A vertical information intelligent acquisition system in the security field

Abstract

The application relates to the technical field of information collection in the security field, and discloses a vertical information intelligent collection system in the security field, which comprises a data collection module, a data security module, a data classification module and an intelligent analysis module. The data collection module collects global internet open data through distributed collection technology. The data security module adopts anti-collection and anti-tracing technology in the collection process to prevent external tracking and data tracing. The data classification module automatically classifies and structurally processes the collected multi-source information and stores the information in a preset vertical database. The intelligent analysis module analyzes, judges and aggregates the classified information based on an artificial intelligence big model. Through the whole-process closed-loop processing from the collection of vertical information in the security field to the generation of a report, the vertical information intelligent collection system in the security field deeply adapts to the special requirements of regional security events, specific types of public security events, policy and regulation change information and the like in the security field, and prevents the generalization of collection, the shallow analysis and the insufficient report pertinence of a general information collection system in the security field.

Description

Technical Field

[0001] This invention relates to the field of information acquisition technology in the security field, specifically a vertical intelligent information acquisition system for the security field. Background Technology

[0002] Intelligent information collection in the security field is a process of automatically and intelligently collecting and integrating various security-related information using advanced technologies such as artificial intelligence, big data, and the Internet of Things. It can cover multiple dimensions such as cyberspace, physical environment, and social dynamics. For example, it can capture malicious code characteristics and vulnerability information in the network in real time, monitor abnormal behavior data in public places, and summarize comments related to security risks in global public opinion. Through intelligent algorithms, information can be filtered, cleaned, and correlated to quickly identify potential security threats, providing comprehensive and accurate data support for early warning decisions and emergency response, and effectively improving the initiative and timeliness of security protection.

[0003] In existing technologies, general information collection systems are mostly geared towards broad fields, such as news and e-commerce, and lack in-depth adaptation to the special needs of security fields, such as regional security incidents, specific types of public security incidents, and information on changes in policies and regulations. As a result, the systems collect generalized information in the security field, perform superficial analysis, and fail to provide targeted reports. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a vertical information intelligent acquisition system for the security field, which solves the problem that existing technologies lack adaptability to information collected from multiple fields, resulting in superficial analysis and insufficient targeting of reports.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a vertically integrated intelligent information acquisition system for the security field, comprising: The data acquisition module collects publicly available data from the global internet using distributed acquisition technology; The data security module employs anti-collection and anti-tracing technologies during the data acquisition process to prevent external tracking and data traceability. The data classification module automatically classifies and structures the collected multi-source information and stores it in a preset vertical database; The intelligent analysis module analyzes, assesses, and aggregates classified information based on a large-scale artificial intelligence model. The report generation module generates safety briefings, bulletins, daily reports, weekly reports, and special reports based on the analysis results.

[0006] Preferably, the data acquisition process includes: The task of collecting data across the entire network is divided according to different information source types, geographical scope, and subject keywords; The divided data collection tasks are distributed to multiple distributed data collection nodes, and each node independently performs information capture. Each data collection node acquires target web pages, social media content, and open database data in real time and in parallel based on preset data collection rules and scheduling strategies. The collected information is transmitted to the central processing server through an encrypted channel; The central processing server integrates, deduplicates, and verifies data from different nodes to form a preliminary global dataset.

[0007] Preferably, the anti-collection and anti-tracing process includes: During the data collection process, the network identity information of the data collection nodes is dynamically changed, including IP address, User-Agent parameter and request header information, to simulate normal user behavior; By setting randomized request intervals and access frequencies, traffic can be avoided from being detected as abnormal traffic by the target website. Insert human-computer interaction features such as click, scroll, and pause during the data collection process; End-to-end encryption is applied to the data transmission between the data acquisition nodes and the central server to prevent data interception and tracing during transmission. By desensitizing and obfuscating the access logs of the data collection nodes, the actual source and execution path of the tasks are hidden, thereby achieving anti-tracking and anti-source tracing.

[0008] Preferably, the automatic classification process includes: Natural language processing algorithms are used to identify keywords, perform semantic analysis and topic modeling on multi-source information, and extract core features such as event type, time of occurrence, location, subject and influencing factors; The extracted features are compared with the preset classification rules to preliminarily determine the category to which the information belongs; The initial judgment results are reclassified and assessed for confidence by calling a large artificial intelligence model to ensure the accuracy and consistency of the classification.

[0009] Preferably, the structured processing flow includes: The categorized results are transformed into a unified data structure format, event entries are created and standardized fields are populated, including time, location, category, risk level and related descriptive information; The structured information is stored in the corresponding vertical database.

[0010] Preferably, the information storage process includes: Establish a mapping relationship between different categories of information and the target database, and determine the corresponding database type and data table structure; Before storage, generate unique identifiers for the information and build multidimensional indexes, including time indexes, geographic indexes, subject indexes, and risk level indexes; The structured information is written into the corresponding database table according to the mapping relationship, while maintaining the association with the original data; The data verification mechanism verifies the integrity, consistency, and deduplication of stored information.

[0011] Preferably, the processing flow for the classified information includes: Using a pre-trained language model, semantic parsing is performed on classified information to identify causal relationships, temporal order, and potential influencing factors among events; The severity, scope of impact, and development trend of an event are quantitatively analyzed using a risk assessment model, and corresponding risk levels are generated. By cross-referencing and multi-dimensionally integrating information collected from different databases and time periods, hidden related events or potential patterns can be discovered. By using time series modeling and prediction algorithms, we can analyze the development direction and evolution trend of events; The analysis and assessment results are aggregated according to theme, region, or industry, and output as standardized assessment items.

[0012] Preferably, the process of cross-comparison and multi-dimensional fusion includes: Information from different databases is standardized according to timestamps, geographic codes, and event categories to ensure data consistency in both time and spatial dimensions. Map the key elements of an event to a unified feature vector; Based on semantic matching algorithms and vector retrieval models, similarity analysis is performed on information from different sources to identify multiple data records that may belong to the same event; By merging highly similar data entries, a complete event chain is generated, and the correction results are filled in with missing or contradictory information. By using cluster analysis and graph association techniques, we can discover recurring patterns, potential regularities, or hidden cross-regional and cross-temporal related events from the fused event chains.

[0013] Preferably, the report generation process includes: The report type is determined by matching the analysis results of the intelligent analysis module with the preset report type trigger conditions. Apply corresponding information filtering rules to different report types; Fill in the filtered information using the standardized templates for each type of report; Perform completeness and logical verification on the generated report; Once the verification is successful, the report is stored in the report library and distributed through the system interaction function.

[0014] Preferably, a vertical information intelligent collection method in the security field includes: Collect publicly available data from the global internet using distributed data collection technology; Anti-collection and anti-tracing technologies are employed during the data collection process to prevent external tracking and data tracing. The collected multi-source information is automatically classified and structured, and then stored in a pre-defined vertical database; Based on a large-scale artificial intelligence model, classified information is analyzed, assessed, and aggregated. Based on the analysis results, generate safety briefings, bulletins, daily reports, weekly reports, and special reports.

[0015] This invention provides a vertically integrated intelligent information acquisition system for the security field. It has the following beneficial effects: 1. This invention achieves a closed-loop processing of vertical information in the security field, from collection to report generation. It is deeply adapted to the special needs of the security field, such as regional security incidents, specific types of public safety incidents, and policy and regulatory change information. This prevents the generalization of collection, superficial analysis, and insufficient reporting of general information collection systems in the security field, and improves the professionalism and accuracy of security information processing.

[0016] 2. This invention utilizes distributed acquisition technology in conjunction with multi-layered anti-acquisition and anti-tracing technologies. It improves the efficiency of global security information acquisition by task splitting and multi-node parallel acquisition, while enhancing the concealment and security of the acquisition process through dynamic replacement of network identity information, simulation of human-computer interaction features, and end-to-end encryption, thus balancing information acquisition efficiency and anti-tracking capabilities.

[0017] 3. This invention combines natural language processing algorithms with large-scale artificial intelligence models to achieve accurate classification of collected information. By unifying data structure formats and standardizing field filling, it completes structured processing, providing a high-quality data foundation for subsequent analysis and improving the accuracy and structuring of information classification.

[0018] 4. This invention utilizes pre-trained language models, risk assessment models, cross-comparison and fusion, and time series prediction techniques to not only analyze the causal relationships and risk levels of single events, but also to discover hidden related events across regions and time periods and predict development trends, thereby enhancing the ability to assess potential security risks. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the system architecture of the present invention; Figure 2 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0020] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example: Please see the appendix Figure 1 - Appendix Figure 2 This invention provides a vertical information intelligent acquisition system for the security field, comprising: The data acquisition module collects publicly available data from the global internet using distributed acquisition technology. The data security module employs anti-collection and anti-tracing technologies during the data acquisition process to prevent external tracking and data traceability. The data classification module automatically classifies and structures the collected multi-source information and stores it in a preset vertical database; The intelligent analysis module analyzes, assesses, and aggregates classified information based on a large-scale artificial intelligence model. The report generation module generates safety briefings, bulletins, daily reports, weekly reports, and special reports based on the analysis results.

[0022] Furthermore, the data collection process includes: The task of collecting data across the entire network is divided according to different information source types, geographical scope, and subject keywords; The divided data collection tasks are distributed to multiple distributed data collection nodes, and each node independently performs information capture. Each data collection node acquires target web pages, social media content, and open database data in real time and in parallel based on preset data collection rules and scheduling strategies. The collected information is transmitted to the central processing server through an encrypted channel; The central processing server integrates, deduplicates, and verifies data from different nodes to form a preliminary global dataset.

[0023] Specifically, for the highly sensitive, multi-source, and real-time needs of global security information, such as regional security incidents, specific types of public safety incidents, policy and regulatory changes, and public health risks, a distributed data acquisition architecture and a refined task management strategy are adopted. The specific implementation process is as follows: The system first divides the entire network collection tasks into multiple dimensions based on a pre-set vertical collection directory for the security field. This includes division by information source type, by geographical scope, and by topic keywords. The division by information source type specifically includes, but is not limited to, security news websites, government and international organization announcement platforms, social media, industry databases, etc., ensuring coverage of more than 10,000 security-related information sources. The division by geographical scope specifically divides the tasks into sub-regions such as Asia-Pacific, Middle East and Africa, Europe and the Americas, etc., based on the user's area of ​​interest. Each region corresponds to a specific collection target. The division by topic keywords specifically combines the core needs of the security field and pre-sets a topic keyword library, including information related to specific types of security incidents and personnel, information on specific economic policy and regulatory adjustments, information on specific changes in the operating environment of enterprises, information on specific political agendas and policy inclinations, natural disasters and impact assessment information, etc. Each keyword combination corresponds to a type of special collection task. For example, for keywords related to security incidents of specific entities, relevant news and social media reports are targeted for collection. The task division is based on the importance of the information source, the frequency of information updates, and the user's priority needs, and the weight of each sub-task is dynamically adjusted. For example, the task weight of information sources from high-risk countries is higher than that of low-risk countries. The system deploys several distributed acquisition nodes, physically distributed across different network environments, and also belongs to the central scheduling system. The number of nodes can be dynamically expanded according to the acquisition task volume. The central scheduling system allocates tasks based on the principles of load balancing and regional adaptation, prioritizing the allocation of acquisition tasks in the Middle East and Africa to nodes with lower network latency in that region, and allocating high-frequency updated source tasks to nodes with stronger processing capabilities, ensuring that the workload of each node matches its computing power. Each node independently maintains its local task queue, and after receiving task instructions from the central scheduling system, it automatically loads the corresponding acquisition rules and can start capturing data without relying on other nodes. Each node has pre-defined collection rules for different information sources, including webpage parsing rules (such as extracting structured fields like event time, location, and number of affected people from news pages), API call specifications (such as interface parameters for connecting to open databases), anti-scraping adaptation strategies (such as request header simulation for specific websites), and cookie pool management. The scheduling strategy combines real-time triggering and scheduled inspections. For information sources related to sudden security incidents, such as reports of specific types of public safety incidents, real-time triggering (interval ≤ 1 minute) is used. For routine policy-related information sources, scheduled inspections are used, such as once per hour. Nodes acquire data in parallel through the following methods for the target webpage. It employs a headless browser to simulate human browsing behavior, such as scrolling and clicking, parses HTML / JS to dynamically load content, and extracts text, images, and attachments, such as government official PDFs. It also crawls posts, comments, and reposts under specific topic tags from social media through official APIs or compliant crawlers, filtering out invalid information, such as advertisements and irrelevant discussions. It calls data interfaces of open databases, such as the World Health Organization's public health event database, to obtain structured data in batches, such as confirmed cases and distribution areas of infectious diseases. During the collection process, nodes record the task status in real time, such as successful crawling, access restrictions, and data anomalies, and synchronize this information to the central dispatch system. Each node transmits the raw information it collects, including text, images, and metadata such as collection time and source URL, to the central processing server through an SSL / TLS encrypted channel. The transmitted content includes the node ID and data verification code to ensure that the data is not tampered with or leaked during transmission. For large-capacity data, a fragmented transmission and breakpoint resume mechanism is adopted to avoid data loss due to network interruption. The central server categorizes multi-node data by source type, region, and topic, establishes a global index, and removes duplicates based on content fingerprints and source priority. For multiple records of the same event, such as the same public safety event reported by different media, content fingerprints are generated using text similarity algorithms, such as cosine similarity. The record with the highest source authority or the most complete information is retained, and duplicate data is removed. At the same time, the data is checked to see if it conforms to the preset structure, such as whether the event time is in a standard time format. Unstructured data is preliminarily cleaned, such as removing garbled characters and standardizing place names. Data from multiple sources is compared, such as whether the key indicators of a certain event are consistent in different reports. Conflicting information is marked and its confidence level is labeled. Finally, a preliminary global dataset is formed and stored in a temporary database for subsequent data classification module to use. Through the above process, this system achieves broad coverage, high timeliness, and high security in the collection of security information. It not only meets the demand for targeted acquisition of vertical security information globally, but also balances collection efficiency and data security through distributed architecture and encryption mechanisms, providing high-quality raw data support for subsequent classification, analysis, and report generation.

[0024] Furthermore, the anti-scraping and anti-tracing process includes: During the data collection process, the network identity information of the data collection nodes is dynamically changed, including IP address, User-Agent parameter and request header information, to simulate normal user behavior; By setting randomized request intervals and access frequencies, traffic can be avoided from being detected as abnormal traffic by the target website. Insert human-computer interaction features such as click, scroll, and pause during the data collection process; End-to-end encryption is applied to the data transmission between the data acquisition nodes and the central server to prevent data interception and tracing during transmission. By desensitizing and obfuscating the access logs of the data collection nodes, the actual source and execution path of the tasks are hidden, thereby achieving anti-tracking and anti-source tracing.

[0025] Specifically, addressing the highly sensitive needs during information collection in the security field, a multi-layered technical strategy is employed to implement anti-collection and anti-source tracing, ensuring that collection activities are not identified and blocked by target websites, and preventing the collection source and path from being traced. The specific process is as follows: To simulate the network behavior characteristics of normal users, the system configures a dynamic identity pool for each distributed collection node, changing network identity information in real time. This includes IP address rotation, dynamic generation of User-Agent parameters, and adjustment of request header information. Specifically, IP address rotation involves nodes accessing a proxy IP pool covering multiple regions globally, employing a timed and triggered rotation mechanism. By default, the IP is randomly switched every 5 minutes. When abnormal responses such as return from the target website, access restrictions, or CAPTCHA blocking are detected, a forced IP switch is immediately triggered, and the geographic attribute of the switched IP matches the region of the target website. Dynamic generation of User-Agent parameters involves the system having a built-in User-Agent template library for mainstream browsers and mobile devices. Each collection request randomly calls one template, dynamically adjusting details such as version number and device model to avoid fixed parameters being flagged as crawlers. Personalized adjustment of request header information involves, in addition to User-Agent, fields such as Accept, Referer, and Cookie in the request header are dynamically generated with the request. Referer simulates a jump from a search engine or related page to the target page, and Cookie is dynamically generated based on previous normal browsing behavior, with old Cookies being cleaned up periodically to simulate the user session cycle. To circumvent abnormal traffic detection by target websites for high-frequency, regular requests, the system employs a random scheduling strategy based on user behavior models. This strategy includes randomized request intervals and dynamic adaptation of access frequency. Request interval randomization involves setting a base interval range based on the target website type, and generating a specific interval within that range before each request using a random algorithm. The difference between adjacent request intervals is no less than 1 second to avoid mechanical patterns. Dynamic access frequency adaptation involves the system monitoring the target website's response status in real time, such as page loading speed and return codes. When a website is detected to be under high load, such as a response delay greater than 3 seconds, the system automatically reduces the collection frequency for that website. For different pages on the same website, a differentiated strategy is adopted: low frequency for popular pages and high frequency for less popular pages, simulating real user browsing habits. By simulating human behavior, the probability of being identified as an automated program by the target website is reduced. This includes click behavior simulation, scrolling and dwell behavior simulation, and form interaction simulation. Click behavior simulation involves randomly triggering 1-2 click operations on the page before collecting webpage content, such as clicking navigation bar buttons, scroll bars, or blank areas. The click positions are generated based on the page layout algorithm, which prioritizes areas that users frequently click, such as titles and links. The click interval is 0.5-2 seconds to simulate the physical delay of mouse clicks. Scrolling and dwell behavior simulation involves simulating mouse scrolling after the page loads using a random trajectory algorithm. After scrolling to different areas of the page, the mouse dwells for a random duration (2-8 seconds) before continuing to scroll. For long text pages, the simulation simulates the dwell pattern of users reading in segments. Form interaction simulation involves simulating user input for some websites that require simple interaction, such as randomly generating a name, email address, or checking an agreement to terms and conditions in a valid format. The interaction interval and input speed match human operating habits, such as a typing speed of 30-60 words per minute. To prevent interception or tracing during data transmission, the system employs a full-link encryption mechanism, specifically including transmission channel encryption, data content encryption, and transmission behavior concealment. Transmission channel encryption involves establishing an encrypted channel between nodes and the central server based on the SSL / TLS 1.3 protocol, using the ECDHE key exchange algorithm and the AES-256-GCM encryption algorithm. Certificates are dynamically updated to prevent malicious interception. Data content encryption, in addition to channel encryption, involves secondary encryption of the collected raw data before transmission using a self-developed symmetric encryption algorithm (the key is dynamically distributed by the central server and updated hourly). The encrypted content includes the data itself and metadata, such as collection time and node ID. Even if the channel is cracked, valid information cannot be directly obtained. Transmission behavior concealment involves randomizing data transmission times (avoiding peak traffic monitoring periods of the target website) and controlling the amount of data transmitted in a single session within a reasonable range, such as ≤1MB. Large files, such as multi-image reports, are transmitted in chunks with random intervals, such as 1-5 seconds, simulating normal user file download behavior. By processing node operation logs, the source and execution path of the actual data collection task are hidden. Specifically, in the local logs of the collection nodes, all core sensitive information is hashed (SHA-256 algorithm) or replaced with meaningless identifiers, such as using source A and keyword X to replace the actual content. Only non-sensitive operation records are retained. If the collection is successful or the transmission is completed, the system periodically inserts false records into the logs (accounting for 10%-20%), such as fictitious access to non-existent web pages or transmission failures. The format and timestamp of the false records are consistent with the real records, interfering with the tracking of the real collection path. The local logs of the nodes are only retained for 24 hours and are automatically deleted after the expiration and cannot be recovered. The logs received by the central server only store the de-identified operation results. If data collection in a certain area is completed, the specific execution details are not recorded, further cutting off the traceability link. Through the above process, the system achieves end-to-end protection for the collection of highly sensitive information in the security field. This not only prevents the collection behavior from being identified and intercepted by the target website, but also prevents the collection nodes, task sources, and data transmission paths from being traced and tracked by external parties. This ensures the continuity and security of global security information collection and provides a reliable data foundation for subsequent classification, analysis, and report generation.

[0026] Furthermore, the automatic classification process includes: Natural language processing algorithms are used to identify keywords, perform semantic analysis and topic modeling on multi-source information, and extract core features such as event type, time of occurrence, location, subject and influencing factors; The extracted features are compared with the preset classification rules to preliminarily determine the category to which the information belongs; The initial judgment results are reclassified and assessed for confidence by calling a large artificial intelligence model to ensure the accuracy and consistency of the classification.

[0027] Specifically, given the diversity and complexity of information in the security field, a three-tiered processing mechanism of feature extraction, rule matching, and intelligent verification is used to achieve accurate classification of collected information, providing a structured foundation for subsequent storage and analysis. The specific process is as follows: The system extracts key features from collected multi-source information, such as news texts, government announcements, and social media posts, using Natural Language Processing (NLP) technology. This includes keyword recognition, semantic parsing, topic modeling, and core feature output. Keyword recognition is based on a pre-defined security domain keyword library, covering core terms in areas such as regional events, specific types of public safety incidents, policy and regulatory changes, specific behavioral patterns, economic measures, political agendas, strikes, and infectious diseases. It uses a bidirectional longest matching algorithm to identify entity words and event words in the text, while simultaneously using a Named Entity Recognition (NER) model to locate proper nouns such as countries / regions, organizations, and people. Semantic parsing utilizes pre-trained language models, such as a security-domain fine-tuned version of BERT, to perform deep semantic understanding of the text, identifying the subject-verb-object structure of events and analyzing causal relationships and temporal order. Topic modeling uses the Latent Dirichlet Allocation (LDA) algorithm to cluster batches of text into topics, uncovering hidden topics and mapping individual information to corresponding topic dimensions. Core feature output integrates the above processing to extract standardized features for each piece of information, including event type, time of occurrence, location, subject, and influencing factors. The system compares the extracted core features with the security domain classification rule base to complete the preliminary classification. This includes the construction of the classification rule base and the rule matching logic. Specifically, the classification rule base is designed based on the classification system of the vertical database in the document. Each rule contains feature matching conditions and corresponding categories. For example, if the event type = specific public safety event and the subject = citizen of a specific region / enterprise entity of a specific region, it is initially classified into the regional security event database - personal and property safety sub-database. If the event type = policy adjustment and the content = tax and labor policy, it is initially classified into the legal and compliance risk database. If the event type = specific political agenda and the country involved = specific country, it is initially classified into the policy and political dynamics database - political agenda sub-database. The rule matching logic adopts a multi-feature weighted matching strategy, which sets weights for features such as event type, location, and subject. For example, the event type has the highest weight. When the matching degree exceeds the preset threshold, it is determined to be the corresponding category. If multiple rules are matched at the same time, the preliminary category is determined according to the principle of prioritizing the most relevant features. To correct the initial classification error, the system calls upon a large-scale AI model for the security industry for secondary verification. This model is trained on over a million annotated security-related data points collected historically by the system, possessing deep semantic understanding and category judgment capabilities for security events. The initial classification results and the original text are input into the large-scale model. By comparing the feature distribution of similar events, such as the difference between specific public security events and ordinary public security cases, the model corrects specific events that were misclassified as ordinary public security cases to specific public security events. The model outputs a confidence score (0-100) for the classification results. If the score is ≥90, it is considered high confidence and the classification result is directly confirmed. If the score is 70 ≤ score <90, it is considered medium confidence and suspicious points requiring manual review are marked. If the score is <70, it is considered low confidence and returns to the feature extraction stage for reprocessing. For similar events, such as information related to global specific political agenda predictions, the model ensures consistent classification standards through cross-time and cross-source comparisons. For example, political agenda information from different countries is categorized into the policy and political dynamics database. Through the above process, the system achieves accurate classification of multi-source information in the security field. It relies on the rule base to ensure the efficiency and standardization of classification, and improves the classification accuracy of complex events through secondary verification by the AI ​​big model. The information is stored in the corresponding vertical database, providing high-quality structured data support for subsequent intelligent analysis and report generation.

[0028] Furthermore, the structured processing flow includes: The categorized results are transformed into a unified data structure format, event entries are created and standardized fields are populated, including time, location, category, risk level and related descriptive information; The structured information is stored in the corresponding vertical database.

[0029] Specifically, after the data is automatically categorized, it needs to be transformed from unstructured / semi-structured information into standardized data through structuring processing. This provides a unified format foundation for subsequent storage, analysis, and report generation. The specific process is as follows: The system addresses the common characteristics and vertical classification needs of security information by pre-setting structured templates for security events, defining a unified data structure format, and using JSON as the standard exchange format to ensure that information from different sources and categories can be parsed according to consistent rules. The template includes core standardized fields such as unique event identifier, event category, event occurrence time, event location, involved parties, event risk level, brief event description, event impact, information source URL, and information collection time, as shown in Table 1 below: Table 1

[0030] For categorized information, the system establishes event entries and fills in fields according to the following steps: Based on the core features extracted by previous natural language processing (event type, time, location, etc.), it automatically maps to the corresponding fields. For example, the time information in the original report of a specific public safety event is parsed into event_time, and the location information is broken down into the country / province / city level. The classification result of regional safety event - personal and property safety subcategory is directly filled into the event_type field. The risk level of the event is automatically determined by the risk assessment model and filled into risk_level. For example, the default risk level of an event that causes serious consequences is ≥4. For information with an AI classification confidence level of less than 70% or missing fields, the system automatically marks it as pending verification and completes it after manual review. The filled fields are formatted and uniformly converted. For example, non-standard time formats such as March 26, 2024.03.26 are uniformly converted into ISO8601 format. The location names are standardized. For example, non-standard place names are uniformly converted into standard geocoding and latitude and longitude are added. After structuring, the system stores the data in the corresponding database tables according to the preset mapping relationship between event type (event_type) and vertical database. The specific process is as follows: The system presets an event type-database mapping table. If event_type is a regional security event, it is mapped to the regional security information database; if event_type is a specific political agenda and policy change, it is mapped to the political and economic dynamics database; if event_type is a public safety event of a specific type, it is mapped to the public safety event database; if event_type is an infectious disease outbreak / food and drug safety, it is mapped to the public health database. The structured data is written into the table structure of the corresponding database through the database interface. A hybrid architecture of relational and non-relational databases is adopted. For example, MySQL stores the core structured fields, and MongoDB stores the long text descriptions. The event_id is associated with the original collected data to ensure traceability. Then, the integrity of the fields and the consistency of the format are verified. If the verification fails, it returns to the field filling stage for reprocessing. Database indexes are automatically created for event_time (time index), location (region index), event_type (category index), and risk_level (risk level index) to improve the efficiency of subsequent queries and analysis. Through the above process, the system transforms scattered unstructured security information into structured data with unified format and standardized fields, and stores it in a vertical database. This not only solves the problem of chaotic information formats and difficulty in correlation analysis in the security field, but also provides high-quality data support for the intelligent analysis module and report generation module.

[0031] Furthermore, the information storage process includes: Establish a mapping relationship between different categories of information and the target database, and determine the corresponding database type and data table structure; Before storage, generate unique identifiers for the information and build multidimensional indexes, including time indexes, geographic indexes, subject indexes, and risk level indexes; The structured information is written into the corresponding database table according to the mapping relationship, while maintaining the association with the original data; The data verification mechanism verifies the integrity, consistency, and deduplication of stored information.

[0032] Specifically, considering the multi-source, highly sensitive, and highly correlated characteristics of vertical information in the security field, a full-process management system is implemented, encompassing mapping rule establishment, unique identifier and index construction, targeted writing, and verification optimization, to ensure the standardization, traceability, and availability of data storage. The specific process is as follows: Based on a pre-defined security domain information classification system, the system matches corresponding storage media and table structures for different categories of information. Specifically, it clarifies the relationships through a classification-database mapping table, as shown in Table 2 below: Table 2

[0033] The structure of Table 2 is optimized for different data characteristics. Structured information, such as event time and location, uses a fixed-field table of a relational database, while unstructured information, such as detailed event descriptions and original news text, uses a flexible document structure of a non-relational database (MongoDB) to ensure a balance between storage efficiency and query convenience. To achieve accurate data location and efficient retrieval, the system generates identifiers and constructs indexes before storage. Specifically, it generates globally unique IDs using a composite rule of time, category coding, and random sequences, such as EVT202403260830_RA001. Here, "EVT" represents event-type information, "202403260830" is the information collection timestamp, "RA" is the category code for "regional security event" (a pre-defined coding library corresponds to various databases), and "001" is the sequence number of that category of information on that day. This unique ID ensures that each piece of information can be individually identified, and it allows for quick association with its category and collection time. A four-level index is constructed based on the core features of the information to support multi-dimensional query needs, including a time index, denoted by event_time. A B-tree index is built using "e" (event occurrence time) and "collect_time" (collection time) as keywords, supporting filtering by time ranges such as "last 24 hours" and "last 7 days". A geographic index is created, with a hierarchical index on the "location" field (country / province / city), and multi-level geographic queries ("Asia-country-province") are enabled by combining geocoding (such as ISO country codes). A topic index is built based on "event_type" (event type) and "subject" (involved entities), allowing for combined queries such as "public safety incidents associated with regional entities" and "political agendas associated with specific regions and countries". A risk level index is created, with a hash index on "risk_level" (levels 1-5), supporting quick filtering of "high-risk (levels 4-5) events". The system writes structured information into the target database according to the mapping relationship and maintains a traceability association with the original collected data. The structured data, such as event entries and standardized fields, is written to the corresponding tables in batches through database interfaces (such as JDBC and MongoDBDriver). The writing process adopts a transaction mechanism to ensure atomicity, that is, all fields of a piece of information are either all written successfully or all fail. For large-volume data, a sharding writing and breakpoint resume strategy is adopted. Data is sharded by time or region to avoid excessive pressure in a single write. The original collected data is stored in a distributed file system (such as HDFS) and a unique file path is generated. The file path of the original data is recorded in the source_path field of the structured data. A two-way association between structured information and original data is realized through a unique ID to ensure traceability, such as querying the original source content by event ID. To ensure the quality of stored data, the system employs a multi-layered verification mechanism to validate the integrity, consistency, and deduplication effectiveness of information. This includes checking for missing fields in structured data, such as event_id, event_time, location, and event_type. If any are missing, the data is marked as incomplete and returned to the structured processing stage for supplementation. The system also checks for format consistency, verifying that the time field conforms to the ISO8601 standard and that the region name matches the preset geocoding database. Logical consistency is also verified by checking the rationality of associated fields. Deduplication is based on a dual approach using unique IDs and content fingerprints. For newly written information, the system checks for duplicate submissions using the unique ID. For information with different IDs but highly similar content, such as different sources reporting the same event, a content fingerprint is generated using the SimHash algorithm. If the similarity is ≥90%, the data is considered duplicate, and only the record with the highest source authority is retained. A note indicating a duplicate is added. Data that passes the verification is officially stored, while data that fails is placed in a waiting queue where the system automatically attempts to correct it or triggers manual intervention to ensure the reliability of the final stored data. Through the above process, the system achieves standardized storage of vertical information in the security field, which not only ensures accurate matching and efficient retrieval of data with the database, but also ensures traceability by associating the data with the original data through a unique identifier. At the same time, the verification mechanism improves data quality, providing an effective data foundation for subsequent intelligent analysis and report generation.

[0034] Furthermore, the processing flow for categorized information includes: Using a pre-trained language model, semantic parsing is performed on classified information to identify causal relationships, temporal order, and potential influencing factors among events; The severity, scope of impact, and development trend of an event are quantitatively analyzed using a risk assessment model, and corresponding risk levels are generated. By cross-referencing and multi-dimensionally integrating information collected from different databases and time periods, hidden related events or potential patterns can be discovered. By using time series modeling and prediction algorithms, we can analyze the development direction and evolution trend of events; The analysis and assessment results are aggregated according to theme, region, or industry, and output as standardized assessment items.

[0035] Specifically, for security information already categorized into vertical databases, a progressive processing approach—semantic parsing, risk quantification, correlation fusion, trend prediction, and aggregated output—is used to uncover the deeper value of the information and provide decision support for report generation. The specific process is as follows: The system employs a pre-trained language model, such as BERT, fine-tuned for the security domain, to perform deep semantic analysis on classified information. This includes model training foundations and core relationship identification. The model training foundation uses historical data from over 10,000 security-related information sources in documents as training corpus. Model parameters are optimized by labeling causal relationships and time sequences to adapt to specific expressions in the security domain, such as the correlation between specific economic measures and industry barriers, or the progressive relationship between specific group activities and policy and regulatory changes. Core relationship identification specifically includes causal relationship identification, time sequence identification, and potential influencing factor identification. Causal relationship identification involves analyzing the triggering and being triggered logic between events. For example, from texts about policy and regulatory adjustments in a specific region and changes in the operational status of a specific entity, it identifies that policy adjustments are the direct cause of changes in the operating environment. Time sequence identification sorts multiple events in the same region or subject by timestamp to clarify the chronological relationship. For example, a specific public safety incident occurs first in region A, subsequently triggering the evacuation of personnel from an entity in region B. Potential influencing factor identification involves mining indirect related elements. For example, from the policy claims of participants in a specific region's political agenda, it predicts the potential impact of the agenda's advancement on the operational policies of local entities. The system calls a risk assessment model adapted to the security field to quantify the severity, scope of impact, and development trend of an event, generating a risk level of 1-5. Specifically, the risk level is determined by the degree of loss of life and property caused by the event (e.g., causing significant loss of life and property is considered high), the nature of the event (e.g., a specific type of public safety event is scored higher than a general individual event), the geographical location (e.g., cross-regional events are scored higher than single-city events), the industry (e.g., the energy industry is scored higher than ordinary commerce), the degree of association with a specific entity (e.g., directly associated with a specific entity is scored higher than indirectly associated with a specific entity), and the event's diffusion potential (e.g., whether it triggers a series of chain reactions or is imitated by other relevant parties). The model calculates the risk level by weighting the above dimensions. For example, an event involving specific entities and causing serious consequences is classified as a level 4 risk level. To uncover hidden correlations or potential patterns, the system performs cross-domain fusion of information from different databases and time periods. Specifically, it compares information by region (e.g., comparing a specific event in a region from the regional security information database with the dynamics of enterprises in that region); it compares information by subject (e.g., comparing a political agenda in a region from the political and economic dynamics database with policy adjustments in the legal and compliance risk database to analyze the comprehensive impact on specific entities); it compares information by time (e.g., comparing an earthquake in a region from the natural disaster database with a surge in traffic accidents in the road traffic safety database to discover the indirect impact of disasters on public security); and for highly correlated information (similarity ≥ 80%), it aligns entities, merging entities from the same region or the same event into event chains (e.g., the advancement of a political agenda in a region - the occurrence of a public safety incident - the impact on the operation of a specific entity), supplementing missing links. The system employs time series forecasting algorithms, such as ARIMA and LSTM, to analyze the development trends of continuously occurring events. Specifically, it uses historical time series data of the same type of events in the database as input, such as the frequency of specific types of public safety incidents in a region over the past 6 months, or the weekly changes in support rates for candidates in a region with a specific political agenda. Logical prediction is divided into short-term trends, medium- and long-term trends, and impact prediction. For sudden and continuous events, such as specific types of public safety incidents, the short-term trend forecast predicts the development over the next 48 hours based on data from the past 72 hours. For periodic events, such as specific political agendas, the medium- and long-term trend forecast predicts the final outcome based on months of public opinion and voting data. Impact prediction combines event trends with the associated database to predict the potential impact on entities in a specific region, such as "if a political agenda in a region leads to changes in the policy environment, the probability of specific industry entities facing operational risks increases." The system aggregates the above analysis and assessment results in a unified format to generate standardized assessment items, providing direct input for the report generation module. Specifically, these include thematic aggregation, regional aggregation, and industry aggregation. Thematic aggregation, such as under the theme of regional security incidents, summarizes assessment results such as security incidents encountered by personnel of specific entities and economic measures suffered by specific entities. Regional aggregation, such as under the Southeast Asia region, integrates security dynamics of relevant regions. Industry aggregation, such as under the energy industry, links information such as oil transportation safety and security incidents encountered by mining enterprises. The standardized item format includes a core summary of the event, such as the serious consequences of a specific security incident in region A on personnel of entity B in region B, the risk level (level 4), the trend prediction (the risk level of similar events will remain high in the short term), and the impact on specific entities (related projects need to strengthen security measures), ensuring that the fields match the fields in subsequent report templates (such as a briefing on overseas security incidents of specific entities). Through the above process, the system realizes the transformation from "fragmented classified information" to structured judgment conclusions, relies on AI models and algorithms to explore the deep correlation and trends of information, and supports the automated generation of various reports through standardized output.

[0036] Furthermore, the process of cross-comparison and multi-dimensional fusion includes: Information from different databases is standardized according to timestamps, geographic codes, and event categories to ensure data consistency in both time and spatial dimensions. Map the key elements of an event to a unified feature vector; Based on semantic matching algorithms and vector retrieval models, similarity analysis is performed on information from different sources to identify multiple data records that may belong to the same event; By merging highly similar data entries, a complete event chain is generated, and the correction results are filled in with missing or contradictory information. By using cluster analysis and graph association techniques, we can discover recurring patterns, potential regularities, or hidden cross-regional and cross-temporal related events from the fused event chains.

[0037] Specifically, to address the need for correlation mining of multi-source heterogeneous information in the security field, a progressive processing approach—standardization, feature mapping, similarity matching, fusion completion, and pattern discovery—is adopted to achieve deep correlation of information across databases, time periods, and geographical regions. The specific process is as follows: To address the issue of inconsistent data formats caused by differences in data collection rules across different databases, standardization is necessary to ensure consistency in both time and spatial dimensions. This standardization includes time dimension standardization, spatial dimension standardization, and event category standardization. Time dimension standardization specifically involves unifying the timestamp format. This converts time records from different databases, such as 2024 / 3 / 26, 26-03-2024, March 26, into the ISO 8601 standard format, such as 2024-03-26T08:30:00 and 05:00. It also includes time zone information, such as +5:00 for the second time zone and +8:00 for the first time zone, avoiding cross-regional time comparison errors. Finally, it aligns the time granularity by adjusting the time precision according to the event type, such as accurate to the minute for specific public safety events. The policy adjustments are precise down to the day. For time-limited events, such as a specific group activity lasting 3 days, start and end timestamps are marked (start_time and end_time). Spatial dimension standardization specifically involves mapping vague regional names such as "a province in the second major region" or "northern part of the second region" to standardized codes based on ISO3166 country codes and multi-level administrative division codes, ensuring consistency of different expressions for the same region. For events containing specific locations, latitude and longitude coordinates are matched through geographic information APIs to provide quantitative basis for spatial correlation analysis. Event category standardization specifically involves unifying the category expressions of different databases based on a preset security event classification system. For example, public safety events of a specific type are uniformly classified into the public safety event - specific event subclass, ensuring consistency of cross-database category comparison. The standardized event information is transformed into a computable multidimensional feature vector, providing a quantitative basis for subsequent similarity analysis. The core elements and mapping rules include core element extraction and feature vector construction. Core element extraction extracts five key elements from each event record, specifically including: time features (event_time, a numerical representation of a timestamp, such as a Unix timestamp) and time_window (event duration in hours); spatial features (country_code, such as PK), region_code, such as PK-KP), and coordinates (latitude and longitude coordinates, split into latitude and longitude numerical features); subject features (subjects involved in the event, such as specific individuals or names of relevant parties); event features (event_type, such as code 101 for "specific public safety event") and risk_level (risk level 1-5); and semantic features (keywords). ords (core keywords, such as "specific event method" or "specific entity asset"), description (event description text), feature vector construction is as follows: numerical features, such as timestamp, latitude and longitude, risk level, are directly standardized to values ​​in the range [0,1]; categorical features, such as country code, event type are converted into binary vectors using one-hot encoding, such as the first region (PK) corresponding vector has a PK dimension of 1, other countries have 0; textual features (such as keywords, descriptions) are converted into semantic vectors through a security domain pre-trained word vector model (such as a security event lexicon trained by Word2Vec), such as the semantic vectors of specific event type A and specific event type B are close, and finally form a fixed-dimensional feature vector (such as 128 dimensions), such as the vector of a certain regional security event includes: timestamp (0.65), region code (1), event type code (1), risk level 4 (0.8), "specific entity" semantic vector (0.72), etc. Algorithms are used to calculate the similarity of feature vectors from events from different sources to identify potential associations. Cosine similarity is used to calculate the cosine of the angle between the feature vectors of two events, using the following formula: ; in, Representing vectors The One portion, Representing vectors The There are several components, where n is the vector dimension, sim(A,B)∈[0,1], and the closer the value is to 1, the higher the similarity. The semantic matching enhances the text description part, and the BERT model is used to calculate the sentence-level semantic similarity. For example, the semantic similarity between a specific entity asset encountering an event and a specific entity personnel encountering a major event is ≥0.9. This is then weighted and fused with vector similarity (50% weight each) to improve the accuracy of text association recognition. An efficient vector retrieval engine (such as FAISS) is used to retrieve the Top 100 similar vectors of the target event in cross-database data. At the same time, a dynamic similarity threshold is set. The direct association threshold for the same event is ≥0.8 (such as two reports describing the same public safety event), and the causal association threshold (such as policy adjustments and changes in the entity's operating environment) is ≥0.6 (features overlap but are not completely identical). Event records with similarity exceeding a threshold are merged to fill in missing information, correct contradictions, and form a complete event chain. This includes data fusion rules and event chain construction. Specifically, for different records of the same event, non-conflicting fields are merged. For example, record A has time but no location, and record B has location but no time; the merged record will include both time and location. When fields conflict, such as record A affecting 5 people and record B affecting 7 people, the fusion is weighted by source authority (e.g., official media > mainstream media > self-media) and timeliness (latest report > old report), e.g., latest report from official media. The seven individuals were used as the final value, and a conflict was noted in the remarks field. Authoritative sources were adopted, and completely duplicated fields were deleted. If different records all involve a specific entity, only one record was retained. The event chain was constructed by linking related events in chronological order. For example, Event 1: A relevant party issued a statement targeting a specific entity; Event 2: Personnel of that specific entity encountered a public safety incident; Event 3: The specific entity initiated an emergency response plan. After merging, a chain was generated: relevant information release - security incident occurrence - entity emergency response. The causal relationship within the chain was also marked, such as Event 1 being the precursor to Event 2. By employing clustering and knowledge graph technologies, deep cross-regional and cross-temporal patterns are extracted from the fused event chains. Specifically, the DBSCAN clustering algorithm is used to group large-scale fused data based on event feature vectors. Parameter settings include a distance threshold ε=0.3 (based on the statistical distribution of security event features) and a minimum sample size min_samples=5. For example, clustering reveals a recurring pattern in a region where the frequency of group activities targeting specific entities increases one month before each specific political agenda in the past six months, providing a basis for risk warning. A security event knowledge graph is constructed through knowledge graph association, with nodes representing events, subjects, regions, and times, and edges representing causal, accompanying, and sequential relationships. Hidden associations are mined using graph neural networks (GNNs), such as cross-regional associations where the event pattern vectors of stakeholders in region A and region B are highly similar, indicating a correlation; and cross-temporal associations where the trend of specific policy adjustments in a country before two political agendas is consistent, predicting that similar policies may continue after a specific political agenda in 2024. Through the above process, the system realizes the transformation of multi-source information in the security field from scattered to related, and from fragmented to complete. It not only solves the problem of data heterogeneity between different databases, but also uses algorithms to mine explicit correlations and implicit patterns between events, providing cross-dimensional correlation evidence chains for global risk assessment.

[0038] Furthermore, the report generation process includes: The report type is determined by matching the analysis results of the intelligent analysis module with the preset report type trigger conditions. Apply corresponding information filtering rules to different report types; Fill in the filtered information using the standardized templates for each type of report; Perform completeness and logical verification on the generated report; Once the verification is successful, the report is stored in the report library and distributed through the system interaction function.

[0039] Specifically, addressing the diverse information presentation needs in the security field, based on the analysis results output by the intelligent analysis module, various reports that meet user requirements are automatically generated through a standardized process of type matching, information filtering, template filling, verification, and distribution. The specific process is as follows: The system automatically determines the report type by comparing the analysis results output by the intelligent analysis module with the preset report type trigger rule base. The rule base design takes into account the timeliness and content characteristics of the first report, quick report, and daily report in the document to determine the report type, as shown in Table 3 below: Table 3

[0040] Specifically, when a major public safety incident involving specific entities and personnel occurs in area A of the intelligent analysis module, with a risk level of 4 and directly associated with a specific entity, the system matches the quick report triggering conditions and determines to generate an emergency safety incident quick report for the specific entity. After determining the report type, the system invokes the corresponding information filtering rules to extract core information from the intelligent analysis module's assessment results, ensuring the report's relevance and conciseness. Specifically, the rapid report filtering rules focus on a single emergency event, extracting the "5W1H" core elements (Who: specific entity / personnel, When: time of the event, Where: region of the event, What: major security event in a specific manner; Why: not yet clear, How: specific manner of the event), filtering out secondary information (such as historical event background), and prioritizing the retention of authoritative information from sources (such as television reports). The daily report filtering rules categorize and summarize by region and event type, such as Category A events in Asia, Category B events in Europe, and only retaining the details of each event. The report should include three core elements: event summary, risk level, and impact on specific entities. Only one duplicate event should be retained (based on the best source). The weekly report selection rules are to add trend analysis elements to the daily report, such as a 20% increase in specific types of public safety events this week compared to last week, mainly concentrated in the Middle East. High-frequency risk points should be extracted, such as a 60% proportion of specific types of events in Southeast Asia. A risk forecast for next week should be added, such as a high risk level for specific types of public safety events in a certain region. The special report selection rules are to integrate information from multiple time periods and databases around a user-specified theme, such as a specific political agenda. It should also include four in-depth dimensions: event background, development context, impact analysis, and countermeasures and recommendations. Information sources should cover authoritative sources such as government websites and think tank reports. The system automatically fills in the filtered information according to the standardized template of the corresponding report type. The template design strictly matches the format characteristics of the report in the document and adopts a field mapping mechanism to automatically fill the filtered information into the fixed fields of the template. Redundant words are automatically removed from text information and charts are automatically generated from data information. To ensure report quality, the system performs dual verification on generated reports using preset rules. The verification standards reference the accuracy and rigor requirements for research reports in the documentation, specifically including completeness and logical consistency checks. Completeness checks include mandatory field checks; for example, a quick report must include four mandatory fields: time, location, specific entity involved, and nature of the event. Missing fields are marked as incomplete and need to be filled in. Information granularity checks are also performed; for example, the special report's countermeasures recommendation section must include at least two actionable suggestions, avoiding vague statements such as "strengthening prevention." Logical consistency checks include timeline verification; for example, if event A in the report... Incident B occurred on March 26th. It was triggered by A. It is necessary to check whether the time of A is earlier than that of B to avoid time reversal. Causal relationship verification is also required. For example, if the policy and regulations of a certain region change the operational status of a specific entity, it is necessary to verify whether there is a direct relationship between the two. Data consistency verification is also required. For example, the 42 regional security incidents in Southeast Asia in the weekly report must be consistent with the 42 regional security incidents in Southeast Asia in February in the monthly report. Reports that fail the verification will be automatically returned to the information filtering stage, indicating missing fields or logical contradictions. If there are minor flaws in high-priority reports (such as quick reports), manual intervention can be triggered for correction. Reports that pass verification are stored and distributed according to the following process to ensure users can obtain the information they need in a timely manner. They are stored in a three-level directory system based on report type, time, and region. Each report is assigned a unique identifier, linked to the original data, and supports traceability. Distribution is conducted through system interactive functions, with the distribution recipients automatically matched to target users based on the report type. For example, bulletins are pushed to overseas security officers of specific regional entities and security specialists at embassies and consulates abroad; monthly reports are pushed to corporate strategy departments and government research institutions. Distribution methods are as follows: emergency reports (such as bulletins) are pushed via system pop-ups and SMS / email; regular reports (such as daily reports) are updated only through the system's internal report center, with simultaneous reminder notifications; classified reports (such as special reports) require user permission verification, such as for corporate executives and government departments, and support watermark traceability. Through the above process, the system achieves fully automated conversion from intelligent analysis results to usable reports, ensuring the timeliness and accuracy of the reports, while meeting the needs of different users in different scenarios through standardized templates and targeted distribution.

[0041] Example 2: A vertically integrated intelligent information acquisition method for the security field includes: Collect publicly available data from the global internet using distributed data collection technology; Anti-collection and anti-tracing technologies are employed during the data collection process to prevent external tracking and data tracing. The collected multi-source information is automatically classified and structured, and then stored in a pre-defined vertical database; Based on a large-scale artificial intelligence model, classified information is analyzed, assessed, and aggregated. Based on the analysis results, generate safety briefings, bulletins, daily reports, weekly reports, and special reports.

[0042] Specifically, the implementation process of the specific steps has been described in detail based on Embodiment 1, so it will not be repeated here. Based on the content disclosed in Embodiment 1, the overall workflow of the system of this application is described below. Through distributed data collection technology, publicly available security-related data from the global internet is divided into tasks based on source type, geographical scope, and keywords. Multiple distributed nodes crawl the data in parallel and transmit it through an encrypted channel to a central server for integration and deduplication, forming a preliminary dataset. During the collection process, anti-collection and anti-source tracing technologies such as dynamically changing network identity information, simulating human-computer interaction features, encrypted transmission, and log anonymization are employed to ensure the covert and secure collection activities. For the collected multi-source information, core features are extracted using natural language processing algorithms and automatically classified using AI models. This data is then transformed into structured data with standardized fields, stored in corresponding vertical databases, and indexed using multi-dimensional indexes. Subsequently, based on AI models, the classified information undergoes semantic analysis, risk quantification, cross-database correlation and fusion, and time series trend prediction. Standardized judgment items are aggregated by theme, region, or industry. Finally, based on the judgment results, report type trigger conditions are matched, corresponding filtering rules are invoked, and security briefings, bulletins, daily reports, weekly reports, and special reports are generated according to standardized templates. After integrity and logical verification, these reports are stored in a report library and distributed through system interaction functions.

[0043] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A vertically integrated intelligent information acquisition system for the security field, characterized in that, include: The data acquisition module collects publicly available data from the global internet using distributed acquisition technology; The data security module employs anti-collection and anti-tracing technologies during the data acquisition process to prevent external tracking and data traceability. The data classification module automatically classifies and structures the collected multi-source information and stores it in a preset vertical database; The intelligent analysis module analyzes, assesses, and aggregates classified information based on a large-scale artificial intelligence model. The report generation module generates safety briefings, bulletins, daily reports, weekly reports, and special reports based on the analysis results.

2. The security field vertical information intelligent acquisition system according to claim 1, characterized in that, The data acquisition process includes: The task of collecting data across the entire network is divided according to different information source types, geographical scope, and subject keywords; The divided data collection tasks are distributed to multiple distributed data collection nodes, and each node independently performs information capture. Each data collection node acquires target web pages, social media content, and open database data in real time and in parallel based on preset data collection rules and scheduling strategies. The collected information is transmitted to the central processing server through an encrypted channel; The central processing server integrates, deduplicates, and verifies data from different nodes to form a preliminary global dataset.

3. The security field vertical information intelligent acquisition system according to claim 1, characterized in that, The anti-scraping and anti-tracing process includes: During the data collection process, the network identity information of the data collection nodes is dynamically changed, including IP address, User-Agent parameter and request header information, to simulate normal user behavior; By setting randomized request intervals and access frequencies, traffic can be avoided from being detected as abnormal traffic by the target website. Insert human-computer interaction features such as click, scroll, and pause during the data collection process; End-to-end encryption is applied to the data transmission between the data acquisition nodes and the central server to prevent data interception and tracing during transmission. By desensitizing and obfuscating the access logs of the data collection nodes, the actual source and execution path of the tasks are hidden, thereby achieving anti-tracking and anti-source tracing.

4. The security field vertical information intelligent acquisition system according to claim 1, characterized in that, The automatic classification process includes: Natural language processing algorithms are used to identify keywords, perform semantic analysis and topic modeling on multi-source information, and extract core features such as event type, time of occurrence, location, subject and influencing factors; The extracted features are compared with the preset classification rules to preliminarily determine the category to which the information belongs; The initial judgment results are reclassified and assessed for confidence by calling a large artificial intelligence model to ensure the accuracy and consistency of the classification.

5. The security field vertical information intelligent acquisition system according to claim 1, characterized in that, The structured processing flow includes: The categorized results are transformed into a unified data structure format, event entries are created and standardized fields are populated, including time, location, category, risk level and related descriptive information; The structured information is stored in the corresponding vertical database.

6. The security field vertical information intelligent acquisition system according to claim 1, characterized in that, The information storage process includes: Establish a mapping relationship between different categories of information and the target database, and determine the corresponding database type and data table structure; Before storage, generate unique identifiers for the information and build multidimensional indexes, including time indexes, geographic indexes, subject indexes, and risk level indexes; The structured information is written into the corresponding database table according to the mapping relationship, while maintaining the association with the original data; The data verification mechanism verifies the integrity, consistency, and deduplication of stored information.

7. The intelligent information acquisition system for vertical security applications according to claim 1, characterized in that, The processing flow for the classified information includes: Using a pre-trained language model, semantic parsing is performed on classified information to identify causal relationships, temporal order, and potential influencing factors among events; The severity, scope of impact, and development trend of an event are quantitatively analyzed using a risk assessment model, and corresponding risk levels are generated. By cross-referencing and multi-dimensionally integrating information collected from different databases and time periods, hidden related events or potential patterns can be discovered. By using time series modeling and prediction algorithms, we can analyze the development direction and evolution trend of events; The analysis and assessment results are aggregated according to theme, region, or industry, and output as standardized assessment items.

8. A vertical information intelligent acquisition system for the security field according to claim 7, characterized in that, The process of cross-comparison and multi-dimensional fusion includes: Information from different databases is standardized according to timestamps, geographic codes, and event categories to ensure data consistency in both time and spatial dimensions. Map the key elements of an event to a unified feature vector; Based on semantic matching algorithms and vector retrieval models, similarity analysis is performed on information from different sources to identify multiple data records that may belong to the same event; By merging highly similar data entries, a complete event chain is generated, and the correction results are filled in with missing or contradictory information. By using cluster analysis and graph association techniques, we can discover recurring patterns, potential regularities, or hidden cross-regional and cross-temporal related events from the fused event chains.

9. A vertical information intelligent acquisition system for the security field according to claim 1, characterized in that, The report generation process includes: The report type is determined by matching the analysis results of the intelligent analysis module with the preset report type trigger conditions. Apply corresponding information filtering rules to different report types; Fill in the filtered information using the standardized templates for each type of report; Perform completeness and logical verification on the generated report; Once the verification is successful, the report is stored in the report library and distributed through the system interaction function.

10. A method for intelligent vertical information acquisition in the security field, used in the intelligent vertical information acquisition system for the security field as described in any one of claims 1-9, characterized in that, include: Collect publicly available data from the global internet using distributed data collection technology; Anti-collection and anti-tracing technologies are employed during the data collection process to prevent external tracking and data tracing. The collected multi-source information is automatically classified and structured, and then stored in a pre-defined vertical database; Based on a large-scale artificial intelligence model, classified information is analyzed, assessed, and aggregated. Based on the analysis results, generate safety briefings, bulletins, daily reports, weekly reports, and special reports.

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