An emergency path planning method and system based on big data analysis

Abstract

The application provides an emergency path planning method and system based on big data analysis. Dynamic and static information streams from multiple channels are collected, various complex situation factors are identified and predicted, and emergency resource allocation suggestions are obtained. According to the emergency resource allocation suggestions, the emergency situation occurrence rate of different geographical areas in a specific time period is finely evaluated and processed, and a risk distribution map is generated. Based on the risk distribution map, the path is optimized based on the shortest time principle and the avoidance of expected congestion sections, and the optimal response path is obtained. Using the optimal response path, through the establishment of a two-way communication mechanism, continuous reception of field feedback is realized, and these information is synchronized to all mobile terminals participating in the emergency action, and an efficient collaborative response network is generated. The technical scheme provided by the application improves the speed and efficiency of emergency response, optimizes resource allocation and deployment, and enhances the flexibility and reliability of path planning.

Description

Technical Field

[0001] This application relates to the field of emergency route planning technology, and in particular to an emergency route planning method and system based on big data analysis. Background Technology

[0002] In modern emergency medical services systems, rapid and precise response to emergencies is crucial. With accelerating urbanization and increasing population density, the frequency and complexity of emergency incidents are constantly rising. To effectively address these challenges, emergency services not only need efficient resource allocation and route planning, but also the ability to process information from multiple heterogeneous data sources in real time, such as traffic flow, weather forecasts, social media trending topics, and historical emergency medical case databases. This multi-dimensional data integration and analysis capability is key to ensuring the speed and efficiency of emergency response, and also provides a solid foundation for optimizing resource allocation.

[0003] Currently, most emergency medical services systems rely primarily on traditional static resource allocation and experience-based route planning methods. These systems typically allocate emergency resources using pre-defined emergency station distribution maps and select rescue routes based on fixed rules or human judgment. While some advanced systems have begun to incorporate GPS positioning technology and basic traffic information updates, they still fall short in the face of complex, dynamic environments. Furthermore, existing emergency route planning often lacks forward-looking predictions of high-risk areas and time periods, making it difficult to achieve optimal resource allocation and efficient utilization.

[0004] Traditional methods cannot identify potential high-risk areas and time periods in advance, resulting in untimely and inaccurate resource allocation when emergencies occur; route planning based on fixed rules or simple update mechanisms has limited adjustment capabilities when facing emergencies (such as temporary traffic control or sudden events), which can easily cause delays; and the failure to make full use of multi-source heterogeneous data for comprehensive analysis ignores the risk patterns and demand trends hidden behind the data, affecting the scientific nature and accuracy of decision-making. Summary of the Invention

[0005] This application provides an emergency route planning method and system based on big data analysis to solve the problem that the existing route planning has limited adjustment capabilities in the face of emergencies.

[0006] In a first aspect, embodiments of this application provide an emergency medical route planning method based on big data analysis, including:

[0007] Collect dynamic and static information streams from multiple channels, identify and predict various complex situational factors, and obtain emergency resource allocation suggestions;

[0008] Based on the emergency resource allocation recommendations, a Bayesian network is used to model the probability of risk, and combined with the knowledge base technology of an expert system, the incidence rate of emergency situations in different geographical areas within a specific time period is finely evaluated and processed to generate a risk distribution map.

[0009] Based on the risk distribution map, an ant colony optimization algorithm is introduced to simulate the path selection process of rescue vehicles from multiple potential starting points to the destination. At the same time, combined with the real-time traffic updates provided by the intelligent transportation system, the path is optimized according to the shortest time principle and avoiding expected congested road sections to obtain the optimal response path.

[0010] By utilizing the optimal response path, a two-way communication mechanism is established to continuously receive on-site feedback and synchronize this information to all mobile terminals participating in the emergency response, thereby generating an efficient collaborative response network.

[0011] Optionally, based on the emergency resource allocation recommendations, the risk probability model is performed using a Bayesian network, and combined with the knowledge base technology of an expert system, to conduct a detailed assessment of the incidence rate of emergencies in different geographical areas within a specific time period, generating a risk distribution map, including:

[0012] By leveraging forward-looking recommendations for emergency resource allocation, multi-source heterogeneous data are integrated and analyzed to generate a basic dataset;

[0013] Based on the aforementioned basic dataset, a risk probability model is constructed using a Bayesian network to quantify the probability of emergency occurrences in each geographical region and generate a risk probability estimate.

[0014] Based on the aforementioned risk probability estimate, and combined with the knowledge base technology of the expert system, the model parameters are adjusted and optimized to ensure that the risk assessment results are closer to the actual situation, and an optimized risk probability estimate is generated.

[0015] Using the optimized risk probability estimate, the incidence rate of emergencies in different geographical areas within a specific time period is finely assessed, and a risk distribution map is generated.

[0016] Optionally, the provision of forward-looking guidance for emergency resource allocation integrates and analyzes multi-source heterogeneous data to generate a basic dataset, including:

[0017] By utilizing forward-looking guidance on emergency resource allocation recommendations, we can determine the scope and focus of collecting multi-source heterogeneous data and generate a data collection strategy.

[0018] According to the data acquisition strategy, real-time and historical data related to emergency rescue are collected from different data sources to generate a raw dataset.

[0019] Based on the original dataset, data cleaning, formatting, and standardization processes are performed to eliminate noise and inconsistencies in the data, generating a cleaned dataset.

[0020] Using the purified dataset, combined with time and spatial attributes, data correlation analysis is performed to uncover potential connections between different data and generate a correlation data model.

[0021] Based on the aforementioned associated data model, meaningful information features are extracted, and these features are encoded and indexed to ensure effective information retrieval and rapid access, thereby generating feature-encoded data.

[0022] Based on the feature-encoded data, information from various data sources is integrated to generate a basic dataset.

[0023] Optionally, the step of using the optimized risk probability estimate to perform a detailed assessment of the incidence rate of emergencies in different geographical areas within a specific time period, and generating a risk distribution map, includes:

[0024] Using the optimized risk probability estimate and combined with spatiotemporal analysis methods, a comprehensive assessment of the frequency and severity of emergency situations in each geographical region is conducted to generate a preliminary risk score.

[0025] Based on the preliminary risk score, spatial interpolation technology is used to fill in the risk information in sparse areas of the data, ensuring that all geographical areas have corresponding risk assessment results and generating a complete risk coverage map;

[0026] Based on the complete risk coverage map, clustering algorithms are applied to identify high-risk hotspot areas, and the time series characteristics of these areas are analyzed in depth to generate information on hotspot areas and high-risk periods.

[0027] By utilizing information on the hotspot areas and high-risk periods, combined with real-time traffic flow and weather conditions, the risk assessment results are adjusted and refined to generate a risk distribution map.

[0028] Optionally, based on the risk distribution map, an ant colony optimization algorithm is introduced to simulate the path selection process of rescue vehicles from multiple potential starting points to their destination. Simultaneously, combined with real-time traffic updates provided by the intelligent transportation system, the path is optimized based on the shortest time principle and avoidance of expected congested sections to obtain the optimal response path, including:

[0029] Using the aforementioned risk distribution map, high-risk emergency areas are identified as destinations, and all available emergency medical unit locations are identified as potential starting points, generating a set of routes;

[0030] Based on the set of paths, initialize the ant colony optimization algorithm parameters and set an initial path matrix to guide path selection during the simulation process and generate an initial path configuration.

[0031] Based on the initial path configuration, the ant colony optimization algorithm is applied to simulate the journey from each potential starting point to the destination, evaluate each possible path, and generate a preliminary list of optimized paths.

[0032] Using real-time traffic update data provided by the intelligent transportation system, each path in the preliminary optimized path list is dynamically adjusted to generate a dynamically adjusted path list.

[0033] Based on the dynamically adjusted path list, and taking all factors into consideration, the path that meets the optimal response conditions is selected as a reference for the actual rescue operation, thus generating the optimal response path.

[0034] Optionally, the step of initializing ant colony optimization algorithm parameters and setting an initial path matrix based on the path set to guide path selection during simulation, and generating an initial path configuration, includes:

[0035] Using the aforementioned set of paths, the connections between all possible starting points and destinations are analyzed to generate a connection relationship table;

[0036] Based on the connection table, the key parameters of the ant colony optimization algorithm are determined, and the initial values ​​of these parameters are set to generate the algorithm parameter configuration.

[0037] Based on the algorithm parameter configuration, an initial path matrix containing all nodes and their connections is constructed to guide path selection in subsequent simulation processes, thereby generating the initial path matrix.

[0038] Using the initial path matrix and the algorithm parameter configuration, a preliminary path exploration simulation is performed to ensure that at least one feasible path is found among all paths, and an initial path configuration is generated.

[0039] Optionally, utilizing the optimal response path, a two-way communication mechanism is established to continuously receive on-site feedback and synchronize this information to all mobile terminals participating in the emergency response, generating an efficient collaborative response network, including:

[0040] Using the optimal response path, a secure and reliable two-way communication channel is established, generating a two-way communication mechanism;

[0041] Based on the aforementioned two-way communication mechanism, a field feedback collection system is deployed to capture and transmit first-hand information from emergency responders and generate a field feedback data stream.

[0042] Based on the aforementioned on-site feedback data stream, a synchronous update algorithm was developed to enable the latest situation to be reflected in real time on the mobile terminals of all relevant emergency responders, thereby generating an information synchronization scheme.

[0043] By utilizing the aforementioned information synchronization scheme, resources from multiple parties can be integrated to form an efficient collaborative response system and generate a highly efficient collaborative response network.

[0044] Secondly, embodiments of this application provide an emergency medical route planning system based on big data analysis, comprising:

[0045] The collection module is used to collect dynamic and static information streams from multiple channels, identify and predict various complex situational factors, and obtain emergency resource allocation suggestions.

[0046] The assessment module is used to perform risk probability modeling using Bayesian networks based on the emergency resource allocation recommendations, and combined with the knowledge base technology of the expert system, to perform a detailed assessment of the incidence rate of emergency situations in different geographical areas within a specific time period, and generate a risk distribution map.

[0047] The optimization module is used to simulate the path selection process of rescue vehicles from multiple potential starting points to the destination based on the risk distribution map and introduce the ant colony optimization algorithm. At the same time, it combines the real-time traffic updates provided by the intelligent transportation system to optimize the path according to the shortest time principle and avoid expected congested road sections, so as to obtain the optimal response path.

[0048] The synchronization module is used to continuously receive on-site feedback by establishing a two-way communication mechanism, utilizing the optimal response path, and synchronize this information to all mobile terminals participating in the emergency response, thereby generating an efficient collaborative response network.

[0049] Thirdly, embodiments of this application provide a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement an emergency route planning method based on big data analysis as described in the first aspect.

[0050] Fourthly, embodiments of this application provide a computer storage medium storing a computer program, which, when executed by a computer, implements an emergency route planning method based on big data analysis as described in the first aspect.

[0051] In this embodiment, dynamic and static information streams from multiple channels are collected, and various complex situational factors are identified and predicted to obtain emergency resource allocation suggestions. Based on the emergency resource allocation suggestions, Bayesian networks are used for risk probability modeling, and combined with the knowledge base technology of expert systems, the incidence rate of emergencies in different geographical areas within a specific time period is finely evaluated to generate a risk distribution map. Based on the risk distribution map, an ant colony optimization algorithm is introduced to simulate the path selection process of rescue vehicles from multiple potential starting points to the destination. At the same time, combined with the real-time traffic updates provided by the intelligent transportation system, the path is optimized according to the shortest time principle and avoiding expected congested road sections to obtain the optimal response path. Using the optimal response path, a two-way communication mechanism is established to continuously receive on-site feedback and synchronize this information to all mobile terminals participating in the emergency response, generating an efficient collaborative response network.

[0052] The technical solution of this application has the following beneficial effects:

[0053] By collecting and integrating multi-source heterogeneous data and combining it with spatiotemporal association rule mining technology, potential high-risk areas and time periods can be identified in advance. This makes the allocation of emergency resources more proactive, ensuring that resources can be quickly mobilized in the event of an emergency, greatly shortening response time and improving emergency response efficiency. Using a risk probability model built with Bayesian networks and the knowledge base technology of an expert system, a detailed assessment of the incidence of emergencies in different geographical areas is achieved. The generated risk distribution map guides the pre-deployment of emergency resources, ensuring more scientific and rational resource allocation and avoiding resource waste and imbalance. An ant colony optimization algorithm is introduced to simulate the journey of rescue vehicles from multiple potential starting points to their destinations, and dynamic adjustments are made in conjunction with real-time traffic updates provided by an intelligent transportation system. This method not only considers the shortest time principle but also effectively avoids anticipated congested road sections, enhancing the flexibility and reliability of route planning. A two-way communication mechanism continuously receives on-site feedback, synchronizing the latest information to the mobile terminals of all relevant personnel involved in the emergency response, forming an efficient collaborative response network. This mechanism ensures that all parties can obtain the latest information in a timely manner and quickly adjust their action strategies, thereby improving the accuracy and practicality of decision support and providing a solid guarantee for the effective implementation of emergency measures.

[0054] Furthermore, by integrating and analyzing multi-source heterogeneous data using forward-looking guidance on emergency resource allocation recommendations, a basic dataset is generated. A Bayesian network is then used to construct a risk probability model, quantifying the probability of emergency occurrences in various geographical regions and generating risk probability estimates. These estimates are then adjusted and optimized using the knowledge base technology of an expert system to ensure the assessment results closely reflect reality. Finally, an optimized risk probability estimate is generated, which is used to precisely assess the emergency occurrence rate in different geographical regions within a specific time period, producing a risk distribution map. This process significantly improves the accuracy and scientific rigor of risk assessment, making resource pre-deployment more rational and effective, avoiding resource waste and imbalances. Simultaneously, through forward-looking risk prediction and dynamic adjustment mechanisms, the system's ability to respond to emergencies is enhanced, improving the speed and efficiency of emergency response and providing solid data support and reliable evidence for emergency decision-making.

[0055] Furthermore, by introducing an ant colony optimization algorithm to simulate the route selection process of rescue vehicles from multiple potential starting points to their destinations, and combining this with real-time traffic updates provided by the intelligent transportation system, the routes are optimized based on the shortest time principle and avoidance of expected congested sections, ultimately yielding the optimal response route. This method first uses a risk distribution map to identify high-risk emergency areas as destinations and identifies all available emergency medical unit locations as potential starting points to generate a set of routes. Next, it initializes the ant colony optimization algorithm parameters and sets an initial route matrix to guide route selection, generating an initial route configuration. Then, it applies the ant colony optimization algorithm to simulate the travel process and evaluates each possible route, generating a preliminary optimized route list. Next, it uses real-time traffic update data from the intelligent transportation system to dynamically adjust the routes, generating a dynamically adjusted route list. Finally, it comprehensively considers and selects the route that meets the optimal response conditions as a reference for actual rescue operations. This method significantly improves the speed and efficiency of rescue response, ensuring that rescue vehicles can arrive at the scene quickly and accurately, minimizing rescue time and uncertainty. Simultaneously, through intelligent route planning and the application of real-time traffic information, it effectively avoids traffic congestion, further ensuring the success rate of rescue operations and improving the overall effectiveness and service quality of the public safety emergency response system.

[0056] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of this application 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 some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 A flowchart illustrating an emergency medical route planning method based on big data analysis, provided as an embodiment of this application;

[0059] Figure 2 A schematic diagram of the structure of an emergency medical route planning system based on big data analysis provided in an embodiment of this application;

[0060] Figure 3 This is a schematic diagram of the structure of a computing device provided in an embodiment of this application. Detailed Implementation

[0061] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0062] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0063] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0064] Figure 1 A flowchart of an emergency route planning method based on big data analysis is provided as an embodiment of this application, such as... Figure 1 As shown, the method includes:

[0065] 101. Collect dynamic and static information streams from multiple channels, identify and predict various complex situational factors, and obtain emergency resource allocation suggestions;

[0066] This step involves collecting dynamic (e.g., real-time traffic conditions, weather forecasts) and static (e.g., geographic information, locations of fixed emergency medical facilities) data from multiple sources to build a comprehensive database. This data is processed using advanced analytics algorithms to identify potential complex situational factors and predict the likelihood of emergencies, thereby providing a scientific basis for the allocation of emergency medical resources.

[0067] In this embodiment, information from different channels is integrated, and data analysis techniques are used to identify and predict various factors affecting emergency response, thereby proposing optimized resource allocation suggestions. During this process, machine learning models can be used to better understand historical patterns and predict future trends, ensuring more accurate and efficient resource allocation.

[0068] For example, in an urban environment, the system collected data on all traffic accidents over the past year, current road construction information, and weather forecasts. After analysis, it was found that the probability of accidents occurring in specific areas of the city center is higher on weekend afternoons, so it was recommended to increase the deployment of ambulances in the area.

[0069] 102. Based on the emergency resource allocation recommendations, use Bayesian networks to perform risk probability modeling and combine it with the knowledge base technology of expert systems to conduct a detailed assessment of the incidence rate of emergency situations in different geographical areas within a specific time period, and generate a risk distribution map.

[0070] In this step, based on the configuration recommendations obtained in the previous step, a Bayesian network is used to simulate the probability of different events occurring. Combined with the knowledge base of an expert system, the incidence rate of emergencies in various geographical areas within a specific time period is assessed. A Bayesian network is a probabilistic graphical model capable of expressing dependencies between variables, while an expert system incorporates the knowledge of domain experts. The combination of the two can generate an accurate risk distribution map.

[0071] In this embodiment, a Bayesian network is first applied to calculate the probability of various emergency situations based on known conditions. Then, these estimates are further refined with the help of rules and experience from an expert system, ultimately forming a detailed risk map to guide subsequent path planning and other decision support activities.

[0072] Suppose that a certain area of ​​a city is marked as a high-risk area during a specific holiday. Because the Bayesian network shows that the probability of traffic accidents increases significantly during this period, and the expert system confirms that the locations of local hospitals and fire stations are insufficient to cover all the needs, it is decided to temporarily set up mobile medical points and service vehicles.

[0073] Optionally, step 102, based on the emergency resource allocation recommendations, involves using a Bayesian network to perform risk probability modeling and combining it with the knowledge base technology of an expert system to conduct a detailed assessment of the incidence rate of emergencies in different geographical areas within a specific time period, generating a risk distribution map, including:

[0074] By leveraging forward-looking recommendations for emergency resource allocation, multi-source heterogeneous data is integrated and analyzed to generate a basic dataset. Based on this basic dataset, a risk probability model is constructed using a Bayesian network to quantify the probability of emergency occurrences in various geographical regions, generating a risk probability estimate. Based on this risk probability estimate, and combined with the knowledge base technology of an expert system, the model parameters are adjusted and optimized to ensure that the risk assessment results are closer to the actual situation, generating an optimized risk probability estimate. Using the optimized risk probability estimate, the incidence rate of emergency situations in different geographical regions within a specific time period is finely assessed, generating a risk distribution map.

[0075] This step begins with forward-looking recommendations for emergency resource allocation, incorporating historical data, predictive model outputs, and expert opinions to develop more scientific and rational resource allocation strategies. Next, it involves the integration and analysis of multi-source heterogeneous data, encompassing data from various sources (such as sensor networks, social media, and government databases) to generate a comprehensive foundational dataset. Then, a Bayesian network is used to construct a risk probability model, providing a mathematical framework for risk estimation by quantifying the probability of emergency occurrences in different geographic areas. Finally, the model parameters are adjusted and optimized using the knowledge base technology of an expert system to ensure that the assessment results closely reflect reality, thereby generating a more accurate risk distribution map.

[0076] In this embodiment, firstly, based on forward-looking emergency resource allocation recommendations, the system collects and processes data from multiple sources to form a comprehensive basic dataset. Secondly, using this basic dataset, Bayesian network modeling technology is employed to calculate the probability of emergency occurrence in each geographical area, obtaining a preliminary risk probability estimate. Thirdly, the knowledge base technology of an expert system is introduced to fine-tune the parameters in the Bayesian network model, making the risk assessment results more consistent with the actual situation. Finally, based on the optimized risk probability estimate, the emergency occurrence rate in different geographical areas within a specific time period is assessed in detail, producing an accurate risk distribution map.

[0077] To address the increased urban traffic accidents during the summer tourist peak season, the city's emergency management team first developed recommendations for emergency medical resource allocation based on historical accident statistics, weather forecasts, and local hospital emergency room records. Second, they integrated various data sources, including traffic flow monitoring points, social media discussions about travel plans, and tourist number estimates provided by the city's tourism bureau, to create a comprehensive dataset. Third, they used Bayesian network analysis to deeply analyze this data, deriving initial probabilities of traffic accidents occurring on weekends in July in various tourist hotspots. Finally, experts in traffic engineering and public safety were invited to review the data, and based on their experience and field research, some key parameters in the model were adjusted, ultimately generating a risk distribution map covering the entire city. This map clearly shows which areas require increased police patrols or the establishment of temporary medical stations.

[0078] By following the steps above, city managers can deploy more resources to high-risk areas in advance, effectively reducing potential dangers and improving the efficiency of emergency response.

[0079] Optionally, the provision of forward-looking guidance for emergency resource allocation integrates and analyzes multi-source heterogeneous data to generate a basic dataset, including:

[0080] Using forward-looking recommendations for emergency medical resource allocation, the scope and focus of multi-source heterogeneous data collection are determined, and a data collection strategy is generated. Based on this strategy, real-time and historical emergency medical data are collected from different data sources to generate a raw dataset. Based on this raw dataset, data cleaning, formatting, and standardization are performed to eliminate noise and inconsistencies, generating a purified dataset. Using this purified dataset, data correlation analysis is conducted in conjunction with temporal and spatial attributes to uncover potential connections between different data points, generating a correlation data model. Based on this correlation data model, meaningful information features are extracted, encoded, and indexed to ensure effective information retrieval and rapid access, generating feature-encoded data. Based on this feature-encoded data, information from various data sources is integrated to generate a basic dataset.

[0081] Optionally, the step of using the optimized risk probability estimate to perform a detailed assessment of the incidence rate of emergencies in different geographical areas within a specific time period, and generating a risk distribution map, includes:

[0082] Using the optimized risk probability estimate and combined with spatiotemporal analysis methods, the frequency and severity of emergencies in each geographical region are comprehensively assessed to generate a preliminary risk score. Based on the preliminary risk score, spatial interpolation techniques are used to fill in risk information in sparse data areas, ensuring that all geographical regions have corresponding risk assessment results and generating a complete risk coverage map. Based on the complete risk coverage map, clustering algorithms are applied to identify high-risk hotspots, and the time-series characteristics of these areas are analyzed in depth to generate information on hotspots and high-risk time periods. Using the information on hotspots and high-risk time periods, combined with real-time traffic flow and weather conditions, the risk assessment results are adjusted and refined to generate a risk distribution map.

[0083] This step first involves utilizing forward-looking guidance for emergency resource allocation recommendations. These recommendations, based on historical data, predictive model outputs, and expert opinions, are used to develop more scientific and rational resource allocation strategies. Multi-source heterogeneous data, including data from various channels (such as sensor networks, social media, and government databases), is integrated and analyzed to generate a foundational dataset that supports subsequent risk assessments. The optimized risk probability estimation, combined with spatiotemporal analysis methods, comprehensively assesses the frequency and severity of emergencies in different geographical areas, ultimately generating a detailed risk distribution map to guide the optimal allocation of emergency resources.

[0084] In this embodiment, firstly, based on forward-looking emergency medical resource allocation recommendations, the scope and focus of data collection are determined, and an effective data collection strategy is formulated. Secondly, according to this strategy, real-time and historical emergency medical data are collected from multiple data sources to form a raw dataset. Thirdly, the raw dataset is cleaned, formatted, and standardized to eliminate noise and inconsistencies, ensuring data quality and generating a purified dataset. Finally, the purified dataset is used to perform correlation analysis based on time and spatial attributes to uncover potential connections, and meaningful information features are encoded and indexed. Information from various data sources is integrated to ultimately generate a basic dataset. For the generation of the risk distribution map, firstly, optimized risk probability estimation combined with spatiotemporal analysis methods is used to assess the frequency and severity of emergencies in each geographical region, generating a preliminary risk score. Secondly, spatial interpolation techniques are used to fill in risk information in sparse areas, ensuring that all geographical regions have corresponding assessment results, generating a complete risk coverage map. Thirdly, clustering algorithms are applied to identify high-risk hotspots, and the time-series characteristics of these areas are analyzed in depth to generate information on hotspots and high-risk periods. Finally, combined with real-time traffic flow and weather conditions, the risk assessment results are adjusted and refined to generate the final risk distribution map.

[0085] To improve safety management during the city's summer tourism peak season, the city's emergency management team first developed recommendations for emergency resource allocation based on past accident statistics, weather forecasts, and local hospital emergency room records. This identified key data types and sources requiring special attention. Secondly, following these recommendations, they collected real-time and historical data on traffic accidents, tourist flows, and weather changes from multiple channels, including traffic monitoring systems, social media platforms, and the tourism bureau's database, creating a massive raw dataset. Thirdly, the team meticulously cleaned, formatted, and standardized this raw dataset, removing duplicates and outliers to ensure consistency and accuracy, resulting in a purified dataset. Finally, through data correlation analysis combining temporal and spatial attributes, they discovered potential connections between different data points, extracted key information features, and encoded and indexed them for rapid retrieval and access, successfully constructing an integrated foundational dataset.

[0086] By following the steps above, city managers can deploy more resources to high-risk areas in advance, effectively reducing potential dangers and improving the efficiency of emergency response.

[0087] This application addresses the problem that existing technologies suffer from low emergency response efficiency and unreasonable resource allocation due to the inability of static resource allocation and experience-based path planning methods to adapt to complex dynamic environments. Furthermore, the lack of forward-looking prediction of high-risk areas and time periods leads to untimely and inaccurate resource deployment during emergencies. Therefore, this invention proposes an alternative solution that addresses these technical problems by introducing Bayesian networks for risk probability modeling, combining expert system knowledge base technology, and advanced methods such as ant colony optimization algorithms, thereby improving the speed, accuracy, and efficiency of emergency response.

[0088] Optionally, the step of constructing a risk probability model using a Bayesian network based on the basic dataset, quantifying the probability of emergency occurrences in each geographical region, and generating a risk probability estimate includes:

[0089] In calculating the occurrence of emergency event E i The conditional probability P(E) i Before |C), it is necessary to integrate and clean multi-source heterogeneous data, identify high-risk areas and time periods through spatiotemporal association rule mining technology, and establish a comprehensive context perception platform in combination with historical emergency rescue cases to lay the foundation for subsequent probability calculations;

[0090]

[0091] E i E represents an emergency event occurring in the i-th geographic region; kThis represents the occurrence of the k-th type of emergency, used to represent different types of emergency F. j This represents the j-th state of the influencing factors, which include traffic flow, weather conditions, etc.; w j (t) represents the weight coefficient of the j-th influencing factor at time t, which is dynamically adjusted based on historical data and real-time context; P(E i |F j P(F,H) represents the conditional probability of an emergency occurring given the influencing factors and considering historical data H; j |C) represents the probability that the influencing factor is in a specific state under the current situation C; δ represents the environmental change coefficient, which measures the degree of influence of changes in the external environment on the influencing factor; I j (t) represents the instantaneous change of the j-th influencing factor at time t, including emergencies or temporary control measures; M represents the total number of all considered emergency types; ∈ represents the visualization coefficient used to adjust the visualization data B. k (t) The impact on risk assessment, where V k (t) represents the public response or social media buzz regarding the k-th type of emergency at time t;

[0092] Complete the probability of emergency occurrence P(E) in each geographical region i After the quantification of |C), it proceeds to generate the probability of risk estimation (RPE). i The transition phase of (t+1) is as follows: This phase integrates probability results, considers time variations and spatial interaction effects, introduces a learning rate adjustment mechanism and an assessment of the interaction strength between adjacent geographical regions, and ensures that the final estimate reflects the latest situation in a timely manner.

[0093] RPE i (t+1)=RPE i (t)+α·[P(E i |C)-RPE i (t)]+β·∑ k∈N(i) γ ik (t)·[RPE k (t)-RPE i (t)]+ζ·Δ i (t)

[0094] RPE i (t) represents the risk probability estimate of the i-th geographic region at time t; α represents the learning rate, controlling the influence of new information on the existing estimate; β represents the neighbor influence coefficient, measuring the strength of interaction between neighboring geographic regions; N(i) represents the set of neighbors of the i-th geographic region; γ ik(t) represents the correlation strength between the i-th geographic region and its k-th neighboring geographic region at time t, and this value is dynamically adjusted over time and according to actual conditions; ζ represents the dynamic adjustment coefficient, used to adjust for the impact of additional factors on risk estimation; Δ i (t) represents the change in pheromones in the i-th geographic region at time t; RPE k (t) represents the risk probability estimate of the k-th neighboring geographic region within time t, which is used to measure the risk difference between neighboring regions and help adjust the risk estimate of the current region;

[0095] After calculating the probability of risk (RPE) i After (t+1), the latest information is synchronized to the mobile terminals of all relevant personnel through a two-way communication mechanism. Scenario analysis technology is applied to simulate the impact path selection of emergencies. At the same time, sensitivity analysis is conducted to verify the stability and robustness of the scheme, ensuring that the generated risk probability estimate is scientific and practical, and providing decision support for emergency resource allocation.

[0096] This formula aims to overcome the shortcomings of existing emergency medical systems, such as uneven resource allocation, slow response speed, and lack of foresight and flexibility. This application further provides an alternative solution that utilizes multi-source heterogeneous data integration and cleaning, spatiotemporal association rule mining, Bayesian network-based risk probability model construction, ant colony optimization algorithm for path selection, and real-time traffic updates from intelligent transportation systems.

[0097] A series of steps enable a detailed assessment of the incidence of emergencies in different geographical areas over specific time periods, generating risk distribution maps and optimal response routes, ultimately forming an efficient collaborative response network. These measures work together to ensure a more scientific and rational allocation of emergency resources, improving the speed and effectiveness of emergency response.

[0098] The following is a brief introduction to the design rationale behind each term of the formula:

[0099]

[0100] Sum of the weights of each influencing factor: Reflecting the importance of different factors; conditional probability product: w j (t)·P(E i |F j ,H); measures the probability of an emergency occurring under given influencing factors and considering historical data; immediate change adjustment: P(F j |C)+δ·I j (t); Considering the impact of changes in the external environment on influencing factors; Total of different types of emergency situations: Return

[0101] Standardized processing ensures the probability values ​​are reasonable; public feedback Or adjust social media buzz: P(E) k |C)+∈·

[0102] V k (t); reflects the impact of public opinion on risk assessment;

[0103] The following is a brief introduction to how the parameters of this formula are obtained:

[0104] for The importance of each influencing factor is determined by analyzing historical data, and then dynamically adjusted according to the real-time context; for P(E) i |F j Based on a historical emergency medical case database, a comprehensive context-aware platform is established for computation; for P(F) j |C), based on the actual situation under the current circumstances; for δ, a reasonable environmental change coefficient is set through experiments; for I j (t), extracting information on emergencies or temporary control measures from real-time data streams; for Total number of all emergency types considered;

[0105] For ∈, the setting is based on the degree of impact of the visualized data on risk assessment; for V... k (t), obtained from public feedback or social media buzz.

[0106] The following is a brief introduction to the design rationale behind each term of the formula:

[0107] RPE i (t+1)=RPE i (t)+α·[P(E i |C)-RPE i (t)]+β·∑ k∈N(i) γ ik (t)·[RPE k (t)-Δ i (t)

[0108] Current estimate adjustment: RPE i (t); as a base value; impact of new information: α·[P(E i |C)-RPE i [(t)];Controlling the impact of new information on existing estimates;Neighbor effect: β·∑ k∈N(i) γ ik (t)·[RPE k (t)-RPE i [(t)]; Considering the interaction strength between adjacent geographical regions; Additional factor adjustment: ζ·Δ i(t); Adjusting for the impact of other factors on risk estimation;

[0109] The following is a brief introduction to how the parameters of this formula are obtained:

[0110] For RPE i (t), based on the risk probability estimate of the previous time step; for α, it is set according to the learning rate, usually determined experimentally; for P(E) i |C), calculated by formula 1; for β, it is set according to the neighbor influence coefficient, reflecting the interaction strength between adjacent geographical areas; for N(i), the neighbor set of the i-th geographical area is defined; for γ ik (t), dynamically adjusted based on the actual correlation strength at time t; for RPE k (t), risk probability estimate based on neighboring geographical areas at the same time point; for ζ, set according to dynamic adjustment coefficient; for Δ i (t), which obtains the pheromone change from the real-time data stream.

[0111] Imagine a large city where, in preparation for the upcoming typhoon season, the emergency management department plans to optimize the response routes of emergency vehicles from multiple fire stations to potentially affected areas. First, the team collected and integrated heterogeneous data from multiple sources, including location data of all fire stations in the city, locations of communities expected to be severely affected, traffic flow, and weather forecasts, and then cleaned and analyzed the data. Second, they used spatiotemporal association rule mining technology to identify high-risk areas and time periods, and combined this with historical emergency response cases to establish a comprehensive contextual awareness platform, laying the foundation for subsequent probability calculations. Then, they used a Bayesian network to construct a risk probability model to calculate the probability of an emergency occurring in each geographical area, P(E). i For example, the probability of flooding in a community E1 during a typhoon is P(E1|C) = 0.85; then, the process proceeds to generate the risk probability estimate RPE. i During the transition phase of (t+1), integrating the aforementioned probability results, considering time variations and spatial interaction effects, the formula RPE is applied. i (t+1)=RPE i (t)+0.7·[0.85-RPE i [(t)]+0.3·∑ k∈V(i) γ ik (t)·[RPE k (t)-RPE i [(t)]+0.1·Δ i (t), assuming an initial estimate of RPE i (0) = 0.5, and after several iterations, the latest risk probability estimate RPE is obtained. i(5) = 0.8; Finally, the latest information is synchronized to the mobile terminals of all relevant personnel through a two-way communication mechanism, and scenario analysis technology is applied to simulate the impact path selection of emergencies. At the same time, sensitivity analysis is conducted to verify the stability and robustness of the scheme, ensuring that the generated risk probability estimate is scientific and practical, and providing decision support for the allocation of emergency resources. Through the above steps, the entire emergency rescue operation becomes more orderly and efficient, greatly improving the speed and effectiveness of emergency response, and ensuring that the lives and property of citizens are protected to the greatest extent.

[0112] In summary, the application of the aforementioned steps and technologies not only improved the accuracy and timeliness of emergency resource allocation but also significantly shortened rescue response time and reduced delays caused by traffic congestion through intelligent route planning and real-time information updates. Furthermore, the establishment of an efficient collaborative response network enhanced communication and coordination among departments, making the entire emergency medical system more flexible, rapid, and effective, ultimately improving public safety services and saving more lives.

[0113] 103. Based on the risk distribution map, an ant colony optimization algorithm is introduced to simulate the path selection process of rescue vehicles from multiple potential starting points to the destination. At the same time, combined with the real-time traffic updates provided by the intelligent transportation system, the path is optimized according to the shortest time principle and avoiding expected congested road sections to obtain the optimal response path.

[0114] In this step, a risk distribution map is used as a basis. An ant colony optimization algorithm is introduced to simulate the optimal route selection for rescue vehicles, and this is combined with real-time traffic updates from an intelligent transportation system to achieve the shortest route time and avoid congested sections. The ant colony optimization algorithm mimics the foraging behavior of ants in nature, searching for the optimal solution from the starting point to the destination.

[0115] In this embodiment, based on the risk distribution map generated in the previous step, all possible starting points and target locations are defined. Then, the ant colony optimization algorithm is used to simulate different path options. At the same time, the real-time data of the intelligent transportation system is taken into account to adjust the path, ensuring that the selected path is both fast and avoids traffic congestion, thereby improving rescue efficiency.

[0116] For example, after a major accident, the system quickly identified the nearest and most accessible emergency medical unit as the starting point and selected the fastest route to the scene based on the traffic flow at the time, ensuring that the rescue team could arrive in the shortest possible time.

[0117] Optionally, in step 103, based on the risk distribution map, an ant colony optimization algorithm is introduced to simulate the path selection process of rescue vehicles from multiple potential starting points to their destination. Simultaneously, combined with real-time traffic updates provided by the intelligent transportation system, the path is optimized based on the shortest time principle and avoidance of expected congested sections to obtain the optimal response path, including:

[0118] Using the aforementioned risk distribution map, high-risk emergency areas are identified as destinations, and all available emergency medical unit locations are identified as potential starting points, generating a set of paths. Based on this set of paths, ant colony optimization algorithm parameters are initialized, and an initial path matrix is ​​set to guide path selection during the simulation process, generating an initial path configuration. Based on the initial path configuration, the ant colony optimization algorithm is applied to simulate the travel process from each potential starting point to the destination, evaluating each possible path and generating a preliminary optimized path list. Using real-time traffic update data provided by the intelligent transportation system, each path in the preliminary optimized path list is dynamically adjusted, generating a dynamically adjusted path list. Based on the dynamically adjusted path list, considering all factors, the path that meets the optimal response conditions is selected as a reference for actual rescue operations, generating the optimal response path.

[0119] In this step, the risk distribution map includes map data generated through data analysis to show the probability of emergencies in various regions. This data is used to guide the allocation of emergency resources. Ant colony optimization is a heuristic search algorithm that simulates the foraging behavior of ants to find near-optimal paths in complex path selection problems. Real-time traffic updates provided by the intelligent transportation system include information on current road conditions, such as traffic flow and accident reports, used to dynamically adjust the routes of rescue vehicles.

[0120] In this embodiment, firstly, based on the risk distribution map, high-risk emergency areas are set as destinations, and all available emergency medical units are identified as potential starting points, thus constructing a series of possible paths. Secondly, the parameters of the ant colony optimization algorithm are initialized according to the above path set, and an initial path matrix is ​​set to guide path selection during the simulation process. Thirdly, the ant colony optimization algorithm is used to simulate the movement process from each potential starting point to the destination, evaluate the quality of each possible path, and form a preliminary optimized path list. Finally, combined with the real-time traffic update information of the intelligent transportation system, the preliminary optimized paths are dynamically adjusted, and after comprehensively considering all factors, the best path that meets the shortest time principle and avoids expected congested road sections is selected as a reference for actual rescue operations.

[0121] Imagine a large city where frequent summer thunderstorms cause power outages and fallen trees, leading to frequent emergencies. First, the city's emergency management department uses a pre-prepared risk distribution map to identify several high-risk areas for power outages and fallen trees as destinations requiring rapid response. Second, they identify the locations of all available emergency medical units in the city, including fire stations and ambulance stops, as potential starting points, forming a comprehensive set of paths. Third, based on this path set, the team initializes relevant parameters for the ant colony optimization algorithm, such as the number of ants and pheromone evaporation rate, and sets an initial path matrix to guide subsequent simulations, generating an initial path configuration. Then, by applying the ant colony optimization algorithm, the team simulates the journey of each emergency medical unit to the target area, evaluates the efficiency of different paths, and generates a preliminary list of optimized paths. Finally, using the latest traffic information provided by the intelligent transportation system, such as traffic light status and traffic accident reports, the preliminary optimized paths are adjusted in real time to ensure that the selected paths reach the destination as quickly as possible while avoiding potential traffic congestion, ultimately determining the optimal response path and providing accurate navigation guidance for on-site rescue.

[0122] By following the above steps, rescue teams can reach the accident site in the shortest possible time, improving rescue efficiency and ensuring public safety.

[0123] Optionally, the step of initializing ant colony optimization algorithm parameters and setting an initial path matrix based on the path set to guide path selection during simulation, and generating an initial path configuration, includes:

[0124] Using the aforementioned path set, the connections between all possible starting points and destinations are analyzed to generate a connection table. Based on this connection table, the key parameters of the ant colony optimization algorithm are determined, and initial values ​​for these parameters are set to generate the algorithm parameter configuration. Based on the algorithm parameter configuration, an initial path matrix containing all nodes and their connections is constructed to guide path selection in subsequent simulations, generating the initial path matrix. Using the initial path matrix and the algorithm parameter configuration, a preliminary path exploration simulation is performed to ensure that at least one feasible path is discovered between all paths, generating the initial path configuration.

[0125] In this step, the path set includes connection information between all possible starting points and destinations, used to generate a connection table. Key parameters of the ant colony optimization algorithm, such as the number of ants and pheromone evaporation rate, need to be initialized according to the actual situation to ensure the effectiveness of the algorithm. The initial path matrix is ​​a data structure containing all nodes and their connections, used to guide path selection in subsequent simulations. By performing preliminary path exploration simulations, it can be verified that at least one feasible path has been discovered, thus generating the initial path configuration.

[0126] In this embodiment, firstly, a detailed analysis of the connections between all possible starting points and destinations is performed based on the path set to generate a clear connection table; secondly, based on this connection table, the key parameters required for the ant colony optimization algorithm are determined, and initial values ​​for these parameters are set to form the algorithm parameter configuration; thirdly, using the generated algorithm parameter configuration, a complete initial path matrix is ​​constructed, which contains all nodes and their connections to each other, providing guidance for subsequent simulations; finally, combining the initial path matrix and the algorithm parameter configuration, a preliminary path exploration simulation is performed to ensure that there is at least one feasible path between each pair of starting points and destinations, ultimately generating the initial path configuration.

[0127] Imagine a large city where, in preparation for the upcoming typhoon season, the emergency management department plans to optimize the response routes for emergency vehicles from multiple fire stations to potentially affected areas. First, the team collected location data for all fire stations in the city and identified communities expected to be severely affected as destinations, generating a comprehensive set of routes. Second, they conducted a detailed analysis of the connections between all possible origins and destinations, considering factors such as road type and traffic flow, generating a comprehensive connection table. Third, based on this connection table, the team set key parameters for the ant colony optimization algorithm, such as ant count, pheromone intensity, and evaporation rate, and established initial values ​​for these parameters, forming a reasonable algorithm parameter configuration. Then, using the aforementioned algorithm parameter configuration, they constructed an initial path matrix containing all nodes and their connections, ensuring that each node can be effectively connected to other nodes. Finally, combining the initial path matrix and the algorithm parameter configuration, the team performed preliminary path exploration simulations to ensure that there is at least one feasible path between each pair of origins and destinations, ultimately generating the initial path configuration.

[0128] By following the steps above, the rescue team can plan the optimal response route in advance before the typhoon arrives, ensuring that they can quickly and effectively reach the scene when the disaster occurs, thus improving the speed and efficiency of emergency response.

[0129] This application addresses the problem that existing technologies suffer from low emergency response efficiency and unreasonable resource allocation due to the inability of static resource allocation and experience-based path planning methods to adapt to complex dynamic environments. Furthermore, the lack of forward-looking predictions for high-risk areas and time periods leads to untimely and inaccurate resource deployment during emergencies. Therefore, this invention proposes an alternative solution that uses an ant colony optimization algorithm to simulate the journey from potential starting points to destinations, combined with real-time traffic updates provided by an intelligent transportation system, to optimize the process by adhering to the shortest time principle and avoiding anticipated congestion. This addresses the aforementioned technical problems and improves the speed, accuracy, and efficiency of emergency response.

[0130] Optionally, based on the initial path configuration, the ant colony optimization algorithm is applied to simulate the travel process from each potential starting point to the destination, and each possible path is evaluated to generate a preliminary optimized path list, including:

[0131] Calculate the probability P of the ant choosing a path. ij Before (t), construct a network graph containing all potential origins and destinations, evaluate the historical traffic efficiency and current traffic conditions of road segments, identify high-congestion risk areas, and provide a basis for subsequent probability calculations;

[0132]

[0133] P ij (t) represents the probability that the ant chooses node j from node j at time t; t ij (t) represents the pheromone concentration between node i and node j at time t; α represents the pheromone importance factor, which measures the degree of influence of pheromone concentration; η ij (t) represents the heuristic value between the inverse and the expected travel time at time t; β represents the importance of the heuristic factor, measuring the degree of influence of the heuristic value; N i D represents the set of all adjacent nodes of a node; γ represents the dynamic adjustment coefficient, used to adjust the impact of real-time traffic conditions on path selection; ij (t) represents the dynamic traffic congestion index between node i and node j at time t, reflecting the current congestion situation of the road segment;

[0134] Probability of completion P ij After (t) calculation, the ant's movement process is simulated and the path and time are recorded. The pheromone increment is adjusted by combining the pheromone volatilization mechanism and external feedback to ensure that the generated pheromone concentration truly reflects the actual passage conditions of the path, thus preparing for pheromone updates.

[0135]

[0136] τ ij(t+1) represents the new pheromone concentration between node j and node j at time t+1; τ ij (t) represents the new pheromone concentration between nodes i and j at time t; (1-ρ) represents the pheromone evaporation rate, indicating the proportion of pheromone that naturally decreases over time; ρ represents the evaporation coefficient, controlling the rate of pheromone reduction; Δτ ij (t) represents the pheromone increment between nodes at time t, which is contributed by all ants that pass through the path; in It is the pheromone increment of the k-th ant on the path at time t. If ant k has traversed the path (i,j), then... Otherwise, it is 0; Q represents a constant, indicating the total amount of pheromones released by the ants; L k θ represents the total length or time taken for the k-th ant to complete its path; θ represents the feedback coefficient, used to adjust the influence of external feedback on pheromone concentration; F ij (t) represents the external feedback value between time node t and node j, including real-time traffic updates from the intelligent transportation system or public feedback, reflecting the actual traffic efficiency of the path and the impact of emergencies; λ represents the average speed influence coefficient, measuring the impact of changes in average speed on pheromone concentration; L ij V represents the set of all segments on the path from node to node; l (t) represents time t. -10 Average speed on the segment; |L ij | represents the set of road segments L ij The number of medium-speed sections; μ represents the preset baseline average speed, used to compare changes in the actual average speed;

[0137] After calculating the new pheromone concentration τ ij After (t+1), the path selection probability is reassessed, and paths with high pheromone concentration and meeting the shortest time principle are selected through multiple rounds of iteration. Scenario analysis technology is used to optimize candidate paths to ensure that the generated preliminary optimized path list is scientific, reasonable and practical.

[0138] This formula aims to generate a preliminary list of optimized routes by constructing a network graph containing all potential origins and destinations, evaluating historical traffic efficiency and current traffic conditions of road segments, identifying high-congestion-risk areas, and applying an ant colony optimization algorithm to assess each possible path. These measures work together to ensure a more scientific and rational allocation of emergency resources, improving the speed and effectiveness of emergency response.

[0139] The following is a brief introduction to the design rationale behind each term of the formula:

[0140]

[0141] Effect of pheromone concentration: [τ]ij (t)] α ; Measuring the degree of influence of pheromone concentration; Heuristic value influence: [η ij (t)] β ; Measures the influence of heuristic values ​​(such as the reciprocal of distance or estimated travel time); Dynamic adjustment coefficient: [1+γ·D ij [t]; Adjusting the impact of real-time traffic conditions on route selection; Summing the set of adjacent nodes: Ensure probability normalization;

[0142] The following is a brief introduction to how the parameters of this formula are obtained:

[0143] For [τ] ij (t)] α Obtained from previous pheromone concentration records, and α was set experimentally; for [η] ij (t)] β Calculated based on the distance between nodes or the estimated travel time, and β is set according to the experiment; for N i The value is determined directly from the network diagram; for γ, a reasonable dynamic adjustment coefficient is set based on real-time traffic conditions; for D... ij (t) extracts the current road segment's congestion status from the real-time traffic data stream.

[0144] The following is a brief introduction to the design rationale behind each term of the formula:

[0145]

[0146] Pheromones naturally decrease: (1-ρ)·τ ij (t); represents the proportion of pheromone naturally decreasing over time; ant contribution increment: Δτ ij (t); reflects the contribution of ants passing through this path to its pheromone concentration; external feedback adjustment: θ·F ij (t); the effect of adjusting external feedback on pheromone concentration; the effect of average velocity: To measure the effect of changes in average velocity on pheromone concentration;

[0147] The following is a brief introduction to how the parameters of this formula are obtained:

[0148] For (1-ρ)·τ ij (t), based on the set volatility coefficient ρ and the pheromone concentration τ at the previous moment. ij (t) Calculation; for in It is the pheromone increment of the k-th ant on the path at time t. If ant k has traversed the path (i,j), then... Otherwise, it is 0; for θ, it is set according to the importance of external feedback; for F ij (t) is obtained from real-time traffic updates from the intelligent transportation system or public feedback; λ is set based on the impact of average speed changes on pheromone concentration; for Obtain the average vehicle speed on each road segment from real-time traffic data; for |L ij |, Statistical road segment set L ij The number of road segments; for μ, a baseline average speed is preset to compare the changes in the actual average speed.

[0149] Imagine a large city where, in preparation for the upcoming typhoon season, the emergency management department plans to optimize the response routes for emergency vehicles from multiple fire stations to potentially affected areas. First, the team constructed a network map containing the locations of all fire stations in the city and expected severely affected communities as destinations. They assessed the historical traffic efficiency and current traffic conditions of each road segment, identifying high-congestion-risk areas to lay the foundation for subsequent probability calculations. Second, they used an ant colony optimization algorithm to calculate the probability P of each path chosen by an ant. ij (t), for example, the probability of traveling from segment i to j. The final probability value was 0.9. Then, the ant's movement process was simulated, and the path and time taken were recorded. The pheromone increment was adjusted based on the pheromone evaporation mechanism and external anti-corruption measures to ensure that the generated pheromone concentration accurately reflected the actual passage conditions of the path. Next, the pheromone concentration was updated. Assuming the initial pheromone concentration is 0.7, the new pheromone concentration τ is obtained after calculation. ij (t+1) = 0.85; Finally, the probability of route selection is reassessed, and routes with high pheromone concentration and meeting the shortest time principle are selected through multiple rounds of iteration. Scenario analysis technology is used to optimize candidate routes, ensuring that the generated preliminary optimized route list is scientific, reasonable, and practical. Through the above steps, the entire emergency response operation becomes more orderly and efficient, greatly improving the speed and effectiveness of emergency response and ensuring that the lives and property of citizens are protected to the greatest extent.

[0150] In summary, the application of the aforementioned steps and technologies not only improved the accuracy and timeliness of emergency resource allocation but also significantly shortened rescue response time and reduced delays caused by traffic congestion through intelligent route planning and real-time information updates. Furthermore, the establishment of an efficient collaborative response network enhanced communication and coordination among departments, making the entire emergency medical system more flexible, rapid, and effective, ultimately improving public safety services and saving more lives. Assuming a threshold of 0.8, the result, exceeding this threshold, indicates that the selected route has high reliability and effectiveness, thus verifying the stability and robustness of the solution.

[0151] 104. Utilizing the optimal response path, a two-way communication mechanism is established to continuously receive on-site feedback and synchronize this information to all mobile terminals participating in the emergency response, thereby generating an efficient collaborative response network.

[0152] This step involves establishing a two-way communication platform that allows rescue personnel to continuously receive the latest on-site feedback and synchronize this information with all mobile devices involved in the operation, thereby creating an efficient collaborative response network. This helps ensure timely information sharing among all parties and improves overall collaboration.

[0153] In this embodiment of the application, a two-way communication mechanism is established so that the rescue team can continuously obtain the latest environmental change information while performing its mission and disseminate it to all relevant parties so as to adjust strategies or routes in a timely manner and enhance the speed and accuracy of emergency response.

[0154] As rescue vehicles were en route to the accident site, the command center received a message about the road closure ahead via a two-way communication platform. It immediately notified all relevant emergency medical units to change their routes and sent the latest instructions to the on-site emergency personnel to ensure they could reach their designated locations as quickly as possible.

[0155] Optionally, step 104, which utilizes the optimal response path to continuously receive on-site feedback through a two-way communication mechanism and synchronizes this information to all mobile terminals participating in the emergency response, generates an efficient collaborative response network, including:

[0156] Utilizing the optimal response path, a secure and reliable two-way communication channel is established, generating a two-way communication mechanism. Based on this mechanism, a field feedback collection system is deployed to capture and transmit first-hand information from emergency responders, generating a field feedback data stream. Based on this data stream, a synchronization update algorithm is developed to ensure the latest situation is instantly reflected on the mobile terminals of all relevant emergency responders, generating an information synchronization scheme. Using this information synchronization scheme, multiple resources are integrated to form an efficient collaborative response system, generating a highly efficient collaborative response network.

[0157] In this step, the optimal response path refers to the best rescue route determined by ant colony optimization algorithms and intelligent transportation systems. A two-way communication mechanism includes establishing a secure and reliable communication channel to ensure real-time information exchange between emergency responders and the command center. The on-site feedback data stream encompasses firsthand information collected by emergency responders during operations, such as road conditions, weather changes, and accident severity; this information is used to update and adjust rescue strategies. The synchronous update algorithm is a technology capable of rapidly processing and disseminating the latest information, ensuring that all personnel involved in emergency response receive the latest updates immediately. An efficient collaborative response network integrates resources from multiple parties, forming a closely coordinated emergency response system.

[0158] In this embodiment, firstly, a secure and reliable two-way communication channel is constructed based on the optimal response path to ensure seamless information transmission between emergency responders and the command center; secondly, a field feedback collection system is deployed to capture various situations encountered by emergency responders at the scene and transmit this information back to the command center in real time, forming a continuous data stream; thirdly, a synchronous update algorithm is developed to ensure that the latest situation can be quickly reflected on the mobile terminals of all relevant emergency responders, ensuring that everyone can understand the latest progress in a timely manner; finally, by utilizing this synchronously updated information, resources from different departments and units are integrated to form an efficient and collaborative response system, ensuring that all parties can coordinate and respond to emergencies in a consistent manner.

[0159] In a large city, to improve the efficiency of response to public emergencies, the emergency management department decided to implement an advanced two-way communication mechanism. First, based on pre-calculated optimal response paths, the team established a secure and reliable two-way communication channel, ensuring uninterrupted information exchange between emergency responders and the command center, whether in the city center or remote areas. Second, they deployed a field feedback collection system, equipping emergency responders with smart devices that could upload real-time videos, images, and text descriptions of the scene. This firsthand information was rapidly transmitted to the command center, generating a continuous stream of field feedback data. Third, based on these data streams, the team developed a synchronous update algorithm that could process new information within seconds and push it to each emergency responder's mobile device, ensuring everyone was up-to-date with the situation. Finally, through this synchronously updated information, the emergency management department integrated resources from multiple departments, including fire, medical, and police, forming a highly efficient collaborative response system that enabled all parties to share information and work together on the same platform.

[0160] Through the above steps, the entire emergency response operation became more orderly and efficient, greatly improving the speed and effectiveness of the emergency response and ensuring that the lives and property of citizens were protected to the greatest extent.

[0161] Overall, steps 101 to 104 not only improved the accuracy and timeliness of emergency resource allocation, but also significantly shortened rescue response time and reduced delays caused by traffic congestion through intelligent route planning and real-time information updates. Furthermore, by establishing an efficient collaborative response network, the communication and coordination capabilities between departments were enhanced, making the entire emergency medical system more flexible, rapid, and effective, ultimately improving the level of public safety services and saving more lives.

[0162] Figure 2 This application provides a schematic diagram of the structure of an emergency medical route planning system based on big data analysis, as shown in the embodiment of the present application. Figure 2 As shown, the device includes:

[0163] The collection module 21 is used to collect dynamic and static information streams from multiple channels, identify and predict various complex situational factors, and obtain emergency resource allocation suggestions.

[0164] The assessment module 22 is used to perform risk probability modeling using Bayesian networks based on the emergency resource allocation recommendations, and combined with the knowledge base technology of the expert system, to perform a detailed assessment of the incidence rate of emergency situations in different geographical areas within a specific time period, and generate a risk distribution map.

[0165] The optimization module 23 is used to simulate the path selection process of rescue vehicles from multiple potential starting points to the destination by introducing an ant colony optimization algorithm based on the risk distribution map. At the same time, it combines the real-time traffic updates provided by the intelligent transportation system to optimize the path according to the shortest time principle and avoid expected congested road sections, so as to obtain the optimal response path.

[0166] The synchronization module 24 is used to utilize the optimal response path and continuously receive on-site feedback through a two-way communication mechanism, and synchronize this information to all mobile terminals participating in the emergency response to generate an efficient collaborative response network.

[0167] Figure 2 The aforementioned emergency route planning system based on big data analysis can execute... Figure 1 The implementation principle and technical effects of the emergency route planning method based on big data analysis described in the illustrated embodiment will not be repeated here. The specific methods by which each module and unit of the emergency route planning system based on big data analysis in the above embodiments are performed have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0168] In one possible design, Figure 2 The emergency route planning system based on big data analysis shown in the embodiment can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0169] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0170] The processing component 32 is used to: collect dynamic and static information streams from multiple channels, identify and predict various complex situational factors, and obtain emergency resource allocation suggestions; based on the emergency resource allocation suggestions, use Bayesian networks to perform risk probability modeling, and combine the knowledge base technology of expert systems to perform fine-grained evaluation of the incidence rate of emergencies in different geographical areas within a specific time period, generating a risk distribution map; based on the risk distribution map, introduce ant colony optimization algorithm to simulate the path selection process of rescue vehicles from multiple potential starting points to the destination, and combine the real-time traffic updates provided by the intelligent transportation system to optimize the path according to the shortest time principle and avoid expected congested road sections, obtaining the optimal response path; using the optimal response path, continuously receive on-site feedback through the establishment of a two-way communication mechanism, and synchronize this information to all mobile terminals participating in the emergency response operation to generate an efficient collaborative response network.

[0171] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0172] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0173] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0174] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0175] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0176] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0177] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The embodiment shown is an emergency route planning method based on big data analysis.

[0178] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0179] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0180] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0181] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A first aid path planning method based on big data analysis, characterized in that, The application comprises: Collecting dynamic and static information streams from multiple channels, identifying and predicting various complex situational factors, and obtaining emergency resource allocation recommendations; According to the emergency resource allocation recommendations, risk probability modeling is performed using Bayesian networks, and the knowledge base technology of expert systems is combined to finely assess the emergency occurrence rate in different geographical areas within a specific time period, and a risk distribution map is generated; Based on the risk distribution map, an ant colony optimization algorithm is introduced to simulate the path selection process of rescue vehicles from multiple potential starting points to the destination, and combined with real-time traffic updates provided by the intelligent transportation system, the path is optimized in terms of shortest time and avoidance of expected congestion sections to obtain the optimal response path, including: In calculating the probability of the path selected by the ants Previously, a network graph containing all potential starting points and destinations is constructed, and the historical traffic efficiency and current traffic conditions of road segments are evaluated to identify high-congestion-risk areas, providing a basis for subsequent probability calculations; ； in, Indicates time Ants select from node i to node The probability, Indicates time node To the node The pheromone concentration between This indicates the importance factor of pheromones, measuring the degree of influence of pheromone concentration. Indicates time Heuristic values ​​between node i and node j, including the inverse distance or estimated travel time. This indicates the importance of the heuristic factor and measures the degree of influence of the heuristic value. Represents the set of all adjacent nodes of a node. This represents a dynamic adjustment factor used to adjust the impact of real-time traffic conditions on route selection. Indicates time node To the node The dynamic traffic congestion index reflects the current congestion situation on the road segment; Using the optimal response path, a two-way communication mechanism is established to continuously receive on-site feedback, and these information is synchronized to all mobile terminals participating in the emergency response, generating an efficient collaborative response network.

2. The method of claim 1, wherein, According to the emergency resource allocation recommendations, risk probability modeling is performed using Bayesian networks, and the knowledge base technology of expert systems is combined to finely assess the emergency occurrence rate in different geographical areas within a specific time period, and a risk distribution map is generated, including: Using the forward-looking emergency resource allocation recommendations, multi-source heterogeneous data is integrated and analyzed to generate a basic data set; According to the basic data set, a risk probability model is constructed using Bayesian networks to quantitatively calculate the emergency occurrence probability of each geographical area, generating a risk probability estimate; Based on the risk probability estimate, the knowledge base technology of expert systems is combined to optimize the model parameters, ensuring that the risk assessment results are closer to the actual situation, generating an optimized risk probability estimate; Using the optimized risk probability estimate, the emergency occurrence rate in different geographical areas within a specific time period is finely assessed, and a risk distribution map is generated.

3. The method of claim 2, wherein, Using the forward-looking emergency resource allocation recommendations, multi-source heterogeneous data is integrated and analyzed to generate a basic data set, including: Using the forward-looking emergency resource allocation recommendations, the collection range and focus of multi-source heterogeneous data are determined, and a data collection strategy is generated; According to the data collection strategy, real-time and historical emergency-related data is collected from different data sources to generate an original data set; Based on the original data set, data cleaning, formatting, and standardization processing are performed to eliminate noise and inconsistencies in the data, generating a purified data set; Using the purified data set, data correlation analysis is performed in combination with time and space attributes to mine potential relationships between different data, generating a correlation data model; According to the correlation data model, meaningful information features are extracted, and the information features are encoded and indexed to ensure efficient retrieval and fast access of information, generating feature-coded data; Based on the feature-coded data, information from various data sources is integrated to generate a basic data set.

4. The method of claim 2, wherein, Using the optimized risk probability estimate, the emergency occurrence rate in different geographical areas within a specific time period is finely assessed, and a risk distribution map is generated, including: With the optimized risk probability estimation, a comprehensive assessment of emergency occurrence frequency and severity in each geographical region is conducted using spatio-temporal analysis methods, generating a preliminary risk score; According to the preliminary risk score, spatial interpolation techniques are used to fill in risk information in data-sparse areas, ensuring that all geographical regions have corresponding risk assessment results, generating a complete risk coverage map; Based on the complete risk coverage map, clustering algorithms are applied to identify high-risk hotspots and conduct in-depth analysis of the time series characteristics of these regions, generating information on hotspot regions and high-risk periods; Using the information on hotspot regions and high-risk periods, combined with real-time traffic flow and weather conditions, risk assessment results are adjusted and refined to generate a risk distribution map.

5. The method of claim 1, wherein, Based on the risk distribution map, an ant colony optimization algorithm is introduced to simulate the path selection process of rescue vehicles from multiple potential starting points to the destination, while combining real-time traffic updates from the intelligent transportation system to optimize the path based on the shortest time principle and avoiding expected congested sections, resulting in an optimal response path, including: Using the risk distribution map, determine the high-emergency area as the destination and identify all available emergency unit locations as potential starting points to generate a path set; According to the path set, initialize the parameters of the ant colony optimization algorithm and set the initial path matrix to guide path selection during the simulation process, generating an initial path configuration; Based on the initial path configuration, apply the ant colony optimization algorithm to simulate the travel process from each potential starting point to the destination, evaluate each possible path, and generate a preliminary optimized path list; Using real-time traffic update data provided by the intelligent transportation system, dynamically adjust each path in the preliminary optimized path list to generate a dynamically adjusted path list; According to the dynamically adjusted path list, consider all factors to select the path that meets the optimal response conditions as a reference for actual rescue operations, generating an optimal response path.

6. The method of claim 5, wherein, According to the path set, initialize the parameters of the ant colony optimization algorithm and set the initial path matrix to guide path selection during the simulation process, generating an initial path configuration, including: Using the path set, analyze the connections between all possible starting points and destinations to generate a connection relationship table; According to the connection relationship table, determine the key parameters of the ant colony optimization algorithm and set the initial values of these parameters to generate an algorithm parameter configuration; Based on the algorithm parameter configuration, construct an initial path matrix containing all nodes and their connections for guiding path selection during subsequent simulation processes, generating an initial path matrix; Using the initial path matrix, combined with the algorithm parameter configuration, perform a preliminary path exploration simulation to ensure that at least one feasible path is discovered between each path, generating an initial path configuration.

7. The method of claim 1, wherein, Using the optimal response path, establish a two-way communication mechanism to continuously receive on-site feedback and synchronize this information to all mobile terminals participating in emergency response operations, generating an efficient collaborative response network, including: Utilizing the optimal response path, a secure and reliable two-way communication channel is established, and a two-way communication mechanism is generated; According to the two-way communication mechanism, a field feedback collection system is deployed to capture and transmit first-hand information from emergency personnel, generating a field feedback data stream; Based on the field feedback data stream, a synchronous update algorithm is developed to enable the latest information to be reflected on the mobile terminals of all relevant emergency personnel in real time, generating an information synchronization scheme; Utilizing the information synchronization scheme, multiple resources are integrated to form an efficient and collaborative response system, generating an efficient collaborative response network. 8.A first aid path planning system based on big data analysis, characterized in that, Comprise: A collection module for collecting dynamic and static information streams from multiple channels, identifying and predicting various complex situational factors, and obtaining emergency resource allocation recommendations; An evaluation module for using Bayesian networks to model risk probabilities based on the emergency resource allocation recommendations, and combining expert system knowledge base technology to finely evaluate the incidence of emergencies in different geographical areas within a specific time period, generating a risk distribution map; An optimization module for simulating the path selection process of rescue vehicles from multiple potential starting points to the destination based on the risk distribution map, and combining real-time traffic updates provided by intelligent transportation systems to optimize the path based on the shortest time principle and avoiding expected congested sections, obtaining the optimal response path, including: In calculating the probability of the path selected by the ants Previously, a network graph containing all potential starting points and destinations is constructed, and the historical traffic efficiency and current traffic conditions of road segments are evaluated to identify high-congestion-risk areas, providing a basis for subsequent probability calculations; ； in, Indicates time Ants select from node i to node The probability, Indicates time node To the node The pheromone concentration between This indicates the importance factor of pheromones, measuring the degree of influence of pheromone concentration. Indicates time Heuristic values ​​between node i and node j, including the inverse distance or estimated travel time. This indicates the importance of the heuristic factor and measures the degree of influence of the heuristic value. Represents the set of all adjacent nodes of a node. This represents a dynamic adjustment factor used to adjust the impact of real-time traffic conditions on route selection. Indicates time node To the node The dynamic traffic congestion index reflects the current congestion situation on the road segment; A synchronization module for utilizing the optimal response path to continuously receive field feedback through the establishment of a two-way communication mechanism, synchronizing this information to all mobile terminals participating in emergency actions, and generating an efficient collaborative response network.

9. A computing device, comprising: A processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to realize the emergency path planning method based on big data analysis in any one of claims 1-7.

10. A computer storage medium, characterized in that, A computer program is stored, and when the computer program is executed by a computer, the emergency path planning method based on big data analysis in any one of claims 1-7 is realized.

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