, the value of the individual user portrait update frequency index can be directly obtained as 0; if the value of the individual user portrait update frequency index is smaller, the value of the user portrait update frequency index is also smaller. Therefore, the analysis of the user portrait update frequency index is beneficial to monitor the user portrait update frequency and help improve the timeliness of user portrait updates.

[0076] Specifically, the average user portrait update frequency is calculated based on the average number of actual updates within a period of time recorded in historical data. For example, assuming that the Douyin short video platform updates a user's portrait 5 times within a week, then the average user portrait update frequency is 7 days / 5 intervals = 1.4 days / time.

[0077] Specifically, the update frequency assessment value is a metric used to evaluate whether the actual update frequency meets the expected frequency. It is typically set based on business needs and user behavior patterns. For example, if the TikTok short video platform sets a target update frequency of 1.5 days based on business needs analysis, the update frequency assessment value would be 1.5 days.

[0078] Furthermore, the specific process of comparing the user portrait update frequency index with the user portrait update frequency index threshold obtained from the preset database and performing update feedback is as follows: if the user portrait update frequency index is not less than the user portrait update frequency index threshold, feedback is given to the preset analyst that there is no problem with data collection delay and user portrait update frequency; if the user portrait update frequency index is less than the user portrait update frequency index threshold, feedback is given to the preset analyst that there is a problem with the user portrait update frequency, and a command prompt to increase the user portrait update frequency is sent to the preset analyst.

[0079] In this embodiment, it is described how to judge whether the user portrait update frequency meets the requirements by comparing the user portrait update frequency index with the user portrait update frequency index threshold, and to feedback and handle problems based on the judgment result; the user portrait update frequency index is an indicator that measures the update speed of the user portrait, that is, the frequency with which the user portrait reflects the user behavior data. The higher this index is, the more timely the user portrait can reflect the user's latest behavior.

[0080] It's important to understand that if the user profile update frequency index is no less than the preset user profile update frequency index threshold, it indicates that the user profile update frequency is high enough to accurately reflect the user's latest behavior. In this case, the system will provide feedback to the preset analyst, indicating that there are no issues with the user profile update frequency, and that there are no data collection delays or user profile update frequency factors that cause the profile similarity coefficient to fail to meet video marketing requirements; this means that if there is a problem with the profile similarity coefficient, the cause may be elsewhere.

[0081] It's important to understand that if the user profile update frequency index falls below the preset threshold, it means the profile isn't updated promptly enough, potentially leading to inaccurate profiles. Feedback: The system will provide feedback to the designated analyst, indicating that the profile update frequency is causing the profile similarity coefficient to fall short of video marketing requirements. The system will also issue a command prompt to increase the profile update frequency, suggesting that the analyst take steps to gradually increase the profile update rate to improve profile accuracy. Providing specific feedback to the designated analyst can help them pinpoint the root cause of the problem and take targeted measures to resolve it. For example, if the profile update frequency is confirmed to be too low, they can focus on improving it instead of wasting time on other tasks. The system's command prompt reminds the analyst of the importance of profile update frequency and directs them to prioritize measures to increase it in subsequent operations. This helps ensure that the user profile reflects users' latest behavior in a timely manner, thereby enhancing the accuracy of video marketing.

[0082] Specifically, the user profile update frequency index threshold is a preset standard value used to determine whether the user profile update frequency is within an acceptable range. This threshold is set based on historical data analysis or business needs. If the update frequency index falls below this threshold, it means that the profile update may not be timely enough, thus affecting the accuracy of the profile. For example, suppose Company B is a short video e-commerce platform that needs to capture user shopping behavior in real time to make accurate recommendations. After analyzing business needs, the company decided that the user profile should be updated every 6 hours. Therefore, 6 hours is set as the user profile update frequency index threshold.

[0083] To sum up, the embodiment of the present application collects video portrait data within a preset time period, obtains a portrait similarity coefficient based on the collected video portrait data, and then compares the portrait similarity coefficient with the similarity threshold obtained from a preset database to obtain a comparison result. Finally, a portrait data collection strategy is adopted based on the comparison result, thereby realizing dynamic updating of user portraits, and further improving the timeliness of updating user portrait data in marketing data, effectively solving the problem of untimely updating of user portrait data in marketing data in the prior art.

[0084] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0085] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0086] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0087] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0088] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0089] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A marketing data analysis method based on big data portrait, characterized in that: The following steps are involved: Collect video portrait data within a preset time period, and obtain a portrait similarity coefficient based on the collected video portrait data, wherein the video portrait data includes feedback portrait data and user portrait data, and the portrait similarity coefficient is used to measure the similarity between the current user portrait data and the feedback portrait data; Comparing the portrait similarity coefficient with a similarity threshold obtained from a preset database to obtain a comparison result, wherein the comparison result includes whether the portrait meets the standard or not; Adopting a portrait data collection strategy based on the comparison results, wherein the portrait data collection strategy is used to improve the timeliness of the user portrait; The process of obtaining the video image data is as follows: Collect feedback portrait data by sending a portrait questionnaire to the users to be surveyed on the short video platform to be analyzed within a preset time period, and transmit the obtained feedback portrait data to a preset database for storage. The feedback portrait data includes feedback video tags and feedback video duration; Obtain user portrait data of the user to be investigated from the short video platform to be analyzed. The user portrait data includes user video tags and user video duration. The user portrait data is consistent with the content category of the feedback portrait data. The video type similarity between the feedback video tag and the user video tag is obtained through a hash algorithm. At the same time, the video duration similarity between the feedback video duration and the user video duration is obtained through a hash algorithm. The video type similarity indicates the similarity between the user video tag and the feedback video tag, and the video duration similarity indicates the similarity between the user video duration and the feedback video duration. The process of obtaining the portrait similarity coefficient is as follows: Acquire evaluation data from a preset database, wherein the evaluation data includes a video type evaluation threshold and a video duration evaluation threshold; The users to be investigated are numbered, and the individual portrait similarity coefficient is obtained based on the video type similarity and video duration similarity. The portrait similarity coefficient is obtained based on the individual portrait similarity coefficient. The individual portrait similarity coefficient represents the portrait similarity coefficient of a single user to be investigated. The portrait similarity coefficient is calculated using the following formula: ； ； In the formula, f represents the number of the user to be investigated, , F represents the total number of users to be investigated, represents the video type similarity of the fth user to be investigated, represents the video length similarity of the fth user to be investigated, Indicates the video type evaluation threshold, Indicates the video duration evaluation threshold. represents the similarity coefficient of the individual portrait of the fth user to be investigated, Indicates the similarity coefficient of the user portraits to be investigated in the current preset time period on the short video platform to be analyzed.

2. The marketing data analysis method based on big data portraits according to claim 1, characterized in that: The process of obtaining the comparison result is as follows: Get the similarity threshold from the preset database and compare it with the portrait similarity coefficient: If the portrait similarity coefficient is not less than the similarity threshold, the comparison result is that the portrait meets the standard; If the portrait similarity coefficient is less than the similarity threshold, the comparison result is that the portrait does not meet the standards.

3. The marketing data analysis method based on big data portraits as claimed in claim 2, characterized in that: The specific process of adopting the portrait data collection strategy based on the comparison results is as follows: If the comparison result shows that the profile meets the requirements, the current user profile will continue to be used; If the comparison result is that the portrait does not meet the standards, the impact of the current data collection delay on the construction of the user portrait is judged based on the data collection delay index. The data collection delay includes data processing delay and data transmission delay. The data collection delay index is used to quantify the impact of data collection delay on video portrait data collection.

4. The marketing data analysis method based on big data portraits as claimed in claim 3, characterized in that: The process of obtaining the data collection delay index is as follows: Obtain data processing delay and data transmission delay through the log records of the short video platform to be analyzed. The data processing delay represents the time delay generated by the short video platform to be analyzed when processing video portrait data, and the data transmission delay represents the time delay generated by the short video platform to be analyzed when transmitting video portrait data; Acquire reference data from a preset database, the reference data including average data processing delay, average data transmission delay, processing delay evaluation value, and transmission delay evaluation value; An individual delay index is obtained based on the data collection delay and the reference data, and then a data collection delay index is obtained based on the individual delay index. The individual delay index represents the data collection delay index of a single user to be investigated. The data collection delay index is calculated using the following formula: ； ； In the formula, f represents the number of the user to be investigated, , F represents the total number of users to be investigated, represents the data processing delay of the fth user to be investigated, represents the average data processing delay, Indicates the processing delay evaluation value, represents the data transmission delay of the fth user to be investigated, represents the average data transmission delay, represents the estimated transmission delay value, represents the individual delay index of the fth user to be investigated, Indicates the data collection delay index within the preset time period.

5. The marketing data analysis method based on big data portraits according to claim 4, characterized in that: The data collection delay index is obtained, and then the data collection delay index is compared with a data collection delay index threshold obtained from a preset database to obtain a judgment result, which is specifically as follows: The judgment result includes whether the data collection delay is normal or abnormal; The specific method for obtaining the judgment result is as follows: If the data collection delay index is not less than the data collection delay index threshold, the result is that the data collection delay is normal; If the data collection delay index is less than the data collection delay index threshold, the result is determined to be data collection delay abnormality.

6. The marketing data analysis method based on big data portraits according to claim 5, characterized in that: The determination result is obtained, and then feedback on the determination result is provided. The specific process is as follows: If the result of the judgment is that the data collection delay is normal, a profile update judgment is performed based on the user profile update frequency. The profile update judgment is used to determine whether the user profile update frequency has an impact on video marketing; If the judgment result is that the data collection delay is abnormal, the factor that causes the portrait similarity coefficient to fail to meet the video marketing requirements is the data collection delay, and the preset analyst is fed back that the factor is the data collection delay. At the same time, a command prompt for reducing the data collection delay is sent to the preset analyst.

7. The marketing data analysis method based on big data portraits according to claim 6, characterized in that: The specific steps of determining the user portrait update frequency are as follows: Step 1: Obtain the current user portrait update frequency from the log records of the short video platform to be analyzed, and obtain the average user portrait update frequency from a preset database. The user portrait update frequency represents the frequency with which the short video platform to be analyzed updates its user portrait in the current preset time period. Step 2: Obtain an update frequency evaluation value from a preset database, and simultaneously obtain an individual user portrait update frequency index based on the user portrait update frequency, the average user portrait update frequency, and the update frequency evaluation value. Furthermore, obtain a user portrait update frequency index based on the individual user portrait update frequency index. The individual user portrait update frequency index represents the user portrait update frequency index of a single user to be surveyed, and the user portrait update frequency index is used to quantify the degree of influence of the user portrait update frequency on video marketing. Step 3: Compare the user portrait update frequency index with the user portrait update frequency index threshold obtained from the preset database and provide update feedback; The user portrait update frequency index is calculated using the following formula: ； ； In the formula, f represents the number of the user to be investigated, , F represents the total number of users to be investigated, represents the update frequency of the user profile of the fth user to be investigated, Indicates the average user portrait update frequency, represents the update frequency evaluation value, represents the update frequency index of the individual user profile of the fth user to be investigated, Indicates the user profile update frequency index of the short video platform to be analyzed within the preset time period.

8. The marketing data analysis method based on big data portraits according to claim 7, characterized in that: The specific process of comparing the user portrait update frequency index with the user portrait update frequency index threshold obtained from the preset database and performing update feedback is as follows: If the user profile update frequency index is not less than the user profile update frequency index threshold, feedback is given to the preset analyst indicating that there are no issues with data collection delay and user profile update frequency; If the user portrait update frequency index is less than the user portrait update frequency index threshold, a command prompt to increase the user portrait update frequency is sent to the preset analyst.

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