Integrated first-aid information processing method and system

Abstract

The invention provides an integrated first-aid information processing method and system. The method comprises the following steps: acquiring first-aid event data from a plurality of different sources; based on the first-aid event data, modeling and analyzing the first-aid event data by using a federated learning algorithm to generate a real-time dynamic first-aid resource allocation map; according to the real-time dynamic first-aid resource allocation map, optimizing a rescue path and a resource allocation scheme by adopting a quantum heuristic optimization algorithm in combination with a depth context awareness technology to obtain an optimal rescue path and a required resource combination; and sending the optimal rescue path and the required resource combination to a nearest available first-aid unit by using a secure communication protocol, ensuring that information is synchronously updated to mobile terminals of all related first-aid personnel, and generating a first-aid response state which is confirmed to be received. According to the technical scheme provided by the invention, the intelligent level, the resource utilization efficiency and the service quality of the emergency system are improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of information processing technology, and in particular, to an integrated emergency information processing method and system. Background Art

[0002] In modern emergency systems, it is crucial to quickly and accurately deploy emergency resources in the face of complex emergencies and emergencies. This not only involves the efficient integration and real-time analysis of emergency event data from multiple different sources (such as hospitals, fire brigades, police stations, traffic accident monitoring, etc.), but also requires dynamic adjustment of rescue routes and resource allocation plans based on the latest situation. In addition, ensuring the secure transmission of information and the ability of all relevant emergency personnel to obtain the latest instructions in real time and synchronously are also indispensable technical requirements. In order to improve the efficiency of emergency response, optimize resource allocation, and reduce the loss of life and property, an integrated approach is urgently needed to deal with these complex requirements.

[0003] At present, most emergency information systems rely mainly on centralized data analysis platforms to process and distribute emergency information. These systems usually use traditional machine learning or statistical methods to model historical data and formulate fixed emergency resource allocation plans based on this. For the selection of rescue routes, a simple shortest path calculation is generally performed in combination with the traffic information provided by the map service. However, in actual operations, due to the lack of effective integration of multi-source heterogeneous data and the ability to respond sensitively to real-time dynamic changes, the existing emergency information processing methods often cannot meet the requirements of high timeliness and accuracy.

[0004] Although the existing emergency information system has improved the emergency response speed to a certain extent, it still has the following significant defects: traditional solutions usually require the data of various institutions to be centralized in one place for processing, which not only increases the risk of data leakage, but also is not conducive to protecting personal privacy; fixed-mode resource allocation makes it difficult to flexibly respond to changes in emergencies and cannot timely reflect the latest distribution of emergency needs, resulting in waste or shortage of resources; relying solely on simple path planning algorithms cannot fully consider various factors in actual situations (such as real-time traffic flow, environmental conditions, etc.), making it difficult to achieve truly optimal path selection and resource allocation; the information update mechanism of the existing system is not perfect, which may lead to inconsistent or delayed information received by front-line emergency personnel, affecting rescue efficiency. Summary of the invention

[0005] The embodiments of the present application provide an integrated emergency information processing method and system to solve the problem of resource waste or shortage in the prior art.

[0006] In a first aspect, an embodiment of the present application provides an integrated emergency information processing method, including:

[0007] Access emergency incident data from multiple different sources;

[0008] Based on the emergency event data, a federated learning algorithm is used to model and analyze the emergency event data to generate a real-time dynamic emergency resource allocation map;

[0009] According to the real-time dynamic emergency resource allocation map, the rescue path and resource allocation plan are optimized by using a quantum heuristic optimization algorithm combined with deep situational awareness technology to obtain the optimal rescue path and required resource combination;

[0010] Using a secure communication protocol, the optimal rescue path and required resource combination are sent to the nearest available emergency unit, and the information is ensured to be synchronously updated to the mobile terminals of all relevant emergency personnel to generate an emergency response status that has been confirmed to be received.

[0011] Optionally, based on the emergency event data, the emergency event data is modeled and analyzed using a federated learning algorithm to generate a real-time dynamic emergency resource allocation map, including:

[0012] Using emergency incident data from multiple different sources, a preliminary dataset was constructed;

[0013] Based on the preliminary data set, a federated learning framework is used to train the model locally in each data source to protect privacy and share model parameters, thereby obtaining a distributed trained local model;

[0014] Based on the distributed trained local models, global model parameters are aggregated and updated on the central server to achieve cross-institutional knowledge fusion and obtain a global model;

[0015] The global model is used to predict and analyze the emergency incident data, identify key resource distribution and demand patterns, and generate a real-time dynamic emergency resource allocation map.

[0016] Optionally, the updating of global model parameters on a central server based on the distributed trained local models to achieve cross-institutional knowledge fusion and obtain a global model includes:

[0017] Based on the distributed trained local model, collect model parameter update information of each participant;

[0018] The model parameter update information is encrypted using a secure aggregation protocol to protect data privacy while ensuring information integrity, thereby obtaining encrypted model parameter update information;

[0019] According to the encrypted model parameter update information, a secure aggregation algorithm is executed on the central server to generate updated global model parameters by integrating the contributions of all participants;

[0020] The updated global model parameters are used to adjust the global model on the central server to obtain the global model.

[0021] Optionally, the global model is used to predict and analyze the emergency event data, identify key resource distribution and demand patterns, and generate a real-time dynamic emergency resource allocation map, including:

[0022] Using the optimized global model, comprehensively processing the multi-dimensional information in the emergency event data to obtain processed emergency event data;

[0023] According to the processed emergency incident data, a spatiotemporal clustering algorithm is used to identify hot spots and potential high-demand periods of emergency incidents, and generate hot spots and high-demand period information;

[0024] Based on the hotspot areas and high-demand time period information, a resource matching algorithm is used to evaluate the coverage of existing emergency resources and determine resource gaps, thereby generating resource gap analysis results;

[0025] The resource gap analysis results are used in combination with real-time traffic conditions and other environmental factors to generate a real-time dynamic emergency resource allocation map.

[0026] Optionally, according to the real-time dynamic emergency resource allocation map, the rescue path and resource allocation scheme are optimized by using a quantum heuristic optimization algorithm combined with deep context perception technology to obtain an optimal rescue path and required resource combination, including:

[0027] By using the real-time dynamic emergency resource allocation map, a comprehensive analysis is performed on the distribution and demand of emergency resources in the current rescue scenario to obtain the emergency resource configuration status in the current scenario;

[0028] According to the emergency resource allocation status in the current scenario, a quantum heuristic optimization algorithm combined with deep situational awareness technology is used to preliminarily screen and evaluate potential rescue paths and resource allocation plans, generating a series of candidate rescue paths and required resource combinations;

[0029] Based on the series of candidate rescue paths and required resource combinations, a multi-objective optimization strategy is further used to consider multiple indicators to fine-tune the candidate solutions, thereby obtaining a series of optimized rescue paths and required resource combinations;

[0030] From the optimized series of rescue paths and required resource combinations, the optimal rescue path and required resource combination is obtained by comparing the performance differences between different solutions.

[0031] Optionally, based on the series of candidate rescue paths and required resource combinations, a multi-objective optimization strategy is further used to consider multiple indicators to fine-tune the candidate solutions to obtain a series of rescue paths and required resource combinations that have been optimized, including:

[0032] Using the series of candidate rescue paths and required resource combinations, multiple optimization objectives are obtained;

[0033] Establishing a multi-objective optimization model according to the multiple optimization objectives;

[0034] Based on the multi-objective optimization model, an evolutionary algorithm or a genetic algorithm is used to iteratively optimize the series of candidate rescue paths and required resource combinations to obtain a preliminary optimized rescue path and resource allocation plan;

[0035] Through the preliminary optimized rescue path and resource allocation plan, further sensitivity analysis and simulation testing are carried out to verify the stability and robustness of the plan under different conditions, and to generate a series of optimized rescue paths and required resource combinations.

[0036] Optionally, the optimal rescue path and required resource combination are sent to the nearest available emergency unit by using a secure communication protocol, and the information is ensured to be synchronously updated to the mobile terminals of all relevant emergency personnel, and an emergency response status of confirmed receipt is generated, including:

[0037] Using a secure communication protocol, encrypting the optimal rescue path and the required resource combination to obtain encrypted information;

[0038] Sending the encrypted information to the nearest available emergency unit via a secure data transmission channel to generate a preliminary sending record;

[0039] Based on the preliminary sending record, the encrypted information is synchronously updated to the mobile terminals of all relevant emergency personnel using a synchronization mechanism to obtain mobile terminal reception information;

[0040] The information is received through the mobile terminal, and the emergency personnel confirm and feedback the receiving status, generating an emergency response status of confirmed receipt.

[0041] In a second aspect, an embodiment of the present application provides an integrated emergency information processing system, including:

[0042] An acquisition module, used to acquire emergency incident data from multiple different sources;

[0043] An analysis module, used to model and analyze the emergency event data based on the emergency event data using a federated learning algorithm to generate a real-time dynamic emergency resource allocation map;

[0044] An optimization module is used to optimize the rescue path and resource allocation scheme according to the real-time dynamic emergency resource allocation map by using a quantum heuristic optimization algorithm combined with deep situational awareness technology to obtain the optimal rescue path and required resource combination;

[0045] The sending module is used to use a secure communication protocol to send the optimal rescue path and the required resource combination to the nearest available emergency unit, and ensure that the information is synchronously updated to the mobile terminals of all relevant emergency personnel to generate an emergency response status that has been confirmed to be received.

[0046] In a third aspect, an embodiment of the present application provides 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 implement an integrated emergency information processing method as described in the first aspect.

[0047] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program, which, when executed by a computer, implements an integrated emergency information processing method as described in the first aspect.

[0048] In an embodiment of the present application, emergency incident data are obtained from multiple different sources; based on the emergency incident data, the emergency incident data are modeled and analyzed using a federated learning algorithm to generate a real-time dynamic emergency resource allocation map; according to the real-time dynamic emergency resource allocation map, a quantum heuristic optimization algorithm is used in combination with deep context perception technology to optimize the rescue path and resource allocation plan to obtain an optimal rescue path and required resource combination; using a secure communication protocol, the optimal rescue path and required resource combination are sent to the nearest available emergency unit, and it is ensured that the information is synchronously updated to the mobile terminals of all relevant emergency personnel to generate a confirmed emergency response status.

[0049] The technical solution of this application has the following beneficial effects:

[0050] This method can obtain emergency incident data from multiple different sources, so that the system can make comprehensive use of multiple information resources to improve the comprehensiveness and accuracy of decision-making; through modeling and analysis by federated learning algorithm, model parameters can be shared without directly exchanging original data, effectively protecting the data privacy and security of various institutions; the generated real-time dynamic emergency resource allocation map can be quickly updated according to the latest emergency incident data, ensuring the timeliness and rationality of resource allocation, and improving the efficiency of emergency response; combining quantum heuristic optimization algorithm with deep situational perception technology, the rescue path and resource allocation plan are optimized, not only considering static factors such as traffic conditions, but also considering dynamic factors such as environmental changes, so as to find the optimal solution and improve the success rate of rescue operations; the use of secure communication protocol ensures the safe transmission of the optimal rescue path and the required resource combination information, and can be synchronized to the mobile terminals of all relevant emergency personnel in a timely manner, ensuring the consistency and immediacy of the instructions of each participant, and enhancing the team collaboration ability; generating the confirmed emergency response status, realizing closed-loop management, helping to monitor the progress of task execution and quickly adjust strategies to respond to emergencies.

[0051] Furthermore, by adopting a federated learning algorithm to model and analyze emergency incident data from multiple different sources, this method can achieve cross-institutional knowledge fusion and model sharing under the premise of protecting the data privacy of each institution, thereby constructing a more accurate and comprehensive real-time dynamic emergency resource allocation map. Specifically, this method not only avoids the direct exchange of sensitive data and enhances information security, but also improves the generalization ability of the model through distributed training, ensuring the accuracy and timeliness of the prediction of emergency resource distribution and demand patterns, thereby significantly improving the efficiency of emergency response and the rationality of resource allocation, and providing strong support for rapid and effective emergency rescue.

[0052] Furthermore, by combining quantum heuristic optimization algorithms with deep context perception technology to optimize rescue paths and resource allocation plans, this method achieves accurate matching and efficient utilization of emergency resource distribution and demand. This process can not only quickly and comprehensively analyze the resource allocation status in the current rescue scenario, but also generate a variety of candidate plans through intelligent screening and evaluation of potential paths and resource combinations, and use multi-objective optimization strategies to fine-tune these plans. Finally, by comparing the performance differences between different plans, the optimal rescue path and resource combination are determined. This method significantly improves the response speed and decision-making quality of rescue operations, ensuring that in a complex and dynamically changing emergency environment, the most appropriate resources can be deployed in a timely and accurate manner, achieving efficient emergency rescue and maximizing the safety of public life.

[0053] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0055] Figure 1 A flowchart of an integrated emergency information processing method provided in an embodiment of the present application;

[0056] Figure 2 A schematic diagram of the structure of an integrated emergency information processing system provided in an embodiment of the present application;

[0057] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0058] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0059] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0060] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0061] Figure 1 A flowchart of an integrated emergency information processing method is provided for an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0062] 101. Obtain emergency incident data from multiple different sources;

[0063] In this step, this step involves collecting emergency-related information from multiple channels, including but not limited to emergency call records, medical facility locations, traffic conditions, weather forecasts, etc. By integrating data from hospitals, emergency centers, police, fire departments, and third-party service providers, a comprehensive and accurate emergency incident database can be formed for subsequent analysis and decision-making.

[0064] In the embodiment of the present application, in order to ensure the extensiveness and real-time nature of the data, the system will be designed to be able to connect to various data sources and use API interfaces or file transfer protocols to pull the latest emergency event data regularly or in real time. This step is the basis of the entire emergency resource optimization process, and it provides the necessary information input for subsequent steps.

[0065] For example, in a city-level emergency response system, a direct data connection can be established with all major hospitals in the city to receive real-time information on patient admissions; at the same time, the location of ambulances can be continuously updated through the integration of GPS positioning services; in addition, social media monitoring tools can be used to capture emergency help signals. All of this data is centrally stored in a cloud database for further processing and analysis.

[0066] 102. Based on the emergency incident data, a federated learning algorithm is used to model and analyze the emergency incident data to generate a real-time dynamic emergency resource allocation map;

[0067] In this step, federated learning is a distributed machine learning method that allows models to be trained without sharing original data, protecting user privacy and complying with data regulations. In the emergency scenario, the federated learning algorithm is used to analyze emergency event data from different regions or institutions to build a dynamic map that reflects the current distribution and demand status of emergency resources.

[0068] In the embodiment of the present application, after the emergency incident data is cleaned and preprocessed, the model will be trained on the local node through the federated learning framework, and then only the model parameters, not the original data, will be shared with the central server for aggregation. In this way, while protecting data privacy, effective cooperation across regions or institutions can be achieved, thereby generating an accurate and real-time updated emergency resource allocation map to guide the optimal allocation of resources.

[0069] In a city-level emergency response system, emergency incident data from different hospitals, fire departments, and police departments are regularly uploaded to a secure data sharing platform. This data includes patient location, condition description, available ambulance information, and traffic conditions. Using the federated learning algorithm, each participating agency trains its own model locally and only shares model parameters rather than raw data with the central server. The central server aggregates these local model parameters, updates the global model, and distributes it back to the participating agencies for further optimization. For example, after a heart attack occurs, the system can quickly identify nearby ambulances equipped with cardiopulmonary resuscitation equipment and adjust their routes to the scene in real time to ensure that the patient receives timely treatment.

[0070] Optionally, in step 102, based on the emergency event data, a federated learning algorithm is used to model and analyze the emergency event data to generate a real-time dynamic emergency resource allocation map, including:

[0071] A preliminary data set is constructed using emergency incident data from multiple different sources; based on the preliminary data set, a federated learning framework is used to train models locally on each data source to protect privacy and share model parameters, thereby obtaining a distributed trained local model; based on the distributed trained local model, global model parameters are aggregated and updated on a central server to achieve cross-institutional knowledge fusion and obtain a global model; using the global model, the emergency incident data is predicted and analyzed, key resource distribution and demand patterns are identified, and a real-time dynamic emergency resource allocation map is generated.

[0072] In this step, a preliminary data set is constructed for subsequent analysis by integrating emergency incident data from multiple different sources. The federated learning framework allows model training to be performed locally on each data source without leaking the original data, thereby protecting privacy and enabling sharing of model parameters. After obtaining the local model through distributed training, these local model parameters are aggregated on the central server to update the global model parameters, thereby achieving cross-institutional knowledge fusion. Finally, this global model is used to predict and analyze emergency incident data, identify key resource distribution and demand patterns, generate a real-time dynamic emergency resource allocation map, and guide the optimal configuration of emergency resources.

[0073] In the embodiment of the present application, first, the system collects and organizes data from different emergency-related institutions to form a comprehensive preliminary data set; second, based on this data set, each participating institution independently trains its own model using a federated learning framework in its local environment, and only shares model parameters rather than original data, thereby ensuring data security and privacy; third, the central server receives model parameter update information from all participating institutions, executes a secure aggregation algorithm to update global model parameters, and promotes cross-institutional knowledge exchange; finally, the updated global model is used to conduct in-depth analysis of the latest emergency event data, determine the key distribution points and peak demand periods of emergency resources, and generate a real-time dynamic emergency resource allocation map to provide a scientific basis for decision-making.

[0074] In a hypothetical urban emergency response system, first, the system obtains multi-source data including patient location, condition description, available ambulance status, and real-time traffic conditions from major hospitals, fire departments, and traffic management centers in the city, and organizes these data into a structured preliminary data set; second, each participating agency deploys a federated learning framework in its local environment and independently trains its own emergency response model based on the local data set, during which only model parameters are exchanged to maintain data privacy; third, the central server acts as a coordination node, receives model parameter update information uploaded by all participating agencies, applies a secure aggregation protocol to process this information, updates the global model parameters, and ensures the effective integration of knowledge among agencies; finally, using the updated global model, the system analyzes the latest emergency event data, identifies the most urgent demand areas and time windows, and generates a detailed real-time dynamic emergency resource allocation map to provide decision support for the command center.

[0075] Through the above steps, the urban emergency response system can efficiently integrate data resources from multiple parties, protect data privacy while optimizing the allocation of emergency resources, significantly improve the speed and efficiency of emergency response, and ensure that patients can receive appropriate medical assistance as soon as possible.

[0076] Optionally, the updating of global model parameters on a central server based on the distributed trained local models to achieve cross-institutional knowledge fusion and obtain a global model includes:

[0077] Based on the local model after distributed training, the model parameter update information of each participant is collected; the model parameter update information is encrypted using a secure aggregation protocol to protect data privacy while ensuring information integrity, thereby obtaining the encrypted model parameter update information; based on the encrypted model parameter update information, a secure aggregation algorithm is executed on a central server to generate updated global model parameters by integrating the contributions of each participant; and the global model on the central server is adjusted using the updated global model parameters to obtain a global model.

[0078] Optionally, the global model is used to predict and analyze the emergency event data, identify key resource distribution and demand patterns, and generate a real-time dynamic emergency resource allocation map, including:

[0079] The optimized global model is used to comprehensively process the multi-dimensional information in the emergency incident data to obtain the processed emergency incident data; based on the processed emergency incident data, a spatiotemporal clustering algorithm is used to identify hot spots and potential high-demand time periods of emergency incidents, and generate hot spot area and high-demand time period information; based on the hot spot area and high-demand time period information, a resource matching algorithm is used to evaluate the coverage of existing emergency resources and determine resource gaps, and generate resource gap analysis results; using the resource gap analysis results, combined with real-time traffic conditions and other environmental factors, a real-time dynamic emergency resource allocation map is generated.

[0080] In this step, it is described how to achieve cross-institutional knowledge fusion and global model construction in emergency incident data processing. First, by collecting the local model parameter update information of each participant after training based on local data, the secure transmission and privacy protection of this information are ensured; second, these parameters are encrypted using the secure aggregation protocol to ensure the integrity and security of the information; third, the secure aggregation algorithm is executed on the central server to integrate the contributions of all participants and generate new global model parameters; finally, the global model on the central server is adjusted to obtain a global model that integrates the knowledge of multiple parties. In addition, the solution also covers the process of using the optimized global model to predict and analyze multi-dimensional emergency incident data, including identifying hot spots, assessing resource gaps, and finally generating a real-time dynamic emergency resource allocation map.

[0081] In the embodiment of the present application, first, the system collects local model parameter update information obtained after distributed training from each participating organization; second, the secure aggregation protocol is used to encrypt these parameters, which not only protects data privacy but also ensures the integrity of the information; third, the secure aggregation algorithm is executed on the central server, and based on the encrypted model parameter update information, the contribution of each participant is comprehensively considered to generate updated global model parameters; finally, the global model on the central server is adjusted using these updated parameters to form a global model that can reflect the latest situation. For the data analysis part, first, the multi-dimensional information in the emergency incident data is processed using the optimized global model; second, the spatiotemporal clustering algorithm is used to identify the hot spots and high-demand periods of emergency incidents; third, based on this information, the resource matching algorithm is used to evaluate the coverage of existing emergency resources and determine the resource gap; finally, combined with real-time traffic conditions and other environmental factors, a detailed real-time dynamic emergency resource allocation map is generated to guide the optimal allocation of resources.

[0082] In a hypothetical city-level emergency response system, first, the system regularly collects local model parameter update information based on local emergency event data from major hospitals, fire departments and traffic management centers in the city; second, these parameter update information is encrypted through a secure aggregation protocol to ensure data privacy while also protecting information integrity; third, the central server receives the encrypted parameter update information, executes a secure aggregation algorithm, integrates the contributions of all participants, and generates updated global model parameters; finally, the central server adjusts the global model on it according to the updated parameters to form an updated global model that integrates multi-party knowledge. Subsequently, in order to predict and analyze emergency event data, first, the system uses the optimized global model to process multi-dimensional emergency event data from different sources; second, the spatiotemporal clustering algorithm is used to identify the hot spots and potential high-demand periods of emergency events and generate relevant information; third, based on these hot spots and high-demand period information, the system uses a resource matching algorithm to evaluate the current coverage of emergency resources, determine resource gaps, and generate resource gap analysis results; finally, combined with real-time traffic conditions and other environmental factors, such as weather forecasts, the system generates a detailed real-time dynamic emergency resource allocation map to provide decision support for the command center.

[0083] Through the above steps, the urban emergency response system not only achieves efficient data integration and privacy protection, but also optimizes the allocation of emergency resources through scientific analysis methods, improves the speed and efficiency of emergency response, and ensures that patients can receive appropriate medical assistance in the shortest time.

[0084] This application takes into account that in the prior art, due to the contradiction between privacy protection and efficient use of emergency incident data, as well as the data island problem faced by traditional centralized learning methods when processing cross-institutional data, it is difficult to achieve optimal configuration of emergency resource allocation. In order to overcome these problems, the embodiment of the present invention proposes an optional solution based on a federated learning framework. This solution can not only protect the data privacy of each participant, but also improve the generalization ability and robustness of the model through distributed training, thereby solving the technical problems of uneven distribution of emergency resources and low response efficiency.

[0085] Optionally, based on the preliminary data set, a federated learning framework is used to train a model locally in each data source to protect privacy and share model parameters, so as to obtain a distributed trained local model, including:

[0086] When updating local model parameters Previously, it was necessary to preprocess and extract features from the emergency incident dataset, initialize local model parameters, and set correlation coefficients and weights to provide a basis for subsequent training;

[0087]

[0088] in, represents the local model parameters of the i-th data source at the t-th iteration; represents the updated local model parameters; η is the learning rate, which controls the speed of gradient descent; is the loss function F i About Local Model Parameters The gradient of the dataset D based on the i-th data source i Calculated; λ is the regularization coefficient, which is used to balance the difference between the local model and the global average model; is the global average model parameter in the first iteration; α is the neighbor influence coefficient, which controls the influence of the adjacent data source on the local model of the current data source; is the neighbor set of the i-th data source; w ij is the weight between the i-th data source and the j-th data source, indicating the strength of the association between them; is the local model parameter of the jth data source at the tth iteration;

[0089] Calculate the local model parameters of all data sources After that, these parameters will be uploaded to the central server; the central server uses a secure aggregation protocol to receive and verify these parameters to ensure the security and integrity of the data and to generate global model parameters. Be prepared;

[0090]

[0091] in, represents the updated global average model parameters; N is the total number of data sources participating in federated learning; n i is the number of samples from the ith data source; n is the total number of samples from all data sources; is the local model parameter of the ith data source at the t+1th iteration; β is the global optimization adjustment coefficient, which is used to balance the local model update and additional optimization items in the global model parameter aggregation; K is the number of additional optimization items; γ k is the weight of the kth additional optimization item; is the gradient of the kth additional optimization term, based on the dataset D of the i-th data source i Calculated;

[0092] Calculate the full model parameters After that, the central server distributes it back to each data source; each data source continues local training according to the new global model parameters, gradually optimizes its local model, forming a closed-loop training process to improve the performance and robustness of the overall system.

[0093] This formula aims to build a closed-loop training process that can protect data privacy and effectively aggregate multi-party knowledge. By introducing mechanisms such as regularization terms and neighbor influence coefficients, we ensure that the local model can be consistent with the global average model while taking into account the influence of adjacent data sources to improve the generalization ability and adaptability of the model. At the same time, we use global optimization adjustment coefficients to balance local updates and additional optimization terms to further improve the accuracy and stability of the model.

[0094] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0095]

[0096] The local model parameters of the current round; used to initialize the basis for the next round of iterations; Gradient descent term η: learning rate, which controls the speed of gradient descent; The gradient of the loss function with respect to the local model parameters is calculated based on the data set of the i-th data source; it is used to adjust the model parameters according to the current data set to reduce the prediction error; Regularization term λ: Regularization coefficient used to balance the difference between the local model and the global average model; Global average model parameters at the first iteration; ensuring that local models do not overfit local data; Neighbor influence term α: Neighbor influence coefficient, which controls the influence of adjacent data sources on the local model of the current data source; N i : The neighbor set of the i-th data source; wij : The weight between the i-th data source and the j-th data source, indicating the strength of the association between them; considering the influence of surrounding data sources to improve the generalization ability of the model;

[0097] The following is a brief introduction to how to obtain the parameters of the formula:

[0098] Directly obtained from the results of the previous iteration; η is set by the system administrator based on the experiment; Calculated based on local data set by back propagation algorithm; λ and α are tuned based on experience or experiments; Provided by the central server; ij Determined by analyzing the geographical distance or business relevance between data sources;

[0099] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0100]

[0101] Weighted average term n i : The number of samples from the ith data source; n: The total number of samples from all data sources; The local model parameters of the i-th data source at the t+1th iteration; the contributions of all participants are combined to form a new global average model; Global optimization adjustment term β: global optimization adjustment coefficient, used to balance local model updates and additional optimization terms in global model parameter aggregation; K: the number of additional optimization terms; γ k : The weight of the kth additional optimization item; The gradient of the kth additional optimization term is calculated based on the dataset of the first data source; ensuring that the global model not only depends on local updates, but also considers other factors such as historical trends or environmental effects;

[0102] The following is a brief introduction to how to obtain the parameters of the formula:

[0103] n i Each data source counts the amount of data and reports it; n is obtained by the central server summarizing the number of samples from all data sources; Uploaded from each data source to the central server; β and γ k Set by the system administrator according to the specific application scenario; Calculated based on local data sets through specific optimization algorithms;

[0104] In a hypothetical city-level emergency response system, first, the system collects emergency incident data from different hospitals, fire departments, and traffic management centers, performs preprocessing and feature extraction, and initializes the local model parameters of each data source. Secondly, according to the above formula The system calculates the local model parameter update value for each data source. For example, for a data source of a hospital, assuming After one iteration, we get Again, these updated parameters are uploaded to the central server, which receives and verifies these parameters using a secure aggregation protocol to ensure data security and integrity; finally, the central server calculates the parameters based on the formula Calculate new global average model parameters, such as These parameters are distributed back to various data sources for a new round of iterations, gradually optimizing their local models and ultimately improving the performance and robustness of the entire system.

[0105] Through the above steps, the urban emergency response system not only protects the data privacy of each participating institution, but also realizes efficient knowledge sharing and model optimization, significantly improves the emergency response speed and rationality of resource allocation, ensures that each rescue operation can be completed at the fastest speed and highest safety, and maximizes the protection of patients' lives and health. Assuming that the threshold is set to 0.9, since the accuracy and stability of the final global model parameters exceed the set threshold, it shows that this method can effectively improve the overall performance of the emergency response system.

[0106] 103. According to the real-time dynamic emergency resource allocation map, the rescue path and resource allocation plan are optimized by using a quantum heuristic optimization algorithm combined with deep situational awareness technology to obtain the optimal rescue path and required resource combination;

[0107] In this step, this stage uses quantum-inspired optimization algorithms (an optimization algorithm that simulates the principles of quantum computing) and deep situational awareness technology (an artificial intelligence technology that can understand complex environmental conditions) to conduct in-depth analysis of the distribution and demand of emergency resources determined by the real-time dynamic emergency resource allocation map, aiming to find the most effective rescue path and resource allocation plan.

[0108] In the embodiment of the present application, first, through a comprehensive analysis of the emergency resource allocation map, the currently available resources and their locations are identified; then, a quantum-inspired optimization algorithm is used to explore all possible rescue paths and resource allocation schemes, and a number of candidate schemes are preliminarily screened out; finally, with the help of deep situational awareness technology, multiple variables in actual conditions, such as traffic flow, weather conditions, etc., are considered to further optimize these candidate schemes and ultimately determine the optimal solution.

[0109] Based on the generated real-time dynamic emergency resource allocation map, the system starts the quantum heuristic optimization algorithm combined with deep situational awareness technology to evaluate all potential rescue paths and resource allocation plans in the current scenario. For example, at the scene of a large traffic accident, the system first analyzed the distribution of emergency resources in the surrounding area, including information such as the nearest hospital, available ambulances and their status. Then, a variety of possible rescue paths were simulated through the quantum heuristic algorithm, taking into account factors such as real-time traffic flow and weather conditions. Next, deep situational awareness technology further refines these candidate plans, taking into account the specific environment of the accident site (such as road closures, the risk of secondary accidents) and the location and status of other rescue units on duty, and finally determines an optimal path and the most appropriate resource combination, so that the rescue team can arrive at the scene in the shortest time and provide effective assistance.

[0110] Optionally, in step 103, according to the real-time dynamic emergency resource allocation map, a quantum heuristic optimization algorithm combined with deep context perception technology is used to optimize the rescue path and resource allocation scheme to obtain the optimal rescue path and required resource combination, including:

[0111] The real-time dynamic emergency resource allocation map is used to comprehensively analyze the distribution and demand of emergency resources in the current rescue scenario to obtain the emergency resource configuration status in the current scenario; according to the emergency resource configuration status in the current scenario, a quantum heuristic optimization algorithm is used in combination with deep context perception technology to preliminarily screen and evaluate potential rescue paths and resource configuration plans to generate a series of candidate rescue paths and required resource combinations; based on the series of candidate rescue paths and required resource combinations, a multi-objective optimization strategy is further used to consider multiple indicators to fine-tune the candidate plans to obtain a series of optimized rescue paths and required resource combinations; from the optimized series of rescue paths and required resource combinations, the optimal rescue path and required resource combination are obtained by comparing the performance differences between different plans.

[0112] In this step, the allocation of emergency resources is refined and optimized by combining quantum-inspired optimization algorithms and deep situational awareness technology. The real-time dynamic emergency resource allocation map contains information such as the location, status, and demand distribution of all currently available emergency resources, which is used to comprehensively analyze the resource allocation status in the current rescue scenario. The quantum-inspired optimization algorithm is an optimization method that simulates the principles of quantum computing and can efficiently explore a large number of possible solutions; while deep situational awareness technology uses machine learning models to understand complex environmental factors such as traffic flow, weather conditions, etc., to evaluate the effectiveness of different rescue paths and resource allocation plans.

[0113] In the embodiments of the present application, firstly, the system comprehensively analyzes the resource distribution and demand pattern in the current rescue scenario based on the real-time dynamic emergency resource allocation map, and understands the existing resource allocation status; secondly, the system adopts a quantum heuristic optimization algorithm and combines it with deep situational awareness technology to preliminarily screen and evaluate a series of potential rescue paths and resource allocation plans, and generate multiple candidate plans; thirdly, for these candidate plans, a multi-objective optimization strategy is applied, considering multiple indicators such as time, cost, and safety, and further adjusting and optimizing; finally, by comparing the performance differences of each candidate plan, the optimal rescue path and resource combination is determined to ensure that the rescue operation is carried out in the most efficient and safest way.

[0114] In a hypothetical city-level emergency medical response system, first, the system analyzes the distribution of all ambulances, hospital beds, emergency personnel and other resources in the current city and patient needs based on the latest real-time dynamic emergency resource allocation map, and forms a detailed resource allocation status report; secondly, based on this report, the system uses quantum heuristic optimization algorithms combined with deep contextual perception technology, taking into account factors such as real-time traffic flow, road closures, weather changes, etc., to preliminarily screen and evaluate a series of possible rescue paths and resource allocation plans, and generate multiple candidate plans; thirdly, for these candidate plans, the system introduces a multi-objective optimization strategy, comprehensively considers multiple key indicators such as response time, driving distance, traffic congestion, etc., and makes fine adjustments to ensure that each plan can achieve the best results under different conditions; finally, the system makes a detailed comparison of the performance differences between the candidate plans, selects an optimal rescue path that can send patients to the most suitable treatment location in the shortest time, and determines the required resource combination, including which ambulances to dispatch and which emergency personnel to participate in the specific arrangements.

[0115] Through the above steps, the urban emergency medical response system not only improves the speed and accuracy of rescue decisions, but also ensures the rationality and efficiency of resource use, so that each rescue operation can be completed at the fastest speed and highest safety, thereby maximizing the protection of patients' lives and health.

[0116] Optionally, based on the series of candidate rescue paths and required resource combinations, a multi-objective optimization strategy is further used to consider multiple indicators to fine-tune the candidate solutions to obtain a series of rescue paths and required resource combinations that have been optimized, including:

[0117] Using the series of candidate rescue paths and required resource combinations, multiple optimization objectives are obtained; based on the multiple optimization objectives, a multi-objective optimization model is established; based on the multi-objective optimization model, an evolutionary algorithm or a genetic algorithm is used to iteratively optimize the series of candidate rescue paths and required resource combinations to obtain a preliminary optimized rescue path and resource allocation plan; through the preliminary optimized rescue path and resource allocation plan, sensitivity analysis and simulation testing are further implemented to verify the stability and robustness of the plan under different conditions, and a series of optimized rescue paths and required resource combinations are generated.

[0118] In this step, a series of candidate rescue paths and required resource combinations are fine-tuned through a multi-objective optimization strategy. Multiple optimization objectives include but are not limited to response time, driving distance, traffic conditions, resource utilization, etc., and these data are used to establish a comprehensive multi-objective optimization model. Intelligent optimization methods such as evolutionary algorithms or genetic algorithms are applied to the model to iteratively improve candidate solutions, and finally obtain preliminary optimized rescue paths and resource allocation solutions. Subsequently, sensitivity analysis and simulation tests are used to further verify the stability and robustness of these solutions under different conditions to ensure the practical feasibility and efficiency of the solutions.

[0119] In the embodiments of the present application, first, the system determines multiple objectives that need to be optimized based on a series of candidate rescue paths and required resource combinations; second, based on these optimization objectives, a multi-objective optimization model is constructed as the basis for subsequent optimization; third, the candidate solutions are iteratively optimized using intelligent optimization techniques such as evolutionary algorithms or genetic algorithms to gradually improve the quality of the solutions and obtain preliminary optimized rescue paths and resource allocation solutions; finally, sensitivity analysis and simulation tests are performed on these preliminary optimized solutions to evaluate their performance under various possible conditions to ensure the stability and robustness of the solutions, thereby generating a series of rescue paths and required resource combinations that are ultimately optimized.

[0120] In a hypothetical urban emergency medical response system, first, the system determined multiple optimization objectives based on a series of candidate rescue paths and required resource combinations that had been previously screened, such as minimizing response time, maximizing resource utilization, and reducing the impact of traffic congestion; second, based on these optimization objectives, a detailed multi-objective optimization model was established to provide a mathematical basis for subsequent optimization; third, an evolutionary algorithm was used to iteratively optimize these candidate solutions, continuously adjusting the path selection and resource allocation in each iteration, gradually improving the efficiency and effectiveness of the solution, and ultimately obtaining a preliminary optimized rescue path and resource allocation solution; finally, in order to ensure the reliability and adaptability of these preliminary optimized solutions in actual applications, the system implemented extensive sensitivity analysis and simulation tests, simulating different weather conditions, traffic flow changes, and the occurrence of emergencies, verifying the stability and robustness of the solution under various conditions, and generating a series of rescue paths and required resource combinations that were ultimately optimized.

[0121] Through the above steps, the urban emergency medical response system can not only provide more accurate and efficient rescue route planning, but also ensure the rationality of resource allocation, improve the ability to cope with complex and changeable emergency scenarios, so that each rescue operation can be executed in the best way, maximizing the protection of patients' lives.

[0122] This application takes into account that in the prior art, the optimization of the emergency resource allocation plan lacks the ability to effectively respond to real-time dynamic changes, resulting in the lack of flexibility and accuracy in the rescue path and resource allocation. Traditional methods usually rely on static models and fixed rules, which are difficult to adapt to complex and changeable emergency scenarios. In addition, the existing optimization algorithms fail to fully consider environmental factors and the cooperation of other rescue units, affecting the practical feasibility and efficiency of the scheme. In order to overcome these problems, the embodiment of the present invention proposes a method of using a quantum heuristic optimization algorithm combined with deep contextual awareness technology, which comprehensively analyzes and pre-processes the real-time information of emergency resources, and defines the objective function and related constraints to provide a solid data foundation for subsequent optimization algorithms. This solution aims to solve the problem of low rescue efficiency caused by limited optimization and poor adaptability in the prior art.

[0123] Optionally, according to the emergency resource configuration status in the current scenario, a quantum heuristic optimization algorithm combined with deep situational awareness technology is used to preliminarily screen and evaluate potential rescue paths and resource configuration plans, and generate a series of candidate rescue paths and required resource combinations, including:

[0124] In determining the optimal rescue path and resource allocation plan X best Before the optimization, it is necessary to comprehensively analyze and preprocess the real-time information of emergency resources, and define the objective function and related constraints to provide a data basis for the subsequent optimization algorithm.

[0125]

[0126] Among them, X represents potential rescue paths and resource allocation plans; is the feasible solution space; f(X) is the objective function used to evaluate the overall performance of the solution; λ i and μ j They are the inequality constraints g i (X) and the equality constraint h j (X); M and N are the number of inequality constraints and equality constraints respectively; α and β are dynamic adjustment coefficients used to balance the weights between different optimization items; P(X) is a time series prediction item based on historical data, which is used to consider the changes in future resource demand; Q(X) is an impact item based on environmental factors, which is used to dynamically adjust the path and resource configuration;

[0127] Calculate X best Afterwards, deep situational awareness technology is used to further screen and evaluate these solutions, and combined with real-time situational information and the collaboration of other rescue units, candidate rescue paths and required resource combinations Y that better meet actual needs are generated;

[0128] Y=σ(W L ·φ L (X)+b L +γ·R(X)+δ·C(X))

[0129] Where Y is the output after deep context perception technology processing, which represents the candidate rescue path and required resource combination after preliminary selection and evaluation; σ is a nonlinear activation function, which is ReLU or Sigmoid; W L is the weight matrix of the Lth layer; φ L (X) is a high-dimensional vector after multi-layer feature extraction of input X; b L is the bias term of the Lth layer; γ is the situational influence coefficient, which is used to adjust the influence of situational factors on the final decision; R(X) is the situational awareness term based on real-time situational information, which is used to dynamically adjust the output of the model; δ is the collaboration influence coefficient, which is used to adjust the impact of collaboration with other rescue units on the final decision; C(X) is the collaboration awareness term based on the collaboration information of other rescue units, which is used to dynamically adjust the output of the model;

[0130] After completing the calculation of Y, detailed simulation tests are used to verify and optimize each candidate solution to ensure its practical feasibility and efficiency. Finally, the best solution is selected as the target product, that is, the optimal rescue path and the required resource configuration, and deployed in the emergency operation.

[0131] The formula aims to build an optimization framework that can evaluate the overall performance of potential rescue paths and resource allocation solutions, while taking into account future changes in resource demand and the impact of environmental factors. By introducing time series prediction items and environmental factor impact items, it ensures that the optimization process is not only based on current data, but also can proactively adjust strategies. At the same time, deep situational awareness technology is used to further screen and evaluate these solutions, and combined with real-time situational information and the collaboration of other rescue units, candidate solutions that better meet actual needs are generated. Finally, the feasibility and efficiency of the solution are verified through detailed simulation tests to ensure that the optimal solution can be efficiently deployed in emergency operations.

[0132] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0133]

[0134] f(X): used to evaluate the overall performance of the solution; ensure that the selected solution can perform best under all conditions; Control the cost of violating inequality constraints; Ensure that specific equality constraints are met; α·P(X): used to balance the weights between different optimization items, based on the time series prediction item of historical data, considering the changes in future resource demand; β·Q(X): environmental factor impact item

[0135] The following is a brief introduction to how to obtain the parameters of the formula:

[0136] f(X) is obtained by comprehensively evaluating various indicators such as response time, cost, and security; i and μ j Set according to specific application scenarios; i (X) and h j (X) is defined by the system administrator according to the actual situation; α and β are determined through experimental tuning; P(X) and Q(X) are calculated by analyzing historical data and real-time environment information;

[0137] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0138] Y=σ(W L ·φ L (X)+b L +γ·R(X)+δ·C(X))

[0139] σ(W L ·φ L (X)+b L): output of the deep learning layer, ensuring that the model has good expressiveness and generalization performance; γ·R(X): adjusts the influence of situational factors on the final decision and dynamically adjusts the output of the model; δ·C(X): the collaborative perception item adjusts the influence of the collaboration with other rescue units on the final decision, and dynamically adjusts the output of the model based on the collaborative perception item of the collaboration information of other rescue units;

[0140] The following is a brief introduction to how to obtain the parameters of the formula:

[0141] σ selects an appropriate nonlinear activation function; W L and b L Obtained by training the neural network; φ L (X) is generated through a multi-layer feature extraction algorithm; γ and δ are set according to the specific application scenario; R(X) and C(X) are calculated through real-time data collection and analysis;

[0142] In a hypothetical city-level emergency medical response system, first, the system comprehensively analyzes and preprocesses the real-time information of emergency resources, defines the objective function and related constraints, such as setting the minimization of response time and maximization of resource utilization as the main optimization goals, and defines several inequalities and equality constraints; second, according to the above formula The system calculates the optimal solution of potential rescue paths and resource allocation plans. Assume that for a certain accident, the initial solution set S contains multiple possible paths. After one iteration, the optimal solution X is obtained. best =[Path A, Resource Combination 1]; Again, deep context awareness technology is used to further screen and evaluate these solutions, and factors such as real-time traffic flow, weather conditions, and the number of beds in nearby hospitals are combined to generate candidate solutions that better meet actual needs Y = σ(W L ·φ L (X)+b L +γ·R(X)+δ·C(X)), for example, for path A, assuming φ L (X) = [0.8, 0.6], after deep situational awareness processing, we get Y = [path B, resource combination 2]; finally, through detailed simulation tests, the actual feasibility and efficiency of each candidate solution are verified, and finally the best solution is selected as the target product, that is, the optimal rescue path and the required resource configuration, and it is deployed in the emergency operation.

[0143] Through the above steps, the urban emergency medical response system not only improves the accuracy of rescue route planning and the rationality of resource allocation, but also achieves rapid response to real-time dynamic changes, significantly improving the speed and efficiency of emergency response. Assuming that the threshold is set to 0.95, since all indicators of the best solution finally selected exceed the set threshold, it shows that this method can effectively improve the overall efficiency of the emergency response system, ensuring that each rescue operation can be completed at the fastest speed and highest safety, and maximizing the protection of patients' lives and health.

[0144] 104. Using a secure communication protocol, the optimal rescue path and the required resource combination are sent to the nearest available emergency unit, and the information is ensured to be synchronously updated to the mobile terminals of all relevant emergency personnel, generating an emergency response status of confirmed receipt.

[0145] In this step, the secure communication protocol refers to a set of rules and technical measures to ensure confidentiality, integrity and availability during data transmission. In this step, the system will use secure encryption to transmit the optimized rescue path and resource combination information to the designated emergency units and individuals, ensuring the safe and reliable transmission of information and timely feedback on the task confirmation.

[0146] In the embodiment of the present application, once the optimal rescue path and resource allocation plan are determined, the system will immediately push this information to the mobile devices of the relevant emergency units and staff through the secure communication network. At the same time, the system will automatically track the reception status of the information to ensure that every person involved in the rescue can receive the latest instructions and updates at the same time, so as to carry out the rescue work in a coordinated manner.

[0147] After determining the optimal rescue path and resource combination, the system immediately encrypts and sends this information to the nearest available emergency unit, such as the emergency department of a hospital or a fire station, through a secure communication protocol. At the same time, the mobile terminals of all relevant emergency personnel also receive update instructions synchronously to ensure that everyone can receive the latest task arrangements at the same time. For example, when an emergency doctor receives a new task notification, his tablet not only displays detailed patient information and the best driving route, but also automatically updates the location of other emergency vehicles in the area so that he can make the best decision. In addition, the system will continue to monitor the progress of the task. Once the emergency personnel confirms the receipt of the task, it will immediately generate a confirmed emergency response status to ensure that the entire rescue process is seamless and efficient.

[0148] Optionally, in step 104, the optimal rescue path and the required resource combination are sent to the nearest available emergency unit using a secure communication protocol, and the information is ensured to be synchronously updated to the mobile terminals of all relevant emergency personnel, and an emergency response status of confirmed receipt is generated, including:

[0149] The optimal rescue path and the required resource combination are encrypted by using a secure communication protocol to obtain encrypted information; based on the encrypted information, it is sent to the nearest available emergency unit through a secure data transmission channel to generate a preliminary sending record; based on the preliminary sending record, the encrypted information is synchronously updated to the mobile terminals of all relevant emergency personnel by using a synchronization mechanism to obtain mobile terminal reception information; the information is received through the mobile terminal, and the emergency personnel confirm and feedback the receiving status to generate a confirmed emergency response status.

[0150] In this step, it is described how to ensure the secure transmission and synchronous update of the optimal rescue path and the required resource combination information through a secure communication protocol. Encryption processing refers to converting the optimal rescue path and resource configuration plan into a format that can only be decrypted by the authorized party to protect the privacy and integrity of the information; the secure data transmission channel ensures the security of the information during the transmission process; the initial sending record is used to track whether the information is successfully sent to the designated emergency unit; the synchronization mechanism ensures that the mobile terminals of all relevant emergency personnel can receive the latest instructions in real time; finally, the emergency personnel confirm and feedback the receiving status, generate the emergency response status of confirmed receipt, and ensure the consistency and reliability of task execution.

[0151] In the embodiment of the present application, first, the system uses a secure communication protocol to encrypt the optimal rescue path and the required resource combination to ensure the security of the information; secondly, the encrypted information is sent to the nearest available emergency unit through a secure data transmission channel, and the preliminary sending status is recorded; thirdly, based on this preliminary sending record, a synchronization mechanism is used to update the encrypted information to the mobile terminals of all relevant emergency personnel to ensure that each emergency personnel can receive the latest instructions at the same time; finally, the emergency personnel confirm and feedback the receiving status on their respective mobile terminals, and the system generates a confirmed emergency response status based on this to ensure the consistency and accuracy of task execution.

[0152] In a hypothetical urban emergency medical response system, first, the system uses advanced encryption algorithms to encrypt the determined optimal rescue path and required resource combination to ensure that these sensitive information will not be unauthorizedly accessed during transmission; second, the encrypted information is quickly and securely sent to the available emergency unit closest to the scene through an authenticated secure data transmission channel, and a preliminary sending record is generated to prove that the information has been successfully delivered; third, based on this preliminary sending record, the system immediately starts the synchronization mechanism to push the encrypted information to the mobile terminals of all emergency personnel involved in the rescue operation, ensuring that each emergency personnel can receive the latest instructions at the same time, no matter where they are; finally, the emergency personnel confirm and feedback the reception status on their respective mobile terminals, and the command center generates a confirmed emergency response status based on these feedbacks, ensuring that all participants clearly understand their tasks and are ready for the next step.

[0153] Through the above steps, the urban emergency medical response system not only ensures the security and synchronization of information transmission, but also realizes efficient coordination and communication, so that each rescue operation can be carried out quickly and accurately, maximizing the rescue efficiency and success rate, and ensuring the life safety of patients.

[0154] By implementing the emergency resource optimization process from steps 101 to 104, we can achieve an integrated solution from multi-source data integration, intelligent analysis modeling, path and resource allocation optimization to secure information transmission. This not only greatly improves the speed and efficiency of emergency response, ensuring that the most suitable rescue forces are deployed to where they are needed in the shortest time, but also strengthens the collaboration between different emergency units and improves the overall emergency response capabilities. At the same time, with the help of federated learning, data privacy is protected, quantum-inspired algorithms are combined with deep context perception technology to achieve precise optimization, and secure communication protocols are used to ensure the secure transmission of information, thus building an efficient, intelligent and secure modern emergency system, ultimately achieving the goal of saving more lives and reducing injuries. The comprehensive application of this process significantly enhances the ability of cities or regions to respond to emergencies and provides strong support for public safety.

[0155] Figure 2 A schematic diagram of the structure of an integrated emergency information processing system is provided for an embodiment of the present application. Figure 2 As shown, the device comprises:

[0156] An acquisition module 21, used to acquire emergency event data from multiple different sources;

[0157] An analysis module 22 is used to model and analyze the emergency event data based on the emergency event data using a federated learning algorithm to generate a real-time dynamic emergency resource allocation map;

[0158] The optimization module 23 is used to optimize the rescue path and resource allocation scheme according to the real-time dynamic emergency resource allocation map by using a quantum heuristic optimization algorithm combined with deep situational awareness technology to obtain the optimal rescue path and required resource combination;

[0159] The sending module 24 is used to use a secure communication protocol to send the optimal rescue path and the required resource combination to the nearest available emergency unit, and ensure that the information is synchronously updated to the mobile terminals of all relevant emergency personnel to generate an emergency response status that has been confirmed to be received.

[0160] Figure 2 The integrated emergency information processing system can execute Figure 1 The implementation principle and technical effect of the integrated emergency information processing method described in the embodiment are not described in detail. The specific way in which each module and unit performs operations in the integrated emergency information processing system in the above embodiment has been described in detail in the embodiment of the method, and will not be described in detail here.

[0161] In one possible design, Figure 2 An integrated emergency information processing system of the embodiment shown 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;

[0162] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0163] The processing component 32 is used to: obtain emergency incident data from multiple different sources; based on the emergency incident data, use a federated learning algorithm to model and analyze the emergency incident data to generate a real-time dynamic emergency resource allocation map; according to the real-time dynamic emergency resource allocation map, use a quantum heuristic optimization algorithm combined with deep context perception technology to optimize the rescue path and resource allocation plan to obtain the optimal rescue path and required resource combination; use a secure communication protocol to send the optimal rescue path and required resource combination to the nearest available emergency unit, and ensure that the information is synchronously updated to the mobile terminals of all relevant emergency personnel to generate a confirmed emergency response status.

[0164] 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 method. Of course, the processing component may also be implemented by 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 method.

[0165] The 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 memory, flash memory, magnetic disk or optical disk.

[0166] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0167] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0168] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0169] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0170] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 An integrated emergency information processing method of the embodiment shown.

[0171] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0172] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0173] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0174] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An integrated emergency information processing method, characterized in that: include: Access emergency incident data from multiple different sources; Based on the emergency incident data, a federated learning algorithm is used to model and analyze the emergency incident data to generate a real-time dynamic emergency resource allocation map; According to the real-time dynamic emergency resource allocation map, the rescue path and resource allocation plan are optimized by using a quantum heuristic optimization algorithm combined with deep situational awareness technology to obtain the optimal rescue path and required resource combination; Using a secure communication protocol, the optimal rescue path and required resource combination are sent to the nearest available emergency unit, and the information is ensured to be synchronously updated to the mobile terminals of all relevant emergency personnel to generate an emergency response status that has been confirmed to be received.

2. The method according to claim 1, characterized in that Based on the emergency event data, the emergency event data is modeled and analyzed using a federated learning algorithm to generate a real-time dynamic emergency resource allocation map, including: Using emergency incident data from multiple different sources, a preliminary dataset was constructed; Based on the preliminary data set, a federated learning framework is used to train the model locally in each data source to protect privacy and share model parameters, thereby obtaining a distributed trained local model; Based on the distributed trained local models, global model parameters are aggregated and updated on the central server to achieve cross-institutional knowledge fusion and obtain a global model; The global model is used to predict and analyze the emergency incident data, identify key resource distribution and demand patterns, and generate a real-time dynamic emergency resource allocation map.

3. The method according to claim 2, characterized in that The method of aggregating and updating global model parameters on a central server based on the distributed trained local models to achieve cross-institutional knowledge fusion and obtain a global model includes: Based on the distributed trained local model, collect model parameter update information of each participant; The model parameter update information is encrypted using a secure aggregation protocol to protect data privacy while ensuring information integrity, thereby obtaining the encrypted model parameter update information; According to the encrypted model parameter update information, a secure aggregation algorithm is executed on the central server to generate updated global model parameters by integrating the contributions of all participants; The updated global model parameters are used to adjust the global model on the central server to obtain the global model.

4. The method according to claim 2, characterized in that: The global model is used to predict and analyze the emergency event data, identify key resource distribution and demand patterns, and generate a real-time dynamic emergency resource allocation map, including: Using the optimized global model, comprehensively processing the multi-dimensional information in the emergency event data to obtain processed emergency event data; According to the processed emergency incident data, a spatiotemporal clustering algorithm is used to identify hot spots and potential high-demand periods of emergency incidents, and generate hot spots and high-demand period information; Based on the hotspot areas and high-demand time period information, a resource matching algorithm is used to evaluate the coverage of existing emergency resources and determine resource gaps, thereby generating resource gap analysis results; The resource gap analysis results are used in combination with real-time traffic conditions and other environmental factors to generate a real-time dynamic emergency resource allocation map.

5. The method according to claim 1, characterized in that According to the real-time dynamic emergency resource allocation map, the rescue path and resource allocation scheme are optimized by using a quantum heuristic optimization algorithm combined with deep context perception technology to obtain the optimal rescue path and required resource combination, including: By using the real-time dynamic emergency resource allocation map, a comprehensive analysis is performed on the distribution and demand of emergency resources in the current rescue scenario to obtain the emergency resource configuration status in the current scenario; According to the emergency resource allocation status in the current scenario, a quantum heuristic optimization algorithm combined with deep situational awareness technology is used to preliminarily screen and evaluate potential rescue paths and resource allocation plans, generating a series of candidate rescue paths and required resource combinations; Based on the series of candidate rescue paths and required resource combinations, a multi-objective optimization strategy is further used to consider multiple indicators to fine-tune the candidate solutions, thereby obtaining a series of optimized rescue paths and required resource combinations; From the optimized series of rescue paths and required resource combinations, the optimal rescue path and required resource combination is obtained by comparing the performance differences between different solutions.

6. The method according to claim 5, characterized in that Based on the series of candidate rescue paths and required resource combinations, a multi-objective optimization strategy is further used to consider multiple indicators to fine-tune the candidate solutions, thereby obtaining a series of optimized rescue paths and required resource combinations, including: Using the series of candidate rescue paths and required resource combinations, multiple optimization objectives are obtained; Establishing a multi-objective optimization model according to the multiple optimization objectives; Based on the multi-objective optimization model, an evolutionary algorithm or a genetic algorithm is used to iteratively optimize the series of candidate rescue paths and required resource combinations to obtain a preliminary optimized rescue path and resource allocation plan; Through the preliminary optimized rescue path and resource allocation plan, further sensitivity analysis and simulation testing are carried out to verify the stability and robustness of the plan under different conditions, and to generate a series of optimized rescue paths and required resource combinations.

7. The method according to claim 1, characterized in that The optimal rescue path and required resource combination are sent to the nearest available emergency unit by using a secure communication protocol, and the information is ensured to be synchronously updated to the mobile terminals of all relevant emergency personnel, and an emergency response status of confirmed receipt is generated, including: Using a secure communication protocol, encrypting the optimal rescue path and the required resource combination to obtain encrypted information; Sending the encrypted information to the nearest available emergency unit via a secure data transmission channel to generate a preliminary sending record; Based on the preliminary sending record, the encrypted information is synchronously updated to the mobile terminals of all relevant emergency personnel using a synchronization mechanism to obtain mobile terminal reception information; The information is received through the mobile terminal, and the emergency personnel confirm and feedback the receiving status, generating an emergency response status of confirmed receipt.

8. An integrated emergency information processing system, characterized in that: include: An acquisition module, used to acquire emergency incident data from multiple different sources; An analysis module, used to model and analyze the emergency event data based on the emergency event data using a federated learning algorithm to generate a real-time dynamic emergency resource allocation map; An optimization module is used to optimize the rescue path and resource allocation scheme according to the real-time dynamic emergency resource allocation map by using a quantum heuristic optimization algorithm combined with deep situational awareness technology to obtain the optimal rescue path and required resource combination; The sending module is used to use a secure communication protocol to send the optimal rescue path and the required resource combination to the nearest available emergency unit, and ensure that the information is synchronously updated to the mobile terminals of all relevant emergency personnel to generate an emergency response status that has been confirmed to be received.

9. A computing device, characterized in that It comprises 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 implement an integrated emergency information processing method as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, an integrated emergency information processing method as claimed in any one of claims 1 to 7 is implemented.

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