Multifunctional field hospital communication method and system

Abstract

The invention provides a multifunctional field hospital communication method and system. The method comprises the following steps: constructing a distributed communication network with self-adaptive capability, and dynamically adjusting a communication protocol and frequency to obtain a stable communication platform; optimizing a message scheduling mechanism by using the stable communication platform, and generating an optimized resource allocation strategy based on the message scheduling mechanism of deep reinforcement learning; according to the optimized resource allocation strategy, a virtual reality collaborative environment is constructed, an interactive data analysis technology is applied to record the whole process, and a rescue process optimization scheme is generated; and on the basis of the rescue process optimization scheme, potential risks are warned in advance through comprehensive analysis of historical data and real-time feedback, and a decision basis is generated. According to the technical scheme provided by the invention, the reliability and continuity of a communication network are enhanced.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of multifunctional communication technology, and in particular, to a multifunctional field hospital communication method and system. Background Art

[0002] In a modern field hospital environment, medical teams face complex battlefield environments and changing communication conditions. Especially in emergency rescue operations, it is crucial to ensure efficient and reliable information transmission. In order to meet this demand, it is necessary to build a distributed communication network with adaptive capabilities that can integrate satellite communications, radio stations, and mobile network resources to ensure the continuity and reliability of data transmission in an uncertain battlefield environment. In addition, the system needs to dynamically adjust communication protocols and frequencies to cope with changing environmental factors, thereby providing a stable and reliable communication platform. At the same time, priority management for different types of medical needs and precise guidance for frontline medical staff during complex operations or diagnoses also require efficient resource allocation and real-time collaboration tools.

[0003] At present, most field hospital communication systems rely on traditional single-point communication modes, such as fixed radio communication links or limited mobile network coverage. Although these systems can meet basic communication needs to a certain extent, they often seem powerless when faced with complex and changing battlefield environments. For example, when the communication infrastructure is damaged or the signal is interfered with, the flexibility and recovery capabilities of traditional systems are insufficient, and it is difficult to ensure continuous data transmission. In addition, the existing system is also relatively simple in terms of resource allocation. It usually uses preset rules for message scheduling and cannot be flexibly adjusted according to real-time conditions, resulting in high-priority messages may not be delivered in time, affecting rescue efficiency.

[0004] The main drawback of existing communication systems is that they lack 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, which seriously affects the continuity and reliability of information transmission. Secondly, most of the existing message scheduling mechanisms are based on static rules and fail to make full use of advanced machine learning algorithms to optimize resource allocation, which makes resource allocation inaccurate and slow to respond when dealing with emergency medical needs. Finally, the existing system lacks effective remote collaboration tools, especially during complex surgeries or diagnoses. Remote experts cannot provide instant and accurate operational guidance to frontline medical staff through technologies such as virtual reality, which limits the cooperation efficiency and operational accuracy of the medical team. 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] The embodiments of the present application provide 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, an embodiment of the present application provides a multifunctional field hospital communication method, including:

[0007] Build a distributed communication network with adaptive capabilities, integrate satellite communications, radio stations and mobile network resources to ensure the continuity and reliability of data transmission in an uncertain battlefield environment, dynamically adjust communication protocols and frequencies, and obtain a stable communication platform;

[0008] The stable communication platform is used to optimize the message scheduling mechanism. The message scheduling mechanism based on deep reinforcement learning uses a deep Q network algorithm 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. The real-time battlefield situation analysis technology is combined to make dynamic adjustments and generate an optimized resource allocation strategy.

[0009] According to 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 to perform complex operations or diagnoses, and interactive data analysis technology is used to record the entire process to generate a rescue process optimization plan;

[0010] Based on the rescue process optimization plan, an autonomous health monitoring system is deployed to collect the vital signs data of the injured with the help of wearable devices and upload them to the central database in real time. The location tracking of the injured is provided in combination with geo-positioning services, and the status prediction function of the injured is integrated. Through comprehensive analysis of historical data and real-time feedback, potential risks are warned in advance to generate a basis for decision-making.

[0011] Optionally, the stable communication platform is used to optimize the message scheduling mechanism. The message scheduling mechanism based on deep reinforcement learning uses a deep Q network algorithm 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, and combines real-time battlefield situation analysis technology for dynamic adjustment to generate an optimized resource allocation strategy, including:

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

[0013] According to the initial scheduling parameter configuration, a deep Q network algorithm is used to evaluate and process the urgency of the message and the current network status to obtain a message priority evaluation result;

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

[0015] The preliminary resource allocation plan is dynamically adjusted by using real-time battlefield situation analysis technology to ensure the continuous provision of the most appropriate communication resources in a changing battlefield environment and generate an optimized resource allocation strategy.

[0016] Optionally, based on the message priority evaluation result, optimizing and allocating the communication bandwidth and response speed of different types of medical needs to obtain a preliminary resource allocation plan includes:

[0017] Using the message priority evaluation result, classify the importance of various medical needs to obtain a medical needs classification list;

[0018] According to the medical demand classification list and in combination with the currently available communication resources, communication resource estimation processing is performed for different types of medical demands to generate a resource estimation report;

[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 obtain an accurate resource allocation table;

[0020] By using the precise resource allocation table, taking into account the overall load balancing and real-time requirements of the system, the resource allocation plan is initially adjusted to ensure that other communication tasks are not affected while meeting emergency needs, and a preliminary resource allocation plan is generated.

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

[0022] Use real-time battlefield situation analysis technology to monitor and process the current battlefield environment and enemy situation changes to obtain battlefield situation reports;

[0023] According to the battlefield situation report and in combination with 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 plan to ensure that high-priority medical needs can also obtain optimal resource support in a changing environment, and an adjusted resource allocation plan is obtained;

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

[0026] Optionally, the virtual reality collaborative environment is constructed and processed according to the optimized resource allocation strategy, mixed reality technology and high-resolution video streaming are used, and a posture estimation algorithm is combined to guide frontline medical staff to perform complex operations or diagnoses, and interactive data analysis technology is used to record the entire process to generate a rescue process optimization 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] According to the configuration of the virtual reality collaborative environment, an immersive interactive platform between remote experts and frontline medical staff is established by using mixed reality technology and high-resolution video streaming technology to generate an immersive interactive platform;

[0029] Based on the immersive interactive platform, combined with the posture estimation algorithm, the movements of frontline medical staff are tracked and analyzed in real time, providing precise operation guidance, ensuring the accuracy of complex operations or diagnostic processes, and obtaining precise operation guidance results;

[0030] Utilizing the precise operational guidance results and applying interactive data analysis technology, the data of the entire collaborative process is collected, analyzed and recorded, the improveable links are identified, and a rescue process optimization plan is generated.

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

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

[0033] Deploy a high-resolution video streaming system according to the adapted mixed reality device and network parameters to achieve high-definition real-time interaction between remote experts and frontline medical staff, and obtain a high-definition real-time interaction channel;

[0034] Based on the high-definition real-time interactive channel, the virtual object generation and control technology is integrated to enable remote experts to create and operate virtual objects in a shared virtual space, assist in guiding frontline medical staff, and generate a virtual object interaction interface;

[0035] By utilizing the virtual object interaction interface and combining it with 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, thereby generating an immersive interactive platform.

[0036] Optionally, based on the rescue process optimization solution, an autonomous health monitoring system is deployed to collect the vital signs data of the injured with the help of wearable devices and upload them to the central database in real time, provide location tracking of the injured and sick in combination with geographic positioning services, integrate the function of predicting the status of the injured and sick, and warn of potential risks in advance through comprehensive analysis of historical data and real-time feedback, and generate decision-making basis, including:

[0037] Using the rescue process optimization solution, 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, and the health monitoring system architecture is obtained;

[0038] According to the health monitoring system architecture, a wearable device network is deployed to continuously collect the 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, combined with geographic positioning services, a patient location tracking module is developed to achieve real-time monitoring and management of the patient's geographic location and generate location tracking information;

[0040] Utilizing the location tracking information, integrating the patient status prediction function, and using machine learning algorithms to comprehensively analyze historical data and real-time feedback, potential risks can be identified and warned in advance, and risk warning reports can be generated;

[0041] According to the risk warning report, formulate corresponding response measures and resource allocation plans to provide the command center with scientific decision-making basis and ultimately generate decision-making basis.

[0042] In a second aspect, an embodiment of the present application provides a multifunctional field hospital communication system, including:

[0043] Building modules are used to build a distributed communication network with adaptive capabilities. By integrating satellite communications, radio stations 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;

[0044] An optimization module is used to optimize the message scheduling mechanism by using the stable communication platform. The message scheduling mechanism based on deep reinforcement learning uses a deep Q network algorithm 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, and combines real-time battlefield situation analysis technology to make dynamic adjustments and generate an optimized resource allocation strategy;

[0045] A recording module is used to construct and process a virtual reality collaborative environment according to the optimized resource allocation strategy, use mixed reality technology and high-resolution video streaming, and combine posture estimation algorithms to guide frontline medical staff to perform complex operations or diagnoses, apply interactive data analysis technology to record the entire process, and generate a rescue process optimization plan;

[0046] The early warning module is used to deploy an autonomous health monitoring system based on the rescue process optimization plan, collect the vital signs data of the injured with the help of wearable devices and upload them to the central database in real time, provide location tracking of the injured and sick in combination with geographic positioning services, integrate the status prediction function of the injured and sick, and provide early warning of potential risks through comprehensive analysis of historical data and real-time feedback to generate a basis for decision-making.

[0047] In a third aspect, an embodiment of the present application provides a computing device, comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a multifunctional field hospital communication method as described in the first aspect.

[0048] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program, which, when executed by a computer, implements a multifunctional field hospital communication method as described in the first aspect.

[0049] In the embodiment of the present application, a distributed communication network with adaptive capabilities is constructed, and by integrating satellite communications, radio stations and mobile network resources, the continuity and reliability of data transmission are ensured in an uncertain battlefield environment, and the communication protocol and frequency are dynamically adjusted to obtain a stable communication platform; using the stable communication platform, the message scheduling mechanism is optimized, and the message scheduling mechanism based on deep reinforcement learning uses a deep Q network algorithm 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, and combines real-time battlefield situation analysis technology for dynamic adjustment to generate an optimized resource allocation strategy; according to the optimized resource allocation strategy, a virtual reality collaborative environment is constructed and processed, mixed reality technology and high-resolution video streaming are used, and a posture estimation algorithm is combined to guide frontline medical staff to perform complex operations or diagnoses, and interactive data analysis technology is used to record the entire process to generate a rescue process optimization plan; based on the rescue process optimization plan, an autonomous health monitoring system is deployed, and the vital signs data of the wounded are collected with the help of wearable devices and uploaded to the central database in real time, and the location tracking of the wounded is provided in combination with geographic positioning services, and the state prediction function of the wounded is integrated, and potential risks are warned in advance through comprehensive analysis of historical data and real-time feedback to generate a decision basis.

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

[0051] By building a distributed communication network with adaptive capabilities and integrating satellite communications, radio stations 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 enables the platform to flexibly respond to complex environmental changes, providing a stable and reliable communication platform, greatly improving the quality and efficiency of medical information exchange; using the stable communication platform, the message scheduling mechanism based on deep reinforcement learning can 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 for dynamic adjustment, an optimized resource allocation strategy is generated. This not only ensures that high-priority medical needs are handled in a timely manner, but also maximizes the use of limited communication resources and improves the response speed and efficiency of the overall system; according to the optimized resource allocation strategy, the construction of the virtual reality collaborative environment adopts mixed reality technology and high-resolution video streaming, combined with posture estimation algorithms to guide frontline medical staff to perform complex operations or diagnoses. This immersive interactive platform not only enables 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 a rescue process optimization plan. This move significantly improves the accuracy and success rate of complex surgeries and diagnoses, and reduces the possibility of medical errors. Based on the rescue process optimization plan, the deployment of the autonomous health monitoring system uses wearable devices to collect the vital signs of the injured and upload them to the central database in real time. Combined with geo-positioning services to provide location tracking of the injured and integrate the status prediction function of the injured, through comprehensive analysis of historical data and real-time feedback, early warning of potential risks is generated to generate decision-making basis. This system not only realizes real-time monitoring of the status of the injured and sick, but also can identify potential risks in advance, providing scientific decision-making support for the command center, ensuring the best response in the shortest time, and improving the effectiveness and pertinence of rescue operations.

[0052] Furthermore, through the message scheduling mechanism based on 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 optimized resource allocation strategies. This method not only ensures that high-priority medical needs can obtain timely and sufficient communication resource support, but also maximizes the utilization efficiency of limited resources, enhances the flexibility and adaptability of the system, and thus greatly improves the overall response speed and communication quality.

[0053] Furthermore, the virtual reality collaborative environment constructed according to the optimized resource allocation strategy adopts mixed reality technology and high-resolution video streaming, combined with posture estimation algorithms to guide frontline medical staff to perform complex operations or diagnoses. Interactive data analysis technology is used to record the entire process and generate a rescue process optimization plan. This immersive interactive platform not only enables 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 records and analysis. This move significantly improves the accuracy and success rate of complex operations and diagnoses, greatly enhances the cooperation efficiency and operational accuracy of the medical team, and ultimately improves the overall effectiveness and safety of rescue operations.

[0054] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0056] Figure 1 A flowchart of a multifunctional field hospital communication method provided in an embodiment of the present application;

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

[0058] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0059] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0060] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0061] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0062] Figure 1 A flowchart of a multifunctional field hospital communication method is provided for an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0063] 101. Build a distributed communication network with adaptive capabilities, integrate satellite communications, radio stations and mobile network resources to ensure the continuity and reliability of data transmission in an uncertain battlefield environment, dynamically adjust communication protocols and frequencies, and obtain a stable communication platform;

[0064] In this step, building a distributed communication network with adaptive capabilities means integrating satellite communications, radio stations, and mobile network resources to create a system that can automatically adjust its communication protocols and frequencies in a complex and uncertain battlefield environment. This network can not only ensure the continuity and reliability of data transmission, but also dynamically respond to environmental changes and provide a stable and reliable communication platform. It includes the collection and processing of multi-source data to support the effective transmission and real-time update of medical information.

[0065] In the embodiment of the present application, the system first evaluates the current communication environment and selects the most appropriate communication method (such as satellite, radio or mobile network) based on the evaluation results. Then, the system dynamically adjusts the communication protocol and frequency according to actual needs to deal with possible interference or signal attenuation problems. In this way, the system ensures that efficient data transmission can be maintained even under extreme conditions, providing a stable communication foundation for subsequent steps.

[0066] For example, in 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 contact between frontline medical staff and the command center, and ensuring the smooth progress of the rescue operation.

[0067] 102. Utilize the stable communication platform to optimize the message scheduling mechanism. Based on the deep reinforcement learning message scheduling mechanism, a 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, using the stable communication platform to optimize the message scheduling mechanism refers to a message scheduling mechanism based on deep reinforcement learning, which uses a deep Q network algorithm to evaluate the urgency of each message and the current network status, thereby allocating the optimal communication bandwidth and response speed for 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 in a timely manner.

[0069] In the embodiment of the present application, the system uses a deep Q network algorithm to evaluate the urgency of the message, and intelligently allocates appropriate communication resources to different types of messages in combination with the current network status. At the same time, the system will dynamically adjust the allocation of these resources according to the changes in the real-time battlefield situation to ensure the maximum utilization of resources and the optimization of response speed.

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

[0071] Optionally, in step 102, the stable communication platform is used to optimize the message scheduling mechanism. The message scheduling mechanism based on deep reinforcement learning uses a deep Q network algorithm to allocate optimal communication bandwidth and response speed for different types of medical needs according to the urgency of the message and the current network status, and is dynamically adjusted in combination with real-time battlefield situation analysis technology to generate an optimized resource allocation strategy, including:

[0072] The stable communication platform is used to initialize the message scheduling mechanism to obtain the initial scheduling parameter configuration; based on the initial scheduling parameter configuration, the deep Q network algorithm is used to evaluate the urgency of the message and the current network status to obtain the message priority evaluation result; based on the message priority evaluation result, the communication bandwidth and response speed of different types of medical needs are optimized and allocated to obtain a preliminary resource allocation plan; using real-time battlefield situation analysis technology, the preliminary resource allocation plan is dynamically adjusted to ensure the continuous provision of the most appropriate communication resources in a changing battlefield environment and generate an optimized resource allocation strategy.

[0073] In this step, in the 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 the initial scheduling parameter configuration; these configurations include basic data such as network status and bandwidth allocation, which are used to ensure the accuracy of subsequent evaluation and resource allocation. Secondly, the deep Q network algorithm is used to evaluate the urgency of the message and the current network status to generate a message priority evaluation result; this result is based on historical data and real-time conditions to ensure that high-priority messages can be quickly conveyed. Thirdly, according to the priority evaluation results, the communication bandwidth and response speed of 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 that the most appropriate communication resources are continuously provided in a changing environment, and finally an optimized resource allocation strategy is generated.

[0074] In the embodiment of the present application, first, the system uses the stable communication platform to initialize the message scheduling mechanism and sets the initial scheduling parameter configuration; 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, and generates a message priority evaluation result; thirdly, based on the evaluation results, the system optimizes the communication bandwidth and response speed of different types of medical needs, and forms 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 appropriate communication resources are provided in the ever-changing battlefield environment, and finally generates an optimized resource allocation strategy.

[0075] In a complex field rescue scenario, first, the system uses the established stable communication platform to initialize the message scheduling mechanism and sets the initial scheduling parameter configuration, including network status, bandwidth allocation and priority rules; second, based on these initial configurations, the system uses the deep Q network algorithm to evaluate the urgency of a message about first aid for seriously injured patients and a request for logistics supply and the current network status, generates the message priority evaluation result, and determines that first aid for seriously injured patients is the highest priority; third, based on the evaluation results, the system optimizes the communication bandwidth and response speed required for first aid for seriously injured patients, forms a preliminary resource allocation plan to ensure that first aid information can be quickly conveyed; finally, through real-time battlefield situation analysis technology, the system identifies that new conflict points in the nearby area may affect the communication quality, immediately adjusts the resource allocation plan, adds backup communication paths, ensures that the most suitable communication resources are continuously provided in the changing battlefield environment, and finally generates an optimized resource allocation strategy. Through the above steps, the system not only ensures the rapid transmission of high-priority messages, but also improves the flexibility and adaptability of the overall communication system, significantly improving the efficiency and success rate of rescue operations.

[0076] Optionally, based on the message priority evaluation result, optimizing and allocating the communication bandwidth and response speed of different types of medical needs to obtain a preliminary resource allocation plan includes:

[0077] Using the message priority evaluation results, the importance of various types of medical needs are classified to obtain a medical needs classification list; based on the medical needs classification list and in combination with the currently available communication resources, communication resources for different types of medical needs are estimated to generate a resource estimation report; 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 to ensure that high-priority needs can obtain sufficient resource support and obtain an accurate resource allocation table; using the accurate resource allocation table, the resource allocation plan is initially adjusted taking into account the overall load balancing and real-time requirements of the system to ensure that other communication tasks are not affected while meeting emergency needs, and a preliminary resource allocation plan is generated.

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

[0079] Using real-time battlefield situation analysis technology, the current battlefield environment and enemy situation changes are monitored and processed to obtain a battlefield situation report; based on the battlefield situation report and in combination with the preliminary resource allocation plan, factors that may affect communication efficiency are evaluated and processed to generate 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 also obtain optimal resource support in a changing environment, and an adjusted resource allocation plan is obtained; 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 and generate 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 types of medical needs is classified using the message priority evaluation results to obtain a medical needs classification list; these classification data are used to ensure the accuracy of subsequent resource allocation. Secondly, according to the medical needs classification list, combined with the current available communication resources, different types of medical needs are estimated for communication resources to generate a resource estimation report; the report provides a preliminary estimate of the resources required for each need. Thirdly, 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; this process ensures the scientificity and rationality of resource allocation. Finally, using the accurate resource allocation table, the resource allocation plan is adjusted for the first time, taking into account the overall load balancing and real-time requirements of the system, to ensure that other communication tasks are not affected while meeting emergency needs, and to generate a preliminary resource allocation plan. In addition, the preliminary resource allocation plan is dynamically adjusted using real-time battlefield situation analysis technology to ensure that the most appropriate communication resources are continuously provided in a changing battlefield environment and to generate an optimized resource allocation strategy. Specifically, first, the current battlefield environment and enemy situation changes are monitored and processed using real-time battlefield situation analysis technology to obtain a battlefield situation report; second, based on the battlefield situation report and in combination with the preliminary resource allocation plan, the factors that may affect the communication efficiency are evaluated and processed to generate an influencing factor evaluation result; third, 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 also obtain optimal resource support in a changing environment, and to obtain an adjusted resource allocation plan; finally, the adjusted resource allocation plan is used to continuously optimize the adjustment process through a feedback mechanism to ensure the efficiency and adaptability of resource allocation in long-term operation and to generate an optimized resource allocation strategy.

[0081] In an embodiment of the present application, first, the system uses the message priority evaluation results to classify the importance of various types of medical needs and generate a medical needs classification list; secondly, based on this classification list and in combination with the currently available communication resources, communication resources are estimated for different types of medical needs to generate a resource estimation report; thirdly, 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 to form an accurate resource allocation table; finally, using the accurate resource allocation table, the resource allocation plan is initially adjusted taking into account the overall load balancing and real-time requirements of the system, and finally a preliminary resource allocation plan is generated. In addition, first, the system uses real-time battlefield situation analysis technology to monitor and process the current battlefield environment and enemy situation changes, and generates a battlefield situation report; second, based on the battlefield situation report and combined with the preliminary resource allocation plan, the system evaluates the factors that may affect the communication efficiency and generates the evaluation results of the influencing factors; third, based on the evaluation results of the influencing factors, the system uses an adaptive adjustment algorithm to flexibly adjust the communication bandwidth and response speed in the preliminary resource allocation plan to form an adjusted resource allocation plan; finally, the adjustment process is continuously optimized through the feedback mechanism to ensure the efficiency and adaptability of resource allocation in long-term operation and generate an optimized resource allocation strategy.

[0082] In a complex field rescue scenario, first, the system uses the existing message priority evaluation results to classify the importance of various medical needs, and classifies emergency requests, surgical arrangements, and supportive medical services as high, medium, and low priority, respectively, to generate a classification list of medical needs; secondly, based on this classification list and combined with the current available communication resources, the system estimates the communication resources for different types of medical needs, generates a resource estimation report, and determines the communication bandwidth and response speed required for each need; thirdly, 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 such as emergency requests can obtain sufficient resource support, and forming an accurate resource allocation table; finally, using the accurate resource allocation table, the resource allocation plan is initially adjusted by comprehensively considering the overall load balancing and real-time requirements of the system to ensure that other communication tasks are not affected while meeting emergency needs, and a preliminary resource allocation plan is generated. At the same time, firstly, the system uses real-time battlefield situation analysis technology to monitor and process the current battlefield environment and enemy situation changes, generates a battlefield situation report, and records the newly emerged conflict points and signal interference areas; secondly, according to the battlefield situation report, combined with the preliminary resource allocation plan, evaluates the factors that may affect the communication efficiency, such as new obstacles causing signal attenuation or new conflict points causing network congestion, and generates the impact factor evaluation results; thirdly, based on the impact factor evaluation results, the 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 requests, which also obtain the optimal resource support in the changing environment, forming an adjusted resource allocation plan; finally, the feedback mechanism is used to continuously optimize the adjustment process to ensure the efficiency and adaptability of resource allocation in long-term operation, and generate an optimized resource allocation strategy. Through the above steps, the system not only ensures that high-priority medical needs obtain the best communication resource support in a complex and changing battlefield environment, but also improves the flexibility and adaptability of the entire system, significantly improving the efficiency and success rate of rescue operations.

[0083] This application takes into account that in the prior art, due to the lack of a comprehensive evaluation mechanism for the urgency of the message and the current network status, problems such as unreasonable resource allocation, slow response speed and low communication efficiency arise. Especially in a complex and changeable battlefield environment, 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, an embodiment of the present invention proposes a message scheduling mechanism based on a deep Q network algorithm, which aims to solve the above-mentioned technical problems, and ensure that high-priority messages can obtain sufficient communication resource support in a timely manner by dynamically optimizing the resource allocation scheme, thereby improving the flexibility and response speed of the overall system.

[0084] Optionally, the step of using a deep Q network algorithm to evaluate the urgency of the message and the current network status according to the initial scheduling parameter configuration to obtain a message priority evaluation result includes:

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

[0086]

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

[0088] Complete P i After the calculation of the priority score, the system enters the resource allocation optimization stage and adjusts the resource allocation amount R of each message based on the priority score. i ; This process uses deep reinforcement learning algorithms to dynamically optimize resource allocation schemes to ensure that high-priority messages receive sufficient communication resource support, while improving resource utilization efficiency and system flexibility;

[0089]

[0090] Among them, R i represents the adjusted amount of resources allocated to the i-th message; μ, η, ω, θ, v, ξ, χ, τ are the weight coefficients determined in model training; P i represents the priority score of the i-th message calculated by the above formula; Q i represents the basic resource requirement of the i-th message; H i represents the historical similar case impact factor of the i-th message, which is used to reflect the resource consumption in similar situations; χ and τ control H i The degree of impact on resource allocation, ensuring that high-priority and historically based messages receive additional resource support;

[0091] After calculating T iAfter that, the system sorts all messages by resource allocation amount, identifies priority messages, verifies and optimizes resource allocation plans; finally, it generates a detailed report, records the priority score, resource allocation status and operation instructions of each message, and synchronizes it to all emergency action terminals to ensure an efficient coordinated response network and support scientific decision-making and effective rescue.

[0092] This formula is designed to achieve efficient message scheduling and resource allocation. The system introduces two key formulas: Calculate the message priority score P i and the adjusted resource allocation R i These formulas not only take into account multi-dimensional factors such as the urgency of the message, transmission time, importance score and scope of influence, but also combine the influencing factors of similar historical cases to ensure the scientificity and rationality of resource allocation. In addition, by adopting a deep reinforcement learning algorithm, the system can dynamically adjust the resource allocation plan according to the real-time network status, maximizing the resource utilization efficiency and system adaptability.

[0093] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0094]

[0095] Urgency decay term Measures the decay of message urgency over time and the time required for transmission; urgency exponential decay term Reflects the impact of the message's importance score on its priority; emergency response level modifier Adjust priority scores based on emergency response levels;

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

[0097] Parameters α, γ, γ are determined through model training; D i The urgency of the message obtained by natural language processing technology; T i is the estimated transmission time; parameters δ, λ are determined through model training; S i Score the importance of the message; parameters ζ, ψ, φ are determined through model training; F i is the impact range factor; E i It is the emergency response level;

[0098] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0099]

[0100] Priority score power modifier Adjust resource allocation according to priority score; basic resource requirement item ν·Q i: reflects the basic resource requirements for each message; historical similar cases affect the correction items Adjust resource allocation based on historical similar cases;

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

[0102] Parameters μ, η, ω, θ are determined through model training; P i According to the formula P i Calculated; parameter ν is determined by model training; Q i is the basic resource requirement of the message; parameters ξ, χ, τ are determined through model training; H i The impact factor of similar historical cases;

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

[0104] Message 1 (first aid request 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 of 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 of message 2:

[0110]

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

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

[0113]

[0114] Assume 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 is 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 that the priority score threshold P is set threshold = 0.7 and resource allocation threshold R threshold =0.6, since the priority score of message 1 is P1=0.95, message 2 can be processed later. At the same time, since the resource allocation amount of message 1 is R1=0.9, which is larger than R threshold , indicating that message 1 requires more resources to support; while the resource allocation of message 2 R2 = 0.5 is less than R threshold , indicating that message 2 has a lower resource demand. Therefore, the system can judge and optimize resource allocation based on these thresholds to ensure that high-priority messages are processed in a timely manner and improve resource utilization efficiency.

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

[0120] 103. According to the optimized resource allocation strategy, a virtual reality collaborative environment is constructed and processed, mixed reality technology and high-resolution video streaming are used, and posture estimation algorithms are combined to guide frontline medical staff to perform complex operations or diagnoses, and interactive data analysis technology is used to record the entire process to generate a rescue process optimization plan;

[0121] In this step, according to the optimized resource allocation strategy, the construction and processing of the virtual reality collaborative environment refers to the use of mixed reality technology and high-resolution video streaming, combined with posture estimation algorithms to guide frontline medical staff to perform complex operations or diagnoses. Interactive data analysis technology is used to record the entire process and generate a rescue process optimization plan. This environment enables remote experts to provide precise operational guidance as if they were on site, significantly improving the success rate of complex medical operations.

[0122] In the embodiment of the present application, the system establishes an immersive interactive platform through mixed reality technology and high-resolution video streaming, allowing remote experts to guide frontline medical staff to perform complex operations or diagnoses in real time. In addition, the system also uses a posture estimation algorithm to track the movements of medical staff to ensure 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 to complete key steps of the surgery through a virtual reality collaborative environment, greatly improving the success rate of the surgery and providing valuable data for future training through detailed records.

[0124] Optionally, in step 103, according to the optimized resource allocation strategy, a virtual reality collaborative environment is constructed and processed, mixed reality technology and high-resolution video streaming are used, and a posture estimation algorithm is combined to guide frontline medical staff to perform complex operations or diagnoses, and interactive data analysis technology is used to record the entire process to generate a rescue process optimization plan, including:

[0125] The optimized resource allocation strategy is used to configure and process the construction parameters of the virtual reality collaborative environment 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 to generate an immersive interactive platform; based on the immersive interactive platform, combined with a posture estimation algorithm, the movements of the frontline medical staff are tracked and analyzed in real time to provide precise operation guidance, ensure accuracy during complex operations or diagnostic processes, and obtain precise operation guidance results; using the precise operation guidance results, interactive data analysis technology is applied to collect, analyze and record data of the entire collaborative process, identify links that can be improved, and generate a rescue process optimization plan.

[0126] In this step, the virtual reality collaborative environment is constructed and processed according to the optimized resource allocation strategy, including several key steps. First, the construction parameters of the virtual reality collaborative environment are configured and processed using the optimized resource allocation strategy to obtain the virtual reality collaborative environment configuration; these configuration data are used to ensure the effective implementation of mixed reality technology and high-resolution video streaming. Secondly, according to this configuration, an immersive interactive platform between remote experts and frontline medical staff is established using mixed reality technology and high-resolution video streaming technology to generate an immersive interactive platform; this platform enables remote experts to provide guidance as if they were on the scene. Thirdly, based on the immersive interactive platform, combined with the posture estimation algorithm, the actions of frontline medical staff are tracked and analyzed in real time, and precise operation guidance is provided to ensure the accuracy of complex operations or diagnostic processes, and precise operation guidance results are obtained; this process improves the success rate of medical operations. Finally, using the precise operation guidance results, the interactive data analysis technology is applied to collect, analyze and record the data of the entire collaborative process, identify the links that can be improved, and generate a rescue process optimization plan; this step provides valuable data support for subsequent training and process improvement.

[0127] In the embodiment of the present application, firstly, the system uses the optimized resource allocation strategy to configure the construction parameters of the virtual reality collaborative environment, ensures that all equipment and technologies can be seamlessly integrated and run efficiently, and generates a virtual reality collaborative environment configuration; secondly, based on this configuration, the system uses mixed reality technology and high-resolution video streaming technology to establish an immersive interactive platform, so that remote experts can guide frontline medical staff in real time, and generate an immersive interactive platform; thirdly, based on the immersive interactive platform, the system combines the posture estimation algorithm to track and analyze the movements of frontline medical staff in real time, provide precise operational guidance, ensure accuracy in complex operations or diagnostic processes, and obtain precise operational guidance results; finally, the system uses the precise operational guidance results to collect, analyze and record data of the entire collaborative process through interactive data analysis technology, identify links that can be improved, and generate a rescue process optimization plan.

[0128] In a complex field hospital environment, first, the system uses the optimized resource allocation strategy to configure the construction parameters of the virtual reality collaboration environment, ensuring that all equipment (such as head-mounted displays, gesture recognition devices) and technologies (such as mixed reality, high-resolution video streaming) can be seamlessly integrated and run efficiently, and generate a virtual reality collaboration environment configuration; secondly, based on this configuration, the system uses mixed reality technology and high-resolution video streaming technology to establish an immersive interaction platform between remote experts and frontline medical staff, allowing remote experts to provide guidance as if they were on the scene, and generate an immersive interaction platform; thirdly, based on the immersive interaction platform, the system combines the posture estimation algorithm to track and analyze the movements of frontline medical staff in real time, provides precise operation guidance, ensures accuracy in complex operations or diagnostic processes, and obtains precise operation guidance results; finally, the system uses the precise operation guidance results to collect, analyze and record data of the entire collaboration process through interactive data analysis technology, identifies the links that can be improved, and generates a rescue process optimization plan. Through the above steps, the system not only improves the accuracy and success rate of complex operations and diagnoses, but also provides valuable data for subsequent training and process improvements, significantly improving the overall medical level and responsiveness of the field hospital.

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

[0130] The virtual reality collaborative environment configuration is used to adapt the mixed reality device and network parameters to ensure that the system can operate stably in a complex battlefield environment, and to obtain the adapted mixed reality device and network parameters; based on the adapted mixed reality device and network parameters, a high-resolution video stream transmission system is deployed to achieve high-definition real-time interaction between remote experts and frontline medical staff, and to obtain a high-definition real-time interaction channel; based on the high-definition real-time interaction channel, virtual object generation and control technology is integrated to enable remote experts to create and operate virtual objects in a shared virtual space, assist in guiding frontline medical staff, and generate a virtual object interaction interface; using the virtual object interaction interface, combined with 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, and to generate an immersive interactive platform.

[0131] In this step, according to the configuration of the virtual reality collaborative environment, mixed reality technology and high-resolution video streaming technology are used to establish an immersive interactive platform between remote experts and frontline medical staff. First, the mixed reality equipment (such as head-mounted display, gesture recognition device, etc.) and network parameters (such as bandwidth, delay, packet loss rate, etc.) are adapted using the virtual reality collaborative environment configuration to ensure that the system can operate stably in a complex battlefield environment, and the adapted mixed reality equipment and network parameters are obtained; these data are used to ensure the stability and reliability of the system. Secondly, according to the adapted mixed reality equipment 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, and a high-definition real-time interactive channel is obtained; this channel ensures high-quality visual and audio communication. Thirdly, based on the high-definition real-time interactive channel, virtual object generation and control technology is integrated, so that remote experts can create and operate virtual objects in a shared virtual space, assist in guiding frontline medical staff, and generate a virtual object interaction interface; this interface enhances the intuitiveness and accuracy of guidance. Finally, the virtual object interaction interface is used in combination with human-computer interaction design principles to optimize the user interface and user experience, ensuring that remote experts and frontline medical staff can communicate and collaborate efficiently, ultimately generating an immersive interactive platform.

[0132] In the embodiment of the present application, 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, and obtain the adapted mixed reality devices and network parameters; secondly, according to 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 staff, and obtain a high-definition real-time interactive channel; thirdly, based on this interactive channel, the system integrates virtual object generation and control technology, so that remote experts can create and operate virtual objects in a shared virtual space, assist in guiding frontline medical staff, and generate a virtual object interaction interface; finally, using the virtual object interaction interface, the system combines the human-computer interaction design principles to optimize the user interface and user experience, ensuring that remote experts and frontline medical staff can communicate and collaborate efficiently, and generate an immersive interactive platform.

[0133] In a complex surgical scenario in a field hospital, first, the system uses the virtual reality collaborative environment configuration to adapt mixed reality devices (such as head-mounted displays, gesture recognition devices) and network parameters (such as bandwidth, latency, and packet loss rate) to ensure that all equipment and technologies can operate stably in a complex battlefield environment, and obtain the adapted mixed reality devices and network parameters; secondly, based on the adapted mixed reality devices and network parameters, the system deploys a high-resolution video streaming transmission system to achieve high-definition real-time interaction between remote experts and frontline medical staff, obtains a high-definition real-time interaction channel, and ensures high-quality visual and audio communication; thirdly, based on the high-definition real-time interaction channel, the system integrates virtual object generation and control technology, so that remote experts can create and operate virtual objects (such as surgical tools or anatomical models) in a shared virtual space, assist in guiding frontline medical staff, generate a virtual object interaction interface, and enhance the intuitiveness and accuracy of the guidance; finally, using the virtual object interaction interface, the system combines the human-computer interaction design principles to optimize the user interface and user experience, ensure that remote experts and frontline medical staff can communicate and collaborate efficiently, and finally generate an immersive interactive platform. Through the above steps, the system not only ensures that remote experts can provide precise operational guidance during complex operations, but also significantly improves the operational skills and confidence of frontline medical staff, and further enhances the emergency response capabilities and medical service quality of field hospitals in complex environments.

[0134] This application takes into account that in the prior art, due to the lack of accurate tracking and analysis mechanisms for the operating actions of frontline medical staff, the guidance during complex operations or diagnostic processes is not accurate enough, affecting the success rate of medical operations. Especially in emergency rescue scenarios, traditional systems are unable to provide real-time and accurate operational guidance, increasing the risk of operational errors. Therefore, an embodiment of the present invention proposes a method for calculating motion matching based on a posture estimation algorithm, and combines remote expert feedback and comprehensive evaluation of operational safety, aiming to solve the above-mentioned technical problems, by dynamically adjusting the guidance score, ensuring that operations with high matching degrees obtain higher guidance scores, and improving the adaptability and flexibility of the system, thereby significantly improving the accuracy of complex surgical or diagnostic processes.

[0135] Optionally, 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 operation guidance, ensure accuracy during complex surgeries or diagnostic processes, and obtain precise operation guidance results, including:

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

[0137]

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

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

[0140]

[0141] Among them, G jrepresents the guidance score of the j-th operation; μ′, ν′, ω′, θ′, ξ′, χ′, τ′ are the weight coefficients determined in the model training; M j represents the action matching degree of the j-th operation calculated by the above formula; F j ′ represents the feedback factor of the j-th operation; S′ j represents the safety score of the j-th operation; χ′ and τ′ control F′ j and S′ j The degree of impact on the operational guidance score;

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

[0143] This formula is designed to accurately track and analyze the actions of frontline medical staff. The system introduces two key formulas: calculating the action matching degree M j and generate the operation guidance score G j These formulas not only take into account multi-dimensional factors such as execution error between actual operation and standard process, posture angle, and context consistency, but also combine remote expert feedback and operation safety scores to ensure the scientificity and rationality of the guidance scores. In addition, by adopting nonlinear function processing, the system can dynamically adjust the guidance scores according to real-time conditions, maximizing the accuracy of operation guidance and the adaptability of the system.

[0144] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0145]

[0146] Implementing the Error Attenuation Term Measures the execution error between actual operation and ideal state; posture angle matching item Reflects the consistency between the posture angle of the operation and the ideal posture angle; context consistency scoring item η′·C j : Consider the contextual consistency of the operation to ensure that the operation is coherent and reasonable;

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

[0148] Parameters α′, β′ are determined through model training; E′ j is the actual execution error of the j-th step operation; E0 is the execution error under ideal conditions; parameters γ′, δ′ are determined through model training; A jis the posture angle of the j-th step operation; A0 is the ideal posture angle; the parameter η′ is determined by model training; C J Score the contextual consistency of the j-th operation;

[0149] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0150]

[0151] Action matching power correction term Adjust coaching scores based on action matching; remote expert feedback corrections Adjust guidance scores based on remote expert feedback and operational safety scores;

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

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

[0154] In a complex field hospital setting, consider a hypothetical cardiac surgery being performed:

[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 in 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 of the second step:

[0161]

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

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

[0164]

[0165] Assume 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 is 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] Calculation result: G2 = 0.75;

[0170] Assume that the action matching threshold M is set threshold = 0.7 and the guidance score threshold G threshold =0.7, because the action matching degree M1 = 0.9 of the first step is very important and requires more resource support; and the guidance score G2 = 0.75 of the second step is also greater than G threshold However, it is relatively low, indicating that this operation is important but not the most urgent. Therefore, the system can judge and optimize the operation guidance based on these thresholds, ensuring that operations with high matching degrees receive higher guidance scores, while improving the accuracy of operation guidance and the adaptability of the system.

[0171] Through the above steps, the system not only ensures that each step of the operation can obtain accurate operation guidance, but also significantly improves the accuracy of complex operations or diagnostic processes. This not only improves the success rate of medical operations, but also enhances the adaptability and flexibility of the system, further improving the emergency response capabilities and medical service quality of field hospitals in complex environments.

[0172] 104. Based on the rescue process optimization plan, an autonomous health monitoring system is deployed to collect the vital signs data of the injured with the help of wearable devices and upload them to the central database in real time. The location tracking of the injured is provided in combination with geo-positioning services, and the status prediction function of the injured is integrated. Through comprehensive analysis of historical data and real-time feedback, potential risks are warned in advance, and a basis for decision-making is generated.

[0173] In this step, based on the rescue process optimization solution, deploying an autonomous health monitoring system means collecting the vital signs data of the injured with the help of wearable devices and uploading them to the central database in real time, and providing location tracking of the injured in combination with geo-positioning services. Integrating the prediction function of the status of the injured, through comprehensive analysis of historical data and real-time feedback, early warning of potential risks is generated to generate a basis for decision-making. The system aims to achieve comprehensive monitoring and management of the status of the injured and improve the effectiveness and pertinence of rescue operations.

[0174] In the embodiment of the present application, the system uses wearable devices to continuously monitor the vital signs of the injured and uploads these data to the central database in real time. Combined with geolocation services, the system can accurately track the location of each injured person, and identify potential risks in advance through the prediction function of the injured person's status, generate scientific decision-making basis, and ensure rapid and effective rescue response.

[0175] For example, during an evacuation operation, the system detected abnormal vital signs of a casualty through a wearable device and immediately issued an early warning. Based on this, the command center quickly dispatched a medical team to the scene and successfully saved the casualty's life.

[0176] Optionally, in step 104, based on the rescue process optimization solution, an autonomous health monitoring system is deployed to collect the vital signs data of the injured with the help of wearable devices and upload them to the central database in real time, provide location tracking of the injured in combination with geographic positioning services, integrate the function of predicting the status of the injured, and warn of potential risks in advance through comprehensive analysis of historical data and real-time feedback, and generate decision-making basis, including:

[0177] The rescue process optimization scheme is used to design and process the architecture of the autonomous health monitoring system to ensure that the system can efficiently support the dynamic allocation of medical resources, thereby obtaining the architecture of the health monitoring system; according to the architecture of the health monitoring system, a wearable device network is deployed to continuously collect the vital signs data of the injured, and the data is instantly uploaded to the central database through a secure and reliable communication protocol to obtain a vital signs data stream; based on the vital signs data stream, in combination with the geographic positioning service, a wounded position tracking module is developed to achieve real-time monitoring and management of the geographic location of the wounded, and generate location tracking information; using the location tracking information, a wounded status prediction function is integrated, and a machine learning algorithm is used to conduct a comprehensive analysis of historical data and real-time feedback, so as to identify and warn of potential risks in advance and generate a risk warning report; according to the risk warning report, corresponding response measures and resource allocation plans are formulated to provide a scientific decision-making basis for the command center, thereby finally generating a decision-making basis.

[0178] In this step, an autonomous health monitoring system is deployed based on the rescue process optimization scheme, aiming to ensure efficient dynamic allocation of medical resources and real-time monitoring of the status of the injured and sick through a series of key steps. First, the architecture of the autonomous health monitoring system is designed and processed using the rescue process optimization scheme to ensure that the system can efficiently support the dynamic allocation of medical resources and obtain the health monitoring system architecture; these architecture data are used to guide the subsequent configuration and integration of hardware and software. Secondly, according to this architecture, a wearable device network is deployed to continuously collect the vital signs 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 signs data stream; this data stream provides a basis for subsequent analysis. Thirdly, based on the vital signs data stream, combined with the geographic positioning service, a location tracking module for the injured and sick is developed to realize real-time monitoring and management of the geographic location of the injured and sick, and generate location tracking information; this information helps the command center to grasp the specific location of the injured and sick. Finally, the location tracking information is used to integrate the function of predicting the status of the injured and sick, and a machine learning algorithm is used to conduct a comprehensive analysis of historical data and real-time feedback to identify and warn of potential risks in advance and generate a risk warning report; this report provides the command center with a scientific basis for decision-making and ultimately generates a basis for decision-making.

[0179] In the embodiment of the present application, firstly, the system uses the rescue process optimization scheme to design and process the architecture of the autonomous health monitoring system, ensuring that all components can collaborate efficiently and support the dynamic allocation of medical resources, thereby obtaining the architecture of the health monitoring system; secondly, according to this architecture, the system deploys a wearable device network to continuously collect the vital signs data of the wounded, and uploads the data to the central database in real time through a secure and reliable communication protocol to obtain a vital signs data stream; thirdly, based on the vital signs data stream, the system combines the geo-positioning service to develop a wounded location tracking module, realizes real-time monitoring and management of the geographic location of the wounded, and generates location tracking information; finally, using the location tracking information, the system integrates the wounded status prediction function, uses machine learning algorithms to conduct a comprehensive analysis of 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 a scientific decision-making basis for the command center.

[0180] In a complex rescue scenario of a field hospital, firstly, the system uses the rescue process optimization solution to design the architecture of the autonomous health monitoring system to ensure 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 the vital signs data of the injured (such as heart rate, blood pressure, and blood oxygen saturation), and uploads the data to the central database in real time through a secure and reliable communication protocol, thus obtaining the vital signs data stream and ensuring the security and reliability of data transmission. Thirdly, based on the Based on the vital signs data stream, the system combines the geographic positioning service to develop a patient location tracking module, realizes real-time monitoring and management of the patient's geographic location, generates location tracking information, and enables the command center to grasp the specific location of each patient at any time; finally, using the location tracking information, the system integrates the patient status prediction function, uses machine learning algorithms to conduct a comprehensive analysis of historical data and real-time feedback, identifies and warns of potential risks in advance, and generates risk warning reports. Based on these reports, the command center formulates corresponding response measures and resource allocation plans, such as sending medical teams to high-risk areas or adjusting the distribution of drugs and equipment, providing the command center with a scientific decision-making basis. Through the above steps, the system not only realizes comprehensive monitoring and management of the patient's status, but also significantly improves the utilization efficiency of medical resources and the effectiveness of rescue operations, and enhances the emergency response capabilities of field hospitals in complex environments.

[0181] Through steps 101 to 104, this method constructs a distributed communication network with adaptive capabilities, ensuring the continuity and reliability of data transmission; optimizes the message scheduling mechanism through deep reinforcement learning, and realizes efficient resource allocation; establishes a virtual reality collaborative environment, improves the accuracy of complex operations and diagnoses; and realizes comprehensive monitoring and risk management of the status 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 greatly improve the utilization efficiency of medical resources and the overall effectiveness of rescue operations, and significantly improve the emergency response capabilities and medical service quality of field hospitals in complex battlefield environments.

[0182] Figure 2 A structural diagram of a multifunctional field hospital communication system is provided for an embodiment of the present application, such as Figure 2 As shown, the device comprises:

[0183] Building module 21, used to build a distributed communication network with adaptive capabilities, by integrating satellite communications, radio stations and mobile network resources, ensuring the continuity and reliability of data transmission in an uncertain battlefield environment, dynamically adjusting communication protocols and frequencies, and obtaining a stable communication platform;

[0184] The optimization module 22 is used to optimize the message scheduling mechanism by using the stable communication platform. The message scheduling mechanism based on deep reinforcement learning uses a deep Q network algorithm 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, and combines the real-time battlefield situation analysis technology to make dynamic adjustments and generate an optimized resource allocation strategy;

[0185] The 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, and combine the posture estimation algorithm to guide frontline medical staff to perform complex operations or diagnoses, apply interactive data analysis technology to record the entire process, and generate a 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 plan, collect the vital signs data of the injured with the help of wearable devices and upload them to the central database in real time, provide location tracking of the injured in combination with geographic positioning services, integrate the status prediction function of the injured, and provide early warning of potential risks through comprehensive analysis of historical data and real-time feedback to generate a basis for decision-making.

[0187] Figure 2 The multifunctional field hospital communication system can be implemented Figure 1 The implementation principle and technical effect of the multifunctional field hospital communication method described in the embodiment are not described in detail. The specific manner in which each module and unit performs operations in the multifunctional field hospital communication system in the above embodiment has been described in detail in the embodiment of the method, and will not be described in detail here.

[0188] In one possible design, Figure 2 A multifunctional field hospital communication system of the illustrated embodiment may 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 called and executed by the processing component 32 .

[0190] The processing component 32 is used to: construct a distributed communication network with adaptive capabilities, integrate satellite communications, radio stations and mobile network resources to ensure the continuity and reliability of data transmission in an uncertain battlefield environment, dynamically adjust communication protocols and frequencies, and obtain a stable communication platform; use the stable communication platform to optimize the message scheduling mechanism, based on the message scheduling mechanism of deep reinforcement learning, use the deep Q network algorithm 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, and combine the real-time battlefield situation analysis technology to make dynamic adjustments to generate an optimized resource allocation strategy; according to the optimized resource allocation strategy, construct and process the virtual reality collaborative environment, use mixed reality technology and high-resolution video streaming transmission, combine the posture estimation algorithm to guide front-line medical staff to perform complex operations or diagnoses, apply interactive data analysis technology to record the entire process, and generate a rescue process optimization plan; based on the rescue process optimization plan, deploy an autonomous health monitoring system, use wearable devices to collect the vital signs data of the wounded and upload them to the central database in real time, combine geographic positioning services to provide location tracking of the wounded, integrate the state prediction function of the wounded, and use comprehensive analysis of historical data and real-time feedback to warn of potential risks in advance to generate a decision-making basis.

[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 method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

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

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

[0194] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0195] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

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

[0197] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 A multifunctional field hospital communication method according to the illustrated embodiment.

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

[0199] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0200] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0201] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A multifunctional field hospital communication method, characterized in that: include: Build a distributed communication network with adaptive capabilities, integrate satellite communications, radio stations and mobile network resources to ensure the continuity and reliability of data transmission in an uncertain battlefield environment, dynamically adjust communication protocols and frequencies, and obtain a stable communication platform; The stable communication platform is used to optimize the message scheduling mechanism. The message scheduling mechanism based on deep reinforcement learning uses a deep Q network algorithm 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. The real-time battlefield situation analysis technology is combined to make dynamic adjustments and generate an optimized resource allocation strategy. According to 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 to perform complex operations or diagnoses, and interactive data analysis technology is used to record the entire process to generate a rescue process optimization plan; Based on the rescue process optimization plan, an autonomous health monitoring system is deployed to collect the vital signs data of the injured with the help of wearable devices and upload them to the central database in real time. The location tracking of the injured is provided in combination with geo-positioning services, and the status prediction function of the injured is integrated. Through comprehensive analysis of historical data and real-time feedback, potential risks are warned in advance to generate a basis for decision-making.

2. The method according to claim 1, characterized in that The stable communication platform is used to optimize the message scheduling mechanism. The message scheduling mechanism based on deep reinforcement learning uses a deep Q network algorithm 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. The real-time battlefield situation analysis technology is combined for dynamic adjustment to generate an optimized resource allocation strategy, including: Using the stable communication platform, the message scheduling mechanism is initialized to obtain an initial scheduling parameter configuration; According to the initial scheduling parameter configuration, a deep Q network algorithm is used to evaluate and process the urgency of the message and the current network status to obtain a message priority evaluation result; Based on the message priority evaluation result, the communication bandwidth and response speed of different types of medical needs are optimized and allocated to obtain a preliminary resource allocation plan; The preliminary resource allocation plan is dynamically adjusted by using real-time battlefield situation analysis technology to ensure the continuous provision of the most appropriate communication resources in a changing battlefield environment and generate an optimized resource allocation strategy.

3. The method according to claim 2, characterized in that Based on the message priority evaluation result, the communication bandwidth and response speed of different types of medical needs are optimized and allocated to obtain a preliminary resource allocation plan, including: Using the message priority evaluation result, classify the importance of various medical needs to obtain a medical needs classification list; According to the medical demand classification list and in combination with the currently available communication resources, communication resource estimation processing is performed for different types of medical demands to generate a resource estimation report; 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 obtain an accurate resource allocation table; By using the precise resource allocation table, taking into account the overall load balancing and real-time requirements of the system, the resource allocation plan is initially adjusted to ensure that other communication tasks are not affected while meeting emergency needs, and a preliminary resource allocation plan is generated.

4. The method according to claim 2, characterized in that: The method of utilizing real-time battlefield situation analysis technology to dynamically adjust the preliminary resource allocation plan to ensure continuous provision of the most appropriate communication resources in a changing battlefield environment and generate an optimized resource allocation strategy includes: Use real-time battlefield situation analysis technology to monitor and process the current battlefield environment and enemy situation changes to obtain battlefield situation reports; According to the battlefield situation report and in combination with 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 plan to ensure that high-priority medical needs can also obtain optimal resource support in a changing environment, and an adjusted resource allocation plan is obtained; By utilizing the adjusted resource allocation scheme, the adjustment process is continuously optimized through a feedback mechanism to ensure the efficiency and adaptability of resource allocation in long-term operation and generate an optimized resource allocation strategy.

5. The method according to claim 1, characterized in that According to the optimization resource allocation strategy, the virtual reality collaborative environment is constructed and processed, mixed reality technology and high-resolution video streaming are used, and posture estimation algorithms are combined to guide frontline medical staff to perform complex operations or diagnoses, and interactive data analysis technology is used to record the entire process to generate a rescue process optimization plan, including: 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; According to the configuration of the virtual reality collaborative environment, an immersive interactive platform between remote experts and frontline medical staff is established by using mixed reality technology and high-resolution video streaming technology to generate an immersive interactive platform; Based on the immersive interactive platform, combined with the posture estimation algorithm, the movements of frontline medical staff are tracked and analyzed in real time, providing precise operation guidance, ensuring the accuracy of complex operations or diagnostic processes, and obtaining precise operation guidance results; Utilizing the precise operational guidance results and applying interactive data analysis technology, the data of the entire collaborative process is collected, analyzed and recorded, the improveable links are identified, and a rescue process optimization plan is generated.

6. The method according to claim 5, characterized in that The virtual reality collaborative environment configuration uses mixed reality technology and high-resolution video streaming technology to establish an immersive interactive platform between remote experts and frontline medical staff, generating an immersive interactive platform, including: Using the virtual reality collaborative environment configuration, the mixed reality device and network parameters are adapted to ensure that the system can operate stably in a complex battlefield environment, and the adapted mixed reality device and network parameters are obtained; Deploy a high-resolution video streaming system according to the adapted mixed reality device and network parameters to achieve high-definition real-time interaction between remote experts and frontline medical staff, and obtain a high-definition real-time interaction channel; Based on the high-definition real-time interactive channel, the virtual object generation and control technology is integrated to enable remote experts to create and operate virtual objects in a shared virtual space, assist in guiding frontline medical staff, and generate a virtual object interaction interface; By utilizing the virtual object interaction interface and combining it with 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, thereby generating an immersive interactive platform.

7. The method according to claim 1, characterized in that Based on the rescue process optimization solution, an autonomous health monitoring system is deployed to collect the vital signs data of the injured with the help of wearable devices and upload them to the central database in real time. The location tracking of the injured is provided in combination with the geographic positioning service, and the status prediction function of the injured is integrated. Through comprehensive analysis of historical data and real-time feedback, potential risks are warned in advance, and decision-making basis is generated, including: Using the rescue process optimization solution, 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, and the health monitoring system architecture is obtained; According to the health monitoring system architecture, a wearable device network is deployed to continuously collect the 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, combined with geographic positioning services, a patient location tracking module is developed to achieve real-time monitoring and management of the patient's geographic location and generate location tracking information; Utilizing the location tracking information, integrating the patient status prediction function, and using machine learning algorithms to comprehensively analyze historical data and real-time feedback, potential risks can be identified and warned in advance, and risk warning reports can be generated; According to the risk warning report, formulate corresponding response measures and resource allocation plans to provide the command center with scientific decision-making basis and ultimately generate decision-making basis.

8. A multifunctional field hospital communication system, characterized in that: include: Building modules are used to build a distributed communication network with adaptive capabilities. By integrating satellite communications, radio stations 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; An optimization module is used to optimize the message scheduling mechanism by using the stable communication platform. The message scheduling mechanism based on deep reinforcement learning uses a deep Q network algorithm 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, and combines real-time battlefield situation analysis technology to make dynamic adjustments and generate an optimized resource allocation strategy; A recording module is used to construct and process a virtual reality collaborative environment according to the optimized resource allocation strategy, use mixed reality technology and high-resolution video streaming, and combine posture estimation algorithms to guide frontline medical staff to perform complex operations or diagnoses, apply interactive data analysis technology to record the entire process, and generate a rescue process optimization plan; The early warning module is used to deploy an autonomous health monitoring system based on the rescue process optimization plan, collect the vital signs data of the injured with the help of wearable devices and upload them to the central database in real time, provide location tracking of the injured and sick in combination with geographic positioning services, integrate the status prediction function of the injured and sick, and provide early warning of potential risks through comprehensive analysis of historical data and real-time feedback to generate a basis for decision-making.

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

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a multifunctional field hospital communication method as described in any one of claims 1 to 7 is implemented.

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