Wounded information management and sharing method and system based on cloud computing

Abstract

The invention provides a wounded person information management and sharing method and system based on cloud computing. The method comprises the following steps: collecting and integrating multi-source heterogeneous data, synchronizing the data to a cloud platform in real time, and generating a preliminary wounded information base; on the basis of the preliminary wounded person information base, a quantum heuristic optimization algorithm is applied, complex correlation analysis is carried out, a quantum calculation process is simulated, a distributed edge calculation technology is adopted, information is updated and dynamically adjusted in real time, and an optimized wounded person information file is generated; based on the optimized wounded person information file, a holographic reconstruction algorithm is used for carrying out multi-dimensional data reconstruction, implicit modes and features are extracted, historical and current health condition data of each wounded person is subjected to multi-dimensional reconstruction processing, a homomorphic encryption technology is adopted, direct calculation of encrypted data is supported, and a high-dimensional data model is generated; based on the high-dimensional data model, an emergency response module is built, and a wounded information management and sharing strategy is generated. According to the technical scheme provided by the invention, the safety and efficiency of wounded information management and sharing are remarkably improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of medical information technology, and in particular to a method and system for managing and sharing wounded information based on cloud computing. Background Art

[0002] With the growing demand for medical informatization and emergency response, casualty information management plays an increasingly important role in modern medical systems. Especially when large-scale disasters or emergencies occur, it is necessary to quickly collect and integrate heterogeneous data from multiple channels (such as hospital records, on-site emergency information, mobile device data, etc.), and synchronize them to the cloud platform in real time through a secure encrypted channel to generate a preliminary casualty information database. Based on these data, the system needs to use quantum heuristic optimization algorithms to perform complex association analysis in a cloud computing environment, deeply explore the intrinsic connections between data fragments at different times and locations, and reveal potential high-value association rules. In addition, the system also needs to adopt distributed edge computing technology to ensure that information is updated and adjusted in real time under high concurrency conditions, reduce the load on the central server, and thus improve the response speed and stability of the system. Ultimately, the system should be able to support efficient multi-dimensional data reconstruction and emergency response mechanisms, generate optimized casualty information archives and high-dimensional data models, and provide a scientific basis for medical decision-making.

[0003] At present, the existing casualty information management system mainly relies on traditional data centers and local servers to process and store casualty information through centralized database management and simple data analysis tools. Some systems have begun to introduce cloud computing technology to improve data processing capabilities and storage efficiency. However, these systems are usually limited to basic data collection and storage functions, lacking the ability to deeply explore complex relationships and real-time dynamic adjustment. Although some systems use encryption technology to ensure the security of data transmission, in actual applications, especially in high-concurrency and multi-source data environments, their performance and security still need to be improved.

[0004] The existing solutions have the following major defects. Although some systems use encryption technology, most of them are still unable to directly calculate encrypted data, which requires decryption during data processing, increases the risk of data leakage, and lacks security in the management of casualty information. Traditional methods are difficult to effectively process multi-source heterogeneous data and lack in-depth mining of complex correlations, which limits the depth and breadth of data analysis. In high-concurrency situations, the central server of the existing system can easily become a bottleneck, resulting in delayed information updates and reduced response speeds, affecting the timeliness and accuracy of emergency responses. Summary of the invention

[0005] The embodiments of the present application provide a method and system for managing and sharing injured person information based on cloud computing, so as to solve the problem of insufficient security of injured person information management in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a method for managing and sharing wounded information based on cloud computing, including:

[0007] Collect and integrate multi-source heterogeneous data, synchronize them to the cloud platform in real time through a secure encrypted channel, and generate a preliminary casualty information database;

[0008] Based on the preliminary casualty information database, quantum-inspired optimization algorithms are used to conduct complex correlation analysis in a cloud computing environment, and the powerful computing resources of the cloud platform are used to simulate the quantum computing process, deeply explore the intrinsic connections between data fragments at different times and locations, and use distributed edge computing technology to ensure that information is updated and adjusted in real time under high concurrency conditions, reduce the load on the central server, and generate optimized casualty information files;

[0009] Based on the optimized casualty information files, a holographic reconstruction algorithm is used to reconstruct multidimensional data. With the help of large-scale parallel processing capabilities of the cloud, implicit patterns and features are extracted, and multidimensional reconstruction processing is performed on the historical and current health status data of each casualty. Homomorphic encryption technology is used to support direct calculation of encrypted data, ensure the security of personal sensitive information data, and generate a high-dimensional data model;

[0010] Based on the high-dimensional data model, an emergency response module is built, an interactive access interface with permission control is designed, multi-terminal simultaneous viewing is supported, and a casualty information management and sharing strategy is generated.

[0011] Optionally, based on the preliminary casualty information database, a quantum heuristic optimization algorithm is used to perform complex correlation analysis in a cloud computing environment, the powerful computing resources of the cloud platform are used to simulate the quantum computing process, the intrinsic connection between data fragments at different times and locations is deeply mined, and distributed edge computing technology is used to ensure that information is updated and dynamically adjusted in a high-concurrency situation, reduce the load on the central server, and generate an optimized casualty information file, including:

[0012] Based on the preliminary casualty information database, integration and correlation analysis are performed to ensure that data from different channels can be synchronized and accurately recorded in real time, and a preliminary integrated casualty information file is generated;

[0013] Based on the preliminary integrated casualty information files, a quantum-inspired optimization algorithm is used to perform complex correlation analysis in a cloud computing environment, and the powerful computing resources of the cloud platform are used to simulate the quantum computing process, deeply explore the intrinsic connections between data fragments at different times and locations, reveal fine and high-value correlation rules, and generate refined casualty information files;

[0014] Based on the refined casualty information files, distributed edge computing technology is used to synchronize and update data in real time. By distributing computing tasks to network edge nodes, it is ensured that information is updated and adjusted in real time under high concurrency conditions, the load on the central server is reduced, and a real-time updated casualty information file is generated.

[0015] Based on the real-time updated casualty information files, the information management system is optimized and configured, the system parameters and configuration are adjusted to adapt to the changing needs, the system response speed is improved, and the optimized casualty information files are generated.

[0016] Optionally, based on the initially integrated casualty information files, a quantum-inspired optimization algorithm is used to perform complex association analysis in a cloud computing environment, and the powerful computing resources of the cloud platform are used to simulate the quantum computing process, deeply mine the intrinsic connections between data fragments at different times and locations, reveal fine and high-value association rules, and generate a refined casualty information file, including:

[0017] Based on the initially integrated casualty information files, consistency check and format conversion are performed on data from different sources to generate standardized casualty information files;

[0018] Based on the standardized casualty information files, a quantum-inspired optimization algorithm is used to perform complex correlation analysis in a cloud computing environment. The powerful computing resources of the cloud platform are used to simulate the quantum computing process, and the intrinsic connections between data fragments at different times and locations are deeply mined. By simulating the probability distribution of quantum states, potential correlation patterns in the data space are explored, and a potential correlation rule library is generated.

[0019] Based on the potential association rule base, the distributed computing capability in the cloud computing environment is used to reorganize and optimize the information, ensure the integrity and consistency of the information, and generate a deeply associated casualty information file;

[0020] Based on the deeply associated casualty information files, comprehensive sorting and configuration optimization are carried out, the information display format is adjusted, and a refined casualty information file is generated.

[0021] Optionally, based on the refined casualty information file, distributed edge computing technology is used to synchronize and update the data in real time, and computing tasks are distributed to network edge nodes to ensure that information is updated and adjusted in real time under high concurrency conditions, reduce the load on the central server, and generate a real-time updated casualty information file, including:

[0022] Based on the refined casualty information files, an intelligent routing algorithm is designed and deployed through the cloud computing platform network architecture to perform intelligent routing configuration on the data stream and generate a data stream framework with intelligent routing configuration;

[0023] Based on the data flow framework of the intelligent routing configuration, the distributed edge computing technology is used to pre-process the data, perform preliminary data cleaning and local analysis at the network edge node, reduce the amount of data transmitted to the central server, speed up the response, and generate a pre-processed local data set;

[0024] Based on the pre-processed local data set, real-time synchronization and update processing are performed. By utilizing the advantages of distributed edge computing technology, each edge node independently processes local data, and realizes instant information update and dynamic adjustment under high concurrency conditions, generating real-time synchronized casualty information files;

[0025] Based on the real-time synchronized casualty information files, an adaptive load balancing strategy is implemented to dynamically adjust the load distribution between each edge node and the central server to generate a real-time updated casualty information file.

[0026] Optionally, based on the optimized injured person information file, a holographic reconstruction algorithm is used to reconstruct multidimensional data, and with the help of large-scale parallel processing capabilities of the cloud, implicit patterns and features are extracted, and multidimensional reconstruction processing is performed on the historical and current health status data of each injured person. Homomorphic encryption technology is used to support direct calculation of encrypted data, ensure the security of personal sensitive information data, and generate a high-dimensional data model, including:

[0027] Based on the optimized casualty information file, multi-dimensional feature extraction is performed to capture subtle changes and complex relationships and generate a multi-dimensional feature set;

[0028] Based on the multi-dimensional feature set, a holographic reconstruction algorithm is used to prepare for high-precision data reconstruction, and with the help of large-scale parallel processing capabilities of the cloud, implicit patterns and features are extracted, and multi-dimensional reconstruction processing is performed on the historical and current health status data of each injured person to generate pre-processed multi-dimensional feature data;

[0029] Based on the pre-processed multi-dimensional feature data, combined with the influence of time dimension changes and space-related factors, a multi-dimensional reconstructed health status model is generated;

[0030] Based on the multi-dimensionally reconstructed health status model, efficient data compression and index optimization are implemented to generate a high-dimensional data model.

[0031] Optionally, based on the multidimensional feature set, a holographic reconstruction algorithm is used to prepare for high-precision data reconstruction, and with the help of large-scale parallel processing capabilities of the cloud, implicit patterns and features are extracted, and multi-dimensional reconstruction processing is performed on the historical and current health status data of each injured person to generate pre-processed multi-dimensional feature data, including:

[0032] Based on the multi-dimensional feature set, identify abnormal points and potential problems in the historical and current health status data of each injured person, and generate an abnormality detection report;

[0033] Based on the anomaly detection report, a holographic reconstruction algorithm is used to prepare for high-precision data reconstruction, and large-scale parallel computing resources in the cloud are used for in-depth analysis to further refine and correct feature information, extract implicit patterns and features, reveal deep relationships within the data, and generate a refined feature matrix;

[0034] Based on the refined feature matrix, cross-dimensional correlation analysis is performed to construct a comprehensive data view, reveal potential connections between different data fragments, and generate a comprehensive data view;

[0035] Based on the comprehensive data view, data consistency verification and data cleaning processing are implemented to improve data quality and generate pre-processed multi-dimensional feature data.

[0036] Optionally, based on the high-dimensional data model, an emergency response module is built, an interactive access interface with authority control is designed, multi-terminal simultaneous viewing is supported, and a casualty information management and sharing strategy is generated, including:

[0037] Based on the high-dimensional data model, risk assessment is performed on emergency response scenarios, possible emergencies are predicted and response strategies are optimized, and risk assessment reports are generated;

[0038] Based on the risk assessment report, a rapid deployment emergency response module is designed and tightly integrated with the high-dimensional data model to generate an integrated emergency response module;

[0039] Based on the integrated emergency response module, an intelligent workflow engine is introduced to automatically configure the data processing flow and generate an automated data processing flow;

[0040] Based on the automated data processing flow, the data transmission path and cache mechanism are optimized to meet the needs of simultaneous viewing on multiple terminals, and a strategy for managing and sharing the casualty information is generated.

[0041] In a second aspect, the embodiment of the present application provides a cloud computing-based casualty information management and sharing system, including:

[0042] The collection module is used to collect and integrate multi-source heterogeneous data, synchronize it to the cloud platform in real time through a secure encrypted channel, and generate a preliminary casualty information database;

[0043] An analysis module is used to perform complex correlation analysis in a cloud computing environment based on the preliminary casualty information database using a quantum heuristic optimization algorithm, simulate the quantum computing process using the powerful computing resources of the cloud platform, deeply mine the intrinsic connections between data fragments at different times and locations, and use distributed edge computing technology to ensure that information is updated and adjusted in real time under high concurrency conditions, reduce the load on the central server, and generate optimized casualty information files;

[0044] An extraction module is used to reconstruct multidimensional data based on the optimized wounded information archive using a holographic reconstruction algorithm, extract implicit patterns and features with the help of large-scale parallel processing capabilities of the cloud, and perform multidimensional reconstruction processing on the historical and current health status data of each wounded. It uses homomorphic encryption technology to support direct calculation of encrypted data, ensure the security of personal sensitive information data, and generate a high-dimensional data model;

[0045] A design module is used to build an emergency response module based on the high-dimensional data model, design an interactive access interface with permission control, support multi-terminal simultaneous viewing, and generate a wounded information management and sharing strategy.

[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 a cloud computing-based casualty information management and sharing 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. When the computer program is executed by a computer, a cloud computing-based casualty information management and sharing method as described in the first aspect is implemented.

[0048] In the embodiment of the present application, multi-source heterogeneous data are collected and integrated, and synchronized to the cloud platform in real time through a secure encrypted channel to generate a preliminary casualty information database; based on the preliminary casualty information database, a quantum-inspired optimization algorithm is used to perform complex correlation analysis in a cloud computing environment, and the powerful computing resources of the cloud platform are used to simulate the quantum computing process, and the intrinsic connections between data fragments at different times and locations are deeply mined. Distributed edge computing technology is used to ensure that information is updated and adjusted in real time under high concurrency conditions, reduce the load on the central server, and generate an optimized casualty information file; based on the optimized casualty information file, a holographic reconstruction algorithm is used to reconstruct multidimensional data, and with the help of the large-scale parallel processing capabilities of the cloud, implicit patterns and features are extracted, and multi-dimensional reconstruction processing is performed on the historical and current health status data of each casualty. Homomorphic encryption technology is used to support direct calculation of encrypted data, ensure the security of personal sensitive information data, and generate a high-dimensional data model; based on the high-dimensional data model, an emergency response module is built, an interactive access interface with permission control is designed, and multi-terminal simultaneous viewing is supported to generate a casualty information management and sharing strategy. By collecting and integrating multi-source heterogeneous data and synchronizing them to the cloud platform in real time through a secure encrypted channel, the security and integrity of the data are ensured, and a preliminary casualty information database is generated to provide a solid foundation for subsequent processing; quantum-inspired optimization algorithms are used to conduct complex correlation analysis in a cloud computing environment, simulate quantum computing processes, and deeply explore the intrinsic connections between data fragments at different times and locations. Distributed edge computing technology is used to ensure that information is updated and adjusted in real time under high concurrency conditions, reduce the load on the central server, and improve the response speed and stability of the system; based on the optimized casualty information archive, a holographic reconstruction algorithm is used to reconstruct multidimensional data, extract implicit patterns and features, and comprehensively reconstruct the historical and current health status data of each casualty. Homomorphic encryption technology is used to ensure the security of personal sensitive information data, generate high-dimensional data models, support direct calculations on encrypted data, and ensure data privacy and security; an emergency response module is built, an interactive access interface with permission control is designed, multi-terminal simultaneous viewing is supported, and casualty information management and sharing strategies are generated, which improves emergency response capabilities and information sharing efficiency.

[0049] Furthermore, by integrating and analyzing the correlation of the preliminary casualty information database, we ensure that data from different channels can be synchronized and accurately recorded in real time, generate a preliminary integrated casualty information file, and enhance the integrity and consistency of the data; use quantum heuristic optimization algorithms to perform complex correlation analysis in a cloud computing environment, simulate the quantum computing process, deeply explore the intrinsic connections between data fragments at different times and locations, reveal fine and high-value association rules, generate refined casualty information files, and improve the depth and breadth of data analysis; use distributed edge computing technology to synchronize and update data in real time, distribute computing tasks to network edge nodes, and ensure instant information updates and dynamic adjustments under high concurrency conditions, reduce the load on the central server, and improve the stability and response speed of the system; optimize the configuration of the information management system, adjust system parameters and configurations to adapt to changing needs, improve the system response speed, generate optimized casualty information files, and enhance the flexibility and adaptability of the system.

[0050] Furthermore, based on the optimized information files of the injured, multi-dimensional feature extraction is performed to capture subtle changes and complex relationships, generate multi-dimensional feature sets, and ensure the richness and accuracy of the data; the holographic reconstruction algorithm is used to prepare for high-precision data reconstruction, and with the help of the large-scale parallel processing capabilities of the cloud, implicit patterns and features are extracted, and the historical and current health status data of each injured person is multi-dimensionally reconstructed to generate pre-processed multi-dimensional feature data, thereby improving the accuracy and reliability of data reconstruction; based on the pre-processed multi-dimensional feature data, combined with the changes in the time dimension and the influence of space-related factors, a multi-dimensional reconstructed health status model is generated, which provides a more detailed and accurate description of the health status and contributes to more accurate medical decision-making; efficient data compression and index optimization are implemented to generate a high-dimensional data model, which improves data storage and query efficiency and ensures efficient operation and rapid response of the system.

[0051] 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

[0052] 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.

[0053] Figure 1 A flowchart of a method for managing and sharing wounded information based on cloud computing provided in an embodiment of the present application;

[0054] Figure 2A schematic diagram of the structure of a cloud computing-based wounded information management and sharing system provided in an embodiment of the present application;

[0055] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0056] 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.

[0057] 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.

[0058] 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.

[0059] Figure 1 A flowchart of a method for managing and sharing casualty information based on cloud computing is provided for an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0060] 101. Collect and integrate multi-source heterogeneous data, synchronize them to the cloud platform in real time through a secure encrypted channel, and generate a preliminary casualty information database;

[0061] In this step, multi-source heterogeneous data refers to data from different sources, formats, and structures, including but not limited to hospital electronic medical records, on-site emergency information uploaded by ambulances, data from mobile medical devices (such as smart watches, health monitoring devices), social media information, etc.

[0062] A secure encrypted channel refers to a secure communication path established through encryption technology and security protocols (such as SSL / TLS) to ensure that data will not be stolen or tampered with during transmission. Common encryption algorithms include AES (Advanced Encryption Standard) and RSA (asymmetric encryption algorithm), which are used to protect the security of sensitive information.

[0063] The preliminary casualty information database refers to the initial data set after collection and integration, which contains all multi-source heterogeneous data related to the casualties. This database provides basic data support for subsequent complex correlation analysis and data reconstruction.

[0064] In the embodiment of the present application, first, multi-source heterogeneous data are collected through multiple channels; second, a secure encrypted channel is used to ensure the security of data transmission; third, the collected data is synchronized to the cloud platform in real time; finally, a preliminary casualty information database containing all relevant information is generated, providing a solid foundation for subsequent processing.

[0065] Suppose an earthquake occurs in a city, and multiple hospitals, emergency centers, and mobile medical devices simultaneously record a large amount of information about the injured. Through sensors and mobile devices deployed in various locations, this information is collected in real time and transmitted to the central cloud platform through a secure encrypted channel. The platform quickly integrates data from different sources to generate a comprehensive preliminary database of injured information, providing an immediately available information resource for the emergency response team.

[0066] 102. Based on the preliminary casualty information database, quantum-inspired optimization algorithms are used to conduct complex correlation analysis in a cloud computing environment, and the powerful computing resources of the cloud platform are used to simulate the quantum computing process, deeply explore the intrinsic connections between data fragments at different times and locations, and use distributed edge computing technology to ensure that information is updated and adjusted in real time under high concurrency conditions, reduce the load on the central server, and generate optimized casualty information files;

[0067] In this step, the quantum-inspired optimization algorithm is an optimization algorithm that simulates the quantum computing process. It utilizes the probability distribution characteristics of the quantum state and iteratively searches for the optimal solution. It can handle complex correlations in large-scale data sets and reveal potential high-value association rules.

[0068] Complex correlation analysis refers to the identification of intrinsic connections between data fragments at different times and locations through in-depth mining of multi-source heterogeneous data. This analysis not only focuses on explicit direct relationships, but also explores implicit indirect relationships and causal relationships.

[0069] Distributed edge computing technology is a computing model that distributes computing tasks to network edge nodes, reduces data transmission delays and reduces central server load. Edge nodes can be IoT devices, local servers or mobile devices, which work together to achieve real-time data processing.

[0070] The optimized casualty information file is a highly optimized data set generated based on complex correlation analysis, containing high-value information that has been screened and processed. This information not only reflects the current status, but also reveals historical trends and potential risks.

[0071] In the embodiments of the present application, first, data collation and preprocessing are performed based on the preliminary casualty information database; second, a quantum-inspired optimization algorithm is used to perform complex correlation analysis in a cloud computing environment; third, distributed edge computing technology is used to ensure real-time updating and dynamic adjustment of information under high concurrency conditions; finally, an optimized casualty information file is generated to provide high-quality data support for subsequent processing.

[0072] For example, continuing with the above example, assume that a preliminary wounded information database has been established. Next, the system uses the powerful computing resources of the cloud platform to perform complex correlation analysis on the data through quantum heuristic optimization algorithms, and dig out the intrinsic connections between data fragments at different times and locations. At the same time, distributed edge computing technology is used to process data in real time at various emergency sites and mobile devices to ensure that information is updated and adjusted in real time, and finally generate optimized wounded information files to provide a scientific basis for emergency response.

[0073] 103. Based on the optimized wounded information files, a holographic reconstruction algorithm is used to reconstruct multidimensional data. With the help of the large-scale parallel processing capabilities of the cloud, implicit patterns and features are extracted, and multidimensional reconstruction processing is performed on the historical and current health status data of each wounded. Homomorphic encryption technology is used to support direct calculation of encrypted data, ensure the security of personal sensitive information data, and generate a high-dimensional data model;

[0074] In this step, the holographic reconstruction algorithm is a multidimensional data reconstruction technology that generates a high-precision data model by capturing subtle changes and complex relationships in the data. The algorithm can extract implicit patterns and features while maintaining the characteristics of the original data, revealing the deep connections between the data.

[0075] Homomorphic encryption technology is an encryption method that allows calculations to be performed directly on encrypted data without decryption, which ensures the security of data during processing and protects personal sensitive information from being leaked even in a cloud environment.

[0076] High-dimensional data models refer to complex data structures generated after multi-dimensional reconstruction processing, which contain a large number of implicit patterns and features. These models can not only reflect the current status, but also predict future trends, providing strong support for medical decision-making.

[0077] Multidimensional reconstruction processing refers to the comprehensive reconstruction of the historical and current health status data of each injured person, capturing its subtle changes and complex relationships, and generating a detailed description of the health status.

[0078] In the embodiment of the present application, first, multi-dimensional feature extraction is performed based on the optimized information file of the injured person; secondly, a holographic reconstruction algorithm is used to prepare for high-precision data reconstruction; thirdly, with the help of large-scale parallel processing capabilities of the cloud, implicit patterns and features are extracted; finally, a high-dimensional data model is generated to provide detailed and accurate information support for medical decision-making.

[0079] For example, continuing with the previous example, assume that the optimized patient information file has been generated. The system then applies a holographic reconstruction algorithm to perform multi-dimensional reconstruction of each patient's historical and current health data. With the support of large-scale parallel processing capabilities and homomorphic encryption technology in the cloud, the system can efficiently extract implicit patterns and features and generate high-dimensional data models. These models not only retain the core features of the original data, but also enhance the intrinsic connection between features, providing high-quality basic data for subsequent medical decision-making.

[0080] 104. Based on the high-dimensional data model, an emergency response module is built, an interactive access interface with permission control is designed, multi-terminal simultaneous viewing is supported, and a casualty information management and sharing strategy is generated.

[0081] In this step, the emergency response module is a specially designed system component used to respond to emergencies, quickly launch emergency plans, and coordinate resources from all parties. This module is highly flexible and scalable and can be dynamically adjusted according to actual conditions.

[0082] The permission-controlled interactive access interface refers to a strict permission management mechanism that ensures that only authorized users can access specific functions and data. The interactive interface provides an intuitive operating experience, making it easy for users to query, edit and annotate information.

[0083] Multi-terminal synchronous viewing means that the system supports synchronous viewing and operation of data on different types of terminal devices (such as computers, tablets, and mobile phones) to ensure the timeliness and consistency of information sharing.

[0084] The casualty information management and sharing strategy is a complete set of management rules and processes that covers the entire process of information collection, processing, storage, access and sharing, and clarifies the responsibilities and operating procedures of different roles in emergency situations.

[0085] In the embodiment of the present application, first, an emergency response module is built based on a high-dimensional data model; second, an interactive access interface with permission control is designed; third, simultaneous viewing of multiple terminals is supported; finally, a casualty information management and sharing strategy is generated to ensure the security and effectiveness of information management and sharing.

[0086] For example, continuing with the previous example, assume that the high-dimensional data model has been generated. The system then builds an emergency response module and designs an interactive access interface with permission control to ensure that only authorized personnel can access sensitive information. This module supports multi-terminal simultaneous viewing, allowing emergency personnel, doctors, and managers to obtain the latest information on the injured in real time on different devices. Finally, the system generates a complete set of strategies for the management and sharing of injured information to guide the operations of all parties in emergency situations and ensure effective management and rapid response of information.

[0087] In summary, steps 101 to 104 cover the complete process from data collection, complex correlation analysis, multidimensional data reconstruction to emergency response mechanism, aiming to provide an efficient, accurate and secure casualty information management system to meet the needs of modern medical emergency response.

[0088] In order to further improve the accuracy and real-time performance of casualty information management, in some embodiments, the complex association analysis based on the preliminary casualty information database described in step 102 includes: based on the preliminary casualty information database, integration and association analysis are performed to ensure that data from different channels can be synchronized and accurately recorded in real time, and a preliminary integrated casualty information file is generated; based on the preliminary integrated casualty information file, complex association analysis is performed in a cloud computing environment using a quantum heuristic optimization algorithm, and the powerful computing resources of the cloud platform are used to simulate the quantum computing process, deeply mine the intrinsic connections between data fragments at different times and locations, reveal fine and high-value association rules, and generate a refined casualty information file; based on the refined casualty information file, distributed edge computing technology is used to synchronize and update data in real time, and by distributing computing tasks to network edge nodes, it is ensured that information is updated and dynamically adjusted in high concurrency situations, the load on the central server is reduced, and a real-time updated casualty information file is generated; based on the real-time updated casualty information file, the information management system is optimized and configured, system parameters and configurations are adjusted to adapt to changing needs, the system response speed is improved, and an optimized casualty information file is generated.

[0089] In this embodiment, the preliminary injured person information database refers to the initial data set after collection and integration, which includes all multi-source heterogeneous data related to the injured, including but not limited to hospital electronic medical records, on-site first aid information uploaded by ambulances, data from mobile medical devices, social media information, etc.

[0090] Integration and correlation analysis refers to the unified processing of data from different channels to ensure the consistency and integrity of the data. This process involves operations such as data cleaning, format conversion, and duplicate data deletion to generate a preliminary integrated casualty information file.

[0091] The refined casualty information file is a highly optimized data set generated based on integration and correlation analysis, containing high-value information that has been screened and processed. This information not only reflects the current status, but also reveals historical trends and potential risks.

[0092] Real-time updated casualty information files refer to the real-time synchronization and update processing of data through distributed edge computing technology under high concurrency conditions, ensuring that information is updated and adjusted dynamically, reducing the load on the central server.

[0093] System optimization configuration refers to adjusting the parameters and configuration of the information management system according to actual needs to improve the system's response speed and ability to adapt to changes.

[0094] In the embodiment of the present application, first, data collation and preprocessing are performed based on the preliminary casualty information database to ensure that data from different channels can be synchronized and accurately recorded in real time, and a preliminary integrated casualty information file is generated; secondly, a quantum-inspired optimization algorithm is used to perform complex correlation analysis in a cloud computing environment, and the powerful computing resources of the cloud platform are used to simulate the quantum computing process, deeply explore the intrinsic connections between data fragments at different times and places, reveal fine and high-value association rules, and generate a refined casualty information file; thirdly, distributed edge computing technology is used to synchronize and update data in real time, and by distributing computing tasks to network edge nodes, it is ensured that information is updated and dynamically adjusted in high concurrency situations, reducing the load on the central server, and generating a real-time updated casualty information file; finally, the information management system is optimized and configured, and system parameters and configurations are adjusted to adapt to changing needs, improve system response speed, and generate optimized casualty information files.

[0095] Here is a specific example:

[0096] Suppose a serious traffic accident occurs in a city, and multiple emergency stations receive a large number of casualty information at the same time. First, the system integrates and correlates data from different channels through the preliminary casualty information database to ensure real-time synchronization and accurate recording of data, and generate a preliminary integrated casualty information file; for example, continuing the above example, the system then applies quantum heuristic optimization algorithms to perform complex correlation analysis in a cloud computing environment, using the powerful computing resources of the cloud platform to deeply explore the intrinsic connections between data fragments at different times and locations, revealing fine and high-value association rules, and generating refined casualty information files; thirdly, the system uses distributed edge computing technology to distribute computing tasks to various emergency stations and mobile devices, ensuring that information is updated and adjusted in real time and dynamically under high concurrency conditions, reducing the load on the central server, and generating real-time updated casualty information files; finally, the system optimizes the configuration of the information management system, adjusts parameters and configurations to adapt to changing needs, improves the system response speed, generates optimized casualty information files, and provides a scientific basis for emergency response.

[0097] In order to further improve the accuracy and consistency of casualty information management, in some embodiments, the complex association analysis based on the preliminary integrated casualty information file described in step 102 includes: based on the preliminary integrated casualty information file, consistency check and format conversion of data from different sources to generate a standardized casualty information file; based on the standardized casualty information file, complex association analysis is performed in a cloud computing environment using a quantum heuristic optimization algorithm, the powerful computing resources of the cloud platform are used to simulate the quantum computing process, and the intrinsic connection between data fragments at different times and locations is deeply mined. By simulating the probability distribution of quantum states, potential association patterns in the data space are explored to generate a potential association rule library; based on the potential association rule library, reorganization and optimization are performed through the distributed computing power in the cloud computing environment to ensure information integrity and consistency, and generate a deeply associated casualty information file; based on the deeply associated casualty information file, comprehensive organization and configuration optimization are performed, the information display format is adjusted, and a refined casualty information file is generated.

[0098] In this embodiment, the initially integrated casualty information file refers to a data set generated after multi-source heterogeneous data collection and initial organization, ensuring that data from different channels can be synchronized and accurately recorded in real time.

[0099] Consistency check and format conversion refer to the unified processing of data from different sources to ensure the consistency and integrity of the data. This process involves operations such as data cleaning, format standardization, and duplicate data deletion to generate standardized casualty information files.

[0100] The potential association rule base refers to a database containing potential association patterns and rules generated through complex association analysis. These rules not only reflect explicit direct relationships, but also explore implicit indirect relationships and causal relationships.

[0101] Distributed computing capabilities in a cloud computing environment refer to efficient data processing and analysis achieved through the powerful computing resources and distributed architecture of the cloud platform. This computing model can significantly improve the speed and efficiency of data processing.

[0102] The deeply correlated casualty information archive is a highly optimized data set generated based on a latent association rule base, ensuring the integrity and consistency of information that not only reflects the current status but also reveals historical trends and potential risks.

[0103] The refined casualty information file refers to the final data set after comprehensive organization and configuration optimization, which adjusts the information display format and improves the availability and readability of the data.

[0104] In the embodiments of the present application, first, based on the initially integrated casualty information files, data from different sources are checked for consistency and format conversion to ensure data standardization and generate standardized casualty information files; secondly, based on the standardized casualty information files, a quantum-inspired optimization algorithm is used to perform complex correlation analysis in a cloud computing environment, and the powerful computing resources of the cloud platform are used to simulate the quantum computing process, deeply explore the intrinsic connections between data fragments at different times and locations, and explore potential correlation patterns in the data space by simulating the probability distribution of quantum states, and generate a potential correlation rule library; thirdly, based on the potential correlation rule library, reorganization and optimization are performed through the distributed computing power in the cloud computing environment to ensure information integrity and consistency, and generate deeply correlated casualty information files; finally, based on the deeply correlated casualty information files, comprehensive organization and configuration optimization are performed, the information display format is adjusted, and a refined casualty information file is generated.

[0105] Here is a specific example:

[0106] For example, suppose a public health emergency occurs in a city, and multiple medical institutions and emergency sites receive a large amount of information about the injured at the same time. First, based on the preliminary integrated injured information archive, the system performs consistency checks and format conversions on data from different sources to ensure data standardization and generate standardized injured information archives; secondly, based on the standardized injured information archive, the system uses quantum heuristic optimization algorithms to perform complex association analysis in a cloud computing environment, uses the powerful computing resources of the cloud platform to simulate the quantum computing process, deeply mines the intrinsic connections between data fragments at different times and locations, and explores potential association patterns in the data space by simulating the probability distribution of quantum states, generating a potential association rule library; thirdly, based on the potential association rule library, the system reorganizes and optimizes through distributed computing capabilities in a cloud computing environment to ensure information integrity and consistency, and generate deeply associated injured information archives; finally, based on the deeply associated injured information archive, the system conducts comprehensive organization and configuration optimization, adjusts the information display format, and generates refined injured information archives to provide a scientific basis for emergency response.

[0107] In order to further improve the real-time performance and response speed of the wounded information management, in some embodiments, the real-time synchronization and update processing based on the refined wounded information archive in step 102 includes: based on the refined wounded information archive, through the cloud computing platform network architecture, designing and deploying an intelligent routing algorithm, performing intelligent routing configuration on the data stream, and generating a data stream framework with intelligent routing configuration; based on the data stream framework with intelligent routing configuration, using distributed edge computing technology to pre-process the data, performing preliminary data cleaning and local analysis at the network edge node, reducing the amount of data transmitted to the central server, speeding up the response speed, and generating a pre-processed local data set; based on the pre-processed local data set, performing real-time synchronization and update processing, using the advantages of distributed edge computing technology, each edge node independently processes local data, realizes instant information update and dynamic adjustment under high concurrency, and generates a real-time synchronized wounded information archive; based on the real-time synchronized wounded information archive, implementing an adaptive load balancing strategy, dynamically adjusting the load distribution between each edge node and the central server, and generating a real-time updated wounded information archive.

[0108] In this embodiment, the intelligent routing algorithm is a technology for optimizing data transmission paths. By analyzing the network topology and real-time traffic conditions, the optimal path is selected to transmit data, ensuring efficient transmission and low latency of data streams.

[0109] The data flow framework of intelligent routing configuration refers to the intelligent routing algorithm designed and deployed through the network architecture of the cloud computing platform, which performs intelligent routing configuration on the data flow to ensure the optimal path selection during data transmission and reduce transmission delays.

[0110] Distributed edge computing technology: is a computing model that distributes computing tasks to network edge nodes, reducing data transmission delays and reducing central server load. Edge nodes can be IoT devices, local servers, or mobile devices, which work together to achieve real-time data processing.

[0111] Preprocessed local data sets refer to data sets generated after preliminary data cleaning and local analysis are performed at the network edge node. This preprocessing reduces the amount of data transmitted to the central server and speeds up the response.

[0112] Real-time synchronization and update processing refers to taking advantage of distributed edge computing technology, with each edge node independently processing local data, achieving instant updates and dynamic adjustments of information under high concurrency conditions, and ensuring the latest and consistency of data.

[0113] The adaptive load balancing strategy refers to dynamically adjusting the load distribution between each edge node and the central server according to the actual load situation to ensure the stability and efficient operation of the system.

[0114] In the embodiments of the present application, firstly, based on the refined patient information archive, an intelligent routing algorithm is designed and deployed through the network architecture of the cloud computing platform, the data flow is intelligently routed, and a data flow framework of the intelligent routing configuration is generated; secondly, based on the data flow framework of the intelligent routing configuration, the distributed edge computing technology is used to pre-process the data, and preliminary data cleaning and local analysis are performed at the network edge nodes to reduce the amount of data transmitted to the central server, speed up the response speed, and generate a pre-processed local data set; thirdly, based on the pre-processed local data set, real-time synchronization and update processing are performed, and the advantages of distributed edge computing technology are utilized. Each edge node independently processes local data, and real-time information updates and dynamic adjustments are realized under high concurrency conditions to generate a real-time synchronized patient information archive; finally, based on the real-time synchronized patient information archive, an adaptive load balancing strategy is implemented to dynamically adjust the load distribution between each edge node and the central server to generate a real-time updated patient information archive.

[0115] Here is a specific example:

[0116] For example, suppose a large-scale disaster occurs in a city, and multiple emergency sites and mobile medical devices receive a large amount of information about the injured at the same time. First, based on the refined information archives of the injured, the system designs and deploys intelligent routing algorithms through the cloud computing platform network architecture, configures intelligent routing for the data stream, and generates a data stream framework with intelligent routing configuration; secondly, based on the data stream framework with intelligent routing configuration, the distributed edge computing technology is used to pre-process the data, and preliminary data cleaning and local analysis are performed on each emergency site and mobile device to reduce the amount of data transmitted to the central server, speed up the response, and generate a pre-processed local data set; thirdly, based on the pre-processed local data set, real-time synchronization and update processing are performed, and the advantages of distributed edge computing technology are used. Each edge node independently processes local data, and the information is updated and adjusted in real time under high concurrency, generating a real-time synchronized information archive of the injured; finally, based on the real-time synchronized information archive of the injured, an adaptive load balancing strategy is implemented to dynamically adjust the load distribution between each edge node and the central server to ensure the efficient operation of the system, generate a real-time updated information archive of the injured, and provide a scientific basis for emergency response.

[0117] In order to further improve the accuracy and security of casualty information management, in some embodiments, the multidimensional data reconstruction based on the optimized casualty information file in step 103 includes: based on the optimized casualty information file, multidimensional feature extraction is performed to capture subtle changes and complex relationships, and a multidimensional feature set is generated; based on the multidimensional feature set, a holographic reconstruction algorithm is used to prepare for high-precision data reconstruction, and with the help of large-scale parallel processing capabilities of the cloud, implicit patterns and features are extracted, and each casualty's historical and current health status data is multidimensionally reconstructed to generate pre-processed multidimensional feature data; based on the pre-processed multidimensional feature data, a multidimensional reconstructed health status model is generated in combination with the changes in the time dimension and the influence of space-related factors; based on the multidimensional reconstructed health status model, efficient data compression and index optimization are implemented to generate a high-dimensional data model.

[0118] In this embodiment, the optimized casualty information file refers to a highly optimized data set generated after complex correlation analysis and real-time synchronous updating, which contains high-value information that has been screened and processed.

[0119] Multidimensional feature extraction refers to capturing subtle changes and complex relationships from optimized casualty information files to generate a data set containing multiple dimensional features, each dimension representing a different health status indicator or influencing factors such as time and location.

[0120] Preprocessed multidimensional feature data refers to a set of multidimensional features that has undergone preliminary processing to ensure the integrity and consistency of the data and provide a solid foundation for subsequent high-precision data reconstruction.

[0121] The multidimensional reconstruction health status model refers to a multidimensional data model that combines the changes in the time dimension with the influence of space-related factors to generate a detailed description of the health status of each injured person. This model not only reflects the current health status, but also predicts future trends and supports medical decision-making.

[0122] Efficient data compression and index optimization refers to compressing and optimizing the index of the generated multi-dimensional reconstructed health status model, reducing storage space and improving query efficiency to ensure efficient operation of the system.

[0123] In the embodiments of the present application, firstly, based on the optimized information files of the injured, multi-dimensional feature extraction is performed to capture subtle changes and complex relationships, and a multi-dimensional feature set is generated; secondly, based on the multi-dimensional feature set, a holographic reconstruction algorithm is used to prepare for high-precision data reconstruction, and with the help of the large-scale parallel processing capabilities of the cloud, implicit patterns and features are extracted, and the historical and current health status data of each injured person are multi-dimensionally reconstructed to generate pre-processed multi-dimensional feature data; thirdly, based on the pre-processed multi-dimensional feature data, a multi-dimensional reconstructed health status model is generated in combination with the changes in the time dimension and the influence of space-related factors; finally, based on the multi-dimensional reconstructed health status model, efficient data compression and index optimization are implemented to generate a high-dimensional data model to ensure the efficient operation of the system and the security of the data.

[0124] Here is a specific example:

[0125] For example, suppose a serious natural disaster occurs in a city, and multiple medical institutions and emergency stations receive a large number of casualties at the same time. First, the system extracts multidimensional features based on the optimized casualty information archive, captures subtle changes and complex relationships, and generates a multidimensional feature set; second, based on the multidimensional feature set, the system uses a holographic reconstruction algorithm to prepare for high-precision data reconstruction, and with the help of the cloud's large-scale parallel processing capabilities, extracts implicit patterns and features, and performs multidimensional reconstruction processing on the historical and current health status data of each casualty to generate pre-processed multidimensional feature data; third, based on the pre-processed multidimensional feature data, the system combines the changes in the time dimension and the influence of space-related factors to generate a multidimensional reconstructed health status model, which describes the health status of each casualty in detail; finally, based on the multidimensional reconstructed health status model, the system implements efficient data compression and index optimization to generate a high-dimensional data model to ensure the efficient operation of the system and the security of the data, and provide a scientific basis for emergency response.

[0126] In order to further improve the accuracy and reliability of casualty information management, in some embodiments, the high-precision data reconstruction preparation based on the multidimensional feature set described in step 103 includes: based on the multidimensional feature set, identifying abnormal points and potential problems in the historical and current health status data of each casualty, and generating an abnormality detection report; based on the abnormality detection report, using a holographic reconstruction algorithm to prepare for high-precision data reconstruction, using large-scale parallel computing resources in the cloud to conduct in-depth analysis, further refine and correct feature information, extract implicit patterns and features, reveal deep-level relationships within the data, and generate a refined feature matrix; based on the refined feature matrix, perform cross-dimensional correlation analysis, construct a comprehensive data view, reveal potential connections between different data fragments, and generate a comprehensive data view; based on the comprehensive data view, implement data consistency verification and data cleaning processing to improve data quality and generate pre-processed multidimensional feature data.

[0127] In this embodiment, the multidimensional feature set refers to a data set containing multiple dimensional features extracted from the optimized casualty information file, each dimension representing a different health status indicator or influencing factors such as time and place, and is used to capture subtle changes and complex relationships.

[0128] Identification of outliers and potential problems refers to identifying outliers and potential problems in the historical and current health status data of each injured person through statistical analysis and machine learning methods, and generating anomaly detection reports. These outliers may be false alarms or actual health risks and require further verification.

[0129] The refined feature matrix refers to a set of feature information that has been deeply analyzed and corrected, containing more detailed and accurate feature descriptions. These features not only reflect explicit direct relationships, but also reveal implicit indirect relationships and causal relationships.

[0130] Cross-dimensional correlation analysis refers to the comprehensive analysis of data fragments in different dimensions, building a comprehensive data view, and revealing the potential connections between different data fragments. This kind of analysis helps to fully understand the complex structure within the data.

[0131] The comprehensive data view refers to a multidimensional data model generated through cross-dimensional correlation analysis that describes in detail the health status of each injured person. This view not only reflects the current health status, but also predicts future trends and supports medical decision-making.

[0132] Data consistency verification and cleaning processing refers to the quality check of the generated comprehensive data view to ensure the consistency and accuracy of the data, improve the quality of the data by removing noise and erroneous data, and generate pre-processed multi-dimensional feature data.

[0133] In the embodiment of the present application, firstly, based on a multidimensional feature set, anomalies and potential problems in the historical and current health status data of each injured person are identified, and an anomaly detection report is generated; secondly, based on the anomaly detection report, a holographic reconstruction algorithm is used to prepare for high-precision data reconstruction, and in-depth analysis is performed using large-scale parallel computing resources in the cloud to further refine and correct feature information, extract implicit patterns and features, reveal deep internal relationships of the data, and generate a refined feature matrix; thirdly, based on the refined feature matrix, cross-dimensional correlation analysis is performed, a comprehensive data view is constructed, potential connections between different data fragments are revealed, and a comprehensive data view is generated; finally, based on the comprehensive data view, data consistency verification and cleaning processing are implemented to improve data quality and generate pre-processed multidimensional feature data.

[0134] Here is a specific example:

[0135] For example, suppose a major traffic accident occurs on an intercity highway, and multiple emergency stations and mobile medical devices receive a large number of casualties at the same time. First, based on a multidimensional feature set, the system identifies anomalies and potential problems in the historical and current health status data of each injured person, and generates an anomaly detection report; secondly, based on the anomaly detection report, the system uses a holographic reconstruction algorithm to prepare for high-precision data reconstruction, and uses large-scale parallel computing resources in the cloud for in-depth analysis, further refines and corrects feature information, extracts implicit patterns and features, reveals deep relationships within the data, and generates a refined feature matrix; thirdly, based on the refined feature matrix, the system conducts cross-dimensional correlation analysis, constructs a comprehensive data view, reveals potential connections between different data fragments, and generates a comprehensive data view; finally, based on the comprehensive data view, the system implements data consistency verification and cleaning processing to improve data quality, generate pre-processed multidimensional feature data, and provide a scientific basis for subsequent medical decision-making.

[0136] In order to further improve the emergency response capability and decision-making efficiency of casualty information management, in some embodiments, the emergency response module based on the high-dimensional data model described in step 104 includes: based on the high-dimensional data model, risk assessment of emergency response scenarios is performed, possible emergencies are predicted and response strategies are optimized, and a risk assessment report is generated; based on the risk assessment report, a rapid deployment emergency response module is designed and tightly integrated with the high-dimensional data model to generate an integrated emergency response module; based on the integrated emergency response module, an intelligent workflow engine is introduced to automatically configure the data processing flow and generate an automated data processing flow; based on the automated data processing flow, the data transmission path and cache mechanism are optimized for the simultaneous viewing requirements of multiple terminals, and a casualty information management and sharing strategy is generated.

[0137] In this embodiment, the high-dimensional data model refers to a complex data structure generated after multi-dimensional reconstruction processing, which contains a large number of implicit patterns and features. These models can not only reflect the current status, but also predict future trends, providing strong support for medical decision-making.

[0138] Risk assessment refers to the analysis of high-dimensional data models to predict possible emergencies and optimize response strategies. Risk assessment reports are used to guide emergency preparedness and resource allocation to ensure rapid and effective response measures.

[0139] The emergency response module is a specially designed system component used to respond to emergencies, quickly launch emergency plans, and coordinate resources from all parties. The module is highly flexible and scalable and can be dynamically adjusted according to actual conditions.

[0140] The permission-controlled interactive access interface refers to a strict permission management mechanism that ensures that only authorized users can access specific functions and data. The interactive interface provides an intuitive operating experience, making it easy for users to query, edit and annotate information.

[0141] The intelligent workflow engine is an automation tool that can configure and optimize the data processing process, realize tasks scheduling, process monitoring and other functions, and improve the efficiency and accuracy of data processing.

[0142] Automated data processing flow means automating each link of data processing by introducing an intelligent workflow engine, reducing human intervention, and improving processing speed and quality.

[0143] Multi-terminal synchronous viewing means that the system supports synchronous viewing and operation of data on different types of terminal devices (such as computers, tablets, and mobile phones) to ensure the timeliness and consistency of information sharing.

[0144] Optimization of data transmission paths and cache mechanisms means improving the speed and stability of data transmission by optimizing network architecture and cache strategies, ensuring a smooth experience of simultaneous viewing on multiple terminals.

[0145] In the embodiments of the present application, first, based on the high-dimensional data model, a risk assessment is performed on the emergency response scenario, possible emergencies are predicted and the response strategy is optimized, and a risk assessment report is generated; secondly, based on the risk assessment report, a rapid deployment emergency response module is designed and tightly integrated with the high-dimensional data model to generate an integrated emergency response module; thirdly, based on the integrated emergency response module, an intelligent workflow engine is introduced to automatically configure the data processing flow and generate an automated data processing flow; finally, based on the automated data processing flow, the data transmission path and cache mechanism are optimized to meet the needs of simultaneous viewing on multiple terminals, and a strategy for managing and sharing information on the injured is generated.

[0146] Here is a specific example:

[0147] For example, suppose a public health emergency occurs in a school, and multiple medical institutions and emergency sites need to respond quickly. First, based on the high-dimensional data model, the system conducts risk assessment on emergency response scenarios, predicts possible emergencies, optimizes response strategies, and generates risk assessment reports; second, based on the risk assessment report, the system designs a rapid deployment emergency response module, which is tightly integrated with the high-dimensional data model to generate an integrated emergency response module to ensure that all relevant departments can immediately obtain the latest information on the injured; third, based on the integrated emergency response module, the system introduces an intelligent workflow engine to automatically configure the data processing process, generate an automated data processing process, and improve the efficiency and accuracy of data processing; finally, based on the automated data processing process, the system optimizes the data transmission path and cache mechanism for the simultaneous viewing requirements of multiple terminals, generates a strategy for the management and sharing of information on the injured, and ensures that first responders, doctors, and managers can obtain the latest information on the injured in real time on different devices, supporting efficient collaboration and rapid decision-making.

[0148] This application considers that in order to further improve the accuracy and response speed of association analysis of wounded information management, the existing technology has the problem of insufficient mining of internal connections between complex data fragments, so the invention embodiment proposes this optional solution. In order to solve the technical problem that the existing technology is difficult to reveal potential high-value association rules when processing multi-source heterogeneous data, a new optional solution is proposed, which includes:

[0149] Based on the standardized casualty information files, a quantum-inspired optimization algorithm is used to perform complex correlation analysis in a cloud computing environment. The powerful computing resources of the cloud platform are used to simulate the quantum computing process, deeply explore the intrinsic connections between data fragments at different times and locations, and explore potential correlation patterns in the data space by simulating the probability distribution of quantum states. A potential correlation rule base is generated, including:

[0150] Based on the standardized casualty information file, quantum state encoding is performed, each eigenvalue is mapped to a corresponding position in the quantum state space, and a quantum phase factor is introduced to adjust the relative relationship between the eigenvalues ​​to generate a quantum state probability distribution;

[0151] The quantum state probability distribution is calculated using the following formula:

[0152]

[0153] Where P(x) is the quantum state probability distribution of the data segment vector x; β is an adjustment parameter used to control the speed of exponential decay; x i is the i-th eigenvalue in the data segment vector; b is the bias term, representing the basic association strength; i is the feature index, from 1 to N; N is the number of features; λ is the nonlinear enhancement parameter; ∈ is the weight of introducing additional nonlinear terms;

[0154] Based on the quantum state probability distribution, combined with the time phase factor and the angle difference parameter, the similarities and differences between different data segments are evaluated, and the wave function difference is introduced. By comparing the deviation between the quantum wave function of each data segment and the average value, the uniqueness is quantified to generate a potential association rule score;

[0155] The potential association rule score is calculated using the following formula:

[0156]

[0157] Where R(p(x)) is the potential association rule score based on the probability distribution of quantum states; α is the adaptive learning rate, which is used to adjust the speed of parameter update at each iteration; γ and δ are parameters for adjusting the influence of angle difference; P(x k ) is the quantum state probability distribution of the kth data segment; θ k is the angle difference between the kth data segment and the center point; ω is the time phase factor; t k is the time point of the kth data segment; ρ is the influencing factor of the time difference; ν is the power of the time difference; μ is the power of the score; η is the weight of the additional nonlinear term; is the average value of the probability distribution of all quantum states; is the quantum wave function of the kth data segment; is the average value of all quantum wave functions; σ is the influence coefficient of wave function difference; k is the index of the data segment, from 1 to K; K is the number of data segments;

[0158] Based on the potential association rule scores, high-scoring rules are screened out by setting a threshold, a hierarchical clustering algorithm is applied to group the rules according to similarities between the rules, core features are extracted for each cluster, all rule clusters are integrated, and a potential association rule library is generated.

[0159] This method aims to generate more accurate and valuable potential association rule scores by introducing concepts such as quantum state probability distribution and wave function differences, combining time phase factors and angle difference parameters, and evaluating the similarities and differences between different data fragments. This method can not only capture nonlinear relationships in the data, but also filter out high-scoring rules by setting thresholds, apply hierarchical clustering algorithms to group them, extract core features, and finally integrate all rule clusters to generate a potential association rule library.

[0160] In the probability distribution of quantum states, the exponential decay term Used to control the speed at which the probability distribution of quantum states changes with eigenvalues. This part makes a nonlinear transformation of the differences between eigenvalues ​​so that similar eigenvalues ​​have higher probability values, thereby enhancing the model's sensitivity to subtle changes. At the same time, the exponential decay form ensures that the influence of distant eigenvalues ​​gradually weakens. The additional nonlinear term Introducing nonlinear factors to enhance the model's expressiveness. This part introduces logarithmic functions to enable the model to better adapt to data distributions of different scales, especially when dealing with extreme values ​​or outliers.

[0161] Among them, β is set according to the actual application scenario and is usually obtained through experimental parameter adjustment; x i From the standardized casualty information file; b is the basic correlation strength obtained from historical data statistics; λ is obtained through experimental parameter adjustment; ∈ is obtained through experimental parameter adjustment; N is obtained through the number of features;

[0162] In the scoring of latent association rules, the adaptive learning rate term Adjust the speed of parameter update at each iteration. This part takes into account the changes in time and space dimensions by introducing cosine function and time phase factor, so that the model can better capture the trend of dynamic changes; at the same time, the exponential form enhances the focus on important features; additional nonlinear terms Introducing nonlinear factors to enhance the discrimination of scores. This part uses the logarithmic transformation of the score differences to enable the model to more clearly distinguish the importance of different data fragments, especially when processing large amounts of data.

[0163] Among them, α, γ, δ are obtained through experimental parameter adjustment; P(x k ) is calculated by the quantum state probability distribution formula; θ k Calculated based on the angle between the data segment and the center point; ω is determined based on the time series characteristics; t k From the standardized casualty information file; ρ, v, μ, η, σ are obtained through experimental parameter adjustment; P is obtained by adjusting all P(x k ) Find the average value; Calculated according to the characteristics of the data segment; ψ is calculated by The average is obtained; K is obtained from the number of data fragments;

[0164] Assume that during a large international marathon event held in a city, multiple medical emergency stations and mobile medical devices receive a large amount of health information of participants in real time;

[0165] Assume β = 0.6, b = 0.4, λ = 1.5, ∈ = 0.2, N = 80, eigenvalue x i from standardized participant health information;

[0166]

[0167] Assume α=0.7, γ=0.6, δ=0.5, ω=0.4, ρ=0.3, ν=2.5, μ=1.8, η=0.8, σ=0.3, K=60;

[0168]

[0169] Assuming that the threshold is set to 0.9, since the calculated result 0.98 is greater than the set threshold, it shows that the wounded and sick information encryption scheme has high effectiveness and security, and can ensure that the data is not tampered with or leaked during transmission and storage. This is because the higher encryption strength index reflects that the encryption algorithm can effectively protect the security of data under the current network conditions, while not affecting the transmission efficiency and decryption speed. Through the above steps, the security and privacy protection of the wounded and sick information transmission are ensured, the reliability and scientificity of the rescue operation are improved, and the accuracy and response speed of the entire health monitoring system are enhanced.

[0170] This application considers that in order to further improve the accuracy of association analysis and data processing efficiency of wounded information management, the existing technology has the problem of insufficient mining of the internal connection of complex multi-dimensional feature sets, so the embodiment of the invention proposes this optional solution. In order to solve the technical problem that the existing technology is difficult to reveal potential high-value association rules when processing multi-source heterogeneous data, a new optional solution is proposed, which includes:

[0171] Based on the multi-dimensional feature set, a holographic reconstruction algorithm is used to prepare for high-precision data reconstruction. With the help of large-scale parallel processing capabilities of the cloud, implicit patterns and features are extracted, and multi-dimensional reconstruction processing is performed on the historical and current health status data of each injured person to generate pre-processed multi-dimensional feature data, including:

[0172] Based on the multi-dimensional feature set, the holographic transformation technology is applied to map each eigenvalue into the holographic space, capturing the inherent complex structure and potential associations, introducing the phase angle adjustment mechanism, and enhancing the relative relationship between eigenvalues ​​to generate holographic feature weights:

[0173] The holographic feature weight is calculated by the following formula:

[0174]

[0175] Where W(x) is the holographic feature weight of the information vector x; is the holographic transformation result of the i-th eigenvalue; is the average value of all eigenvalue holographic transformation results; i is the feature index, from 1 to N; N is the number of features; p is the norm parameter used to adjust the nonlinearity; α′ is used to adjust the amplitude of the hyperbolic tangent function; β′ is used to control the slope of the hyperbolic tangent function;

[0176] Based on the multidimensional feature set, a dynamic scoring system is constructed to quantify the correlation of information vectors at different time points and angles by calculating the sine square term at each time point, and an exponential decay function is applied to adjust the time difference effect to generate a multidimensional reconstruction association strength;

[0177] The multidimensional reconstruction correlation strength is calculated using the following formula:

[0178]

[0179] Where C(W(x)) is the multidimensional reconstruction correlation strength based on the holographic feature weight; γ′ is the exponential decay rate; ω j is the time phase factor; t j is the time point; δ j is the angle difference parameter; ρ is the influencing factor of the time difference; η is the weight of the additional nonlinear term; is the average value of all holographic feature weights; μ is the power of the score; λ′ is the weight of the additional associated term; θ j is the angle; φ j is the phase angle; ψ j is the additional angle difference parameter; χ is the influence coefficient of the wave function difference; is the holographic wave function; is the average value of all holographic wave functions; v′ is the power of the additional correlation term; j is the index of the record, from 1 to J; J is the number of records; W(x j ) is the information vector x j The holographic feature weight of

[0180] Based on the multi-dimensional reconstructed association strength, logarithmic transformation and product form are used to enhance the importance of high association strength information vectors, suppress the influence of low association strength, introduce additional angle and phase angle difference parameters, refine the association strength evaluation, and generate pre-processed multi-dimensional feature data.

[0181] In the holographic feature weights, the norm term It is used to adjust the nonlinearity so that the model can better adapt to data distributions of different scales, especially when dealing with extreme values ​​or outliers. At the same time, the norm form enhances the focus on important features. The hyperbolic tangent adjustment term α′·tanh(β′·(max i |H(x i )|-min i |H(x i )|)): Introduce the hyperbolic tangent function to adjust the relative relationship between eigenvalues, enhance the model's sensitivity to subtle changes, and make similar eigenvalues ​​have higher weights;

[0182] Among them, H(x i) is obtained by mapping each eigenvalue into the holographic space through the holographic transformation technology; H is obtained by i ) is obtained by averaging; N is obtained from the number of features; p, α′, β′: obtained through experimental parameter adjustment;

[0183] In the multidimensional reconstruction of correlation strength, the exponential decay term By introducing the time phase factor and the angle difference parameter, the model can better capture the trend of dynamic changes by considering the changes in time and space dimensions. At the same time, the exponential decay form ensures that the influence of distant time points gradually weakens. The additional nonlinear term Introducing nonlinear factors to enhance the discrimination of scores, so that the model can more clearly distinguish the importance of different information vectors; adding related terms By introducing additional angle and phase angle difference parameters, the correlation strength evaluation is refined, the importance of high correlation strength information vectors is enhanced, and the influence of low correlation strength is suppressed;

[0184] Among them, γ′, ω j ,t j ,δ j ,ρ,η,W,ρ,λ′,θ j ,φ j , j , χ, v′ are obtained through experimental parameter adjustment; J is obtained through the number of records; W(x j ) is calculated by the holographic feature weight formula; Calculated according to the characteristics of the data segment; Ψ is calculated by Find the average value;

[0185] Assume that during a large-scale international sports event in a city, multiple medical emergency sites and mobile medical devices receive a large amount of health information of participants in real time;

[0186] Assume p = 2, α′ = 0.5, β′ = 0.3, N = 70, eigenvalue x i From the standardized health information of the participants, then:

[0187]

[0188] Assume γ′=0.6,ω j =0.4,δ j =0.3, ρ = 0.2, η = 0.7, μ = 1.5, λ′ = 0.8, θ j =0.5,φ i =0.4,ψ i =0.3, χ=0.2, v′=2.0, J=50;

[0189]

[0190] Assuming that the threshold is set to 0.9, since the calculated result 0.94 is greater than the set threshold, it shows that the wounded and sick information encryption scheme has high effectiveness and security, and can ensure that the data is not tampered with or leaked during transmission and storage. This is because the higher correlation strength index reflects that the encryption algorithm can effectively protect the security of data under the current network conditions, while not affecting the transmission efficiency and decryption speed. Through the above steps, the security and privacy protection of the wounded and sick information transmission are ensured, the reliability and scientificity of the rescue operation are improved, and the accuracy and response speed of the entire health monitoring system are enhanced.

[0191] Figure 2 A schematic diagram of the structure of a cloud computing-based wounded information management and sharing system is provided for the embodiment of the present application. Figure 2 As shown, the device comprises:

[0192] The collection module 21 is used to collect and integrate multi-source heterogeneous data, synchronize them to the cloud platform in real time through a secure encrypted channel, and generate a preliminary casualty information database;

[0193] The analysis module 22 is used to perform complex correlation analysis in a cloud computing environment based on the preliminary casualty information database using a quantum heuristic optimization algorithm, simulate the quantum computing process using the powerful computing resources of the cloud platform, deeply mine the intrinsic connections between data fragments at different times and locations, and use distributed edge computing technology to ensure that information is updated and adjusted in real time under high concurrency conditions, reduce the load on the central server, and generate optimized casualty information files;

[0194] The extraction module 23 is used to reconstruct multi-dimensional data based on the optimized wounded information file using a holographic reconstruction algorithm, extract implicit patterns and features with the help of large-scale parallel processing capabilities of the cloud, and perform multi-dimensional reconstruction processing on the historical and current health status data of each wounded. The homomorphic encryption technology is used to support direct calculation of encrypted data, ensure the security of personal sensitive information data, and generate a high-dimensional data model;

[0195] The design module 24 is used to build an emergency response module based on the high-dimensional data model, design an interactive access interface with authority control, support multi-terminal simultaneous viewing, and generate a wounded information management and sharing strategy.

[0196] Figure 2 The cloud computing-based casualty information management and sharing system can be executed Figure 1The implementation principle and technical effect of the cloud computing-based wounded information management and sharing method described in the illustrated embodiment will not be described in detail. The specific manner in which each module and unit performs operations in the cloud computing-based wounded information management and sharing system in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0197] In one possible design, Figure 2 The cloud computing-based casualty information management and sharing system of the illustrated embodiment can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0198] 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 .

[0199] The processing component 32 is used to: collect and integrate multi-source heterogeneous data, synchronize them to the cloud platform in real time through a secure encrypted channel, and generate a preliminary casualty information database; based on the preliminary casualty information database, use quantum heuristic optimization algorithms to perform complex correlation analysis in a cloud computing environment, use the powerful computing resources of the cloud platform to simulate the quantum computing process, deeply explore the intrinsic connections between data fragments at different times and locations, and use distributed edge computing technology to ensure that information is updated and adjusted in real time under high concurrency conditions, reduce the load on the central server, and generate optimized casualty information files; based on the optimized casualty information files, use a holographic reconstruction algorithm to reconstruct multidimensional data, use the large-scale parallel processing capabilities of the cloud to extract implicit patterns and features, and perform multi-dimensional reconstruction processing on the historical and current health status data of each casualty, use homomorphic encryption technology to support direct calculation of encrypted data, ensure the security of personal sensitive information data, and generate a high-dimensional data model; based on the high-dimensional data model, build an emergency response module, design an interactive access interface with permission control, support multi-terminal simultaneous viewing, and generate a casualty information management and sharing strategy.

[0200] 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.

[0201] 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.

[0202] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0203] 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.

[0204] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0205] 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.

[0206] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The illustrated embodiment is a method for managing and sharing casualty information based on cloud computing.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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. A method for managing and sharing casualty information based on cloud computing, characterized in that: include: Collect and integrate multi-source heterogeneous data, synchronize them to the cloud platform in real time through a secure encrypted channel, and generate a preliminary casualty information database; Based on the preliminary casualty information database, quantum-inspired optimization algorithms are used to conduct complex correlation analysis in a cloud computing environment, and the powerful computing resources of the cloud platform are used to simulate the quantum computing process, deeply explore the intrinsic connections between data fragments at different times and locations, and use distributed edge computing technology to ensure that information is updated and adjusted in real time under high concurrency conditions, reduce the load on the central server, and generate optimized casualty information files; Based on the optimized casualty information files, a holographic reconstruction algorithm is used to reconstruct multidimensional data. With the help of large-scale parallel processing capabilities of the cloud, implicit patterns and features are extracted, and multidimensional reconstruction processing is performed on the historical and current health status data of each casualty. Homomorphic encryption technology is used to support direct calculation of encrypted data, ensure the security of personal sensitive information data, and generate a high-dimensional data model; Based on the high-dimensional data model, an emergency response module is built, an interactive access interface with permission control is designed, multi-terminal simultaneous viewing is supported, and a casualty information management and sharing strategy is generated.

2. The method according to claim 1, characterized in that Based on the preliminary casualty information database, the quantum heuristic optimization algorithm is used to perform complex correlation analysis in a cloud computing environment, the powerful computing resources of the cloud platform are used to simulate the quantum computing process, the intrinsic connection between data fragments at different times and locations is deeply mined, and distributed edge computing technology is used to ensure that information is updated and dynamically adjusted in a high-concurrency situation, reduce the load on the central server, and generate optimized casualty information files, including: Based on the preliminary casualty information database, integration and correlation analysis are performed to ensure that data from different channels can be synchronized and accurately recorded in real time, and a preliminary integrated casualty information file is generated; Based on the preliminary integrated casualty information files, a quantum-inspired optimization algorithm is used to perform complex correlation analysis in a cloud computing environment, and the powerful computing resources of the cloud platform are used to simulate the quantum computing process, deeply explore the intrinsic connections between data fragments at different times and locations, reveal fine and high-value correlation rules, and generate refined casualty information files; Based on the refined casualty information files, distributed edge computing technology is used to synchronize and update data in real time. By distributing computing tasks to network edge nodes, it is ensured that information is updated and adjusted in real time under high concurrency conditions, the load on the central server is reduced, and a real-time updated casualty information file is generated. Based on the real-time updated casualty information files, the information management system is optimized and configured, the system parameters and configuration are adjusted to adapt to the changing needs, the system response speed is improved, and the optimized casualty information files are generated.

3. The method according to claim 2, characterized in that Based on the preliminary integrated casualty information files, the quantum heuristic optimization algorithm is used to perform complex association analysis in a cloud computing environment, and the powerful computing resources of the cloud platform are used to simulate the quantum computing process, deeply explore the intrinsic connections between data fragments at different times and locations, reveal fine and high-value association rules, and generate refined casualty information files, including: Based on the initially integrated casualty information files, consistency check and format conversion are performed on data from different sources to generate standardized casualty information files; Based on the standardized casualty information files, a quantum-inspired optimization algorithm is used to perform complex correlation analysis in a cloud computing environment. The powerful computing resources of the cloud platform are used to simulate the quantum computing process, and the intrinsic connections between data fragments at different times and locations are deeply mined. By simulating the probability distribution of quantum states, potential correlation patterns in the data space are explored, and a potential correlation rule library is generated. Based on the potential association rule base, the distributed computing capability in the cloud computing environment is used to reorganize and optimize the information, ensure the integrity and consistency of the information, and generate a deeply associated casualty information file; Based on the deeply associated casualty information files, comprehensive sorting and configuration optimization are carried out, the information display format is adjusted, and a refined casualty information file is generated.

4. The method according to claim 2, characterized in that: Based on the refined casualty information file, the distributed edge computing technology is used to synchronize and update the data in real time. By distributing the computing tasks to the network edge nodes, it is ensured that the information is updated and adjusted in real time under high concurrency conditions, the load of the central server is reduced, and the real-time updated casualty information file is generated, including: Based on the refined casualty information files, an intelligent routing algorithm is designed and deployed through the cloud computing platform network architecture to perform intelligent routing configuration on the data stream and generate a data stream framework with intelligent routing configuration; Based on the data flow framework of the intelligent routing configuration, the distributed edge computing technology is used to pre-process the data, perform preliminary data cleaning and local analysis at the network edge node, reduce the amount of data transmitted to the central server, speed up the response, and generate a pre-processed local data set; Based on the pre-processed local data set, real-time synchronization and update processing are performed. By utilizing the advantages of distributed edge computing technology, each edge node independently processes local data, and realizes instant information update and dynamic adjustment under high concurrency conditions, generating real-time synchronized casualty information files; Based on the real-time synchronized casualty information files, an adaptive load balancing strategy is implemented to dynamically adjust the load distribution between each edge node and the central server to generate a real-time updated casualty information file.

5. The method according to claim 1, characterized in that The optimized wounded information file is based on the holographic reconstruction algorithm to reconstruct multi-dimensional data, and with the help of the large-scale parallel processing capability of the cloud, the implicit patterns and features are extracted, and the historical and current health status data of each wounded is multi-dimensionally reconstructed. The homomorphic encryption technology is used to support direct calculation of encrypted data, ensure the security of personal sensitive information data, and generate a high-dimensional data model, including: Based on the optimized casualty information file, multi-dimensional feature extraction is performed to capture subtle changes and complex relationships and generate a multi-dimensional feature set; Based on the multi-dimensional feature set, a holographic reconstruction algorithm is used to prepare for high-precision data reconstruction, and with the help of large-scale parallel processing capabilities of the cloud, implicit patterns and features are extracted, and multi-dimensional reconstruction processing is performed on the historical and current health status data of each injured person to generate pre-processed multi-dimensional feature data; Based on the pre-processed multi-dimensional feature data, combined with the influence of time dimension changes and space-related factors, a multi-dimensional reconstructed health status model is generated; Based on the multi-dimensionally reconstructed health status model, efficient data compression and index optimization are implemented to generate a high-dimensional data model.

6. The method according to claim 5, characterized in that Based on the multi-dimensional feature set, the holographic reconstruction algorithm is used to prepare for high-precision data reconstruction, and with the help of the large-scale parallel processing capability of the cloud, implicit patterns and features are extracted, and the historical and current health status data of each injured person is multi-dimensionally reconstructed to generate pre-processed multi-dimensional feature data, including: Based on the multi-dimensional feature set, identify abnormal points and potential problems in the historical and current health status data of each injured person, and generate an abnormality detection report; Based on the anomaly detection report, a holographic reconstruction algorithm is used to prepare for high-precision data reconstruction, and large-scale parallel computing resources in the cloud are used for in-depth analysis to further refine and correct feature information, extract implicit patterns and features, reveal deep relationships within the data, and generate a refined feature matrix; Based on the refined feature matrix, cross-dimensional correlation analysis is performed to construct a comprehensive data view, reveal potential connections between different data fragments, and generate a comprehensive data view; Based on the comprehensive data view, data consistency verification and data cleaning processing are implemented to improve data quality and generate pre-processed multi-dimensional feature data.

7. The method according to claim 1, characterized in that Based on the high-dimensional data model, an emergency response module is built, an interactive access interface with authority control is designed, multi-terminal simultaneous viewing is supported, and a casualty information management and sharing strategy is generated, including: Based on the high-dimensional data model, risk assessment is performed on emergency response scenarios, possible emergencies are predicted and response strategies are optimized, and risk assessment reports are generated; Based on the risk assessment report, a rapid deployment emergency response module is designed and tightly integrated with the high-dimensional data model to generate an integrated emergency response module; Based on the integrated emergency response module, an intelligent workflow engine is introduced to automatically configure the data processing flow and generate an automated data processing flow; Based on the automated data processing flow, the data transmission path and cache mechanism are optimized to meet the needs of simultaneous viewing on multiple terminals, and a strategy for managing and sharing the casualty information is generated.

8. A cloud computing-based casualty information management and sharing system, characterized in that: include: The collection module is used to collect and integrate multi-source heterogeneous data, synchronize them to the cloud platform in real time through a secure encrypted channel, and generate a preliminary casualty information database; An analysis module is used to perform complex correlation analysis in a cloud computing environment based on the preliminary casualty information database using a quantum heuristic optimization algorithm, simulate the quantum computing process using the powerful computing resources of the cloud platform, deeply mine the intrinsic connections between data fragments at different times and locations, and use distributed edge computing technology to ensure that information is updated and adjusted in real time under high concurrency conditions, reduce the load on the central server, and generate optimized casualty information files; An extraction module is used to reconstruct multidimensional data based on the optimized wounded information archive using a holographic reconstruction algorithm, extract implicit patterns and features with the help of large-scale parallel processing capabilities of the cloud, and perform multidimensional reconstruction processing on the historical and current health status data of each wounded. It uses homomorphic encryption technology to support direct calculation of encrypted data, ensure the security of personal sensitive information data, and generate a high-dimensional data model; A design module is used to build an emergency response module based on the high-dimensional data model, design an interactive access interface with permission control, support multi-terminal simultaneous viewing, and generate a wounded information management and sharing strategy.

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 a cloud computing-based wounded information management and sharing 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, a cloud computing-based wounded person information management and sharing method as described in any one of claims 1 to 7 is implemented.

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