Intelligent early warning communication system for public safety events based on AI semantic analysis

Abstract

The application discloses an intelligent early warning communication system for public safety events based on AI semantic analysis, relates to the technical field of public safety event early warning, and comprises four core modules.A public event safety evaluation module collects text, audio and video stream data of a target region, analyzes an event threat comprehensive evaluation value to determine whether a public safety event occurs, a public event safety authenticity verification module acquires group behavior dynamic trend indexes to verify the authenticity of an event when it is determined that an event occurs, an event safety risk level analysis module analyzes the safety risk level of an event and matches an early warning communication scheme according to the threat comprehensive evaluation value when the event is true, and an event diffusion risk level analysis module derives a surrounding area cooperative early warning scheme according to the safety risk level of an event, and constructs an intelligent public safety early warning and prevention and control system.

Description

Technical Field

[0001] This invention relates to the field of public safety incident early warning technology, and specifically to an intelligent early warning communication system for public safety incidents based on AI semantic analysis. Background Technology

[0002] With the acceleration of urbanization and the popularization of information technology, public safety incidents are characterized by their suddenness, rapid spread, and wide impact. Traditional public safety early warning methods, relying on manual patrols and single-channel monitoring, suffer from problems such as delayed response, fragmented information, and low coordination efficiency, making it difficult to quickly and accurately assess incident risks and take effective measures. Against this backdrop, intelligent early warning and communication systems for public safety incidents based on AI semantic analysis have emerged.

[0003] Existing technologies, such as the invention patent application CN115545573B, disclose a risk warning method, device, equipment, and storage medium based on social events, belonging to the field of data processing technology. The method includes: acquiring current social event text information within the region to be warned; performing word segmentation on the social event text information to obtain multiple keywords; classifying the multiple keywords based on the KNN text classification model to determine the dimension to which each keyword belongs; performing lexical analysis on each keyword to obtain lexical analysis results; forming word groups based on the lexical analysis results of the keywords; performing correlation analysis on at least two keywords forming the word groups to determine the evaluation value corresponding to each keyword in its dimension; summing the evaluation values ​​corresponding to keywords belonging to the same dimension to obtain the risk value for that dimension; and determining the warning level for the region to be warned based on the risk values ​​of each dimension. This application improves the accuracy of risk warnings based on social events.

[0004] Regarding the above-mentioned solutions, the applicant of this invention has discovered at least the following technical problems: 1. The data acquisition and processing capabilities of existing technologies have multiple limitations. On the one hand, data sources rely excessively on traditional channels such as surveillance cameras and official reports, neglecting unstructured data such as social media and public reports, making it difficult to capture early clues of events. On the other hand, even when multi-source data is involved, the lack of unified standards and efficient fusion technologies makes it difficult to collaboratively analyze text, audio, video, and other information, resulting in severe fragmentation. Simultaneously, the analysis technology is lagging behind; traditional statistical methods cannot deeply mine the value of data, making it difficult to extract key risk indicators, directly weakening the accuracy of early warnings.

[0005] 2. Current technologies, in the risk assessment stage, rely on simple rules or manual verification for authenticity checks, lacking quantitative indicators and intelligent models. This makes them highly susceptible to false alarms and underreporting when facing online rumors and other misinformation. The risk level classification standards are vague, failing to fully consider dynamic factors such as the event's spread trend and scope of impact, thus failing to provide accurate references for emergency decision-making and potentially leading to resource misallocation or inadequate response. Furthermore, the lengthy process from data collection to risk assessment makes real-time monitoring and early warning difficult, often missing the golden opportunity for event response.

[0006] 3. Existing communication technologies rely heavily on traditional methods such as SMS and broadcasting, failing to develop differentiated strategies for different groups and risk levels. This results in information blind spots for vulnerable groups. Early warning information is generated mechanically using fixed templates, unable to provide targeted content based on event type and risk level, leading to low public awareness and understanding. In extreme disasters or network failures, the lack of backup communication links and emergency support poses a high risk of communication interruption, severely impacting the delivery of early warning information.

[0007] 4. Existing technologies have significant shortcomings in regional coordination and resource allocation. Systems in different regions and departments operate independently, lacking data sharing interfaces and collaborative mechanisms, making cross-regional joint prevention and control difficult. Emergency resource reserves and allocation lack scientific planning, failing to be deployed in advance based on event risk levels and spread trends, resulting in haphazard resource allocation and low emergency response efficiency. Furthermore, the systems are primarily reactive, lacking the ability to dynamically predict and proactively intervene in the spread of events, making it difficult to effectively curb their escalation. Summary of the Invention

[0008] To address the aforementioned technical shortcomings, the purpose of this invention is to provide an intelligent early warning and communication system for public safety incidents based on AI semantic analysis.

[0009] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides an intelligent early warning communication system for public safety incidents based on AI semantic analysis, including: a public incident security assessment module: used to acquire text stream data, audio stream data and video stream data corresponding to the target area, thereby analyzing and obtaining a comprehensive assessment value of the incident threat corresponding to the target area, and then assessing whether a public safety incident has occurred in the target area.

[0010] Public safety incident verification module: When a public safety incident occurs in a target area, it is used to obtain the dynamic situation indicators of the corresponding group behavior in the target area, and then assess the authenticity of the public safety incident in the target area.

[0011] Event security risk level analysis module: When assessing the likelihood of a public security incident occurring in a target area, it analyzes the event security risk level of the target area based on the comprehensive threat assessment value of the target area, and also analyzes the corresponding early warning communication scheme for the target area.

[0012] Event Propagation Risk Level Analysis Module: This module is used to analyze the collaborative early warning plan for the surrounding areas based on the event security risk level of the target area.

[0013] The beneficial effects of this invention are as follows: 1. In the embodiments of this invention, technologies such as deep web crawling, voiceprint array acquisition, and multi-source monitoring integration are comprehensively utilized to acquire text, audio, and video stream data. Through AI semantic analysis technology, starting from key elements such as the proportion of negative keywords, spectral amplitude, and group aggregation degree, quantitative indicators such as semantic threat entropy and audio emotional energy density are constructed. After normalization processing, a comprehensive event threat assessment value is formed. This multimodal data linkage analysis breaks through the limitations of single data, achieves comprehensive perception of public safety events, significantly improves the accuracy and reliability of early warning information, and provides solid data support for subsequent risk assessment.

[0014] 2. In this embodiment of the invention, a public event security authenticity verification module is used to accurately determine the authenticity of an event by comparing the information dissemination speed, traffic flow mutation rate, and base station signaling peak value with corresponding thresholds, effectively reducing the false alarm rate. Based on this, risk levels I-IV are divided according to the comprehensive threat assessment value of the event, and the event dissemination trend is analyzed using a propagation map, ensuring a high degree of consistency between the risk level determination and the actual event evolution. From initial event assessment to risk level subdivision, the system provides a progressive and precise evaluation, offering a scientific and reliable basis for emergency decision-making, ensuring the rational allocation of resources, and improving emergency response efficiency.

[0015] 3. In this embodiment of the invention, the system automatically matches differentiated early warning communication schemes according to different risk levels. For Level I risks, a comprehensive, emergency, full-coverage early warning communication scheme is activated, ensuring information reaches every corner through high-frequency push notifications across all channels. For Level II risks, a targeted, precise, and regionally-oriented collaborative early warning communication scheme is adopted to achieve accurate dissemination. For Level III and IV risks, communication strategies are optimized, focusing on strengthening communication among key populations and routine dynamic monitoring, respectively. Simultaneously, the system supports intelligent generation of multimodal communication content to adapt to the needs of different audiences. Combined with emergency communication redundancy, it can still ensure efficient delivery of early warning information even in extreme situations, improving public responsiveness.

[0016] 4. In this embodiment of the invention, based on the security risk level of an event, a collaborative early warning plan for surrounding areas is formulated, forming a 30-kilometer to 5-kilometer concentric prevention and control system. Level I risk triggers a nationwide red alert and a 30-kilometer concentric emergency joint defense, achieving cross-regional resource sharing and collaborative response; Levels II-IV risks narrow the scope of collaboration according to their level, refining prevention and control measures. Through mechanisms such as sharing propagation maps and pre-allocating emergency resources, the cross-regional risk response time is shortened, effectively curbing the spread of the event. The regional collaboration mechanism breaks down information barriers, integrates the strengths of multiple parties, and builds a joint prevention and control public safety defense line, significantly improving overall prevention and control effectiveness. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the system module connections of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below 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.

[0020] Examples of embodiments of the present invention Figure 1 As shown, the intelligent early warning and communication system for public safety incidents based on AI semantic analysis includes: a public incident security assessment module, a public incident security authenticity verification module, an incident security risk level analysis module, an incident propagation risk level analysis module, and a database.

[0021] The public event security authenticity verification module is connected to the public event security assessment module and the event security risk level analysis module, respectively. The event security risk level analysis module is connected to the event propagation risk level analysis module, and the database is connected to the event security risk level analysis module.

[0022] It should be noted that the database is used to store the range of comprehensive threat assessment values ​​corresponding to the security risk level of each event.

[0023] Public Event Security Assessment Module: This module is used to acquire text stream data, audio stream data, and video stream data corresponding to the target area, thereby analyzing and obtaining a comprehensive assessment value of the event threat corresponding to the target area, and then assessing whether a public security event has occurred in the target area.

[0024] In a specific embodiment, the acquisition of text stream data, audio stream data and video stream data corresponding to the target region is specifically analyzed as follows: A1. Text stream data is collected by using a deep web crawling engine to capture key semantic fragments from social media, news platforms and government systems in real time.

[0025] It should be noted that, in addition to capturing key semantic fragments from social media, news platforms, and government systems in real time, deep web crawling engines also need to use natural language processing technology to deduplicatize, reduce noise, and semantically clean the crawled text, filtering out invalid information and extracting core keyword groups.

[0026] A2. Audio stream acquisition uses a voiceprint array deployed in urban surveillance equipment, combined with microphone beamforming technology to separate human voice from environmental noise.

[0027] It should be noted that after separating human voice from environmental noise, the voiceprint array and microphone beamforming technology need to improve the clarity of the effective sound signal through audio enhancement algorithms and use frequency band analysis technology to accurately locate abnormal sound spectrum features.

[0028] A2. The video stream data integration system integrates existing surveillance camera resources in the target area, including traffic monitoring, security monitoring, and public area cameras, and transmits video footage back in real time via network transmission protocols; it also deploys drones equipped with camera devices for aerial video acquisition.

[0029] It should be noted that after integrating camera resources and video streams collected by drones, computer vision algorithms are used to analyze the images in real time to obtain target detection and tracking technology to quantify the density of crowds.

[0030] In a specific embodiment, the analysis yields a comprehensive event threat assessment value for the target region. The specific analysis process is as follows: the semantic threat entropy value, audio emotion energy density, and abnormal behavior index corresponding to the target region are analyzed and normalized. Simultaneously, these values ​​are substituted into the analysis module for the comprehensive event threat assessment value to obtain the comprehensive event threat assessment value for the target region.

[0031] It should be noted that the analysis process for the comprehensive threat assessment value of the target region is as follows: the semantic threat entropy value of the target region is calculated. Audio emotional energy density and abnormal behavior index Substitute into the calculation formula: In the process, the comprehensive threat assessment value corresponding to the target area is obtained. .

[0032] In a specific embodiment, the analysis of the semantic threat entropy value, audio emotion energy density, and abnormal behavior index corresponding to the target region is carried out in the following specific process: B1. Obtain the text stream data, audio stream data, and video stream data corresponding to the target region. Extract the proportion of each type of negative keywords from the text stream data, extract the spectral amplitude corresponding to each frequency from the audio stream data, and extract the group aggregation degree and motion conflict vector from the video stream data.

[0033] It should be noted that the extraction of the proportions of each type of negative keyword from the text stream data involves the following steps: Using natural language processing technology, the text data, obtained and cleaned by a deep web crawling engine, is first segmented into independent word units. Then, a pre-constructed negative keyword dictionary, covering public safety-related negative words such as "conflict," "accident," and "danger," is used, categorized by type (e.g., disaster, public security, public health). Keyword matching and identification are then performed on the segmented text. The frequency of each type of negative keyword in the total vocabulary is calculated and divided by the total number of words in the text to obtain the proportion of each type of negative keyword.

[0034] Extracting spectral amplitudes for each frequency from audio stream data: Audio data processed using voiceprint array and microphone beamforming techniques, and optimized with audio enhancement algorithms, is converted from time-domain audio signals to frequency-domain spectra using spectral analysis methods such as short-time Fourier transform. Based on a defined frequency range, multiple frequency bands are divided, and the spectral energy within each band is statistically analyzed to obtain the spectral amplitudes for the corresponding frequencies. Combining frequency band analysis techniques, a focus is placed on frequency ranges potentially associated with abnormal behavior, such as characteristic frequencies corresponding to sounds like shouts or object collisions, filtering out spectral amplitude data containing potential danger signals to provide a foundation for subsequent audio emotional energy density calculations.

[0035] Extracting crowd density and motion conflict vectors from video stream data: Computer vision algorithms are used to process the integrated video footage in real time. For crowd density, target detection algorithms such as YOLO and Faster R-CNN are used to identify human targets in the footage. Density estimation methods, such as Gaussian kernel density estimation, are applied to calculate the number of people per unit area. Combined with the scene area, the crowd density is derived. A density threshold is set; when it is exceeded, the scene is considered a crowd gathering, and the gathering area and size are recorded. For motion conflict vectors, target tracking algorithms are used to track the movement trajectories of people in the footage, calculating the displacement, velocity, and direction changes of people between adjacent frames. When a sudden change in movement direction, an abnormal increase in velocity, or a collision / intersection of multiple people's trajectories is detected, a motion conflict vector is generated based on preset conflict determination rules, quantifying the intensity, location, and range of people involved in the conflict.

[0036] B2. The analysis process for semantic threat entropy is as follows: the proportion of each type of negative keyword in the target region is recorded as follows: ,in, This indicates the number corresponding to each type of negative keyword. , It is a positive integer. Furthermore, the formula was used to calculate the collection of various types of negative keywords: In this process, the semantic threat entropy value corresponding to the target region is obtained. ,in, This is represented as the set polarization factor.

[0037] It should be noted that the polarization factor α is set based on the correlation between the proportion of different types of negative keywords in historical data of public safety incidents and the actual severity of the incidents. It is combined with experts' experience in assessing the risks of various incidents, as well as factors such as population density, geographical features, and social environment of the target area. The aim is to strengthen the influence weight of high-threat negative keywords on the semantic threat entropy value and accurately reflect the actual threat level of semantic information in different scenarios.

[0038] B3. The analysis process of audio emotional energy density is as follows: the spectral amplitude corresponding to each frequency in the target area is recorded as... ,in, This indicates the number corresponding to each frequency. , It is a positive integer. We also substitute the values ​​of each frequency into the calculation formula: The audio emotional energy density corresponding to the target region is obtained. ,in, , These are the upper and lower limits corresponding to the set frequency range of the acoustic spectrum amplitude, respectively. The set stress response factor.

[0039] It should be noted that the upper and lower limits of the frequency range of the sound spectrum amplitude are determined based on the acoustic characteristics of typical abnormal sounds in public safety scenarios, such as panic shouts and violent conflict sounds, combined with the frequency distribution statistics of audio data of similar events in the target area in the past; the stress response factor is set after multiple rounds of simulation tests and expert calibration, referring to the intensity of physiological and psychological stress response of different groups to dangerous audio stimuli in psychological experiments, as well as the requirements of emergency management for early warning sensitivity.

[0040] B4. The analysis process of the abnormal behavior index is as follows: the group aggregation degree and movement conflict vector corresponding to the target area are respectively denoted as... and Substitute into the calculation formula: In the process, the abnormal behavior index corresponding to the target region is obtained. ,in, and It is divided into a set group aggregation threshold and a motion conflict vector threshold.

[0041] It should be noted that the group aggregation threshold and the motion conflict vector threshold are determined by analyzing the critical density data of abnormal group aggregation in historical public safety incidents in the target area, combined with the regional spatial carrying capacity, such as the area of ​​the venue and the standard safe distance per person, and by using simulation experiments and expert experience for calibration. For different scenarios, such as squares, streets, and venues, the vector characteristic patterns of motion conflicts, such as speed, direction changes, and collision intensity, are statistically analyzed and set by experts.

[0042] In a specific embodiment, the process of assessing whether a public safety incident has occurred in the target area is as follows: the comprehensive threat assessment value of the target area is compared with the comprehensive threat assessment value of the set standard area. If the comprehensive threat assessment value of the target area is greater than or equal to the comprehensive threat assessment value of the set standard area, then the target area is assessed to have experienced a public safety incident. If the comprehensive threat assessment value of the target area is less than the comprehensive threat assessment value of the set standard area, then the target area is assessed to have not experienced a public safety incident.

[0043] Public safety incident verification module: When a public safety incident occurs in a target area, it is used to obtain the dynamic situation indicators of the corresponding group behavior in the target area, and then assess the authenticity of the public safety incident in the target area.

[0044] In one specific embodiment, the dynamic situation indicators of group behavior include information dissemination speed, traffic flow mutation rate, and base station signaling peak value.

[0045] It should be noted that the information dissemination speed is calculated by capturing the dissemination timestamps, reposts, and comments of relevant topics in the target area on social media platforms and fitting them with a dissemination model; the traffic flow mutation rate relies on real-time data of road traffic and pedestrian flow in the target area collected by the traffic monitoring system, and compares it with the normal traffic flow in the same period in history to obtain the mutation amplitude and rate; the base station signaling peak value is obtained from the number of base station access requests and signaling interaction frequency in the target area during the monitoring period obtained from the telecommunications operator, and the maximum peak value is selected to obtain the information dissemination speed, traffic flow mutation rate, and base station signaling peak value in the dynamic situation indicators of group behavior.

[0046] In a specific embodiment, the evaluation process for determining the authenticity of a public safety incident in the target area is as follows: The information diffusion speed, traffic flow mutation rate, and base station signaling peak value corresponding to the target area are compared with the set information diffusion speed threshold, traffic flow mutation rate threshold, and base station signaling peak value threshold, respectively. If the information diffusion speed, traffic flow mutation rate, and base station signaling peak value corresponding to the target area are all greater than the set information diffusion speed threshold, traffic flow mutation rate threshold, and base station signaling peak value threshold, then the public safety incident in the target area is considered genuine. If any one of the information diffusion speed, traffic flow mutation rate, and base station signaling peak value corresponding to the target area is less than or equal to the set information diffusion speed threshold, traffic flow mutation rate threshold, and base station signaling peak value threshold, then the public safety incident in the target area is considered false.

[0047] Event security risk level analysis module: When assessing the likelihood of a public security incident occurring in a target area, it analyzes the event security risk level of the target area based on the comprehensive threat assessment value of the target area, and also analyzes the corresponding early warning communication scheme for the target area.

[0048] In a specific embodiment, the analysis of the event security risk level corresponding to the target area is carried out as follows: the comprehensive threat assessment value of the event corresponding to the target area is compared with the range of comprehensive threat assessment values ​​of each event security risk level in the database. If the comprehensive threat assessment value of the event corresponding to the target area is within the range of comprehensive threat assessment values ​​of a certain event security risk level in the database, then the event security risk level in the database is recorded as the event security risk level corresponding to the target area.

[0049] The incident security risk levels are classified into Level I, Level II, Level III, and Level IV.

[0050] In a specific embodiment, the analysis process of the early warning communication scheme corresponding to the target area is as follows: C1. If the event security risk level corresponding to the target area is Level I, then the early warning communication scheme corresponding to the target area is: full-domain attack - emergency full-domain coverage early warning communication scheme.

[0051] It should be noted that the comprehensive attack emergency warning communication plan employs a comprehensive, high-frequency, multi-channel emergency communication approach. Detailed warning information is immediately disseminated to the entire population of the target area through all available channels, including radio, television, SMS, social media push notifications, and outdoor electronic screens. This information includes the event type, level of danger, escape routes, and evacuation guidelines, and is repeated every 10 minutes to ensure complete coverage. Simultaneously, real-time communication links between the emergency command center and grassroots units are activated to ensure rapid transmission of instructions and feedback on the situation on the ground.

[0052] C2. If the event security risk level corresponding to the target area is Level I, then the early warning communication scheme corresponding to the target area is: Targeted Precision Targeting - Regional Directional Collaborative Early Warning Communication Scheme.

[0053] It should be noted that the targeted and precise early warning communication solution for the region is as follows: It primarily relies on targeted and precise communication, while also utilizing SMS messages, community announcements, and local media platforms to send early warnings to affected areas and surrounding populations. Emphasis is placed on potential risks, preventative measures, and precautions, with information updated every 30 minutes. Simultaneously, an emergency communication group is established to organize community staff and volunteers to maintain close communication with affected individuals, promptly answer questions, and collect on-site updates.

[0054] C3. If the event security risk level corresponding to the target area is Level I, then the early warning communication scheme for the target area is: conventional stability control - enhanced early warning communication scheme for key populations.

[0055] It should be noted that the routine stability control and enhanced early warning communication plan for key populations adopts a communication strategy that combines routine information dissemination with targeted reminders. Event progress and risk warning information will be released through official websites, local government WeChat accounts, and community WeChat groups, updated at least three times daily. For specific high-risk groups, such as the elderly, children, and people with disabilities, designated personnel will be assigned to notify them by phone or in person to ensure that key groups understand the response methods. At the same time, emergency communication channels will be kept open to respond to any sudden changes.

[0056] C4. If the event security risk level corresponding to the target area is Level I, then the early warning communication scheme for the target area is: online publicity - daily dynamic monitoring and early warning communication scheme.

[0057] It should be noted that the online publicity and daily dynamic monitoring and early warning communication plan is as follows: Early warnings are issued through online information disclosure. Brief descriptions of the event and safety tips are published on local government websites and official social media accounts, updated 1-2 times daily. A hotline is available with dedicated personnel to answer public inquiries and guide the public on daily precautions. Simultaneously, the event's dynamics are closely monitored, and communication strategies are adjusted promptly based on the development of the situation to ensure the timeliness and accuracy of information dissemination.

[0058] Event Propagation Risk Level Analysis Module: This module is used to analyze the collaborative early warning plan for the surrounding areas based on the event security risk level of the target area.

[0059] In a specific embodiment, the analysis of the coordinated early warning scheme for the surrounding areas corresponding to the target area is carried out as follows: D1. If the event security risk level corresponding to the target area is Level I, then the emergency joint defense scheme of the entire red alert 30-kilometer circle is executed.

[0060] It should be noted that the emergency joint defense plan for the 30-kilometer radius of the red alert area is as follows: Immediately activate the cross-regional collaborative early warning mechanism, and push the red alert to all administrative regions within a 30-kilometer radius of the target area through the provincial emergency command platform, sharing detailed event information, propagation maps, and real-time dynamic data. Surrounding areas must simultaneously activate a 24-hour emergency response mode, urgently assemble rescue teams, inventory and reserve emergency supplies, implement pre-control measures on major traffic arteries near the target area, reserve green channels for emergency rescue, and issue evacuation warnings to residents in their jurisdictions through all channels, including television, radio, and SMS, and be prepared to provide immediate support to the target area and respond to risk spillover.

[0061] D2. If the event security risk level corresponding to the target area is Level I, then the orange linkage 15-30 km area dynamic joint defense plan shall be implemented.

[0062] It should be noted that the dynamic collaborative prevention and control plan for the 15-30 km radius of the orange alert is as follows: Relying on the regional joint prevention and control data platform, an orange alert is sent to administrative regions within a 15-30 km radius of the target area, sharing event risk assessment reports and potential spread path analyses. Surrounding areas activate emergency duty, strengthen monitoring of key transportation hubs and densely populated areas, and organize professional teams to conduct hazard investigations; risk warning information is released through government new media, community announcements, and other channels to remind residents to pay attention to event updates; simultaneously, a real-time information exchange channel is established with the target area, and collaborative measures are flexibly adjusted according to the development of the situation.

[0063] D3. If the event security risk level corresponding to the target area is Level I, then implement the yellow-level control 5-15 km boundary forward defense plan.

[0064] It should be noted that the yellow alert deployment plan, covering a 5-15 km border, involves: using the municipal emergency communication network to send yellow alerts to administrative areas within a 5-15 km radius of the target area, sharing a brief overview of the event and preliminary risk assessment results. Surrounding areas will maintain close monitoring, conduct focused patrols of border areas adjacent to the target area, organize community workers to carry out safety awareness campaigns, establish pre-positioned emergency supply depots at key locations to ensure rapid deployment of supplies, and simultaneously conduct emergency drills to test and improve regional collaborative response capabilities.

[0065] D4. If the event security risk level corresponding to the target area is Level I, then the routine joint control plan within 5 kilometers of the blue alert shall be implemented.

[0066] It should be noted that the routine joint control plan within a 5-kilometer radius of a blue alert is as follows: Utilizing the regional emergency early warning information system, a blue alert is sent to the administrative regions within a 5-kilometer radius of the target area, informing them of basic information about the incident and prevention recommendations. Surrounding areas strengthen daily monitoring, informing residents of precautions through community WeChat groups, bulletin boards, and other means; organizing volunteer teams to conduct safety awareness campaigns; and establishing a routine information sharing mechanism with the target area to regularly communicate on the progress of the incident and prepare for risk prevention and control.

[0067] The above description is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the protection scope of the present invention.

Claims

1. A public safety incident intelligent early warning and communication system based on AI semantic analysis, characterized in that, include: Public Event Security Assessment Module: This module is used to acquire text stream data, audio stream data, and video stream data corresponding to the target area, thereby analyzing and obtaining a comprehensive threat assessment value for the target area, and then assessing whether a public security event has occurred in the target area. The analysis yields a comprehensive assessment value of the event threat corresponding to the target region. The specific analysis process is as follows: The semantic threat entropy, audio emotion energy density, and abnormal behavior index of the target region are analyzed and normalized. Then, they are substituted into the analysis module of the comprehensive event threat assessment value to obtain the comprehensive event threat assessment value of the target region. The analysis process for the semantic threat entropy value, audio emotion energy density, and abnormal behavior index corresponding to the target region is as follows: B1. Obtain text stream data, audio stream data and video stream data corresponding to the target area. Extract the proportion of each type of negative keywords from the text stream data, extract the spectral amplitude of each frequency from the audio stream data, and extract the group aggregation degree and motion conflict vector from the video stream data. B2. The analysis process for semantic threat entropy is as follows: the proportion of each type of negative keyword in the target region is recorded as follows: ,in, This indicates the number corresponding to each type of negative keyword. , It is a positive integer. Furthermore, the formula was used to calculate the collection of various types of negative keywords: In this process, the semantic threat entropy value corresponding to the target region is obtained. ,in, Represented as the set polarization factor; B3. The analysis process of audio emotional energy density is as follows: the spectral amplitude corresponding to each frequency in the target area is recorded as... ,in, This indicates the number corresponding to each frequency. , It is a positive integer. We also substitute the values ​​of each frequency into the calculation formula: The audio emotional energy density corresponding to the target region is obtained. ,in, , These are the upper and lower limits corresponding to the set frequency range of the acoustic spectrum amplitude, respectively. The set stress response factor; B4. The analysis process of the abnormal behavior index is as follows: the group aggregation degree and movement conflict vector corresponding to the target area are respectively denoted as... and Substitute into the calculation formula: In the process, the abnormal behavior index corresponding to the target region is obtained. ,in, and It is divided into a set group aggregation threshold and a motion conflict vector threshold; Public safety incident authenticity verification module: When a public safety incident occurs in a target area, it is used to obtain the dynamic situation indicators of the group behavior in the target area, and then assess the authenticity of the public safety incident in the target area. Event security risk level analysis module: When assessing the likelihood of a public security event occurring in a target area, it analyzes the event security risk level of the target area based on the comprehensive assessment value of the event threat corresponding to the target area, and also analyzes the early warning communication scheme corresponding to the target area. Event Propagation Risk Level Analysis Module: This module is used to analyze the collaborative early warning plan for the surrounding areas based on the event security risk level of the target area.

2. The intelligent early warning and communication system for public safety incidents based on AI semantic analysis as described in claim 1, characterized in that, The specific analysis process for obtaining the text stream data, audio stream data, and video stream data corresponding to the target region is as follows: A1. Text stream data is collected using a deep web crawling engine to capture key semantic fragments from social media, news platforms, and government systems in real time. A2. Audio stream acquisition uses a voiceprint array deployed in urban surveillance equipment, combined with microphone beamforming technology to separate human voice from environmental noise; A2. The video stream data integration system integrates existing surveillance camera resources in the target area, including traffic monitoring, security monitoring, and public area cameras, and transmits video footage back in real time via network transmission protocols; it also deploys drones equipped with camera devices for aerial video acquisition.

3. The intelligent early warning and communication system for public safety incidents based on AI semantic analysis as described in claim 1, characterized in that, The specific analysis process for assessing whether a public safety incident has occurred in the target area is as follows: The comprehensive threat assessment value of the target area is compared with the comprehensive threat assessment value of the set standard area. If the comprehensive threat assessment value of the target area is greater than or equal to the comprehensive threat assessment value of the set standard area, then the target area is assessed to have experienced a public safety incident. If the comprehensive threat assessment value of the target area is less than the comprehensive threat assessment value of the set standard area, then the target area is assessed to have not experienced a public safety incident.

4. The intelligent early warning and communication system for public safety incidents based on AI semantic analysis as described in claim 3, characterized in that, The dynamic situation indicators of group behavior include information dissemination speed, traffic flow mutation rate, and base station signaling peak value.

5. The intelligent early warning and communication system for public safety incidents based on AI semantic analysis as described in claim 4, characterized in that, The assessment process for verifying the authenticity of public safety incidents in the target area is as follows: The information dissemination speed, traffic flow mutation rate, and base station signaling peak value corresponding to the target area are compared with the set information dissemination speed threshold, traffic flow mutation rate threshold, and base station signaling peak value threshold, respectively. If the information dissemination speed, traffic flow mutation rate, and base station signaling peak value corresponding to the target area are all greater than the set information dissemination speed threshold, traffic flow mutation rate threshold, and base station signaling peak value threshold, then the public safety incident in the target area is real. If any one of the information dissemination speed, traffic flow mutation rate, and base station signaling peak value corresponding to the target area is less than or equal to the set information dissemination speed threshold, traffic flow mutation rate threshold, and base station signaling peak value threshold, then the public safety incident in the target area is false.

6. The intelligent early warning and communication system for public safety incidents based on AI semantic analysis as described in claim 5, characterized in that, The analysis of the event security risk level corresponding to the target area is carried out in the following specific process: The comprehensive threat assessment value of the target region is compared with the range of comprehensive threat assessment values ​​of each event security risk level in the database. If the comprehensive threat assessment value of the target region is within the range of comprehensive threat assessment values ​​of a certain event security risk level in the database, then the security risk level of that event in the database is recorded as the security risk level of the target region. The incident security risk levels are classified into Level I, Level II, Level III, and Level IV.

7. The intelligent early warning and communication system for public safety incidents based on AI semantic analysis as described in claim 6, characterized in that, The analysis process for the early warning communication scheme corresponding to the target area is as follows: C1. If the event security risk level corresponding to the target area is Level I, then the early warning communication scheme corresponding to the target area is: Full-domain attack - emergency full-domain coverage early warning communication scheme; C2. If the event security risk level corresponding to the target area is Level I, then the early warning communication scheme corresponding to the target area is: Targeted Precision Targeting - Regional Directional Collaborative Early Warning Communication Scheme; C3. If the event security risk level corresponding to the target area is Level I, then the early warning communication scheme corresponding to the target area is: conventional stability control - enhanced early warning communication scheme for key populations; C4. If the event security risk level corresponding to the target area is Level I, then the early warning communication scheme for the target area is: online publicity - daily dynamic monitoring and early warning communication scheme.

8. The intelligent early warning and communication system for public safety incidents based on AI semantic analysis as described in claim 7, characterized in that, The analysis of the coordinated early warning scheme for the surrounding areas corresponding to the target area is as follows: D1. If the security risk level of the incident corresponding to the target area is Level I, then the emergency joint defense plan of the entire red alert 30-kilometer circle shall be implemented. D2. If the event security risk level corresponding to the target area is Level I, then implement the orange linkage 15-30 km area dynamic joint defense plan; D3. If the security risk level of the event corresponding to the target area is Level I, then implement the yellow-controlled 5-15 km boundary forward defense plan. D4. If the event security risk level corresponding to the target area is Level I, then the routine joint control plan within 5 kilometers of the blue alert shall be implemented.

Images