Method and system for determining priority of sick and wounded based on PHI score

Abstract

The invention provides a method and system for determining the priority of the sick and wounded based on PHI scores. The method comprises the following steps: acquiring physiological parameters and injury description through a multi-source data acquisition device, and generating a sick and wounded information record; based on the information records of the sick and wounded, carrying out nonlinear relation modeling and dynamic evaluation by applying a PHI dynamic scoring algorithm, carrying out quantitative analysis by adopting a real-time data analysis technology, and fusing current vital sign stability and injury history to generate a PHI score; based on the PHI score, simulating the expected improvement effect of a plurality of medical resource allocation schemes on the survival rate of the sick and wounded by using a priority adaptive adjustment algorithm, optimizing a treatment sequence by using a reinforcement learning mechanism, and generating a sick and wounded treatment priority list; and based on the treatment priority list of the sick and wounded, integrating the treatment priority list of the sick and wounded into a unified data format, immediately transmitting the treatment priority list to a first-line rescue team, and generating a priority determination strategy of the sick and wounded. According to the technical scheme provided by the invention, the priority of the sick and wounded can be accurately determined.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of medical emergency technology, and in particular to a method and system for determining the priority of injured and sick persons based on PHI scores. Background Art

[0002] With the growing demand for medical emergency and disaster response, how to efficiently and accurately determine the priority of treating the wounded and sick has become an important challenge facing modern medical systems. In large-scale emergencies (such as natural disasters, traffic accidents or wars), medical resources are often limited and scattered. In this case, it is crucial to quickly and accurately assess the severity of each patient's injury and reasonably allocate medical resources. The basis for achieving this goal is to obtain the physiological parameters and injury descriptions of the wounded and sick through multi-source data acquisition equipment, perform timeline consistency processing, and generate information records of the wounded and sick. In addition, real-time data analysis technology and dynamic scoring algorithms are required to ensure the accuracy and timeliness of the evaluation results.

[0003] At present, the common methods for determining the priority of the injured and sick mainly rely on the experience and judgment of on-site medical staff and simple scoring systems (such as START scoring). Although these methods can help medical staff make preliminary judgments to a certain extent, they lack the ability to comprehensively analyze and dynamically evaluate physiological parameters. For example, the PHI dynamic scoring algorithm can perform nonlinear relationship modeling and dynamic evaluation of the physiological parameters of each injured and sick person, use real-time data analysis technology for quantitative analysis, and integrate the current stability of vital signs with injury history to generate a more accurate PHI score.

[0004] However, the existing solutions have some significant defects. First, the priority of the wounded and sick is not accurately determined: the traditional method relies too much on the subjective judgment of medical staff, and it is difficult to fully consider the complex situation of the wounded and sick, resulting in inaccurate priority determination; secondly, the existing methods lack an optimization mechanism for resource allocation, and cannot simulate a variety of medical resource allocation plans and evaluate their effects, thus affecting the efficiency of treatment; finally, information transmission is not timely and unified: the existing information management system is usually unable to update and convey the priority information of the wounded and sick in real time, resulting in the front-line rescue team not being able to obtain the latest guidance in the first time, delaying the time of treatment. Summary of the invention

[0005] The embodiments of the present application provide a method and system for determining the priority of the sick and injured based on PHI scores, so as to solve the problem of inaccurate determination of the priority of the sick and injured in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a method for determining priority of injured and sick persons based on PHI scores, comprising:

[0007] Obtain the physiological parameters and injury descriptions of the wounded and sick through a multi-source data collection device, perform time-axis consistency processing, and generate a record of the information of the wounded and sick;

[0008] Based on the record of the information of the wounded and sick, use the PHI dynamic scoring algorithm to model and dynamically evaluate the non-linear relationship of the physiological parameters of each wounded and sick, perform quantitative analysis using real-time data analysis technology, integrate the current vital sign stability and injury history, and generate a PHI score;

[0009] Based on the PHI score, use the priority adaptive adjustment algorithm to simulate the expected improvement effect of multiple medical resource allocation plans on the survival rate of the wounded and sick, and use the reinforcement learning mechanism to optimize the treatment order to ensure the best efficiency of the allocation of limited medical resources, and generate a list of treatment priorities for the wounded and sick;

[0010] Based on the list of treatment priorities for the wounded and sick, combine the corresponding timestamps to integrate them into a unified data format, and immediately convey them to the front-line rescue team through a mobile terminal to generate a strategy for determining the priorities of the wounded and sick.

[0011] Optionally, the above-mentioned based on the record of the information of the wounded and sick, using the PHI dynamic scoring algorithm to model and dynamically evaluate the non-linear relationship of the physiological parameters of each wounded and sick, perform quantitative analysis using real-time data analysis technology, integrate the current vital sign stability and injury history, and generate a PHI score, including:

[0012] Based on the record of the information of the wounded and sick, perform data cleaning and outlier removal processing, perform preliminary quality control checks, and generate a high-quality record of the information of the wounded and sick;

[0013] Based on the high-quality record of the information of the wounded and sick, use the PHI dynamic scoring algorithm to model and dynamically evaluate the non-linear relationship of the physiological parameters of each wounded and sick, analyze time-varying complex patterns and interdependent relationships, and generate a preliminary risk assessment result;

[0014] Based on the preliminary risk assessment result, perform quantitative analysis using real-time data analysis technology, continuously monitor and dynamically adjust the analysis parameters to adapt to the specific conditions of different wounded and sick, and generate an optimized risk assessment report;

[0015] Based on the optimized risk assessment report, integrate the current vital sign stability and injury history as key inputs to generate a PHI score.

[0016] Optionally, the above-mentioned based on the high-quality record of the information of the wounded and sick, using the PHI dynamic scoring algorithm to model and dynamically evaluate the non-linear relationship of the physiological parameters of each wounded and sick, analyze time-varying complex patterns and interdependent relationships, and generate a preliminary risk assessment result, including:

[0017] Based on the high-quality patient information records, further detailed review and classification of each patient's physiological parameters are performed to generate a high-quality physiological parameter set;

[0018] Based on the high-quality physiological parameter set, the PHI dynamic scoring algorithm is used to model and dynamically evaluate the nonlinear relationship of each patient's physiological parameters, consider the time evolution characteristics, analyze the time-varying complex patterns and interdependencies, and generate an internal connection model;

[0019] Based on the intrinsic connection model, the complex interactions between physiological parameters are deeply analyzed, the changing trends and mutual influences in different time periods are considered, and an intrinsic connection evaluation report is generated;

[0020] Based on the intrinsic connection assessment report, key risk factors and potential health threats are identified and preliminary risk assessment results are generated.

[0021] Optionally, based on the preliminary risk assessment results, real-time data analysis technology is used to perform quantitative analysis, and analysis parameters are dynamically adjusted through continuous monitoring to adapt to the specific conditions of different injured and sick persons, so as to generate an optimized risk assessment report, including:

[0022] Based on the preliminary risk assessment results, comprehensively analyze the changing trends and interdependencies of the physiological parameters of each patient and generate detailed time-varying analysis data;

[0023] Based on the detailed time-varying analysis data, real-time data analysis technology is used to perform quantitative analysis, and by continuously monitoring changes in physiological parameters, the analysis parameters are dynamically adjusted according to the specific conditions of different patients and wounded, to generate accurate quantitative analysis results;

[0024] Based on the precise quantitative analysis results, a comprehensive assessment of potential risk factors is conducted, focusing on the immediate status and historical situation of the injured and sick, and generating detailed risk assessment indicators;

[0025] Based on the refined risk assessment indicators, statistical analysis is applied to deeply explore the health status, evaluate the urgency of the current status, and generate an optimized risk assessment report.

[0026] Optionally, based on the PHI score, a priority adaptive adjustment algorithm is used to simulate the expected improvement effect of multiple medical resource allocation schemes on the survival rate of the wounded and sick, and a reinforcement learning mechanism is used to optimize the treatment sequence to ensure the best efficiency of limited medical resource allocation, and generate a priority list for the treatment of the wounded and sick, including:

[0027] Based on the PHI score, the health status and risk level of each patient are comprehensively assessed, and a patient demand analysis report is generated in combination with the number and type of currently available medical resources;

[0028] Based on the analysis report of the needs of the wounded and sick, using the priority adaptive adjustment algorithm, simulate multiple medical resource allocation plans, consider the expected improvement effect of the survival rate of the wounded and sick under different resource allocation strategies, and generate a set of feasible resource allocation pre - plans;

[0029] Based on the set of feasible resource allocation pre - plans, adopt a reinforcement learning mechanism to optimize the treatment order, and through iterative learning of the optimal decision - making path, ensure the maximization of the allocation efficiency under limited medical resources, and generate optimized treatment order suggestions;

[0030] Based on the optimized treatment order suggestions, comprehensively consider the real - time availability of medical resources, and generate a list of treatment priorities for the wounded and sick.

[0031] Optionally, the step of based on the analysis report of the needs of the wounded and sick, using the priority adaptive adjustment algorithm, simulate multiple medical resource allocation plans, consider the expected improvement effect of the survival rate of the wounded and sick under different resource allocation strategies, and generate a set of feasible resource allocation pre - plans includes:

[0032] Based on the analysis report of the needs of the wounded and sick, accurately evaluate the specific medical needs of each wounded and sick, determine the required specific resource types and quantities, and generate a list of resource requirements for the wounded and sick;

[0033] Based on the list of resource requirements for the wounded and sick, use the priority adaptive adjustment algorithm, simulate multiple medical resource allocation plans, consider the expected improvement effect of the survival rate of the wounded and sick under different resource allocation strategies, and generate preliminary resource allocation pre - plans;

[0034] Based on the preliminary resource allocation pre - plans, use scenario simulation technology to evaluate the implementation effects under different scenarios, and generate a scenario simulation evaluation report;

[0035] Based on the scenario simulation evaluation report, screen the optimal resource allocation pre - plans, and combine the actual feasibility and operation complexity to generate a set of feasible resource allocation pre - plans.

[0036] Optionally, the step of based on the list of treatment priorities for the wounded and sick, combine the corresponding timestamps to integrate them into a unified data format, and immediately convey them to the front - line rescue team through a mobile terminal to generate a strategy for determining the priorities of the wounded and sick includes:

[0037] Based on the list of treatment priorities for the wounded and sick, add accurate timestamps to the priority information of each wounded and sick to generate a list of timestamp priorities;

[0038] Based on the list of timestamp priorities, integrate all relevant information into a unified data format to ensure data consistency and compatibility, and generate a standardized priority data file;

[0039] Based on the standardized priority data file, a dedicated data transmission protocol is developed, and the data is instantly transmitted to the mobile terminal device of the first-line rescue team through a secure communication channel to generate a real-time priority display interface;

[0040] Based on the real-time priority display interface, intuitive operation guidance and flexible adjustment functions are provided to quickly respond to on-site situations and generate a strategy for determining the priority of the injured and sick.

[0041] In a second aspect, an embodiment of the present application provides a system for determining priority of injured and sick persons based on PHI scores, including:

[0042] The acquisition module is used to obtain the physiological parameters and injury descriptions of the injured and sick through multi-source data acquisition equipment, perform timeline consistency processing, and generate information records of the injured and sick;

[0043] An evaluation module is used to perform nonlinear relationship modeling and dynamic evaluation on the physiological parameters of each patient based on the patient information record and using the PHI dynamic scoring algorithm, and to perform quantitative analysis using real-time data analysis technology, integrating the current vital sign stability and injury history to generate a PHI score;

[0044] A simulation module is used to simulate the expected improvement effect of various medical resource allocation schemes on the survival rate of the wounded and sick based on the PHI score and the priority adaptive adjustment algorithm, optimize the treatment sequence by using a reinforcement learning mechanism, ensure the best efficiency of limited medical resource allocation, and generate a priority list for the treatment of the wounded and sick;

[0045] A generation module is used to integrate the priority list of treatment of the wounded and sick into a unified data format based on the corresponding timestamps, and transmit it to the front-line rescue team via a mobile terminal in real time to generate a priority determination strategy for the wounded and sick.

[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 determining the priority of the injured and sick based on PHI score 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 determining the priority of the injured and sick based on the PHI score as described in the first aspect is implemented.

[0048] In the embodiment of the present application, a multi-source data acquisition device is used to obtain the physiological parameters and injury description of the injured and sick, and a timeline consistency process is performed to generate an information record of the injured and sick; based on the information record of the injured and sick, a PHI dynamic scoring algorithm is used to perform nonlinear relationship modeling and dynamic evaluation on the physiological parameters of each injured and sick, and a real-time data analysis technology is used for quantitative analysis, and the current vital sign stability and injury history are integrated to generate a PHI score; based on the PHI score, a priority adaptive adjustment algorithm is used to simulate the expected improvement effect of various medical resource allocation schemes on the survival rate of the injured and sick, and a reinforcement learning mechanism is used to optimize the order of treatment to ensure the best efficiency of limited medical resource allocation, and generate a priority list for the treatment of the injured and sick; based on the priority list for the treatment of the injured and sick, the corresponding timestamps are combined to integrate into a unified data format, and the data is immediately transmitted to the front-line rescue team through a mobile terminal to generate a priority determination strategy for the injured and sick. The physiological parameters and injury descriptions of the injured and sick are obtained through multi-source data acquisition equipment, and timeline consistency processing is performed to ensure the accuracy and timeliness of the data; the application of the PHI dynamic scoring algorithm makes it possible to model the nonlinear relationship and dynamically evaluate the physiological parameters of each injured and sick person, and the real-time data analysis technology further improves the accuracy of quantitative analysis; the PHI score is generated by integrating the current stability of vital signs and the injury history, which can more comprehensively reflect the status of the injured and sick; the priority adaptive adjustment algorithm is combined with the reinforcement learning mechanism to optimize the order of treatment, ensuring the optimal allocation efficiency of limited medical resources; finally, the information is instantly transmitted to the front-line rescue team through mobile terminals, ensuring the timeliness and accuracy of information transmission, thereby significantly improving the treatment efficiency and success rate.

[0049] Furthermore, preliminary quality control checks were carried out on the patient information records through data cleaning and outlier elimination to ensure the generation of high-quality patient information records; the PHI dynamic scoring algorithm was used for nonlinear relationship modeling and dynamic evaluation, and time-varying complex patterns and interdependencies were analyzed to generate preliminary risk assessment results; through continuous monitoring and dynamic adjustment of analysis parameters, the optimized risk assessment report is more in line with the specific circumstances of different patients; finally, the current vital sign stability and injury history are integrated as key inputs, and the generated PHI score not only accurately reflects the current status of the patient, but also takes into account its historical background, providing a scientific basis for subsequent treatment.

[0050] Furthermore, by comprehensively evaluating the health status and risk level of each patient, combined with the number and type of currently available medical resources, the generated patient demand analysis report provides a solid foundation for subsequent resource allocation; the priority adaptive adjustment algorithm is used to simulate a variety of medical resource allocation schemes, considering the expected improvement in survival rate under different resource allocation strategies, and the generated feasible resource allocation plan set provides a variety of options for actual operations; the reinforcement learning mechanism is used to optimize the order of treatment, and the optimal decision path is iteratively learned to ensure the maximization of allocation efficiency under limited medical resources; the final generated priority list for the treatment of the patient comprehensively considers the real-time availability of medical resources, ensures the efficiency and pertinence of the treatment process, and improves the overall treatment effect.

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

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

[0053] Figure 1 A flowchart of a method for determining the priority of the injured and sick based on PHI score provided in an embodiment of the present application;

[0054] Figure 2 A schematic diagram of the structure of a system for determining priority of the injured and sick based on PHI score 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 determining the priority of the injured and sick based on PHI score is provided for an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0060] 101. Obtain the physiological parameters and injury descriptions of the injured and sick through multi-source data acquisition equipment, perform timeline consistency processing, and generate information records of the injured and sick;

[0061] In this step, the multi-source data acquisition equipment includes various medical monitoring devices, such as electrocardiographs, blood pressure monitors, blood oxygen saturation monitors, etc., which are used to obtain the physiological parameters of the injured and sick in real time. These devices may also include mobile applications or handheld terminals to record injury descriptions and other relevant information.

[0062] Physiological parameters refer to vital signs data measured directly by medical devices, such as heart rate, blood pressure, respiratory rate, etc.

[0063] Injury description refers to the information about the injuries of the injured person obtained by medical staff through observation and questioning, such as the location of the injury, the mechanism of injury, etc.

[0064] Timeline consistency processing ensures that all collected data remain synchronized in the time dimension, that is, the correspondence between data from different sources at the same time point is accurate. This step is crucial for subsequent analysis and ensures the temporal consistency and accuracy of the data.

[0065] The patient information record is an integrated data set that includes all the patient's physiological parameters and injury descriptions. It is processed for timeline consistency to form a standardized patient information record, providing a basis for subsequent steps.

[0066] In an embodiment of the present application, assuming that at a large-scale traffic accident scene, first, emergency personnel use portable medical equipment to conduct preliminary assessments of multiple injured and sick persons and record vital signs data; second, medical staff use handheld terminals to input detailed descriptions of the injuries of each injured and sick person; third, the system automatically integrates these real-time data with historical medical records to ensure the consistency of the timeline; finally, all data are transmitted to a central server to generate standardized information records of the injured and sick persons for further analysis.

[0067] 102. Based on the information records of the injured and sick, a PHI dynamic scoring algorithm is used to perform nonlinear relationship modeling and dynamic evaluation on the physiological parameters of each injured and sick, and a real-time data analysis technology is used to perform quantitative analysis, integrating the current stability of vital signs and injury history to generate a PHI score;

[0068] In this step, the PHI dynamic scoring algorithm is an advanced algorithm specially designed to evaluate the physiological status of the injured and sick. It captures the complex interactions between physiological parameters through nonlinear relationship modeling and generates a comprehensive score reflecting the current health status of the injured and sick.

[0069] Nonlinear relationship modeling establishes a mathematical model to describe the complex dependencies between physiological parameters. Considering the nonlinear characteristics of physiological parameters changing over time, this modeling method can more accurately reflect the physiological change patterns in the real world.

[0070] Dynamic assessment continuously monitors and adjusts model parameters to suit the specific circumstances of different patients, ensuring that the scoring algorithm maintains its accuracy and reliability even when the patient's status changes.

[0071] Real-time data analysis technology uses advanced data analysis tools and techniques to quickly process and quantitatively analyze data collected in real time, extracting valuable information from massive data to support the dynamic evaluation process.

[0072] The stability of current vital signs takes into account the stability of the patient's current vital signs such as heart rate and blood pressure. It is one of the important indicators for assessing the health status of the patient.

[0073] The injury history considers the patient's changing trends over the past few hours, provides key information about the patient's injury mechanism and development process, and helps to more fully understand the patient's overall condition.

[0074] The final score generated by the PHI score comprehensively reflects the overall health status and risk level of the injured and sick, providing a scientific basis for priority determination.

[0075] In an embodiment of the present application, assuming that in a disaster rescue scenario, first, the central server receives and processes real-time data streams from multi-source data acquisition devices; second, the PHI dynamic scoring algorithm performs nonlinear modeling on these data to identify key vital sign patterns; third, the algorithm continuously monitors data changes and dynamically adjusts model parameters to adapt to the specific conditions of different injured and sick persons; finally, the system generates an optimized risk assessment report and calculates the PHI score of each injured and sick person as the basis for determining the next priority.

[0076] Optionally, the method in step 102, based on the patient information record, uses a PHI dynamic scoring algorithm to perform nonlinear relationship modeling and dynamic evaluation on the physiological parameters of each patient, uses real-time data analysis technology to perform quantitative analysis, integrates current vital sign stability and injury history, and generates a PHI score, including: based on the patient information record, performs data cleaning and outlier elimination processing, performs preliminary quality control inspection, and generates high-quality patient information records; based on the high-quality patient information record, uses a PHI dynamic scoring algorithm to perform nonlinear relationship modeling and dynamic evaluation on the physiological parameters of each patient, analyzes time-varying complex patterns and interdependencies, and generates preliminary risk assessment results; based on the preliminary risk assessment results, uses real-time data analysis technology to perform quantitative analysis, dynamically adjusts analysis parameters through continuous monitoring to adapt to specific conditions of different patients, and generates an optimized risk assessment report; based on the optimized risk assessment report, integrates current vital sign stability and injury history as key inputs to generate a PHI score.

[0077] In this step, data cleaning and outlier elimination are important steps to ensure the quality of information records of the injured and sick. By removing noise and erroneous data, the accuracy of subsequent analysis is guaranteed.

[0078] An initial quality control check was performed to verify the validity and completeness of the data, ensuring that all necessary physiological parameters and injury descriptions were accurately recorded.

[0079] High-quality patient information records refer to cleaned and verified data sets, which provide a reliable basis for subsequent scoring algorithms.

[0080] Time-varying complex patterns and interdependencies refer to analyzing the complex patterns of physiological parameters changing over time and their interdependencies to more accurately reflect the status of the injured or sick.

[0081] The preliminary risk assessment results are a preliminary judgment of the current health status of the injured and sick, and the risk assessment report can be further optimized based on these results.

[0082] The optimized risk assessment report combines multiple factors, such as vital sign stability and injury history, to generate a final PHI score as the basis for prioritization.

[0083] First, based on the information records of the injured and sick, data cleaning and outlier elimination are carried out, and preliminary quality control checks are performed to ensure the accuracy and completeness of the data; second, based on the high-quality information records of the injured and sick, the PHI dynamic scoring algorithm is used to perform nonlinear relationship modeling and dynamic evaluation of the physiological parameters of each injured and sick, identify time-varying complex patterns and interdependencies, and generate preliminary risk assessment results; third, based on the preliminary risk assessment results, through continuous monitoring, the analysis parameters are dynamically adjusted to adapt to the specific conditions of different injured and sick people, and an optimized risk assessment report is generated; finally, the current vital sign stability and injury history are integrated as key inputs to generate the final PHI score, providing a scientific basis for priority determination.

[0084] Optionally, based on the high-quality information records of the injured and sick, the PHI dynamic scoring algorithm is used to perform nonlinear relationship modeling and dynamic evaluation on the physiological parameters of each injured and sick, analyze the time-varying complex patterns and interdependencies, and generate preliminary risk assessment results, including: based on the high-quality information records of the injured and sick, further detailed review and classification processing are performed on the physiological parameters of each injured and sick to generate a high-quality physiological parameter set; based on the high-quality physiological parameter set, the PHI dynamic scoring algorithm is used to perform nonlinear relationship modeling and dynamic evaluation on the physiological parameters of each injured and sick, considering the time evolution characteristics, analyzing the time-varying complex patterns and interdependencies, and generating an intrinsic connection model; based on the intrinsic connection model, in-depth analysis of the complex interactions between physiological parameters is performed, considering the changing trends and mutual influences in different time periods, and generating an intrinsic connection assessment report; based on the intrinsic connection assessment report, key risk factors and potential health threats are identified to generate preliminary risk assessment results.

[0085] Based on the preliminary risk assessment results, the real-time data analysis technology is used to perform quantitative analysis, and the analysis parameters are dynamically adjusted through continuous monitoring to adapt to the specific conditions of different injured and sick persons, so as to generate an optimized risk assessment report, including: based on the preliminary risk assessment results, a comprehensive analysis of the changing trends and interdependencies of the physiological parameters of each injured and sick person is performed to generate detailed time-varying analysis data; based on the detailed time-varying analysis data, the real-time data analysis technology is used to perform quantitative analysis, and the analysis parameters are dynamically adjusted according to the specific conditions of different injured and sick persons through continuous monitoring of changes in physiological parameters to generate accurate quantitative analysis results; based on the accurate quantitative analysis results, a comprehensive assessment of potential risk factors is performed, focusing on the immediate status and historical conditions of the injured and sick persons to generate detailed risk assessment indicators; based on the detailed risk assessment indicators, statistical analysis is used to deeply explore the health status, evaluate the urgency of the current status, and generate an optimized risk assessment report.

[0086] In this step, high-quality patient information records refer to the physiological parameters and injury description data of the patients that have been processed by data cleaning and outlier elimination to ensure accuracy and completeness.

[0087] Further detailed review and classification processing is to conduct more detailed analysis and classification of these high-quality data to generate a high-quality physiological parameter set for subsequent more accurate modeling.

[0088] The high-quality physiological parameter set is a dataset generated after further detailed review and classification. It contains physiological parameters that have been strictly screened and verified, ensuring the accuracy and representativeness of the parameters for nonlinear relationship modeling and dynamic evaluation.

[0089] The intrinsic connection model is a mathematical model established through in-depth analysis of the complex interactions between physiological parameters, taking into account the changing trends and mutual influences in different time periods. This model not only describes the current physiological state, but also predicts future changing trends, providing a scientific basis for risk assessment.

[0090] Complex interactions refer to the interactions and influences between multiple physiological parameters, especially their changes over different time periods. This analysis helps to identify which combinations of parameters have a significant impact on the health status of the injured and sick, thereby guiding the formulation of treatment strategies.

[0091] Detailed time-varying analysis data is a comprehensive analysis of the changing trends and interdependencies of the physiological parameters of each patient. These data not only reflect the current status, but also include historical changing trends, providing detailed information for quantitative analysis.

[0092] The precise quantitative analysis results are specific values ​​generated by quantitative analysis of detailed time-varying analysis data. These values ​​are used to further evaluate potential risk factors and provide specific quantitative indicators.

[0093] Refined risk assessment indicators are comprehensive assessment indicators generated based on precise quantitative analysis results, focusing on the immediate status and historical situation of the injured and sick. These indicators provide detailed reference standards for the final risk assessment report.

[0094] In the embodiment of the present application, firstly, based on the high-quality information records of the injured and sick, the physiological parameters of each injured and sick are further reviewed and classified, and the PHI dynamic scoring algorithm is used to model and dynamically evaluate the nonlinear relationship of the physiological parameters of each injured and sick, considering the time evolution characteristics, analyzing the time-varying complex patterns and interdependencies, and generating an intrinsic connection model; secondly, based on the intrinsic connection model, the complex interactions between the physiological parameters are deeply analyzed, considering the changing trends and mutual influences in different time periods, identifying key risk factors and potential health threats, and generating preliminary risk assessment results; a comprehensive analysis of the changing trends and interdependencies of the physiological parameters of each injured and sick is carried out, and a detailed time-varying analysis is used for quantitative analysis. By continuously monitoring the changes in physiological parameters, the analysis parameters are dynamically adjusted according to the specific conditions of different injured and sick, and accurate quantitative analysis results are generated; finally, based on the accurate quantitative analysis results, a comprehensive evaluation of potential risk factors is carried out, focusing on the immediate status and historical conditions of the injured and sick, applying statistical analysis to deeply explore the health status, evaluate the urgency of the current status, and generate an optimized risk assessment report.

[0095] In the embodiment of the present application, it is assumed that in the emergency department of a large hospital, firstly, high-quality patient information records are received from multi-source data acquisition equipment, and the physiological parameters of each patient are further reviewed and classified. The system uses the PHI dynamic scoring algorithm to perform nonlinear relationship modeling and dynamic evaluation on the physiological parameters of each patient, considering the time evolution characteristics, analyzing the time-varying complex patterns and interdependencies, and generating an intrinsic connection model; secondly, based on the intrinsic connection model, the system conducts an in-depth analysis of the complex interactions between physiological parameters, considering the changing trends and mutual influences in different time periods, identifying key risk factors and potential health threats, and generating preliminary risk assessment results;

[0096] Thirdly, based on the preliminary risk assessment results, the system comprehensively analyzes the changing trends and interdependencies of the physiological parameters of each patient, uses detailed time-varying analysis for quantitative analysis, and continuously monitors the changes in physiological parameters. It dynamically adjusts the analysis parameters according to the specific circumstances of different patients, and generates accurate quantitative analysis results. Finally, based on the accurate quantitative analysis results, the system conducts a comprehensive assessment of potential risk factors, focusing on the immediate status and historical situation of the patients. The system uses statistical analysis to deeply explore health conditions, assess the urgency of the current status, generate optimized risk assessment reports, and guide emergency doctors to efficiently perform rescue tasks.

[0097] This application takes into account that the existing technology lacks accuracy and timeliness in assessing the health status of the injured and sick due to insufficient analysis of the changing trends and interdependencies of the physiological parameters of the injured and sick. Therefore, the embodiment of the invention proposes this optional solution, which introduces methods such as principal component analysis, time window sliding method and nonlinear model output to solve the technical problem that the existing technology cannot effectively capture the time-evolution characteristics and complex interactions of physiological parameters. This not only improves the accuracy of PHI scoring, but also provides a more scientific basis for the priority confirmation of the injured and sick.

[0098] Optionally, based on the high-quality physiological parameter set, the PHI dynamic scoring algorithm is used to perform nonlinear relationship modeling and dynamic evaluation on the physiological parameters of each patient, taking into account the time evolution characteristics, analyzing the time-varying complex patterns and interdependencies, and generating an internal connection model, including:

[0099] Based on the high-quality physiological parameter set, the most representative feature vector is extracted using principal component analysis;

[0100] Capture the time-varying trend through the time window sliding method, fully consider the time evolution characteristics, introduce periodic and non-periodic fluctuation analysis, identify and analyze potential periodic patterns to generate nonlinear model output;

[0101] The nonlinear model output is calculated using the following formula:

[0102]

[0103] Where M(t) is the output of the nonlinear model at time t; w i is the weight coefficient of the ith physiological parameter; f(x i ,t) is the ith physiological parameter x i Function that changes with time t; η i is the time periodic amplitude coefficient of the i-th physiological parameter; ω i is the time periodic angular frequency of the i-th physiological parameter; φ i is the time periodic phase offset of the i-th physiological parameter; α is the time attenuation coefficient of the time evolution characteristic; β is the time attenuation rate of the time evolution characteristic; ∈ is the logarithmic growth coefficient of the time evolution characteristic; τ is the logarithmic growth offset of the time evolution characteristic; i is the index of the physiological parameter, from 1 to n; n is the number of physiological parameters; t is the time of physiological parameter measurement;

[0104] Based on the output of the nonlinear model, residual analysis is performed to detect and correct potential systematic deviations, and the trend of physiological parameter changes in the future is predicted using the autoregressive integrated moving average method, and the interdependence between multiple physiological parameters is evaluated to generate dynamic evaluation results;

[0105] The dynamic evaluation result is calculated by the following formula:

[0106]

[0107] Where P(t) is the dynamic evaluation result at time t; w i is the weight coefficient of the ith physiological parameter; f(x i ,t) is the ith physiological parameter x i A function that changes with time t; is the historical average value of the ith physiological parameter; σ 3 is the cube of the variance, measuring data fluctuations; γ is the adjustment factor; g(y j ,t) are other related factors y j Function of change over time t; δ is the amplitude coefficient; ω is the angular frequency; φ is the phase shift; λ is the time decay coefficient of the time evolution characteristic; μ is the time decay rate of the time evolution characteristic; i is the index of the physiological parameter, from 1 to n; n is the number of physiological parameters; j is the index of other related factors, from 1 to m; m is the number of other related factors; t is the time of physiological parameter measurement;

[0108] Based on the dynamic evaluation results, the causal relationship between different physiological parameters is analyzed through causal inference, and the probability dependency diagram between parameters is constructed through the Bayesian network to intuitively display the complex interactions and generate an intrinsic connection model.

[0109] This method aims to more comprehensively capture the changing patterns of physiological parameters of the injured and sick over time and their interdependencies. It adopts a multi-step formulaic processing method. First, the most representative eigenvectors are extracted through principal component analysis to reduce data dimensions and retain key information. Second, the time window sliding method is used to capture the time evolution characteristics, and the potential periodic pattern is identified by combining periodic and non-periodic fluctuation analysis. Third, residual analysis is performed based on the output of the nonlinear model to predict the trend of physiological parameters in the future and evaluate the interdependencies between multiple physiological parameters. Finally, an intrinsic connection model is constructed through causal inference to intuitively display complex interactions. These steps together ensure the accuracy and reliability of the model.

[0110] In the nonlinear model output, the time evolution and periodic fluctuation term w i ·(f(x i ,t)+η i ·sin(ω i ·t+φ i )): This sub-item is used to capture the time evolution characteristics and periodic changes of each physiological parameter. By introducing time periodic fluctuations, it can more accurately reflect the change pattern of physiological parameters in different time periods, thereby better capturing the potential periodic characteristics; the time decay term α·exp(-β·t2 ): This sub-item simulates the effect of physiological parameters gradually weakening over time. As time goes by, the effect of some physiological parameters may gradually weaken, so an exponential decay function is introduced to simulate this characteristic; Logarithmic growth term ∈·ln(t+τ): This sub-item considers the growth trend of physiological parameters over time. For some physiological parameters, their changes may show a slowly increasing pattern, and the introduction of a logarithmic growth function can simulate this gradual growth characteristic;

[0111] Among them, the weight coefficient w i Assign values ​​according to the importance of physiological parameters; the function of physiological parameters changing with time f(x i ,t) directly obtained from high-quality physiological parameter sets; time periodicity amplitude coefficient η i Calculated through periodic fluctuation analysis; time periodic angular frequency ω i Calculated by periodic fluctuation analysis; time periodic phase shift φ i It is calculated through periodic fluctuation analysis; the time decay coefficient α and the time decay rate β are fitted according to historical data; the logarithmic growth coefficient ∈ and the logarithmic growth offset τ are fitted according to historical data;

[0112] In the dynamic evaluation results, the dynamic deviation term This sub-item is used to measure the degree to which physiological parameters deviate from their historical averages. By introducing cubic deviation, changes in physiological parameters can be captured more sensitively, especially those that deviate significantly from the historical averages. This sub-item considers the impact of other related factors on physiological parameters. The changes of many physiological parameters are not only affected by their own historical data, but also related to other external or internal factors; Periodic fluctuation term δ·cos(ω·t+φ): This sub-item is used to capture the periodic fluctuation of physiological parameters. The amplitude coefficient δ controls the amplitude of the fluctuation, and the angular frequency ω determines the frequency of the fluctuation; the phase shift φ describes the initial position of the fluctuation. By introducing the cosine function, the periodic changes of physiological parameters can be simulated more accurately; the time evolution characteristic term λ·exp(-μ·t 3 ): This sub-item simulates the characteristics of physiological parameters evolving over time. For some parameters, their effects may be significantly weakened as events develop. The time decay coefficient λ controls the magnitude of the decay, while the time decay rate μ determines the speed of the decay. By introducing a higher-order time decay function, this complex evolution process can be simulated more carefully;

[0113] Among them, the weight coefficient w i Assign values ​​according to the importance of physiological parameters; the function of physiological parameters changing with time f(x i ,t) Directly obtained from high-quality physiological parameter sets; historical averages Calculated from a high-quality set of physiological parameters; variance cubic σ 3 Measuring data fluctuations; the adjustment factor γ is obtained by fitting historical data; other relevant factors change over time function g(y j ,t) directly obtained from high-quality physiological parameter sets; the amplitude coefficient δ is calculated by periodic fluctuation analysis; the angular frequency ω is calculated by periodic fluctuation analysis; the phase shift φ is calculated by periodic fluctuation analysis; the time decay coefficient λ and the time decay rate μ are both obtained by fitting historical data;

[0114] Assume that the medical security site is at a large outdoor music festival; assume that the weight coefficient w 1 =0.8; Physiological parameter time-varying function f(x 1 ,t)=75+5sin(2πt / 60); time periodic amplitude coefficient η 1 =5; time periodic angular frequency ω 1 =2π / 60; time periodic phase shift φ 1 =0; time decay coefficient α = 1; time decay rate β = 0.01; logarithmic growth coefficient ∈ = 0.5; logarithmic growth offset τ = 1;

[0115] At t = 30,

[0116] Assume that the weight coefficient w 2 =0.7; Physiological parameter time-varying function f(x 2 ,t)=120+10sin(2πt / 120); historical average Variance cubic σ 3 =27; other related factors change over time g(y 1 ,t)=0.5sin(2πt / 60); amplitude coefficient δ=10; angular frequency ω=2π / 120; phase shift φ=0; time decay coefficient λ=1; time decay rate μ=0.005; adjustment factor γ=0.5;

[0117] At t = 60,

[0118]

[0119] Assuming the threshold is 0.2, since the calculated result 0.12 is less than the set threshold, it indicates that the current physiological parameters of the patient are relatively stable and no emergency measures need to be taken immediately. Through the above steps, the accurate assessment and priority confirmation of the physiological parameters of the patient are ensured, the scientificity and reliability of the treatment decision are improved, and the accuracy and response speed of the entire health monitoring system are enhanced.

[0120] 103. Based on the PHI score, a priority adaptive adjustment algorithm is used to simulate the expected improvement effect of various medical resource allocation schemes on the survival rate of the wounded and sick, and a reinforcement learning mechanism is used to optimize the treatment sequence to ensure the best efficiency in the allocation of limited medical resources, and generate a priority list for the treatment of the wounded and sick;

[0121] In this step, the priority adaptive adjustment algorithm is an intelligent algorithm that is used to simulate multiple medical resource allocation schemes and evaluate their effects on the expected improvement in the survival rate of the wounded and sick. It selects the optimal scheme based on different resource allocation strategies to ensure the best utilization of limited medical resources.

[0122] Simulate a variety of medical resource allocation plans. Use computer simulation technology to simulate the allocation of medical resources such as drugs, equipment, and medical staff in different situations to ensure the reality and feasibility of the simulation results.

[0123] The expected improvement effect of survival rate evaluates the impact of different resource allocation schemes on the survival rate of the wounded and sick. Based on historical data and simulation results, the changes in the survival rate of the wounded and sick under different schemes are predicted to help select the most effective resource allocation strategy.

[0124] The reinforcement learning mechanism is a machine learning method that continuously improves the treatment order through iterative learning and optimization of decision paths, ensures maximum resource allocation efficiency, and gradually optimizes the treatment order to improve the overall treatment effect.

[0125] Optimize the treatment sequence to determine the best treatment sequence based on simulation results and reinforcement learning mechanisms, taking into account multiple factors such as the patient's PHI score, resource availability, treatment time and location, etc., to ensure the most effective use of limited medical resources.

[0126] The priority list for treating the wounded and sick is an ordered list generated based on the optimization results, which clarifies the order of treating each wounded and sick person, guides the actual treatment actions, and ensures that the front-line rescue team can perform the treatment tasks in the optimal order.

[0127] In an embodiment of the present application, assuming that in an emergency medical rescue environment, first, the system comprehensively evaluates the health status and risk level of each patient based on the PHI score, and generates a patient demand analysis report; secondly, the priority adaptive adjustment algorithm simulates a variety of medical resource allocation schemes to evaluate the expected survival rate improvement effect under different strategies; thirdly, the order of treatment is iteratively optimized through the reinforcement learning mechanism, and the optimal decision path is selected; finally, the system generates a priority list for the treatment of the patient to guide the resource allocation and treatment actions of the front-line rescue team.

[0128] Optionally, the method in step 103, based on the PHI score, uses a priority adaptive adjustment algorithm to simulate the expected improvement effect of various medical resource allocation schemes on the survival rate of the wounded and sick, uses a reinforcement learning mechanism to optimize the treatment order, ensures the best efficiency of limited medical resource allocation, and generates a priority list for the treatment of the wounded and sick, including: based on the PHI score, comprehensively assessing the health status and risk level of each wounded and sick, and generating a wounded and sick demand analysis report in combination with the current number and type of available medical resources; based on the wounded and sick demand analysis report, uses a priority adaptive adjustment algorithm to simulate various medical resource allocation schemes, considers the expected improvement effect of the survival rate of the wounded and sick under different resource allocation strategies, and generates a feasible resource allocation plan set; based on the feasible resource allocation plan set, uses a reinforcement learning mechanism to optimize the treatment order, and through iterative learning of the optimal decision path, ensures the maximum allocation efficiency under limited medical resources, and generates an optimized treatment order suggestion; based on the optimized treatment order suggestion, comprehensively considers the real-time availability of medical resources to generate a priority list for the treatment of the wounded and sick.

[0129] Among them, based on the demand analysis report of the wounded and sick, a priority adaptive adjustment algorithm is used to simulate various medical resource allocation schemes, and the expected improvement effect of the survival rate of the wounded and sick under different resource allocation strategies is considered to generate a feasible resource allocation plan set, including: based on the demand analysis report of the wounded and sick, accurately evaluate the specific medical needs of each wounded and sick, determine the required specific resource types and quantities, and generate a list of resource needs for the wounded and sick; based on the resource demand list of the wounded and sick, a priority adaptive adjustment algorithm is used to simulate various medical resource allocation schemes, and the expected improvement effect of the survival rate of the wounded and sick under different resource allocation strategies is considered to generate a preliminary resource allocation plan; based on the preliminary resource allocation plan, scenario simulation technology is used to evaluate the implementation effects under different scenarios, and a scenario simulation evaluation report is generated; based on the scenario simulation evaluation report, the optimal resource allocation plan is screened, and a feasible resource allocation plan set is generated in combination with actual feasibility and operation complexity.

[0130] In this step, the patient needs analysis report is a document generated after a comprehensive assessment of the health status and risk level of each patient. It lists in detail the specific medical needs of each patient, combined with the number and type of currently available medical resources.

[0131] The set of feasible resource allocation plans is a series of plans generated by simulating various medical resource allocation schemes based on the expected improvement in the survival rate of the wounded and sick under different resource allocation strategies. These plans not only include the specific methods of resource allocation, but also take into account the feasibility and complexity in actual operations.

[0132] Scenario simulation technology uses computer simulation technology to evaluate the implementation effects under different scenarios and generate scenario simulation evaluation reports. This method can help screen out the optimal resource allocation plan and ensure the effectiveness and feasibility of the plan in actual operations.

[0133] The sick and wounded resource demand list accurately assesses the specific medical needs of each sick and wounded patient and determines the type and quantity of specific resources required. This list provides detailed data support for the generation of subsequent resource allocation plans.

[0134] The preliminary resource allocation plan is based on the resource demand list of the wounded and sick. It uses a priority adaptive adjustment algorithm to simulate various medical resource allocation schemes and considers the expected improvement in the survival rate of the wounded and sick under different resource allocation strategies.

[0135] The scenario simulation evaluation report is a document generated after evaluating the implementation effects in different scenarios through scenario simulation technology. The report records in detail the performance of each plan in different scenarios, providing a basis for selecting the optimal plan.

[0136] The optimized treatment sequence recommendation is based on the scenario simulation evaluation report and is generated after iteratively learning the optimal decision path through a reinforcement learning mechanism.

[0137] In the embodiment of the present application, firstly, the health status and risk level of each patient are comprehensively evaluated based on the PHI score, and the specific medical needs of each patient are accurately evaluated in combination with the number and type of currently available medical resources, the required specific resource types and quantities are determined, and a list of patient resource needs is generated; secondly, based on the patient resource need list, a priority adaptive adjustment algorithm is used to simulate a variety of medical resource allocation schemes, the expected improvement effect of the patient survival rate under different resource allocation strategies is considered, and the implementation effect under different scenarios is evaluated by scenario simulation technology, a scenario simulation evaluation report is generated, and the optimal resource allocation plan is screened, and a feasible resource allocation plan set is generated in combination with actual feasibility and operation complexity; thirdly, based on the feasible resource allocation plan set, the system adopts a reinforcement learning mechanism to optimize the treatment order, and through iterative learning of the optimal decision path, the allocation efficiency under limited medical resources is maximized, and the real-time availability of medical resources is comprehensively considered to generate a priority list for the treatment of the patient; finally, the system integrates the priority list for the treatment of the patient into a unified data format, and instantly transmits it to the front-line rescue team through a mobile terminal. The rescue personnel determine the strategy based on the updated priority, quickly adjust the treatment plan, and ensure efficient execution.

[0138] Assuming that at the medical support site of a large international marathon event, first, the system comprehensively evaluates the health status and risk level of each participant based on the PHI score, and combines the number and type of currently available medical resources to accurately evaluate the specific medical needs of each participant, determine the type and quantity of specific resources required, and generate a list of resource requirements for the injured and sick; secondly, based on the list of resource requirements for the injured and sick, the system uses a priority adaptive adjustment algorithm to simulate multiple medical resource allocation plans, considers the expected improvement in the survival rate of participants under different resource allocation strategies, uses scenario simulation technology to evaluate the implementation effect under different scenarios, generates a scenario simulation evaluation report, and selects the optimal resource allocation plan, and generates a feasible resource allocation plan set based on actual feasibility and operational complexity; thirdly, based on the feasible resource allocation plan set, the system uses a reinforcement learning mechanism to optimize the treatment order, and through iterative learning of the optimal decision path, ensures the maximum allocation efficiency under limited medical resources, and comprehensively considers the real-time availability of medical resources to generate a priority list for the treatment of the injured and sick; finally, the system integrates the priority list for the treatment of the injured and sick into a unified data format, and instantly transmits it to the front-line rescue team through mobile terminals. The rescuers determine the strategy based on the updated priority, quickly adjust the treatment plan, and ensure efficient execution.

[0139] This application takes into account that in the prior art, due to the problems of insufficient analysis of the resource needs of the wounded and sick, insufficient simulation of the resource allocation plan, and insufficient refinement of the optimization scoring method, it is difficult to effectively improve the expected survival rate of the wounded and sick in the actual allocation of medical resources. Therefore, the embodiment of the invention proposes this optional solution, which introduces methods such as time series decomposition, exponential smoothing, and multi-factor correlation analysis to solve the technical problem that the prior art cannot accurately capture the time characteristics and mutual influence of resource allocation. This not only improves the efficiency and accuracy of resource allocation, but also provides a more scientific basis for the priority confirmation of the wounded and sick.

[0140] Optionally, based on the resource demand list of the sick and wounded, a priority adaptive adjustment algorithm is used to simulate multiple medical resource allocation schemes, and the expected improvement effect of the survival rate of the sick and wounded under different resource allocation strategies is considered to generate a preliminary resource allocation plan, including:

[0141] Based on the resource demand list of the sick and wounded, the time series is decomposed into trend and random components to accurately capture the time characteristics;

[0142] Identify long-term trends through exponential smoothing, introduce multi-factor correlation analysis, evaluate the mutual influence between different resources, eliminate redundant information, and generate resource allocation benefits;

[0143] The resource allocation benefit is calculated using the following formula:

[0144]

[0145] Among them, B i is the resource allocation benefit of the i-th patient; i is the index of the patient; j is the index of the resource type, from 1 to m; m is the total number of resource types; r ij is the allocation of the j-th resource to the i-th injured or sick person; S j is the basic benefit coefficient of the jth resource; α j is the time attenuation coefficient; β j is the time attenuation coefficient; t j is the allocation time of the jth resource; λ is the adjustment factor; τ is the time offset; ρ is the periodic fluctuation coefficient; ω t is the time period angular frequency; φ t is the time period phase shift;

[0146] Based on the resource allocation benefits, sensitivity analysis is performed to evaluate the stability under different parameter settings, a large number of possible resource allocation scenarios are generated through Monte Carlo simulation methods, and multiple evaluation indicators are comprehensively considered to generate an optimized resource allocation score;

[0147] The optimized resource allocation score is calculated using the following formula:

[0148]

[0149] Among them, O i is the optimal resource allocation score for the i-th patient; i is the index of the patient; j is the index of the resource type, from 1 to m; m is the total number of resource types; k is the index of other influencing factors, from 1 to p; p is the number of other influencing factors; l is the index of the additional factor, from 1 to q; q is the number of additional factors; r ij is the allocation of the j-th resource to the i-th injured or sick person; S j is the basic benefit coefficient of the jth resource; α j is the time attenuation coefficient; β j is the time attenuation coefficient; t j is the allocation time of the jth resource; γ j is the periodic fluctuation coefficient; ω j is the angular frequency; φ j is the phase shift; σ 2 is the variance, which measures data fluctuations; δ is the adjustment factor; V k is the weight of the kth influencing factor; g(x i ,t j ) is the kth influencing factor at time t j The function value; η is the amplitude coefficient; μ is the time decay rate; h(y l ,t j ) is the lth additional factor at time t jThe function value of; λ is the adjustment factor, which is used to weigh the impact of the basic resource allocation efficiency on the dynamic resource allocation optimization result; B i The resource allocation benefit for the i-th patient;

[0150] Based on the optimized resource allocation score, further feasibility verification is carried out to ensure that it is feasible in actual operation. The decision tree is used to comprehensively analyze the risks and benefits of resource allocation and generate a preliminary resource allocation plan.

[0151] This method aims to more comprehensively evaluate the impact of different resource allocation strategies on the expected survival rate of the wounded and sick. This scheme adopts a multi-step formulaic processing method. First, the time series is decomposed into trend and random components to accurately capture the time characteristics; second, the exponential smoothing method is used to identify long-term trends, and multi-factor correlation analysis is introduced to eliminate redundant information and generate resource allocation benefits; third, sensitivity analysis is performed based on resource allocation benefits, and multiple evaluation indicators are comprehensively considered through Monte Carlo simulation methods to generate optimized resource allocation scores; finally, a decision tree is used to comprehensively analyze the risks and benefits of resource allocation to ensure feasibility in actual operations. These steps together ensure the optimality and reliability of the resource allocation scheme.

[0152] In the resource allocation benefit, the basic contribution term r ij ·S j : measure the basic contribution of each resource to the wounded and sick, and ensure that the basic benefits of resource allocation are reflected; time decay effect item Simulate the effect of resources gradually weakening over time, taking into account that the effect of some resources will weaken over time; adjust the factor term λ. ln(t j +τ): Considering the logarithmic growth characteristics of resource allocation time, it ensures that certain benefits can be maintained when allocating resources for a long time; the periodic fluctuation term ρ.sin(ω t ·t j +φ t ): Capturing the periodic changes in resource allocation, considering that some resources may have periodic usage patterns;

[0153] Among them, the weight coefficient w i Assign values ​​according to the importance of physiological parameters; the function of physiological parameters changing with time f(x i ,t) directly obtained from high-quality physiological parameter sets; time periodicity amplitude coefficient η i Calculated through periodic fluctuation analysis; time periodic angular frequency ω i Calculated by periodic fluctuation analysis; time periodic phase shift φ i Calculated by periodic fluctuation analysis; time attenuation coefficient α j and the time decay rate β jare all obtained by fitting historical data; the logarithmic growth coefficient ∈ and the logarithmic growth offset τ are all obtained by fitting historical data; the adjustment factor λ is obtained by fitting historical data; the periodic fluctuation coefficient ρ is calculated through periodic fluctuation analysis; the time period angular frequency ω t and the time period phase shift φ t Calculated through periodic fluctuation analysis;

[0154] In the optimization resource allocation score, the basic contribution term r ij ·S j : measure the basic contribution of each resource to the wounded and sick, and ensure that the basic benefits of resource allocation are reflected; time decay effect item Simulates the effect of resources gradually weakening over time, taking into account that the effect of some resources will weaken over time; the periodic fluctuation term γ j ·sin(ω j ·t j +φ j ): captures the periodic changes in resource allocation, considering that some resources may have periodic usage patterns; the variance term σ 2 Measure data fluctuations to ensure that the model can adapt to different data distributions; other influencing factors Consider the impact of other relevant factors to ensure the comprehensiveness of the model; additional factors Consider the impact of additional factors on resource allocation to ensure the flexibility of the model; adjust the factor term λ·B i : Weigh the impact of basic resource allocation benefits on dynamic resource allocation optimization results to ensure the stability of the model;

[0155] Among them, the weight coefficient w i Assign values ​​according to the importance of physiological parameters; the function of physiological parameters changing with time f(x i ,t) directly obtained from high-quality physiological parameter sets; time periodicity amplitude coefficient η i Calculated through periodic fluctuation analysis; time periodic angular frequency ω i Calculated by periodic fluctuation analysis; time periodic phase shift φ i Calculated by periodic fluctuation analysis; time attenuation coefficient α j and the time decay rate β j All are obtained by fitting based on historical data; the cyclical fluctuation coefficient γ j Calculated by periodic fluctuation analysis; angular frequency ω j and phase shift φ j Calculated by periodic fluctuation analysis; variance σ 2Measure data fluctuations; adjustment factor δ is obtained by fitting historical data; weights of other influencing factors V k According to the historical data fitting, other influencing factors function g(x k ,t j ) is directly obtained from the high-quality physiological parameter set; the amplitude coefficient η and the time decay rate μ are fitted according to the historical data; the additional factor function h(y l ,t j ) is directly obtained from a high-quality physiological parameter set; the adjustment factor λ is obtained by fitting based on historical data;

[0156] Suppose for a patient x who was injured in a traffic accident 1 , three resources need to be allocated, namely oxygen, transfusion and ECG monitoring; assuming that the resources are sufficient for patient x 1 The distribution of oxygen r 11 =2L / min; infusion 12 =1L / hour; ECG monitoring 13 = 1 unit; the basic efficiency coefficients are oxygen S 1 =1.2; Infusion S 2 =1.0; ECG monitoring S 3 =0.8; the time decay coefficients are oxygen α 1 =0.6; infusion α 2 =0.7; ECG monitoring α 3 =0.5; the time decay rates are oxygen β 1 =0.02; infusion β 2 =0.01; ECG monitoring β 3 =0.005; resource allocation time is oxygen t 1 =1 hour; infusion t 2 =2 hours; ECG monitoring t 3 = 3 hours; adjustment factor λ = 0.5; time offset τ = 1; periodic fluctuation coefficient ρ = 0.3; time period angular frequency ω t =2π / 60; time period phase shift φ t =0;

[0157]

[0158] Assume that resources are available for patient x 1 The distribution of oxygen r 11 =2L / min; infusion 12 =1L / hour; ECG monitoring 13 = 1 unit; the basic efficiency coefficients are oxygen S 1 =1.2; Infusion S 2 =1.0; ECG monitoring S 3=0.8; the time decay coefficients are oxygen α 1 =0.6; infusion α 2 =0.7; ECG monitoring α 3 =0.5; the time decay rates are oxygen β 1 =0.02; infusion β 2 =0.01; ECG monitoring β 3 =0.005; resource allocation time is oxygen t 1 = 1 hour; input night t 2 =2 hours; ECG monitoring t 3 = 3 hours; the periodic fluctuation coefficients are oxygen γ 1 =0.3; infusion γ 2 =0.4; ECG monitoring γ 3 =0.2; the angular frequencies are respectively oxygen ω 1 =2π / 60; infusionω 2 =2π / 60; ECG monitoring ω 3 =2π / 60; the phase shifts are respectively oxygen φ 1 =0; Infusionφ 2 =0; ECG monitoring φ 3 =0; variance σ 2 =1; adjustment factor δ = 0.5; weight of other influencing factors V k =0.5 (assuming there is only one influencing factor); other influencing factor functions g(x k ,t j )=0.5sin(2πt j / 60); amplitude coefficient η = 0.5; time decay rate μ = 0.005; additional factor function h(y l ,t j )=0.5sin(2πt j / 60) (assuming only one additional factor); adjustment factor λ = 0.5;

[0159] O i =(2·(1.2+0.6·exp(-0.02·1 3 )+0.3·sin(2π / 60·1+0))+1·(1.0+0.7·exp(-0.01·2 3 )+0.4·sin(2π / 60·2+0))+1·(0.8+0.5·exp(-0.005·3 3 )+0.2·sin(2π / 60·3+0))) / 1+0.5·ln(1+0.5·sin(2π·60 / 60) 2 )+0.5·exp(-0.005·(0.5sin(2π·60 / 60)) 2)+0.5·3.7=85.2;

[0160] Assuming that the threshold is set to 80, since the calculated result 85.2 is greater than the set threshold, it shows that the current resource allocation plan for the injured has a high optimization effect and is expected to significantly improve their survival rate. Through the above steps, the accurate assessment and priority confirmation of the resource needs of the injured are ensured, the scientificity and reliability of the treatment decision-making are improved, and the accuracy and response speed of the entire health monitoring system are enhanced.

[0161] 104. Based on the priority list of treatment for the wounded and sick, combined with the corresponding timestamps, the list is integrated into a unified data format, and instantly transmitted to the front-line rescue team via a mobile terminal to generate a strategy for determining the priority of the wounded and sick.

[0162] In this step, the timestamp marks the time point of each patient’s information to ensure the time accuracy of the data, so that the rescue team can accurately understand the latest status of each patient.

[0163] Unified data format converts all relevant data into a standard format, ensuring compatibility of different devices and systems, facilitating information transmission and processing, and reducing the risk of information loss or misunderstanding.

[0164] Instant communication on mobile terminals pushes the priority list to the mobile devices of the front-line rescue team in real time through a secure and reliable communication network, ensuring the timeliness and accuracy of information transmission, enabling rescue personnel to obtain the latest treatment guidance at the first time, and improving response speed and efficiency.

[0165] The strategy for determining the priority of the wounded and sick is the final strategy document that guides the rescue team on how to perform specific rescue actions based on the priority list. It not only includes the priority sorting but also details the specific content of each step of the operation, ensuring that the rescue team can perform the rescue mission efficiently and accurately.

[0166] In an embodiment of the present application, assuming that at a large-scale natural disaster rescue site, first, the system combines the generated priority list of treatment for the wounded and sick with the timestamp to form a standardized data file; secondly, these data files are converted into a unified format suitable for display on mobile terminals; thirdly, through a secure and reliable communication network, the data files are instantly pushed to the mobile terminals of the front-line rescue team; finally, the rescue personnel determine the strategy based on the updated priority, quickly adjust the treatment plan, and ensure efficient execution.

[0167] Optionally, the priority list for treating the wounded and sick in step 104 is combined with corresponding timestamps to be integrated into a unified data format, and is instantly transmitted to the front-line rescue team via a mobile terminal to generate a strategy for determining the priority of the wounded and sick, including: based on the priority list for treating the wounded and sick, adding an accurate timestamp to each priority information of the wounded and sick, to generate a timestamp priority list; based on the timestamp priority list, integrating all relevant information into a unified data format to ensure data consistency and compatibility, and generating a standardized priority data file; based on the standardized priority data file, developing a dedicated data transmission protocol, and instantly transmitting it to the mobile terminal device of the front-line rescue team via a secure communication channel to generate a real-time priority display interface; based on the real-time priority display interface, providing intuitive operation instructions and flexible adjustment functions to quickly respond to on-site conditions and generate a strategy for determining the priority of the wounded and sick.

[0168] In this step, precise timestamp refers to the time mark added to the priority information of each injured and sick person to ensure the time accuracy of the data. Timestamp is crucial for tracking changes in the status of the injured and sick, auditing the treatment process and ensuring the timeliness of information transmission.

[0169] The timestamp priority list is a new list generated by adding an accurate timestamp to each priority information of the wounded and sick based on the priority list of treatment of the wounded and sick. This ensures the accuracy and consistency of all priority information in the time dimension, facilitating subsequent data integration and transmission.

[0170] Unifying data formats is to convert all relevant data into a standard format to ensure compatibility between different devices and systems. This format facilitates information transmission and processing, reduces the risk of information loss or misunderstanding, and ensures that data can be seamlessly transmitted between different platforms.

[0171] The standardized priority data file is a file generated by integrating all relevant information in the time-stamped priority list into a unified data format. The file ensures data consistency and compatibility, facilitating subsequent data transmission and display.

[0172] The dedicated data transmission protocol is a communication protocol developed to ensure data security and real-time transmission. The protocol uses encryption technology and secure communication channels to ensure that data is not tampered with or leaked during transmission, while ensuring that data can reach the mobile terminal devices of the front-line rescue team in real time.

[0173] The real-time priority display interface is an operation interface specially designed for front-line rescue teams, providing intuitive operation guidance and flexible adjustment functions.

[0174] In the embodiment of the present application, first, the system adds an accurate timestamp to each priority information of the wounded and sick based on the priority list for treating the wounded and sick, and generates a timestamp priority list; secondly, based on the timestamp priority list, all relevant information is integrated into a unified data format to ensure data consistency and compatibility, and generate a standardized priority data file; thirdly, based on the standardized priority data file, a dedicated data transmission protocol is developed, which is instantly transmitted to the mobile terminal device of the front-line rescue team through a secure communication channel to generate a real-time priority display interface; finally, based on the real-time priority display interface, intuitive operation instructions and flexible adjustment functions are provided to quickly respond to on-site conditions and generate a strategy for determining the priority of the wounded and sick.

[0175] Assuming that in a busy urban emergency center, first, the system adds an accurate timestamp to the priority information of each patient based on the priority list of the patient's treatment, and generates a timestamp priority list; secondly, the system integrates all relevant information into a unified data format to ensure data consistency and compatibility, and generates a standardized priority data file; thirdly, the system develops a dedicated data transmission protocol, which is instantly transmitted to the mobile terminal devices of the front-line rescue team through a secure communication channel to generate a real-time priority display interface; finally, dispatchers and rescue personnel respond quickly to on-site conditions and adjust treatment plans based on the intuitive operating instructions and flexible adjustment functions provided on the real-time priority display interface to ensure efficient execution of treatment tasks. At the same time, the system continuously monitors the progress of treatment and automatically updates priority information to ensure that the rescue team always has the latest guidance.

[0176] In summary, steps 101 to 104 cover the complete process from analyzing the resource needs of the wounded and sick to optimizing the resource allocation score, aiming to provide a scientific and systematic medical resource allocation plan to meet the needs of efficient and accurate allocation of limited medical resources in emergency situations, so as to maximize the survival rate and treatment effect of the wounded and sick.

[0177] Figure 2 A schematic diagram of a system for determining the priority of patients based on PHI scores is provided for an embodiment of the present application. Figure 2 As shown, the device comprises:

[0178] The acquisition module 21 is used to obtain the physiological parameters and injury description of the injured and sick through multi-source data acquisition equipment, perform time axis consistency processing, and generate information records of the injured and sick;

[0179] An evaluation module 22 is used to perform nonlinear relationship modeling and dynamic evaluation on the physiological parameters of each patient based on the patient information record and using a PHI dynamic scoring algorithm, and to perform quantitative analysis using real-time data analysis technology, integrating current vital sign stability and injury history to generate a PHI score;

[0180] A simulation module 23 is used to simulate the expected improvement effect of various medical resource allocation schemes on the survival rate of the wounded and sick based on the PHI score and use a priority adaptive adjustment algorithm, optimize the treatment sequence by using a reinforcement learning mechanism, ensure the best efficiency of limited medical resource allocation, and generate a priority list for the treatment of the wounded and sick;

[0181] The generating module 24 is used to integrate the priority list of the injured and sick into a unified data format based on the corresponding timestamps, and transmit it to the front-line rescue team via the mobile terminal in real time to generate a priority determination strategy for the injured and sick.

[0182] Figure 2 The system for determining the priority of the injured and sick based on PHI score can be implemented Figure 1 The implementation principle and technical effect of the method for determining the priority of the sick and injured based on PHI score in the embodiment shown are not described in detail. The specific way in which each module and unit performs operations in the system for determining the priority of the sick and injured based on PHI score in the above embodiment has been described in detail in the embodiment of the method, and will not be described in detail here.

[0183] In one possible design, Figure 2 The illustrated embodiment of a system for determining priority of patients based on PHI scores 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;

[0184] 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 .

[0185] The processing component 32 is used to: obtain the physiological parameters and injury descriptions of the injured and sick through multi-source data acquisition equipment, perform timeline consistency processing, and generate information records of the injured and sick; based on the information records of the injured and sick, use the PHI dynamic scoring algorithm to perform nonlinear relationship modeling and dynamic evaluation on the physiological parameters of each injured and sick, use real-time data analysis technology to perform quantitative analysis, integrate the current vital sign stability and injury history, and generate a PHI score; based on the PHI score, use the priority adaptive adjustment algorithm to simulate the expected improvement effect of various medical resource allocation schemes on the survival rate of the injured and sick, use the reinforcement learning mechanism to optimize the treatment order, ensure the best efficiency of limited medical resource allocation, and generate a priority list for the treatment of the injured and sick; based on the priority list for the treatment of the injured and sick, combine the corresponding timestamps to integrate into a unified data format, and instantly transmit it to the front-line rescue team through the mobile terminal to generate a priority determination strategy for the injured and sick.

[0186] 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.

[0187] 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.

[0188] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0189] 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.

[0190] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0191] 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.

[0192] 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 determining the priority of the sick and injured based on PHI scores.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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 determining the priority of the injured and sick based on PHI score, characterized in that: include: Through multi-source data acquisition equipment, the physiological parameters and injury descriptions of the injured and sick are obtained, and the time axis consistency processing is performed to generate information records of the injured and sick; Based on the patient information records, a PHI dynamic scoring algorithm is used to perform nonlinear relationship modeling and dynamic evaluation on the physiological parameters of each patient, and real-time data analysis technology is used to perform quantitative analysis, integrating the current vital sign stability and injury history to generate a PHI score; Based on the PHI score, a priority adaptive adjustment algorithm is used to simulate the expected improvement effect of various medical resource allocation schemes on the survival rate of the wounded and sick, and a reinforcement learning mechanism is used to optimize the treatment sequence to ensure the best efficiency of limited medical resource allocation, and generate a priority list for the treatment of the wounded and sick; Based on the priority list of treatment for the wounded and sick, combined with the corresponding timestamps to be integrated into a unified data format, it is instantly transmitted to the front-line rescue team through mobile terminals to generate a strategy for determining the priority of the wounded and sick.

2. The method according to claim 1, characterized in that: Based on the patient information record, the PHI dynamic scoring algorithm is used to perform nonlinear relationship modeling and dynamic evaluation on the physiological parameters of each patient, and real-time data analysis technology is used for quantitative analysis, integrating the current vital sign stability and injury history to generate a PHI score, including: Based on the patient information records, data cleaning and outlier elimination are performed, and preliminary quality control checks are performed to generate high-quality patient information records; Based on the high-quality patient information records, the PHI dynamic scoring algorithm is used to perform nonlinear relationship modeling and dynamic evaluation on the physiological parameters of each patient, analyze the time-varying complex patterns and interdependencies, and generate preliminary risk assessment results; Based on the preliminary risk assessment results, real-time data analysis technology is used to conduct quantitative analysis, and the analysis parameters are dynamically adjusted through continuous monitoring to adapt to the specific conditions of different injured and sick people, so as to generate an optimized risk assessment report; Based on the optimized risk assessment report, a PHI score is generated by integrating current vital sign stability and injury history as key inputs.

3. The method according to claim 2, characterized in that Based on the high-quality patient information records, the PHI dynamic scoring algorithm is used to perform nonlinear relationship modeling and dynamic evaluation on the physiological parameters of each patient, analyze the time-varying complex patterns and interdependencies, and generate preliminary risk assessment results, including: Based on the high-quality patient information records, further detailed review and classification of each patient's physiological parameters are performed to generate a high-quality physiological parameter set; Based on the high-quality physiological parameter set, the PHI dynamic scoring algorithm is used to model and dynamically evaluate the nonlinear relationship of each patient's physiological parameters, consider the time evolution characteristics, analyze the time-varying complex patterns and interdependencies, and generate an internal connection model; Based on the intrinsic connection model, the complex interactions between physiological parameters are deeply analyzed, the changing trends and mutual influences in different time periods are considered, and an intrinsic connection evaluation report is generated; Based on the intrinsic connection assessment report, key risk factors and potential health threats are identified and preliminary risk assessment results are generated.

4. The method according to claim 2, characterized in that: Based on the preliminary risk assessment results, real-time data analysis technology is used for quantitative analysis, and analysis parameters are dynamically adjusted through continuous monitoring to adapt to the specific conditions of different injured and sick persons, and an optimized risk assessment report is generated, including: Based on the preliminary risk assessment results, comprehensively analyze the changing trends and interdependencies of the physiological parameters of each patient and generate detailed time-varying analysis data; Based on the detailed time-varying analysis data, real-time data analysis technology is used to perform quantitative analysis, and by continuously monitoring changes in physiological parameters, the analysis parameters are dynamically adjusted according to the specific conditions of different patients and wounded, to generate accurate quantitative analysis results; Based on the precise quantitative analysis results, a comprehensive assessment of potential risk factors is conducted, focusing on the immediate status and historical situation of the injured and sick, and generating detailed risk assessment indicators; Based on the refined risk assessment indicators, statistical analysis is applied to deeply explore the health status, evaluate the urgency of the current status, and generate an optimized risk assessment report.

5. The method according to claim 1, characterized in that Based on the PHI score, the priority adaptive adjustment algorithm is used to simulate the expected improvement effect of various medical resource allocation schemes on the survival rate of the wounded and sick, and the reinforcement learning mechanism is used to optimize the treatment order to ensure the best efficiency of limited medical resource allocation, and generate a priority list for the treatment of the wounded and sick, including: Based on the PHI score, the health status and risk level of each patient are comprehensively assessed, and a patient demand analysis report is generated in combination with the number and type of currently available medical resources; Based on the demand analysis report of the injured and sick, a priority adaptive adjustment algorithm is used to simulate various medical resource allocation schemes, and the expected improvement effect of the survival rate of the injured and sick under different resource allocation strategies is considered to generate a feasible resource allocation plan set; Based on the feasible resource allocation plan set, a reinforcement learning mechanism is used to optimize the treatment order, and the optimal decision path is learned iteratively to ensure the maximum allocation efficiency under limited medical resources and generate optimized treatment order recommendations; Based on the optimized treatment sequence recommendation, a priority list for treatment of the wounded and sick is generated by comprehensively considering the real-time availability of medical resources.

6. The method according to claim 5, characterized in that Based on the demand analysis report of the injured and sick, a priority adaptive adjustment algorithm is used to simulate multiple medical resource allocation schemes, consider the expected improvement effect of the survival rate of the injured and sick under different resource allocation strategies, and generate a feasible resource allocation plan set, including: Based on the patient needs analysis report, accurately assess the specific medical needs of each patient, determine the type and quantity of specific resources required, and generate a list of patient resource needs; Based on the resource demand list of the sick and wounded, a priority adaptive adjustment algorithm is used to simulate various medical resource allocation schemes, and the expected improvement effect of the survival rate of the sick and wounded under different resource allocation strategies is considered to generate a preliminary resource allocation plan; Based on the preliminary resource allocation plan, scenario simulation technology is used to evaluate the implementation effects under different scenarios and generate a scenario simulation evaluation report; Based on the scenario simulation evaluation report, the optimal resource allocation plan is screened, and a feasible resource allocation plan set is generated in combination with actual feasibility and operational complexity.

7. The method according to claim 1, characterized in that The priority list of the injured and sick is combined with the corresponding timestamp to integrate into a unified data format, and is immediately transmitted to the front-line rescue team through the mobile terminal to generate a priority determination strategy for the injured and sick, including: Based on the priority list of treatment of the wounded and sick, an accurate timestamp is added to each priority information of the wounded and sick to generate a timestamp priority list; Based on the timestamp priority list, all relevant information is integrated into a unified data format to ensure data consistency and compatibility, and generate a standardized priority data file; Based on the standardized priority data file, a dedicated data transmission protocol is developed, and the data is instantly transmitted to the mobile terminal device of the first-line rescue team through a secure communication channel to generate a real-time priority display interface; Based on the real-time priority display interface, intuitive operation guidance and flexible adjustment functions are provided to quickly respond to on-site situations and generate a strategy for determining the priority of the injured and sick.

8. A system for determining the priority of the injured and sick based on PHI score, characterized in that: include: The acquisition module is used to obtain the physiological parameters and injury descriptions of the injured and sick through multi-source data acquisition equipment, perform timeline consistency processing, and generate information records of the injured and sick; An evaluation module is used to perform nonlinear relationship modeling and dynamic evaluation on the physiological parameters of each patient based on the patient information record and using the PHI dynamic scoring algorithm, and to perform quantitative analysis using real-time data analysis technology, integrating the current vital sign stability and injury history to generate a PHI score; A simulation module is used to simulate the expected improvement effect of various medical resource allocation schemes on the survival rate of the wounded and sick based on the PHI score and the priority adaptive adjustment algorithm, optimize the treatment sequence by using a reinforcement learning mechanism, ensure the best efficiency of limited medical resource allocation, and generate a priority list for the treatment of the wounded and sick; A generation module is used to integrate the priority list of treatment of the wounded and sick into a unified data format based on the corresponding timestamps, and transmit it to the front-line rescue team via a mobile terminal in real time to generate a priority determination strategy for the wounded and sick.

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 determining priority of injured and sick persons based on PHI score 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 determining the priority of the injured and sick based on the PHI score as claimed in any one of claims 1 to 7 is implemented.

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