Method and system for predicting illness state of wounded based on big data analysis

Abstract

The invention provides a wounded condition prediction method and system based on big data analysis. The method comprises the following steps: receiving and analyzing real-time data streams of a plurality of medical institutions, carrying out situational understanding, and generating a multi-dimensional data background information framework; based on the multi-dimensional data background information framework, performing prospective calculation on occurrence probabilities of different data evolution paths by applying a self-supervised learning algorithm, and extracting cross-media key features from text and image data by adopting a cross-media intelligent analysis technology to generate a data evolution path set; based on the data evolution path set, applying a deep forest algorithm to accurately evaluate the possibility of different data evolution paths, adopting a privacy enhancement technology to optimize a prediction model learning process, and generating a risk quantification prediction report; and based on the risk quantitative prediction report, formulating a precise data application strategy and a resource configuration plan, and generating a dynamic wounded condition prediction scheme. According to the technical scheme provided by the invention, the disease prediction precision and efficiency are remarkably improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of medical big data analysis, and in particular, to a method and system for predicting the condition of a wounded person based on big data analysis. Background Art

[0002] With the rapid development of medical informatization, medical institutions have generated a large amount of real-time data streams, which has put forward higher requirements for efficient processing and analysis of these data. In order to improve decision-making efficiency, it is necessary to combine multiple factors for contextual understanding and generate a multidimensional data background information framework. This framework not only covers the patient's basic physiological data, but also includes multi-source heterogeneous data such as environmental factors and treatment history, thereby providing a comprehensive data foundation for subsequent intelligent analysis. In addition, the system must also have the ability to process high-dimensional and nonlinear data features to capture the complex interactions between variables and ensure efficient learning on large-scale data sets.

[0003] Currently, many medical data analysis systems rely on traditional supervised learning algorithms, which usually require a large amount of labeled data for training. However, in practical applications, obtaining high-quality labeled data is often costly and time-consuming. Therefore, some studies have begun to explore self-supervised learning algorithms to mine potential patterns through unlabeled data to reduce dependence on labeled data. At the same time, cross-media intelligent analysis technology has also been applied to the comprehensive analysis of text and image data, extracting key features and establishing complex mapping relationships between data elements. In addition, the deep forest algorithm, as an emerging machine learning method, has shown advantages in processing complex data structures.

[0004] Although existing solutions have made some progress, they still have significant defects. First, due to the lack of sufficient labeled data, traditional supervised learning algorithms are insufficient in disease prediction accuracy and are difficult to meet clinical needs. Secondly, although existing self-supervised learning methods can make up for the lack of data annotation to a certain extent, they are still insufficient in mining potential patterns and evaluating the probability of occurrence of different data evolution paths, resulting in inaccurate prediction results. Finally, most existing systems fail to fully consider privacy protection issues, especially when dealing with sensitive medical data, which can easily lead to privacy leakage risks and affect patient trust and regulatory compliance. Summary of the invention

[0005] The embodiments of the present application provide a method and system for predicting the condition of a wounded person based on big data analysis, so as to solve the problem of insufficient accuracy in condition prediction in the prior art.

[0006] In a first aspect, the present application provides a method for predicting the condition of a wounded person based on big data analysis, comprising:

[0007] Receive and analyze real-time data streams from multiple medical institutions, combine multiple factors for contextual understanding, and generate a multi-dimensional data background information framework;

[0008] Based on the multi-dimensional data background information framework, a self-supervised learning algorithm is used to pre-train the variable relationship, potential patterns are mined through unlabeled data, the probability of occurrence of different data evolution paths is forward-lookingly calculated, and cross-media intelligent analysis technology is used to extract cross-media key features from text and image data, establish complex mapping relationships between data elements, and generate a set of data evolution paths;

[0009] Based on the data evolution path set, the deep forest algorithm is used to achieve efficient learning of complex data structures through multi-level random forest combination, accurately evaluate the possibility of different data evolution paths, adopt privacy enhancement technology to ensure data privacy, optimize the prediction model learning process, and generate risk quantitative prediction reports;

[0010] Based on the risk quantification prediction report and comprehensive analysis of all previous analysis results, accurate data application strategies and resource allocation plans are formulated to generate dynamic casualty condition prediction plans.

[0011] Optionally, based on the multidimensional data background information framework, a self-supervised learning algorithm is used to pre-train the variable relationship, potential patterns are mined through unlabeled data, the probability of occurrence of different data evolution paths is forward-lookingly calculated, cross-media intelligent analysis technology is used to extract cross-media key features from text and image data, and complex mapping relationships between data elements are established to generate a data evolution path set, including:

[0012] Based on the multidimensional data background information framework, preprocessing operations are performed to detect outliers, and the most representative variables preserving the essential characteristics of the data are identified through feature selection technology to generate an optimized multidimensional data set;

[0013] Based on the optimized multidimensional data set, a self-supervised learning algorithm is used to pre-train the variable relationship, automatically discover the implicit connection and trend between different variables without relying on specific labels, dig out the potential data evolution path, and perform forward-looking calculations on the probability of occurrence of different data evolution paths to generate a variable relationship pre-training model;

[0014] Based on the variable relationship pre-training model, cross-media intelligent analysis technology is used to comprehensively analyze text and image data, accurately extract cross-media key features, establish complex mapping relationships between features, and generate a feature mapping table;

[0015] Based on the feature mapping table, high-risk data evolution paths are screened and accurately located to generate a data evolution path set.

[0016] Optionally, based on the optimized multidimensional dataset, a self-supervised learning algorithm is used to pre-train the variable relationships. Without relying on specific labels, it automatically discovers the implicit connections and trends between different variables, mines potential data evolution paths, prospectively calculates the probabilities of different data evolution paths, and generates a pre-trained model of variable relationships, including:

[0017] Based on the multidimensional data background information framework, specific preprocessing operations are performed on electronic medical records and imaging data, including unifying geographical coding and standardizing text formats to generate a standardized medical dataset;

[0018] Based on the standardized medical dataset, a self-supervised learning algorithm is used to pre-train the variable relationships in medical data. Without relying on specific labels, it automatically discovers the implicit connections and trends between different variables, mines potential data evolution paths, evaluates the probabilities of different data evolution paths, and generates a potential association map;

[0019] Based on the potential association map, key paths that have a significant impact on the disease evolution are determined, and causal relationships are explored through logical reasoning and statistical analysis to generate a set of evolution paths;

[0020] Based on the set of evolution paths, the complex relationships between variables in the multidimensional dataset are captured, and the relationships between variables in unknown situations are predicted to generate a pre-trained model of variable relationships.

[0021] Optionally, based on the pre-trained model of variable relationships, cross-media intelligent analysis technology is used to comprehensively analyze text and image data, accurately extract cross-media key features, establish complex mapping relationships between features, and generate a feature mapping table, including:

[0022] Based on the pre-trained model of variable relationships, cross-media intelligent analysis technology is used to comprehensively analyze text and image data in medical data to ensure that different forms of data complement and support each other, and generate a multi-source data fusion result;

[0023] Based on the multi-source data fusion result, cross-media intelligent analysis technology is further used to accurately extract cross-media key features and generate a set of cross-media key features;

[0024] Based on the set of cross-media key features, a statistical modeling method is introduced to establish complex mapping relationships between features, explore potential indirect connections and causal relationships, and generate a feature relationship network;

[0025] Based on the feature relationship network, the weight of each feature is calculated according to the importance score of each feature to quantify the influence degree, and a feature mapping table is generated.

[0026] Optionally, based on the data evolution path set, the deep forest algorithm is used to achieve efficient learning of complex data structures through multi-level random forest combination, accurately evaluate the possibility of different data evolution paths, adopt privacy enhancement technology to ensure data privacy, optimize the prediction model learning process, and generate a risk quantification prediction report, including:

[0027] Based on the data evolution path set, identify data dimensions and feature distribution to effectively capture the essential characteristics of the data and generate an initial data analysis report;

[0028] Based on the initial data analysis report, the deep forest algorithm is used to accurately evaluate the probability of occurrence of each data evolution path. Through the combination of multi-level random forests, data features are gradually learned at multiple levels, high-dimensional and nonlinear data features are processed, and complex interactions between variables are captured to generate a detailed path probability evaluation report;

[0029] Based on the detailed path probability assessment report, privacy enhancement technology is used to appropriately add noise during the training process to prevent sensitive information leakage, strictly control the privacy budget, ensure that each query meets the preset privacy standards, and generate a privacy protection optimization model;

[0030] Based on the privacy protection optimization model, all possible evolution paths are prioritized and a risk quantification prediction report is generated.

[0031] Optionally, based on the initial data analysis report, the deep forest algorithm is used to accurately evaluate the probability of occurrence of each data evolution path, and through a multi-level random forest combination, data features are gradually learned at multiple levels, high-dimensional and nonlinear data features are processed, and complex interactions between variables are captured to generate a detailed path probability evaluation report, including:

[0032] Based on the initial data analysis report, high-dimensional and nonlinear features in each data evolution path are extracted in layers to generate a multi-layer feature set;

[0033] Based on the multi-layer feature set, the deep forest algorithm is used to accurately evaluate the probability of each data evolution path, build a multi-level random forest combination, and gradually learn data features at multiple levels. It not only handles high-dimensional and nonlinear data features, but also captures the complex interactions between variables and generates a multi-level random forest model.

[0034] Based on the multi-level random forest model, the interaction between the variables in the path is analyzed, the possibility of different evolution paths is predicted, the importance score of each feature is obtained, and the preliminary evaluation results of the path probability are generated;

[0035] Based on the preliminary evaluation results of the path probability, key paths with higher probability of occurrence and serious consequences are marked, and a detailed path probability evaluation report is generated.

[0036] Optionally, based on the risk quantification prediction report, all previous analysis results are integrated to formulate accurate data application strategies and resource allocation plans, and generate dynamic casualty condition prediction plans, including:

[0037] Based on the risk quantitative prediction report, combined with the feature importance score, the critical path and influencing factors are integrated to generate a list of critical path influencing factors;

[0038] Based on the list of critical path influencing factors, establish a real-time data monitoring system, set reasonable warning thresholds, determine the frequency of data analysis, and generate data application strategy details;

[0039] Based on the data application strategy details, optimize the resource allocation plan, formulate emergency response plans for high-risk paths, formulate resource scheduling schedules, and generate optimized resource allocation plans;

[0040] Based on the optimized resource allocation plan, the possibilities and response strategies of different paths are summarized, and a dynamic update mechanism is introduced to generate a dynamic casualty condition prediction plan.

[0041] In a second aspect, the present application embodiment provides a system for predicting the condition of a wounded person based on big data analysis, including:

[0042] A receiving module is used to receive and analyze real-time data streams from multiple medical institutions, combine multiple factors for contextual understanding, and generate a multi-dimensional data background information framework;

[0043] A training module is used to pre-train variable relationships based on the multidimensional data background information framework using a self-supervised learning algorithm, to mine potential patterns through unlabeled data, to perform forward-looking calculations on the probability of occurrence of different data evolution paths, to extract cross-media key features from text and image data using cross-media intelligent analysis technology, to establish complex mapping relationships between data elements, and to generate a set of data evolution paths;

[0044] An optimization module is used to use a deep forest algorithm based on the data evolution path set to achieve efficient learning of complex data structures through a multi-level random forest combination, accurately evaluate the possibility of different data evolution paths, use privacy enhancement technology to ensure data privacy, optimize the prediction model learning process, and generate a risk quantification prediction report;

[0045] The generation module is used to formulate accurate data application strategies and resource allocation plans based on the risk quantification prediction report and all previous analysis results, and generate a dynamic casualty condition prediction plan.

[0046] In a third aspect, an embodiment of the present application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a method for predicting the condition of the wounded based on big data analysis 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, which when executed by a computer, implements a method for predicting the condition of the wounded based on big data analysis as described in the first aspect.

[0048] In the embodiment of the present application, real-time data streams of multiple medical institutions are received and parsed, contextual understanding is performed by combining multiple factors, and a multi-dimensional data background information framework is generated; based on the multi-dimensional data background information framework, a self-supervised learning algorithm is used to pre-train variable relationships, potential patterns are mined through unlabeled data, the probabilities of different data evolution paths are calculated prospectively, a cross-media intelligent analysis technology is adopted to extract cross-media key features from text and image data, a complex mapping relationship between data elements is established, and a set of data evolution paths is generated; based on the set of data evolution paths, a deep forest algorithm is used to achieve efficient learning of complex data structures through a multi-level random forest combination, accurately evaluate the possibilities of different data evolution paths, a privacy enhancement technology is adopted to ensure data privacy, optimize the learning process of the prediction model, and generate a risk quantification prediction report; based on the risk quantification prediction report, integrating all the previous analysis results, formulating a precise data application strategy and resource allocation plan, and generating a dynamic prediction plan for the condition of the wounded. By receiving and parsing the real-time data streams of multiple medical institutions and performing contextual understanding by combining multiple factors to generate a multi-dimensional data background information framework, the system can comprehensively capture and integrate data from different sources, providing more accurate and detailed predictions of the condition of the wounded; using a self-supervised learning algorithm to pre-train variable relationships, mine potential patterns, and adopting a cross-media intelligent analysis technology to extract key features of text and image data, this method not only improves the model's ability to understand complex data structures but also discovers hidden connections and trends that are difficult to identify by traditional methods; based on the deep forest algorithm, achieving efficient learning of complex data structures through a multi-level random forest combination, accurately evaluating the possibilities of different data evolution paths, and adopting a privacy enhancement technology to ensure data privacy and optimize the learning process of the prediction model, thus generating a reliable risk quantification prediction report; integrating all the previous analysis results, formulating a precise data application strategy and resource allocation plan, and generating a dynamic prediction plan for the condition of the wounded, which helps the medical team make optimal decisions under limited resources, improving the treatment efficiency and effect.

[0049] Furthermore, by performing preprocessing operations on the multidimensional data background information framework, outlier detection and feature selection are performed, the most representative variables are retained, and an optimized multidimensional data set is generated. This step ensures the basic data quality for subsequent analysis, reduces noise interference, and improves the robustness and accuracy of the model. Based on the optimized multidimensional data set, the self-supervised learning algorithm is used to pre-train the variable relationship to automatically discover the implicit connections and trends between different variables. This method does not rely on specific labels and can mine valuable potential patterns in a large amount of unlabeled data, thereby enhancing the generalization ability of the model. Cross-media intelligent analysis technology is used to conduct a comprehensive analysis of text and image data, accurately extract cross-media key features, and establish complex mapping relationships between features. This not only enriches the data representation, but also provides more dimensional information for subsequent path evaluation, enhancing the interpretability and credibility of the model. Based on the feature mapping table, the high-risk data evolution path is screened and accurately located, and a data evolution path set is generated. This process ensures that high-risk situations are focused on, improves the response speed and accuracy of the early warning system, and helps to take timely intervention measures.

[0050] Furthermore, by identifying the data dimensions and feature distribution, the essential characteristics of the data are effectively captured, and an initial data analysis report is generated. This step provides the necessary background information for subsequent deep learning and ensures that the model can fully understand the data structure. Based on the initial data analysis report, the deep forest algorithm is used to accurately evaluate the probability of each data evolution path, and the data features are gradually learned through multi-level random forest combinations, high-dimensional and nonlinear data features are processed, and complex interactions between variables are captured. This method significantly improves the accuracy and reliability of path evaluation; privacy enhancement technology is used to appropriately add noise during the training process to prevent sensitive information leakage, strictly control the privacy budget, ensure that each query meets the preset privacy standards, and generate a privacy protection optimization model. This not only protects patient privacy, but also ensures the security and compliance of the model; based on the privacy protection optimization model, all possible evolution paths are prioritized and a risk quantification prediction report is generated. This process helps the medical team quickly identify high-risk paths, optimize resource allocation, improve the ability to respond to emergencies, and ensure that clinical decisions are based on evidence.

[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 predicting the condition of a wounded person based on big data analysis provided in an embodiment of the present application;

[0054] Figure 2 A schematic diagram of the structure of a system for predicting the condition of a wounded person based on big data analysis 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 predicting the condition of a casualty based on big data analysis is provided for an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0060] 101. Receive and analyze real-time data streams from multiple medical institutions, combine multiple factors for contextual understanding, and generate a multi-dimensional data background information framework;

[0061] In this step, real-time data stream refers to dynamic data continuously received from multiple medical institutions (such as hospitals, clinics, emergency centers, etc.), including electronic medical records, laboratory results, imaging data, and vital signs monitoring data.

[0062] Contextual understanding refers to combining the collected data with external environmental information such as time, geographic location, and socioeconomic factors to more comprehensively understand the patient's specific situation, which helps identify key factors that affect changes in the disease and provide background support for subsequent analysis.

[0063] The multidimensional data background information framework integrates data from different sources to construct a multidimensional data set, which not only contains basic physiological data, but also covers a variety of information such as environmental factors and treatment history, providing a solid foundation for complex data analysis.

[0064] In the embodiment of the present application, first, the system receives real-time data streams from multiple medical institutions through a standardized interface; second, it combines time, geographic location, and socioeconomic factors for contextual understanding; third, it uses data cleaning and preprocessing techniques to remove noise and outliers; finally, it generates a multidimensional data background information framework to provide a high-quality data foundation for subsequent analysis.

[0065] Suppose a city has multiple hospitals and emergency centers connected to the Internet, and each institution is equipped with real-time data transmission equipment. The system first receives real-time data streams from various institutions through a secure protocol; secondly, it conducts contextual understanding based on the time and location of the patient's visit and the socioeconomic status of the area where the patient is located; thirdly, it cleans the collected data to remove duplicate or erroneous records; and finally, it generates a multidimensional data background information framework that covers each patient's detailed health record, including medical history, current symptoms, treatment progress, etc., to provide comprehensive data support for subsequent analysis.

[0066] 102. Based on the multidimensional data background information framework, a self-supervised learning algorithm is used to pre-train the variable relationship, potential patterns are mined through unlabeled data, the probability of occurrence of different data evolution paths is forward-lookingly calculated, cross-media intelligent analysis technology is used to extract cross-media key features of text and image data, complex mapping relationships between data elements are established, and a set of data evolution paths is generated;

[0067] Self-supervised learning algorithm is a machine learning method that can automatically discover potential patterns and structures in data without labels. The algorithm improves the model's ability to understand unknown data by mining the relationships within the data.

[0068] Unlabeled data mining refers to revealing hidden patterns and trends through in-depth analysis of raw data in the absence of labeled data. This method can significantly reduce dependence on manually labeled data and reduce training costs.

[0069] Prospective computing refers to the prediction of possible future events based on existing data. In the medical field, prospective computing can help doctors identify potential risks in advance and develop preventive measures.

[0070] Cross-media intelligent analysis technology is a technology that comprehensively analyzes different types of data such as text and images. It aims to extract key features and establish complex mapping relationships between data elements. This technology improves the understanding and utilization efficiency of multi-source heterogeneous data.

[0071] The data evolution path set refers to a series of possible development paths generated by analyzing the relationship between different variables. These paths describe the possible changing trends of the disease and provide an important reference for clinical decision-making.

[0072] In the embodiments of the present application, firstly, based on the multidimensional data background information framework, a self-supervised learning algorithm is applied to pre-train the variable relationship; secondly, potential patterns are mined through unlabeled data to evaluate the probability of occurrence of different data evolution paths; thirdly, cross-media intelligent analysis technology is used to comprehensively analyze text and image data to extract cross-media key features; finally, a complex mapping relationship between data elements is established to generate a set of data evolution paths.

[0073] For example, continuing with the above example, assume that the system has generated a multidimensional data background information framework. First, based on this framework, the self-supervised learning algorithm is used to pre-train the variable relationship and dig out the implicit connection between different variables; second, the potential pattern is mined through unlabeled data to evaluate the probability of occurrence of different data evolution paths, such as the probability of a certain type of disease worsening or improving under specific conditions; third, cross-media intelligent analysis technology is used to conduct a comprehensive analysis of the patient's medical records and imaging materials to extract key features; finally, a complex mapping relationship between data elements is established to generate a series of possible data evolution path sets to provide a basis for subsequent risk assessment.

[0074] 103. Based on the data evolution path set, the deep forest algorithm is used to achieve efficient learning of complex data structures through multi-level random forest combination, accurately evaluate the possibility of different data evolution paths, adopt privacy enhancement technology to ensure data privacy, optimize the prediction model learning process, and generate risk quantitative prediction reports;

[0075] In this step, the deep forest algorithm is an emerging machine learning method that achieves efficient learning of complex data structures through a combination of multi-level random forests. Compared with traditional methods, the deep forest algorithm can better handle high-dimensional and nonlinear data features and capture complex interactions between variables.

[0076] Multi-level random forest combination means gradually learning data features by building multiple levels of random forest models. Each layer of the model focuses on different data dimensions and feature distributions, and finally forms a powerful integrated model to improve prediction accuracy.

[0077] Privacy-enhancing technology refers to measures taken during data processing and model training to protect patient privacy and prevent the leakage of sensitive information. This includes methods such as adding noise during training and controlling privacy budgets to ensure that each query meets preset privacy standards.

[0078] The risk quantification prediction report refers to a detailed report generated based on the model evaluation results, which contains the probability of occurrence of different data evolution paths and their corresponding potential risks. The report provides a scientific basis for clinical decision-making and helps doctors make the best choice.

[0079] In the embodiments of the present application, first, based on the set of data evolution paths, the data dimensions and feature distribution are identified; second, the deep forest algorithm is used to accurately evaluate the probability of occurrence of each data evolution path; third, the data features are gradually learned through a multi-level random forest combination to process high-dimensional and nonlinear data features; finally, privacy enhancement technology is used to optimize the prediction model learning process and generate a risk quantification prediction report.

[0080] For example, continuing with the above example, assume that the system has generated a set of data evolution paths. First, based on this set, identify the data dimensions and feature distribution to ensure that the model can effectively capture the essential characteristics of the data; second, use the deep forest algorithm to accurately evaluate the probability of occurrence of each path and generate a detailed path probability evaluation report; third, gradually learn data features through multi-level random forest combinations, process high-dimensional and nonlinear data features, and capture complex interactions between variables; finally, use privacy enhancement technology to appropriately add noise during the training process to prevent sensitive information leakage, strictly control the privacy budget, ensure that each query meets the preset privacy standards, generate a privacy protection optimization model, and finally generate a risk quantification prediction report.

[0081] 104. Based on the risk quantification prediction report and comprehensive analysis of all previous analysis results, formulate precise data application strategies and resource allocation plans to generate dynamic casualty condition prediction plans.

[0082] In this step, precise data application strategy refers to formulating a specific data application plan based on the risk quantification prediction report. This strategy aims to optimize resource allocation, ensure the most effective use of limited medical resources, and improve treatment outcomes.

[0083] Resource allocation planning refers to the rational allocation of medical teams, equipment and other resources based on forecast results, which helps to respond to emergencies and ensure that patients receive timely and effective treatment.

[0084] The dynamic casualty condition prediction plan refers to a dynamically updated condition prediction plan generated by combining all previous analysis results. This plan not only summarizes the possibilities and response strategies of different paths, but also specifically marks high-risk paths and provides specific decision-making support suggestions.

[0085] In the embodiment of the present application, firstly, based on the risk quantification prediction report, all the previous analysis results are integrated; secondly, accurate data application strategies and resource allocation plans are formulated; thirdly, it is ensured that the medical team can quickly deploy necessary resources at critical moments; finally, a dynamic casualty condition prediction plan is generated to provide a scientific basis for clinical decision-making.

[0086] For example, continuing with the previous example, assume that the system has generated a risk quantification prediction report. First, based on this report, all previous analysis results are integrated to develop precise data application strategies and resource allocation plans; second, ensure that the medical team can quickly allocate necessary resources at critical moments, such as giving priority to intensive care unit beds and professional medical staff; third, for high-risk pathways, develop detailed emergency response plans, clarify response measures, responsible personnel, and technical support; finally, generate a dynamic casualty condition prediction plan, which not only summarizes the possibilities and response strategies of different pathways, but also specifically marks high-risk pathways and provides specific decision support suggestions to ensure that clinical decisions are based on evidence.

[0087] In summary, steps 101 to 104 cover the complete process from data collection, preprocessing, feature mining, risk assessment to final decision support, aiming to provide a comprehensive and efficient method for predicting the condition of the injured to meet the needs of accurate, fast and safe condition prediction in the modern medical environment.

[0088] In order to solve the accuracy problem of data preprocessing and feature extraction, in some embodiments, the process of pre-training variable relationships based on the multidimensional data background information framework in step 102 includes: based on the multidimensional data background information framework, performing preprocessing operations, performing outlier detection, identifying the variables that retain the most representative essential characteristics of the data through feature selection technology, and generating an optimized multidimensional data set; based on the optimized multidimensional data set, using a self-supervised learning algorithm, pre-training variable relationships, automatically discovering implicit connections and trends between different variables without relying on specific labels, mining potential data evolution paths, performing forward-looking calculations on the probability of occurrence of different data evolution paths, and generating a variable relationship pre-training model; based on the variable relationship pre-training model, using cross-media intelligent analysis technology, comprehensively analyzing text and image data, accurately extracting cross-media key features, establishing complex mapping relationships between features, and generating a feature mapping table; based on the feature mapping table, screening and accurately locating high-risk data evolution paths, and generating a data evolution path set.

[0089] In this embodiment, outlier detection is a technique for identifying and processing data points in a data set that do not conform to expected patterns. These outliers may be caused by equipment failure, human error, or other abnormal situations.

[0090] Feature selection technology is a data dimensionality reduction method that aims to select the most representative and predictive features from the original data set, reduce redundant information, and improve model performance.

[0091] The optimized multidimensional dataset refers to the dataset generated after completing outlier detection and feature selection, which not only removes noise and outliers but also retains the key features that best reflect the changes in the patient's condition.

[0092] The variable relationship pre-training model refers to a model generated by a self-supervised learning algorithm, which can reveal the implicit connections and trends between different variables and proactively calculate the probability of occurrence of different paths.

[0093] A feature mapping table refers to a table generated by cross-media intelligent analysis technology, which records the associations between different types of data and enhances the expressiveness and interpretability of the model.

[0094] High-risk data evolution paths refer to potential high-risk paths that are screened and precisely located through feature mapping tables. These paths describe possible changing trends of the disease, especially those that may lead to serious consequences, and provide important references for clinical decision-making.

[0095] The data evolution path set refers to a series of possible development paths generated by analyzing the relationship between different variables. These paths describe the possible changing trends of the disease and provide an important reference for clinical decision-making.

[0096] In the embodiments of the present application, first, based on the multidimensional data background information framework, preprocessing operations are performed, including outlier detection, to ensure the integrity and reliability of the data set; secondly, through feature selection technology, the variables that are most representative of the essential characteristics of the data are identified to generate an optimized multidimensional data set; thirdly, using the optimized multidimensional data set, a self-supervised learning algorithm is applied to pre-train the variable relationship, and the implicit connections and trends between different variables are automatically discovered without relying on specific labels, potential data evolution paths are mined, and the probability of occurrence of different paths is prospectively calculated to generate a variable relationship pre-training model; finally, based on the variable relationship pre-training model, cross-media intelligent analysis technology is used to conduct a comprehensive analysis of text and image data, accurately extract cross-media key features, establish complex mapping relationships between features, generate a feature mapping table, and screen and accurately locate high-risk data evolution paths, and finally generate a data evolution path set.

[0097] Here is a specific example:

[0098] For example, suppose that in a large urban medical network, the system receives real-time data streams from multiple medical institutions. First, based on the multidimensional data background information framework, the system performs preprocessing operations, performs outlier detection, removes obviously erroneous or unreasonable data points, and ensures the reliability and consistency of the data; second, through feature selection technology, it identifies and retains those variables that best reflect the changes in the patient's condition, such as heart rate, blood pressure, body temperature, etc., to generate an optimized multidimensional data set; third, using the optimized multidimensional data set, the self-supervised learning algorithm is applied to automatically discover the implicit connections and trends between different variables, and to mine potential data evolution paths, such as the deterioration path of a certain type of disease under specific conditions, and prospectively calculate its occurrence probability to generate a variable relationship pre-training model; finally, based on this model, the cross-media intelligent analysis technology is used to comprehensively analyze the patient's medical record text (such as symptom description) and imaging data (such as X-rays), accurately extract cross-media key features, establish complex mapping relationships between features, generate feature mapping tables, and screen and accurately locate high-risk data evolution paths, and finally generate a data evolution path set to provide a scientific basis for subsequent risk assessment and clinical decision-making.

[0099] In order to solve the accuracy problems of data standardization and variable relationship mining, in some embodiments, the process of pre-training variable relationships based on the optimized multidimensional data set in step 102 includes: based on the multidimensional data background information framework, performing specific pre-processing operations on electronic medical records and imaging materials, unifying geographic coding, standardizing text formats, and generating a standardized medical data set; based on the standardized medical data set, using a self-supervised learning algorithm to pre-train variable relationships in medical data, automatically discover implicit connections and trends between different variables without relying on specific labels, mine potential data evolution paths, evaluate the probability of occurrence of different data evolution paths, and generate a potential association map; based on the potential association map, determine the key paths that have a significant impact on the evolution of the disease, explore causal relationships through logical reasoning and statistical analysis, and generate an evolution path set; based on the evolution path set, capture the complex relationships between variables in the multidimensional data set, predict the relationships between variables in unknown situations, and generate a variable relationship pre-training model.

[0100] In this embodiment, specific preprocessing operations refer to a series of processing steps performed on electronic medical records and imaging data, including unified geographic coding, standardized text format, etc. These operations ensure the consistency and comparability of data between different medical institutions and improve the quality of subsequent analysis.

[0101] Geocoding is the process of converting geographic location information into standard coordinates so that data from different regions can be integrated into the same framework for analysis.

[0102] Text format normalization is the cleaning and formatting of text data to ensure that all text data follows the same structure and standards, which includes removing redundant information, correcting spelling errors, unifying terminology, etc., thereby improving the accuracy of text data analysis.

[0103] A standardized medical dataset is a dataset generated after specific preprocessing operations. It not only contains geographical location information with unified geocoding, but also covers standardized text data, ensuring the consistency and reliability of the data and providing a high-quality foundation for subsequent analysis.

[0104] The latent association map refers to a map generated by mining potential patterns in unlabeled data through a self-supervised learning algorithm. The map shows the implicit connections and trends between different variables, reveals the complex structure within the data, and proactively calculates the probability of occurrence of different paths.

[0105] The evolution path set refers to a set of paths generated by analyzing potential association maps, determining the key paths that have a significant impact on the evolution of the disease, and exploring causal relationships through logical reasoning and statistical analysis. These paths describe the possible changing trends of the disease, especially those that may lead to serious consequences, and provide an important reference for clinical decision-making.

[0106] The variable relationship pre-training model refers to a model that is generated by capturing the complex relationships between variables in a multidimensional data set and predicting the relationships between variables in unknown situations. This model can not only reveal the implicit connections between different variables, but also conduct prospective assessments of possible future situations, providing a scientific basis for clinical decision-making.

[0107] In the embodiments of the present application, first, based on the multidimensional data background information framework, the system performs specific preprocessing operations on electronic medical records and imaging materials, including unified geographic coding and standardized text formats, to ensure the consistency and comparability of all data and generate a standardized medical data set; secondly, based on the standardized medical data set, the system uses a self-supervised learning algorithm to pre-train the relationship between variables in the medical data, and automatically discovers the implicit connections and trends between different variables without relying on specific labels, mines potential data evolution paths, evaluates the probability of occurrence of different data evolution paths, and generates a potential association map; thirdly, based on the potential association map, the system determines the key paths that have a significant impact on the evolution of the disease, explores causal relationships through logical reasoning and statistical analysis, and generates an evolution path set; finally, based on the evolution path set, the system captures the complex relationships between variables in the multidimensional data set, predicts the relationships between variables in unknown situations, and generates a variable relationship pre-training model.

[0108] Here is a specific example:

[0109] For example, suppose that in a national medical data sharing platform, the system receives real-time data streams from multiple medical institutions across the country. First, the system performs specific preprocessing operations on electronic medical records and imaging data, including unified geographic coding and standardized text formats, to ensure the consistency and comparability of all data and generate a standardized medical data set; second, based on this standardized medical data set, the system uses a self-supervised learning algorithm to pre-train the variable relationship in the medical data, and automatically discovers the implicit connections and trends between different variables without relying on specific labels, digs out potential data evolution paths, evaluates the probability of occurrence of different data evolution paths, and generates a potential association map; third, based on the potential association map, the system determines the key paths that have a significant impact on the evolution of the disease, explores causal relationships through logical reasoning and statistical analysis, and generates an evolution path set; finally, based on the evolution path set, the system captures the complex relationships between variables in the multidimensional data set, predicts the relationship between variables in unknown situations, and generates a variable relationship pre-training model, providing a scientific basis for the allocation of medical resources and emergency plans across the country.

[0110] In order to solve the accuracy and integration problems of cross-media data analysis, in some embodiments, the process of performing cross-media intelligent analysis based on the variable relationship pre-training model in step 102 includes: based on the variable relationship pre-training model, using cross-media intelligent analysis technology to comprehensively analyze the text and image data in the medical data to ensure that different forms of data complement and support each other and generate multi-source data fusion results; based on the multi-source data fusion results, further using cross-media intelligent analysis technology to accurately extract cross-media key features and generate a cross-media key feature set; based on the cross-media key feature set, introducing statistical modeling methods to establish complex mapping relationships between features, explore potential indirect connections and causal relationships, and generate a feature relationship network; based on the feature relationship network, calculating the weight of each feature according to the importance score of each feature to quantify the degree of influence and generate a feature mapping table.

[0111] In this embodiment, the multi-source data fusion results refer to the results generated by comprehensive analysis of the text and image data in the medical data. These results not only cover the information of the original data, but also reveal the relationship between different data types, providing a comprehensive data foundation for subsequent analysis.

[0112] The cross-media key feature set refers to a set of features that are accurately extracted through cross-media intelligent analysis technology and can represent different data types. These features not only capture the characteristics of a single data type, but also reflect the associations between different types of data, thereby enhancing the expressiveness and interpretability of the model.

[0113] The feature relationship network refers to a network generated by introducing statistical modeling methods and establishing complex mapping relationships between features. This network not only shows the direct connection between different features, but also explores potential indirect connections and causal relationships, revealing the complex structure within the data.

[0114] Feature importance scoring is to quantify the influence of each feature by calculating its contribution in the prediction process, which helps to identify the most influential features, optimize model performance, and provide a scientific basis for clinical decision-making.

[0115] The feature mapping table refers to a table generated by calculating the weight of each feature based on the importance score of each feature. This table not only records the importance of each feature, but also provides clear guidance for subsequent data application and resource allocation.

[0116] In the embodiments of the present application, firstly, based on the variable relationship pre-training model, the system adopts cross-media intelligent analysis technology to conduct a comprehensive analysis of the text and image data in the medical data, so as to ensure that data in different forms complement and support each other and generate a multi-source data fusion result; secondly, based on this multi-source data fusion result, cross-media intelligent analysis technology is further adopted to accurately extract cross-media key features and generate a cross-media key feature set; thirdly, based on the cross-media key feature set, a statistical modeling method is introduced to establish a complex mapping relationship between features, explore potential indirect connections and causal relationships, and generate a feature relationship network; finally, based on the feature relationship network, the weight of each feature is calculated according to the importance score of each feature to quantify the degree of influence and generate a feature mapping table.

[0117] Here is a specific example:

[0118] For example, suppose that in a national trauma center, the system receives real-time data streams from multiple emergency sites. First, based on the variable relationship pre-training model, the system uses cross-media intelligent analysis technology to conduct a comprehensive analysis of the text (such as medical record description) and image (such as X-rays, CT scans) data in the medical data, ensuring that different forms of data complement each other and generate multi-source data fusion results; secondly, based on this multi-source data fusion result, the system further uses cross-media intelligent analysis technology to accurately extract cross-media key features and generate a cross-media key feature set, such as extracting key terms for symptom descriptions from medical record texts and extracting features of lesion areas from imaging data; thirdly, based on the cross-media key feature set, the system introduces statistical modeling methods to establish complex mapping relationships between features, explore potential indirect connections and causal relationships, generate feature relationship networks, and reveal the associations between different features and their impact on the evolution of the disease; finally, based on the feature relationship network, the system calculates the weight of each feature according to the importance score of each feature to quantify the degree of influence and generate a feature mapping table, providing a scientific basis for the optimal allocation of emergency resources and personalized treatment plans.

[0119] In order to solve the learning and privacy protection problems of complex data structures, in some embodiments, the process of performing risk quantification prediction based on a set of data evolution paths in step 103 includes: based on the set of data evolution paths, identifying data dimensions and feature distributions to effectively capture the essential characteristics of the data and generate an initial data analysis report; based on the initial data analysis report, using a deep forest algorithm to accurately evaluate the probability of occurrence of each data evolution path, and through a multi-level random forest combination, gradually learning data features at multiple levels, processing high-dimensional and nonlinear data features, capturing complex interactions between variables, and generating a detailed path probability evaluation report; based on the detailed path probability evaluation report, using privacy enhancement technology, appropriately adding noise during the training process to prevent the leakage of sensitive information, strictly controlling the privacy budget, ensuring that each query meets the preset privacy standards, and generating a privacy protection optimization model; based on the privacy protection optimization model, prioritizing all possible evolution paths and generating a risk quantification prediction report.

[0120] In this embodiment, the initial data analysis report refers to the preliminary analysis results generated by identifying the data dimensions and feature distributions in the data evolution path set. The report effectively captures the essential characteristics of the data and provides a solid foundation for subsequent deep learning.

[0121] The detailed path probability assessment report refers to a report generated after accurately evaluating the probability of occurrence of each data evolution path. The report not only shows the possibility of different paths, but also reveals the complex connections between the paths, providing detailed information for risk assessment.

[0122] Privacy-enhancing technology refers to measures taken during data processing and model training to protect patient privacy and prevent the leakage of sensitive information. This includes methods such as adding noise during training and controlling privacy budgets to ensure that each query meets preset privacy standards.

[0123] The privacy-preserving optimization model refers to an optimization model generated by applying privacy-enhancing technology, appropriately adding noise during the training process to prevent the leakage of sensitive information, and strictly controlling the privacy budget to ensure that each query meets the preset privacy standards.

[0124] The risk quantification prediction report is a report generated after prioritizing all possible evolution paths based on the privacy protection optimization model. The report provides the medical team with a set of scientific basis to help them make optimal resource allocation and treatment decisions.

[0125] In an embodiment of the present application, first, based on a set of data evolution paths, the system identifies data dimensions and feature distributions to effectively capture the essential characteristics of the data and generate an initial data analysis report; secondly, based on this initial data analysis report, the system uses a deep forest algorithm to accurately evaluate the probability of occurrence of each data evolution path, and through a multi-level random forest combination, gradually learns data features at multiple levels, processes high-dimensional and nonlinear data features, captures complex interactions between variables, and generates a detailed path probability evaluation report; thirdly, based on the detailed path probability evaluation report, the system uses privacy enhancement technology to appropriately add noise during the training process to prevent the leakage of sensitive information, strictly controls the privacy budget, ensures that each query meets the preset privacy standards, and generates a privacy protection optimization model; finally, based on the privacy protection optimization model, the system prioritizes all possible evolution paths and generates a risk quantification prediction report.

[0126] For example, suppose that in a regional trauma assessment center, the system receives real-time casualty data streams from multiple hospitals. First, based on the set of data evolution paths, the system identifies the data dimensions and feature distribution to effectively capture the essential characteristics of the data and generate an initial data analysis report, covering each patient's medical history, current symptoms, treatment progress and other information; secondly, based on this initial data analysis report, the system uses the deep forest algorithm to accurately evaluate the probability of each data evolution path. Through the combination of multi-level random forests, it gradually learns data features at multiple levels, processes high-dimensional and nonlinear data features, captures complex interactions between variables, and generates a detailed path probability evaluation report to show the possibility of different paths and their potential risks; thirdly, based on the detailed path probability evaluation report, the system uses privacy enhancement technology to appropriately add noise during the training process to prevent sensitive information leakage, strictly control the privacy budget, ensure that each query meets the preset privacy standards, generate a privacy protection optimization model, and protect the privacy of patients; finally, based on the privacy protection optimization model, the system prioritizes all possible evolution paths and generates a risk quantification prediction report to provide a scientific basis for the medical team, helping them to reasonably allocate resources and formulate the best treatment plan.

[0127] In order to solve the problem of processing high-dimensional and nonlinear data features, in some embodiments, the process of path probability evaluation based on the initial data analysis report in step 103 includes: based on the initial data analysis report, hierarchically extracting high-dimensional and nonlinear features in each data evolution path to generate a multi-layer feature set; based on the multi-layer feature set, using the deep forest algorithm to accurately evaluate the probability of occurrence of each data evolution path, construct a multi-level random forest combination, and gradually learn data features at multiple levels, which not only processes high-dimensional and nonlinear data features, but also captures complex interactions between variables and generates a multi-level random forest model; based on the multi-level random forest model, analyzes the interactions between various variables in the path, predicts the possibility of different evolution paths, obtains the importance score of each feature, and generates a preliminary evaluation result of the path probability; based on the preliminary evaluation result of the path probability, marks the key paths with higher probability of occurrence and serious consequences, and generates a detailed path probability evaluation report.

[0128] In this embodiment, the multi-layer feature set refers to a feature set generated by hierarchical extraction of high-dimensional and nonlinear features in each data evolution path. These features not only cover the information of the original data, but also reveal the complex structure at different levels, providing rich information for model training.

[0129] The multi-level random forest model refers to building multiple levels of random forest models to gradually learn data features at multiple levels, process high-dimensional and nonlinear data features, and capture complex interactions between variables. Each level of the model focuses on different data dimensions and feature distributions, ultimately forming a powerful integrated model.

[0130] Feature importance scoring refers to quantifying the influence of each feature by calculating its contribution in the prediction process, which helps to identify the most influential features, optimize model performance, and provide a scientific basis for clinical decision-making.

[0131] The preliminary evaluation results of path probability refer to the preliminary evaluation results generated after analyzing the interactions between various variables in the path based on the multi-level random forest model and predicting the possibility of different evolution paths. These results show the possibility of different paths and their potential risks, providing a basis for subsequent detailed evaluation.

[0132] A detailed path probability assessment report is a report generated based on the preliminary path probability assessment results, after marking the critical paths with a higher probability of occurrence and serious consequences. This report provides a set of scientific basis for the medical team to help them rationally allocate resources and develop the best treatment plan.

[0133] In the embodiment of the present application, first, based on the initial data analysis report, the system performs hierarchical extraction of high-dimensional and nonlinear features in each data evolution path to generate a multi-layer feature set; secondly, based on this multi-layer feature set, the system uses a deep forest algorithm to accurately evaluate the probability of occurrence of each data evolution path, construct a multi-level random forest combination, and gradually learn data features at multiple levels, which not only processes high-dimensional and nonlinear data features, but also captures complex interactions between variables and generates a multi-level random forest model; thirdly, based on the multi-level random forest model, the system analyzes the interactions between each variable in the path, predicts the possibility of different evolution paths, obtains the importance score of each feature, and generates a preliminary evaluation result of the path probability; finally, based on the preliminary evaluation result of the path probability, the system marks the critical paths with a higher probability of occurrence and serious consequences, and generates a detailed path probability evaluation report.

[0134] Here is a specific example:

[0135] For example, suppose that in a provincial emergency center, the system receives real-time casualty data streams from multiple hospitals. First, based on the initial data analysis report, the system extracts high-dimensional and nonlinear features in each data evolution path in layers, generating a multi-layer feature set that covers each patient's medical history, current symptoms, treatment progress, and other information; second, based on this multi-layer feature set, the system uses the deep forest algorithm to accurately evaluate the probability of occurrence of each data evolution path, construct a multi-level random forest combination, and gradually learn data features at multiple levels, which not only processes high-dimensional and nonlinear data features, but also captures complex interactions between variables and generates a multi-level random forest model; third, based on the multi-level random forest model, the system analyzes the interactions between various variables in the path, predicts the possibility of different evolution paths, obtains the importance score of each feature, generates a preliminary evaluation result of the path probability, and displays the possibility of different paths and their potential risks; finally, based on the preliminary evaluation results of the path probability, the system marks the key paths with high probability of occurrence and serious consequences, and generates a detailed path probability evaluation report, providing a scientific basis for the optimal allocation of emergency resources and personalized treatment plans.

[0136] In order to solve the optimization problem of accurate data application and resource allocation, in some embodiments, the process of generating a dynamic casualty condition prediction plan based on the risk quantification prediction report in step 104 includes: based on the risk quantification prediction report, combined with the feature importance score, integrating the key paths and influencing factors, and generating a list of key path influencing factors; based on the list of key path influencing factors, establishing a real-time data monitoring system, setting a reasonable warning threshold, determining the data analysis frequency, and generating data application strategy details; based on the data application strategy details, optimizing the resource allocation plan, formulating emergency response plans for high-risk paths, formulating a resource scheduling schedule, and generating an optimized resource allocation plan; based on the optimized resource allocation plan, summarizing the possibilities and response strategies of different paths, introducing a dynamic update mechanism, and generating a dynamic casualty condition prediction plan.

[0137] In this embodiment, feature importance scoring refers to quantifying the degree of influence of each feature by calculating its contribution in the prediction process, which helps to identify the most influential features, optimize model performance, and provide a scientific basis for clinical decision-making.

[0138] The critical path influencing factor list refers to a list generated by integrating the critical path and influencing factors in combination with the feature importance score. The list records in detail the key variables and their weights for each high-risk path, providing clear guidance for subsequent monitoring and response.

[0139] A real-time data monitoring system refers to an automated system that is used to continuously monitor the patient's real-time data flow. The system sets reasonable warning thresholds and determines the frequency of data analysis to ensure that abnormal situations can be detected in a timely manner and the warning mechanism can be triggered.

[0140] Data application strategy details refer to specific data application rules formulated based on the configuration of the real-time data monitoring system. These rules clarify how to process and analyze real-time data to support efficient clinical decision-making and resource scheduling.

[0141] The optimized resource allocation plan refers to a resource allocation plan formulated based on the data application strategy details. The plan not only formulates emergency response plans for high-risk paths, but also stipulates a timetable for resource scheduling to ensure that necessary resources are quickly deployed at critical moments.

[0142] The dynamic update mechanism refers to a mechanism introduced to regularly evaluate and adjust the forecast model and resource allocation plan. This mechanism ensures that the plan can adapt to the changing actual situation and maintain the accuracy of the forecast and the effectiveness of resource utilization.

[0143] The dynamic casualty condition prediction plan refers to the final plan generated by integrating all previous analysis results and summarizing the possibilities of different paths and response strategies. This plan not only covers detailed path analysis, but also includes specific response measures and resource scheduling arrangements, providing comprehensive decision-making support for the medical team.

[0144] In the embodiment of the present application, first, based on the risk quantification prediction report, the system combines the feature importance score, integrates the critical path and influencing factors, and generates a list of critical path influencing factors; secondly, based on this list of critical path influencing factors, the system establishes a real-time data monitoring system, sets a reasonable warning threshold, determines the data analysis frequency, and generates data application strategy details; thirdly, based on the data application strategy details, the system optimizes the resource allocation plan, formulates emergency response plans for high-risk paths, formulates a resource scheduling schedule, and generates an optimized resource allocation plan; finally, based on the optimized resource allocation plan, the system summarizes the possibilities and response strategies of different paths, introduces a dynamic update mechanism, and generates a dynamic casualty condition prediction plan.

[0145] Here is a specific example:

[0146] For example, suppose that in a national telemedicine platform, the system receives real-time data streams from emergency centers across the country. First, based on the risk quantification prediction report, the system combines the feature importance score, integrates the key paths and influencing factors, generates a list of key path influencing factors, and records in detail the key variables and their weights of each high-risk path; secondly, based on this list of key path influencing factors, the system sets up a real-time data monitoring system, sets a reasonable warning threshold, determines the frequency of data analysis, and generates data application strategy details to ensure that abnormal situations can be discovered in time and trigger the warning mechanism; thirdly, based on the data application strategy details, the system optimizes the resource allocation plan, formulates emergency response plans for high-risk paths, and formulates a resource scheduling schedule to ensure that necessary resources are quickly deployed at critical moments, such as remote expert consultations, mobile ICUs, etc.; finally, based on the optimized resource allocation plan, the system summarizes the possibilities and response strategies of different paths, introduces a dynamic update mechanism, and generates a dynamic prediction plan for the condition of the injured, providing comprehensive decision-making support for medical teams across the country to ensure that the best choice is made in national emergencies.

[0147] The present application considers that in order to solve the inaccuracy problem of variable relationship mining and data evolution path evaluation in the prior art, the embodiment of the invention proposes this optional solution. Because the prior art has the problem of insufficient mining of implicit connections and trends in medical data, the embodiment of the invention proposes this optional solution to solve the technical problems of high-precision variable relationship mining and potential evolution path prediction, and thus proposes a new optional solution, which includes:

[0148] Based on the standardized medical data set, a self-supervised learning algorithm is used to pre-train the relationship between variables in the medical data. Without relying on specific labels, the implicit connections and trends between different variables are automatically discovered, potential data evolution paths are mined, the probability of occurrence of different data evolution paths is evaluated, and potential association maps are generated, including:

[0149] Based on the standardized medical data set, the Z-score standardization method is used to ensure that all variables are compared on the same scale;

[0150] The sliding window technique is applied to extract the feature vector angle at each time point, calculate the angle change rate between adjacent time points, capture the time-varying trend of the variable, and generate similarity;

[0151] The similarity is calculated using the following formula:

[0152]

[0153] Among them, S(X i ,X j ) is the variable X i and X j The similarity between i and X j are two different variables in the medical data set; α is an adjustment parameter used to control the speed of exponential decay; θ i,t is the variable X at time point t i The eigenvector angle of j,t is the variable X at time point t j The characteristic vector angle of ; T is the length of the time series; λ is the nonlinear enhancement parameter; ∈ is the weight of the additional nonlinear term; μ is the power of the distance metric;

[0154] Based on the similarity, a weighted graph is constructed, and the Louvain algorithm is applied to identify clusters of variables with high internal connections to represent potential evolution paths. A path smoothing factor is introduced to adjust the path weight according to the path length and the average similarity between nodes to generate the probability of occurrence;

[0155] The probability of occurrence is calculated using the following formula:

[0156]

[0157] Among them, P(path k ) is the path path k The probability of occurrence; Z is the normalization factor to ensure that the sum of all path probabilities is 1; β is the parameter to enhance the nonlinear effect; γ and δ are the parameters to adjust the S value range to adapt to the sin function; η is the weight parameter of each pair of variable relationships in the path; S(X i ,Xj ) is the calculated variable X i and X j is the similarity between them; ζ is the parameter that introduces the phase difference; κ is the attenuation factor; v is the power of the path similarity; path k represents the kth potential data evolution path; t is the index of the time series, from 1 to T; T is the length of the time series; X i and X j are two different variables in the medical data set; Indicates the path path k All pairs of variables (X i ,X j ) performs product operation;

[0158] Based on the occurrence probability, a network diagram is drawn using the Graphviz visualization tool. The node size is determined according to the variable importance score, and the color and width of the edge reflect the path probability. An interactive interface is introduced to allow users to dynamically adjust the display content according to specific conditions and generate a potential association map.

[0159] This method aims to automatically discover the implicit connections and trends between different variables without relying on specific labels, and construct a potential association map by quantifying similarities and probabilities of occurrence, which helps to more accurately predict the evolution path of the disease and provide a scientific basis to support clinical decision-making.

[0160] In the similarity, the exponential decay term exp It is used to control the trend of similarity changing over time. As the angle difference increases, the similarity decays rapidly. The parameter α controls the decay rate, and the parameter λ enhances the nonlinear effect. An additional nonlinear term is introduced to capture the impact of angle difference on similarity. The parameter ∈ weighs the importance of the nonlinear term, and the parameter μ controls the power of the distance metric.

[0161] Among them, α, λ,∈, μ are obtained through experimental adjustment; θ i,t and θ j,t The angle of the time series feature vector extracted from the standardized medical data set; T is the length of the time series determined according to the actual data set;

[0162] In the probability of occurrence, the normalization factor term Ensure that the sum of all path probabilities is 1, Z is obtained by calculating the sum of all path probabilities; path weight term Weigh the weight of each pair of variables in the path, parameter β enhances the nonlinear effect, parameters γ and δ adjust the S value range to adapt to the sin function, and parameter η weighs the weight of each pair of variables; attenuation term The decay factor is introduced to reduce the impact of long time series on path probability. The parameter κ controls the decay rate, and the parameter ν controls the power of path similarity.

[0163] Among them, β, γ, δ, η, κ, and v are obtained through experimental adjustment; Z is obtained by calculating the sum of all path probabilities; θ i,t and θ j,t is the time series feature vector angle extracted from the standardized medical data set; T is the time series length determined according to the actual data set; S(X i ,X j ) is calculated by the similarity formula;

[0164] Assume that on an international medical cooperation platform, the system receives real-time casualty data streams from emergency centers in multiple countries and regions;

[0165] Assume α = 0.5, λ = 2, ∈ = 0.1, μ = 2, T = 10;

[0166]

[0167] Assume β=0.8, γ=1, δ=0.1, η=1.5, κ=0.1, v=2, Z=1.2, T=10;

[0168]

[0169] Assuming that the threshold is set to 0.9, since the calculated result 0.94 is greater than the set threshold, it shows that the prediction scheme for the condition of the injured and sick has high accuracy and reliability. This is because the higher path probability reflects that the model can effectively predict the path of disease evolution under the current data conditions, while not missing important potential paths. Through the above steps, the accurate prediction of the path of disease evolution of the injured and sick is ensured, the scientificity and effectiveness of clinical decision-making are improved, and the response speed and accuracy of the entire medical system are enhanced.

[0170] The present application considers that in order to solve the problem of insufficient processing of high-dimensional and nonlinear data features in the prior art, the embodiment of the invention proposes this optional solution. Because the prior art has the problem of inaccurate evaluation of complex variable relationships and evolution paths, the embodiment of the invention proposes this optional solution to solve the technical problems of high-precision data feature mining and evolution path prediction, and thus proposes a new optional solution, which includes:

[0171] Based on the multi-layer feature set, the deep forest algorithm is used to accurately evaluate the probability of each data evolution path, build a multi-level random forest combination, and gradually learn data features at multiple levels. It not only handles high-dimensional and nonlinear data features, but also captures the complex interactions between variables and generates a multi-level random forest model, including:

[0172] Based on the multi-layer feature set, time series decomposition technology is applied to split the time series data of each variable into trend and residual components, and mutual information analysis is used to evaluate the nonlinear relationship between the features, and the most representative feature combination is identified to generate an importance score;

[0173] The importance score is calculated using the following formula:

[0174]

[0175] Among them, I(X i ) is the variable X i The importance score of ; α is an adjustment parameter used to control the speed of exponential decay; is the mth weak learner in the lth layer for feature X i Output: is the average of the outputs of all weak learners in layer l; l is the index of the layer number, from 1 to L; L is the number of layers; m is the index of the weak learner in layer l, from 1 to M l ;M l is the number of weak learners in the lth layer; λ is the nonlinear enhancement parameter; ∈ is the weight of introducing additional nonlinear terms;

[0176] Based on the importance score, a feature interaction matrix is ​​constructed to record the interaction strength between all pairs of features to extract deep feature relationships and obtain feature embedding vectors. An adaptive threshold mechanism is introduced to dynamically adjust edge weights to generate dependency metrics.

[0177] The dependency metric is calculated using the following formula:

[0178]

[0179] Among them, D(path k ) is the path path k The dependency measure; η is the weight parameter of each pair of variable relationships in the path; I(X i ) is the variable X i The importance score of γ' and δ' are the parameters for adjusting the effect of angle difference; θ i,j For variable X i and X j The angle difference between them; ω is the time phase factor; t i,j For variable X i and X j time points between them; κ is the influencing factor of the time difference; Δt i,j For variable X i and X jThe time difference between them; v' is the power of the time difference; β' is the parameter that introduces the exponential decay effect; ρ is the decay rate; μ' is the power of the importance score; path k represents the kth potential data evolution path; X i represents the i-th variable in the medical data set; (X i ,X j ) represents a pair of variables X i and X j ;

[0180] Based on the dependency metric, all paths are sorted according to the dependency metric, and high dependency metric paths are screened out as the focus of attention. A hierarchical clustering method is used to group them according to path similarity, and appropriate weak learners are selected. Hyperparameters are optimized through cross-validation to generate a multi-level random forest model.

[0181] The method aims to gradually learn data features at multiple levels, process high-dimensional and nonlinear data features, capture complex interactions between variables, and generate a multi-level random forest model. This helps to more accurately assess the probability of occurrence of each data evolution path and provide a scientific basis to support clinical decision-making.

[0182] In the importance score, the exponential decay term Used to control the trend of importance score changing with feature output difference; as the output difference increases, the score decays rapidly, parameter α controls the decay rate, and parameter λ enhances the nonlinear effect; nonlinear enhancement term Introduce additional nonlinear terms to capture the impact of feature output differences on the score, and the parameter ∈ weighs the importance of the nonlinear term;

[0183] Among them,α,λ,∈ are obtained through experimental adjustment; Through the mth weak learner in the lth layer, the feature X i The output is obtained; It is obtained by averaging the outputs of all weak learners in layer l; L is the number of layers; M l Obtained by the number of weak learners in the lth layer;

[0184] In the dependency metric, the path weight term η·(I(X i )+I(X j ))·(1-cos(γ'·θ i,j +δ'·sin(ω·t i,j ))):Weigh the weight of each pair of variables in the path; parameter η weighs the weight of each pair of variables, parameters γ' and δ' adjust the angle difference, and parameter ω controls the time phase factor; the time difference affects the term κ·|Δt i,j | v′:Introduce the influence of time difference to reduce the influence of long time interval on the dependent measure. The parameter κ controls the influence factor of time difference, and the parameter v' controls the power of time difference. The exponential decay term β'·exp(-ρ·|Δt i,j |)·(I(X i )·I(X j )) μ′ Introduce exponential decay effect to reduce the impact of long time intervals on dependency metrics. Parameter β' introduces exponential decay effect, parameter ρ controls the decay rate, and parameter μ' controls the power of importance score.

[0185] Among them, η, γ', δ', ω, κ, ν', β', ρ, μ' are obtained through experimental adjustment; I(X i ) through the variable X i The importance score of i,j Through the variable X i and X j The angle difference between the two is obtained; t i,j Through the variable X i and X j The time points between Δt i,j Through the variable X i and X j The time difference between them is obtained;

[0186] Assume that on a global medical cooperation platform, the system receives real-time casualty data streams from emergency centers in multiple countries and regions;

[0187] Assume α=0.6,λ=2.5,∈=0.15,L=3,M 1 =4,M 2 =5,M 3 =6;

[0188]

[0189] Assume η=1.2, γ'=0.8, δ'=0.2, ω=0.5, κ=0.1, v'=2, β'=0.7, ρ=0.1, μ'=1.5;

[0190]

[0191] Assuming that the threshold is set to 1.0, since the calculated result 1.12 is greater than the set threshold, it shows that the prediction scheme for the condition of the injured and sick has high accuracy and reliability. This is because the higher dependency measure reflects that the model can effectively capture the complex interactions between variables under the current data conditions, while not missing important potential paths. Through the above steps, the accurate prediction of the evolution path of the patient's condition is ensured, the scientificity and effectiveness of clinical decision-making are improved, and the response speed and accuracy of the entire medical system are enhanced.

[0192] Figure 2 A schematic diagram of the structure of a system for predicting the condition of a casualty based on big data analysis is provided for an embodiment of the present application. Figure 2 As shown, the device comprises:

[0193] A receiving module 21 is used to receive and analyze real-time data streams from multiple medical institutions, combine multiple factors for contextual understanding, and generate a multi-dimensional data background information framework;

[0194] The training module 22 is used to pre-train the variable relationship based on the multi-dimensional data background information framework using a self-supervised learning algorithm, to mine potential patterns through unlabeled data, to perform forward-looking calculations on the probability of occurrence of different data evolution paths, to extract cross-media key features from text and image data using cross-media intelligent analysis technology, to establish complex mapping relationships between data elements, and to generate a set of data evolution paths;

[0195] The optimization module 23 is used to use the deep forest algorithm based on the data evolution path set to achieve efficient learning of complex data structures through multi-level random forest combination, accurately evaluate the possibility of different data evolution paths, use privacy enhancement technology to ensure data privacy, optimize the prediction model learning process, and generate a risk quantification prediction report;

[0196] The generation module 24 is used to formulate accurate data application strategies and resource allocation plans based on the risk quantification prediction report and all previous analysis results, and generate a dynamic casualty condition prediction plan.

[0197] Figure 2 The system for predicting the condition of the injured based on big data analysis can be implemented Figure 1 The implementation principle and technical effect of the method for predicting the condition of the injured based on big data analysis described in the embodiment shown are not repeated here. The specific way in which each module and unit performs operations in the system for predicting the condition of the injured based on big data analysis in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0198] In one possible design, Figure 2The patient condition prediction system based on big data analysis of the embodiment shown 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;

[0199] 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 .

[0200] The processing component 32 is used to: receive and analyze real-time data streams from multiple medical institutions, combine multiple factors for contextual understanding, and generate a multi-dimensional data background information framework; based on the multi-dimensional data background information framework, use a self-supervised learning algorithm to pre-train variable relationships, mine potential patterns through unlabeled data, and perform forward-looking calculations on the probability of occurrence of different data evolution paths, use cross-media intelligent analysis technology to extract cross-media key features from text and image data, establish complex mapping relationships between data elements, and generate a set of data evolution paths; based on the set of data evolution paths, use a deep forest algorithm to achieve efficient learning of complex data structures through a multi-level random forest combination, accurately evaluate the possibility of different data evolution paths, use privacy enhancement technology to ensure data privacy, optimize the prediction model learning process, and generate a risk quantification prediction report; based on the risk quantification prediction report, integrate all previous analysis results, formulate accurate data application strategies and resource allocation plans, and generate dynamic casualty condition prediction plans.

[0201] 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.

[0202] 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.

[0203] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0204] 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.

[0205] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0206] 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.

[0207] 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 predicting the condition of a wounded person based on big data analysis.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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 predicting the condition of a wounded person based on big data analysis, characterized in that: include: Receive and analyze real-time data streams from multiple medical institutions, combine multiple factors for contextual understanding, and generate a multi-dimensional data background information framework; Based on the multi-dimensional data background information framework, a self-supervised learning algorithm is used to pre-train the variable relationship, potential patterns are mined through unlabeled data, the probability of occurrence of different data evolution paths is forward-lookingly calculated, and cross-media intelligent analysis technology is used to extract cross-media key features from text and image data, establish complex mapping relationships between data elements, and generate a set of data evolution paths; Based on the data evolution path set, the deep forest algorithm is used to achieve efficient learning of complex data structures through multi-level random forest combination, accurately evaluate the possibility of different data evolution paths, adopt privacy enhancement technology to ensure data privacy, optimize the prediction model learning process, and generate risk quantitative prediction reports; Based on the risk quantification prediction report and comprehensive analysis of all previous analysis results, accurate data application strategies and resource allocation plans are formulated to generate dynamic casualty condition prediction plans.

2. The method according to claim 1, characterized in that Based on the multidimensional data background information framework, the self-supervised learning algorithm is used to pre-train the variable relationship, the potential pattern is mined through unlabeled data, the probability of occurrence of different data evolution paths is forward-lookingly calculated, and cross-media intelligent analysis technology is used to extract cross-media key features of text and image data, establish complex mapping relationships between data elements, and generate a data evolution path set, including: Based on the multidimensional data background information framework, preprocessing operations are performed to detect outliers, and the most representative variables preserving the essential characteristics of the data are identified through feature selection technology to generate an optimized multidimensional data set; Based on the optimized multidimensional data set, a self-supervised learning algorithm is used to pre-train the variable relationship, automatically discover the implicit connection and trend between different variables without relying on specific labels, dig out the potential data evolution path, and perform forward-looking calculations on the probability of occurrence of different data evolution paths to generate a variable relationship pre-training model; Based on the variable relationship pre-training model, cross-media intelligent analysis technology is used to comprehensively analyze text and image data, accurately extract cross-media key features, establish complex mapping relationships between features, and generate a feature mapping table; Based on the feature mapping table, high-risk data evolution paths are screened and accurately located to generate a data evolution path set.

3. The method according to claim 2, characterized in that Based on the optimized multidimensional data set, the self-supervised learning algorithm is used to pre-train the variable relationship, automatically discover the implicit connection and trend between different variables without relying on specific labels, dig out the potential data evolution path, and perform forward-looking calculations on the probability of occurrence of different data evolution paths to generate a variable relationship pre-training model, including: Based on the multidimensional data background information framework, specific preprocessing operations are performed on electronic medical records and imaging data, geographic coding is unified, text format is standardized, and standardized medical data sets are generated; Based on the standardized medical data set, a self-supervised learning algorithm is used to pre-train the relationship between variables in the medical data, automatically discover implicit connections and trends between different variables without relying on specific labels, mine potential data evolution paths, evaluate the probability of occurrence of different data evolution paths, and generate potential association maps; Based on the potential association map, determine the key paths that have a significant impact on the evolution of the disease, explore the causal relationship through logical reasoning and statistical analysis, and generate a set of evolution paths; Based on the evolution path set, the complex relationship between variables in the multidimensional data set is captured, the relationship between variables in unknown situations is predicted, and a variable relationship pre-training model is generated.

4. The method according to claim 2, characterized in that: Based on the variable relationship pre-training model, cross-media intelligent analysis technology is used to comprehensively analyze text and image data, accurately extract cross-media key features, establish complex mapping relationships between features, and generate a feature mapping table, including: Based on the variable relationship pre-training model, cross-media intelligent analysis technology is used to comprehensively analyze the text and image data in the medical data to ensure that different forms of data complement each other and generate multi-source data fusion results; Based on the multi-source data fusion result, cross-media intelligent analysis technology is further used to accurately extract cross-media key features and generate a cross-media key feature set; Based on the cross-media key feature set, a statistical modeling method is introduced to establish a complex mapping relationship between features, explore potential indirect connections and causal relationships, and generate a feature relationship network; Based on the feature relationship network, each feature weight is calculated according to the importance score of each feature to quantify the degree of influence and generate a feature mapping table.

5. The method according to claim 1, characterized in that Based on the data evolution path set, the deep forest algorithm is used to achieve efficient learning of complex data structures through multi-level random forest combination, accurately evaluate the possibility of different data evolution paths, adopt privacy enhancement technology to ensure data privacy, optimize the prediction model learning process, and generate risk quantitative prediction reports, including: Based on the data evolution path set, identify data dimensions and feature distribution to effectively capture the essential characteristics of the data and generate an initial data analysis report; Based on the initial data analysis report, the deep forest algorithm is used to accurately evaluate the probability of occurrence of each data evolution path. Through the combination of multi-level random forests, data features are gradually learned at multiple levels, high-dimensional and nonlinear data features are processed, and complex interactions between variables are captured to generate a detailed path probability evaluation report; Based on the detailed path probability assessment report, privacy enhancement technology is used to appropriately add noise during the training process to prevent sensitive information leakage, strictly control the privacy budget, ensure that each query meets the preset privacy standards, and generate a privacy protection optimization model; Based on the privacy protection optimization model, all possible evolution paths are prioritized and a risk quantification prediction report is generated.

6. The method according to claim 5, characterized in that Based on the initial data analysis report, the deep forest algorithm is used to accurately evaluate the probability of each data evolution path. Through the combination of multi-level random forests, data features are gradually learned at multiple levels, high-dimensional and nonlinear data features are processed, and complex interactions between variables are captured to generate a detailed path probability evaluation report, including: Based on the initial data analysis report, high-dimensional and nonlinear features in each data evolution path are extracted in layers to generate a multi-layer feature set; Based on the multi-layer feature set, the deep forest algorithm is used to accurately evaluate the probability of each data evolution path, build a multi-level random forest combination, and gradually learn data features at multiple levels. It not only handles high-dimensional and nonlinear data features, but also captures the complex interactions between variables and generates a multi-level random forest model. Based on the multi-level random forest model, the interaction between the variables in the path is analyzed, the possibility of different evolution paths is predicted, the importance score of each feature is obtained, and the preliminary evaluation results of the path probability are generated; Based on the preliminary evaluation results of the path probability, key paths with higher probability of occurrence and serious consequences are marked, and a detailed path probability evaluation report is generated.

7. The method according to claim 1, characterized in that Based on the risk quantification prediction report, all previous analysis results are integrated to formulate accurate data application strategies and resource allocation plans, and generate dynamic casualty condition prediction plans, including: Based on the risk quantitative prediction report, combined with the feature importance score, the critical path and influencing factors are integrated to generate a list of critical path influencing factors; Based on the list of critical path influencing factors, establish a real-time data monitoring system, set reasonable warning thresholds, determine the frequency of data analysis, and generate data application strategy details; Based on the data application strategy details, optimize the resource allocation plan, formulate emergency response plans for high-risk paths, formulate resource scheduling schedules, and generate optimized resource allocation plans; Based on the optimized resource allocation plan, the possibilities and response strategies of different paths are summarized, and a dynamic update mechanism is introduced to generate a dynamic casualty condition prediction plan.

8. A system for predicting the condition of the injured based on big data analysis, characterized in that: include: A receiving module is used to receive and analyze real-time data streams from multiple medical institutions, combine multiple factors for contextual understanding, and generate a multi-dimensional data background information framework; A training module is used to pre-train variable relationships based on the multi-dimensional data background information framework using a self-supervised learning algorithm, to mine potential patterns through unlabeled data, to perform forward-looking calculations on the probability of occurrence of different data evolution paths, to extract cross-media key features from text and image data using cross-media intelligent analysis technology, to establish complex mapping relationships between data elements, and to generate a set of data evolution paths; An optimization module is used to use a deep forest algorithm based on the data evolution path set to achieve efficient learning of complex data structures through a multi-level random forest combination, accurately evaluate the possibility of different data evolution paths, use privacy enhancement technology to ensure data privacy, optimize the prediction model learning process, and generate a risk quantification prediction report; The generation module is used to formulate accurate data application strategies and resource allocation plans based on the risk quantification prediction report and all previous analysis results, and generate a dynamic casualty condition prediction plan.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a method for predicting the condition of a wounded person based on big data analysis 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 predicting the condition of a wounded person based on big data analysis as described in any one of claims 1 to 7 is implemented.

Images