First-aid information intelligent research and judgment platform for war trauma combined with biological injury

Abstract

The present application relates to the technical field of intelligent medical first aid and data processing, in particular to an intelligent first aid information research and judgment platform for war trauma combined with biological injury; comprising data acquisition, feature reconstruction, entropy value research and decision generation modules; the system performs causal inference decoupling based on composite physiological data and environmental parameters; the core is to separate the characteristics of overt trauma and latent biological injury, calculate the confidence and user execution entropy, and determine the first aid treatment strategy according to the game relationship between the two; the present application effectively removes the nonlinear physiological masking effect caused by biological factors, solves the misjudgment problem caused by biological toxins covering up trauma signs, and realizes the sensitive capture and early warning of latent biological injury.

Description

Technical Field

[0001] This invention relates to the field of intelligent medical emergency care and data processing technology, specifically to an intelligent assessment platform for emergency information on combat trauma combined with biological injury. Background Technology

[0002] With the evolution of modern warfare, the battlefield environment has become increasingly complex, and extreme scenarios involving both combat trauma and latent biological damage have become more frequent.

[0003] Currently, battlefield emergency medical care mainly relies on medical personnel's observation and experience in assessing macroscopic vital signs such as heart rate and blood pressure. However, in biochemical threat environments, the nonlinear physiological masking effect caused by biological agents can lead to confusion of trauma characteristics, and environmental toxins can easily cause sensor drift, resulting in distorted data. Faced with high-dimensional and conflicting monitoring data, frontline personnel are prone to cognitive overload and decision paralysis, leading to misjudgments and omissions, or the collapse of trust caused by frequent changes in auxiliary decision-making instructions, which can result in delays in treatment or even the risk of biological contamination escape.

[0004] Therefore, how to accurately decouple complex concurrent injury characteristics, eliminate environmental interference, and generate adaptive emergency rescue strategies while balancing model credibility and personnel execution capabilities has become an urgent problem to be solved in this field. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides an intelligent assessment platform for emergency medical information related to combat trauma combined with biological injury. Specifically, the technical solution of this invention includes:

[0006] The data acquisition module is used to acquire composite physiological monitoring data of the target object and current environmental interference parameters. The composite physiological monitoring data includes: macroscopic vital sign sequences, sensor drift coefficients, and medical resource supply-demand ratios. The feature reconstruction module is used to perform causal inference decoupling based on the composite physiological monitoring data and the environmental interference parameters to separate overt trauma feature components and latent biological injury feature components. The entropy judgment module is used to calculate the judgment confidence and user execution entropy based on the degree of conflict between the overt trauma feature components and the latent biological injury feature components. The decision generation module is used to determine the emergency treatment strategy for the target object based on the game relationship between the judgment confidence and the user execution entropy. The emergency treatment strategy includes: a precise isolation strategy based on the medical optimal solution or a tactical degrading strategy based on the principle of minimum regret.

[0007] Preferably, based on the composite physiological monitoring data and the environmental interference parameters, causal inference decoupling is performed to separate the overt trauma feature components and the latent biological damage feature components, including: calling the macroscopic vital sign sequence and the environmental interference parameters; constructing a multimodal temporal causal model to identify the linear physiological response caused by kinetic trauma and the nonlinear physiological masking effect caused by biological factors in the macroscopic vital sign sequence; stripping the nonlinear physiological masking effect from the macroscopic vital sign sequence, reconstructing and generating latent biological damage feature components that can characterize the true degree of biological damage, and defining the remaining signal as the overt trauma feature components.

[0008] Preferably, the judgment confidence and user execution entropy are calculated based on the degree of conflict between the explicit trauma feature component and the implicit biological injury feature component, including: calculating the feature distance between the explicit trauma feature component and the implicit biological injury feature component in the temporal evolution; generating the judgment confidence based on the feature distance, wherein the larger the feature distance, the lower the judgment confidence; monitoring the jump frequency of the judgment confidence within a preset time window, and mapping the jump frequency to user execution entropy representing the user's cognitive load.

[0009] Preferably, the emergency treatment strategy for the target object is determined based on the game relationship between the assessment confidence level and the user execution entropy, including: setting a confidence level threshold and an entropy alarm threshold; generating a precise isolation strategy based on the medical optimal solution in response to the assessment confidence level being higher than the confidence level threshold and the user execution entropy being lower than the entropy alarm threshold; and generating a tactical degradation strategy based on the principle of minimum regret in response to the assessment confidence level being lower than the confidence level threshold or the user execution entropy being higher than the entropy alarm threshold.

[0010] Preferably, the generation of a tactical downgrade strategy based on the principle of least regret includes: invoking the supply-demand ratio of medical resources; abandoning the precise biosafety classification for a single target object, and instead generating a preset downgrade processing instruction for the target objects in the current batch; the preset downgrade processing instruction includes: tactical decisions to adjust the transport sequence to maintain the overall resilience of the transport link, and instructions to exempt certain biological testing steps for rapid passage.

[0011] Preferably, generating a precise isolation strategy based on the medical optimal solution includes: predicting the infectivity level of the target object's biological factors based on the latent biological damage feature components; and generating a composite medical instruction containing a specific isolation level and treatment priority based on the infectivity level and the trauma severity corresponding to the overt trauma feature components.

[0012] Preferably, the data acquisition module is further configured to: monitor the signal baseline fluctuation of the sensor in real time; generate the sensor drift coefficient using the signal baseline fluctuation; and pre-calibrate the original acquired signal using the sensor drift coefficient when acquiring the composite physiological monitoring data, so as to eliminate the toxic interference of environmental toxins on the sensor itself.

[0013] Preferably, it further includes: a feedback correction module, used to collect actual physiological feedback data after the execution of the emergency treatment strategy; calculate the residual between the actual physiological feedback data and the predicted feature components; and dynamically update the causal inference weight parameters in the feature reconstruction module using the residual.

[0014] Compared with the prior art, the present invention has the following beneficial effects:

[0015] 1. This system, by constructing a multimodal temporal causal model and utilizing causal inference decoupling technology, can identify and remove the nonlinear physiological masking effect caused by biological factors from macroscopic vital sign sequences. This mechanism effectively solves the problem of misjudgment caused by biological toxins masking trauma signs in scenarios where combat trauma and biological injury occur simultaneously. This enables the system to keenly capture the characteristics of latent biological injury before conventional physiological indicators have deteriorated significantly, thus significantly improving the early warning capability and diagnostic accuracy of latent biological injury.

[0016] 2. To address the issue of sensor drift caused by toxin corrosion in biochemical threat environments, this system introduces a real-time monitoring and reverse compensation mechanism based on signal baseline fluctuations. By calculating the sensor drift coefficient and pre-calibrating the original acquired signal, the toxic interference of environmental toxins on the sensor itself is effectively eliminated. This prevents data distortion caused by sensor physical performance degradation and ensures the quality of input data and system robustness of the analysis platform in complex biochemical pollution environments.

[0017] 3. This system innovatively introduces user execution entropy as a decision variable, and quantifies the cognitive load of frontline personnel by monitoring and judging the frequency of confidence jumps. This mechanism establishes a mapping from data feature conflicts to personnel psychological states, avoiding the forced output of frequently changing high-risk instructions when features are unclear. It effectively prevents cognitive overload and trust collapse of frontline personnel caused by the instability of AI-assisted decision-making, and ensures the stability and effectiveness of the human-machine collaboration channel in high-pressure battlefield environments.

[0018] 4. Based on the game-theoretic relationship between assessment confidence and user execution entropy, this system establishes a dual-modal handling strategy of precise isolation and tactical degradation. When information is sufficient and reliable, it aims to maximize individual survival rate and biosafety. Under data loss or extreme pressure, it automatically switches to a batch processing mode based on the principle of least regret, releasing the throughput by adjusting the transport sequence and exempting some detection steps. This fault-oriented safety design achieves a dynamic balance between medical precision and tactical execution, avoiding systemic collapse caused by the pursuit of perfect medicine, and realizing the optimal allocation of medical resources under extreme pressure. Attached Figure Description

[0019] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0020] Figure 1 This is a structural diagram of the system of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0022] Example 1:

[0023] Please see Figure 1 An intelligent assessment platform for emergency medical information in combat trauma combined with biological injury includes: a data acquisition module for acquiring composite physiological monitoring data and current environmental interference parameters of the target object, the composite physiological monitoring data including: macroscopic vital sign sequences, sensor drift coefficients, and medical resource supply-demand ratio; a feature reconstruction module for performing causal inference decoupling based on the composite physiological monitoring data and environmental interference parameters to separate overt trauma feature components and latent biological injury feature components; an entropy assessment module for calculating assessment confidence and user execution entropy based on the degree of conflict between overt trauma feature components and latent biological injury feature components; and a decision generation module for determining the emergency treatment strategy for the target object based on the game relationship between assessment confidence and user execution entropy; wherein the emergency treatment strategy includes: a precise isolation strategy based on medical optimal solution or a tactical degradation strategy based on the principle of minimum regret.

[0024] This embodiment details the overall architecture and operating mechanism of the intelligent emergency information analysis platform. This platform aims to address the decision-making paralysis caused by the masking effect of physiological signs in scenarios involving both combat trauma and latent biological injury. The system activates a data acquisition module as the sensing front end, acquiring high-dimensional input tensors in real time from individual soldier wearable devices and environmental monitoring nodes. Specifically, this includes composite physiological monitoring data of the target object and current environmental interference parameters. During this process, the system not only collects macroscopic vital sign sequences including time-series waveforms of heart rate, blood pressure, blood oxygen saturation, and respiratory rate, but also... It also simultaneously monitors the sensor drift coefficient, which characterizes the degree of signal baseline shift caused by environmental toxins or physical impacts. And the medical resource supply-demand ratio, defined as the ratio of currently available emergency medical units to the number of casualties awaiting treatment. Simultaneously, the system captures current environmental interference parameters, including chemical or biological agent concentration readings, ambient temperature, and electromagnetic noise levels, through an array of environmental sensors. The feature reconstruction module utilizes causal AI technology to perform causal inference decoupling, aiming to solve the feature confusion problem under complex injuries. It separates the observed chaotic signals into explicit trauma feature components that characterize the degree of direct physical damage caused by kinetic energy impact. And latent biological damage characteristic components that characterize pathophysiological changes caused by biological factors. The entropy analysis module incorporates a human-machine trust dimension, based on... and The degree of physiological conflict between the two factors is used to calculate the statistical confidence level of the model for the current injury classification results. Simultaneously, it calculates the user execution entropy, which characterizes the probability of cognitive overload experienced by frontline medical workers when receiving high-frequency changing instructions. The decision generation module acts as the decision-making center, based on... and The game theory relationship determines the emergency response strategy, and a balance is found between the credibility of the model and human execution ability;

[0025] This embodiment introduces user execution entropy as a decision variable to construct an adaptive judgment mechanism that can perceive the cognitive load of frontline personnel. In the face of extreme battlefield environments with missing data or conflicting features, the platform avoids forcibly outputting high-risk instructions that may lead to misjudgment, effectively preventing the collapse of trust caused by frequent false alarms from AI. This design reduces the risk of biological contamination escape caused by violations of regulations by frontline personnel on a macro level, achieves a dynamic balance between treatment efficiency and biosafety, and ensures the decision-making resilience of the battlefield medical system under complex biochemical threats.

[0026] Example 2:

[0027] Based on composite physiological monitoring data and environmental interference parameters, causal inference decoupling is performed to separate overt trauma feature components from latent biological damage feature components. This includes: calling macroscopic vital sign sequences and environmental interference parameters; constructing a multimodal temporal causal model to identify linear physiological responses caused by kinetic trauma and nonlinear physiological masking effects caused by biological factors in the macroscopic vital sign sequences; stripping the nonlinear physiological masking effects from the macroscopic vital sign sequences, reconstructing and generating latent biological damage feature components that can characterize the true degree of biological damage, and defining the remaining signals as overt trauma feature components.

[0028] This embodiment further specifies the execution logic of the feature reconstruction module in Embodiment 1, focusing on the technical details of processing physiological signal aliasing through structural equation modeling; the system executes the multimodal data retrieval step, extracting macroscopic vital sign sequences respectively. As observed variables, and environmental disturbance parameters As an exogenous variable, the system constructs a multimodal temporal causal model incorporating structural equation modeling (SEM). This model pre-defines two types of causal paths, used to identify the linear physiological response to kinetic trauma and the nonlinear physiological masking effect of biological factors. Based on this, to extract true features from the aliased signal, considering that the masking effect of biological factors inhibits physiological signals (i.e., a subtraction operation), the system employs a signal decoupling algorithm for iterative calculation. The corrected residual calculation formula is as follows:

[0029]

[0030] in, The source is the calculation result, the physical meaning is the residual signal, and the unit is mV or mmHg;

[0031] The source is sensor data, and the physical meaning is the original macroscopic vital sign sequence, with units of mV or mmHg;

[0032] The source is a medical pathology database for training, and the physical meaning is a preset linear response function for trauma.

[0033] The source is the model definition, and the physical meaning is a nonlinear physiological masking function that simulates the inhibitory effect of biological factors on physiological signs;

[0034] The source is an estimated value, and its physical meaning is the current estimated value of the overt trauma feature components;

[0035] The source is an estimated value, and its physical meaning is the current estimated value of the latent biological damage characteristic components;

[0036] To ensure the mathematical solvability and uniqueness of the above decoupling calculation, this embodiment clarifies the specific mathematical form of the function and the solution objective:

[0037] Define a linear response function In matrix operation form: ,in, This is a linear mapping matrix pre-trained based on a trauma database; the notation here is consistent with the matrix notation updated by the feedback correction module in the embodiment.

[0038] Define a nonlinear physiological masking function For the Logistic activation model that incorporates feature mapping:

[0039]

[0040] in, This is a biomarker mapping matrix pre-trained based on a pathological database, used to map the feature space... Mapping back to the signal space of macroscopic vital signs, its dimensional structure and Maintain consistency; among which, The slope of the toxin's action, expressed as the reciprocal of the concentration unit, for example... or , This is the half-maximal effect concentration, with units equal to the environmental concentration. The values ​​are consistent; both specific values ​​are derived from publicly available standard toxicology databases, such as the RTECS database, or pre-set calibration tables based on specific biological agents; in this calculation step, Specifically refers to the tensor of current multidimensional environmental disturbance parameters. The effective concentration scalar value extracted for a specific biological agent is used; if multiple toxins are present, a weighted equivalent concentration is used to ensure consistency with the scalar threshold. Dimensional consistency in algebraic operations; this formula ensures consistency when environmental toxin concentrations... Masking effect when rising The subsequent increase is consistent with pathological logic;

[0041] Based on this, the system constructs the following optimization objective function. The optimal solution is obtained by proximal gradient descent. and :

[0042]

[0043] in, , The pre-defined regularization hyperparameters with balancing dimensionality are used to balance the physical units of the square of the signal amplitude and the norm of the eigenvector, respectively constraining the sparsity and smoothness of the solution. The total number of sampling points within the preset analysis time window; to ensure the convergence of the gradient descent algorithm and the uniqueness of the solution, the system will, at the initial moment of iteration, Initialize it to a linear inverse solution assuming no biological disturbance, and then... Initialized as a prior value vector based on a linear mapping of the current environmental concentration. The calculation formula is: ,in, The preset empirical coefficient vector is used as the starting search point for convex optimization.

[0044] By minimizing the residual With the aforementioned regularization terms, the system successfully extracted the sequence from the original sequence. The nonlinear physiological masking effect is defined, and the stripping component is reconstructed to generate latent biological damage feature components. That is, the optimized Meanwhile, the remaining signals that conform to a linear law are defined as overt trauma feature components. ;

[0045] This embodiment constructs a causal model that distinguishes between linear response and nonlinear masking effect, accurately identifying false-negative casualties who appear to have stable vital signs but have actually suffered severe damage from biological toxins. This technique effectively solves the problem of feature confusion under complex injuries, enabling the system to keenly capture subtle pathological changes caused by biological factors before conventional physiological indicators deteriorate, significantly improving the detection rate and early warning capability of latent biological damage.

[0046] Example 3:

[0047] Based on the degree of conflict between the overt trauma feature components and the latent biological injury feature components, the assessment confidence and user execution entropy are calculated, including: calculating the feature distance between the overt trauma feature components and the latent biological injury feature components in the temporal evolution; generating the assessment confidence based on the feature distance, where the larger the feature distance, the lower the assessment confidence; monitoring the jump frequency of the assessment confidence within a preset time window, and mapping the jump frequency to the user execution entropy that represents the user's cognitive load.

[0048] This embodiment further specifies the calculation logic of the entropy value assessment module in Embodiment 2, detailing how to quantify the uncertainty of the system and its impact on people; the system uses the Dynamic Time Warping (DTW) algorithm to calculate the overt trauma feature components. Characteristic components of latent biological damage Feature distance in normalized space This distance aims to quantify the degree of contradiction between the pathological states pointed to by the two sets of features; the system generates a judgment confidence level based on this feature distance. The calculation formula is as follows:

[0049]

[0050] in, The source is a preset constant, and its physical meaning is the sensitivity coefficient of the Sigmoid function. It is dimensionless and used to distinguish the regularization parameter in Example 2.

[0051] The source is the calculation of the previous level; its physical meaning is the characteristic distance, which is dimensionless.

[0052] The source is historical data statistics, and the physical meaning is the conflict tolerance threshold, which is dimensionless.

[0053] System monitoring and analysis confidence level Within the preset time window internal switching frequency And calculate the user execution entropy based on the following mapping logic. :

[0054]

[0055] in, The source is real-time monitoring, and the physical meaning is the switching frequency of the confidence level category, with the unit being Hz;

[0056] The source is statistical calculation, and the physical meaning is time window. The average confidence level within the range is dimensionless.

[0057] The source is the preset weight, the physical meaning is the frequency normalization coefficient, the unit is s, and it is used to convert the frequency dimension into a dimensionless entropy component.

[0058] The source is a preset weight, and its physical meaning is a confidence level adjustment coefficient, which is dimensionless. Greater than ;

[0059] This embodiment establishes a mapping mechanism from data conflict to cognitive load through the composite calculation of feature distance and jump frequency. The system not only focuses on the accuracy of pathological diagnosis, but also on the impact of the stability of command output on the operator's psychology, thereby preventing cognitive confusion caused by frequent jumps in AI judgment in a short period of time and ensuring the effectiveness of the human-machine collaboration channel in a high-pressure battlefield environment.

[0060] Example 4:

[0061] Based on the game-like relationship between the assessment confidence level and the user's execution entropy, an emergency treatment strategy for the target object is determined, including: setting a confidence level threshold and an entropy alarm threshold; generating a precise isolation strategy based on the medical optimal solution in response to the assessment confidence level being higher than the confidence level threshold and the user's execution entropy being lower than the entropy alarm threshold; and generating a tactical degradation strategy based on the principle of minimum regret in response to the assessment confidence level being lower than the confidence level threshold or the user's execution entropy being higher than the entropy alarm threshold.

[0062] This embodiment further specifies the logic control strategy of the decision generation module in Embodiment 3, establishing the system's dual-modal operation mechanism; the system sets a confidence threshold. Entropy alarm threshold As the decision boundary; the system evaluates the current assessment status in real time, responding to the assessment confidence level. Higher than And user execution entropy Below If the system determines that the current AI judgment is accurate and stable, and the user's cognitive load is within a controllable range, it will generate a precise isolation strategy based on the optimal medical solution to maximize individual survival rate and epidemic prevention safety; otherwise, it will respond to the assessment of confidence level. Less than or equal to Or user execution entropy Higher than or equal to If the system determines that there is a risk of feature conflict or user crash, it will generate a tactical degradation strategy based on the principle of least regret, abandoning the pursuit of accuracy and instead executing the solution with the highest fault tolerance.

[0063] This embodiment implements a dynamic fail-safe strategy by setting a dual threshold triggering mechanism. In extreme cases of insufficient information or strained human-computer interaction, the system can automatically degrade to tactical mode, effectively avoiding the risk of systemic collapse caused by forcibly pursuing precision medicine when data support is insufficient, and embodying the survival-first battlefield emergency rescue philosophy.

[0064] Example 5:

[0065] Generate tactical degradation strategies based on the principle of least regret, including: adjusting the supply-demand ratio of medical resources; abandoning precise biosafety classification for a single target object, and instead generating preset degradation processing instructions for the target objects in the current batch; preset degradation processing instructions include: tactical decisions to adjust the transfer sequence to maintain the overall resilience of the back-end transport link, and instructions to expedite the passage of some biosafety testing steps.

[0066] This embodiment further specifies the tactical degradation strategy in Embodiment 4, and describes in detail the batch processing logic under resource constraints; the system calls the real-time updated medical resource supply-demand ratio. As a basis for decision-making; on this basis, the system abandons precise biosafety classification for a single target object, that is, it no longer focuses on the specific infection level of an individual, but instead generates preset downgrade processing instructions for the current batch of target objects; in order to make tactical decisions have executable quantitative standards and avoid logical conflicts between tactical instructions, the instruction includes algorithmic operations and priority determination in two key dimensions:

[0067] In response to adjustments in transit timing, the system calculates a dynamic retention coefficient. :

[0068]

[0069] like If the supply-demand ratio is extremely unbalanced, an in-situ freeze order will be issued, temporarily storing suspected infected but still viable wounded in a buffer zone; otherwise, the transfer interval will be adjusted to... ,in, Standard interval;

[0070] For exemptions from the biological detection step, the system executes conditional logic with mutually exclusive priorities: prior to this, the system defines the average latent biological damage characteristic components of the current batch. Calculation method: Obtain the latent biological damage feature components of all target objects in the current batch. Calculate its vector magnitude And take the arithmetic mean, that is:

[0071]

[0072] in, This represents the total number of injured in the current batch; if all conditions are met... , and If so, an exemption from nucleic acid retesting is generated, and only a rapid pass instruction for antigen rapid screening is executed; if the conditions are met... but If the test result is negative, the standard isolation and transfer procedure will be followed without a testing exemption; otherwise, full-process testing will be maintained.

[0073] This embodiment transforms vague tactical principles into concrete mathematical logic by implementing the principle of least regret, and sets up a priority mutual exclusion mechanism between instructions. It accepts the cost of individual misjudgments in exchange for the survival of the overall evacuation link. Through batch processing and detection exemption strategies, the system maximizes the evacuation throughput under the premise of controllable biosafety risks, effectively preventing mass deaths caused by the backlog of wounded soldiers at the front line, and ensuring the optimal allocation of medical resources under extreme pressure.

[0074] Example 6:

[0075] Generate a precise isolation strategy based on medical optimal solutions, including: predicting the infectivity level of biological agents of the target object based on the latent biological damage characteristic components; and generating a composite medical instruction containing specific isolation levels and treatment priorities based on the infectivity level and the trauma severity corresponding to the overt trauma characteristic components.

[0076] This embodiment further specifies the precise isolation strategy in Embodiment 4, illustrating a refined treatment process under reliable data conditions; the system utilizes a piecewise mapping function to process latent biological injury feature components. Predict the infectivity level of biological agents in the target population. To ensure comparability between vector and scalar thresholds, the system calculates... The L2 norm is used as the criterion; the specific mapping logic is set as follows: if ,determination Level 0, meaning no infection; if ,determination Level 1, meaning contact transmission; if ,determination Level 2, meaning aerosol transmission; among which, and The system uses a preset pathological threshold to determine the overt trauma feature components. The modulus length is mapped to the level of trauma severity. For example: minor injury, serious injury, critical injury; the system calls the preset two-dimensional decision matrix Matrix_decision[ ][ Generate composite medical instructions; for example, when the index is [2][critical], the matrix output instruction set isolation level: negative pressure stretcher Level A; treatment priority: red, i.e., immediate treatment; special operation: bone marrow infusion; this embodiment realizes a refined stratified treatment strategy by transforming fuzzy medical judgments into a deterministic calculation process based on threshold discrimination and matrix lookup table; while ensuring that high-risk infectious sources are strictly physically isolated, their key vital signs are not delayed, and the optimal medical solution is achieved between ensuring biosafety and improving individual survival rate.

[0077] Example 7:

[0078] The data acquisition module is also used for: real-time monitoring of sensor signal baseline fluctuations; generating sensor drift coefficients using signal baseline fluctuations; and pre-calibrating the original acquired signals using sensor drift coefficients when acquiring composite physiological monitoring data, so as to eliminate the toxic interference of environmental toxins on the sensor itself.

[0079] This embodiment improves upon the data acquisition module functionality in Embodiment 1 by introducing an anti-interference mechanism to address sensor poisoning; the system monitors the sensor's signal baseline fluctuations in real time without any physiological contact. The system uses an integral algorithm to generate the sensor drift coefficient. The calculation formula is as follows:

[0080]

[0081] in, The physical meaning is the average baseline drift rate within the monitoring window, measured in units of... or ; The source is sensor no-load monitoring, and the physical meaning is the baseline voltage or current value, with units of mV or mA;

[0082] The source is a system setting, the physical meaning is the monitoring window, and the unit is seconds (s).

[0083] The source is a mathematical definition, and its physical meaning is an integral variable;

[0084] This coefficient aims to reflect the rate of sensor performance degradation caused by chemical corrosion or biofilm adhesion in the environment; here, based on the principle of electrochemical sensors, an empirical correlation model between baseline noise accumulation and sensitivity decay is constructed, assuming baseline instability... The integral of the value is statistically positively correlated with the degree of attenuation of sensor sensitivity;

[0085] When acquiring composite physiological monitoring data, the system utilizes For the raw acquired signal Perform reverse compensation pre-calibration; the calculation formula is as follows:

[0086]

[0087] in, The source is the sensor's factory calibration; its physical meaning is the sensitivity attenuation coefficient caused by unit drift rate, with units of... Right now or Used to balance dimensions to generate dimensionless calibration factors The average drift rate is mapped to a percentage of sensitivity decay.

[0088] At the same time, to prevent severe poisoning of the sensor due to excessively high concentrations of environmental toxins, i.e. The denominator is maximized, making the denominator Approaching zero can cause numerical calculation divergence; therefore, the system sets a safety cutoff threshold. When this threshold is detected... When this happens, the system no longer performs division operations, but instead directly marks the channel data as invalid and triggers a hardware replacement alarm, thereby eliminating the toxic interference of environmental toxins on the sensor itself.

[0089] This embodiment effectively solves the unique technical problem of sensor poisoning in battlefield environments by introducing baseline fluctuation monitoring and reverse compensation mechanisms; by calibrating drift in real time, it ensures the authenticity and reliability of the input data source, prevents judgment errors caused by sensor physical failure, and significantly improves the robustness of the system in biochemical pollution environments.

[0090] Example 8:

[0091] It also includes: a feedback correction module, used to collect actual physiological feedback data after the execution of emergency treatment strategies; calculate the residual between the actual physiological feedback data and the predicted feature components; and use the residual to dynamically update the causal inference weight parameters in the feature reconstruction module.

[0092] This embodiment supplements the platform of Embodiment 1 by adding a closed-loop learning and evolution mechanism. The system, through a feedback correction module, collects actual physiological feedback data after diagnosis following the execution of the emergency treatment strategy and maps it back into the signal space to generate a target response signal. System calculation The residual vector between the predicted linear response signal and the actual linear response signal The system uses this residual, based on the Widrow-Hoff Delta rule, to dynamically update the linear mapping matrix in the feature reconstruction module. The updated formula is as follows:

[0093]

[0094] in, The learning rate, which includes a normalization factor, has physical dimensions that cancel out the dimensions of the squared eigenvectors, ensuring that the units on both sides of the formula are consistent. This is the transpose of the eigenvector;

[0095] This embodiment establishes a closed-loop feedback mechanism based on gradient descent, which endows the system with the ability to evolve online. This enables it to continuously adapt to changes in physiological characteristics brought about by new biological agents on the battlefield, realizes adaptive updates of the model, and ensures that the judgment algorithm can maintain high diagnostic accuracy when facing unknown or mutated biological threats.

[0096] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An intelligent research and judgment platform for first aid information of war trauma combined with biological injury, characterized in that, include: The data acquisition module is used to acquire composite physiological monitoring data of the target object and current environmental interference parameters. The composite physiological monitoring data includes: macroscopic vital sign sequences, sensor drift coefficients, and medical resource supply-demand ratios. The feature reconstruction module is used to perform causal inference decoupling based on the composite physiological monitoring data and the environmental interference parameters to separate overt trauma feature components and latent biological damage feature components. The entropy judgment module is used to calculate the judgment confidence and user execution entropy based on the degree of conflict between the overt trauma feature components and the latent biological damage feature components. The decision generation module is used to determine the emergency treatment strategy for the target object based on the game relationship between the judgment confidence and the user execution entropy. The emergency treatment strategy includes: a precise isolation strategy based on the medical optimal solution or a tactical degradation strategy based on the principle of minimum regret. Based on the composite physiological monitoring data and the environmental interference parameters, causal inference decoupling is performed to separate the overt trauma feature components and the latent biological damage feature components. This includes: calling the macroscopic vital sign sequence and the environmental interference parameters; constructing a multimodal temporal causal model to identify the linear physiological response caused by kinetic trauma and the nonlinear physiological masking effect caused by biological factors in the macroscopic vital sign sequence; stripping the nonlinear physiological masking effect from the macroscopic vital sign sequence, reconstructing and generating latent biological damage feature components that can characterize the true degree of biological damage, and defining the remaining signal as the overt trauma feature components. Based on the degree of conflict between the explicit trauma feature components and the implicit biological injury feature components, the assessment confidence and user execution entropy are calculated, including: calculating the feature distance between the explicit trauma feature components and the implicit biological injury feature components in the temporal evolution; generating the assessment confidence based on the feature distance, wherein the larger the feature distance, the lower the assessment confidence; monitoring the jump frequency of the assessment confidence within a preset time window, and mapping the jump frequency to user execution entropy representing the user's cognitive load; Based on the game relationship between the assessment confidence level and the user execution entropy, an emergency treatment strategy for the target object is determined, including: setting a confidence level threshold and an entropy alarm threshold; generating a precise isolation strategy based on the medical optimal solution in response to the assessment confidence level being higher than the confidence level threshold and the user execution entropy being lower than the entropy alarm threshold; and generating a tactical degradation strategy based on the principle of minimum regret in response to the assessment confidence level being lower than the confidence level threshold or the user execution entropy being higher than the entropy alarm threshold. The system builds the following optimization objective function , and solves the optimal and by the proximal gradient descent method. ； in, For residual signals, , This is a pre-defined regularization hyperparameter with balancing dimensionality, used to balance the physical units of the square of the signal amplitude and the norm of the eigenvector. The total number of sampling points within the preset analysis time window; to ensure the convergence of the gradient descent algorithm and the uniqueness of the solution, the system will, at the initial moment of iteration, Initialize it to a linear inverse solution assuming no biological disturbance, and then... Initialized as a prior value vector based on a linear mapping of the current environmental concentration. The calculation formula is: ,in, The preset empirical coefficient vector is used as the starting search point for convex optimization. The system generates the assessment confidence level based on this feature distance. The calculation formula is as follows: ； wherein, : the source is a preset constant, and the physical meaning is a sensitivity coefficient of the Sigmoid function; : physical meaning is characteristic distance, dimensionless; : source is historical data statistics, physical meaning is conflict tolerance threshold, dimensionless; System monitors researches and judges confidence Within a preset time window The frequency of jumping And according to the following mapping logic to calculate the user to perform entropy : ； in, The source is real-time monitoring, and the physical meaning is the switching frequency of the confidence level category, with the unit being Hz; : Origin is statistical calculation, physical meaning is average confidence within a time window : Origin is statistical calculation, physical meaning is average confidence within a time window : source is preset weight, physical meaning is frequency normalization coefficient, unit is s; : source is preset weight, physical meaning is confidence adjustment coefficient, dimensionless, and greater than .

2. The intelligent research and judgment platform for first-aid information of war trauma combined with biological injury according to claim 1, characterized in that, Generate a tactical downgrade strategy based on the principle of least regret, including: invoking the supply-demand ratio of medical resources; abandoning the precise biosafety classification for a single target object, and instead generating a preset downgrade processing instruction for the target objects in the current batch; the preset downgrade processing instruction includes: tactical decisions to adjust the transport sequence to maintain the overall resilience of the transport link, and instructions to exempt certain biological testing steps for rapid passage. 3.The first aid information intelligent research and judgment platform for war trauma combined with biological injury according to claim 2, characterized in that, Generating a precise isolation strategy based on medical optimal solutions includes: predicting the infectivity level of biological agents of the target object based on the latent biological damage feature components; and generating a composite medical instruction containing specific isolation levels and treatment priorities based on the infectivity level and the trauma severity corresponding to the overt trauma feature components.

4. The intelligent research and judgment platform for first-aid information of war trauma combined with biological injury according to claim 1, characterized in that, The data acquisition module is also used for: real-time monitoring of the sensor's signal baseline fluctuations; generating the sensor drift coefficient using the signal baseline fluctuations; and pre-calibrating the original acquired signal using the sensor drift coefficient when acquiring the composite physiological monitoring data, so as to eliminate the toxic interference of environmental toxins on the sensor itself.

5. The intelligent assessment platform for emergency information on combat trauma combined with biological injury according to claim 1, characterized in that, Also includes: The feedback correction module is used to collect actual physiological feedback data after the execution of emergency treatment strategies; Calculate the residual between the actual physiological feedback data and the predicted feature components; The causal inference weight parameters in the feature reconstruction module are dynamically updated using the residuals.

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