Traffic platform monitoring method and system based on word frequency weight

Abstract

The present invention provides a traffic platform monitoring method and system based on word frequency weights. By building a cloud computing platform, obtaining Internet data streams, using syntactic analysis and semantic analysis feature vectors, assigning weight values according to the frequency of occurrence of word components, calculating cosine values to obtain the centroid vectors of relevant comments, and performing alarm judgment on the centroid vectors, it is easier to determine whether compliance is achieved, greatly improving protection efficiency.

Description

Technical Field

[0001] The present application relates to the field of network multimedia, and in particular to a method and system for monitoring a traffic platform based on word frequency weights. Background Art

[0002] The problem faced by existing traffic platforms is that vocabulary is fragmented and it is difficult to extract key words. Although there are filtering methods based on centroid vectors in the existing technology, when the frequency of vocabulary appearance is disorderly, the filtering method based on centroid vectors is difficult to achieve the desired effect.

[0003] Therefore, there is an urgent need for a targeted method and system for monitoring traffic platforms based on word frequency weights. Summary of the Invention

[0004] The purpose of the present invention is to provide a traffic platform monitoring method and system based on word frequency weights. By building a cloud computing platform, obtaining Internet data streams, using syntactic analysis and semantic analysis feature vectors, assigning weight values according to the frequency of occurrence of word components, calculating cosine values to obtain the centroid vectors of relevant comments, and performing alarm judgment on the centroid vectors, it is easier to determine whether compliance is achieved, greatly improving protection efficiency.

[0005] In a first aspect, the present application provides a traffic platform monitoring method based on word frequency weight, the method comprising:

[0006] Building a cloud computing platform on a server, constructing a syntactic model and a semantic analysis model, wherein the syntactic model and the semantic analysis model are respectively located on different core entities of the cloud computing platform, wherein the core entity is a physical server at a central position in the cloud computing platform;

[0007] According to the acquisition strategy, a data stream from an Internet platform is acquired, and feature vectors in the data stream are input into a syntactic model for sentence segmentation, emoticons are removed, and word components are obtained;

[0008] Count the number of times the word component appears in a unit time, and assign a weight value according to the number of times;

[0009] Input the word components into a semantic analysis model, output word meanings, wherein the word meanings are sentences that remove modal particles, are concise, have unique meanings, and use words from a large category; reorganize the word meanings into a new sentence; insert the weight values into the new sentence, complete vectorization, and obtain a second feature vector;

[0010] wherein the second feature vector includes a plurality of weight values corresponding to different word meanings;

[0011] Calculating cosine values of angles between a plurality of second eigenvectors, and forming a centroid vector from the second eigenvectors whose cosine values are higher than a threshold;

[0012] Calculating a cumulative value of the weight values of the centroid vectors to reflect the measure of relevance of the comments;

[0013] Filter word meanings whose centroid vector values are lower than the second threshold, and determine whether the word meanings include specified keywords. If so, continue to determine whether the sentence containing the word meaning has formed the specified meaning. If the sentence has formed the specified meaning, determine that the corresponding second eigenvector belongs to a situation requiring an alarm, and issue an alarm message; if the sentence does not form the specified meaning, determine that the corresponding second eigenvector is compliant.

[0014] In combination with the first aspect, in a first possible implementation manner of the first aspect, the method further includes risk assessment, attack correlation analysis, and situation awareness.

[0015] In combination with the first aspect, in a second possible implementation manner of the first aspect, obtaining the data stream of the Internet platform includes encoding and decoding the data stream.

[0016] In combination with the first aspect, in a third possible implementation of the first aspect, the kernels of the semantic analysis model and the syntactic model both use a neural network model.

[0017] In a second aspect, the present application provides a traffic platform monitoring system based on word frequency weight, the system comprising a processor and a memory:

[0018] The memory is used to store program code and transmit the program code to the processor;

[0019] The processor is configured to execute any one of the four possible methods of the first aspect according to instructions in the program code.

[0020] In a third aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store program code, and the program code is used to execute any one of the four possible methods in the first aspect.

[0021] The present invention provides a traffic platform monitoring method and system based on word frequency weights. By building a cloud computing platform, obtaining Internet data streams, using syntactic analysis and semantic analysis feature vectors, assigning weight values according to the frequency of occurrence of word components, calculating cosine values to obtain the centroid vectors of relevant comments, and performing alarm judgment on the centroid vectors, it is easier to determine whether compliance is achieved, greatly improving protection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0023] Figure 1 Flowchart of the method of the present invention. DETAILED DESCRIPTION

[0024] The preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making a clearer and more precise definition of the protection scope of the present invention.

[0025] Figure 1 The flow chart of the traffic platform monitoring method based on word frequency weight provided in this application includes:

[0026] Building a cloud computing platform on a server, constructing a syntactic model and a semantic analysis model, wherein the syntactic model and the semantic analysis model are respectively located on different core entities of the cloud computing platform, wherein the core entity is a physical server at a central position in the cloud computing platform;

[0027] According to the acquisition strategy, a data stream from an Internet platform is acquired, and feature vectors in the data stream are input into a syntactic model for sentence segmentation, emoticons are removed, and word components are obtained;

[0028] Count the number of times the word component appears in a unit time, and assign a weight value according to the number of times;

[0029] Input the word components into a semantic analysis model, output word meanings, wherein the word meanings are sentences that remove modal particles, are concise, have unique meanings, and use words from a large category; reorganize the word meanings into a new sentence; insert the weight values into the new sentence, complete vectorization, and obtain a second feature vector;

[0030] wherein the second feature vector includes a plurality of weight values corresponding to different word meanings;

[0031] Calculating cosine values of angles between a plurality of second eigenvectors, and forming a centroid vector from the second eigenvectors whose cosine values are higher than a threshold;

[0032] Calculating a cumulative value of the weight values of the centroid vectors to reflect the measure of relevance of the comments;

[0033] Filter word meanings whose centroid vector values are lower than the second threshold, and determine whether the word meanings include specified keywords. If so, continue to determine whether the sentence containing the word meaning has formed the specified meaning. If the sentence has formed the specified meaning, determine that the corresponding second eigenvector belongs to a situation requiring an alarm, and issue an alarm message; if the sentence does not form the specified meaning, determine that the corresponding second eigenvector is compliant.

[0034] The cloud computing platform also includes calling a physical server at an edge position, tracing the corresponding word components and the cluster structure, and sending the suspected trajectory and suspected source point to the physical server at a central position. The physical server at the central position calls the computing power of the cloud computing platform, determines the source point of the corresponding data flow, and notifies the physical server at the edge position to block the source point.

[0035] In some preferred embodiments, the method further includes risk assessment, attack correlation analysis, and situation awareness.

[0036] In some preferred embodiments, obtaining the data stream of the Internet platform includes encoding and decoding the data stream.

[0037] In some preferred embodiments, the kernels of the semantic analysis model and the syntactic model both use neural network models.

[0038] The present application provides a traffic platform monitoring system based on word frequency weight, the system comprising: the system comprising a processor and a memory:

[0039] The memory is used to store program code and transmit the program code to the processor;

[0040] The processor is configured to execute the method described in any one of all embodiments of the first aspect according to instructions in the program code.

[0041] The present application provides a computer-readable storage medium, which is used to store program code, and the program code is used to execute the method described in any one of the embodiments of the first aspect.

[0042] In a specific implementation, the present invention further provides a computer storage medium, wherein the computer storage medium may store a program that, when executed, may include some or all of the steps of various embodiments of the present invention. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0043] Those skilled in the art will clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus the necessary general-purpose hardware platform. Based on this understanding, the technical solutions in the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention or certain portions of the embodiments.

[0044] In particular, for the embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiments.

[0045] The above-described embodiments of the present invention do not limit the protection scope of the present invention.

Claims

1. A traffic platform monitoring method based on word frequency weight, characterized in that: The method comprises: Building a cloud computing platform on a server, constructing a syntactic model and a semantic analysis model, wherein the syntactic model and the semantic analysis model are respectively located on different core entities of the cloud computing platform, wherein the core entity is a physical server at a central position in the cloud computing platform; According to the acquisition strategy, a data stream from an Internet platform is acquired, and feature vectors in the data stream are input into a syntactic model for sentence segmentation, emoticons are removed, and word components are obtained; Count the number of times the word component appears in a unit time, and assign a weight value according to the number of times; Input the word components into a semantic analysis model, output word meanings, wherein the word meanings are sentences that remove modal particles, are concise, have unique meanings, and use words from a large category; reorganize the word meanings into a new sentence; insert the weight values into the new sentence, complete vectorization, and obtain a second feature vector; wherein the second feature vector includes a plurality of weight values corresponding to different word meanings; Calculating cosine values of angles between a plurality of second eigenvectors, and forming a centroid vector from the second eigenvectors whose cosine values are higher than a threshold; Calculating a cumulative value of the weight values of the centroid vectors to reflect the measure of relevance of the comments; Filter word meanings whose centroid vector values are lower than the second threshold, and determine whether the word meanings include specified keywords. If so, continue to determine whether the sentence containing the word meaning has formed the specified meaning. If the sentence has formed the specified meaning, determine that the corresponding second eigenvector belongs to a situation requiring an alarm, and issue an alarm message; if the sentence does not form the specified meaning, determine that the corresponding second eigenvector is compliant.

2. The method according to claim 1, wherein: The method also includes risk assessment, attack correlation analysis and situation awareness.

3. The method according to any one of claims 1-2, characterized in that: The obtaining of the data stream of the Internet platform includes encoding and decoding the data stream.

4. The method according to any one of claims 1-2, characterized in that: The kernels of the semantic analysis model and the syntactic model both use neural network models.

5. A traffic platform monitoring system based on word frequency weight, characterized in that: The system includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is configured to implement the method according to any one of claims 1 to 4 according to the instructions in the program code.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium is used to store program code, and the program code is used to execute the method according to any one of claims 1 to 4.

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