Travel safety early warning system and method based on artificial intelligence

Abstract

The invention relates to the technical field of tourism safety management, and discloses a tourism safety early warning system and method based on artificial intelligence. According to the system, accurate early warning is realized through cooperation of multiple modules. The data acquisition module uses a multi-source heterogeneous data fusion algorithm to integrate multiple types of data such as tourist positioning, meteorology, terrain and the like in a target area; the risk assessment module extracts multi-dimensional risk features and outputs a safety risk probability value by means of a space-time attention mechanism deep learning model; the dynamic adjustment module optimizes an early warning trigger threshold and a response strategy by using a reinforcement learning algorithm; the early warning generation module is combined with a fuzzy logic algorithm to generate multi-stage early warning signals and is associated with emergency resources; and the user interaction module converts early warning into multi-language prompts through a natural language processing technology and pushes the multi-language prompts to tourists and a management platform. Wherein the risk assessment module outputs a probability value through specific input and model training. According to the system, intelligent monitoring and early warning of tourism safety risks can be realized, and the tourism safety management level is improved.

Description

Technical Field

[0001] This invention relates to the field of tourism safety management technology, specifically to a tourism safety early warning system and method based on artificial intelligence. Background Technology

[0002] With the booming development of the global tourism industry and the increasing number of tourists year by year, tourism safety has become a growing concern. Tourism activities involve multiple complex factors, such as the natural environment, population movement, and transportation conditions, making the assessment and early warning of tourism safety risks extremely challenging. Regarding the natural environment, weather conditions are unpredictable, with frequent extreme weather events and geological disasters such as torrential rains, typhoons, and mudslides, seriously threatening the lives of tourists. For example, in mountainous areas, heavy rainfall can trigger landslides, blocking roads and trapping tourists; while at the coast, disasters such as hurricanes and tsunamis are even more difficult to prevent. However, traditional meteorological monitoring methods often have limited coverage and insufficient data accuracy, failing to provide timely and accurate disaster warnings to tourists and tourism management departments, leaving tourists unknowingly exposed to danger.

[0003] Managing the flow of people also presents challenges. During peak tourist seasons, the number of visitors to popular scenic spots far exceeds their carrying capacity, easily leading to stampedes and other accidents. For example, in some famous ancient towns or temple fairs, narrow streets are packed with tourists, making evacuation difficult and increasing the risk of injury or death in the event of an emergency. Moreover, traditional methods of monitoring tourist flow rely heavily on manual statistics or simple counting devices, which cannot provide real-time information on the distribution and flow trends of tourists, making it difficult to implement effective crowd control measures in a timely manner.

[0004] Traffic conditions also affect tourist safety. Traffic flow is heavy around tourist attractions, road congestion is common, and traffic accidents occur frequently. Especially during holidays, roads around attractions are often paralyzed, not only delaying tourists' itineraries but also potentially causing secondary accidents. While existing traffic monitoring systems can obtain some basic traffic flow data, they lack accuracy and foresight in predicting traffic congestion and assessing accident risks, and therefore cannot provide strong support for early warning systems for tourist safety.

[0005] Furthermore, tourism safety management also faces problems such as data fragmentation and information silos. Meteorological data, topographical data, traffic data, and tourist data are managed and maintained by different departments, with varying data formats, making integration and utilization difficult. This results in a lack of comprehensive and accurate data foundation for tourism safety risk assessment, leading to poor early warning effectiveness. Moreover, most existing tourism safety early warning systems are functionally limited and cannot dynamically adjust warning strategies based on real-time conditions, resulting in insufficient timeliness and accuracy in warnings, making it difficult to meet the growing demands for tourism safety. Summary of the Invention

[0006] The purpose of this invention is to provide an artificial intelligence-based tourism safety early warning system and method to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a tourism safety early warning system based on artificial intelligence, the system comprising: The data acquisition module is used to collect and integrate tourist location data, meteorological monitoring data, terrain sensor data, traffic flow data, historical accident data, and social media sentiment data of the target area in real time through multi-source heterogeneous data fusion algorithms. The risk assessment module is used by a deep learning model based on a spatiotemporal attention mechanism to extract multi-dimensional risk features from the integrated data and output the security risk probability value of the target area. The dynamic adjustment module is used to optimize the warning trigger threshold and response strategy based on the dynamic deviation between the real-time risk probability value and the preset risk threshold using a reinforcement learning algorithm. The early warning generation module is used to generate multi-level safety early warning signals by using fuzzy logic algorithms, combining risk probability values ​​and early warning thresholds, and to associate them with emergency resource allocation plans. The user interaction module is used to convert warning signals into multilingual text and voice prompts through natural language processing technology and push them to the visitor terminal and management platform. The execution steps of the risk assessment module include: The time-series data of tourist density, meteorological fluctuation curve, terrain complexity matrix, traffic congestion index, historical accident distribution heat map and public opinion sentiment polarity value of the target area are input into the spatiotemporal attention mechanism model. Extract the spatiotemporal correlation weights of each data source and construct a multidimensional risk feature tensor; The security risk probability value is output by jointly training a convolutional neural network and a long short-term memory network.

[0008] Preferably, the execution steps of the data acquisition module include: Sensor data is denoised and formatted using edge computing nodes; Anonymize and encrypt privacy information of multi-source data based on a federated learning framework; Knowledge graph technology is used to establish a mapping relationship between tourist behavior, environmental factors, and historical events.

[0009] Preferably, the execution steps of the dynamic adjustment module include: Construct a Markov decision process model, defining the state space as a continuous interval between the real-time risk value and the threshold deviation; The reward function is designed as a weighted reciprocal of the false alarm rate and the false negative rate, and its formula is as follows: in, This represents the reward function value. Indicates the false alarm rate. Indicates the false negative rate. and Preset weighting coefficients and satisfying ; The warning trigger threshold and response strategy parameters are iteratively optimized using the Q-learning algorithm.

[0010] Preferably, the execution steps of the early warning generation module include: The risk probability values ​​are used to divide the warning intervals into low, medium, and high levels. The membership function is defined as follows: in, Risk value The membership degree for a given warning interval ranges from [0,1]. This represents the risk probability value. and Dynamic thresholds for different warning intervals; A fuzzy rule base is used to match the current risk value with the availability of emergency resources; Generate multimodal early warning commands that include evacuation route planning, medical resource allocation, and communication priorities.

[0011] Preferably, the execution steps of the user interaction module include: Semantic compression and multilingual translation of the warning text; The speech is generated using a speech synthesis engine to produce audio broadcasts in different dialects. The regional relevance weight of the pushed content is dynamically adjusted based on the GPS coordinates of the tourist's terminal.

[0012] Preferably, the system further includes: The feedback optimization module is used to perform correlation analysis on user behavior data and early warning response results based on a collaborative filtering algorithm, and to update the parameters of the deep learning model. The execution steps of the feedback optimization module include: Collect data on tourists' click-through rates on early warning information, response delay times, and emergency response implementation rates; Construct a user-alert behavior matrix and calculate the similarity of potential needs using the following formula: in, For users With users Potential demand similarity and Representing users respectively and For warning types Response rating and For the average score, This represents the total number of warning signal types. The attention weights and convolution kernel parameters of the deep learning model are updated using a matrix factorization algorithm.

[0013] Preferably, the execution steps of the dynamic adjustment module further include: Introducing adversarial generative networks to simulate risk threshold drift in extreme scenarios; Optimize Q-learning exploration using Monte Carlo tree search algorithm – leveraging balancing strategy.

[0014] Preferably, the execution steps of the early warning generation module further include: Based on knowledge graph reasoning technology, causal chains are extracted from historical accident data; Embedding causal chains into a fuzzy rule base enhances the logical interpretability of multi-level early warning systems.

[0015] Preferably, the execution steps of the data acquisition module further include: The BERT model was used to classify sentiment polarity in social media sentiment data. By using graph neural networks, we can identify key nodes and the speed of spread in the path of public opinion dissemination.

[0016] Preferably, the present invention also includes an artificial intelligence-based tourism safety early warning method, the method comprising the following steps: Step 1: Using a multi-source heterogeneous data fusion algorithm, the data acquisition module collects and integrates tourist location data, meteorological monitoring data, terrain sensor data, traffic flow data, historical accident data, and social media sentiment data of the target area in real time. Step 2: Using a deep learning model based on a spatiotemporal attention mechanism, the risk assessment module inputs the time series data of tourist density, meteorological fluctuation curves, terrain complexity matrix, traffic congestion index, historical accident distribution heat map, and public opinion sentiment polarity value of the target area into the spatiotemporal attention mechanism model, extracts the spatiotemporal correlation weights of each data source, constructs a multidimensional risk feature tensor, and then outputs the safety risk probability value of the target area through joint training of convolutional neural network and long short-term memory network. Step 3: Using reinforcement learning algorithms, the module dynamically adjusts the warning trigger threshold and response strategy based on the dynamic deviation between the real-time risk probability value and the preset risk threshold. Step 4: Using a fuzzy logic algorithm, and with the help of the early warning generation module, multi-level safety early warning signals are generated by combining risk probability values ​​and early warning thresholds, and then linked to the emergency resource allocation plan; Step 5: Using natural language processing technology and the user interaction module, the warning signal is converted into multilingual text and voice prompts and pushed to the tourist terminal and management platform.

[0017] Compared with the prior art, the beneficial effects of the present invention are: In the data acquisition phase, the system utilizes a multi-source heterogeneous data fusion algorithm to collect and integrate real-time tourist location data, meteorological monitoring data, terrain sensor data, traffic flow data, historical accident data, and social media sentiment data for the target area. This fusion of multi-source data significantly enriches the dimensions and depth of the data. For example, combining tourist location data with terrain data allows for precise understanding of tourist distribution in complex terrain areas, enabling early detection of potential hazards; social media sentiment data reflects tourists' real-time perceptions and feedback on the safety of the scenic area, providing a more comprehensive perspective for risk assessment. Simultaneously, the data acquisition module uses edge computing nodes to denoise and standardize the format of sensor data, and anonymizes and encrypts privacy information from the multi-source data based on a federated learning framework. This ensures both data accuracy and usability while protecting user privacy, laying a solid and reliable data foundation for subsequent risk assessment and early warning.

[0018] The risk assessment module utilizes a deep learning model based on a spatiotemporal attention mechanism to extract multi-dimensional risk features from the integrated data and output a safety risk probability value for the target area. Compared to traditional risk assessment methods, this approach can more accurately capture the spatiotemporal correlations between various data types. For example, when analyzing meteorological and tourist density data, the model can consider the impact of weather changes in different time periods and regions on tourist activities, thus more accurately assessing the risk. By jointly training a convolutional neural network and a long short-term memory network, the advantages of both are fully utilized. The convolutional neural network excels at extracting local features from the data, while the long short-term memory network can effectively handle long-term dependencies in time-series data, further improving the accuracy and reliability of risk assessment.

[0019] The dynamic adjustment module utilizes reinforcement learning algorithms to optimize the early warning trigger threshold and response strategy based on the dynamic deviation between the real-time risk probability value and the preset risk threshold. This allows the early warning system to flexibly adjust according to the actual risk situation, avoiding the rigidity and lag of traditional early warning systems. For example, during peak tourist seasons or under extreme weather conditions, risk probability values ​​change frequently. The dynamic adjustment module can promptly optimize the early warning trigger threshold, improving the timeliness and relevance of early warnings. Simultaneously, it adjusts the response strategy according to different risk conditions, rationally allocates emergency resources, improves the efficiency of emergency response, and minimizes losses caused by safety accidents.

[0020] The early warning generation module employs a fuzzy logic algorithm, combining risk probability values ​​and early warning thresholds to generate multi-level safety early warning signals and linking them to emergency resource allocation plans. By dividing the warning into low, medium, and high levels, the warning information becomes clearer and more explicit, facilitating rapid responses from tourism management departments and tourists. Furthermore, the use of a fuzzy rule base and its matching with emergency resource availability ensures the rational allocation of emergency resources across different risk levels. For example, during high-risk warnings, priority is given to allocating substantial medical resources and evacuating personnel to dangerous areas to ensure tourist safety; during low-risk warnings, a limited number of resources are allocated for prevention and monitoring to avoid resource waste.

[0021] The user interaction module utilizes natural language processing technology to convert warning signals into multilingual text and voice prompts, which are then pushed to tourist terminals and the management platform. This function greatly facilitates tourists from different language backgrounds in obtaining warning information, eliminating language barriers. Both domestic and international tourists can understand the warning content promptly and accurately and take appropriate preventative measures. Simultaneously, the module dynamically adjusts the regional relevance weight of the pushed content based on the tourist terminal's GPS coordinates, ensuring that the warning information received by each tourist is closely related to their location, thus improving the practicality and effectiveness of the warning information.

[0022] The system also includes a feedback optimization module that uses a collaborative filtering algorithm to correlate user behavior data with early warning response results and update the parameters of the deep learning model. By collecting data such as visitor click-through rates on early warning information, response latency, and emergency response execution rates, the system gains a deeper understanding of user acceptance and response to early warning information. Based on this feedback, the attention weights and convolutional kernel parameters of the deep learning model are continuously optimized, making risk assessment and early warning more consistent with reality, further improving the system's performance and reliability. Attached Figure Description

[0023] Figure 1 This is a schematic diagram illustrating the working principle of the tourism safety early warning system described in this invention. Figure 2 A flowchart illustrating the optimization strategy for dynamically adjusting modules; Figure 3 A flowchart for the early warning generation module to generate early warnings and instructions; Figure 4 A flowchart for information processing and push notifications in the user interaction module. Detailed Implementation

[0024] 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.

[0025] Please see Figure 1-4 This invention provides a technical solution: an artificial intelligence-based tourism safety early warning system and method, to achieve accurate assessment and timely early warning of safety risks in tourist areas, ensuring the personal safety of tourists and the smooth progress of tourism activities. The system mainly consists of a data acquisition module, a risk assessment module, a dynamic adjustment module, an early warning generation module, and a user interaction module.

[0026] The data acquisition module is responsible for collecting and integrating various key data from the target area in real time using multi-source heterogeneous data fusion algorithms. This includes tourist location data, meteorological monitoring data, terrain sensor data, traffic flow data, historical accident data, and social media sentiment data. These data come from a wide range of sources and are in diverse formats, providing a comprehensive information foundation for subsequent risk assessment.

[0027] The risk assessment module operates based on a deep learning model employing a spatiotemporal attention mechanism. This module first inputs time-series data on tourist density, meteorological fluctuation curves, terrain complexity matrix, traffic congestion index, historical accident distribution heatmaps, and public opinion sentiment polarity values ​​of the target area into the spatiotemporal attention model. Through this process, it extracts the spatiotemporal correlation weights of each data source, thereby constructing a multidimensional risk feature tensor. Subsequently, using a convolutional neural network and a long short-term memory network for joint training, it ultimately outputs the safety risk probability value of the target area, providing a quantitative basis for risk assessment.

[0028] The dynamic adjustment module utilizes reinforcement learning algorithms to optimize the early warning trigger threshold and response strategy based on the dynamic deviation between the real-time risk probability value and the preset risk threshold. It can flexibly adjust the early warning and response methods according to changes in the actual risk situation, improving the accuracy and adaptability of the early warning system.

[0029] The early warning generation module employs a fuzzy logic algorithm, combining risk probability values ​​and early warning thresholds to generate multi-level safety early warning signals, and associates them with emergency resource allocation plans. By rationally classifying risk probability values, different early warning levels are determined, and corresponding emergency resource allocation plans are formulated based on these levels, ensuring a rapid and effective response when danger occurs.

[0030] The user interaction module uses natural language processing technology to convert warning signals into multilingual text and voice prompts, which are then pushed to tourist terminals and the management platform. This allows tourists with different language backgrounds to easily access warning information, while also enabling the management platform to promptly grasp the safety status of the tourist area and take appropriate management measures.

[0031] In actual operation, the data acquisition module continuously collects various types of data, the risk assessment module analyzes the data in real time to obtain risk probability values, the dynamic adjustment module optimizes early warning and response strategies based on the deviation between the risk probability value and the threshold, the early warning generation module generates early warning signals based on the optimized threshold and risk probability value and associates them with emergency resource plans, and the user interaction module pushes early warning signals to tourists and the management platform in various forms. All modules work together to form a complete and efficient tourism safety early warning system.

[0032] The present invention will be further described below with reference to Examples 1 to 5: Example 1: This embodiment further elaborates on the workflow of the data acquisition module. The data acquisition module is the foundation of the entire tourism safety early warning system; the accuracy and completeness of the data it collects directly affect the effectiveness of subsequent risk assessments and early warnings.

[0033] The data acquisition module first performs noise reduction and format standardization on the sensor data through edge computing nodes. In tourist areas, numerous sensors are distributed, such as those monitoring weather and acquiring terrain data. The data collected by these sensors may be affected by environmental noise and other factors, leading to errors. Edge computing nodes can perform preliminary data processing close to the data source to remove noise interference and make the data more accurate. Simultaneously, since different types of sensors may output data in different formats, the edge computing nodes also perform format standardization, converting the data into a unified format to facilitate subsequent data fusion and analysis.

[0034] This paper utilizes a federated learning framework to anonymize and encrypt privacy information from multi-source data. Tourism data involves a large amount of user privacy information, such as tourist location data. The federated learning framework can achieve collaborative learning and analysis of multi-source data without disclosing the original data. In this process, specific encryption algorithms are used to anonymize privacy information in the data, ensuring that user privacy is not leaked during data sharing and use, thus guaranteeing data security.

[0035] Knowledge graph technology is employed to establish a mapping relationship between tourist behavior, environmental factors, and historical events. This technology can organize and represent complex tourism data in the form of a graph. By analyzing the correlations between tourist behavior data, environmental factor data (such as weather and topography), and historical accident data, potential risk factors and patterns can be uncovered. For example, knowledge graphs can reveal that under specific weather conditions, certain terrain areas are prone to accidents, and the concentration of tourists in these areas increases the risk. Such mapping relationships contribute to a more comprehensive understanding of the safety status of tourist areas, providing richer information for risk assessment.

[0036] For social media sentiment data, the BERT model is used for sentiment polarity classification. Information on social media reflects tourists' real-time feelings and evaluations of tourist areas. The BERT model is a powerful natural language processing model that can analyze text on social media and determine its sentiment polarity—whether the text expresses positive, negative, or neutral sentiment. By classifying the sentiment polarity of sentiment data, we can promptly understand tourists' views on the safety of tourist areas and whether there are any potential safety hazards. For example, if a large amount of sentiment data shows negative sentiment, it may mean that there are some safety issues in the tourist area that require attention.

[0037] Graph neural networks (GNNs) are used to uncover key nodes and their spread speed in the dissemination of public opinion. GNNs can analyze the network structure of public opinion dissemination, identifying nodes that play a crucial role in the process. These nodes may be information initiators, disseminators, or users with significant influence. Simultaneously, they can calculate the spread speed of public opinion, understanding its scope and rate of dissemination on social media. This is crucial for timely monitoring of public opinion dynamics and taking appropriate countermeasures. For example, if a negative public opinion is found to be spreading rapidly, and key nodes have significant influence, timely measures need to be taken to prevent further escalation and damage to the image of the tourist area and the safety of tourists.

[0038] Example 2: This embodiment describes the specific working process of the dynamic adjustment module. The dynamic adjustment module plays a crucial optimization role in the tourism safety early warning system, enabling the system to better adapt to changes in actual conditions.

[0039] The dynamic adjustment module first constructs a Markov decision process model, defining the state space as a continuous interval of deviation between the real-time risk value and the threshold. The real-time risk value is output by the risk assessment module, while the preset risk threshold is set based on historical data and safety standards of the tourist area. The deviation between the real-time risk value and the threshold is divided into different intervals, each interval representing a state. For example, when the real-time risk value is much lower than the threshold, it is in one state; when the real-time risk value is close to or exceeds the threshold, it is in another state. In this way, the dynamic adjustment problem is transformed into a Markov decision process, facilitating subsequent algorithmic processing.

[0040] The reward function is designed as a weighted reciprocal of the false alarm rate and the false negative rate, and the formula is: in, This represents the reward function value. This indicates the false alarm rate, which is the proportion of warnings issued when there is actually no risk. This indicates the underreporting rate, which is the proportion of risks that actually exist but for which no warning has been issued. and Preset weighting coefficients and satisfying These two weighting coefficients are set according to the importance that the tourism region attaches to false reporting and underreporting. For example, if the tourism region attaches more importance to avoiding underreporting, then the weighting coefficients can be appropriately increased. The value of the reward function is designed to reduce false alarms and false negatives, and improve the accuracy of the early warning system by optimizing the early warning trigger threshold and response strategy.

[0041] The Q-learning algorithm iteratively optimizes the warning trigger threshold and response strategy parameters. Q-learning is a commonly used reinforcement learning algorithm that finds the optimal decision-making strategy through continuous trial and error. In this embodiment, the Q-learning algorithm selects an action (adjusting the warning trigger threshold or the response strategy) based on the current state (the deviation between the real-time risk value and the threshold) and obtains a reward value according to the reward function. Through multiple iterations, the action is continuously adjusted to maximize the reward value, thereby finding the optimal warning trigger threshold and response strategy.

[0042] Furthermore, the dynamic adjustment module incorporates a Generative Adversarial Network (GAN) to simulate risk threshold drift under extreme scenarios. Tourist areas may encounter various extreme situations, such as severe weather or large-scale emergencies, which can cause changes in risk thresholds. The GAN consists of a generator and a discriminator. The generator simulates risk data under extreme scenarios, while the discriminator judges whether the generated data is realistic. In this way, the drift of risk thresholds under extreme scenarios can be simulated, enabling the early warning system to make reasonable adjustments when facing extreme situations.

[0043] The Monte Carlo Tree Search algorithm is employed to optimize the exploration-utilization balance strategy in Q-learning. In Q-learning, there exists a balance between exploring new strategies and utilizing existing ones. The Monte Carlo Tree Search algorithm optimizes this balance by constructing a search tree to evaluate and select different strategies. In tourism safety early warning scenarios, it can help the dynamic adjustment module continuously try new warning thresholds and response strategies while fully leveraging existing experience, thus improving the efficiency and accuracy of adjustments.

[0044] Example 3: The early warning generation module is a crucial link in transforming risk assessment results into specific early warning information, and its accuracy and effectiveness are of paramount importance.

[0045] The early warning generation module first divides the warning intervals into low, medium, and high levels based on the membership function of the risk probability values. The membership function is defined as follows: in, Risk value The membership degree for a given warning interval ranges from [0,1]. This represents the risk probability value. and These are dynamic thresholds for different warning intervals. For example, when the risk probability value... Less than or equal to When the risk value has a membership degree of 0 to the low-risk warning interval, it is in a low-risk state; when Greater than or equal to When the membership degree is 1, it is in a high-risk state; when At that time, the membership degree is calculated according to the formula to determine whether it is in a medium-risk state. These dynamic thresholds are adjusted according to the actual situation of the tourist area and historical data to ensure the accuracy of the early warning.

[0046] A fuzzy rule base is used to match the current risk value with the availability of emergency resources. The fuzzy rule base stores a series of rules derived from experience and data analysis to determine the allocation of emergency resources under different risk values. For example, if the risk value is in a high-risk range and medical resources are sufficient, the fuzzy rule base will instruct that medical resources be prioritized for allocation to the high-risk area; if transportation resources are strained, evacuation route planning will be adjusted accordingly. In this way, a reasonable match between risk values ​​and emergency resources is achieved, improving the efficiency of emergency response.

[0047] The system generates multimodal early warning instructions, including evacuation route planning, medical resource allocation, and communication prioritization. After determining the risk level and emergency resource allocation plan, the early warning generation module generates detailed early warning instructions. Evacuation route planning will determine the safest and fastest evacuation routes based on the terrain, traffic conditions, and tourist distribution of the tourist area; medical resource allocation will specify the number of medical equipment and personnel to be allocated; and communication prioritization will ensure that important communication information is transmitted first in emergencies, guaranteeing smooth emergency command.

[0048] Based on knowledge graph reasoning technology, causal chains are extracted from historical accident data. This technology can uncover the causal relationships between various factors in historical accident data, forming causal chains. For example, analysis might reveal that under specific weather conditions, the terrain of a certain road section is prone to traffic accidents, which in turn cause traffic congestion, affecting tourist evacuation. Such causal chains can help better understand the causes and impacts of accidents, providing a more in-depth basis for early warning.

[0049] Embedding causal chains into a fuzzy rule base enhances the logical interpretability of multi-level early warning systems. By integrating extracted causal chains into the fuzzy rule base, the rules become more logical and interpretable. When an early warning is triggered based on a risk value, it not only provides the corresponding warning level and emergency measures but also explains why these measures are taken and the causal relationships upon which the decisions were made. This helps managers and tourists better understand the warning information and improves their ability to respond to risks.

[0050] Example 4: The user interaction module serves as a bridge between the tourism safety early warning system and users, and the completeness of its functions directly affects the user's reception and processing of early warning information.

[0051] The user interaction module first performs semantic compression and multilingual translation on the warning text. Warning text may contain a large amount of information, but to facilitate quick user understanding, semantic compression is necessary to remove redundant information and highlight key content. Simultaneously, considering that tourist areas may receive visitors from different countries and regions, the user interaction module utilizes multilingual translation technology to translate the warning text into multiple languages. For example, it translates Chinese warning text into common languages ​​such as English, Japanese, and Korean, ensuring that tourists with different language backgrounds can understand the warning information.

[0052] The system generates voice announcements in different dialects using a speech synthesis engine. In addition to text-based warnings, voice announcements are more intuitive and convenient. The speech synthesis engine can generate voice announcements in different dialects based on a preset speech model. In some tourist attractions, many local tourists may be accustomed to speaking their own dialects. Providing dialect-based voice announcements allows them to understand the warning content more quickly and accurately, improving the effectiveness of warning information dissemination.

[0053] The system dynamically adjusts the regional relevance weight of push notifications based on the GPS coordinates of the tourist's device. Tourists in different locations may face different risks, therefore, it's necessary to dynamically adjust the regional relevance weight of the push notifications based on the tourist's device's GPS coordinates. For example, when a risk event occurs in a certain area, tourists closer to that area will receive more detailed and targeted warning information, while tourists farther away will receive relatively general information. This ensures that each tourist receives warning information relevant to their specific situation, improving the effectiveness of the warnings.

[0054] In practical applications, after the warning generation module generates a warning signal, the user interaction module quickly processes the warning text, first performing semantic compression, and then translating it into multiple languages ​​according to pre-set language options. Simultaneously, the speech synthesis engine generates voice broadcasts in different dialects based on the translated text. When pushing warning information, the system obtains the GPS coordinates of the tourist's terminal in real time, calculates the distance to the risk area based on the coordinates, and then dynamically adjusts the regional relevance weight of the pushed content, pushing the most suitable warning information to each tourist.

[0055] Example 5: The feedback optimization module can continuously optimize the deep learning model based on user feedback on the early warning information, thereby improving the performance of the tourism safety early warning system.

[0056] The feedback optimization module first collects data on tourists' click-through rate of warning information, response delay time, and emergency response execution rate. The click-through rate reflects tourists' level of attention to the warning information; a low click-through rate may indicate problems with the way the warning information is delivered or its content. Response delay time refers to the time interval between when a tourist receives the warning information and when they respond; it measures the speed at which tourists react to the warning. The emergency response execution rate reflects whether tourists took the required emergency measures after receiving the warning information. By collecting this data, a comprehensive understanding of tourists' acceptance and response to warning information can be obtained.

[0057] Construct a user-alert behavior matrix and calculate the similarity of potential needs using the following formula: in, For users With users Potential demand similarity and Representing users respectively and For warning types Response rating and For the average score, This represents the total number of warning signal types. This formula allows analysis of differences in user responses to different types of warning information, identifying user groups with similar needs. For example, if two users both give high ratings to a certain type of warning information, it indicates their needs in that area are similar, which helps in providing more personalized warning services to different users.

[0058] The attention weights and convolutional kernel parameters of a deep learning model are updated using matrix factorization algorithms. Matrix factorization decomposes the user-warning behavior matrix into multiple low-dimensional matrices. By analyzing these low-dimensional matrices, latent patterns and features in the data can be discovered. This information can then be used to update the attention weights and convolutional kernel parameters of the deep learning model. Adjusting the attention weights allows the model to focus more on data features relevant to user needs, while optimizing the convolutional kernel parameters improves the model's ability to extract features from the data, thereby enhancing the performance of the deep learning model and making risk assessment and warnings more accurate.

[0059] In actual operation, the feedback optimization module continuously collects tourist behavior data, periodically constructs a user-warning behavior matrix, and calculates the similarity of potential needs. Based on the calculation results, the parameters of the deep learning model are updated using a matrix factorization algorithm. Through continuous feedback optimization, the tourism safety early warning system can better adapt to the needs of different users, improving the overall early warning effect and safety.

[0060] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0061] 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 tourism safety early warning system based on artificial intelligence, characterized in that, include: The data acquisition module is used to collect and integrate tourist location data, meteorological monitoring data, terrain sensor data, traffic flow data, historical accident data, and social media sentiment data of the target area in real time through multi-source heterogeneous data fusion algorithms. The risk assessment module is used by a deep learning model based on a spatiotemporal attention mechanism to extract multi-dimensional risk features from the integrated data and output the security risk probability value of the target area. The dynamic adjustment module is used to optimize the warning trigger threshold and response strategy based on the dynamic deviation between the real-time risk probability value and the preset risk threshold using a reinforcement learning algorithm. The early warning generation module is used to generate multi-level safety early warning signals by using fuzzy logic algorithms, combining risk probability values ​​and early warning thresholds, and to associate them with emergency resource allocation plans. The user interaction module is used to convert warning signals into multilingual text and voice prompts through natural language processing technology and push them to the visitor terminal and management platform. The execution steps of the risk assessment module include: The time-series data of tourist density, meteorological fluctuation curve, terrain complexity matrix, traffic congestion index, historical accident distribution heat map and public opinion sentiment polarity value of the target area are input into the spatiotemporal attention mechanism model. Extract the spatiotemporal correlation weights of each data source and construct a multidimensional risk feature tensor; The security risk probability value is output by jointly training a convolutional neural network and a long short-term memory network.

2. The system as described in claim 1, characterized in that, The execution steps of the data acquisition module include: Sensor data is denoised and formatted using edge computing nodes; Anonymize and encrypt privacy information of multi-source data based on a federated learning framework; Knowledge graph technology is used to establish a mapping relationship between tourist behavior, environmental factors, and historical events.

3. The system as described in claim 1, characterized in that, The execution steps of the dynamic adjustment module include: Construct a Markov decision process model, defining the state space as a continuous interval between the real-time risk value and the threshold deviation; The reward function is designed as a weighted reciprocal of the false alarm rate and the false negative rate, and its formula is as follows: in, This represents the reward function value. Indicates the false alarm rate. Indicates the false negative rate. and Preset weighting coefficients and satisfying ; The warning trigger threshold and response strategy parameters are iteratively optimized using the Q-learning algorithm.

4. The system as described in claim 1, characterized in that, The execution steps of the early warning generation module include: The risk probability values ​​are used to divide the warning intervals into low, medium, and high levels. The membership function is defined as follows: in, Risk value The membership degree for a given warning interval ranges from [0,1]. This represents the risk probability value. and Dynamic thresholds for different warning intervals; A fuzzy rule base is used to match the current risk value with the availability of emergency resources; Generate multimodal early warning commands that include evacuation route planning, medical resource allocation, and communication priorities.

5. The system as described in claim 1, characterized in that, The execution steps of the user interaction module include: Semantic compression and multilingual translation of the warning text; The speech is generated using a speech synthesis engine to produce audio broadcasts in different dialects. The regional relevance weight of the pushed content is dynamically adjusted based on the GPS coordinates of the tourist's terminal.

6. The system as described in claim 1, characterized in that, The system also includes: The feedback optimization module is used to perform correlation analysis on user behavior data and early warning response results based on a collaborative filtering algorithm, and to update the parameters of the deep learning model. The execution steps of the feedback optimization module include: Collect data on tourists' click-through rates on early warning information, response delay times, and emergency response implementation rates; Construct a user-alert behavior matrix and calculate the similarity of potential needs using the following formula: in, For users With users Potential demand similarity and Representing users respectively and For warning types Response rating and For the average score, This represents the total number of warning signal types. The attention weights and convolution kernel parameters of the deep learning model are updated using a matrix factorization algorithm.

7. The system as described in claim 3, characterized in that, The execution steps of the dynamic adjustment module also include: Introducing adversarial generative networks to simulate risk threshold drift in extreme scenarios; Optimize Q-learning exploration using Monte Carlo tree search algorithm – leveraging balancing strategy.

8. The system as described in claim 4, characterized in that, The execution steps of the early warning generation module also include: Based on knowledge graph reasoning technology, causal chains are extracted from historical accident data; Embedding causal chains into a fuzzy rule base enhances the logical interpretability of multi-level early warning systems.

9. The system as described in claim 2, characterized in that, The execution steps of the data acquisition module also include: The BERT model was used to classify sentiment polarity in social media sentiment data. By using graph neural networks, we can identify key nodes and the speed of spread in the path of public opinion dissemination.

10. A tourism safety early warning method based on artificial intelligence, characterized in that, Includes the following steps: Step 1: Using a multi-source heterogeneous data fusion algorithm, the data acquisition module collects and integrates tourist location data, meteorological monitoring data, terrain sensor data, traffic flow data, historical accident data, and social media sentiment data of the target area in real time. Step 2: Using a deep learning model based on a spatiotemporal attention mechanism, the risk assessment module inputs the time series data of tourist density, meteorological fluctuation curves, terrain complexity matrix, traffic congestion index, historical accident distribution heat map, and public opinion sentiment polarity value of the target area into the spatiotemporal attention mechanism model, extracts the spatiotemporal correlation weights of each data source, constructs a multidimensional risk feature tensor, and then outputs the safety risk probability value of the target area through joint training of convolutional neural network and long short-term memory network. Step 3: Using reinforcement learning algorithms, the module dynamically adjusts the warning trigger threshold and response strategy based on the dynamic deviation between the real-time risk probability value and the preset risk threshold. Step 4: Using a fuzzy logic algorithm, and with the help of the early warning generation module, multi-level safety early warning signals are generated by combining risk probability values ​​and early warning thresholds, and then linked to the emergency resource allocation plan; Step 5: Using natural language processing technology and the user interaction module, the warning signal is converted into multilingual text and voice prompts and pushed to the tourist terminal and management platform.

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