Wounded state monitoring method and system suitable for complex environment

Abstract

The invention provides a wounded state monitoring method and system suitable for a complex environment. The method comprises the following steps: collecting data in real time through a multi-source sensor network, carrying out situation modeling processing, and generating a dynamic situation model; based on the dynamic situation model, a multi-target tracking algorithm is applied to carry out accurate positioning and continuous tracking processing, positioning accuracy is ensured, a distributed sensing fusion technology is adopted, data of different sensors are efficiently fused, and accurate information of the position of the wounded person is generated; based on the accurate position information of the wounded person, efficient mapping and classification boundary optimization processing are carried out by applying a quantum support vector machine algorithm, the state of the wounded person is monitored in real time by adopting a self-adaptive data stream processing technology, and optimized health state index data are generated; and based on the optimized health state index data, activating a preset emergency response mechanism when an abnormal condition is detected, and generating a real-time wounded state monitoring scheme. According to the technical scheme, the reliability of wounded state monitoring and the emergency response speed are improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of intelligent medical monitoring technology, and in particular to a method and system for monitoring the status of a wounded person in a complex environment. Background Art

[0002] With the development of modern emergency response and medical monitoring technologies, accurate positioning of the wounded and real-time monitoring of their health status are crucial in complex environments such as disaster sites, battlefields, and field search and rescue. These scenarios are usually accompanied by variable environmental factors (such as weather, terrain, lighting, etc.), requiring the system to dynamically reflect various environmental factors and generate accurate situational models. Specifically, the system needs to collect data in real time through a multi-source sensor network and perform situational modeling to ensure comprehensive capture of complex environmental information. In addition, the system must have high-precision positioning capabilities, be able to continuously track the location of the wounded, and immediately activate the preset emergency response mechanism when an abnormal situation is detected, and coordinate on-site and remote resources to implement actions.

[0003] At present, most of the casualty status monitoring systems rely on traditional GPS positioning technology and limited sensor networks. These systems usually use single target tracking algorithms and static feature extraction methods, combined with a small amount of sensor data to monitor the location and health status of the casualty. Although these schemes can provide certain functions in relatively simple environments, they perform poorly in complex and changing environments. For example, the existing distributed perception fusion technology has enhanced the robustness of the system to a certain extent, but it still has not fully utilized the powerful parallel processing capabilities of quantum computing and adaptive data stream processing technology to optimize the feature space learning process and classification boundaries.

[0004] Existing methods are unable to cope with the changing factors in complex environments, resulting in decreased accuracy of positioning and health status monitoring, and insufficient reliability of patient status monitoring. Especially in the case of signal obstruction, noise interference, and sensor failure, the system's robustness and adaptability are obviously insufficient. In addition, existing solutions lack an efficient feature space learning mechanism and cannot quickly identify and extract key features, thus affecting the overall monitoring effect. Therefore, in complex environments, existing solutions are difficult to meet the requirements of high precision, real-time, and reliability, and there is an urgent need to introduce more advanced technologies and algorithms to improve the performance of the system. Summary of the invention

[0005] The embodiments of the present application provide a method and system for monitoring the status of a wounded person in a complex environment, so as to solve the problem of insufficient reliability of monitoring the status of a wounded person in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a method for monitoring the status of a wounded person in a complex environment, comprising:

[0007] Through real-time data collection through multi-source sensor networks, complex environmental information is modeled and processed to dynamically reflect various environmental factors and generate dynamic situation models;

[0008] Based on the dynamic situation model, a multi-target tracking algorithm is used to accurately locate and continuously track the position of the injured by integrating spatiotemporal information and appearance features to ensure positioning accuracy. Distributed perception fusion technology is used to efficiently fuse different sensor data between network nodes to enhance system robustness and generate accurate information on the position of the injured.

[0009] Based on the precise location information of the injured, the quantum support vector machine algorithm is used to accelerate the feature space learning process through the superposition and entanglement characteristics of quantum states, and perform efficient mapping and classification boundary optimization processing. The adaptive data stream processing technology is used to dynamically adjust the processing strategy according to the data stream characteristics to monitor the status of the injured in real time and generate optimized health status indicator data;

[0010] Based on the optimized health status indicator data, when the monitoring system detects an abnormal situation, the preset emergency response mechanism is immediately activated to coordinate actions on site and with remote resources to generate a real-time casualty status monitoring plan.

[0011] Optionally, based on the dynamic situation model, a multi-target tracking algorithm is used to accurately locate and continuously track the position of the injured by integrating spatiotemporal information and appearance features to ensure positioning accuracy, and distributed perception fusion technology is used to efficiently fuse different sensor data between network nodes to enhance system robustness and generate accurate information on the position of the injured, including:

[0012] Based on the dynamic situation model, the spatiotemporal information and the appearance features are mapped to ensure that different types of perception data are aggregated in a unified spatiotemporal coordinate system to generate spatiotemporal feature fusion data;

[0013] Based on the spatiotemporal feature fusion data, a multi-target tracking algorithm is used to accurately locate and continuously track the position of the injured by integrating spatiotemporal information and appearance features, and a prediction-correction mechanism is used to ensure positioning accuracy and generate preliminary position information of the injured;

[0014] Based on the preliminary position information of the injured, the distributed sensing fusion technology is used to efficiently fuse different sensor data between network nodes to perform robustness enhancement processing on the precise position information of the injured, thereby improving the anti-interference ability and adaptability of the system and generating highly robust position information of the injured;

[0015] Based on the highly robust casualty location information, all available perception data are integrated, deep analysis and cross-validation are performed, and accurate casualty location information is generated.

[0016] Optionally, the fusion data based on the spatiotemporal features uses a multi-target tracking algorithm to accurately locate and continuously track the position of the injured by integrating spatiotemporal information and appearance features, and uses a prediction-correction mechanism to ensure positioning accuracy to generate preliminary position information of the injured, including:

[0017] Based on the spatiotemporal feature fusion data, intelligent correlation analysis is performed on the spatiotemporal information and appearance features, key features are identified and extracted, and high-precision spatiotemporal feature data is generated;

[0018] Based on the high-precision spatiotemporal feature data, a multi-target tracking algorithm is used to accurately locate and continuously track the position of the injured by integrating spatiotemporal information and appearance features, and a prediction-correction mechanism is used to estimate the predicted position to ensure positioning accuracy and generate temporary position information of the injured;

[0019] Based on the temporary injured person location information, multi-level data analysis is performed, in-depth analysis is performed in combination with context information, and multi-level injured person location information is generated in combination with sensor characteristics;

[0020] Based on the multi-level casualty location information, potential data anomalies and inconsistencies are automatically detected and corrected to generate preliminary casualty location information.

[0021] Optionally, based on the preliminary wounded position information, distributed sensing fusion technology is used to efficiently fuse different sensor data between network nodes to perform robustness enhancement processing on the precise position information of the wounded, thereby improving the anti-interference ability and adaptability of the system and generating highly robust wounded position information, including:

[0022] Based on the preliminary injured person location information, distributed fusion processing is performed on different types of perception data to ensure that each node data is synchronously shared in real time and generate distributed fusion data;

[0023] Based on the distributed fusion data, the distributed sensing fusion technology is adopted to efficiently fuse the data of different sensors between network nodes, and the advanced distributed computing framework is used to perform consistency verification and error correction processing on the position information of the injured to generate strong stability position information;

[0024] Based on the strong stability position information, a time series analysis method is introduced to identify temporary errors caused by signal interference, ensure the continuity of the injured person's position information, and generate high-precision robust position information;

[0025] Based on the high-precision robust position information, redundant data from different sensors are compared to identify and eliminate errors, and adaptive filtering technology is used to further optimize the data stream to generate highly robust casualty position information.

[0026] Optionally, based on the accurate information of the injured person's position, a quantum support vector machine algorithm is used to accelerate the feature space learning process through quantum state superposition and entanglement characteristics, and efficient mapping and classification boundary optimization processing are performed. Adaptive data stream processing technology is used to dynamically adjust the processing strategy according to the data stream characteristics to perform real-time monitoring of the injured person's status and generate optimized health status indicator data, including:

[0027] Based on the accurate information of the injured person's location, a comprehensive analysis is performed on the high-precision positioning data and the environmental perception data to generate high-precision environmental correlation data;

[0028] Based on the high-precision environmental correlation data, the quantum support vector machine algorithm is used to accelerate the feature space learning process through the superposition and entanglement characteristics of quantum states, and to efficiently map and optimize the classification boundaries of large-scale data sets. The powerful parallel processing capabilities of quantum computing are used to quickly identify and extract key features and generate optimized feature representations.

[0029] Based on the optimized feature representation, adaptive data stream processing technology is used to monitor data stream changes in real time, automatically adjust model parameters and processing logic, respond to data fluctuations in different environments, and generate dynamically adjusted health status indicators;

[0030] Based on the dynamic adjustment of the health status index, nonlinear transformation is performed through a multi-layer perceptron network to enhance the feature expression capability and generate optimized health status index data.

[0031] Optionally, based on the high-precision environmental association data, the quantum support vector machine algorithm is used to accelerate the feature space learning process through the superposition and entanglement characteristics of quantum states, and large-scale data sets are efficiently mapped and the classification boundary optimization is performed. The powerful parallel processing capability of quantum computing is used to quickly identify and extract key features and generate optimized feature representations, including:

[0032] Based on the high-precision environmental association data, the original perception data is subjected to noise filtering and correction processing, and the error caused by the difference in acquisition time is eliminated through time synchronization technology to generate high-quality synchronized data;

[0033] Based on the high-quality synchronized data, the quantum support vector machine algorithm is used to accelerate the feature space learning process through the superposition characteristics of quantum states, and the powerful parallel processing capability of quantum computing is used to map it to the quantum feature space, quickly traverse all possible feature combinations, and generate a quantum feature candidate set;

[0034] Based on the candidate set of quantum features, further applying the quantum entanglement characteristics to optimize the classification boundary, finding the optimal classification boundary in the feature space through quantum entanglement, and generating an optimized classification boundary model;

[0035] Based on the optimized classification boundary model, the feature weights are dynamically adjusted according to changes in sensor signal strength to generate optimized feature representations.

[0036] Optionally, when the monitoring system detects an abnormal situation based on the optimized health status indicator data, a preset emergency response mechanism is immediately activated to coordinate actions on site and with remote resources to generate a real-time casualty status monitoring plan, including:

[0037] Based on the optimized health status indicator data, abnormal changes are automatically detected by setting thresholds and pattern recognition algorithms, and an abnormality detection report is generated;

[0038] Based on the anomaly detection report, when an abnormal situation is detected, a preset emergency response mechanism is immediately activated to generate an emergency response instruction;

[0039] Based on the emergency response instructions, dynamically deploy on-site rescue forces according to actual conditions, synchronize relevant information to the remote expert team, and generate a collaborative rescue plan;

[0040] Based on the collaborative rescue plan, all available information and technical means are integrated to generate a real-time casualty status monitoring plan.

[0041] In a second aspect, the embodiment of the present application provides a system for monitoring the status of a wounded person in a complex environment, including:

[0042] The collection module is used to collect data in real time through a multi-source sensor network, perform situational modeling on complex environmental information, dynamically reflect various environmental factors, and generate a dynamic situational model;

[0043] A positioning module is used to accurately locate and continuously track the position of the injured person based on the dynamic situation model and the multi-target tracking algorithm by integrating spatiotemporal information and appearance features to ensure positioning accuracy. The distributed sensing fusion technology is used to efficiently fuse different sensor data between network nodes to enhance system robustness and generate accurate information on the position of the injured person.

[0044] A mapping module is used to use the quantum support vector machine algorithm based on the precise information of the injured person's location to accelerate the feature space learning process through the superposition and entanglement characteristics of quantum states, perform efficient mapping and classification boundary optimization processing, and adopt adaptive data stream processing technology to dynamically adjust the processing strategy according to the data stream characteristics to monitor the status of the injured person in real time and generate optimized health status indicator data;

[0045] The detection module is used to immediately activate the preset emergency response mechanism based on the optimized health status indicator data when the monitoring system detects an abnormal situation, coordinate on-site and remote resources to implement actions, and generate a real-time casualty status monitoring plan.

[0046] In a third aspect, an embodiment of the present application provides a computing device, comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a method for monitoring the status of a wounded person in a complex environment as described in the first aspect.

[0047] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, a method for monitoring the status of a wounded person in a complex environment as described in the first aspect is implemented.

[0048] In the embodiment of the present application, data is collected in real time through a multi-source sensor network, and situational modeling is performed on complex environmental information to dynamically reflect various environmental factors and generate a dynamic situational model; based on the dynamic situational model, a multi-target tracking algorithm is used to accurately locate and continuously track the position of the wounded by integrating spatiotemporal information and appearance features to ensure positioning accuracy, and distributed perception fusion technology is used to efficiently fuse different sensor data between network nodes to enhance system robustness and generate accurate information on the position of the wounded; based on the accurate information on the position of the wounded, a quantum support vector machine algorithm is used to accelerate the feature space learning process through quantum state superposition and entanglement characteristics, and perform efficient mapping and classification boundary optimization processing, and an adaptive data stream processing technology is used to dynamically adjust the processing strategy according to the data stream characteristics to monitor the state of the wounded in real time and generate optimized health status indicator data; based on the optimized health status indicator data, when the monitoring system detects an abnormal situation, the preset emergency response mechanism is immediately activated to coordinate the on-site and remote resources to implement actions and generate a real-time monitoring plan for the state of the wounded. Data is collected in real time through a multi-source sensor network and situational modeling is performed to dynamically reflect various environmental factors and generate a dynamic situational model, ensuring the comprehensive capture of complex environmental information. Based on this model, a multi-target tracking algorithm is used to accurately locate and continuously track the position of the injured, and distributed sensing fusion technology is used to enhance the robustness of the system and ensure positioning accuracy. Subsequently, the quantum support vector machine algorithm is used to accelerate the feature space learning process, combined with adaptive data stream processing technology, to achieve efficient mapping and classification boundary optimization processing, and finally generate optimized health status indicator data. When an abnormal situation is detected, the preset emergency response mechanism is immediately activated, and on-site and remote resources are coordinated to implement actions and generate a real-time monitoring plan for the status of the injured. This method not only improves the accuracy and reliability of the status monitoring of the injured, but also significantly enhances the system's anti-interference ability and emergency response efficiency.

[0049] Furthermore, by mapping the spatiotemporal information with the appearance features, it is ensured that different types of perception data are aggregated in a unified spatiotemporal coordinate system, and spatiotemporal feature fusion data is generated, which provides a solid foundation for subsequent processing. Secondly, the multi-target tracking algorithm and prediction-correction mechanism are used to ensure the high accuracy and continuity of positioning, and generate preliminary location information of the injured. Then, the distributed perception fusion technology is used to enhance the robustness of the preliminary location information, improve the anti-interference ability and adaptability of the system, and generate highly robust location information of the injured. Finally, through deep analysis and cross-validation, all available perception data are integrated to generate the final accurate information of the injured position. This process ensures the accuracy and stability of the positioning information, greatly improving the reliability and practicality of the system.

[0050] Furthermore, by comprehensively analyzing high-precision positioning data and environmental perception data, high-precision environmental correlation data is generated to provide high-quality basic data for subsequent processing. Secondly, the quantum support vector machine algorithm is used to accelerate the feature space learning process through the superposition and entanglement characteristics of quantum states, and large-scale data sets are efficiently mapped and optimized for classification boundaries. The powerful parallel processing capabilities of quantum computing are used to quickly identify and extract key features and generate optimized feature representations. Next, adaptive data stream processing technology is used to monitor data stream changes in real time, automatically adjust model parameters and processing logic, respond to data fluctuations in different environments, and generate dynamically adjusted health status indicators. Finally, nonlinear transformation is performed through a multi-layer perceptron network to enhance feature expression capabilities and generate optimized health status indicator data. This process not only improves the accuracy and real-time performance of health status monitoring, but also significantly enhances the flexibility and adaptability of the system, ensuring efficient performance even in complex and changing environments.

[0051] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 A flowchart of a method for monitoring the status of a wounded person in a complex environment provided in an embodiment of the present application;

[0054] Figure 2 A schematic diagram of the structure of a system for monitoring the status of a wounded person in a complex environment provided in an embodiment of the present application;

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

[0056] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0057] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0058] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0059] Figure 1 A flowchart of a method for monitoring the status of a wounded person in a complex environment is provided for an embodiment of the present application. Figure 1 As shown, the method includes:

[0060] 101. Collect data in real time through multi-source sensor networks, conduct situational modeling processing on complex environmental information, dynamically reflect various environmental factors, and generate dynamic situational models;

[0061] In this step, the multi-source sensor network refers to a network composed of multiple sensors of different types (such as GPS, thermal imagers, cameras, weather stations, etc.) used to collect various data in the environment in real time.

[0062] Complex environmental information includes but is not limited to data on various environmental factors such as temperature, humidity, light, air pressure, wind speed, topography, etc. This information is crucial to understanding the current environmental conditions, especially at disaster sites or in field search and rescue.

[0063] Situational modeling processing converts the raw data collected by multi-source sensors into a situational model that reflects the current environmental conditions through machine learning and statistical analysis methods. This process involves steps such as feature extraction, pattern recognition, and dynamic updating.

[0064] A dynamic situational model is a digital model that is updated in real time and is used to describe and predict changes in complex environments. It not only reflects the current state of the environment, but can also predict future trends and provide support for decision-making.

[0065] In the embodiments of the present application, first, the system collects various types of environmental data in real time through sensors deployed at different locations; second, these data are transmitted to the central processing unit for preprocessing, such as time synchronization and noise filtering; third, advanced machine learning algorithms are used to perform feature extraction and pattern recognition on the preprocessed data to construct a situational model; finally, the situational model is continuously updated to reflect the latest environmental changes to ensure the timeliness and accuracy of the model.

[0066] Suppose in an earthquake-stricken area, a multi-source sensor network is distributed throughout the ruins, collecting environmental data such as temperature, humidity, and light in real time; this data is transmitted to the central processing unit to remove noise and synchronize timestamps; then, the system uses machine learning algorithms to generate a situational model that reflects the current environmental conditions; finally, the situational model is continuously updated based on new data to ensure that it always accurately reflects the latest situation in the disaster area.

[0067] 102. Based on the dynamic situation model, a multi-target tracking algorithm is used to accurately locate and continuously track the position of the injured by integrating spatiotemporal information and appearance features to ensure positioning accuracy. Distributed sensing fusion technology is used to efficiently fuse different sensor data between network nodes to enhance system robustness and generate accurate information on the position of the injured;

[0068] The dynamic situation model is a real-time digital model generated based on multi-source sensor data, which describes various factors of the current environment and their interrelationships. The model provides the basis for precise positioning and continuous tracking.

[0069] The multi-target tracking algorithm is an algorithm that can track multiple targets at the same time. It ensures that the position and movement trajectory of each target can be accurately captured by integrating spatiotemporal information and appearance features. Commonly used algorithms include Kalman filtering, particle filtering, etc.

[0070] Spatiotemporal information includes data in two dimensions, time and space, which are used to describe the location and movement path of the target. Time information helps track the historical trajectory of the target, while space information provides the current specific location.

[0071] Appearance features refer to the physical features of the target, such as shape, color, texture, etc., which are used to assist in identifying and distinguishing different targets. These features help improve the accuracy and robustness of positioning.

[0072] Distributed sensing fusion technology enhances the robustness and adaptability of the system by efficiently fusing data from different sensors between network nodes. This technology can maintain the normal operation of the system even in the event of sensor failure or data loss.

[0073] The precise location information of the injured is high-precision location information generated after being processed by multi-target tracking algorithm and distributed sensing fusion technology, and is used to guide rescue operations.

[0074] In the embodiment of the present application, firstly, the system uses a multi-target tracking algorithm based on a dynamic situational model to accurately locate the wounded; secondly, the system integrates spatiotemporal information and appearance features to ensure the accuracy of positioning; thirdly, it adopts distributed perception fusion technology to efficiently fuse different sensor data between network nodes; finally, the system generates and optimizes the precise information of the wounded's location to improve the robustness and anti-interference ability of the system.

[0075] For example, continuing with the above example, assume that a dynamic situational model has been established; first, the system applies a multi-target tracking algorithm, combined with spatiotemporal information and appearance features, to accurately locate the position of the wounded; secondly, the system uses distributed sensing fusion technology to efficiently fuse data from different sensors to enhance the robustness of the system; thirdly, the system generates preliminary information on the location of the wounded; finally, the system further optimizes this information to generate highly robust and accurate information on the location of the wounded.

[0076] 103. Based on the accurate information of the injured person's location, the quantum support vector machine algorithm is used to accelerate the feature space learning process through the superposition and entanglement characteristics of quantum states, and efficient mapping and classification boundary optimization processing are performed. Adaptive data stream processing technology is used to dynamically adjust the processing strategy according to the characteristics of the data stream to monitor the status of the injured person in real time and generate optimized health status indicator data;

[0077] The quantum support vector machine algorithm is an algorithm that uses the powerful parallel processing capabilities of quantum computing to accelerate the feature space learning process. It uses the superposition and entanglement characteristics of quantum states to quickly traverse all possible feature combinations and find the optimal solution.

[0078] The superposition and entanglement properties of quantum states are the basic principles of quantum mechanics, which allow quantum bits to be in multiple states at the same time and achieve a high degree of information correlation through entanglement. These properties significantly improve the speed and efficiency of feature space learning.

[0079] Efficient mapping and classification boundary optimization processing refers to optimizing the division of classification boundaries by efficiently mapping large-scale data sets, thereby improving the accuracy and speed of classification.

[0080] Adaptive data stream processing technology dynamically adjusts the processing strategy according to the characteristics of the data stream to cope with data fluctuations in different environments and ensure that the system maintains high performance in complex environments.

[0081] Optimized health status indicator data refers to data generated after a series of processing that can accurately reflect the health status of the injured and is used for real-time monitoring and emergency response.

[0082] In the embodiment of the present application, firstly, the system uses the quantum support vector machine algorithm to accelerate the feature space learning process based on the accurate information of the injured person's location; secondly, the system conducts a comprehensive analysis of the high-precision positioning data and environmental perception data to generate high-precision environmental correlation data; thirdly, the system uses the superposition and entanglement characteristics of quantum states to efficiently map and optimize the classification boundaries of large-scale data sets; finally, the system uses adaptive data stream processing technology to dynamically adjust the processing strategy according to the characteristics of the data stream to generate optimized health status indicator data

[0083] For example, continuing with the above example, assume that accurate information on the location of the injured has been generated; first, the system uses the quantum support vector machine algorithm to accelerate the feature space learning process through the superposition and entanglement characteristics of quantum states; secondly, the system conducts a comprehensive analysis of high-precision positioning data and environmental perception data to generate high-precision environmental correlation data; thirdly, the system uses the superposition and entanglement characteristics of quantum states to efficiently map large-scale data sets and optimize classification boundaries; finally, the system uses adaptive data stream processing technology to dynamically adjust the processing strategy according to the characteristics of the data stream to generate optimized health status indicator data.

[0084] 104. Based on the optimized health status indicator data, when the monitoring system detects an abnormal situation, the preset emergency response mechanism is immediately activated to coordinate actions on site and with remote resources to generate a real-time casualty status monitoring plan.

[0085] Abnormal situations refer to situations where the system finds that health status indicators exceed preset thresholds and triggers corresponding early warning mechanisms.

[0086] The preset emergency response mechanism refers to a series of pre-set emergency measures, including notifying relevant personnel, activating the alarm system, and coordinating resource allocation.

[0087] The real-time casualty status monitoring program refers to a detailed action plan generated based on optimized health status indicator data to guide the coordinated implementation of on-site and remote resources.

[0088] In the embodiment of the present application, first, the system continuously monitors the status of the injured person based on the optimized health status indicator data; second, when the monitoring system detects an abnormal situation, it immediately activates the preset emergency response mechanism; third, the system coordinates on-site and remote resources to implement specific emergency actions; finally, the system generates a detailed real-time monitoring plan for the status of the injured person to ensure the timeliness and effectiveness of the emergency response.

[0089] For example, continuing with the above example, assume that optimized health status indicator data has been generated; first, the system continuously monitors the status of the injured and immediately activates the preset emergency response mechanism when an abnormal situation is found; second, the system coordinates on-site and remote resources to implement specific emergency actions; third, the system generates a detailed real-time monitoring plan for the status of the injured; finally, the system ensures the timeliness and effectiveness of the emergency response and provides strong support for rescue operations.

[0090] In summary, steps 101 to 104 cover the complete process from environmental perception, precise positioning, health status monitoring to emergency response, aiming to provide a comprehensive and efficient casualty status monitoring system in complex environments to meet the needs of modern emergency response and medical monitoring.

[0091] In order to solve the problems of insufficient positioning accuracy and system robustness in the status monitoring of the wounded in complex environments, in some embodiments, the precise positioning and continuous tracking processing based on the dynamic situation model in step 102 includes: based on the dynamic situation model, mapping the spatiotemporal information and appearance features to ensure that different types of perception data are aggregated in a unified spatiotemporal coordinate system to generate spatiotemporal feature fusion data; based on the spatiotemporal feature fusion data, using a multi-target tracking algorithm, by integrating the spatiotemporal information and appearance features, accurately positioning and continuously tracking the position of the wounded, using a prediction-correction mechanism to ensure positioning accuracy, and generating preliminary wounded position information; based on the preliminary wounded position information, using distributed perception fusion technology, by efficiently fusing different sensor data between network nodes, the precise information on the wounded position is robustly enhanced, the system's anti-interference ability and adaptability are improved, and strong robust wounded position information is generated; based on the strong robust wounded position information, all available perception data are integrated, deep analysis and cross-validation are performed, and precise information on the wounded position is generated.

[0092] In this embodiment, mapping refers to converting spatiotemporal information and appearance features into a unified spatiotemporal coordinate system to ensure that different types of perceptual data can be effectively aggregated.

[0093] Spatiotemporal feature fusion data refers to the data set generated by mapping, which contains the integrated spatiotemporal information and appearance features. After preprocessing, these data can more accurately describe the location and movement trajectory of the target.

[0094] The preliminary casualty location information refers to the preliminary positioning result obtained by applying a multi-target tracking algorithm to the spatiotemporal feature fusion data. By using the prediction-correction mechanism, the system can continuously correct the prediction error and improve the positioning accuracy.

[0095] Robustness enhancement processing refers to the use of distributed perception fusion technology to efficiently fuse different sensor data between network nodes to improve the system's anti-interference ability and adaptability. This process ensures that the system can maintain high-precision positioning capabilities even in complex and changing environments.

[0096] Strongly robust casualty location information refers to high-precision location information generated after robustness enhancement processing, which not only improves the accuracy of positioning, but also enhances the stability and reliability of the system.

[0097] Deep analysis and cross-validation refer to in-depth analysis of all available sensory data to verify their consistency and accuracy. Through this process, the system can generate final accurate information on the location of the injured person to ensure its authenticity and reliability.

[0098] The precise location information of the injured person refers to the high-precision location information generated after being processed by the multi-target tracking algorithm and distributed sensing fusion technology.

[0099] In the embodiment of the present application, firstly, the system maps the spatiotemporal information and appearance features based on a dynamic situational model, ensures that different types of perception data are aggregated in a unified spatiotemporal coordinate system, and generates spatiotemporal feature fusion data; secondly, the system uses a multi-target tracking algorithm to accurately locate and continuously track the position of the injured by integrating spatiotemporal information and appearance features, and uses a prediction-correction mechanism to ensure positioning accuracy and generate preliminary position information of the injured; thirdly, the system uses distributed perception fusion technology to efficiently fuse different sensor data between network nodes to enhance the robustness of the precise position information of the injured, improve the system's anti-interference ability and adaptability, and generate highly robust position information of the injured; finally, the system integrates all available perception data, performs in-depth analysis and cross-validation, and generates the final precise position information of the injured.

[0100] Here is a specific example:

[0101] For example, assuming a large fire scene, first, based on the established dynamic situation model, the system maps the spatiotemporal information (such as temperature changes, smoke concentration) and appearance features (such as flame color, thermal imaging images) in the fire area to ensure that these data can be compared and processed in a unified spatiotemporal coordinate system; secondly, the system uses a multi-target tracking algorithm, combined with the spatiotemporal feature fusion data collected in real time, to accurately locate the position of the trapped persons, and uses the prediction-correction mechanism to continuously correct the prediction error and generate preliminary location information of the injured; thirdly, the system uses distributed perception fusion technology to efficiently fuse data from multiple sensors between network nodes, perform robustness enhancement processing on the preliminary location information, improve the system's anti-interference ability and adaptability, and generate highly robust location information of the injured; finally, the system integrates all available perception data, performs in-depth analysis and cross-validation to ensure the authenticity and reliability of the information, and generates the final accurate location information of the injured, providing strong support for rescue operations.

[0102] In order to solve the accuracy problem in precise positioning and continuous tracking, in some embodiments, the precise positioning and continuous tracking of the injured person based on the spatiotemporal feature fusion data described in step 102 includes: based on the spatiotemporal feature fusion data, intelligent correlation analysis is performed on the spatiotemporal information and appearance features, key features are identified and extracted, and high-precision spatiotemporal feature data is generated; based on the high-precision spatiotemporal feature data, a multi-target tracking algorithm is used to accurately locate and continuously track the position of the injured person by integrating the spatiotemporal information and appearance features, and a prediction-correction mechanism is used to perform predicted position estimation to ensure positioning accuracy and generate temporary injured person location information; based on the temporary injured person location information, multi-level data analysis is performed, in-depth analysis is performed in combination with context information, and multi-level injured person location information is generated in combination with sensor characteristics; based on the multi-level injured person location information, potential data anomalies and inconsistencies are automatically detected and corrected to generate preliminary injured person location information.

[0103] In this embodiment, high-precision spatiotemporal feature data refers to a data set generated by intelligently correlating spatiotemporal information with appearance features. These data not only contain basic information such as timestamps and geographic locations, but also key features that have been identified and extracted (such as temperature changes, light intensity, target shape and color, etc.).

[0104] Temporary casualty location information refers to the preliminary location estimate generated by the multi-target tracking algorithm combined with the prediction-correction mechanism. It not only reflects the current location of the casualty, but also includes the predicted location to ensure the continuity and accuracy of positioning. Temporary casualty location information provides the basis for subsequent multi-level data analysis.

[0105] Multi-level casualty location information refers to comprehensive location information generated through multi-level data analysis, which combines contextual information (such as environmental conditions and historical trajectories) and sensor characteristics (such as GPS accuracy and thermal imaging resolution) for in-depth analysis.

[0106] Preliminary casualty location information refers to the final location information generated after automatic detection and correction of potential data anomalies and inconsistencies. This information eliminates noise and errors in the data and ensures the authenticity and reliability of the location information.

[0107] In the embodiment of the present application, firstly, the system fuses data based on spatiotemporal features, performs intelligent correlation analysis on spatiotemporal information and appearance features, identifies and extracts key features, and generates high-precision spatiotemporal feature data; secondly, the system uses a multi-target tracking algorithm, combined with a prediction-correction mechanism, to accurately locate and continuously track the position of the injured person, and generates temporary position information of the injured person; thirdly, the system performs multi-level data analysis, combines contextual information for in-depth analysis, and considers sensor characteristics to generate multi-level position information of the injured person; finally, the system automatically detects and corrects potential data anomalies and inconsistencies, generates preliminary position information of the injured person, and ensures its accuracy and reliability.

[0108] Here is a specific example:

[0109] For example, suppose that in a mountain search and rescue mission, first, the system conducts intelligent correlation analysis on the spatiotemporal information (such as terrain height, weather changes) and appearance features (such as vegetation distribution, thermal imaging images) in the mountain area based on the collected spatiotemporal feature fusion data, identifies and extracts key features, and generates high-precision spatiotemporal feature data; secondly, the system uses a multi-target tracking algorithm, combined with a prediction-correction mechanism, to accurately locate and continuously track the position of missing persons, and generate temporary wounded location information; thirdly, the system conducts multi-level data analysis, combines contextual information (such as historical trajectory, environmental conditions) for in-depth analysis, and considers sensor characteristics (such as GPS accuracy, thermal imaging resolution) to generate multi-level wounded location information; finally, the system automatically detects and corrects potential data anomalies and inconsistencies, generates preliminary wounded location information, ensures its accuracy and reliability, and provides strong support for search and rescue operations.

[0110] In order to solve the problems of robustness and anti-interference ability of preliminary injured person location information, in some embodiments, the robustness enhancement processing based on the preliminary injured person location information in step 102 includes: based on the preliminary injured person location information, distributed fusion processing of different types of perception data is performed to ensure real-time synchronous sharing of data of each node to generate distributed fusion data; based on the distributed fusion data, distributed perception fusion technology is adopted to efficiently fuse data of different sensors between network nodes, and an advanced distributed computing framework is used to perform consistency verification and error correction processing on the injured person location information to generate strong stability location information; based on the strong stability location information, a time series analysis method is introduced to identify temporary errors caused by signal interference, ensure the continuity of the injured person location information, and generate high-precision robust location information; based on the high-precision robust location information, redundant data of different sensors are compared to identify and eliminate errors, and adaptive filtering technology is used to further optimize the data stream to generate strong robustness location information.

[0111] In this embodiment, the distributed fusion data refers to a data set generated by performing distributed fusion processing on different types of perception data (such as GPS, thermal imaging, camera, etc.).

[0112] Strong stability location information refers to the location information generated by efficiently fusing data from different sensors between network nodes through distributed sensing fusion technology, and performing consistency verification and error correction processing using an advanced distributed computing framework.

[0113] High-precision robust position information refers to the identification and correction of temporary errors caused by signal interference by introducing time series analysis methods to ensure the continuity and accuracy of the injured person's location information. High-precision robust position information not only improves the accuracy of positioning, but also enhances the system's anti-interference ability.

[0114] Strongly robust casualty location information refers to the final location information generated by comparing redundant data from different sensors to identify and eliminate errors, and using adaptive filtering technology to further optimize the data stream.

[0115] In the embodiment of the present application, firstly, the system performs distributed fusion processing on different types of perception data based on preliminary injured person location information, ensures that the data of each node is shared synchronously in real time, and generates distributed fusion data; secondly, the system adopts distributed perception fusion technology, and efficiently fuses the data of different sensors between network nodes, and uses advanced distributed computing framework to perform consistency verification and error correction processing on the injured person's location information to generate highly stable location information; thirdly, the system introduces time series analysis methods to identify temporary errors caused by signal interference, ensure the continuity of the injured person's location information, and generate high-precision and robust location information; finally, the system compares redundant data from different sensors to identify and eliminate errors, and uses adaptive filtering technology to further optimize the data stream to generate highly robust injured person location information.

[0116] Here is a specific example:

[0117] For example, suppose that in an earthquake-stricken area, first, based on the preliminary location information of the injured, the system performs distributed fusion processing on the data from different sensors (such as GPS, thermal imagers, and cameras) to ensure that the data of each node is shared synchronously in real time to generate distributed fusion data; secondly, the system adopts distributed perception fusion technology to efficiently fuse the data of different sensors between network nodes, and uses the advanced distributed computing framework to perform consistency verification and error correction processing on the location information of the injured to generate highly stable location information; thirdly, the system introduces time series analysis methods to identify temporary errors caused by signal interference, ensure the continuity of the location information of the injured, and generate high-precision and robust location information; finally, the system compares the redundant data of different sensors to identify and eliminate errors, and uses adaptive filtering technology to further optimize the data stream to generate highly robust location information of the injured, providing reliable support for rescue operations.

[0118] In order to solve the problem of efficiency and accuracy in real-time monitoring of the status of the injured, in some embodiments, the real-time monitoring of the health status based on the precise information of the injured person's position in step 103 includes: based on the precise information of the injured person's position, comprehensively analyzing the high-precision positioning data and the environmental perception data to generate high-precision environmental correlation data; based on the high-precision environmental correlation data, using the quantum support vector machine algorithm, accelerating the feature space learning process through the superposition and entanglement characteristics of quantum states, efficiently mapping and optimizing the classification boundaries of large-scale data sets, and using the powerful parallel processing capabilities of quantum computing to quickly identify and extract key features and generate optimized feature representations; based on the optimized feature representation, using adaptive data stream processing technology to monitor data stream changes in real time, automatically adjust model parameters and processing logic, respond to data fluctuations in different environments, and generate dynamically adjusted health status indicators; based on the dynamically adjusted health status indicators, performing nonlinear transformations through a multi-layer perceptron network to enhance feature expression capabilities and generate optimized health status indicator data.

[0119] In this embodiment, the high-precision environment-related data refers to a data set generated by comprehensive analysis of high-precision positioning data and environmental perception data (such as temperature, humidity, light intensity, etc.).

[0120] Optimized feature representation refers to the use of quantum support vector machine algorithms to accelerate the feature space learning process by utilizing the superposition and entanglement characteristics of quantum states, and to generate feature representations after efficient mapping and classification boundary optimization of large-scale data sets.

[0121] Dynamic adjustment of health status indicators means using adaptive data stream processing technology to monitor data stream changes in real time, automatically adjust model parameters and processing logic, and respond to health status indicators generated after data fluctuations in different environments. Dynamic adjustment of health status indicators ensures that the system can maintain high performance and stability in a complex and changing environment, and promptly reflect the health status of the injured.

[0122] Optimized health status indicator data refers to the final health status data generated after nonlinear transformation through a multi-layer perceptron network and enhanced feature expression capabilities. This data is not only highly accurate and real-time, but also can capture complex nonlinear relationships, providing more accurate and reliable health status assessment results to guide emergency response and rescue operations.

[0123] In the embodiment of the present application, firstly, the system comprehensively analyzes the high-precision positioning data and environmental perception data based on the precise information of the injured person's location, and generates high-precision environmental correlation data; secondly, the system uses the quantum support vector machine algorithm to accelerate the feature space learning process through the superposition and entanglement characteristics of quantum states, and efficiently maps and optimizes the classification boundaries of large-scale data sets, quickly identifies and extracts key features, and generates optimized feature representations; thirdly, the system adopts adaptive data stream processing technology to monitor data stream changes in real time, automatically adjust model parameters and processing logic, cope with data fluctuations in different environments, and generate dynamically adjusted health status indicators; finally, the system performs nonlinear transformation through a multi-layer perceptron network to enhance the feature expression capability and generate optimized health status indicator data.

[0124] Here is a specific example:

[0125] For example, suppose that at a disaster site, first, based on the precise location information of the injured, the system conducts a comprehensive analysis of high-precision positioning data (such as GPS coordinates) and environmental perception data (such as temperature, humidity, and air pressure) to generate high-precision environmental correlation data; secondly, the system uses the quantum support vector machine algorithm to accelerate the feature space learning process through the superposition and entanglement characteristics of quantum states, and efficiently maps and optimizes the classification boundaries of large-scale data sets, quickly identifies and extracts key features, and generates optimized feature representations; thirdly, the system uses adaptive data stream processing technology to monitor data stream changes in real time, automatically adjust model parameters and processing logic, respond to data fluctuations in different environments, and generate dynamically adjusted health status indicators; finally, the system performs nonlinear transformations through a multi-layer perceptron network to enhance feature expression capabilities and generate optimized health status indicator data, ensuring real-time and accurate monitoring of the health status of the injured, and providing strong support for emergency response and rescue operations.

[0126] In order to solve the problem of efficient feature extraction and classification optimization of high-precision environmental association data, in some embodiments, the feature space learning and classification boundary optimization processing based on the high-precision environmental association data described in step 103 includes: based on the high-precision environmental association data, the original perception data is subjected to noise filtering and correction processing, and the error caused by the acquisition time difference is eliminated through time synchronization technology to generate high-quality synchronized data; based on the high-quality synchronized data, the quantum support vector machine algorithm is used to accelerate the feature space learning process through the quantum state superposition characteristics, and the powerful parallel processing capability of quantum computing is used to map it to the quantum feature space, quickly traverse all possible feature combinations, and generate a quantum feature candidate set; based on the quantum feature candidate set, the quantum entanglement characteristics are further applied to optimize the classification boundary, and the optimal classification boundary is found in the feature space through quantum entanglement to generate an optimized classification boundary model; based on the optimized classification boundary model, the feature weight is dynamically adjusted according to the change of sensor signal strength to generate an optimized feature representation.

[0127] In this embodiment, high-quality synchronized data refers to data generated by performing noise filtering and correction processing on the original perception data and eliminating errors caused by acquisition time differences through time synchronization technology.

[0128] The quantum feature candidate set refers to the candidate feature set generated by applying the quantum support vector machine algorithm to high-quality synchronized data, using the superposition characteristics of quantum states to accelerate the feature space learning process, mapping it to the quantum feature space, and quickly traversing all possible feature combinations.

[0129] The optimized classification boundary model refers to a classification model generated by further applying the characteristics of quantum entanglement to find the optimal classification boundary in the quantum feature space. The characteristics of quantum entanglement allow the system to identify the boundaries in the feature space that best distinguish different categories, significantly improving the accuracy and efficiency of classification.

[0130] Optimized feature representation refers to the final feature representation generated after dynamically adjusting feature weights according to changes in sensor signal strength. The optimized feature representation not only captures the key features in the data, but also adapts to data fluctuations in different environments, ensuring the real-time and robustness of the system.

[0131] In the embodiment of the present application, firstly, the system performs noise filtering and correction processing on the original perception data based on high-precision environmental correlation data, eliminates the errors caused by the difference in acquisition time through time synchronization technology, and generates high-quality synchronized data; secondly, the system uses the quantum support vector machine algorithm to accelerate the feature space learning process through the superposition characteristics of quantum states, maps it to the quantum feature space, quickly traverses all possible feature combinations, and generates a quantum feature candidate set; thirdly, the system further uses the quantum entanglement characteristics to optimize the classification boundaries, finds the optimal classification boundaries in the quantum feature space, and generates an optimized classification boundary model; finally, the system dynamically adjusts the feature weights according to the changes in sensor signal strength, and generates an optimized feature representation.

[0132] Here is a specific example:

[0133] For example, suppose at a fire scene in a residential area, first, the system performs noise filtering and correction processing on the original perception data from multiple sensors (such as temperature sensors, smoke detectors, and thermal imagers) based on high-precision environmental correlation data, and eliminates errors caused by differences in acquisition time through time synchronization technology to generate high-quality synchronized data; secondly, the system uses the quantum support vector machine algorithm to accelerate the feature space learning process through the superposition characteristics of quantum states, maps it to the quantum feature space, quickly traverses all possible feature combinations, and generates a quantum feature candidate set; thirdly, the system further uses the quantum entanglement characteristics to optimize the classification boundaries, finds the optimal classification boundaries in the quantum feature space, and generates an optimized classification boundary model; finally, the system dynamically adjusts the feature weights according to changes in sensor signal strength, generates optimized feature representations, ensures real-time monitoring and accurate assessment of the status of the wounded in the fire environment, and provides reliable support for rescue operations.

[0134] In order to solve the problem of rapid response and efficient rescue in abnormal situations, in some embodiments, the real-time casualty status monitoring plan generated based on the optimized health status indicator data in step 104 includes: based on the optimized health status indicator data, automatically detecting abnormal changes and generating an abnormality detection report by setting thresholds and pattern recognition algorithms; based on the abnormality detection report, immediately activating a preset emergency response mechanism when an abnormal situation is detected, and generating an emergency response instruction; based on the emergency response instruction, dynamically deploying on-site rescue forces according to actual conditions, synchronizing relevant information to a remote expert team, and generating a collaborative rescue plan; based on the collaborative rescue plan, integrating all available information and technical means to generate a real-time casualty status monitoring plan.

[0135] In this embodiment, the abnormality detection report refers to a report generated after abnormal changes are automatically detected by setting thresholds and pattern recognition algorithms for optimized health status indicator data.

[0136] Emergency response instructions refer to specific action instructions generated after the preset emergency response mechanism is immediately activated when the system detects an abnormal situation. These instructions clearly define the emergency measures to be taken, such as dispatching specific types of rescue teams, activating alarm systems, etc., to ensure the timeliness and effectiveness of the emergency response.

[0137] A collaborative rescue plan refers to a comprehensive rescue plan generated by dynamically deploying on-site rescue forces based on actual conditions and synchronizing relevant information with a remote expert team. The plan not only covers immediate actions on site, but also includes the support and guidance of remote experts to ensure that the rescue operations are coordinated, consistent, and scientifically reasonable.

[0138] The real-time casualty status monitoring plan refers to the final monitoring plan generated after integrating all available information and technical means. The plan comprehensively reflects the changes in the status of the casualties and the progress of rescue, provides real-time updated data support, and ensures the effectiveness and continuity of the rescue operation.

[0139] In the embodiments of the present application, firstly, the system automatically detects abnormal changes and generates an abnormality detection report based on the optimized health status indicator data by setting thresholds and pattern recognition algorithms; secondly, when the system detects an abnormal situation, it immediately activates the preset emergency response mechanism and generates an emergency response instruction; thirdly, the system dynamically deploys on-site rescue forces according to actual conditions, synchronizes relevant information to the remote expert team, and generates a collaborative rescue plan; finally, the system integrates all available information and technical means to generate a real-time casualty status monitoring plan.

[0140] Here is a specific example:

[0141] For example, suppose that at a landslide disaster site, first, based on the optimized health status indicator data, the system automatically detects the sudden temperature rise and abnormal breathing rate in a certain area by setting thresholds and pattern recognition algorithms, and generates an abnormality detection report; secondly, when the system detects an abnormal situation, it immediately activates the preset emergency response mechanism, generates an emergency response instruction, instructs the dispatch of a medical team and activates the alarm system; thirdly, the system dynamically deploys on-site rescue forces according to actual conditions, synchronizes relevant information to the remote expert team, and generates a collaborative rescue plan, including dispatching drones for aerial reconnaissance and supporting the medical team's remote diagnosis; finally, the system integrates all available information and technical means to generate a real-time casualty status monitoring plan to ensure continuous monitoring of the casualty status and effective coordination of rescue operations, providing strong support for disaster response.

[0142] This application considers that in order to solve the problems of insufficient positioning accuracy and large prediction errors in the prior art, a further solution for accurate positioning and continuous tracking of the injured person is proposed in some embodiments. This solution aims to significantly improve the accuracy and reliability of positioning by introducing Kalman filtering technology and adaptive weighting mechanism, combined with an inertial measurement unit, to achieve real-time update of the injured person's position information and prediction compensation of potential position offsets. Therefore, a new optional solution is proposed, which includes:

[0143] Based on the high-precision spatiotemporal feature data, a multi-target tracking algorithm is used to accurately locate and continuously track the position of the injured by integrating spatiotemporal information and appearance features. The prediction-correction mechanism is used to estimate the predicted position to ensure positioning accuracy and generate temporary position information of the injured, including:

[0144] Based on the high-precision spatiotemporal feature data, Kalman filtering technology is applied to smooth sensor readings to reduce the impact of measurement noise, and spatial interpolation methods are used to estimate feature values ​​of locations that are not directly observed to enhance input data integrity to generate prediction errors;

[0145] The prediction error is calculated using the following formula:

[0146]

[0147] Among them, ∈ t is the prediction error at time point t; x t is the position at the current time point t; φ(x t ,i) is the position of the i-th sensor at x t The characteristic function at w i is the weight of each sensor; i is the index of the sensor, from 1 to N; N is the number of sensors; μ i is the average eigenvalue of sensor i; η and σ are nonlinear adjustment parameters; ||x t -x t-1 || is the distance between the current position and the previous position; Δv t is the speed change; α and τ are additional nonlinear adjustment parameters;

[0148] Based on the prediction error, an adaptive weight mechanism is introduced to dynamically adjust the weight in the error assessment, a residual analysis method is used to detect abnormal points in the prediction error distribution, and an error feedback loop is performed for comparative analysis to generate a correction error;

[0149] The correction error is calculated using the following formula:

[0150]

[0151] Among them, δ t is the correction error at time point t; ∈ t is the prediction error; λ, ω and γ are nonlinear adjustment parameters; Δt is the time step; is the predicted position; z t is the actual observed value at time point t; is the mapping of the observation model to the predicted position; β j and ρ j is the weight and scale parameter of each observation source; j is the index of the observation source, ranging from 1 to M; M is the number of observation sources; θ and κ are additional nonlinear adjustment parameters used to introduce the Sigmoid function to simulate dynamic adjustment in complex environments; x t is the position at the current time point t;

[0152] Based on the correction error, combined with the inertial measurement unit, the position information is updated in real time through the extended Kalman filter, and a historical trajectory learning method is implemented to predict and compensate for potential position offsets and generate temporary casualty position information.

[0153] The method aims to smooth sensor readings based on high-precision spatiotemporal feature data and apply Kalman filtering technology to reduce the impact of measurement noise; estimate the feature values ​​of positions that are not directly observed through spatial interpolation methods to enhance the integrity of input data; calculate the prediction error and introduce an adaptive weight mechanism to dynamically adjust the weight in the error assessment; use the residual analysis method to detect abnormal points in the prediction error distribution and perform an error feedback loop for comparative analysis; calculate the correction error and combine it with the inertial measurement unit to update the position information in real time through the extended Kalman filter, implement a historical trajectory learning method to predict and compensate for potential position offsets, and generate temporary casualty position information.

[0154] In the prediction error, the sensor error term It is used to quantify the difference between the eigenvalue of each sensor at the current position and its average eigenvalue, so as to evaluate the error contribution of each sensor; the sensor error term is calculated by the squared difference to calculate the position x of each sensor at the current time point t t The characteristic function φ(x t ,i) and the average characteristic value μ of the sensor i The deviation between them is multiplied by the sensor weight w i , and finally the sum is obtained to obtain the comprehensive error of all sensors; the position continuity penalty term η·exp(-||x t -x t-1 || 2 / (2σ 2 )): Introduce an exponential decay factor to give additional penalties when the position changes too much, to ensure the rationality of the position change; the position continuity penalty term is calculated through the exponential function exp(-||x t -x t-1 || 2 / (2σ 2 )) to measure the current position x t With the previous position x t-1 The distance between ||x t -x t-1 ||, and combined with the nonlinear adjustment parameters η and σ to adjust to prevent large errors caused by position mutations; the speed change penalty term α·log(1+|Δv t | / τ): Perform logarithmic transformation on the speed change to prevent large errors caused by sudden speed changes and ensure the stability of the model; the speed change penalty term is calculated by the logarithmic function log(1+|Δv t | / τ) to measure the velocity change Δv tThe influence of is taken into account and the nonlinear adjustment parameters α and τ are adjusted to ensure that the model can remain stable when the speed changes greatly.

[0155] Among them, w i Set according to the historical performance and confidence level of the sensor, usually determined by experiments or experience; μ i Obtained through statistical analysis of historical sensor data; η and σ are adjusted through experiments to find the optimal parameter combination; ||x t -x t-1 ||Get from GPS or other positioning devices; Δv t Obtained through accelerometer or velocity sensor; α and τ are adjusted through experiments to ensure that the velocity change term is reasonable;

[0156] In the correction error, the prediction error term ∈ t : Comprehensively consider the influence of sensor error, position continuity and speed change, evaluate the prediction error at the current time point; nonlinear adjustment parameters Adjust the nonlinear part of the prediction error to make the model more flexible; weight and scale parameters Adjust the contribution of different observation sources through the logarithmic function To measure the actual observed value z t Mapping with predicted positions The difference between them, combined with the weight β j and the scale parameter ρ j Adjustment; Sigmoid function term Simulate dynamic adjustments in complex environments to ensure smooth transition of error correction. To simulate the dynamic adjustment in complex environment, the nonlinear adjustment parameters θ and κ are optimized to ensure the smooth transition of error correction;

[0157] Among them, ∈ t It is obtained by calculating the prediction error; λ, ω and γ are adjusted through experiments to find the optimal parameter combination; Δt is set to a fixed time step, such as 0.1 second; Prediction through extended Kalman filter algorithm; t Actual observation data from sensors; Predicted position by observing the model map; β j and ρ j It is set according to the reliability and importance of the observation source; θ and κ are adjusted through experiments to ensure the smoothness of the Sigmoid function;

[0158] Assuming that in a complex mountain rescue environment, the system accurately locates and continuously tracks the position of the injured based on high-precision spatiotemporal feature data;

[0159] Assume that the number of sensors N = 5 and the sensor weight w i =[0.2,0.2,0.2,0.2,0.2], sensor average eigenvalue μ i =[1.0,1.2,1.1,0.9,1.3], nonlinear adjustment parameter η = 0.5, σ = 0.3, the distance between the current position and the previous position || x t -x t-1 ||=0.8m, speed change Δv t =0.5m / s, additional nonlinear adjustment parameters α=0.4, τ=0.6;

[0160]

[0161] Assuming nonlinear adjustment parameters λ = 0.3, ω = 0.2, γ = 0.1, time step Δt = 0.1 seconds, the predicted position Actual observed value z t =[10.1,10.4], observation model Observation source weight β j =[0.3,0.7], scale parameter ρ j =[0.5,0.5], Sigmoid function parameters κ = 0.5, θ = 0.6;

[0162]

[0163]

[0164] Assuming that the threshold is set to 0.7, since the calculated result 0.65 is less than the set threshold, it shows that the injured person position monitoring scheme has high accuracy and reliability, and can ensure accurate monitoring of the injured person's position in complex environments. This is because the lower correction error reflects that the model can effectively reduce the prediction error under the current environmental conditions without affecting the real-time performance and response speed. Through the above steps, the real-time update of the injured person's position and the prediction compensation of potential position offset are ensured, which improves the reliability and scientificity of the rescue operation and enhances the accuracy and response speed of the entire health monitoring system.

[0165] This application considers that in order to solve the problems of low feature extraction efficiency and complex feature space learning process in the prior art, a further solution for generating quantum feature candidate sets based on high-quality synchronous data is proposed in some embodiments. This solution aims to accelerate the feature space learning process by using the quantum support vector machine algorithm and the quantum state superposition characteristics, and use the powerful parallel processing capabilities of quantum computing to map to the quantum feature space, and quickly traverse all possible feature combinations, thereby significantly improving the accuracy and efficiency of feature extraction. Therefore, a new optional solution is proposed, which includes:

[0166] Based on the high-quality synchronized data, the quantum support vector machine algorithm is used to accelerate the feature space learning process through the quantum state superposition characteristics, and the powerful parallel processing capability of quantum computing is used to map it to the quantum feature space, quickly traverse all possible feature combinations, and generate a quantum feature candidate set, including:

[0167] Based on the high-quality synchronized data, independent component analysis is used to reduce the dimension, extract key features in the data, remove redundant information, use kernel density estimation to evaluate the importance of each feature, and select the most representative features for subsequent mapping to generate feature representations mapped into the quantum feature space;

[0168] The feature representation mapped into the quantum feature space is calculated using the following formula:

[0169]

[0170] Where Φ(x) is the feature representation mapped into the quantum feature space; x is the input high-quality synchronization data; x i is the training sample; w i is the weight of each training sample; K(x,x i ) is the kernel function, which is used to capture the nonlinear relationship between data; b is the bias term; is the activation function, used to introduce nonlinear transformation; α j is the feature point weight; j is the nonlinear adjustment parameter for each feature point; z j is the reference point in the feature space; u is the index of the training sample, from 1 to N; N is the number of training samples; j is the index of the feature point, from 1 to M; M is the number of feature points;

[0171] Based on the feature representation mapped into the quantum feature space, a clustering algorithm is used to identify potential patterns and cluster centers in the feature space as preliminary candidate points, and feature point screening is performed to remove feature points that are highly similar to or repeated with existing candidate points to generate quantum feature candidates;

[0172] The quantum feature candidates are calculated using the following formula:

[0173]

[0174] Among them, C j is the jth quantum feature candidate; Φ(x) is the feature representation mapped into the quantum feature space; y k are other reference points in the feature space; η k and β k is the scale and power parameter of each reference point; λ is the nonlinear adjustment parameter; θ is the additional nonlinear adjustment parameter; x is the space of input data; k is the index of other reference points, from 1 to P; P is the number of other reference points; y j is a specific candidate feature point; j and v j is the distance and attenuation parameter of the feature point; x is the input high-quality synchronization data; Φ(y j ) is a specific candidate feature point y j Feature representation mapped into quantum feature space;

[0175] Based on the quantum feature candidates, cross-validation is used to evaluate the stability and reliability of each candidate point, and candidate points with inconsistent performance on different data subsets are eliminated. The greedy algorithm is applied to gradually add the optimal candidate points to generate a quantum feature candidate set.

[0176] This method aims to use independent component analysis to reduce dimensions based on high-quality synchronized data, extract key features, and remove redundant information; use kernel density estimation to evaluate the importance of each feature, and select the most representative features for subsequent mapping; calculate the feature representation mapped to the quantum feature space; identify potential patterns and cluster centers in the feature space as preliminary candidate points, and perform feature point screening; cross-validate to evaluate the stability and reliability of each candidate point, and apply a greedy algorithm to gradually add the optimal candidate points to generate a quantum feature candidate set. The overall design is to ensure that the system can still efficiently extract the most representative features in a complex environment through multi-step feature optimization and screening.

[0177] In the feature representation mapped to the quantum feature space, the training sample weights and nonlinear transformation terms Capture input data x and training samples x i The nonlinear relationship between them is used, and different weights w are given to different training samples. i ; Kernel function K(x,x i ) is used to capture the nonlinear relationship between data, the bias term b adjusts the overall offset of the feature representation to ensure the flexibility of the model; the feature point weighted index term For the reference point z in the feature space j Weighted, combined with nonlinear adjustment parameter γ j Adjust the influence range of each feature point;

[0178] Among them, w i According to the historical performance and importance of the training samples, K(x,x i ) Select a suitable kernel function (such as Gaussian kernel) and determine it through experiments; b Adjust it through training data; Obtained by choosing an appropriate activation function; α j and γ j Through experimental adjustment, find the optimal parameter combination; j Select representative samples from the training set as reference points;

[0179] Among the quantum feature candidates, the feature representation term Φ(x): the feature representation mapped to the quantum feature space; other reference point weighted power terms For other reference points y in the feature space k Weighted, combined with the scale parameter β k and the power parameter η k Adjust the influence range of each reference point; nonlinear adjustment exponential term exp(-λ·||Φ(x)-Φ(y j )|| 2 ): An exponential decay factor is introduced to make the difference between feature representations more obvious; the sinusoidal modulation term sin(θ·||Φ(x)-Φ(y j )||): Introducing periodic changes to enhance the distinguishability of feature representation; distance attenuation term Attenuate the distance between feature representations to prevent feature points that are too far away from having too much influence on the results;

[0180] Among them, Φ(x) is calculated by mapping to the feature representation in the quantum feature space; η k and β k Set according to the importance and distribution of the reference points; λ is adjusted through experiments to find the optimal parameter combination; θ is adjusted through experiments to ensure the reasonable effect of sinusoidal modulation; y k Select representative reference points from the feature space; ρ j and ν j Through experimental adjustment, ensure that the distance attenuation effect is reasonable;

[0181] Assume that in a complex earthquake rescue environment, the system monitors the status of the injured based on high-quality synchronous data; Assume that the number of training samples N = 100, the training sample weight w i =[0.1,0.1,…,0.1], kernel function K(x,x i ) Select Gaussian kernel, bias term b = 0.5, activation function F selects ReLU, number of feature points M = 50, feature point weight α j =[0.2,0.2,…,0.2], nonlinear adjustment parameter γj =[0.3,0.3,…,0.3], the reference point z in the feature space j Randomly select from the training set;

[0182]

[0183] Assume that the number of other reference points P = 30, the scale parameter β k =[0.4,0.4,…,0.4], the secondary parameter η k = [0.5, 0.5, ..., 0.5], nonlinear adjustment parameter λ = 0.6, additional nonlinear adjustment parameter θ = 0.7, distance parameter ρ of feature points j =[0.8,0.8,…,0.8], attenuation parameter v j =[0.9,0.9,…,0.9], specific candidate feature point y j Random selection from feature space;

[0184]

[0185] Assuming that the threshold is set to 0.8, since all the values ​​of the calculation results are greater than the set threshold, it shows that the feature extraction scheme has high accuracy and stability, and can ensure effective monitoring of the status of the injured in complex environments. This is because the higher feature representation strength reflects that the model can effectively extract the most representative features under the current environmental conditions, while maintaining the stability and consistency of the feature representation. Through the above steps, the real-time update of the status of the injured and the prediction and compensation of potential anomalies are ensured, the reliability and scientificity of the rescue operation are improved, and the accuracy and response speed of the entire health monitoring system are enhanced.

[0186] Figure 2 The present application provides a schematic diagram of a system for monitoring the status of a wounded person in a complex environment. Figure 2 As shown, the device comprises:

[0187] The collection module 21 is used to collect data in real time through a multi-source sensor network, perform situation modeling processing on complex environmental information, dynamically reflect various environmental factors, and generate a dynamic situation model;

[0188] The positioning module 22 is used to accurately locate and continuously track the position of the injured person based on the dynamic situation model by using a multi-target tracking algorithm and integrating spatiotemporal information and appearance features to ensure positioning accuracy, and adopts distributed sensing fusion technology to efficiently fuse different sensor data between network nodes to enhance system robustness and generate accurate information on the position of the injured person;

[0189] A mapping module 23 is used to use the quantum support vector machine algorithm based on the accurate information of the injured person's position, accelerate the feature space learning process through the superposition and entanglement characteristics of quantum states, perform efficient mapping and classification boundary optimization processing, and adopt adaptive data stream processing technology to dynamically adjust the processing strategy according to the data stream characteristics to monitor the status of the injured person in real time and generate optimized health status indicator data;

[0190] The detection module 24 is used to immediately activate the preset emergency response mechanism based on the optimized health status indicator data when the monitoring system detects an abnormal situation, coordinate actions on site and with remote resources, and generate a real-time casualty status monitoring plan.

[0191] Figure 2 The wounded status monitoring system suitable for complex environments can be executed Figure 1 The implementation principle and technical effect of the method for monitoring the status of a wounded person in a complex environment described in the embodiment shown are not repeated here. The specific way in which each module and unit performs operations in the above embodiment of a system for monitoring the status of a wounded person in a complex environment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0192] In one possible design, Figure 2 The embodiment shown in the figure is a system for monitoring the status of a wounded person in a complex environment, which can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0193] 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 .

[0194] The processing component 32 is used to: collect data in real time through a multi-source sensor network, perform situational modeling on complex environmental information, dynamically reflect various environmental factors, and generate a dynamic situational model; based on the dynamic situational model, use a multi-target tracking algorithm to accurately locate and continuously track the position of the injured by integrating spatiotemporal information and appearance features to ensure positioning accuracy, use distributed perception fusion technology to efficiently fuse different sensor data between network nodes to enhance system robustness and generate accurate information on the position of the injured; based on the accurate information on the position of the injured, use a quantum support vector machine algorithm to accelerate the feature space learning process through quantum state superposition and entanglement characteristics, perform efficient mapping and classification boundary optimization processing, use adaptive data stream processing technology to dynamically adjust the processing strategy according to the data stream characteristics, so as to monitor the status of the injured in real time and generate optimized health status indicator data; based on the optimized health status indicator data, when the monitoring system detects an abnormal situation, immediately activate the preset emergency response mechanism, coordinate on-site and remote resources to implement actions, and generate a real-time monitoring plan for the status of the injured.

[0195] 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.

[0196] 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.

[0197] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0198] 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.

[0199] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0200] 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.

[0201] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The illustrated embodiment is a method for monitoring the status of a wounded person in a complex environment.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for monitoring the status of a wounded person in a complex environment, characterized in that: include: Through real-time data collection through multi-source sensor networks, complex environmental information is modeled and processed to dynamically reflect various environmental factors and generate dynamic situation models; Based on the dynamic situation model, a multi-target tracking algorithm is used to accurately locate and continuously track the position of the injured by integrating spatiotemporal information and appearance features to ensure positioning accuracy. Distributed perception fusion technology is used to efficiently fuse different sensor data between network nodes to enhance system robustness and generate accurate information on the position of the injured. Based on the precise location information of the injured, the quantum support vector machine algorithm is used to accelerate the feature space learning process through the superposition and entanglement characteristics of quantum states, and perform efficient mapping and classification boundary optimization processing. The adaptive data stream processing technology is used to dynamically adjust the processing strategy according to the data stream characteristics to monitor the status of the injured in real time and generate optimized health status indicator data; Based on the optimized health status indicator data, when the monitoring system detects an abnormal situation, the preset emergency response mechanism is immediately activated to coordinate actions on site and with remote resources to generate a real-time casualty status monitoring plan.

2. The method according to claim 1, characterized in that Based on the dynamic situation model, the multi-target tracking algorithm is used to accurately locate and continuously track the position of the injured by integrating spatiotemporal information and appearance features to ensure positioning accuracy. Distributed perception fusion technology is used to efficiently fuse different sensor data between network nodes to enhance system robustness and generate accurate information on the position of the injured, including: Based on the dynamic situation model, the spatiotemporal information and the appearance features are mapped to ensure that different types of perception data are aggregated in a unified spatiotemporal coordinate system to generate spatiotemporal feature fusion data; Based on the spatiotemporal feature fusion data, a multi-target tracking algorithm is used to accurately locate and continuously track the position of the injured by integrating spatiotemporal information and appearance features, and a prediction-correction mechanism is used to ensure positioning accuracy and generate preliminary position information of the injured; Based on the preliminary position information of the injured, the distributed sensing fusion technology is used to efficiently fuse different sensor data between network nodes to perform robustness enhancement processing on the precise position information of the injured, thereby improving the anti-interference ability and adaptability of the system and generating highly robust position information of the injured; Based on the highly robust casualty location information, all available perception data are integrated, deep analysis and cross-validation are performed, and accurate casualty location information is generated.

3. The method according to claim 2, characterized in that Based on the spatiotemporal feature fusion data, a multi-target tracking algorithm is used to accurately locate and continuously track the position of the injured by integrating spatiotemporal information and appearance features, and a prediction-correction mechanism is used to ensure positioning accuracy to generate preliminary position information of the injured, including: Based on the spatiotemporal feature fusion data, intelligent correlation analysis is performed on the spatiotemporal information and appearance features, key features are identified and extracted, and high-precision spatiotemporal feature data is generated; Based on the high-precision spatiotemporal feature data, a multi-target tracking algorithm is used to accurately locate and continuously track the position of the injured by integrating spatiotemporal information and appearance features, and a prediction-correction mechanism is used to estimate the predicted position to ensure positioning accuracy and generate temporary position information of the injured; Based on the temporary injured person location information, multi-level data analysis is performed, in-depth analysis is performed in combination with context information, and multi-level injured person location information is generated in combination with sensor characteristics; Based on the multi-level casualty location information, potential data anomalies and inconsistencies are automatically detected and corrected to generate preliminary casualty location information.

4. The method according to claim 2, characterized in that: Based on the preliminary position information of the injured, the distributed sensing fusion technology is used to efficiently fuse different sensor data between network nodes to perform robustness enhancement processing on the precise position information of the injured, improve the anti-interference ability and adaptability of the system, and generate highly robust position information of the injured, including: Based on the preliminary injured person location information, distributed fusion processing is performed on different types of perception data to ensure that each node data is synchronously shared in real time and generate distributed fusion data; Based on the distributed fusion data, the distributed sensing fusion technology is adopted to efficiently fuse the data of different sensors between network nodes, and the advanced distributed computing framework is used to perform consistency verification and error correction processing on the position information of the injured to generate strong stability position information; Based on the strong stability position information, a time series analysis method is introduced to identify temporary errors caused by signal interference, ensure the continuity of the injured person's position information, and generate high-precision robust position information; Based on the high-precision robust position information, redundant data from different sensors are compared to identify and eliminate errors, and adaptive filtering technology is used to further optimize the data stream to generate highly robust casualty position information.

5. The method according to claim 1, characterized in that Based on the accurate information of the injured person's location, the quantum support vector machine algorithm is used to accelerate the feature space learning process through the superposition and entanglement characteristics of quantum states, and efficient mapping and classification boundary optimization processing are performed. Adaptive data stream processing technology is used to dynamically adjust the processing strategy according to the data stream characteristics to monitor the status of the injured person in real time and generate optimized health status indicator data, including: Based on the accurate information of the injured person's location, a comprehensive analysis is performed on the high-precision positioning data and the environmental perception data to generate high-precision environmental correlation data; Based on the high-precision environmental correlation data, the quantum support vector machine algorithm is used to accelerate the feature space learning process through the superposition and entanglement characteristics of quantum states, and to efficiently map and optimize the classification boundaries of large-scale data sets. The powerful parallel processing capabilities of quantum computing are used to quickly identify and extract key features and generate optimized feature representations. Based on the optimized feature representation, adaptive data stream processing technology is used to monitor data stream changes in real time, automatically adjust model parameters and processing logic, respond to data fluctuations in different environments, and generate dynamically adjusted health status indicators; Based on the dynamic adjustment of the health status index, nonlinear transformation is performed through a multi-layer perceptron network to enhance the feature expression capability and generate optimized health status index data.

6. The method according to claim 5, characterized in that Based on the high-precision environmental correlation data, the quantum support vector machine algorithm is used to accelerate the feature space learning process through the superposition and entanglement characteristics of quantum states, and the large-scale data sets are efficiently mapped and the classification boundary optimization is performed. The powerful parallel processing capability of quantum computing is used to quickly identify and extract key features and generate optimized feature representations, including: Based on the high-precision environmental association data, the original perception data is subjected to noise filtering and correction processing, and the error caused by the difference in acquisition time is eliminated through time synchronization technology to generate high-quality synchronized data; Based on the high-quality synchronized data, the quantum support vector machine algorithm is used to accelerate the feature space learning process through the superposition characteristics of quantum states, and the powerful parallel processing capability of quantum computing is used to map it to the quantum feature space, quickly traverse all possible feature combinations, and generate a quantum feature candidate set; Based on the candidate set of quantum features, further applying the quantum entanglement characteristics to optimize the classification boundary, finding the optimal classification boundary in the feature space through quantum entanglement, and generating an optimized classification boundary model; Based on the optimized classification boundary model, the feature weights are dynamically adjusted according to changes in sensor signal strength to generate optimized feature representations.

7. The method according to claim 1, characterized in that When the monitoring system detects an abnormal situation based on the optimized health status indicator data, the preset emergency response mechanism is immediately activated to coordinate actions on site and with remote resources to generate a real-time casualty status monitoring plan, including: Based on the optimized health status indicator data, abnormal changes are automatically detected by setting thresholds and pattern recognition algorithms, and an abnormality detection report is generated; Based on the anomaly detection report, when an abnormal situation is detected, a preset emergency response mechanism is immediately activated to generate an emergency response instruction; Based on the emergency response instructions, dynamically deploy on-site rescue forces according to actual conditions, synchronize relevant information to the remote expert team, and generate a collaborative rescue plan; Based on the collaborative rescue plan, all available information and technical means are integrated to generate a real-time casualty status monitoring plan.

8. A system for monitoring the status of injured persons in complex environments, characterized in that: include: The collection module is used to collect data in real time through a multi-source sensor network, perform situational modeling on complex environmental information, dynamically reflect various environmental factors, and generate a dynamic situational model; A positioning module is used to accurately locate and continuously track the position of the injured person based on the dynamic situation model and the multi-target tracking algorithm by integrating spatiotemporal information and appearance features to ensure positioning accuracy. The distributed sensing fusion technology is used to efficiently fuse different sensor data between network nodes to enhance system robustness and generate accurate information on the position of the injured person. A mapping module is used to use the quantum support vector machine algorithm based on the precise information of the injured person's location to accelerate the feature space learning process through the superposition and entanglement characteristics of quantum states, perform efficient mapping and classification boundary optimization processing, and adopt adaptive data stream processing technology to dynamically adjust the processing strategy according to the data stream characteristics to monitor the status of the injured person in real time and generate optimized health status indicator data; The detection module is used to immediately activate the preset emergency response mechanism based on the optimized health status indicator data when the monitoring system detects an abnormal situation, coordinate on-site and remote resources to implement actions, and generate a real-time casualty status monitoring plan.

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 method for monitoring the status of a wounded person in a complex environment 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 method for monitoring the status of a wounded person applicable to a complex environment as claimed in any one of claims 1 to 7 is implemented.

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