An all-media operation intelligent analysis platform based on big data

Abstract

The application discloses a kind of full media operation intelligent analysis platform based on big data, it is related to computer technology field, the platform includes: data acquisition module, data acquisition module: using distributed crawler technology, write customization script, in combination with social media official API call and the data interface of cooperation platform are connected or push, real-time from webpage, social media, news client collection including text, image, audio, video in full media data;The application is connected by distributed crawler technology, social media official API call and the data interface of cooperation platform, real-time collection including text, image, audio, video in full media data, and using dynamic weight fusion algorithm will these cross-modal data be converted into uniform format feature vector and be fused, the problem of limited single modal data information in traditional analysis method is solved, also by cross-modal dynamic fusion formula.

Description

Technical Field

[0001] This invention relates to the field of computer technology, specifically to a big data-based intelligent analysis platform for multimedia operations. Background Technology

[0002] With the rapid development of internet technology, the era of multimedia has arrived, and the way information is disseminated has undergone tremendous changes. Diverse channels such as web pages, social media, and news apps are constantly emerging, generating massive amounts of text, images, audio, and video multimedia data every day. This data contains rich market information, user preferences, and valuable content related to social hotspots. For enterprises, governments, and research institutions, how to efficiently collect, process, and analyze this multimedia data to uncover its potential value has become an urgent problem to be solved. The intelligent analysis platform for multimedia operations has emerged to address this need, aiming to use advanced technologies to deeply mine and analyze multimedia data, providing strong support for decision-making.

[0003] However, traditional multimedia data analysis techniques have many shortcomings. First, in terms of data collection, traditional methods often rely on a single data source or simple web crawling technology, making it difficult to cover the wide range of data in the multimedia field, and the real-time performance and accuracy of data collection are difficult to guarantee. Second, in terms of data processing, traditional technologies often lack the ability to deeply integrate multimodal data, making it difficult to effectively integrate different types of data such as text, images, audio, and video, resulting in one-sided analysis results that cannot fully reflect the true situation of media content. In addition, in terms of sentiment and semantic analysis and trend prediction, traditional technologies often rely on manual annotation or simple keyword matching, making it difficult to accurately capture the sentiment tendencies and semantic relationships in the text, and also unable to effectively predict the development trends and hot topics of media content. Finally, in terms of decision support, traditional technologies often lack scientific and reasonable decision-making basis and visualization methods, resulting in a highly subjective and inefficient decision-making process.

[0004] Therefore, developing a big data-based intelligent analysis platform for all-media operations will greatly improve the efficiency and accuracy of all-media data analysis, provide more scientific and reasonable decision support for enterprises, governments and research institutions, and promote the intelligent development of the all-media operation field. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a big data-based intelligent analysis platform for multimedia operations. Through distributed crawling, API calls, and data interface integration, the platform achieves real-time collection of multimedia data. The platform uses a dynamic weight fusion algorithm to process multimodal data, and combines sentiment judgment models and semantic role labeling technology to deeply analyze the relationship between data sentiment and semantics. At the same time, through hot topic screening and trend prediction models, it accurately grasps the development trend of media content. Finally, the platform integrates the analysis results to provide visualized decision-making suggestions, effectively improving the efficiency and scientific nature of multimedia operations.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a big data-based intelligent analysis platform for all-media operations, the platform comprising:

[0007] Data Acquisition Module: Utilizing distributed crawler technology, custom scripts are written and combined with official API calls from social media platforms and data interface connections or pushes from partner platforms to collect multimedia data, including text, images, audio, and video, from web pages, social media, and news clients in real time.

[0008] Data preprocessing module: Cleans the collected raw data, converts text, image, audio and video data into feature vectors in a unified format, and fuses them using a dynamic weight fusion algorithm;

[0009] Sentiment and Semantic Analysis Module: Creates a sentiment tendency judgment model to judge the sentiment tendency of the fused data, understands the semantic relationships and implicit information in the text through semantic role labeling technology, and associates entities and relationships in the text with the help of knowledge graphs;

[0010] Trend and Hotspot Analysis Module: Organizes media data into time series and captures its temporal characteristics; uses a hotspot cluster dynamic filtering algorithm to cluster texts, filters potential hotspot clusters, and combines a hotspot trend dynamic prediction model to analyze and predict the development trend of media content and hot topics;

[0011] Decision Support Module: Integrates the results of the sentiment semantic analysis module and the trend hotspot analysis module, generates decision suggestions based on the decision suggestion knowledge base, and displays the analysis results and suggestions in the form of charts and text through visualization tools;

[0012] Interactive Feedback Module: Develop a responsive interface compatible with multiple terminals, allowing users to customize data query and analysis tasks through natural language input or operation commands. Collect user feedback on analysis results and suggestions through the feedback entry point, and use the results to optimize platform models and strategies.

[0013] Furthermore, the multimedia data collected by the data acquisition module includes:

[0014] Text data includes: web page content, social media text, news client content, and user interaction data;

[0015] Image data includes: web page images, social media images, and images from news apps;

[0016] Audio data includes: social media audio, news client audio, and webpage audio;

[0017] Video data includes: web videos, social media videos, and news app videos.

[0018] Furthermore, the data preprocessing module fuses text, image, and audio data of the same standard format using a dynamic weighted fusion algorithm. The calculation formula for the dynamic weighted fusion algorithm is as follows:

[0019] ,in, It is the fused multimodal feature vector. It is a modal index. These represent text, image, audio, and video modalities, respectively. It is the first The eigenvectors of a mode, It is a text modal feature vector. It is an image modal feature vector. It is an audio modal feature vector. It is a video modal feature vector. It is the first Time of the first Modal weights, It is the first Attention weights for modalities.

[0020] Furthermore, the calculation of parameters in the dynamic weight fusion algorithm, the first... Time of the first Modal weights The calculation formula is: ,in This represents the natural exponential function, used to assign importance scores to modes. Converted into non-linear weight values, For the first Time-mode Importance score, calculation formula: ,in It is modal The time variance of the feature It is modal Correlation with features of other modalities It is modal The variance-correlation balance coefficient was obtained through statistical analysis of historical data; Modal attention weights The calculation formula is: ,in It is modal The weight matrix, It is modal Key feature vectors, This is the attention dimension, used to scale the dot product calculation; its value is determined based on the model design and task requirements. It is the first The eigenvectors of a mode, It is a sign function used to convert multiple real numbers into a probability distribution.

[0021] Furthermore, the construction formula for the sentiment tendency judgment model in the sentiment semantic analysis module is as follows: ,in It is the fused multimodal feature vector. It is the first A projection vector of emotional polarity. These are the projection vector weight coefficients, obtained by optimizing the sentiment classification task using training data. It is an emotion intensity modulator that dynamically adjusts based on modal credibility. It is a symbolic function, outputting... They represent negative, neutral, and positive emotions, respectively. This represents the number of emotional polarity projection vectors.

[0022] Furthermore, the specific steps in the sentiment semantic analysis module to understand the semantic relationships and implicit information of the text through semantic role labeling technology are as follows:

[0023] (1) Extract verbs / adjectives from the sentence as predicates and distinguish semantics through context disambiguation;

[0024] (2) Label the core arguments and peripheral arguments of the predicate, and construct the predicate argument relation matrix;

[0025] (3) Identify the modification relationship between arguments through dependency parsing and extract the emotional polarity of modifiers;

[0026] (4) Complete the omitted components, identify metaphorical expressions, and deduce the implicit logical relationships;

[0027] (5) Align entities in images / audio with text arguments to verify cross-modal semantic consistency;

[0028] (6) Map semantic roles to knowledge graph nodes and complete implicit semantics through graph relationships;

[0029] (7) Generate results containing predicate argument structure, implicit information, and multimodal associations.

[0030] Furthermore, the trend hotspot analysis module uses a dynamic hotspot cluster filtering algorithm to cluster text and filter potential hotspot clusters. The formula is as follows: ,in It is a cluster Text density, Clusters formed after text clustering It is a cluster Mean semantic similarity of the inner text It is a cluster Normalized value of semantic distance to the nearest high-density cluster, It is a dynamic threshold that is dynamically adjusted based on time and data volume.

[0031] Furthermore, the trend and hot topic analysis module uses a dynamic prediction model to predict media content development trends and hot topics. The calculation formula is as follows:

[0032] ,in, Clusters formed after text clustering Hotspot cluster Importance weights, the formula is: , hotspot clusters The popularity growth rate reflects how the popularity of this hot topic cluster increases over time. represent Hotspot clusters selected in real time Except Other hotspots besides yes The set of hotspot clusters selected at any given time contains all hotspot clusters that meet the hotspot criteria at that time. Indicates in Hotspot clusters selected in real time In addition to specific hotspot clusters Another hotspot cluster Text density, It is a prediction The hot topic trend value at any given moment. yes The set of hotspot clusters selected in real time, Hotspot cluster The predicted trend of heat change, Hotspot cluster The semantic similarity between the topic and the historical outbreak. It is the trend factor weight, determined through training and validation on historical hot data, with a value range of [value range missing]. , It is a moment.

[0033] Furthermore, the decision suggestion knowledge base in the decision support module contains the following content:

[0034] Industry case study library: success stories and failure stories;

[0035] Strategy Template Library: General strategy templates and industry-customized strategy templates;

[0036] Market Environment Information Database: Macroeconomic data, policy and regulatory information, and industry trend analysis reports;

[0037] Expert experience knowledge base: expert opinions and suggestions, and expert case interpretations;

[0038] Internal enterprise data and experience base: historical decision-making data and internal business processes and standards.

[0039] Furthermore, the decision support module generates decision suggestions based on a decision suggestion knowledge base using a dual-source fusion decision algorithm, with the following formula: ,in This is the optimal decision-making recommendation. These are candidate suggestions from the decision-making advice knowledge base. It is the feature vector of the current analysis result. Candidate Recommendation The corresponding result feature vector, It is a characteristic vector of the current market environment. Candidate Recommendation The corresponding environmental feature vector, This refers to the similarity weight of the analysis results, which is set according to the specific application scenario and decision-making objectives, and its value range is as follows: , It is the cosine similarity function.

[0040] Compared with existing technologies, this big data-based intelligent analysis platform for multimedia operations has the following advantages:

[0041] I. This invention utilizes distributed web crawling technology, official social media API calls, and data interface integration with partner platforms to collect multimedia data in real time, including text, images, audio, and video. A dynamic weighted fusion algorithm is then used to convert this cross-modal data into feature vectors in a unified format for fusion. This not only solves the problem of limited information from single-modal data in traditional analysis methods but also ensures that the importance and relevance of different modalities are fully considered during the fusion process through a cross-modal dynamic fusion formula. Furthermore, the platform creates a sentiment judgment model, combining semantic role labeling technology and knowledge graphs to judge sentiment and understand semantic relationships in the fused data, generating structured data. This allows the platform to more comprehensively and deeply understand the sentiment and semantic relationships within media content, providing a richer and more accurate data foundation for subsequent trend analysis and decision support.

[0042] Second, this invention organizes media data into a time series and uses a dynamic hotspot cluster filtering algorithm and a dynamic hotspot trend prediction model to accurately predict the development trend of media content and hot topics. The dynamic hotspot cluster filtering algorithm effectively filters out potential hotspot clusters by calculating text density, mean semantic similarity, and normalized semantic distance. The dynamic hotspot trend prediction model further combines the importance weight of hotspot clusters, popularity growth rate, and semantic similarity factors to accurately predict future hotspot trends. In addition, the platform integrates the results of the sentiment semantic analysis module and the trend hotspot analysis module, generates decision suggestions based on the decision suggestion knowledge base, and displays the analysis results and suggestions in the form of charts and text through visualization tools. This intelligent trend hotspot prediction and accurate decision support function can not only help users grasp the dynamic changes of media content in a timely manner, but also provide users with scientific and reasonable decision-making basis, improving users' operational efficiency and decision-making quality.

[0043] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0045] Figure 1 A flowchart illustrating the workflow of a big data-based intelligent analysis platform for multimedia operations.

[0046] Figure 2 This is a modular framework diagram of a big data-based intelligent analysis platform for multimedia operations. Detailed Implementation

[0047] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0048] Example 1: An in-depth implementation of a big data-based intelligent analysis platform for multimedia operations in the K-12 online education field.

[0049] To improve course conversion rates and user retention rates, a leading K-12 online education platform leveraged multimedia data analysis to optimize its operational strategies and achieve precise user growth. The platform integrated dynamic weight fusion algorithms, sentiment judgment models, and hot topic cluster dynamic filtering algorithms to build a full-link intelligent analysis system from data collection to decision support.

[0050] Data acquisition module: Real-time capture of user behavior and public opinion across all channels.

[0051] We employ distributed crawler technology and interface with the official API to build a multi-dimensional data collection network.

[0052] Internal behavioral data: Collect user click patterns, course playback duration, conversion path from trial class to full-price class, and homework submission rate within the APP through SDK tracking, covering text (user comments), video (lecture clips), and audio (live class recordings) modalities;

[0053] External public opinion data: Utilizing customized scripts to capture multimedia content from the WeChat ecosystem (comments on WeChat Moments ads, discussions in parent communities), short video platforms (Douyin videos and comments on "XX course reviews," interactive teaching clips on Kuaishou), and vertical communities (posts on Mama.cn on "recommended courses for early childhood education," and Q&A on Zhihu on "how to choose online math courses"), simultaneously acquiring image (course promotional posters, screenshots of user assignments) and video data, such as... Figure 1 As shown.

[0054] Data collection follows the principle of dynamic adaptation. For example, based on the algorithm recommendation characteristics of the Douyin platform, user interaction data under popular education topics is obtained in real time through the official social media API to ensure the data integrity of high-conversion channels.

[0055] Data preprocessing module: standardization and dynamic fusion of multimodal features.

[0056] Perform a cleaning process on the raw data: improve data quality through text deduplication, outlier filtering, and format standardization.

[0057] The dynamic weight fusion algorithm is used to convert data from different modalities into a unified feature vector. The calculation formula for the dynamic weight fusion algorithm is as follows: ,in, It is the fused multimodal feature vector. It is a modal index. These represent text, image, audio, and video modalities, respectively. It is the first The eigenvectors of a mode, It is a text modal feature vector. It is an image modal feature vector. It is an audio modal feature vector. It is a video modal feature vector. It is the first Time of the first Modal weights, It is the first The attention weight of a modality, for example, when processing data related to "winter break intensive training courses," the algorithm automatically calculates the weight of each modality in the current scenario based on dimensions such as Douyin ad clicks (image / video modality), WeChat group inquiries (text modality), and trial class completion rate (video modality). If the conversion rate of the Douyin channel is significantly higher than that of other channels, the algorithm will increase the attention weight of that modality to highlight the dissemination effect characteristics of the advertising material, such as... Figure 2 As shown.

[0058] Sentiment semantic analysis module: Deep understanding of user needs and emotional insights.

[0059] The formula for constructing a sentiment tendency judgment model is as follows: ,in It is the fused multimodal feature vector. It is the first A projection vector of emotional polarity. These are the projection vector weight coefficients. It is an emotion intensity modulator that dynamically adjusts based on modal credibility. It is a symbolic function, outputting... They represent negative, neutral, and positive emotions, respectively. The number of sentiment polarity projection vectors is used for triadic classification (positive / neutral / negative) based on the core dimensions of the educational scenario:

[0060] Course Experience: The course analyzes the predicate argument structure in comments using semantic role annotation technology. For example, it extracts the semantic relationship of "the teacher explained the techniques" from "the geometric auxiliary line techniques explained by the teacher are very practical" and combines it with the sentiment polarity projection vector to judge positive sentiment.

[0061] Service quality: Identify negative emotions in "the homeroom teacher did not provide timely feedback on homework problems", and use a knowledge graph to link the "after-sales service" entity to pinpoint specific service loopholes.

[0062] Semantic role labeling technology can be used to uncover implicit information: for example, if a parent comments, "My child likes the math teacher, but always gets distracted in English class," the model can identify potential needs such as "positive interest in math class" and "negative attention in English class," and confirm the authenticity of the problem through cross-modal semantic verification (such as linking the playback progress data of English class videos).

[0063] Knowledge graph technology integrates the entity relationships of "grade, subject, knowledge points, and difficulties". For example, it associates the high-frequency inquiries about "buoyancy calculation in eighth grade physics" with the corresponding course chapters to form a mapping network of "user questions and course content".

[0064] Trend and Hotspot Analysis Module: Dynamic capture and prediction of educational demand.

[0065] The hotspot cluster dynamic filtering algorithm is used to identify cyclical hotspots in the industry. The formula is as follows: ,in It is a cluster Text density, Clusters formed after text clustering It is a cluster Mean semantic similarity of the inner text It is a cluster Normalized value of semantic distance to the nearest high-density cluster, It is a dynamic threshold that is dynamically adjusted based on time and data volume. Before the midterm exam, the platform clusters texts related to "pre-exam sprint" and selects potential hot topics clusters for "breaking through the final math problem in the third year of junior high school" by calculating the text density, semantic similarity and distance from historical hot topics within the cluster.

[0066] In response to policy trends (such as the release of the "new curriculum standards"), we identify emerging topics such as "core competency development" and "project-based learning," assess their dissemination potential through the semantic features of hot topic clusters, and analyze their development trajectory using a dynamic prediction model for hot topic trends. The calculation formula is as follows: ,in, Clusters formed after text clustering Hotspot cluster Importance weights, the formula is: , hotspot clusters The popularity growth rate reflects how the popularity of this hot topic cluster increases over time. represent Hotspot clusters selected in real time Except Other hotspots besides yes The set of hotspot clusters selected at any given time contains all hotspot clusters that meet the hotspot criteria at that time. Indicates in Hotspot clusters selected in real time In addition to specific hotspot clusters Another hotspot cluster Text density, It is a prediction The hot topic trend value at any given moment. yes The set of hotspot clusters selected in real time, Hotspot cluster The predicted trend of heat change, Hotspot cluster The semantic similarity between the topic and the historical outbreak. It is the trend factor weight, and its value range is... , At that moment, when it was discovered that the search volume for "primary school English grammar special course" increased by 150% within a week, the model calculated the popularity growth rate of this hot topic cluster and matched it with the historical explosion pattern of the "grammar learning" topic in the same period, predicting that it would reach the peak demand within 3 weeks, and suggested preparing the course launch and promotion materials in advance.

[0067] Decision Support Module: Intelligent Strategy Generation and Visualized Decision Assistance.

[0068] Based on a dual-source fusion decision-making algorithm, decision suggestions are generated according to a decision suggestion knowledge base. The formula is as follows: ,in This is the optimal decision-making recommendation. These are candidate suggestions from the decision-making advice knowledge base. It is the feature vector of the current analysis result. Candidate Recommendation The corresponding result feature vector, It is a characteristic vector of the current market environment. Candidate Recommendation The corresponding environmental feature vector, This refers to the similarity weight of the analysis results, which is set according to the specific application scenario and decision-making objectives, and its value range is as follows: , It uses a cosine similarity function to integrate sentiment analysis results (e.g., 30% of fifth-grade parents reported negative feedback about "the course being too difficult") with trend predictions (increased search volume for the keyword "differentiated instruction"), and then calls upon a decision-making advice knowledge base:

[0069] Industry Case Studies: Referencing competitors' promotional strategies for "adaptive difficulty courses," we retrieved implementation cases demonstrating a 12% increase in conversion rates.

[0070] Strategy Template Library: Enables operational templates that combine "tiered trial lessons + personalized recommendations" with dynamic weight fusion algorithms to generate course recommendation schemes at different difficulty levels;

[0071] Expert Experience Base: Integrating suggestions from educational psychology experts on "age-appropriate content design" to optimize the curriculum syllabus.

[0072] Generate visual decision-making suggestions: Display negative keyword clouds for courses in various subjects through dynamic charts (such as "fast explanation of math word problems") and hot topic trend prediction curves (such as the conversion probability of "winter break preview class" in the next two weeks), and output strategy solutions in natural language, such as: "Place short videos of 'fifth grade math tiered trial classes' on Douyin, paired with a 'difficulty test + intelligent recommendation' landing page, and optimize ad creatives by referring to the A / B testing schemes in competitor cases."

[0073] Interactive feedback module: Human-machine collaborative optimization and model iteration.

[0074] It provides a multi-terminal responsive interface, and operators can customize analysis tasks through natural language commands, such as "analyze the reasons for user churn in junior high school Chinese courses in Shanghai". The platform automatically calls the sentiment semantic analysis module to parse the comment data and locate the specific problem of "insufficient depth of classical Chinese explanation" through semantic role labeling.

[0075] Collect user feedback on recommended courses (such as "the recommended math olympiad course is beyond the child's ability"), input the feedback data into the hot topic trend prediction model and dynamic weight fusion algorithm, adjust the "difficulty matching degree" parameter and modal weight, for example, increase the weight of user behavior data (such as trial class completion rate) in the recommendation model, optimize the accuracy of subsequent course recommendations, and form a closed-loop mechanism of "data collection, analysis, decision-making, feedback and optimization".

[0076] In summary, when applied in the K-12 online education field, this platform collects user behavior and public opinion data from all channels, utilizes a dynamic weighted fusion algorithm to standardize multimodal data processing, leverages a sentiment and semantic analysis module to uncover user needs and emotional tendencies, employs a dynamic hotspot cluster filtering algorithm and a trend prediction model to grasp the dynamics of educational demand, generates operational strategies based on a dual-source fusion decision algorithm combined with a knowledge base, and achieves model iteration through interactive feedback, thus forming a precise user growth solution. This effectively improves course conversion rates and user retention rates, providing strong intelligent analysis support for the operation of online education platforms.

[0077] Example 2: An in-depth example of a big data-based intelligent analysis platform for multimedia operations in the education field.

[0078] A holographic monitoring platform for university brand image and intelligent management of public opinion.

[0079] To enhance brand communication effectiveness, universities are building a public opinion monitoring and response system covering all media touchpoints to achieve dynamic maintenance and strategic optimization of brand reputation.

[0080] Data Acquisition Module: Capture dynamic data from all channels.

[0081] We use distributed crawler technology to connect with the official API and collect three main types of data in real time:

[0082] Our own media matrix includes: official website news, WeChat official account articles, Weibo topics (such as campus open days), Douyin campus account videos and user comments, and simultaneously captures user interaction data (likes, reposts, comments).

[0083] Social media discourse: In-depth discussions on Zhihu under the topic "How to evaluate XX University", text and images in the Douban group "Experience of studying at XX University", and campus Vlogs and bullet comments posted by Bilibili UP masters;

[0084] Third-party media: Education-related vertical platforms (such as "Jiemodui") report on school research achievements, local news clients provide information on school-enterprise cooperation, and unofficial accounts on short video platforms release campus-related content (such as classroom clips secretly filmed by students).

[0085] The data types cover text (press releases, comments), images (event posters, campus scenery photos), audio (recordings of admissions presentations), and video (live replays of graduation ceremonies), ensuring comprehensive capture of all media data.

[0086] Data preprocessing module: multimodal feature fusion and standardization.

[0087] Perform a cleaning process on the raw data: remove duplicate information, filter spam comments, and correct formatting errors to ensure data quality;

[0088] A dynamic weight fusion algorithm is used to convert text, images, audio, and video into a unified feature vector. The formula is as follows: For example, when processing data related to the "anniversary celebration live stream", the algorithm automatically assigns dynamic weights to different modalities based on dimensions such as live stream viewership, bullet screen word frequency (text), on-site photo dissemination (images), and theme song playback count (audio), highlighting data with high dissemination value (such as anniversary celebration video clips that have gone viral).

[0089] Sentiment and semantic analysis module: deep semantic understanding and sentiment insight.

[0090] The formula for constructing a sentiment tendency judgment model is as follows: The data is classified into three categories (positive / neutral / negative). For example, when analyzing comments related to "dormitory conditions", the model extracts the predicate argument structure of "distant dormitory facilities" through semantic role labeling technology, and combines it with the knowledge graph to associate the entity of "logistics management" to determine the specific direction of negative sentiment.

[0091] Semantic role labeling is used to parse implicit information: For example, if a Weibo post mentions "the library reservation platform has crashed again", the model can identify "platform crash" as the core event and "affecting learning" as the implicit consequence, and link it to the "information construction" node through the knowledge graph to locate management loopholes;

[0092] Cross-modal semantic verification: Align scenes from campus activity photos (such as library study rooms) with descriptions in text comments to verify the consistency of positive evaluations of "strong learning atmosphere".

[0093] Trend and Hotspot Analysis Module: Dynamic hotspot capture and trend prediction.

[0094] A dynamic hotspot cluster selection algorithm is used to perform cluster analysis on time series data to select potential hotspot clusters. The formula is as follows: For example, during the admissions season, the platform automatically identifies topic clusters such as "admission scores", "top majors", and "employment quality", and filters potential hot topics based on text density and semantic similarity;

[0095] Combining a dynamic trend prediction model for hot topics, the development trajectory of hot topics is analyzed, and the calculation formula is as follows: If the discussion volume of the topic "admission score line" increases by 300% within 24 hours, the model can predict that the topic may evolve into a regional hotspot by using the growth rate of popularity and semantic similarity with historical public opinion events, thus triggering an early warning mechanism in advance.

[0096] Decision Support Module: Intelligent Strategy Generation and Visualization

[0097] Integrating sentiment analysis (e.g., negative sentiment accounting for 45% of public opinion about a certain college) and trend prediction (the sentiment is likely to spread within 48 hours), and drawing on resources from the decision-making suggestion knowledge base, a decision-making suggestion is generated through a dual-source fusion decision-making algorithm. The formula is as follows: The resources in the knowledge base are:

[0098] Industry Case Studies: Referencing the public opinion handling procedures for "laboratory safety accidents" in other universities;

[0099] Strategy Template Library: Enables crisis response templates that combine "official statement + live on-site rectification";

[0100] Expert Experience Base: Retrieve advice from public relations experts on "how to rebuild trust through data visualization".

[0101] Generate specific decision-making suggestions, such as "posting a timeline of laboratory rectification on Weibo and conducting on-site interviews with local media," and displaying public opinion heat curves and sentiment distribution cloud maps through dynamic charts to assist the school's publicity department in formulating the dissemination rhythm.

[0102] Interactive feedback module: Human-machine collaborative optimization mechanism.

[0103] It provides a multi-terminal responsive interface, allowing publicity personnel to query "public opinion related to international cooperation in the past week" through natural language. The platform automatically retrieves the corresponding data and generates analysis reports.

[0104] Collect staff feedback on the suggestions (such as "the number of viewers for the live stream exceeded expectations"), input the feedback data into the model, optimize the modal weights of sentiment analysis and the trend factors of hotspot prediction, and form a closed-loop optimization.

[0105] In summary, the big data-based intelligent analysis platform for all-media operations, in the monitoring of university brand image and public opinion management, achieves full-channel data collection through distributed crawling and API integration, integrates multimodal information using dynamic weight fusion algorithms, analyzes public opinion sentiment and implicit information using sentiment judgment models and semantic role labeling technology, captures public opinion hotspots and development trends using dynamic hotspot cluster filtering algorithms and trend prediction models, and finally generates strategies based on a decision suggestion knowledge base. Through interactive feedback, it optimizes the model, constructing a closed-loop system from data collection to decision support, providing an intelligent solution for university brand maintenance and public opinion response.

[0106] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A big data-based intelligent analysis platform for multimedia operations, characterized in that: The platform includes: Data Acquisition Module: Utilizing distributed crawler technology, custom scripts are written and combined with official API calls from social media platforms and data interface connections or pushes from partner platforms to collect multimedia data, including text, images, audio, and video, from web pages, social media, and news clients in real time. Data preprocessing module: Cleans the collected raw data, converts text, image, audio and video data into feature vectors in a unified format, and fuses them using a dynamic weight fusion algorithm; Sentiment and Semantic Analysis Module: Creates a sentiment tendency judgment model to judge the sentiment tendency of the fused data, understands the semantic relationships and implicit information in the text through semantic role labeling technology, and associates entities and relationships in the text with the help of knowledge graphs; Trend and Hotspot Analysis Module: This module organizes media data into time series, capturing its temporal characteristics; it uses a dynamic hotspot cluster filtering algorithm to cluster text, filtering potential hotspot clusters; and combines this with a dynamic hotspot trend prediction model to analyze and predict media content development trends and hot topics. The calculation formula for the dynamic hotspot trend prediction model is as follows: ,in, Clusters formed by text clustering Hotspot cluster Importance weights, the formula is: , hotspot clusters The popularity growth rate reflects how the popularity of this hot topic cluster increases over time. represent Hotspot clusters selected in real time Except Other hotspots besides yes The set of hotspot clusters selected at any given time contains all hotspot clusters that meet the hotspot criteria at that time. Indicates in Hotspot clusters selected in real time In addition to specific hotspot clusters Another hotspot cluster Text density, It is a prediction The hot topic trend value at any given moment. yes The set of hotspot clusters selected in real time, Hotspot cluster The predicted trend of heat change, Hotspot cluster The semantic similarity between the topic and the historical outbreak. It is the trend factor weight, determined through training and validation on historical hot data, with a value range of [value range missing]. , It is a moment; Decision Support Module: Integrates the results of the sentiment semantic analysis module and the trend hotspot analysis module, generates decision suggestions based on the decision suggestion knowledge base, and displays the analysis results and suggestions in the form of charts and text through visualization tools; Interactive Feedback Module: Develop a responsive interface compatible with multiple terminals, allowing users to customize data query and analysis tasks through natural language input or operation commands. Collect user feedback on analysis results and suggestions through the feedback entry point, and use the results to optimize platform models and strategies.

2. The intelligent analysis platform for all-media operations based on big data as described in claim 1, characterized in that, The multimedia data collected by the data acquisition module is as follows: Text data includes: web page content, social media text, news client content, and user interaction data; Image data includes: web page images, social media images, and images from news apps; Audio data includes: social media audio, news client audio, and webpage audio; Video data includes: web videos, social media videos, and news app videos.

3. The intelligent analysis platform for all-media operation based on big data as described in claim 1, characterized in that, The data preprocessing module uses a dynamic weighted fusion algorithm to fuse text, image, and audio data of the same standard format. The calculation formula for the dynamic weighted fusion algorithm is as follows: ,in, It is the fused multimodal feature vector. It is a modal index. These represent text, image, audio, and video modalities, respectively. It is the first The eigenvectors of a mode, It is a text modal feature vector. It is an image modal feature vector. It is an audio modal feature vector. It is a video modal feature vector. It is the first Time of the first Modal weights, It is the first Attention weights for modalities.

4. The intelligent analysis platform for all-media operation based on big data according to claim 3, characterized in that, The calculation of parameters in the dynamic weight fusion algorithm, the first Time of the first Modal weights The calculation formula is: ,in This represents the natural exponential function, used to assign importance scores to modes. Converted into non-linear weight values, For the first Time-mode Importance score, calculation formula: ,in It is modal The time variance of the feature It is modal Correlation with features of other modalities It is modal The variance-correlation balance coefficient was obtained through statistical analysis of historical data; Modal attention weights The calculation formula is: ,in It is modal The weight matrix, It is modal Key feature vectors, This is the attention dimension, used to scale the dot product calculation; its value is determined based on the model design and task requirements. It is the first The eigenvectors of a mode, It is a sign function used to convert multiple real numbers into a probability distribution.

5. The intelligent analysis platform for all-media operation based on big data according to claim 1, characterized in that, The construction formula for the sentiment tendency judgment model in the sentiment semantic analysis module is as follows: ,in It is the fused multimodal feature vector. It is the first A projection vector of emotional polarity. These are the projection vector weight coefficients, obtained by optimizing the sentiment classification task using training data. It is an emotion intensity modulator that dynamically adjusts based on modal credibility. It is a symbolic function, outputting... They represent negative, neutral, and positive emotions, respectively. This represents the number of emotional polarity projection vectors.

6. The intelligent analysis platform for all-media operations based on big data according to claim 1, characterized in that, The specific steps in the sentiment semantic analysis module to understand the semantic relationships and implicit information of text through semantic role labeling technology are as follows: (1) Extract verbs / adjectives from the sentence as predicates and distinguish semantics through context disambiguation; (2) Label the core arguments and peripheral arguments of the predicate, and construct the predicate argument relation matrix; (3) Identify the modification relationship between arguments through dependency parsing and extract the emotional polarity of modifiers; (4) Complete the omitted components, identify metaphorical expressions, and deduce the implicit logical relationships; (5) Align entities in images / audio with text arguments to verify cross-modal semantic consistency; (6) Map semantic roles to knowledge graph nodes and complete implicit semantics through graph relationships; (7) Generate results containing predicate argument structure, implicit information, and multimodal associations.

7. The intelligent analysis platform for all-media operation based on big data according to claim 1, characterized in that, The trend hotspot analysis module uses a dynamic hotspot cluster filtering algorithm to cluster text and filter potential hotspot clusters. The formula is as follows: ,in It is a cluster Text density, Clusters formed by text clustering It is a cluster Mean semantic similarity of the inner text It is a cluster Normalized value of semantic distance to the nearest high-density cluster, It is a dynamic threshold that is dynamically adjusted based on time and data volume.

8. The intelligent analysis platform for all-media operation based on big data according to claim 1, characterized in that, The decision support module includes the following content in its decision suggestion knowledge base: Industry case study library: success stories and failure stories; Strategy Template Library: General strategy templates and industry-customized strategy templates; Market Environment Information Database: Macroeconomic data, policy and regulatory information, and industry trend analysis reports; Expert experience knowledge base: expert opinions and suggestions, and expert case interpretations; Internal enterprise data and experience base: historical decision-making data and internal business processes and standards.

9. The intelligent analysis platform for all-media operation based on big data according to claim 1, characterized in that, The decision support module generates decision suggestions based on a decision suggestion knowledge base using a dual-source fusion decision algorithm, with the following formula: ,in This is the optimal decision-making recommendation. These are candidate suggestions from the decision-making advice knowledge base. It is the feature vector of the current analysis result. Candidate Recommendation The corresponding result feature vector, It is a characteristic vector of the current market environment. Candidate Recommendation The corresponding environmental feature vector, This refers to the similarity weight of the analysis results, which is set according to the specific application scenario and decision-making objectives, and its value range is as follows: , It is the cosine similarity function.

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