System for realizing all-media content security detection based on radio and television technology

Abstract

The invention relates to the technical field of content security detection, and particularly discloses a system for realizing all-media content security detection based on a radio and television technology, and the system comprises a model building module which is used for setting a plurality of emotional polarity labels, labeling emotional polarity labels of historical texts, and forming sample data; training according to the sample data by using a convolutional neural network model to obtain an emotion label analysis model; the initial module is used for presetting initial keywords, acquiring original texts, and performing sentiment polarity analysis on the original texts by using a sentiment label analysis model to obtain sentiment polarity labels of the original texts; the keyword analysis module is used for setting an analysis period, acquiring a similar context text, counting an occurrence frequency ratio and a remaining frequency ratio, obtaining a keyword quantity curve and a remaining quantity curve, and judging whether the remaining quantity curve meets complementary constraints or not; and the detection module is used for performing content security detection on subsequent media texts by using the initial keywords and the alternative keywords.

Description

Technical Field

[0001] The present invention relates to the technical field of content security detection, and in particular to a system for realizing full-media content security detection based on broadcasting and television technology. Background Art

[0002] All-media content security detection mainly involves the use of advanced information technology to conduct all-round, multi-level security monitoring and management of radio, television and new media content; it mainly includes image review, video review, text review, real-time monitoring, security protection and data analysis.

[0003] In the prior art, when performing content security detection on the text content in the media file, the method mainly relies on presetting illegal keywords. This method searches for these illegal keywords in the text content and shields the retrieved illegal keywords to achieve the purpose of maintaining content security. However, with the improvement of the audience's or users' thinking and creativity, some audiences or users are gradually able to cleverly avoid the retrieval of these preset illegal keywords. They will use homophones, homophones, pinyin abbreviations, decomposition, similar characters, etc. to replace the original illegal keywords, thereby circumventing system detection. In addition, the speed of change of Internet buzzwords is very fast, and new Internet terms and expressions are constantly emerging. Simply relying on the preset illegal keyword list cannot keep up with this change in time, resulting in the latest illegal keywords often being difficult to retrieve in time. Summary of the invention

[0004] The purpose of the present invention is to provide a system for realizing security detection of all media contents based on broadcasting and television technology, so as to solve the above technical problems.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A system for detecting the security of all media contents based on broadcasting and television technology, comprising: Model building module: set a number of sentiment polarity labels, obtain historical media files, extract the text content in the historical media files, record them as historical texts, mark the sentiment polarity labels of each historical text, and obtain sample data; establish a convolutional neural network model, train the convolutional neural network model according to the sample data, and obtain the final sentiment label analysis model; Initial module: preset a number of initial keywords, obtain the historical text where the initial keywords are located, and record it as the original text; obtain the sentiment polarity label of the original text through the sentiment label analysis model, and record it as the original sentiment label; Keyword analysis module: set an analysis period; obtain all media files within the analysis period in real time, and extract the text content in the media files, which is recorded as media text; select the media text whose sentiment polarity label is the original sentiment label, which is recorded as context-like text; obtain all words in the context-like text, and obtain the occurrence ratio of the initial keyword in all words; record the words in all words except the initial keyword as the remaining words, obtain the occurrence ratio of each remaining word, and record it as the remaining frequency ratio; According to the proportion of the number of occurrences in each analysis period, the keyword quantity curve is fitted, and according to the proportion of the remaining times of each remaining word in each analysis period, the remaining quantity curves of each remaining word are obtained; if the keyword quantity curve G(t) , where t is the number of the analysis period, t i represents the i-th analysis cycle, then it is determined in turn whether the keyword quantity curve and the other quantity curves satisfy the complementary constraint: , where n is the number of analysis cycles, t n represents the nth analysis period, Q(t) is the remaining quantity curve, G´(t i ) means that G(t) is at t=t i The derivative value at Q´(t i ) means Q(t) at t=t i The derivative value at , Cv is a preset error threshold, k is a preset slope error threshold; if the keyword quantity curve and the remaining quantity curves Q(t) satisfy the complementary constraint, the remaining words corresponding to the remaining quantity curves Q(t) are recorded as replacement keywords for the initial keywords; Detection module: performs content security detection on subsequent media texts based on the initial keywords and alternative keywords.

[0006] As a further solution of the present invention: the setting of the sentiment polarity label includes positive, negative and neutral.

[0007] As a further solution of the present invention: the establishment of the convolutional neural network model is based on natural language processing technology.

[0008] As a further solution of the present invention: the setting process of the analysis cycle includes: A time interval threshold is set, and the moment of setting the initial keyword is set as the initial moment. Starting from the initial moment, a time node is obtained every time interval threshold, and each two adjacent time nodes constitute an analysis cycle, and the composition of the analysis cycle includes the initial moment.

[0009] As a further solution of the present invention: the process of obtaining the occurrence frequency ratio includes: Obtain the total number M of all words in the class context text, and obtain the number of occurrences m of the initial keyword among all words, then obtain the occurrence ratio p = m / M.

[0010] As a further solution of the present invention: The fitting process of the keyword quantity curve includes: Number each analysis period, and obtain the occurrence ratio under each numbered analysis period; use the number as the abscissa and the occurrence ratio as the ordinate to establish a coordinate system; obtain several coordinate points in the coordinate system according to each numbered analysis period and its corresponding occurrence ratio; connect each coordinate point with a smooth curve, and record the curve as the keyword quantity curve.

[0011] As a further solution of the present invention: When obtaining all words in the class context text, filter out the stop words in the class context text, and the stop words include "of" and "I".

[0012] As a further solution of the present invention: The process of performing content security detection on subsequent media texts according to the initial keyword and the alternative keyword includes: Retrieve the words in the media text. If the initial keyword or the alternative keyword is retrieved, it is prompted that there is an abnormality in the content of the media text.

[0013] Advantages of the present invention: The present invention sets emotion polarity labels and uses a convolutional neural network model to train historical texts, so that the system can learn and identify texts with different emotion polarities. This deep learning-based method is more accurate and flexible than traditional rule-based or statistical methods because it can capture complex structures and patterns in texts. By setting the analysis period, media files in each period can be obtained and analyzed in real time. This real-time nature enables the system to quickly respond to newly emerging content and perform security detection in a timely manner. At the same time, by continuously updating and optimizing the keyword library (including the initial keyword and the alternative keyword), it can adapt to the dynamic changes of media content and maintain high accuracy. And by analyzing all words in the class context text and their occurrence ratios, possible alternative keywords can be discovered. This method ensures comprehensive coverage and detailed analysis of media content, reducing the risk of missing important information. Set several initial keywords according to the current network language situation, and then collect the occurrence ratios of the initial keywords in each analysis period. If the occurrence ratio of the initial keyword begins to decrease, analyze whether the occurrence ratios of the remaining words in each analysis period become more. If the complementary constraint is satisfied, it indicates that the remaining word gradually replaces the initial keyword and becomes a newly emerging keyword. This enables the setting of keywords to follow the latest changes in network terms when performing content security detection on the text content of media files. Description of the Drawings

[0014] The present invention will be further described below in conjunction with the accompanying drawings.

[0015] Figure 1 It is a flow chart of a system for realizing a security detection of all-media content based on broadcasting and television technology according to the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] See also Figure 1 As shown, the present invention is a system for realizing security detection of all media content based on broadcasting and television technology, comprising: Model building module: set a number of sentiment polarity labels, obtain historical media files, extract the text content in the historical media files, record them as historical texts, mark the sentiment polarity labels of each historical text, and obtain sample data; establish a convolutional neural network model, train the convolutional neural network model according to the sample data, and obtain the final sentiment label analysis model; It is understandable that a large number of historical media files are collected, such as news reports, social media posts, comments, etc.; text content is extracted from these files as the basis of training data; the extracted text content is manually or automatically annotated and assigned corresponding sentiment polarity labels. This step is the key to building a supervised learning model, because the model needs to learn how to identify the sentiment tendency of the text through annotated data; the annotated historical texts and their corresponding sentiment polarity labels are combined to form a sample data set, which will be used for subsequent model training; the convolutional neural network model is trained using the obtained sample data set; during the training process, the model will learn how to adjust its internal parameters to minimize the difference between the predicted sentiment polarity label and the actual label. Through multiple iterations and optimization, the model gradually improves its ability to recognize the sentiment tendency of the text; As a preferred embodiment of the present invention, the setting of the sentiment polarity label includes positive, negative and neutral; It should be noted that sentiment polarity refers to the positive, negative or neutral sentiment tendency expressed in a text; the sentiment polarity tag is used to annotate the sentiment polarity of a text; sentiment polarity analysis is a research field in natural language processing (NLP), which focuses on extracting and identifying the author's emotional attitude from text data; As a preferred embodiment of the present invention, the establishment of the convolutional neural network model is based on natural language processing technology; It is worth noting that before building a convolutional neural network model, the sample data needs to be fully preprocessed, including text cleaning, word segmentation, and stop word removal; It should be noted that the architecture of the convolutional neural network is designed according to the requirements, including convolutional layers, pooling layers and fully connected layers; the convolutional layer is used to extract text features, the pooling layer is used for dimensionality reduction, and the fully connected layer maps the features to the output space to achieve classification or regression tasks; during the model training process, the input sample is passed layer by layer through the convolutional layer, activation layer (such as ReLU), pooling layer, etc., and finally the output result is obtained. This process is called forward propagation; during the forward propagation process, each neuron receives the input from the neurons in the previous layer, and calculates according to its weight and bias, and then passes the result to the neurons in the next layer; after the forward propagation, it is necessary to calculate the difference between the model prediction result and the true label, that is, Loss; Common loss functions include cross entropy loss and mean square error; According to the definition of loss function, the loss of each sample can be calculated, and then the average loss of the entire training set can be calculated; After calculating the loss, the backpropagation algorithm needs to be used to update the parameters of the model to reduce the loss value; The backpropagation is to transfer the loss from the output layer to the input layer layer by layer through the chain rule, and update the value of each parameter according to the gradient descent algorithm; After completing a forward propagation and backpropagation, a new set of parameter values ​​will be obtained; In the process of multiple iterations, the model will continuously optimize its parameters to gradually reduce the loss value and improve the prediction accuracy; Initial module: preset a number of initial keywords, obtain the historical text where the initial keywords are located, and record it as the original text; obtain the sentiment polarity label of the original text through the sentiment label analysis model, and record it as the original sentiment label; It can be understood that the acquired original text is input into the previously trained sentiment label analysis model, which processes the text content and outputs one or more sentiment polarity labels; the sentiment polarity labels of the original text obtained from the sentiment label analysis model are recorded as a reference or benchmark for subsequent analysis; these original sentiment labels reflect the emotional color of the text in the context of specific keywords; Keyword analysis module: set an analysis period; obtain all media files within the analysis period in real time, and extract the text content in the media files, which is recorded as media text; select the media text whose sentiment polarity label is the original sentiment label, which is recorded as context-like text; obtain all words in the context-like text, and obtain the occurrence ratio of the initial keyword in all words; record the words in all words except the initial keyword as the remaining words, obtain the occurrence ratio of each remaining word, and record it as the remaining frequency ratio; According to the proportion of the number of occurrences in each analysis period, the keyword quantity curve is fitted, and according to the proportion of the remaining times of each remaining word in each analysis period, the remaining quantity curves of each remaining word are obtained; if the keyword quantity curve G(t) , where t is the number of the analysis period, t i represents the i-th analysis cycle, then it is determined in turn whether the keyword quantity curve and the other quantity curves satisfy the complementary constraint: , where n is the number of analysis cycles, t n represents the nth analysis period, Q(t) is the remaining quantity curve, G´(t i ) means that G(t) is at t=t i The derivative value at Q´(t i ) means Q(t) at t=t i The derivative value at , Cv is a preset error threshold, k is a preset slope error threshold; if the keyword quantity curve and the remaining quantity curves Q(t) satisfy the complementary constraint, the remaining words corresponding to the remaining quantity curves Q(t) are recorded as replacement keywords for the initial keywords; It is understandable that a fixed time period needs to be set as the analysis cycle, which can be adjusted according to actual needs, such as daily, weekly or monthly; the purpose of setting the analysis cycle is to regularly collect and analyze media files so as to timely discover content change trends and potential risks; in each analysis cycle, the system automatically obtains all media files in the cycle; these media files may include news reports, social media posts, blog posts and other forms of content; by obtaining these files in real time, the system can ensure that the analyzed data is up to date, thereby improving the accuracy and timeliness of the analysis; according to the sentiment polarity labels previously obtained through the sentiment label analysis model, media texts matching the original sentiment labels are screened out; these texts are called context-like texts, which reflect the text content in the same sentiment background as the initial keywords; further analysis is performed on the context-like texts to extract all the words therein, and the proportion of the number of occurrences of each word to the total number of words is calculated, which helps to understand which words are more common in the same sentiment background as the initial keywords; the words in the context-like texts are divided into two categories: initial keywords and remaining words; the initial keywords refer to those preset keywords, and the remaining words are all words except the initial keywords, and their proportion of occurrences is calculated respectively; It is worth noting that in order to determine whether there are other words that can replace the initial keyword, the system needs to check whether there is a complementary relationship between the keyword quantity curve and the remaining quantity curves; specifically, the following two conditions need to be met: Integration threshold condition: Cv is a preset error threshold used to measure the difference between the two curves. If the difference is less than or equal to Cv, the two curves are considered to be complementary in terms of the value of the integral area. The closer the absolute value of the difference between the integral values ​​of the two curves is to 0, the more complementary they are. Slope error threshold condition: , where k is a preset slope error threshold, which is used to limit the sum of the change rates of the two curves; if the sum of the change rates of the two curves belongs to [0, k], it is considered that the change trends of the two curves are just opposite; if both of the above two conditions are met, it is considered that there is a complementary relationship between the keyword quantity curve and other quantity curves, that is, there are potential alternative keywords; As a preferred embodiment of the present invention, the setting process of the analysis cycle includes: Setting a time interval threshold, taking the time when the initial keyword is set as the initial time, starting from the initial time, obtaining a time node every time interval threshold, and each two adjacent time nodes constitute an analysis cycle, and the composition of the analysis cycle includes the initial time; It is understandable that a time interval threshold needs to be determined; this threshold defines the time length between two adjacent time nodes; for example, if the time interval threshold is set to 24 hours, then a new time node will be generated every 24 hours; starting from the initial moment, the time is gradually increased according to the previously set time interval threshold to generate new time nodes; for example, if the initial moment is 8 am on Monday and the time interval threshold is 24 hours, then the next time node will be 8 am on Tuesday, and so on; each analysis period consists of two adjacent time nodes; for example, the first analysis period may be from 8 am on Monday to 8 am on Tuesday, and the second analysis period is from 8 am on Tuesday to 8 am on Wednesday, and so on; In a preferred embodiment of the present invention, the obtaining process of the occurrence frequency ratio includes: Obtain the total number M of all words in the class context text, and obtain the occurrence times m of the initial keyword among all words, then the occurrence frequency ratio p = m / M can be obtained; It is understandable that all words are extracted from the class context text, and through natural language processing (NLP) techniques, such as word segmentation, stop word removal, etc., all words in the text can be obtained. Then, the total number M of these words is counted; In a preferred embodiment of the present invention, the fitting process of the keyword quantity curve includes: Number each analysis period, and obtain the occurrence frequency ratio under each numbered analysis period; use the number as the abscissa and the occurrence frequency ratio as the ordinate to establish a coordinate system; according to each numbered analysis period and its corresponding occurrence frequency ratio, obtain several coordinate points in the coordinate system; connect the coordinate points with a smooth curve, and record the curve as the keyword quantity curve; It is understandable that the obtained coordinate points are connected by a smooth curve using mathematical methods (such as the least squares method, spline interpolation, etc.); this curve is the keyword quantity curve, which reflects the change trend of the initial keyword in different analysis periods; In a preferred embodiment of the present invention, when obtaining all words in the class context text, stop words in the class context text are filtered, and the stop words include "of" and "I"; It should be noted that the stop words refer to the words that appear frequently in the text but contribute little to the analysis task; these words usually include common words without practical meaning such as "的" (de) and "我" (I). Removing them can improve the accuracy and efficiency of the analysis; the stop word filtering technology identifies and removes irrelevant words by using a predefined stop word list; common stop word libraries include the Harbin Institute of Technology stop word list, the Baidu stop word list, etc. These tools can effectively reduce the impact of noise data; the filtering of the stop words first loads the text data and performs tokenization to split the text into words; then obtains the stop word list and filters out the stop words in the text, and finally counts the frequency of the remaining words. Detection module: Perform content security detection on subsequent media texts according to the initial keyword and the alternative keyword. In a preferred embodiment of the present invention, the process of performing content security detection on subsequent media texts according to the initial keyword and the alternative keyword includes: Retrieve the words in the media text. If the initial keyword or the alternative keyword is retrieved, it is prompted that the content of the media text is abnormal. It can be understood that the system will perform natural language processing (NLP) on the input media text, including steps such as word segmentation and stop word removal, so as to extract all the words in the text; then, the system will retrieve these words to check whether they match the initial keyword or the alternative keyword; if the system finds the initial keyword or its alternative keyword during the retrieval process, it means that the media text may contain sensitive or inappropriate content. At this time, a warning or prompt is generated to inform the user that the content of the media text is abnormal.

[0018] The above has described an embodiment of the present invention in detail, but the above content is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the application of the present invention should still fall within the scope covered by the patent of the present invention.

Claims

1. A system for realizing all-media content security detection based on broadcasting and television technology, characterized in that: Including: Model establishment module: Set a number of sentiment polarity labels, obtain historical media files, extract the text content in the historical media files, record it as historical text, label the sentiment polarity labels of each historical text to obtain sample data; establish a convolutional neural network model, and train the convolutional neural network model according to the sample data to obtain a final sentiment label analysis model; Initial module: Preset a number of initial keywords, obtain the historical text where the initial keywords are located, record it as the original text; through the sentiment label analysis model, obtain the sentiment polarity label of the original text, record it as the original sentiment label; Keyword analysis module: Set an analysis period; obtain all media files within the analysis period in real time, and extract the text content in the media files, record it as media text; Select the media text with the sentiment polarity label being the original sentiment label, record it as the context-like text; obtain all the words in the context-like text, and obtain the proportion of the number of occurrences of the initial keywords in all the words; Record the words other than the initial keywords in all the words as the remaining words, and obtain the proportion of the number of occurrences of each remaining word, record it as the remaining proportion; According to the proportion of the number of occurrences in each analysis period, the keyword quantity curve is fitted, and according to the proportion of the remaining times of each remaining word in each analysis period, the remaining quantity curves of each remaining word are obtained; if the keyword quantity curve G(t) , where t is the number of the analysis period, t i represents the i-th analysis cycle, then it is determined in turn whether the keyword quantity curve and the other quantity curves satisfy the complementary constraint: , where n is the number of analysis cycles, t n represents the nth analysis period, Q(t) is the remaining quantity curve, G´(t i ) means that G(t) is at t=t i The derivative value at Q´(t i ) means Q(t) at t=t i The derivative value at , Cv is a preset error threshold, k is a preset slope error threshold; if the keyword quantity curve and the remaining quantity curves Q(t) satisfy the complementary constraint, the remaining words corresponding to the remaining quantity curves Q(t) are recorded as replacement keywords for the initial keywords; Detection module: Conduct content security detection on subsequent media text according to the initial keywords and alternative keywords.

2. According to claim 1, a system for realizing all-media content security detection based on broadcasting and television technology is characterized in that: The setting of the sentiment polarity labels includes positive, negative, and neutral.

3. According to claim 1, a system for realizing all-media content security detection based on broadcasting and television technology is characterized in that: The establishment of the convolutional neural network model is based on natural language processing technology.

4. According to claim 1, a system for realizing all-media content security detection based on broadcasting and television technology is characterized in that: The setting process of the analysis period includes: Set a time interval threshold. Take the moment of setting the initial keywords as the initial moment. Starting from the initial moment, obtain a time node every time interval threshold. Each adjacent two time nodes form an analysis period, and the composition of the analysis period includes the initial moment.

5. According to claim 1, a system for realizing all-media content security detection based on broadcasting and television technology is characterized in that: The obtaining process of the proportion of the number of occurrences includes: Obtain the total number M of all the words in the context-like text, and obtain the number of occurrences m of the initial keywords in all the words, then obtain the proportion of the number of occurrences p = m / M.

6. The system for realizing all-media content security detection based on broadcasting and television technology according to claim 1 is characterized in that: The fitting process of the keyword quantity curve includes: Number each analysis period, and obtain the proportion of the number of occurrences in each numbered analysis period; use the number as the abscissa and the proportion of the number of occurrences as the ordinate to establish a coordinate system; obtain a number of coordinate points in the coordinate system according to each numbered analysis period and its corresponding proportion of the number of occurrences; connect each coordinate point with a smooth curve, and record the curve as the keyword quantity curve.

7. The system for realizing all-media content security detection based on broadcasting and television technology according to claim 1 is characterized in that: When obtaining all the words in the context-like text, filter out the stop words in the context-like text, and the stop words include "de" and "wo".

8. The system for realizing all-media content security detection based on broadcasting and television technology according to claim 1 is characterized in that: The process of conducting content security detection on subsequent media text according to the initial keywords and alternative keywords includes: Retrieve the words in the media text. If the initial keyword or alternative keyword is retrieved, it is prompted that there is an abnormality in the content of the media text.

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