Information feature information analysis and extraction system based on effective feature point comparison

Abstract

The invention discloses an information feature information analysis and extraction system based on effective feature point comparison, which relates to the technical field of information analysis and comprises a data acquisition module, a data preprocessing module, a data analysis module, a data mining module and an information evaluation and feedback module. The data acquisition module is used for performing unified collection operation on various information basic data, and acquiring real-time video, image, text and language information from social media, news websites, databases and public channels by utilizing web crawlers and API interface technologies; parameters in the acquisition stage comprise crawling frequency and data volume. According to the intelligence feature information analysis and extraction system based on effective feature point comparison, intelligence feature information is judged and extracted one by one in a more systematic judgment mode, key intelligence information is preferentially processed, intelligence data can be efficiently processed, analysis and prediction are achieved according to the distribution of the extracted feature points, and the analysis and extraction efficiency is improved. And the method has a positive promotion effect on development of an intelligence data mining technology.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligence analysis, and in particular to an intelligence feature information analysis and extraction system based on effective feature point comparison. Background Art

[0002] Against the backdrop of the rapid development of informatization, intelligence analysis and mining have become important tools for decision-making support in many fields, including society, economy, and military. The core of intelligence analysis lies in the collection, organization, analysis, and interpretation of data, aiming to extract valuable knowledge from massive amounts of information. The use of artificial intelligence (AI) and machine learning (ML) technologies can significantly improve analysis efficiency and accuracy.

[0003] Intelligence analysis is the process of systematizing, structuring and interpreting collected information in order to support decision-making and problem solving. Its basic concepts cover the acquisition, processing and analysis of information, emphasizing the generation of actionable intelligence on an accurate and timely basis. Intelligence analysis usually relies on a variety of methods, including statistical analysis, data mining and pattern recognition, and is applicable to a variety of fields such as business, military and security.

[0004] In the future, the development of intelligence analysis will gradually move towards intelligence and automation. Adaptive algorithms and deep learning will become the core technologies to improve the effectiveness of analysis. At the same time, the analysis process will pay more attention to interaction and visualization to help decision makers quickly understand complex data relationships and situations. In the context of globalization, the integration of intelligence analysis and risk management will also be strengthened to support more complex decision-making needs. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides an intelligence feature information analysis and extraction system based on effective feature point comparison, which solves the problems raised in the above-mentioned background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligence feature information analysis and extraction system based on effective feature point comparison, including a data acquisition module, a data preprocessing module, a data analysis module, a data mining module and an intelligence evaluation and feedback module;

[0007] The data collection module is used to uniformly collect various basic intelligence data, using web crawlers and API interface technology to obtain real-time video, image, text, and language information from social media, news websites, databases, and public channels;

[0008] The data preprocessing module is used to analyze the collected data, including noise removal, data cleaning, and data conversion. It uses Pandas and Numpy tools to process structured data. It also uses natural language processing technology to perform text analysis to transform text into structured data. It uses SpaCy and NLTK, combined with the TF-IDF algorithm to extract keywords, and mathematically expresses the feature extraction formula F(x). i With each feature x i The linear combination of is used to obtain the final feature representation. The feature extraction formula is as follows:

[0009]

[0010] Data analysis module, which is used to integrate data preprocessing into the entire intelligence data collection and analysis process to ensure the validity and availability of intelligence information;

[0011] Determine the nature of the problem and the characteristics of the data, extract multiple feature points according to the content of the problem and arrange them,

[0012] Select five valid feature points in the problem, specifically marked as (0, 0, 0, 0, 0), and set the data set arrangement number to AA, AB, AC, AD...BC, BD, BE,..., ZY, ZZ;

[0013] The comparison and judgment are as follows:

[0014] Compare the dataset AA with feature points I.

[0015] If there are corresponding feature points, the data set is marked as AA (1, 0, 0, 0, 0).

[0016] If there is no corresponding feature point, the data set is marked as AA (0, 0, 0, 0, 0).

[0017] And enter the next analysis and judgment instruction;

[0018] Compare the dataset AA with feature points II.

[0019] If there are corresponding feature points, the data set is marked as AA (1, 1, 0, 0, 0).

[0020] If there is no corresponding feature point, the data set is marked as AA (0, 0, 0, 0, 0).

[0021] Then enter the next analysis and judgment instruction;

[0022] Compare the dataset AA with feature points III.

[0023] If there are corresponding feature points, the data set is marked as AA (1, 1, 1, 0, 0).

[0024] If there is no corresponding feature point, the data set is marked as AA (0, 0, 0, 0, 0).

[0025] Then enter the next analysis and judgment instruction;

[0026] Compare dataset AA with feature IV.

[0027] If there are corresponding feature points, the data set is marked as AA (1, 1, 1, 1, 0).

[0028] If there is no corresponding feature point, the data set is marked as AA (0, 0, 0, 0, 0).

[0029] Then enter the next analysis and judgment instruction;

[0030] Compare dataset AA with feature V,

[0031] If there are corresponding feature points, the data set is marked as AA (1, 1, 1, 1).

[0032] If there is no corresponding feature point, the data set is marked as AA (0, 0, 0, 0, 0).

[0033] Then enter the next analysis and judgment instruction;

[0034] Repeat the above comparative analysis process for each data set, and determine the labeling for each data set;

[0035] According to the result of the mark determination, the data set is reclassified based on the feature point range as the main item, and arranged in descending order according to the feature point repetition rate, and the data set with a higher feature point repetition rate is analyzed first;

[0036] Determine whether to extract features from the data based on the nature of the problem and the characteristics of the data, and use the high-ratio priority principle to extract effective information from the data; otherwise, directly use the original data instead;

[0037] The high proportion priority principle is as follows:

[0038] Dataset AA and data set AB are text information, both consisting of several characters, specifically marked as AA(0,0) and AB(0,0);

[0039] The characteristic points of intelligence questions are set as special characters and special phrases;

[0040] The marks after feature point comparison and determination are AA(3,16), AB(4,12);

[0041] Calculate the ratio of special characters and special phrases to constituent characters in datasets AA and AB in turn, arrange the ratio values ​​from large to small, determine the dataset to be extracted first, then determine the priority extraction item according to the ratio value of the single feature in the dataset, and extract the dataset features according to the feature points;

[0042] In the data analysis process, statistical analysis, machine learning and image analysis are used accordingly; analysis models are built based on multiple algorithms, and model evaluation is performed through accuracy, recall rate and F1 value; data results can be presented in charts using visualization tools to facilitate decision makers' understanding and application;

[0043] The data mining module, as a transformation path from raw data to valuable intelligence, provides intelligence workers with key information to support decision-making through in-depth processing and intelligent analysis of data. This module involves identifying specific patterns and trends from processed data. The data mining module uses customized mining algorithm code to achieve effective analysis and extraction of intelligence data by accurately setting parameters and decision logic.

[0044] Determine the input parameters according to the requirements, including the size of the data set, feature selection, and internal parameters of the model; optimize the parameter configuration according to the specific intelligence analysis needs to ensure the efficient operation of the algorithm and the accuracy of the results; then initialize the required model and its parameters through the class `MiningAlgorithm` to ensure the accuracy of the algorithm implementation;

[0045] The intelligence evaluation and feedback module uses a feedback mechanism to regularly review analysis results, verify their accuracy and make continuous improvements. The evaluation indicators used include user satisfaction surveys and false alarm rates.

[0046] Optionally, the parameters of the collection phase include crawling frequency and data volume;

[0047] The crawling frequency is set to 10 times / h and the data volume is set to 10GB / d.

[0048] Optionally, the data analysis module also includes a social network analysis unit, which provides data analysis on user social behavior by quantifying the user's influence in the network, the closeness of connections, and the composition of the social circle; and uses graph network analysis and community discovery algorithms to mine and identify key nodes in the information network.

[0049] Optionally, the algorithm in the data mining module is used to process the input data set and adjust the model to suit specific data features through a training process; after training, the model can reflect the internal relationships of the data and perform predictive analysis on new data sets.

[0050] Optionally, the data mining module also includes an error handling mechanism, and the algorithm logic includes a check on the validity of input data to handle data anomalies.

[0051] The present invention provides an intelligence feature information analysis and extraction system based on effective feature point comparison, which has the following beneficial effects:

[0052] The intelligence feature information analysis and extraction system based on effective feature point comparison, the entire intelligence data mining process is a transformation path from raw data to valuable intelligence, through deep processing and intelligent analysis of data, to provide intelligence workers with key information to support decision-making; in the face of evolving security threats, the application of these technologies is particularly important, for this reason, our work not only pursues theoretical rigor, but also focuses on practicality and pertinence, providing tailor-made solutions for specific regions and issues

[0053] The algorithm output of this study is a model object that contains training parameters and prediction methods. This object can be used for further data analysis, such as predicting trends in intelligence data or identifying specific patterns and abnormal behaviors, providing strong technical support for intelligence analysis.

[0054] In general, the mining algorithm code designed in this study can efficiently process intelligence data and provide accurate analysis and prediction; the implementation of this algorithm illustrates the high degree of integration of information technology in the intelligence field and has a positive role in promoting the development of intelligence data mining technology;

[0055] The conclusion of intelligence analysis and mining emphasizes the necessity and effectiveness of multiple methods in data processing and analysis. Deep learning frameworks such as TensorFlow and PyTorch have been used to perform well in natural language processing tasks. The accuracy of the model can be significantly improved by adjusting model parameters, such as setting the learning rate to 0.001 and the batch size to 32. With the BERT model as the core, the F1 value in specific domain tasks can reach more than 90% through the Fine-tuning method, providing strong support for text classification and sentiment analysis.

[0056] In data mining, clustering algorithms such as K-means and DBSCAN show potential for application in large-scale data sets. In K-means, the selection of K value is crucial. The elbow rule determines that K value is 5, which can effectively separate clusters. DBSCAN uses ε-neighborhood and minPts parameters, specifically set to ε = 0.5 and minPts = 5, which can accurately identify clustering structures in the presence of noise and has strong applicability.

[0057] Supervised learning methods are also the core of intelligence analysis. Support vector machine (SVM) uses radial basis function (RBF) kernel in binary classification tasks. The optimal configuration of parameters C and γ can achieve a classification accuracy of 95%. The decision tree model is sensitive to feature selection and uses information gain or Gini coefficient as the division criterion to provide a clear decision path for complex nonlinear problems.

[0058] In the process of intelligence fusion, the reliability and comprehensiveness of intelligence are improved through the integration and analysis of multi-source data; the diversity of information sources and the selection of information fusion algorithms, such as the weighted average method and the Dempster-Shafer theory, ensure the quality of information in complex environments, and the growth rate of the cumulative number of effective intelligence can reach more than 30%;

[0059] In social network analysis, the combination of graph theory techniques and social network analysis tools, such as Gephi and NetworkX, can visualize group behavior. The centrality analysis of social nodes (such as degree centrality and betweenness centrality) reveals the information dissemination mechanism and optimizes the intelligence acquisition path. The gradual application of all these methods has enhanced the depth and breadth of intelligence analysis and has had a positive impact on the improvement of the decision support system. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a schematic diagram of the intelligence analysis workflow in the invention;

[0061] Figure 2 A schematic diagram of the mining algorithm code in the invention;

[0062] Figure 3 This is a schematic diagram of the intelligence analysis type in Case 1 of the invention. DETAILED DESCRIPTION

[0063] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0064] See also Figure 1 to Figure 2 ,The present invention provides a technical solution: an intelligence feature information analysis and extraction system based on effective feature point comparison, including a data acquisition module, a data preprocessing module, a data analysis module, a data mining module and an intelligence evaluation and feedback module;

[0065] The data collection module is used to uniformly collect various basic intelligence data, using web crawlers and API interface technology to obtain real-time video, image, text, and language information from social media, news websites, databases, and public channels;

[0066] The parameters of its collection phase include crawling frequency and data volume;

[0067] The crawling frequency is set to 10 times / h, and the data volume is set to 10GB / d;

[0068] The data preprocessing module is used to analyze the collected data, including noise removal, data cleaning, and data conversion. Pandas and Numpy tools are used to effectively process structured data, with a processing speed of 1GB / min. Text analysis is performed through natural language processing technology, so that scattered text can be transformed into structured data. SpaCy and NLTK are used in combination with the TF-IDF algorithm to extract keywords. Data preprocessing involves cleaning, normalization, and missing value processing to ensure that the data quality meets the requirements of subsequent mining algorithms. Feature extraction further extracts features that are beneficial to solving specific intelligence tasks from the processed data. This process requires mathematical expression with the help of the feature extraction formula F(x), which is expressed through the weight coefficient w. i With each feature x i The linear combination of to obtain the final feature representation;

[0069] The feature extraction formula is as follows:

[0070]

[0071] During the experiment, according to the feature extraction result table, TF-IDF, Bigram model, time series analysis, statistical analysis and other methods were used for features of different dimensions, including text features, behavior features, transaction features, network features and social features;

[0072] The above methods not only cover the diversity of algorithms, but also include the statistical properties of features, such as minimum, maximum, mean, and standard deviation;

[0073] For example, when processing text features, word frequency and bigram frequency are used to capture the basic semantic information of the text; when mining transaction features, the statistical characteristics of transaction frequency and transaction amount can reveal potential consumption patterns and risky behaviors;

[0074] As shown in the following table:

[0075]

[0076]

[0077] Data analysis module, which is used to integrate data preprocessing into the entire intelligence data collection and analysis process to ensure the validity and availability of intelligence information;

[0078] Determine the nature of the problem and the characteristics of the data, extract multiple feature points according to the content of the problem and arrange them,

[0079] Select five valid feature points in the problem, specifically marked as (0, 0, 0, 0, 0), and set the data set arrangement number to AA, AB, AC, AD...BC, BD, BE,..., ZY, ZZ;

[0080] The comparison and judgment are as follows:

[0081] Compare the dataset AA with feature points I.

[0082] If there are corresponding feature points, the data set is marked as AA (1, 0, 0, 0, 0).

[0083] If there is no corresponding feature point, the data set is marked as AA (0, 0, 0, 0, 0).

[0084] And enter the next analysis and judgment instruction;

[0085] Compare the dataset AA with feature points II.

[0086] If there are corresponding feature points, the data set is marked as AA (1, 1, 0, 0, 0).

[0087] If there is no corresponding feature point, the data set is marked as AA (0, 0, 0, 0, 0).

[0088] Then enter the next analysis and judgment instruction;

[0089] Compare the dataset AA with feature points III.

[0090] If there are corresponding feature points, the data set is marked as AA (1, 1, 1, 0, 0).

[0091] If there is no corresponding feature point, the data set is marked as AA (0, 0, 0, 0, 0).

[0092] Then enter the next analysis and judgment instruction;

[0093] Compare dataset AA with feature IV.

[0094] If there are corresponding feature points, the data set is marked as AA (1, 1, 1, 1, 0).

[0095] If there is no corresponding feature point, the data set is marked as AA (0, 0, 0, 0, 0).

[0096] Then enter the next analysis and judgment instruction;

[0097] Compare dataset AA with feature V,

[0098] If there are corresponding feature points, the data set is marked as AA (1, 1, 1, 1).

[0099] If there is no corresponding feature point, the data set is marked as AA (0, 0, 0, 0, 0).

[0100] Then enter the next analysis and judgment instruction;

[0101] Repeat the above comparative analysis process for each data set, and determine the labeling for each data set;

[0102] According to the result of the mark determination, the data set is reclassified based on the feature point range as the main item, and arranged in descending order according to the feature point repetition rate, and the data set with a higher feature point repetition rate is analyzed first;

[0103] Determine whether to extract features from the data based on the nature of the problem and the characteristics of the data, and use the high-ratio priority principle to extract effective information from the data; otherwise, directly use the original data instead;

[0104] The high proportion priority principle is as follows:

[0105] Dataset AA and data set AB are text information, both consisting of several characters, specifically marked as AA(0,0) and AB(0,0);

[0106] The characteristic points of intelligence questions are set as special characters and special phrases;

[0107] The marks after feature point comparison and determination are AA(3,16), AB(4,12);

[0108] Calculate the ratio of special characters and special phrases to constituent characters in datasets AA and AB in turn, arrange the ratio values ​​from large to small, determine the dataset to be extracted first, then determine the priority extraction item according to the ratio value of the single feature in the dataset, and extract the dataset features according to the feature points;

[0109] In the data analysis process, statistical analysis, machine learning and image analysis are used accordingly; analysis models are built based on a variety of algorithms (such as decision trees, support vector machines, and random forests), and model evaluation is performed through accuracy, recall and F1 value; using visualization tools (such as Tableau and Matplotlib), data results can be presented in charts to facilitate decision makers' understanding and application;

[0110] The data analysis module also includes a social network analysis unit, which provides data analysis on user social behavior by quantifying the user's influence in the network, the closeness of connections, and the composition of social circles; using graph network analysis and community discovery algorithms to effectively mine and identify key nodes and patterns in information networks;

[0111] The data mining module, as a transformation path from raw data to valuable intelligence, provides intelligence workers with key information to support decision-making through in-depth processing and intelligent analysis of data. This module involves identifying specific patterns and trends from processed data. The data mining module uses customized mining algorithm code to achieve effective analysis and extraction of intelligence data by accurately setting parameters and decision logic.

[0112] Determine the input parameters according to the requirements, including the size of the data set, feature selection, and internal parameters of the model; optimize the parameter configuration according to the specific intelligence analysis needs to ensure the efficient operation of the algorithm and the accuracy of the results; then initialize the required model and its parameters through the class `MiningAlgorithm` to ensure the accuracy of the algorithm implementation;

[0113] The algorithms in the data mining module are used to process the input data set and adjust the model to suit the specific data characteristics through the training process; after training, the model can reflect the internal relationships of the data and perform predictive analysis on new data sets;

[0114] The data mining module also includes an error handling mechanism. The algorithm logic includes a strict check on the validity of the input data, which can handle various abnormal situations, avoid the deviation of the results caused by the input of illegal data, and ensure the stability of the mining process and the reliability of data analysis.

[0115] The intelligence evaluation and feedback module uses a feedback mechanism to regularly review analysis results, verify their accuracy and make continuous improvements. The evaluation indicators used include user satisfaction surveys and false alarm rates.

[0116] Case 1: Political intelligence analysis case, such as Figure 3 As shown;

[0117] In the process of conducting political intelligence analysis, a data-driven analysis framework is established, involving a wide range of political texts and diverse data sources; the data collection module realizes automatic crawling for specific political events, and the cumulative amount of text collected exceeds that of various news and policy documents, ensuring the breadth and depth of analysis; the text preprocessing stage uses natural language processing technology for word segmentation, part-of-speech tagging and named entity recognition, and the processed text data is standardized, laying the foundation for subsequent analysis;

[0118] Design a machine learning-based intelligence analysis model to identify and classify key political events and figures in texts; through supervised learning algorithm training, the model gradually optimizes parameters and achieves an accuracy of more than 85% on the validation set; to ensure the generalization ability of the model, select corpus covering multiple time periods and multiple political topics as the training set, and the performance on the test set remains stable;

[0119] In the model training phase, multiple algorithms such as logistic regression, random forest and support vector machine are selected for comparative analysis, and the best model is selected based on the cross-validation strategy. For each algorithm, important parameters such as regularization strength, tree depth, kernel function parameters, etc. are fine-tuned to improve the accuracy of model prediction. At the same time, technologies such as SMOTE are used to balance data with uneven categories and enhance the model's prediction ability in minority classes.

[0120] In the interpretation of the results of the analytical framework, we not only focus on the interpretation of quantitative indicators, but also combine the types of intelligence analysis; specifically, intelligence analysis is divided into three types: strategic intelligence analysis, tactical intelligence analysis, and operational intelligence analysis, which correspond to the needs at different levels in the political decision-making process; this classification is reflected in the captions, providing decision makers with a full range of perspectives from macro to micro;

[0121] By analyzing historical data of a large number of samples, the framework can predict the probability of a specific political event and generate corresponding intelligence reports based on the action patterns of various political actors;

[0122] To verify the practicality and effectiveness of the framework, a retrospective analysis of several historical political events was conducted, and the prediction results were compared with the actual situation. The accuracy rate met the predetermined scientific research standards. Finally, this intelligence analysis case study showed that the comprehensive use of data mining and machine learning techniques can effectively support the collection, analysis and early warning of political intelligence. Our research is not only innovative in theory, but also shows obvious application value in practice. It is expected to provide a scientific basis for regional security policy formulation.

[0123] Case 2: Business intelligence analysis case;

[0124] In the business field, the depth and breadth of intelligence analysis are crucial to the formulation of corporate strategies; specific case studies reveal the efficient application of multiple intelligence analysis methods in various business scenarios; this study investigates in detail the detailed configuration of these methods, the amount of data they are adapted to, and the actual results achieved;

[0125] Taking the decision tree algorithm as an example, its application in product quality analysis sets the tree depth to 5 layers and processes about 10,000 data items. By comparing with the company's existing fault detection process, it is proved that this method can significantly improve the fault detection rate. For example, in the actual application of a certain electronics company, the detection rate has been improved by nearly 20%. This not only optimizes production quality, but also saves a lot of later maintenance costs for the company.

[0126] In the task of exploring consumer purchase patterns, the association analysis method showed its unique advantages. By setting the confidence level to 0.8 and analyzing 50,000 pieces of consumer data, it successfully discovered potential commodity purchase associations, which enabled a clothing chain brand to achieve a 30% increase in cross-selling. This analysis method was highly efficient in discovering potential product combinations and provided data support for marketing strategies.

[0127] The K-means clustering analysis algorithm also performs well in the field of customer segmentation; by processing 5,000 customer data and setting the number of clusters to 8, this method helped a financial institution identify different customer groups more accurately. As an optimized marketing strategy, the company's marketing conversion rate increased by 15%, fully proving the importance and efficiency of customer analysis in the big data era;

[0128] Quantitative forecasting technology was applied to market trend analysis. By setting a quarterly forecasting cycle for 20,000 market data, it helped a cosmetics company significantly reduce the forecast error of quarterly sales to 5%, thus enhancing the company's ability to respond to market changes.

[0129] Another technology, the application of text mining in social media sentiment analysis, brought a 25% increase in user satisfaction for an Internet company by analyzing 100,000 user comments with the number of feature words set to 1000, which reflects the important role of social media analysis in customer relationship management.

[0130] In summary, various methods of business intelligence analysis can significantly improve the operational efficiency and market competitiveness of enterprises under the condition of ensuring correct parameter settings and reasonable data volume, and provide scientific and efficient data support for enterprise strategic decision-making. Through systematic analysis methods, precise parameter adjustments, and sufficient and effective data input, various key indicators have been significantly improved, reflecting the real value and application potential of intelligence analysis in enterprises.

[0131] The application of intelligence analysis methods in enterprises is shown in the following table:

[0132]

[0133]

[0134] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An intelligence feature information analysis and extraction system based on effective feature point comparison, including a data acquisition module, a data preprocessing module, a data analysis module, a data mining module, and an intelligence evaluation and feedback module; Features: The data collection module is used to uniformly collect various basic intelligence data, using web crawlers and API interface technology to obtain real-time video, image, text, and language information from social media, news websites, databases, and public channels; The data preprocessing module is used to analyze the collected data, including noise removal, data cleaning, and data conversion. It uses Pandas and Numpy tools to process structured data. It also uses natural language processing technology to perform text analysis to transform text into structured data. It uses SpaCy and NLTK, combined with the TF-IDF algorithm to extract keywords, and mathematically expresses the feature extraction formula F(x). i With each feature x i The linear combination of is used to obtain the final feature representation. The feature extraction formula is as follows: Data analysis module, which is used to integrate data preprocessing into the entire intelligence data collection and analysis process to ensure the validity and availability of intelligence information; Determine the nature of the problem and the characteristics of the data, extract multiple feature points according to the content of the problem and arrange them, Select five valid feature points in the problem, specifically marked as (0, 0, 0, 0, 0), and set the data set arrangement number to AA, AB, AC, AD...BC, BD, BE,..., ZY, ZZ; The comparison and judgment are as follows: Compare the dataset AA with feature points I. If there are corresponding feature points, the data set is marked as AA (1, 0, 0, 0, 0). If there is no corresponding feature point, the data set is marked as AA (0, 0, 0, 0, 0). And enter the next analysis and judgment instruction; Compare the dataset AA with feature points II. If there are corresponding feature points, the data set is marked as AA (1, 1, 0, 0, 0). If there is no corresponding feature point, the data set is marked as AA (0, 0, 0, 0, 0). Then enter the next analysis and judgment instruction; Compare the dataset AA with feature points III. If there are corresponding feature points, the data set is marked as AA (1, 1, 1, 0, 0). If there is no corresponding feature point, the data set is marked as AA (0, 0, 0, 0, 0). Then enter the next analysis and judgment instruction; Compare dataset AA with feature IV. If there are corresponding feature points, the data set is marked as AA (1, 1, 1, 1, 0). If there is no corresponding feature point, the data set is marked as AA (0, 0, 0, 0, 0). Then enter the next analysis and judgment instruction; Compare dataset AA with feature V, If there are corresponding feature points, the data set is marked as AA (1, 1, 1, 1). If there is no corresponding feature point, the data set is marked as AA (0, 0, 0, 0, 0). Then enter the next analysis and judgment instruction; Repeat the above comparative analysis process for each data set, and determine the labeling for each data set; According to the result of the mark determination, the data set is reclassified based on the feature point range as the main item, and arranged in descending order according to the feature point repetition rate, and the data set with a higher feature point repetition rate is analyzed first; Determine whether to extract features from the data based on the nature of the problem and the characteristics of the data, and use the high-ratio priority principle to extract effective information from the data; otherwise, directly use the original data instead; The high proportion priority principle is as follows: Dataset AA and data set AB are text information, both consisting of several characters, specifically marked as AA(0,0) and AB(0,0); The characteristic points of intelligence questions are set as special characters and special phrases; The marks after feature point comparison and determination are AA(3,16), AB(4,12); Calculate the ratio of special characters and special phrases to constituent characters in datasets AA and AB in turn, arrange the ratio values ​​from large to small, determine the dataset to be extracted first, then determine the priority extraction item according to the ratio value of the single feature in the dataset, and extract the dataset features according to the feature points; In the data analysis process, statistical analysis, machine learning and image analysis are used accordingly; Build analytical models based on multiple algorithms, and evaluate models through accuracy, recall, and F1 value. Use visualization tools to present data results in charts to facilitate decision makers’ understanding and application. The data mining module, as a transformation path from raw data to valuable intelligence, provides intelligence workers with key information to support decision-making through in-depth processing and intelligent analysis of data. This module involves identifying specific patterns and trends from processed data. The data mining module uses customized mining algorithm code to achieve effective analysis and extraction of intelligence data by accurately setting parameters and decision logic. Determine the input parameters according to the requirements, including the size of the data set, feature selection, and internal parameters of the model; optimize the parameter configuration according to the specific intelligence analysis needs to ensure the efficient operation of the algorithm and the accuracy of the results; then initialize the required model and its parameters through the class `MiningAlgorithm` to ensure the accuracy of the algorithm implementation; The intelligence evaluation and feedback module uses a feedback mechanism to regularly review analysis results, verify their accuracy and make continuous improvements. The evaluation indicators used include user satisfaction surveys and false alarm rates.

2. The intelligence feature information analysis and extraction system based on effective feature point comparison according to claim 1 is characterized in that: The parameters of the acquisition phase include crawling frequency and data volume; The crawling frequency is set to 10 times / h and the data volume is set to 10GB / d.

3. The intelligence feature information analysis and extraction system based on effective feature point comparison according to claim 1 is characterized in that: The data analysis module also includes a social network analysis unit, which provides data analysis on user social behavior by quantifying the user's influence in the network, the closeness of connections, and the composition of the social circle; and uses graph network analysis and community discovery algorithms to mine and identify key nodes in the information network.

4. The intelligence feature information analysis and extraction system based on effective feature point comparison according to claim 1 is characterized in that: The algorithm in the data mining module is used to process the input data set and adjust the model to suit specific data characteristics through a training process; After training, the model can reflect the internal relationships of the data and perform predictive analysis on new data sets.

5. The intelligence feature information analysis and extraction system based on effective feature point comparison according to claim 4 is characterized in that: The data mining module also includes an error handling mechanism, and the algorithm logic includes a check on the validity of input data to handle data anomalies.

Images