A multifunctional field hospital communication method and system

Abstract

This application provides a multifunctional field hospital communication method and system. It involves constructing an adaptive distributed communication network, dynamically adjusting the communication protocol and frequency to obtain a stable communication platform; utilizing this stable communication platform, optimizing the message scheduling mechanism, and generating an optimized resource allocation strategy based on a deep reinforcement learning-based message scheduling mechanism; constructing a virtual reality collaborative environment according to the optimized resource allocation strategy, applying interactive data analysis technology to record the entire process, and generating an optimized rescue process plan; based on the optimized rescue process plan, providing early warnings of potential risks through comprehensive analysis of historical data and real-time feedback, and generating decision-making basis. The technical solution provided by this application enhances the reliability and continuity of the communication network.

Description

Technical Field

[0001] This application relates to the field of multifunctional communication technology, and in particular to a multifunctional field hospital communication method and system. Background Technology

[0002] In modern field hospital environments, medical teams face complex battlefield conditions and fluctuating communication requirements. Especially during emergency rescue operations, ensuring efficient and reliable information transmission is crucial. To meet this need, an adaptive distributed communication network is required, capable of integrating satellite communications, radio stations, and mobile network resources to ensure the continuity and reliability of data transmission in uncertain battlefield environments. Furthermore, the system must dynamically adjust communication protocols and frequencies to cope with constantly changing environmental factors, thereby providing a stable and reliable communication platform. Simultaneously, prioritizing different types of medical needs and providing precise guidance to frontline medical personnel during complex surgeries or diagnoses also necessitates efficient resource allocation and real-time collaboration tools.

[0003] Currently, most field hospital communication systems rely on traditional point-to-point communication methods, such as fixed radio communication links or limited mobile network coverage. While these systems can meet basic communication needs to a certain extent, they often fall short in the face of complex and ever-changing battlefield environments. For example, when communication infrastructure is damaged or signals are interfered with, traditional systems lack the flexibility and resilience to guarantee continuous data transmission. Furthermore, existing systems are relatively simple in their resource allocation, typically using preset rules for message scheduling, which cannot be flexibly adjusted according to real-time conditions. This can lead to high-priority messages failing to be delivered in a timely manner, impacting rescue efficiency.

[0004] The main shortcomings of existing communication systems lie in their lack of adaptive capabilities and intelligent resource management mechanisms. First, due to the inability to dynamically adjust communication protocols and frequencies, traditional systems are prone to communication interruptions or data loss in complex battlefield environments, severely impacting the continuity and reliability of information transmission. Second, existing message scheduling mechanisms are mostly based on static rules, failing to fully utilize advanced machine learning algorithms to optimize resource allocation. This results in inaccurate resource allocation and slow response times when handling emergency medical needs. Finally, existing systems lack effective remote collaboration tools, especially during complex surgeries or diagnoses. Remote experts cannot provide immediate and precise operational guidance to frontline medical personnel using technologies such as virtual reality, limiting the efficiency and precision of medical team collaboration. Therefore, a more intelligent and flexible solution is urgently needed to overcome these shortcomings and comprehensively improve the performance of field hospital communication systems and the effectiveness of rescue operations. Summary of the Invention

[0005] This application provides a multifunctional field hospital communication method and system to solve the problems of poor continuity and reliability of information transmission in the prior art.

[0006] In a first aspect, embodiments of this application provide a multifunctional field hospital communication method, including:

[0007] Construct an adaptive distributed communication network, integrate satellite communication, radio station and mobile network resources to ensure the continuity and reliability of data transmission in uncertain battlefield environments, dynamically adjust communication protocols and frequencies to obtain a stable communication platform;

[0008] Using the stable communication platform, the message scheduling mechanism is optimized. Based on the deep reinforcement learning message scheduling mechanism, the deep Q-network algorithm is used to allocate the optimal communication bandwidth and response speed for different types of medical needs according to the urgency of the message and the current network status. Combined with real-time battlefield situation analysis technology, dynamic adjustments are made to generate an optimized resource allocation strategy.

[0009] Based on the optimized resource allocation strategy, a virtual reality collaborative environment is constructed and processed. Mixed reality technology and high-resolution video streaming are used, combined with posture estimation algorithms to guide frontline medical staff in performing complex surgeries or diagnoses. Interactive data analysis technology is applied to record the entire process and generate an optimized rescue process plan.

[0010] Based on the aforementioned rescue process optimization scheme, an autonomous health monitoring system is deployed. Wearable devices are used to collect vital sign data of the injured and upload it to the central database in real time. Combined with geolocation services, the system provides location tracking of the injured and sick, integrates the function of predicting the condition of the injured and sick, and provides early warning of potential risks through comprehensive analysis of historical data and real-time feedback, thereby generating decision-making basis.

[0011] Optionally, the stable communication platform is used to optimize the message scheduling mechanism. Based on deep reinforcement learning, the message scheduling mechanism employs a deep Q-network algorithm to allocate optimal communication bandwidth and response speed to different types of medical needs according to the urgency of the message and the current network status. This is combined with real-time battlefield situation analysis technology for dynamic adjustment, generating an optimized resource allocation strategy, including:

[0012] Using the stable communication platform, the message scheduling mechanism is initialized to obtain the initial scheduling parameter configuration;

[0013] Based on the initial scheduling parameter configuration, a deep Q-network algorithm is used to evaluate the urgency of messages and the current network status to obtain message priority evaluation results.

[0014] Based on the message priority evaluation results, the communication bandwidth and response speed for different types of medical needs are optimized and allocated to obtain a preliminary resource allocation scheme.

[0015] By utilizing real-time battlefield situation analysis technology, the initial resource allocation scheme is dynamically adjusted to ensure that the most suitable communication resources are continuously provided in a changing battlefield environment, thereby generating an optimized resource allocation strategy.

[0016] Optionally, based on the message priority evaluation results, the communication bandwidth and response speed for different types of medical needs are optimized and allocated to obtain a preliminary resource allocation scheme, including:

[0017] Using the message priority evaluation results, the importance of various medical needs is classified to obtain a list of medical needs classifications;

[0018] Based on the medical needs classification list and the current available communication resources, communication resource estimation is performed for different types of medical needs, and a resource estimation report is generated.

[0019] Based on the resource estimation report, an intelligent optimization algorithm is used to accurately calculate the communication bandwidth and response speed required for each medical need, ensuring that high-priority needs can obtain sufficient resource support and obtaining an accurate resource allocation table.

[0020] Using the precise resource allocation table, and taking into account the overall system load balancing and real-time requirements, the resource allocation scheme is initially adjusted to ensure that urgent needs are met without affecting other communication tasks, thus generating a preliminary resource allocation scheme.

[0021] Optionally, the step of using real-time battlefield situation analysis technology to dynamically adjust the initial resource allocation scheme to ensure the continuous provision of the most suitable communication resources in a changing battlefield environment and to generate an optimized resource allocation strategy includes:

[0022] Real-time battlefield situation analysis technology is used to monitor and process changes in the current battlefield environment and enemy situation to obtain a battlefield situation report;

[0023] Based on the battlefield situation report and the preliminary resource allocation plan, factors that may affect communication efficiency are evaluated and processed to generate an evaluation result of the influencing factors.

[0024] Based on the evaluation results of the influencing factors, an adaptive adjustment algorithm is used to flexibly adjust the communication bandwidth and response speed in the preliminary resource allocation scheme to ensure that high-priority medical needs can obtain optimal resource support in changing environments, resulting in an adjusted resource allocation scheme.

[0025] By utilizing the adjusted resource allocation scheme and continuously optimizing the adjustment process through a feedback mechanism, the efficiency and adaptability of resource allocation in long-term operation are ensured, and an optimized resource allocation strategy is generated.

[0026] Optionally, the virtual reality collaborative environment is constructed and processed according to the optimized resource allocation strategy, using mixed reality technology and high-resolution video streaming, combined with pose estimation algorithms to guide frontline medical staff in performing complex surgeries or diagnoses, and interactive data analysis technology is applied to record the entire process to generate an optimized rescue process plan, including:

[0027] Using the optimized resource allocation strategy, the construction parameters of the virtual reality collaborative environment are configured to obtain the virtual reality collaborative environment configuration.

[0028] Based on the virtual reality collaborative environment configuration, mixed reality technology and high-resolution video streaming technology are used to establish an immersive interactive platform between remote experts and frontline medical staff, thereby generating an immersive interactive platform.

[0029] Based on the immersive interactive platform and combined with the posture estimation algorithm, the actions of frontline medical staff are tracked and analyzed in real time, providing precise operation guidance to ensure accuracy in complex surgical or diagnostic processes and obtain precise operation guidance results.

[0030] Using the precise operational guidance results, interactive data analysis technology is applied to collect, analyze, and record data throughout the entire collaboration process, identify areas for improvement, and generate optimization plans for the rescue process.

[0031] Optionally, based on the virtual reality collaborative environment configuration, a mixed reality technology and high-resolution video streaming technology are used to establish an immersive interactive platform between remote experts and frontline medical staff, generating the immersive interactive platform, including:

[0032] Using the virtual reality collaborative environment configuration, the mixed reality devices and network parameters are adapted to ensure that the system can operate stably in a complex battlefield environment, and the adapted mixed reality devices and network parameters are obtained.

[0033] Based on the adapted mixed reality device and network parameters, a high-resolution video streaming system is deployed to achieve high-definition real-time interaction between remote experts and frontline medical staff, thus obtaining a high-definition real-time interaction channel.

[0034] Based on the aforementioned high-definition real-time interactive channel, virtual object generation and control technology is integrated, enabling remote experts to create and operate virtual objects in a shared virtual space, assisting and guiding frontline medical staff, and generating virtual object interactive interfaces.

[0035] By utilizing the virtual object interaction interface and combining human-computer interaction design principles, the user interface and user experience are optimized to ensure that remote experts and frontline medical staff can communicate and collaborate efficiently, thus creating an immersive interactive platform.

[0036] Optionally, based on the optimized rescue process scheme, an autonomous health monitoring system is deployed. This system uses wearable devices to collect vital sign data of the injured and uploads it to a central database in real time. It also integrates geolocation services to track the location of the injured, incorporates a patient status prediction function, and provides early warnings of potential risks through comprehensive analysis of historical data and real-time feedback, generating decision-making basis, including:

[0037] Using the aforementioned rescue process optimization scheme, the architecture of the autonomous health monitoring system is designed and processed to ensure that the system can efficiently support the dynamic allocation of medical resources, thus obtaining the health monitoring system architecture.

[0038] According to the health monitoring system architecture, a wearable device network is deployed to continuously collect vital sign data of the injured, and the data is uploaded to the central database in real time through a secure and reliable communication protocol to obtain a vital sign data stream.

[0039] Based on the vital signs data stream and combined with geolocation services, a patient location tracking module was developed to achieve real-time monitoring and management of the patient's geographical location and generate location tracking information.

[0040] By utilizing the location tracking information, the system integrates the function of predicting the status of the wounded and sick, and uses machine learning algorithms to comprehensively analyze historical data and real-time feedback, identify and warn of potential risks in advance, and generate risk warning reports.

[0041] Based on the aforementioned risk warning report, corresponding countermeasures and resource allocation plans are formulated to provide the command center with a scientific basis for decision-making, ultimately generating a decision-making basis.

[0042] Secondly, embodiments of this application provide a multifunctional field hospital communication system, comprising:

[0043] The building module is used to construct an adaptive distributed communication network. By integrating satellite communication, radio station and mobile network resources, it ensures the continuity and reliability of data transmission in uncertain battlefield environments, dynamically adjusts communication protocols and frequencies, and obtains a stable communication platform.

[0044] The optimization module is used to optimize the message scheduling mechanism using the stable communication platform. Based on the message scheduling mechanism of deep reinforcement learning, the deep Q-network algorithm is used to allocate the optimal communication bandwidth and response speed for different types of medical needs according to the urgency of the message and the current network status. Combined with real-time battlefield situation analysis technology, dynamic adjustments are made to generate an optimized resource allocation strategy.

[0045] The recording module is used to construct and process the virtual reality collaborative environment according to the optimized resource allocation strategy, adopt mixed reality technology and high-resolution video streaming, combine posture estimation algorithm to guide front-line medical staff to perform complex surgeries or diagnoses, apply interactive data analysis technology to record the entire process, and generate an optimized rescue process plan.

[0046] The early warning module is used to deploy an autonomous health monitoring system based on the rescue process optimization scheme. It uses wearable devices to collect vital sign data of the wounded and uploads it to the central database in real time. It combines geolocation services to provide location tracking of the wounded and sick, integrates the function of predicting the status of the wounded and sick, and provides early warning of potential risks by comprehensively analyzing historical data and real-time feedback, thereby generating decision-making basis.

[0047] Thirdly, embodiments of this application provide a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a multifunctional field hospital communication method as described in the first aspect.

[0048] Fourthly, embodiments of this application provide a computer storage medium storing a computer program, which, when executed by a computer, implements a multifunctional field hospital communication method as described in the first aspect.

[0049] In this embodiment, an adaptive distributed communication network is constructed. By integrating satellite communication, radio stations, and mobile network resources, the continuity and reliability of data transmission are ensured in uncertain battlefield environments. The communication protocol and frequency are dynamically adjusted to obtain a stable communication platform. Using this stable communication platform, the message scheduling mechanism is optimized. Based on a deep reinforcement learning-based message scheduling mechanism, a deep Q-network algorithm is used to allocate optimal communication bandwidth and response speed to different types of medical needs according to the urgency of the message and the current network status. This is combined with real-time battlefield situation analysis technology for dynamic adjustment, generating an optimized resource allocation strategy. Based on the optimized resource allocation strategy, a virtual reality collaborative environment is constructed. Mixed reality technology and high-resolution video streaming are used, combined with attitude estimation algorithms to guide frontline medical personnel in performing complex surgeries or diagnoses. Interactive data analysis technology is applied to record the entire process, generating an optimized rescue process plan. Based on the optimized rescue process plan, an autonomous health monitoring system is deployed. Wearable devices are used to collect vital sign data of the wounded and upload it to a central database in real time. Geolocation services are used to provide location tracking of the wounded and sick, and the function of predicting the status of the wounded and sick is integrated. By comprehensively analyzing historical data and real-time feedback, potential risks are warned in advance, generating a basis for decision-making.

[0050] The technical solution of this application has the following beneficial effects:

[0051] By constructing an adaptive distributed communication network and integrating satellite communication, radio station, and mobile network resources, the system can ensure the continuity and reliability of data transmission in uncertain battlefield environments. The ability to dynamically adjust communication protocols and frequencies allows the platform to flexibly respond to complex environmental changes, providing a stable and reliable communication platform that greatly improves the quality and efficiency of medical information exchange. Utilizing this stable communication platform, a message scheduling mechanism based on deep reinforcement learning can allocate optimal communication bandwidth and response speed to different types of medical needs according to the urgency of the message and the current network status. Combined with real-time battlefield situation analysis technology, dynamic adjustments are made to generate optimized resource allocation strategies. This not only ensures that high-priority medical needs are processed promptly but also maximizes the use of limited communication resources, improving the overall system's response speed and efficiency. Based on the optimized resource allocation strategy, the virtual reality collaborative environment is constructed using mixed reality technology and high-resolution video streaming, combined with attitude estimation algorithms to guide frontline medical personnel in performing complex surgeries or diagnoses. This immersive interactive platform not only allows remote experts to provide precise operational guidance as if they were on-site but also uses interactive data analysis technology to record the entire process and generate optimized rescue procedures. This significantly improves the accuracy and success rate of complex surgeries and diagnoses, reducing the possibility of medical errors. Based on the optimized rescue process, the deployment of an autonomous health monitoring system utilizes wearable devices to collect vital sign data of the injured and upload it to a central database in real time. Combined with geolocation services for tracking the location of the injured, and integrating a patient status prediction function, the system provides early warnings of potential risks through comprehensive analysis of historical data and real-time feedback, generating decision-making basis. This system not only achieves real-time monitoring of the patient's condition but also identifies potential risks in advance, providing scientific decision support for the command center, ensuring the best response in the shortest possible time, and improving the effectiveness and targeting of rescue operations.

[0052] Furthermore, by employing a message scheduling mechanism based on deep reinforcement learning, a deep Q-network algorithm is used to allocate optimal communication bandwidth and response speed to different types of medical needs based on the urgency of the message and the current network state. Combined with real-time battlefield situation analysis technology, dynamic adjustments are made to generate optimized resource allocation strategies. This approach not only ensures that high-priority medical needs receive timely and sufficient communication resource support but also maximizes the utilization efficiency of limited resources, enhances the system's flexibility and adaptability, and thus significantly improves overall response speed and communication quality.

[0053] Furthermore, the virtual reality collaborative environment constructed based on the optimized resource allocation strategy employs mixed reality technology and high-resolution video streaming, combined with posture estimation algorithms to guide frontline medical personnel in performing complex surgeries or diagnoses. Interactive data analysis technology is used to record the entire process, generating optimized rescue procedures. This immersive interactive platform not only allows remote experts to provide precise operational guidance as if they were on-site, reducing the possibility of medical errors, but also provides valuable data support for subsequent training and process improvement through detailed recording and analysis. This significantly improves the accuracy and success rate of complex surgeries and diagnoses, greatly enhances the collaborative efficiency and operational precision of the medical team, and ultimately improves the overall effectiveness and safety of rescue operations.

[0054] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 A flowchart illustrating a multifunctional field hospital communication method provided in this application embodiment;

[0057] Figure 2 A schematic diagram of the structure of a multifunctional field hospital communication system provided in this application embodiment;

[0058] Figure 3 This is a schematic diagram of the structure of a computing device provided in an embodiment of this application. Detailed Implementation

[0059] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0060] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0061] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0062] Figure 1 A flowchart illustrating a multifunctional field hospital communication method provided in this application embodiment is shown below. Figure 1 As shown, the method includes:

[0063] 101. Construct an adaptive distributed communication network, integrating satellite communication, radio stations and mobile network resources to ensure the continuity and reliability of data transmission in uncertain battlefield environments, dynamically adjust communication protocols and frequencies, and obtain a stable communication platform.

[0064] In this step, building an adaptive distributed communication network refers to creating a system that can automatically adjust its communication protocols and frequencies in complex and uncertain battlefield environments by integrating satellite communications, radio stations, and mobile network resources. This network not only ensures the continuity and reliability of data transmission but also dynamically responds to environmental changes, providing a stable and reliable communication platform. It includes the acquisition and processing of multi-source data to support the effective delivery and real-time updates of medical information.

[0065] In this embodiment, the system first assesses the current communication environment and selects the most suitable communication method (such as satellite, radio, or mobile network) based on the assessment results. Then, the system dynamically adjusts the communication protocol and frequency according to actual needs to address potential interference or signal attenuation issues. In this way, the system ensures efficient data transmission even under extreme conditions, providing a stable communication foundation for subsequent steps.

[0066] For example, during a field rescue operation, when some ground communication facilities were damaged, the system automatically switched to satellite communication mode and optimized the communication frequency, ensuring continuous communication between frontline medical personnel and the command center and guaranteeing the smooth progress of the rescue operation.

[0067] 102. Using the stable communication platform, the message scheduling mechanism is optimized. Based on the message scheduling mechanism of deep reinforcement learning, the deep Q-network algorithm is used to allocate the optimal communication bandwidth and response speed for different types of medical needs according to the urgency of the message and the current network status. Combined with real-time battlefield situation analysis technology, dynamic adjustments are made to generate an optimized resource allocation strategy.

[0068] In this step, optimizing the message scheduling mechanism using the stable communication platform refers to a message scheduling mechanism based on deep reinforcement learning. This mechanism employs a deep Q-network algorithm to evaluate the urgency of each message and the current network status, thereby allocating optimal communication bandwidth and response speed to different types of medical needs. Combined with real-time battlefield situation analysis technology, this mechanism can dynamically adjust resource allocation strategies to ensure that high-priority messages are processed promptly.

[0069] In this embodiment, the system uses a deep Q-network algorithm to assess the urgency of messages and, combined with the current network status, intelligently allocates appropriate communication resources to different types of messages. Simultaneously, the system dynamically adjusts the allocation of these resources based on changes in the real-time battlefield situation to ensure maximum resource utilization and optimized response speed.

[0070] For example, during a large-scale rescue operation, the system identified a message about emergency treatment for a seriously injured person as having the highest priority and immediately allocated the maximum communication bandwidth to it, ensuring that this critical information could be quickly transmitted to the relevant personnel so that the injured person could receive timely treatment.

[0071] Optionally, in step 102, the message scheduling mechanism is optimized using the stable communication platform. Based on deep reinforcement learning, the message scheduling mechanism employs a deep Q-network algorithm to allocate optimal communication bandwidth and response speed to different types of medical needs according to the urgency of the message and the current network status. This is combined with real-time battlefield situation analysis technology for dynamic adjustment, generating an optimized resource allocation strategy, including:

[0072] Using the stable communication platform, the message scheduling mechanism is initialized to obtain initial scheduling parameter configurations. Based on these initial configurations, a deep Q-network algorithm is employed to evaluate the urgency of messages and the current network status, yielding message priority evaluation results. Based on these priority evaluation results, communication bandwidth and response speed for different types of medical needs are optimized and allocated to obtain a preliminary resource allocation scheme. Real-time battlefield situation analysis technology is used to dynamically adjust the preliminary resource allocation scheme, ensuring the continuous provision of the most suitable communication resources in a changing battlefield environment and generating an optimized resource allocation strategy.

[0073] In this step, in an optional scheme, the message scheduling mechanism is optimized using the stable communication platform, including several key steps. First, the message scheduling mechanism is initialized to obtain initial scheduling parameter configurations; these configurations include basic data such as network status and bandwidth allocation to ensure the accuracy of subsequent evaluation and resource allocation. Second, a deep Q-network algorithm is used to evaluate the urgency of messages and the current network status, generating message priority evaluation results; these results are based on historical data and real-time conditions to ensure high-priority messages can be delivered quickly. Third, based on the priority evaluation results, the communication bandwidth and response speed for different types of medical needs are optimized to form a preliminary resource allocation plan; this process aims to maximize resource utilization and meet different needs. Finally, combined with real-time battlefield situation analysis technology, the preliminary resource allocation plan is dynamically adjusted to ensure the continuous provision of the most suitable communication resources in a changing environment, ultimately generating an optimized resource allocation strategy.

[0074] In this embodiment, firstly, the system uses the stable communication platform to initialize the message scheduling mechanism and set initial scheduling parameter configurations; secondly, based on these initial configurations, the system uses a deep Q-network algorithm to evaluate the urgency of each message and the current network status, generating message priority evaluation results; thirdly, based on the evaluation results, the system optimizes the communication bandwidth and response speed for different types of medical needs, forming a preliminary resource allocation plan; finally, through real-time battlefield situation analysis technology, the system dynamically adjusts the preliminary resource allocation plan to ensure that the most suitable communication resources are provided in the constantly changing battlefield environment, ultimately generating an optimized resource allocation strategy.

[0075] In a complex field rescue scenario, the system first initializes its message scheduling mechanism using a pre-built stable communication platform, setting initial scheduling parameters including network status, bandwidth allocation, and priority rules. Second, based on these initial configurations, the system uses a deep Q-network algorithm to evaluate the urgency of a message regarding emergency medical care for critically wounded personnel and a logistical supply request, along with the current network status, generating message priority assessment results and determining emergency medical care for critically wounded personnel as the highest priority. Third, based on the assessment results, the system optimizes the communication bandwidth and response speed required for emergency medical care for critically wounded personnel, forming a preliminary resource allocation plan to ensure rapid transmission of emergency information. Finally, through real-time battlefield situation analysis technology, the system identifies potential new conflict points in the vicinity that may affect communication quality, immediately adjusting the resource allocation plan and adding backup communication paths to ensure the continuous provision of the most suitable communication resources in a changing battlefield environment, ultimately generating an optimized resource allocation strategy. Through these steps, the system not only ensures the rapid transmission of high-priority messages but also improves the overall flexibility and adaptability of the communication system, significantly enhancing the efficiency and success rate of rescue operations.

[0076] Optionally, based on the message priority evaluation results, the communication bandwidth and response speed for different types of medical needs are optimized and allocated to obtain a preliminary resource allocation scheme, including:

[0077] Using the message priority assessment results, the importance of various medical needs is categorized to obtain a medical need classification list. Based on this list and the current available communication resources, communication resource estimation is performed for different types of medical needs, generating a resource estimation report. Based on this report, an intelligent optimization algorithm is used to accurately calculate the required communication bandwidth and response speed for each medical need, ensuring that high-priority needs receive sufficient resource support, resulting in a precise resource allocation table. Using this precise allocation table, and considering overall system load balancing and real-time requirements, the resource allocation scheme is initially adjusted to ensure that urgent needs are met without affecting other communication tasks, generating a preliminary resource allocation scheme.

[0078] Optionally, the step of using real-time battlefield situation analysis technology to dynamically adjust the initial resource allocation scheme to ensure the continuous provision of the most suitable communication resources in a changing battlefield environment and to generate an optimized resource allocation strategy includes:

[0079] Real-time battlefield situation analysis technology is used to monitor and process changes in the current battlefield environment and enemy situation, resulting in a battlefield situation report. Based on the battlefield situation report and the preliminary resource allocation plan, factors that may affect communication efficiency are evaluated, generating an influencing factor evaluation result. Based on the influencing factor evaluation result, an adaptive adjustment algorithm is used to flexibly adjust the communication bandwidth and response speed in the preliminary resource allocation plan to ensure that high-priority medical needs can receive optimal resource support even in changing environments, resulting in an adjusted resource allocation plan. Using the adjusted resource allocation plan, the adjustment process is continuously optimized through a feedback mechanism to ensure the efficiency and adaptability of resource allocation in long-term operation, generating an optimized resource allocation strategy.

[0080] In this step, in the optional scheme, the communication bandwidth and response speed of different types of medical needs are optimized and allocated based on the message priority evaluation results, including several key steps. First, the importance of various medical needs is classified using the message priority evaluation results, resulting in a list of medical need classifications; this classification data is used to ensure the accuracy of subsequent resource allocation. Second, based on the medical need classification list and the current available communication resources, communication resource estimation is performed for different types of medical needs, generating a resource estimation report; this report provides a preliminary estimate of the resources required for each need. Third, based on the resource estimation report, an intelligent optimization algorithm is used to accurately calculate the communication bandwidth and response speed required for each medical need, ensuring that high-priority needs receive sufficient resource support, resulting in an accurate resource allocation table; this process ensures the scientific and rational nature of resource allocation. Finally, using the accurate resource allocation table, and comprehensively considering the overall system load balancing and real-time requirements, the resource allocation scheme is initially adjusted to ensure that urgent needs are met without affecting other communication tasks, generating a preliminary resource allocation scheme. Furthermore, real-time battlefield situation analysis technology is used to dynamically adjust the preliminary resource allocation scheme, ensuring the continuous provision of the most suitable communication resources in a changing battlefield environment and generating an optimized resource allocation strategy. Specifically, firstly, real-time battlefield situation analysis technology is used to monitor and process changes in the current battlefield environment and enemy situation, obtaining a battlefield situation report; secondly, based on the battlefield situation report and the preliminary resource allocation scheme, factors that may affect communication efficiency are evaluated, generating an influencing factor evaluation result; thirdly, based on the influencing factor evaluation result, an adaptive adjustment algorithm is used to flexibly adjust the communication bandwidth and response speed in the preliminary resource allocation scheme, ensuring that high-priority medical needs can also receive optimal resource support in a changing environment, resulting in an adjusted resource allocation scheme; finally, using the adjusted resource allocation scheme, a feedback mechanism is used to continuously optimize the adjustment process, ensuring the efficiency and adaptability of resource allocation in long-term operation, and generating an optimized resource allocation strategy.

[0081] In this embodiment, firstly, the system uses the message priority evaluation results to classify the importance of various medical needs and generate a medical need classification list; secondly, based on this classification list and the current available communication resources, it performs communication resource estimation for different types of medical needs and generates a resource estimation report; thirdly, based on the resource estimation report, it uses an intelligent optimization algorithm to accurately calculate the communication bandwidth and response speed required for each medical need, forming an accurate resource allocation table; finally, using the accurate resource allocation table, it comprehensively considers the overall system load balancing and real-time requirements to perform an initial adjustment of the resource allocation scheme, ultimately generating a preliminary resource allocation scheme. Furthermore, firstly, the system utilizes real-time battlefield situation analysis technology to monitor and process changes in the current battlefield environment and enemy situation, generating a battlefield situation report; secondly, based on the battlefield situation report and the preliminary resource allocation plan, it assesses factors that may affect communication efficiency, generating an influencing factor assessment result; thirdly, based on the influencing factor assessment result, it uses an adaptive adjustment algorithm to flexibly adjust the communication bandwidth and response speed in the preliminary resource allocation plan, forming an adjusted resource allocation plan; finally, through a feedback mechanism, it continuously optimizes the adjustment process to ensure the efficiency and adaptability of resource allocation in long-term operation, generating an optimized resource allocation strategy.

[0082] In a complex field rescue scenario, the system first categorizes medical needs based on existing message priority assessment results, classifying them as high, medium, and low priorities, respectively, generating a medical need classification list. Second, based on this list and the available communication resources, it performs communication resource estimation for different types of medical needs, generating a resource estimation report that determines the required communication bandwidth and response speed for each need. Third, based on the resource estimation report, an intelligent optimization algorithm precisely calculates the required communication bandwidth and response speed for each medical need, ensuring that high-priority needs such as emergency requests receive sufficient resource support, resulting in a precise resource allocation table. Finally, using the precise resource allocation table, and considering overall system load balancing and real-time requirements, the resource allocation scheme is initially adjusted to ensure that emergency needs are met without affecting other communication tasks, generating a preliminary resource allocation plan. Simultaneously, firstly, the system utilizes real-time battlefield situation analysis technology to monitor and process changes in the current battlefield environment and enemy situation, generating a battlefield situation report that records newly emerging conflict points and signal interference zones. Secondly, based on the battlefield situation report and the preliminary resource allocation plan, it assesses factors that may affect communication efficiency, such as signal attenuation caused by new obstacles or network congestion caused by new conflict points, generating an influencing factor assessment result. Thirdly, based on the influencing factor assessment result, an adaptive adjustment algorithm is used to flexibly adjust the communication bandwidth and response speed in the preliminary resource allocation plan, especially for high-priority needs, such as emergency medical requests, achieving optimal resource support even in changing environments, resulting in an adjusted resource allocation plan. Finally, through a feedback mechanism, the adjustment process is continuously optimized to ensure the efficiency and adaptability of resource allocation in long-term operation, generating an optimized resource allocation strategy. Through these steps, the system not only ensures that high-priority medical needs receive optimal communication resource support in complex and ever-changing battlefield environments but also improves the flexibility and adaptability of the entire system, significantly enhancing the efficiency and success rate of rescue operations.

[0083] This application addresses the problems in existing technologies, such as unreasonable resource allocation, slow response speed, and low communication efficiency due to the lack of a comprehensive assessment mechanism for message urgency and current network status. Especially in complex and ever-changing battlefield environments, traditional static rules cannot flexibly respond to emergencies, and high-priority messages may be delayed due to insufficient resources, affecting the effectiveness of rescue operations. Therefore, this invention proposes a message scheduling mechanism based on a deep Q-network algorithm to solve the above-mentioned technical problems. By dynamically optimizing the resource allocation scheme, it ensures that high-priority messages can obtain sufficient communication resource support in a timely manner, improving the overall system's flexibility and response speed.

[0084] Optionally, the step of using a deep Q-network algorithm to evaluate the urgency of messages and the current network state based on the initial scheduling parameters to obtain message priority evaluation results includes:

[0085] In calculating message priority score P i Previously, the system first pre-analyzed and classified the messages, using natural language processing technology to parse the message content, assess its urgency, transmission time, and importance score, and consider the scope of the message's impact; these steps provided the necessary input data for subsequent priority score calculation.

[0086]

[0087] Among them, P i Let represent the priority score of the i-th message; α, β, γ, δ, λ, ζ, ψ, φ are the weight coefficients determined during model training; D i Indicates the urgency level of the i-th message; T i S represents the estimated time required to transmit the i-th message; i F represents the importance score of the i-th message; i E represents the influence range factor of the i-th message. i Indicates the emergency response level of the i-th message;

[0088] Complete P i After the calculation, the system enters the resource allocation optimization phase, adjusting the resource allocation R for each message based on the priority score. i This process utilizes deep reinforcement learning algorithms to dynamically optimize resource allocation schemes, ensuring that high-priority messages receive sufficient communication resources, while improving resource utilization efficiency and system flexibility.

[0089]

[0090] Among them, R i This represents the adjusted resource amount allocated to the i-th message; μ, η, ω, θ, v, ξ, χ, τ are the weight coefficients determined during model training; P i Q represents the priority score of the i-th message calculated using the above formula; i H represents the basic resource requirements of the i-th message; i χ and τ represent the historical similarity case impact factor of the i-th message, used to reflect resource consumption under similar circumstances; H is controlled by χ and τ. i The degree of impact on resource allocation should be assessed to ensure that high-priority messages with historical basis receive additional resource support.

[0091] After calculating T iSubsequently, the system sorts all messages by resource allocation, identifies messages that require priority processing, and verifies and optimizes resource allocation schemes. Finally, it generates a detailed report that records the priority score, resource allocation status, and operation instructions for each message, and synchronizes it to all emergency response terminals to ensure an efficient collaborative response network that supports scientific decision-making and effective rescue.

[0092] This formula aims to achieve efficient message scheduling and resource allocation. The system introduces two key formulas: calculating the message priority score P. i and the adjusted resource allocation R i These formulas not only consider multiple dimensions such as message urgency, transmission time, importance score, and scope of impact, but also incorporate the influence factors of similar historical cases, ensuring the scientific and rational allocation of resources. Furthermore, by employing deep reinforcement learning algorithms, the system can dynamically adjust the resource allocation scheme based on real-time network conditions, maximizing resource utilization efficiency and system adaptability.

[0093] The following is a brief introduction to the design rationale behind each term of the formula:

[0094]

[0095] Urgency attenuation term The decay of message urgency over time and transmission time; urgency index decay term. The impact of the importance score on the message's priority; Emergency response level correction items. Priority scores are adjusted based on the emergency response level;

[0096] The following is a brief introduction to how the parameters of this formula are obtained:

[0097] Parameters α, β, γ are determined through model training; D i The urgency level of a message as determined by natural language processing techniques; T i The expected transmission time; parameters δ and λ are determined through model training; S i The importance of the message is scored; parameters ζ, ψ, φ are determined through model training; F i E is the influence range factor. i Emergency response level;

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

[0099]

[0100] Priority score power correction term Adjust resource allocation based on priority scores; basic resource requirement item ν·Q i: Reflects the basic resource requirements of each message; correction items affected by similar historical cases. Adjust resource allocation based on similar historical cases;

[0101] The following is a brief introduction to how the parameters of this formula are obtained:

[0102] Parameters μ, η, ω, θ are determined through model training; P i From formula P i Calculated; parameter ν is determined through model training; Q i The basic resource requirements for the message; parameters ξ, χ, τ are determined through model training; H i Influence factors for historically similar cases;

[0103] In a field hospital scenario, suppose two messages are received:

[0104] Message 1 (Request for emergency medical assistance for seriously injured person): D1 = 0.9, T1 = 0.2 hours, S1 = 0.85, F1 = 0.7, E1 = 0.9;

[0105] Message 2 (Logistics Supply Request): D2 = 0.3, T2 = 0.5 hours, S2 = 0.6, F2 = 0.4, E2 = 0.5;

[0106] First, calculate the priority score P1 for message 1:

[0107]

[0108] Assume α=0.5, β=0.1, γ=0.1, δ=0.3, λ=0.5, ζ=0.2, ψ=0.4, φ=0.5, the calculation result is P1=0.95;

[0109] Next, calculate the priority score P2 for message 2:

[0110]

[0111] Assuming the same parameters, the calculated result is P2 = 0.6;

[0112] Then, calculate the resource allocation R1 for message 1:

[0113]

[0114] Assuming μ = 0.8, η = 0.7, ω = 0.5, θ = 0.5, v = 0.2, ξ = 0.3, χ = 0.4, τ = 0.5, and Q1 = 0.8, H1 = 0.9, the calculated result R1 = 0.9;

[0115] Finally, calculate the resource allocation R2 for message 2:

[0116]

[0117] Assuming the same parameters, and Q2 = 0.5, H2 = 0.6, the calculated result is R2 = 0.5;

[0118] Assume a priority score threshold P is set. threshold =0.7 and resource allocation threshold R threshold =0.6, since message 1 has a priority score P1 = 0.95, message 2 can be processed later. Also, since the resource allocation for message 1, R1 = 0.9, is greater than R... threshold This indicates that message 1 requires more resource support; while the resource allocation for message 2, R2 = 0.5, is less than R. threshold This indicates that message 2 has low resource requirements. Therefore, the system can use these thresholds to determine and optimize resource allocation, ensuring that high-priority messages are processed in a timely manner while improving resource utilization efficiency.

[0119] Through the above steps, the system not only ensures that high-priority messages (such as emergency medical requests for seriously injured personnel) receive timely and sufficient communication resource support, but also significantly improves the efficiency of resource allocation and the flexibility of the system. This not only enhances the utilization efficiency of medical resources, but also strengthens the effectiveness and targeting of rescue operations, further improving the emergency response capabilities and medical service quality of field hospitals in complex environments.

[0120] 103. Based on the optimized resource allocation strategy, the virtual reality collaborative environment is constructed and processed. Mixed reality technology and high-resolution video streaming are used, combined with posture estimation algorithms to guide frontline medical staff in performing complex surgeries or diagnoses. Interactive data analysis technology is applied to record the entire process and generate an optimized rescue process plan.

[0121] In this step, constructing the virtual reality collaborative environment according to the optimized resource allocation strategy involves using mixed reality technology and high-resolution video streaming, combined with pose estimation algorithms, to guide frontline medical personnel in performing complex surgeries or diagnoses. Interactive data analysis technology is applied to record the entire process, generating optimized rescue workflow solutions. This environment enables remote experts to provide precise operational guidance as if they were on-site, significantly improving the success rate of complex medical procedures.

[0122] In this embodiment, the system establishes an immersive interactive platform through mixed reality technology and high-resolution video streaming, allowing remote experts to guide frontline medical staff in performing complex surgeries or diagnoses in real time. Furthermore, the system employs a pose estimation algorithm to track the movements of medical staff, ensuring the accuracy of the operation, and records the entire process through interactive data analysis technology for subsequent analysis and improvement.

[0123] For example, in a complex heart surgery, remote experts directly guided frontline doctors through a virtual reality collaborative environment to complete key steps of the surgery, greatly improving the success rate of the operation and providing valuable data for future training through detailed records.

[0124] Optionally, step 103 involves constructing a virtual reality collaborative environment based on the optimized resource allocation strategy, employing mixed reality technology and high-resolution video streaming, combining pose estimation algorithms to guide frontline medical personnel in performing complex surgeries or diagnoses, and using interactive data analysis technology to record the entire process to generate an optimized rescue process plan, including:

[0125] Using the optimized resource allocation strategy, the construction parameters of the virtual reality collaborative environment are configured to obtain the virtual reality collaborative environment configuration. Based on the virtual reality collaborative environment configuration, mixed reality technology and high-resolution video streaming technology are used to establish an immersive interactive platform between remote experts and frontline medical staff, generating an immersive interactive platform. Based on the immersive interactive platform, combined with a posture estimation algorithm, the actions of frontline medical staff are tracked and analyzed in real time to provide precise operational guidance, ensuring accuracy in complex surgical or diagnostic processes, and obtaining precise operational guidance results. Using the precise operational guidance results, interactive data analysis technology is applied to collect, analyze, and record data throughout the collaborative process, identify areas for improvement, and generate an optimized rescue process plan.

[0126] In this step, the virtual reality collaborative environment is constructed according to the optimized resource allocation strategy, including several key steps. First, the construction parameters of the virtual reality collaborative environment are configured using the optimized resource allocation strategy to obtain the virtual reality collaborative environment configuration; this configuration data is used to ensure the effective implementation of mixed reality technology and high-resolution video streaming. Second, based on this configuration, an immersive interactive platform is established between remote experts and frontline medical staff using mixed reality technology and high-resolution video streaming technology, generating an immersive interactive platform; this platform allows remote experts to provide guidance as if they were on-site. Third, based on the immersive interactive platform, combined with a posture estimation algorithm, the movements of frontline medical staff are tracked and analyzed in real time, providing precise operational guidance to ensure accuracy in complex surgical or diagnostic processes, obtaining precise operational guidance results; this process improves the success rate of medical operations. Finally, using the precise operational guidance results, interactive data analysis technology is applied to collect, analyze, and record data from the entire collaborative process, identify areas for improvement, and generate optimized rescue process solutions; this step provides valuable data support for subsequent training and process improvement.

[0127] In this embodiment, firstly, the system uses the optimized resource allocation strategy to configure the construction parameters of the virtual reality collaborative environment, ensuring seamless integration and efficient operation of all devices and technologies, thus generating a virtual reality collaborative environment configuration. Secondly, based on this configuration, the system employs mixed reality technology and high-resolution video streaming technology to establish an immersive interactive platform, enabling remote experts to provide real-time guidance to frontline medical personnel, thus generating an immersive interactive platform. Thirdly, based on the immersive interactive platform, the system combines a posture estimation algorithm to track and analyze the actions of frontline medical personnel in real time, providing precise operational guidance to ensure accuracy in complex surgical or diagnostic processes, resulting in precise operational guidance results. Finally, the system utilizes the precise operational guidance results to collect, analyze, and record data from the entire collaborative process through interactive data analysis technology, identifying areas for improvement and generating an optimized rescue process plan.

[0128] In a complex field hospital environment, firstly, the system utilizes the optimized resource allocation strategy to configure the construction parameters of the virtual reality collaborative environment, ensuring seamless integration and efficient operation of all devices (such as head-mounted displays and gesture recognition devices) and technologies (such as mixed reality and high-resolution video streaming), generating a virtual reality collaborative environment configuration. Secondly, based on this configuration, the system employs mixed reality and high-resolution video streaming technologies to establish an immersive interactive platform between remote experts and frontline medical staff, allowing remote experts to provide guidance as if they were on-site, generating an immersive interactive platform. Thirdly, based on the immersive interactive platform, the system combines posture estimation algorithms to track and analyze the movements of frontline medical staff in real time, providing precise operational guidance and ensuring accuracy in complex surgical or diagnostic processes, obtaining precise operational guidance results. Finally, the system utilizes the precise operational guidance results to collect, analyze, and record data from the entire collaborative process through interactive data analysis technology, identifying areas for improvement and generating an optimized rescue process plan. Through the above steps, the system not only improves the accuracy and success rate of complex surgeries and diagnoses, but also provides valuable data for subsequent training and process improvement, significantly enhancing the overall medical level and responsiveness of field hospitals.

[0129] Optionally, based on the virtual reality collaborative environment configuration, a mixed reality technology and high-resolution video streaming technology are used to establish an immersive interactive platform between remote experts and frontline medical staff, generating the immersive interactive platform, including:

[0130] By utilizing the virtual reality collaborative environment configuration, the mixed reality devices and network parameters are adapted to ensure stable operation of the system in complex battlefield environments, resulting in adapted mixed reality devices and network parameters. Based on these adapted parameters, a high-resolution video streaming system is deployed to enable high-definition real-time interaction between remote experts and frontline medical personnel, creating a high-definition real-time interaction channel. Based on this channel, virtual object generation and control technologies are integrated, allowing remote experts to create and manipulate virtual objects in a shared virtual space to assist and guide frontline medical personnel, generating a virtual object interaction interface. Using this interface, and combining human-computer interaction design principles, the user interface and user experience are optimized to ensure efficient communication and collaboration between remote experts and frontline medical personnel, creating an immersive interactive platform.

[0131] In this step, based on the virtual reality collaborative environment configuration, an immersive interactive platform is established between remote experts and frontline medical staff using mixed reality technology and high-resolution video streaming technology. First, using the virtual reality collaborative environment configuration, the mixed reality devices (such as head-mounted displays, gesture recognition devices, etc.) and network parameters (such as bandwidth, latency, packet loss rate, etc.) are adapted to ensure the system can operate stably in complex battlefield environments, resulting in adapted mixed reality devices and network parameters; these data are used to ensure the stability and reliability of the system. Second, based on the adapted mixed reality devices and network parameters, a high-resolution video streaming system is deployed to achieve high-definition real-time interaction between remote experts and frontline medical staff, resulting in a high-definition real-time interaction channel; this channel ensures high-quality visual and audio communication. Third, based on the high-definition real-time interaction channel, virtual object generation and control technology is integrated, enabling remote experts to create and manipulate virtual objects in a shared virtual space to assist and guide frontline medical staff, generating a virtual object interactive interface; this interface enhances the intuitiveness and accuracy of the guidance. Finally, by utilizing virtual object interaction interfaces and combining human-computer interaction design principles, the user interface and user experience are optimized to ensure that remote experts and frontline medical staff can communicate and collaborate efficiently, ultimately creating an immersive interactive platform.

[0132] In this embodiment, firstly, the system utilizes the virtual reality collaborative environment configuration to adapt the mixed reality devices and network parameters, ensuring that all devices and technologies can operate stably in a complex battlefield environment, thus obtaining the adapted mixed reality devices and network parameters. Secondly, based on the adapted parameters, the system deploys a high-resolution video streaming transmission system to achieve high-definition real-time interaction between remote experts and frontline medical personnel, thus obtaining a high-definition real-time interaction channel. Thirdly, based on this interaction channel, the system integrates virtual object generation and control technology, enabling remote experts to create and operate virtual objects in a shared virtual space to assist and guide frontline medical personnel, thus generating a virtual object interaction interface. Finally, utilizing the virtual object interaction interface, the system combines human-computer interaction design principles to optimize the user interface and user experience, ensuring that remote experts and frontline medical personnel can communicate and collaborate efficiently, thus generating an immersive interactive platform.

[0133] In a complex surgical scenario within a field hospital, the system first adapts mixed reality devices (such as head-mounted displays and gesture recognition devices) and network parameters (such as bandwidth, latency, and packet loss rate) using the virtual reality collaborative environment configuration. This ensures that all devices and technologies can operate stably in the complex battlefield environment, resulting in adapted mixed reality devices and network parameters. Secondly, based on these adapted parameters, the system deploys a high-resolution video streaming system, enabling high-definition real-time interaction between remote experts and frontline medical staff. This provides a high-definition real-time interaction channel, ensuring high-quality visual and audio communication. Thirdly, based on this high-definition real-time interaction channel, the system integrates virtual object generation and control technology, allowing remote experts to create and manipulate virtual objects (such as surgical tools or anatomical models) in a shared virtual space to assist and guide frontline medical staff. This generates a virtual object interaction interface, enhancing the intuitiveness and accuracy of the guidance. Finally, utilizing the virtual object interaction interface, the system optimizes the user interface and user experience based on human-computer interaction design principles, ensuring efficient communication and collaboration between remote experts and frontline medical staff, ultimately creating an immersive interactive platform. Through the above steps, the system not only ensures that remote experts can provide precise operational guidance during complex surgeries, but also significantly improves the operational skills and confidence of frontline medical staff, further enhancing the emergency response capabilities and medical service quality of field hospitals in complex environments.

[0134] This application addresses the fact that, in existing technologies, the lack of precise tracking and analysis mechanisms for the actions of frontline medical personnel leads to inaccurate guidance during complex surgeries or diagnoses, impacting the success rate of medical procedures. Particularly in emergency rescue scenarios, traditional systems cannot provide real-time, precise operational guidance, increasing the risk of operational errors. Therefore, this invention proposes a method for calculating action matching degree based on a posture estimation algorithm, combined with remote expert feedback and a comprehensive assessment of operational safety. This aims to solve the aforementioned technical problems by dynamically adjusting the guidance score, ensuring that operations with high matching degrees receive higher guidance scores, improving the system's adaptability and flexibility, and thus significantly enhancing the accuracy of complex surgical or diagnostic procedures.

[0135] Optionally, based on the immersive interactive platform and combined with a pose estimation algorithm, the actions of frontline medical staff are tracked and analyzed in real time to provide precise operational guidance, ensuring accuracy in complex surgical or diagnostic procedures and obtaining precise operational guidance results, including:

[0136] In calculating the action matching degree M j Previously, the system used a pose estimation algorithm to track the movements of medical staff in real time, assess the execution error between the actual operation and the standard procedure, and consider the contextual consistency of the operation; these steps provided the necessary input data for subsequent calculation of the action matching degree.

[0137]

[0138] Among them, M j α′, β′, γ′, δ′, η′ represent the action matching degree of the j-th step operation; α′, β′, γ′, δ′, η′ are the weight coefficients determined during model training; E′ j This represents the actual execution error of the j-th step, which is different from E. i E0 represents the execution error under ideal conditions; A j A represents the attitude angle of the j-th operation; A0 represents the ideal attitude angle; C j Represents the context consistency score of the j-th step operation;

[0139] Complete M j After calculation, the system generates an operation guidance score G based on the matching degree result. j This stage comprehensively considers action matching degree, remote expert feedback, and operational safety. The guidance score is dynamically adjusted through nonlinear function processing to ensure that operations with high matching degree receive higher guidance scores, while enhancing the system's adaptability and flexibility.

[0140]

[0141] Among them, G jThe instruction score represents the instruction score for the j-th step; μ′,ν′,ω′,θ′,ξ′,χ′,τ′ are the weight coefficients determined during model training; M j F represents the action matching degree of the j-th step operation calculated by the above formula; j S′ represents the feedback factor of the j-th step operation; S′ j The safety score for step j is represented by χ′ and τ′; F′ controls the operation. j and S′ j The degree of influence on the operational guidance score;

[0142] After calculating G j Subsequently, the system sorts all operation steps according to guidance scores, identifies steps that require special attention, and verifies and optimizes guidance scores; finally, it generates a detailed report, recording the matching degree of each operation, guidance score and suggestions, and synchronizes it to all emergency response terminals to ensure an efficient collaborative response network and the accuracy of complex surgical or diagnostic procedures.

[0143] This formula aims to achieve precise tracking and analysis of the actions of frontline medical staff. The system introduces two key formulas: calculating the action matching degree M. j And the score G for generating operation guidance j These formulas not only consider multiple dimensions such as execution errors, posture angles, and contextual consistency between actual operations and standard procedures, but also incorporate remote expert feedback and operational safety scores to ensure the scientific validity and rationality of the guidance scores. Furthermore, by employing nonlinear function processing, the system can dynamically adjust the guidance scores based on real-time conditions, maximizing the accuracy of operational guidance and the system's adaptability.

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

[0145]

[0146] Execution error attenuation term Measuring the execution error between actual operation and ideal conditions; attitude angle matching term Reflects the consistency between the operational attitude angle and the ideal attitude angle; context consistency scoring item η′·C j Consider the contextual consistency of operations to ensure that operations are coherent and reasonable;

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

[0148] Parameters α′ and β′ are determined through model training; E′ j E0 represents the actual execution error of the j-th step; E0 represents the execution error under ideal conditions; parameters γ′ and δ′ are determined through model training; A jLet A0 be the pose angle for the j-th operation; A0 is the ideal pose angle; parameter η′ is determined through model training; C j Score the context consistency of the operation in step j;

[0149] The following is a brief introduction to the design rationale behind each term of the formula:

[0150]

[0151] Action matching degree power correction term The guidance score is adjusted based on the degree of motion matching; feedback from remote experts is used to correct errors. Combine remote expert feedback and operational safety scoring to adjust guidance scores;

[0152] The following is a brief introduction to how the parameters of this formula are obtained:

[0153] The parameters μ′, ν′, ω′, θ′ are determined through model training; M j From formula M j Calculated; parameters ξ′, χ′, τ′ are determined through model training; F′ j S′ is the anti-noble factor for the j-th step operation; j Score the safety of the operation in step j;

[0154] Imagine a heart surgery taking place in a complex field hospital setting:

[0155] Step 1 (cutting the skin): E′1=0.1, A1=45°, C1=0.9;

[0156] Step 2 (Exposing the Heart): E′2 = 0.3, A2 = 60°, C2 = 0.8;

[0157] First, calculate the action matching degree M1 for the first step:

[0158]

[0159] Assuming α′=0.5, β′=0.1, γ′=0.3, δ′=0.5, η′=0.2, and E0=0.05, A0=45°, the calculated result is M1=0.9;

[0160] Next, calculate the action matching degree M2 in the second step:

[0161]

[0162] Assuming the same parameters, the calculated result is M² = 0.7;

[0163] Then, calculate the guidance score G1 for the first step:

[0164]

[0165] Assuming μ′=0.8, ν′=0.7, ω′=0.5, θ′=0.5, ξ′=0.3, χ′=0.4, τ′=0.5, and F′1=0.9, S′1=0.9, the calculated result G1=0.95;

[0166] Finally, calculate the guidance score G2 for the second step:

[0167]

[0168] Assuming the same parameters, and F′2 = 0.8, S′2 = 0.8,

[0169] The calculated result is G2 = 0.75;

[0170] Assuming an action matching threshold M is set... threshold =0.7 and the guidance score threshold G threshold =0.7, because the action matching degree of the first step, M1 = 0.9, is very important and requires more resource support; while the guidance score of the second step, G2 = 0.75, is also greater than G. threshold However, a relatively low score indicates that while the step is important, it is not the most urgent. Therefore, the system can use these thresholds to judge and optimize operational guidance, ensuring that highly relevant actions receive higher guidance scores, while improving the accuracy of operational guidance and the adaptability of the system.

[0171] Through these steps, the system not only ensures precise operational guidance for each step but also significantly improves the accuracy of complex surgical or diagnostic procedures. This not only increases the success rate of medical operations but also enhances the system's adaptability and flexibility, further improving the emergency response capabilities and medical service quality of field hospitals in complex environments.

[0172] 104. Based on the aforementioned rescue process optimization scheme, deploy an autonomous health monitoring system, use wearable devices to collect vital sign data of the injured and upload it to the central database in real time, combine geolocation services to provide location tracking of the injured and sick, integrate the function of predicting the status of the injured and sick, and generate decision-making basis by comprehensively analyzing historical data and real-time feedback to provide early warning of potential risks.

[0173] In this step, based on the aforementioned optimized rescue process, deploying an autonomous health monitoring system involves using wearable devices to collect vital sign data of the injured and uploading it in real time to a central database, combined with geolocation services to provide location tracking of the injured. The system also integrates a patient condition prediction function, using comprehensive analysis of historical data and real-time feedback to provide early warnings of potential risks and generate decision-making support. This system aims to achieve comprehensive monitoring and management of the injured's condition, improving the effectiveness and targeting of rescue operations.

[0174] In this embodiment, the system utilizes wearable devices to continuously monitor the vital signs of the injured and uploads this data to a central database in real time. Combined with geolocation services, the system can accurately track the location of each injured person and, through a patient condition prediction function, identify potential risks in advance, generating scientific decision-making basis to ensure a rapid and effective rescue response.

[0175] For example, during an evacuation operation, the system detected abnormal vital signs in an injured person through wearable devices and immediately issued an alert. Based on this, the command center quickly dispatched a medical team to the scene, successfully saving the injured person's life.

[0176] Optionally, in step 104, based on the optimized rescue process, an autonomous health monitoring system is deployed. This system uses wearable devices to collect vital sign data of the injured and uploads it to a central database in real time. It also integrates geolocation services to track the location of the injured, incorporates a patient status prediction function, and provides early warnings of potential risks through comprehensive analysis of historical data and real-time feedback, generating decision-making basis, including:

[0177] Using the aforementioned rescue process optimization scheme, the architecture of the autonomous health monitoring system is designed and processed to ensure that the system can efficiently support the dynamic allocation of medical resources, resulting in the health monitoring system architecture. Based on this architecture, a wearable device network is deployed to continuously collect vital sign data from the injured, and the data is uploaded to a central database in real time via a secure and reliable communication protocol, resulting in a vital sign data stream. Based on this vital sign data stream, combined with geolocation services, a patient location tracking module is developed to achieve real-time monitoring and management of the injured's geographical location, generating location tracking information. Using this location tracking information, a patient status prediction function is integrated, and machine learning algorithms are used to comprehensively analyze historical data and real-time feedback to identify and warn of potential risks in advance, generating risk warning reports. Based on these risk warning reports, corresponding response measures and resource allocation plans are formulated to provide the command center with a scientific basis for decision-making, ultimately generating a decision-making basis.

[0178] In this step, an autonomous health monitoring system is deployed based on the aforementioned rescue process optimization scheme. This aims to ensure efficient dynamic allocation of medical resources and real-time monitoring of the injured and sick through a series of key steps. First, the architecture of the autonomous health monitoring system is designed using the rescue process optimization scheme to ensure the system can efficiently support the dynamic allocation of medical resources, resulting in the health monitoring system architecture. This architecture data guides the subsequent configuration and integration of hardware and software. Second, based on this architecture, a wearable device network is deployed to continuously collect the vital signs data of the injured and upload the data to a central database in real time via a secure and reliable communication protocol, resulting in a vital signs data stream. This data stream provides the foundation for subsequent analysis. Third, based on the vital signs data stream and combined with geolocation services, a patient location tracking module is developed to achieve real-time monitoring and management of the geographical location of the injured and sick, generating location tracking information. This information helps the command center determine the specific location of the injured and sick. Finally, by utilizing the location tracking information, the system integrates the function of predicting the status of the wounded and sick, and uses machine learning algorithms to comprehensively analyze historical data and real-time feedback, identify and warn of potential risks in advance, and generate risk warning reports. These reports provide the command center with scientific decision-making basis and ultimately generate decision-making support.

[0179] In this embodiment, firstly, the system utilizes the rescue process optimization scheme to design the architecture of the autonomous health monitoring system, ensuring efficient collaboration among all components and supporting dynamic allocation of medical resources, thus obtaining the health monitoring system architecture. Secondly, based on this architecture, the system deploys a wearable device network to continuously collect vital sign data from the injured and uploads the data to the central database in real time via a secure and reliable communication protocol, obtaining a vital sign data stream. Thirdly, based on the vital sign data stream, the system, combined with geolocation services, develops a patient location tracking module, enabling real-time monitoring and management of the patient's geographical location and generating location tracking information. Finally, using the location tracking information, the system integrates a patient status prediction function, employs machine learning algorithms to comprehensively analyze historical data and real-time feedback, identifies and warns of potential risks in advance, generates risk warning reports, and formulates corresponding response measures and resource allocation plans based on these reports, providing the command center with a scientific basis for decision-making.

[0180] In a complex rescue scenario at a field hospital, firstly, the system utilizes the aforementioned rescue process optimization scheme to design and process the architecture of the autonomous health monitoring system, ensuring that all components (such as servers, communication modules, and data analysis engines) can collaborate efficiently and support the dynamic allocation of medical resources, thus obtaining the health monitoring system architecture; secondly, based on this architecture, the system deploys a wearable device network, including smart bracelets and chest straps, to continuously collect vital sign data (such as heart rate, blood pressure, and blood oxygen saturation) of the wounded, and uploads the data to the central database in real time through a secure and reliable communication protocol, obtaining a vital sign data stream and ensuring the security and reliability of data transmission; thirdly, based on the... Based on the vital signs data stream, the system, combined with geolocation services, developed a patient location tracking module. This module enables real-time monitoring and management of the geographical locations of the wounded and sick, generating location tracking information that allows the command center to know the exact location of each patient at any time. Finally, utilizing this location tracking information, the system integrates a patient condition prediction function. Machine learning algorithms are used to comprehensively analyze historical data and real-time feedback to identify and warn of potential risks in advance, generating risk warning reports. Based on these reports, the command center formulates corresponding response measures and resource allocation plans, such as dispatching medical teams to high-risk areas or adjusting the allocation of medicines and equipment, providing the command center with a scientific basis for decision-making. Through these steps, the system not only achieves comprehensive monitoring and management of the patient's condition but also significantly improves the efficiency of medical resource utilization and the effectiveness of rescue operations, enhancing the emergency response capabilities of field hospitals in complex environments.

[0181] Through steps 101-104, this method constructs an adaptive distributed communication network, ensuring the continuity and reliability of data transmission; optimizes the message scheduling mechanism through deep reinforcement learning, achieving efficient resource allocation; establishes a virtual reality collaborative environment, improving the accuracy of complex surgeries and diagnoses; and achieves comprehensive monitoring and risk management of the wounded and sick through an autonomous health monitoring system. These measures not only enhance the flexibility and stability of the communication system but also significantly improve the utilization efficiency of medical resources and the overall effectiveness of rescue operations, substantially enhancing the emergency response capabilities and medical service quality of field hospitals in complex battlefield environments.

[0182] Figure 2 This application provides a schematic diagram of the structure of a multifunctional field hospital communication system, as shown in the embodiments. Figure 2 As shown, the device includes:

[0183] Module 21 is used to build an adaptive distributed communication network. By integrating satellite communication, radio station and mobile network resources, it ensures the continuity and reliability of data transmission in an uncertain battlefield environment, dynamically adjusts communication protocols and frequencies, and obtains a stable communication platform.

[0184] Optimization module 22 is used to optimize the message scheduling mechanism using the stable communication platform. Based on the message scheduling mechanism of deep reinforcement learning, the deep Q-network algorithm is used to allocate the optimal communication bandwidth and response speed for different types of medical needs according to the urgency of the message and the current network status. Combined with real-time battlefield situation analysis technology, dynamic adjustments are made to generate an optimized resource allocation strategy.

[0185] Recording module 23 is used to construct and process the virtual reality collaborative environment according to the optimized resource allocation strategy, adopt mixed reality technology and high-resolution video streaming, combine posture estimation algorithm to guide front-line medical staff to perform complex surgery or diagnosis, apply interactive data analysis technology to record the whole process, and generate rescue process optimization plan.

[0186] The early warning module 24 is used to deploy an autonomous health monitoring system based on the rescue process optimization scheme. It uses wearable devices to collect vital sign data of the wounded and uploads it to the central database in real time. It combines geolocation services to provide location tracking of the wounded and sick, integrates the function of predicting the status of the wounded and sick, and provides early warning of potential risks by comprehensively analyzing historical data and real-time feedback, thereby generating decision-making basis.

[0187] Figure 2 The aforementioned multi-functional field hospital communication system can perform... Figure 1 The implementation principle and technical effects of the multifunctional field hospital communication method described in the illustrated embodiment will not be repeated here. The specific methods by which each module and unit of the multifunctional field hospital communication system in the above embodiments perform operations have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0188] In one possible design, Figure 2 The multifunctional field hospital communication 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;

[0189] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0190] The processing component 32 is used to: construct an adaptive distributed communication network, integrate satellite communication, radio station and mobile network resources to ensure the continuity and reliability of data transmission in uncertain battlefield environments, dynamically adjust communication protocols and frequencies to obtain a stable communication platform; optimize the message scheduling mechanism using the stable communication platform, based on a deep reinforcement learning message scheduling mechanism, using a deep Q-network algorithm to allocate optimal communication bandwidth and response speed to different types of medical needs according to the urgency of the message and the current network status, and dynamically adjust using real-time battlefield situation analysis technology to generate an optimized resource allocation strategy; construct a virtual reality collaborative environment based on the optimized resource allocation strategy, using mixed reality technology and high-resolution video streaming, combined with attitude estimation algorithms to guide frontline medical personnel in performing complex surgeries or diagnoses, applying interactive data analysis technology to record the entire process, and generating an optimized rescue process plan; and deploy an autonomous health monitoring system based on the optimized rescue process plan, using wearable devices to collect vital sign data of the wounded and uploading it to a central database in real time, combining geolocation services to provide location tracking of the wounded and sick, integrating wounded and sick status prediction functions, and generating decision-making basis by comprehensively analyzing historical data and real-time feedback to provide early warning of potential risks.

[0191] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0192] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0193] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0194] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0195] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0196] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0197] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The embodiment shown illustrates a multifunctional field hospital communication method.

[0198] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0199] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0200] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0201] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A multifunctional field hospital communication method, characterized in that, include: Construct an adaptive distributed communication network, integrate satellite communication, radio station and mobile network resources to ensure the continuity and reliability of data transmission in uncertain battlefield environments, dynamically adjust communication protocols and frequencies to obtain a stable communication platform; Using the stable communication platform, the message scheduling mechanism is initialized to obtain the initial scheduling parameter configuration; Based on the initial scheduling parameter configuration, a deep Q-network algorithm is used to evaluate the urgency of messages and the current network status to obtain message priority evaluation results. Based on the message priority evaluation results, the communication bandwidth and response speed for different types of medical needs are optimized and allocated to obtain a preliminary resource allocation scheme. By utilizing real-time battlefield situation analysis technology, the preliminary resource allocation scheme is dynamically adjusted to ensure that the most suitable communication resources are continuously provided in a changing battlefield environment, thereby generating an optimized resource allocation strategy. Using the optimized resource allocation strategy, the construction parameters of the virtual reality collaborative environment are configured to obtain the virtual reality collaborative environment configuration; based on the virtual reality collaborative environment configuration, mixed reality technology and high-resolution video streaming technology are used to establish an immersive interactive platform between remote experts and frontline medical staff, thus generating the immersive interactive platform. Based on the immersive interactive platform and combined with the posture estimation algorithm, the actions of frontline medical staff are tracked and analyzed in real time, providing precise operation guidance and obtaining precise operation guidance results. Using the precise operational guidance results, interactive data analysis technology is applied to collect, analyze, and record data throughout the entire collaboration process, identify areas for improvement, and generate optimization plans for the rescue process. Based on the aforementioned rescue process optimization scheme, an autonomous health monitoring system is deployed. Wearable devices are used to collect vital sign data of the injured and upload it to the central database in real time. Combined with geolocation services, the system provides location tracking of the injured and sick, integrates the function of predicting the condition of the injured and sick, and provides early warning of potential risks through comprehensive analysis of historical data and real-time feedback, thereby generating decision-making basis.

2. The method according to claim 1, characterized in that, Based on the message priority evaluation results, the communication bandwidth and response speed for different types of medical needs are optimized and allocated to obtain a preliminary resource allocation scheme, including: Using the message priority evaluation results, the importance of various medical needs is classified to obtain a list of medical needs classifications; Based on the medical needs classification list and the current available communication resources, communication resource estimation is performed for different types of medical needs, and a resource estimation report is generated. Based on the resource estimation report, an intelligent optimization algorithm is used to accurately calculate the communication bandwidth and response speed required for each medical need, ensuring that high-priority needs can obtain sufficient resource support and obtaining an accurate resource allocation table. Using the precise resource allocation table, and taking into account the overall system load balancing and real-time requirements, the resource allocation scheme is initially adjusted to ensure that urgent needs are met without affecting other communication tasks, thus generating a preliminary resource allocation scheme.

3. The method according to claim 1, characterized in that, The process of dynamically adjusting the initial resource allocation plan using real-time battlefield situation analysis technology ensures the continuous provision of the most suitable communication resources in a changing battlefield environment, and generates an optimized resource allocation strategy, including: Real-time battlefield situation analysis technology is used to monitor and process changes in the current battlefield environment and enemy situation to obtain a battlefield situation report; Based on the battlefield situation report and the preliminary resource allocation plan, factors that may affect communication efficiency are evaluated and processed to generate an evaluation result of the influencing factors. Based on the evaluation results of the influencing factors, an adaptive adjustment algorithm is used to flexibly adjust the communication bandwidth and response speed in the preliminary resource allocation scheme to ensure that high-priority medical needs can obtain optimal resource support in changing environments, resulting in an adjusted resource allocation scheme. By utilizing the adjusted resource allocation scheme and continuously optimizing the adjustment process through a feedback mechanism, the efficiency and adaptability of resource allocation in long-term operation are ensured, and an optimized resource allocation strategy is generated.

4. The method according to claim 1, characterized in that, Based on the virtual reality collaborative environment configuration, and employing mixed reality technology and high-resolution video streaming technology, an immersive interactive platform is established between remote experts and frontline medical staff. This immersive interactive platform includes: Using the virtual reality collaborative environment configuration, the mixed reality devices and network parameters are adapted to ensure that the system can operate stably in a complex battlefield environment, and the adapted mixed reality devices and network parameters are obtained. Based on the adapted mixed reality device and network parameters, a high-resolution video streaming system is deployed to achieve high-definition real-time interaction between remote experts and frontline medical staff, thus obtaining a high-definition real-time interaction channel. Based on the aforementioned high-definition real-time interactive channel, virtual object generation and control technology is integrated, enabling remote experts to create and operate virtual objects in a shared virtual space, assisting and guiding frontline medical staff, and generating virtual object interactive interfaces. By utilizing the virtual object interaction interface and combining human-computer interaction design principles, the user interface and user experience are optimized to ensure that remote experts and frontline medical staff can communicate and collaborate efficiently, thus creating an immersive interactive platform.

5. The method according to claim 1, characterized in that, The aforementioned optimized rescue process scheme deploys an autonomous health monitoring system. This system uses wearable devices to collect vital sign data of the injured and uploads it to a central database in real time. It also integrates geolocation services to track the location of the injured, and incorporates a patient condition prediction function. Through comprehensive analysis of historical data and real-time feedback, it provides early warnings of potential risks and generates decision-making basis, including: Using the aforementioned rescue process optimization scheme, the architecture of the autonomous health monitoring system is designed and processed to ensure that the system can efficiently support the dynamic allocation of medical resources, thus obtaining the health monitoring system architecture. According to the health monitoring system architecture, a wearable device network is deployed to continuously collect vital sign data of the injured, and the data is uploaded to the central database in real time through a secure and reliable communication protocol to obtain a vital sign data stream. Based on the vital signs data stream and combined with geolocation services, a patient location tracking module was developed to achieve real-time monitoring and management of the patient's geographical location and generate location tracking information. By utilizing the location tracking information, the system integrates the function of predicting the status of the wounded and sick, and uses machine learning algorithms to comprehensively analyze historical data and real-time feedback, identify and warn of potential risks in advance, and generate risk warning reports. Based on the aforementioned risk warning report, corresponding countermeasures and resource allocation plans are formulated to provide the command center with a scientific basis for decision-making, ultimately generating a decision-making basis.

6. A multi-functional field hospital communication system, characterized in that, include: The building module is used to construct an adaptive distributed communication network. By integrating satellite communication, radio station and mobile network resources, it ensures the continuity and reliability of data transmission in uncertain battlefield environments, dynamically adjusts communication protocols and frequencies, and obtains a stable communication platform. The optimization module is used to initialize the message scheduling mechanism using the stable communication platform to obtain the initial scheduling parameter configuration. Based on the initial scheduling parameter configuration, a deep Q-network algorithm is used to evaluate the urgency of messages and the current network status to obtain message priority evaluation results. Based on the message priority evaluation results, the communication bandwidth and response speed for different types of medical needs are optimized and allocated to obtain a preliminary resource allocation scheme. By utilizing real-time battlefield situation analysis technology, the preliminary resource allocation scheme is dynamically adjusted to ensure that the most suitable communication resources are continuously provided in a changing battlefield environment, thereby generating an optimized resource allocation strategy. The recording module is used to configure the construction parameters of the virtual reality collaborative environment using the optimized resource allocation strategy to obtain the virtual reality collaborative environment configuration; based on the virtual reality collaborative environment configuration, mixed reality technology and high-resolution video streaming technology are used to establish an immersive interactive platform between remote experts and front-line medical staff, and generate the immersive interactive platform. Based on the immersive interactive platform and combined with the posture estimation algorithm, the actions of frontline medical staff are tracked and analyzed in real time, providing precise operation guidance and obtaining precise operation guidance results. Using the precise operational guidance results, interactive data analysis technology is applied to collect, analyze, and record data throughout the entire collaboration process, identify areas for improvement, and generate optimization plans for the rescue process. The early warning module is used to deploy an autonomous health monitoring system based on the rescue process optimization scheme. It uses wearable devices to collect vital sign data of the wounded and uploads it to the central database in real time. It combines geolocation services to provide location tracking of the wounded and sick, integrates the function of predicting the status of the wounded and sick, and provides early warning of potential risks by comprehensively analyzing historical data and real-time feedback, thereby generating decision-making basis.

7. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a multifunctional field hospital communication method as described in any one of claims 1 to 5.

8. A computer storage medium, characterized in that, The device contains a computer program that, when executed by a computer, implements a multifunctional field hospital communication method as described in any one of claims 1 to 5.

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