A method and system for evaluating the influence of a highway traffic accident in a high-altitude area

Abstract

The application relates to the technical field of traffic safety, and discloses a high-altitude area highway traffic accident influence evaluation method. The evaluation method comprises the following steps: acquiring a current injury index, a current altitude sickness index and a current time length index of a rescue unit arriving at a traffic accident site of a person in a current traffic accident; acquiring an updated hypoxia medical model; taking the updated hypoxia medical model as a calculation framework, determining a current hypoxia deterioration result index corresponding to the current injury index according to the current altitude sickness index and the current time length index; and determining the severity of the current traffic accident according to the current hypoxia deterioration result index. The evaluation method can accurately evaluate the severity of the traffic accident. The application further discloses a high-altitude area highway traffic accident influence evaluation system.

Description

Technical Field

[0001] This application relates to the field of traffic safety technology, such as a method and system for assessing the impact of highway traffic accidents in high-altitude areas. Background Technology

[0002] Currently, in the field of road traffic safety, research on traffic accident assessment can be mainly divided into two aspects: risk assessment before a traffic accident occurs and severity assessment after a traffic accident occurs.

[0003] Risk assessment before a traffic accident mainly involves predicting the probability of an accident by analyzing factors such as driver behavior, road conditions, and weather conditions. For example, Chinese patent CN120218601A proposes a traffic accident risk prediction method and system based on spatiotemporal hypergraph comparative learning, which predicts the probability of an accident by analyzing historical traffic accident risk values. and the spatiotemporal feature tensor of traffic accidents Modeling and analysis are used to obtain traffic accident risk prediction values.

[0004] The severity determination of a traffic accident mainly refers to estimating the severity of the accident by analyzing the influence of various factors on the severity of the accident. For example, Chinese patent CN117829370A proposes a method, system, and computer equipment for predicting the severity of traffic accidents. This method inputs a dataset of traffic accidents to be predicted between motor vehicles and non-motor vehicles into a traffic accident severity prediction model to predict the severity of the traffic accidents.

[0005] In comparison, there are relatively few studies on the dynamic assessment of the comprehensive impact of traffic accidents, and existing technical solutions generally lack research on high-risk scenarios with unique characteristics, such as highways in high-altitude areas. These unique characteristics mean that general solutions cannot be simply applied to traffic accident impact assessment models, mainly in the following aspects: (1) Extreme geographical environment: Located on a plateau, the high altitude, long route, complex road conditions and harsh climate make it exponentially more difficult and time-consuming for rescue personnel and equipment to reach the scene.

[0006] (2) The physiological challenges are enormous: the hypoxic environment of the plateau will significantly accelerate the deterioration of the wounded’s condition, threatening the survivor’s window of opportunity and placing far more stringent requirements on the timeliness of rescue than in plains areas.

[0007] (3) Weak logistical support: Medical points and traffic command centers along the route are sparsely distributed, and rescue resources are limited, which further amplifies the weight of the two factors of "distance" and "time" in the evaluation model.

[0008] In conclusion, current research lacks sophisticated assessment techniques and solutions specifically tailored to the unique challenges of high-altitude environments. Therefore, conducting research on traffic accident impact assessment techniques for high-altitude areas is of significant practical importance and urgency for improving emergency response capabilities in these regions. Summary of the Invention

[0009] To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments, but rather as a prelude to the detailed description that follows.

[0010] This application provides a method for assessing the impact of highway traffic accidents in high-altitude areas. It fully considers the untimely rescue situation caused by the extreme geographical environment and weak logistical support, as well as the enormous physiological challenges faced by the injured. This allows for a more accurate assessment of the severity of traffic accidents, which serves as a basis for medical points and traffic command departments along high-altitude highways to provide rescue resources, thereby improving the emergency rescue capabilities along high-altitude highways.

[0011] In some embodiments, the method for assessing the impact of highway traffic accidents in high-altitude areas includes: Obtain the current injury index, current altitude sickness index, and current time index of rescue units arriving at the accident site in the current traffic accident; Obtain the hypoxia medical model with updated parameters; where parameter update refers to replacing the original medical oxygen injury deterioration coefficient in the hypoxia medical model with the fused hypoxia injury deterioration coefficient; the fused hypoxia injury deterioration coefficient is formed by fusing the medical oxygen injury deterioration coefficient and the statistical hypoxia injury deterioration coefficient. The medical hypoxia injury deterioration coefficient is the injury deterioration coefficient caused by hypoxia determined according to medical theory, and the statistical hypoxia injury deterioration coefficient is the injury deterioration coefficient caused by hypoxia determined by statistical hypoxia injury samples. The first statistical model used for statistical hypoxia injury samples can fit the original hypoxia medical model; Using the updated hypoxia medical model as the computational framework, the current hypoxia deterioration outcome index corresponding to the current injury index is determined based on the current altitude reaction index and the current duration index; among them, the current hypoxia deterioration outcome index is positively correlated with the current injury index, the current altitude reaction index, and the current duration index. The severity of a traffic accident is determined based on the current hypoxia deterioration outcome index; the severity is positively correlated with the current hypoxia deterioration outcome index.

[0012] Optionally, after obtaining the current injury index, current altitude sickness index, and current time taken for rescue units to reach the accident site of the current traffic accident, the following may also be included: Obtain the non-hypoxic medical model with updated parameters; where parameter update refers to replacing the medically inherent injury deterioration rate in the non-hypoxic medical model with the fusion inherent injury deterioration rate; the fusion inherent injury deterioration rate is formed by fusing the medically inherent injury deterioration rate and the statistically inherent injury deterioration rate. The medically inherent injury deterioration rate is the injury deterioration rate in a non-hypoxic environment determined according to medical theory, and the statistically inherent injury deterioration rate is the injury deterioration rate in a non-hypoxic environment determined by statistically analyzing non-hypoxic injury samples. The second statistical model used for statistically analyzing non-hypoxic injury samples can fit the original non-hypoxic medical model; Using the updated non-hypoxic medical model, the current inherent deterioration outcome index corresponding to the current injury index is determined based on the current duration index; among which, the current inherent deterioration outcome index is positively correlated with the current duration index and the current injury index.

[0013] Optionally, the severity of the current traffic accident can be determined based on the current hypoxia deterioration outcome index, including: determining the severity based on the current inherent deterioration outcome index and the current hypoxia deterioration outcome index; wherein the severity is positively correlated with the current inherent deterioration outcome index.

[0014] Optionally, in the hypoxia medical model, the effects of duration index and altitude sickness index on the initial injury index are amplified exponentially, and the medical hypoxia injury deterioration coefficient affects the rate of exponential amplification.

[0015] Optionally, the first statistical model used to statistically analyze hypoxic injury samples can fit the original hypoxia medical model, including: The first statistical model is a linear regression model. The first independent variable of the linear regression model is any one and / or the interaction term of the logarithm of the initial injury index, the duration index, and the altitude reaction index in the hypoxia injury sample. The first dependent variable of the linear regression model is the logarithm of the hypoxia deterioration result index in the hypoxia injury sample. Logarithmic operations were performed on the original hypoxia medical model to transform the initial injury index, altitude sickness index, and duration index into linear terms and / or interaction terms. The linear regression model can fit the first-order terms and / or interaction terms corresponding to the initial injury index, altitude sickness index, and duration index.

[0016] Optionally, in the case of obtaining a non-hypoxic medical model with updated parameters, the duration index in the non-hypoxic medical model has an exponentially amplified effect on the initial injury index, and the inherent medical injury deterioration rate affects the amplification rate of the index.

[0017] Optionally, the second statistical model used to statistically analyze non-hypoxic injury samples can fit the original non-hypoxic medical model, including: The second statistical model is a linear regression model. The second independent variable of the linear regression model is either the logarithm of the initial injury index or the duration index in the non-hypoxic injury samples, or the interaction term of both. The second dependent variable of the linear regression model is the logarithm of the inherent deterioration outcome index in the non-hypoxic injury samples. Logarithmic operation was performed on the original non-hypoxic medical model to convert the initial injury index and duration index into linear terms and / or interaction terms. The linear regression model can fit the first-order terms and / or interaction terms corresponding to the initial injury index and the duration index.

[0018] Optionally, when obtaining a non-hypoxic medical model with updated parameters, the first statistical model and the second statistical model are merged into a single statistical model. Historical accident samples from high-altitude areas were used as fusion samples of hypoxia injury samples and non-hypoxia injury samples; The hypoxic medical model and the non-hypoxic medical model are combined into a medical model of injury evolution. The basic form of the medical model of injury evolution is: ; in, This is the injury deterioration outcome index, derived from the inherent deterioration outcome index. and hypoxia worsening outcome index It is formed through interaction; Initial injury index; The exponent is a base of the natural constant; The rate at which the inherent injury worsens; The factor representing the severity of injury due to hypoxia; Altitude sickness index; Duration index; The logarithmic form of the medical model of injury evolution is: ; The linear regression model is as follows: ; The dependent variable; The set of independent variables is , , , , , , ; , , , , , , is the regression coefficient.

[0019] Optionally, the hypoxia-induced injury deterioration coefficient and the intrinsic injury deterioration rate are obtained as follows: ; in, To statistically analyze the rate of deterioration of the inherent injury, To calculate the severity of injuries due to hypoxia; , , This represents the total number of historical accident samples. And / or, The rate of deterioration of the fused intrinsic injury and the coefficient of deterioration of the fused hypoxic injury were obtained in the following ways: ; ; or, ; ; in, To integrate the rate at which the existing injury deteriorates, To integrate the hypoxia-induced injury deterioration coefficient, Due to the inherent rate of deterioration of medical conditions, The medical hypoxia injury deterioration coefficient, To statistically analyze the rate of deterioration of the inherent injury, To calculate the severity of injuries due to hypoxia, The first influence weight of the statistically inherent rate of injury deterioration on the injury deterioration outcome index, calculated based on historical accident samples. This represents the second influence weight of the statistical hypoxia injury deterioration coefficient, calculated based on historical accident samples, on the injury deterioration outcome index.

[0020] Optionally, the first influence weight Second influence weight The methods for obtaining these variables include: determining the set of independent variables based on the regression coefficients of the linear regression model. The weight corresponding to each element; based on the set of independent variables. middle , and The sum of the corresponding weights determines the first influence weight. According to the set of independent variables middle , and The sum of the corresponding weights determines the second influence weight. .

[0021] Optionally, the set of independent variables can be determined based on the fit coefficients of the linear regression model. The objective weight corresponding to each element in the set of independent variables Perform APH analysis to obtain the variable set The subjective weight corresponding to each element is assigned using game theory based on the objective and subjective weights of all elements to obtain the set of independent variables. The comprehensive weight corresponding to each element in the set of independent variables is determined based on the set of independent variables. middle , and The sum of the corresponding comprehensive weights determines the first influence weight. According to the set of independent variables middle , and The sum of the corresponding comprehensive weights determines the second influence weight. .

[0022] Optionally, the medical model for injury evolution that incorporates both the inherent injury deterioration rate and the hypoxia injury deterioration coefficient is as follows: ; in, for The matrix formed , For the first The first sample One independent variable, , for Construct a matrix, For the first The coefficients obtained by performing univariate regression on 1 independent variable. The elements in the middle are the set of independent variables. The weight of each element in the equation.

[0023] Optionally, during the regression processing of the linear regression model to obtain the regression coefficients, the interaction terms are reconstructed in the following manner. : ; in, For reconstructed interactive items , This serves as the index for the historical accident samples. , , The total number of the historical accident samples; Standardize each regression coefficient: , , ; For interactive items Perform multiple linear diagnosis: ; This represents the 6th independent variable. The regression coefficients are influenced by the regression processes of other independent variables; if This indicates that it is acceptable; if This indicates the existence of some collinearity; if This indicates severe multicollinearity, particularly in the interaction terms of some samples. Perform the deletion operation and fill in the missing values ​​using the interpolation method.

[0024] Optionally, the severity of the current traffic accident can be determined based on the current hypoxia worsening outcome index, including: ;in, As to the degree of severity, The sigma activation function. and This is a linear adjustment factor. This represents the current index indicating the worsening of hypoxia.

[0025] Optionally, in high-altitude areas, when the method for assessing the impact of road traffic accidents includes determining the current inherent deterioration outcome index, the severity is determined based on the current inherent deterioration outcome index and the current hypoxia deterioration outcome index, including: ; in, As to the degree of severity, The sigma activation function. , The parameters are for linear fitting. This is an index representing the current inherent deterioration of outcomes. This is an index indicating the current deterioration of hypoxia. This represents the current injury severity index.

[0026] In some embodiments, the high-altitude highway traffic accident impact assessment system includes a first acquisition module, a second acquisition module, a first determination module, and a second determination module; The first acquisition module is used to acquire the current injury index, current altitude sickness index, and current time index of rescue units arriving at the accident site in the current traffic accident. The second acquisition module is used to acquire the hypoxia medical model after parameter updates. The parameter update refers to replacing the original medical oxygen injury deterioration coefficient in the hypoxia medical model with the fused hypoxia injury deterioration coefficient. The fused hypoxia injury deterioration coefficient is formed by fusing the medical oxygen injury deterioration coefficient and the statistical hypoxia injury deterioration coefficient. The medical hypoxia injury deterioration coefficient is the injury deterioration coefficient caused by hypoxia determined according to medical theory. The statistical hypoxia injury deterioration coefficient is the injury deterioration coefficient caused by hypoxia determined by statistical hypoxia injury samples. The first statistical model used for statistical hypoxia injury samples can fit the original hypoxia medical model. The first determination module is used to use the updated hypoxia medical model as a calculation framework to determine the current hypoxia deterioration result index corresponding to the current injury index based on the current altitude reaction index and the current duration index; wherein, the current hypoxia deterioration result index is positively correlated with the current injury index, positively correlated with the current altitude reaction index, and positively correlated with the current duration index. The second determination module is used to determine the severity of the current traffic accident based on the current hypoxia deterioration result index; wherein the severity is positively correlated with the current hypoxia deterioration result index.

[0027] The method and system for assessing the impact of highway traffic accidents in high-altitude areas provided in this application can achieve the following technical effects: The current altitude sickness index represents the enormous physiological challenges faced by the injured, while the current duration index represents the untimely rescue situation caused by the extreme geographical environment and weak logistical support. In calculating the severity of the accident, the current injury index is used as a basis, taking into account the deterioration effect of the current altitude sickness and current duration index on the current injury index, thereby more accurately assessing the severity of the traffic accident. This serves as a basis for medical points and traffic command departments along the high-altitude highway to provide rescue resources, thereby improving the emergency rescue capabilities along high-altitude highways.

[0028] The specific calculation process uses a hypoxia medical model as the computational framework, and the calculation results are highly interpretable. The hypoxia medical model can reflect the degree to which a hypoxic environment promotes the continuous deterioration of the initial injury; furthermore, the medical hypoxia injury deterioration coefficient has the aforementioned effect.

[0029] The medical hypoxia injury deterioration coefficient is a coefficient of injury deterioration caused by hypoxia, determined based on medical theory. It is interpretable, but the complex environmental and psychological factors occurring at the accident scene, which are not explained by medical theory, limit its interpretability. The statistical hypoxia injury deterioration coefficient, on the other hand, is determined by statistically analyzing hypoxia injury samples. It reflects the overall correlation between the hypoxic environment and the injury deterioration coefficient, but is subject to the randomness of complex environmental and psychological factors.

[0030] The fusion hypoxia injury deterioration coefficient is formed by combining the medical hypoxia injury deterioration coefficient and the statistical hypoxia injury deterioration coefficient. Compared with the original medical hypoxia injury deterioration coefficient, the fusion hypoxia injury deterioration coefficient has more randomness. This randomness gives the updated hypoxia medical model a certain degree of generalization. Under the complex natural environment and complex psychological factors at the accident scene, the generalization of the updated hypoxia medical model allows it to calculate a more accurate current hypoxia deterioration result index, and thus more accurately determine the severity of the current traffic accident. This serves as a basis for medical points and traffic command departments along plateau highways to provide rescue resources, thereby improving the emergency rescue capabilities along high-altitude highways.

[0031] Furthermore, in the technical solution of this application, the reason why the statistical hypoxia injury deterioration coefficient can be integrated with the medical oxygen injury deterioration coefficient is that the first statistical model used to statistically analyze the hypoxia injury samples can fit the original hypoxia medical model. If the first statistical model is determined entirely based on statistical theory, then its statistical method determines the development direction of the correlation between the hypoxic environment and the injury deterioration coefficient, such as whether the relationship between the two is linear or nonlinear, or whether it is a correlation in the time domain or a correlation in a high-dimensional space. If there is a problem with the statistical direction, then integrating the statistical hypoxia injury deterioration coefficient and the medical oxygen injury deterioration coefficient will make the hypoxia medical model with updated parameters even more inaccurate.

[0032] Although the medical hypoxia injury deterioration coefficient in the hypoxia medical model has certain limitations, the overall mechanism of action between the hypoxic environment and the injury deterioration coefficient in the model has a solid scientific basis. The first statistical model used to statistically analyze hypoxia injury samples can fit the original hypoxia medical model. Therefore, in the process of statistically analyzing hypoxia injury samples to determine the injury deterioration coefficient caused by hypoxia, the overall mechanism of action between the hypoxic environment and the injury deterioration coefficient in the hypoxia medical model is actually used to statistically obtain the specific correlation between the hypoxic environment and the injury deterioration coefficient. This statistical method can obtain a more accurate statistical hypoxia injury deterioration coefficient, further making the fusion of hypoxia injury deterioration coefficients more accurate, and ultimately making the current hypoxia deterioration result index more accurate. This provides a more accurate basis for medical points and traffic command departments along plateau highways to accurately allocate rescue resources, thereby improving the emergency rescue capabilities along high-altitude highways.

[0033] The above general description and the description below are exemplary and illustrative only and are not intended to limit this application. Attached Figure Description

[0034] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrative descriptions and drawings do not constitute a limitation on the embodiments. Elements having the same reference numerals in the drawings are considered similar elements, and wherein: Figure 1 This is a flowchart illustrating a method for assessing the impact of highway traffic accidents in high-altitude areas, as provided in an embodiment of this application. Figure 2 This is a flowchart illustrating another method for assessing the impact of highway traffic accidents in high-altitude areas, provided in an embodiment of this application. Figure 3 This is a modular schematic diagram of a highway traffic accident impact assessment system provided in an embodiment of this application; Figure 4 This is a hardware schematic diagram of a highway traffic accident impact assessment system provided in an embodiment of this application. Detailed Implementation

[0035] To provide a more detailed understanding of the features and technical content of the embodiments of this application, the implementation of the embodiments of this application will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for illustrative purposes only and are not intended to limit the embodiments of this application. In the following technical description, for ease of explanation, several details are used to provide a full understanding of the disclosed embodiments. However, one or more embodiments may still be implemented without these details. In other cases, well-known structures and devices may be simplified in their depiction to simplify the drawings.

[0036] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0037] Unless otherwise stated, the term "multiple" means two or more.

[0038] In this embodiment, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0039] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0040] In the embodiments of this application, the term "high altitude" refers to the lowest altitude at which a person can experience altitude sickness.

[0041] Meanwhile, the term "plateau" in the embodiments of this application is also interpreted in a general sense, referring to a region where the altitude can cause altitude sickness.

[0042] Figure 1 This is a flowchart illustrating a method for assessing the impact of highway traffic accidents in high-altitude areas, as provided in an embodiment of this application. This method can be executed on a local computer or on a cloud server.

[0043] Combination Figure 1 As shown, the methods for assessing the impact of highway traffic accidents in high-altitude areas include: S101. Obtain the current injury index, current altitude sickness index, and current time index of rescue units arriving at the accident site in the current traffic accident.

[0044] The current injury index is used to comprehensively reflect the initial injuries caused by the accident. The more people injured, the higher the current injury index; the more severe the injuries, the higher the current injury index.

[0045] The current injury index can be obtained by communicating with personnel at the accident scene, including but not limited to existing fiber optic communication, digital microwave communication, satellite communication, and image communication. Alternatively, the current injury index can be obtained by inquiring with personnel who reported the accident, such as patrol personnel who discovered the accident and came to report it, or personnel at the accident scene who came to report it.

[0046] The current altitude sickness index is used to comprehensively reflect the degree of altitude sickness in the injured. It can also be obtained by communicating with personnel at the accident site or by inquiring with the person who reported the accident.

[0047] The Current Time Index is used to comprehensively reflect the time required for rescue resources to reach the accident site. The Current Time Index is determined based on factors such as the distance between the accident site and medical points and / or traffic control departments along the highway, and the weather conditions of the day. The greater the distance, the higher the Current Time Index; the worse the weather, the higher the Current Time Index.

[0048] The aforementioned rescue resources include, but are not limited to, the number of medical personnel, their knowledge, medical facilities and equipment, and evacuation machinery and equipment at the accident site.

[0049] S102. Obtain the hypoxia medical model with updated parameters.

[0050] Among them, parameter update refers to replacing the original medical oxygen injury deterioration coefficient in the hypoxia medical model with the fused hypoxia injury deterioration coefficient; the fused hypoxia injury deterioration coefficient is formed by fusing the medical oxygen injury deterioration coefficient and the statistical hypoxia injury deterioration coefficient. The medical hypoxia injury deterioration coefficient is the injury deterioration coefficient caused by hypoxia determined according to medical theory, while the statistical hypoxia injury deterioration coefficient is the injury deterioration coefficient caused by hypoxia determined by statistical hypoxia injury samples. The first statistical model used for statistical hypoxia injury samples can fit the original hypoxia medical model.

[0051] The hypoxia medical model is used to represent the trend of the initial injury index gradually deteriorating over time in a hypoxic environment. The medical oxygen injury deterioration coefficient is a key parameter, which can represent the influence of the altitude sickness index on the deterioration rate.

[0052] When assessing the severity of a traffic accident using this method, the initial injury index mentioned above is the current injury index.

[0053] Optionally, in the hypoxia medical model, the effects of the duration index and the altitude reaction index on the initial injury index are amplified exponentially, and the medical hypoxia injury deterioration coefficient affects the rate of exponential amplification. That is, when the duration index and the altitude reaction index remain unchanged, the effects of the medical hypoxia injury deterioration coefficient on the initial injury index are amplified exponentially.

[0054] Those skilled in the art can determine a suitable hypoxia medical model based on the characteristics of the above-mentioned hypoxia medical model and the medical theory that the initial injury gradually deteriorates over time in a hypoxic environment.

[0055] The first statistical model used to statistically analyze hypoxia injury samples can fit the original hypoxia medical model. This can be achieved by directly fitting the original hypoxia medical model with the first statistical model, or by transforming the original hypoxia medical model and then transforming the hypoxia injury samples in the same way, ultimately making the first statistical model fit the transformed original hypoxia medical model.

[0056] Specifically, the first statistical model is a linear regression model. The first independent variable of the linear regression model is any one and / or the interaction term of the logarithm of the initial injury index, the duration index, and the altitude reaction index in the hypoxia injury sample. The first dependent variable of the linear regression model is the logarithm of the hypoxia deterioration result index in the hypoxia injury sample.

[0057] Logarithmic operations were performed on the original hypoxia medical model to transform the initial injury index, altitude sickness index, and duration index into linear terms and / or interaction terms.

[0058] The linear regression model can fit the first-order terms and / or interaction terms corresponding to the initial injury index, altitude sickness index, and duration index.

[0059] The aforementioned interaction terms can represent the interactive promoting effect of two or all of the initial injury index, altitude sickness index, and duration index on the current hypoxia deterioration outcome index. For example, when the initial injury index and altitude sickness index remain unchanged, the larger the duration index, the larger the current hypoxia deterioration outcome index. If the initial injury index remains unchanged, and the altitude sickness index increases, the current hypoxia deterioration outcome index will further increase as the duration index increases.

[0060] Of course, the above linear regression model is only an example. Those skilled in the art can also directly use the first statistical model in exponential form to fit the original hypoxia medical model. Alternatively, those skilled in the art can also perform transformations on the exponential hypoxia medical model other than taking the logarithm based on experience, and adaptively transform the hypoxia injury samples, and finally select a first statistical model that can fit the transformed hypoxia medical model.

[0061] S103. Using the updated hypoxia medical model as the calculation framework, determine the current hypoxia deterioration result index corresponding to the current injury index based on the current altitude reaction index and the current duration index.

[0062] Among them, the current hypoxia deterioration result index is positively correlated with the current injury index, the current altitude sickness index, and the current duration index.

[0063] Specifically, the current duration index and the current altitude sickness index have an exponentially amplified effect on the current injury index, and the amplification rate is influenced by the hypoxia injury deterioration coefficient.

[0064] S104. Determine the severity of the current traffic accident based on the current hypoxia deterioration result index.

[0065] The severity of the hypoxia is positively correlated with the current hypoxia deterioration outcome index.

[0066] Optionally, the severity of the current traffic accident can be determined based on the current hypoxia worsening outcome index, including: ;in, As to the degree of severity, The sigma activation function. and This is a linear adjustment factor. This represents the current index indicating the worsening of hypoxia.

[0067] In the method for assessing the impact of highway traffic accidents in high-altitude areas provided in this application embodiment, the current altitude sickness index represents the enormous physiological challenges faced by the injured, and the current duration index represents the untimely rescue situation caused by the extreme geographical environment and weak logistical support. In the process of calculating the severity of the accident, the current injury index is used as a basis, and the deterioration effect of the current altitude sickness and current duration index on the current injury index is considered, so as to more accurately assess the severity of the traffic accident. This serves as the basis for medical points and traffic command departments along the high-altitude highway to provide rescue resources, thereby improving the emergency rescue capabilities along the high-altitude highway.

[0068] The specific calculation process uses a hypoxia medical model as the computational framework, and the calculation results are highly interpretable. The hypoxia medical model can reflect the degree to which a hypoxic environment promotes the continuous deterioration of the initial injury; furthermore, the medical hypoxia injury deterioration coefficient has the aforementioned effect.

[0069] The medical hypoxia injury deterioration coefficient is a coefficient of injury deterioration caused by hypoxia, determined based on medical theory. It is interpretable, but the complex environmental and psychological factors occurring at the accident scene, which are not explained by medical theory, limit its interpretability. The statistical hypoxia injury deterioration coefficient, on the other hand, is determined by statistically analyzing hypoxia injury samples. It reflects the overall correlation between the hypoxic environment and the injury deterioration coefficient, but is subject to the randomness of complex environmental and psychological factors.

[0070] The fusion hypoxia injury deterioration coefficient is formed by combining the medical hypoxia injury deterioration coefficient and the statistical hypoxia injury deterioration coefficient. Compared with the original medical hypoxia injury deterioration coefficient, the fusion hypoxia injury deterioration coefficient has more randomness. This randomness gives the updated hypoxia medical model a certain degree of generalization. Under the complex natural environment and complex psychological factors at the accident scene, the generalization of the updated hypoxia medical model allows it to calculate a more accurate current hypoxia deterioration result index, and thus more accurately determine the severity of the current traffic accident. This serves as a basis for medical points and traffic command departments along plateau highways to provide rescue resources, thereby improving the emergency rescue capabilities along high-altitude highways.

[0071] Furthermore, in the technical solution of this application, the reason why the statistical hypoxia injury deterioration coefficient can be integrated with the medical oxygen injury deterioration coefficient is that the first statistical model used to statistically analyze the hypoxia injury samples can fit the original hypoxia medical model. If the first statistical model is determined entirely based on statistical theory, then its statistical method determines the development direction of the correlation between the hypoxic environment and the injury deterioration coefficient, such as whether the relationship between the two is linear or nonlinear, or whether it is a correlation in the time domain or a correlation in a high-dimensional space. If there is a problem with the statistical direction, then integrating the statistical hypoxia injury deterioration coefficient and the medical oxygen injury deterioration coefficient will make the hypoxia medical model with updated parameters even more inaccurate.

[0072] Although the medical hypoxia injury deterioration coefficient in the hypoxia medical model has certain limitations, the overall mechanism of action between the hypoxic environment and the injury deterioration coefficient in the model has a solid scientific basis. The first statistical model used to statistically analyze hypoxia injury samples can fit the original hypoxia medical model. Therefore, in the process of statistically analyzing hypoxia injury samples to determine the injury deterioration coefficient caused by hypoxia, the overall mechanism of action between the hypoxic environment and the injury deterioration coefficient in the hypoxia medical model is actually used to statistically obtain the specific correlation between the hypoxic environment and the injury deterioration coefficient. This statistical method can obtain a more accurate statistical hypoxia injury deterioration coefficient, further making the fusion of hypoxia injury deterioration coefficients more accurate, and ultimately making the current hypoxia deterioration result index more accurate. This provides a more accurate basis for medical points and traffic command departments along plateau highways to accurately allocate rescue resources, thereby improving the emergency rescue capabilities along high-altitude highways.

[0073] Figure 2 This is a flowchart illustrating another method for assessing the impact of highway traffic accidents in high-altitude areas, provided in an embodiment of this application. This method can be executed on a local computer or on a cloud server.

[0074] Combination Figure 2 As shown, the methods for assessing the impact of highway traffic accidents in high-altitude areas include: S201. Obtain the current injury index, current altitude sickness index, and current time index of rescue units arriving at the accident site in the current traffic accident.

[0075] S202. Obtain the non-hypoxic medical model with updated parameters.

[0076] Among them, parameter update refers to replacing the medically inherent injury deterioration rate in the non-hypoxic medical model with the fusion inherent injury deterioration rate; the fusion inherent injury deterioration rate is formed by fusing the medically inherent injury deterioration rate and the statistically inherent injury deterioration rate. The medically inherent injury deterioration rate is the injury deterioration rate in a non-hypoxic environment determined according to medical theory, while the statistically inherent injury deterioration rate is the injury deterioration rate in a non-hypoxic environment determined by statistically analyzing non-hypoxic injury samples. The second statistical model used for statistically analyzing non-hypoxic injury samples can fit the original non-hypoxic medical model.

[0077] Hypoxic medical models are used to represent the trend of initial injury indices gradually worsening over time in a hypoxic environment. The medically inherent rate of injury deterioration is a key parameter, representing the natural trend of injury deterioration over time. Of course, in cases of lower injury severity, the rate of injury deterioration can be negative, representing the trend of gradual healing.

[0078] When assessing the severity of a traffic accident using this method, the initial injury index mentioned above is the current injury index.

[0079] Optionally, in the non-hypoxic medical model, the effect of the duration index on the initial injury index is amplified exponentially, and the rate of deterioration of the medically inherent injury affects the rate of amplification of the index. That is, when the duration index remains unchanged, the effect of the rate of deterioration of the medically inherent injury on the initial injury index is amplified exponentially, and the healing status is reduced exponentially.

[0080] Based on the characteristics of the above-mentioned non-hypoxic medical model, and on the medical theory that the initial injury gradually deteriorates (or heals) over time in a non-hypoxic environment, those skilled in the art can determine a suitable non-hypoxic medical model.

[0081] The second statistical model used to statistically analyze non-hypoxic injury samples can fit the original non-hypoxic medical model. This can be achieved by directly fitting the original non-hypoxic medical model with the second statistical model, or by transforming the original non-hypoxic medical model and then transforming the non-hypoxic injury samples in the same way, ultimately making the second statistical model fit the transformed original non-hypoxic medical model.

[0082] Specifically, the second statistical model is a linear regression model. The second independent variable of the linear regression model is either the logarithm of the initial injury index or the duration index in the non-hypoxic injury samples, or the interaction term of both. The second dependent variable of the linear regression model is the logarithm of the inherent deterioration outcome index in the non-hypoxic injury samples. Logarithmic operation was performed on the original non-hypoxic medical model to convert the initial injury index and duration index into linear terms and / or interaction terms. The linear regression model can fit the first-order terms and / or interaction terms corresponding to the initial injury index and the duration index.

[0083] The aforementioned interaction terms can represent the mutually reinforcing effects of the initial injury index and the duration index on the current non-hypoxic deterioration outcome index. For example, when the initial injury index remains constant, the larger the duration index, the larger the current hypoxic deterioration outcome index. If the initial injury index increases, the current non-hypoxic deterioration outcome index will further increase as the duration index increases.

[0084] Of course, the above linear regression model is only an example. Those skilled in the art can also directly use the second statistical model in exponential form to fit the original non-hypoxic medical model. Alternatively, those skilled in the art can also perform transformations on the exponential non-hypoxic medical model based on experience, except for taking the logarithm, and adaptively transform the non-hypoxic injury samples, and finally select a second statistical model that can fit the transformed non-hypoxic medical model.

[0085] S203. Using the updated non-hypoxic medical model, determine the current inherent deterioration outcome index corresponding to the current injury index based on the current duration index.

[0086] Among them, the current inherent deterioration outcome index is positively correlated with the current duration index and the current injury index.

[0087] Specifically, the current duration index has an exponentially amplified effect on the current injury index, and the rate of deterioration of the inherent medical condition affects the rate of index amplification.

[0088] S204. Obtain the hypoxia medical model with updated parameters.

[0089] Among them, parameter update refers to replacing the original medical oxygen injury deterioration coefficient in the hypoxia medical model with the fused hypoxia injury deterioration coefficient; the fused hypoxia injury deterioration coefficient is formed by fusing the medical oxygen injury deterioration coefficient and the statistical hypoxia injury deterioration coefficient. The medical hypoxia injury deterioration coefficient is the injury deterioration coefficient caused by hypoxia determined according to medical theory, while the statistical hypoxia injury deterioration coefficient is the injury deterioration coefficient caused by hypoxia determined by statistical hypoxia injury samples. The first statistical model used for statistical hypoxia injury samples can fit the original hypoxia medical model.

[0090] S205. Using the updated hypoxia medical model as the calculation framework, determine the current hypoxia deterioration result index corresponding to the current injury index based on the current altitude reaction index and the current duration index.

[0091] Among them, the current hypoxia deterioration result index is positively correlated with the current injury index, the current altitude sickness index, and the current duration index.

[0092] S206. Determine the severity based on the current inherent deterioration outcome index and the current hypoxia deterioration outcome index.

[0093] Among them, the severity is positively correlated with the current inherent deterioration outcome index and the current hypoxia deterioration outcome index.

[0094] After a traffic accident occurs in a high-altitude area, the hypoxia medical model focuses more on representing the degree of deterioration of the current injury index by the altitude sickness index, while the non-hypoxia medical model focuses more on representing the trend of the current injury index. By obtaining the current hypoxia deterioration result index and the current non-hypoxia deterioration result index from the two respectively, and using both result indices to determine the severity of the accident, the severity assessment can be more accurate.

[0095] Optionally, in high-altitude areas, when the method for assessing the impact of road traffic accidents includes determining the current inherent deterioration outcome index, the severity is determined based on the current inherent deterioration outcome index and the current hypoxia deterioration outcome index, including: ; in, As to the degree of severity, The sigma activation function. , The parameters are for linear fitting. This is an index representing the current inherent deterioration of outcomes. This is an index indicating the current deterioration of hypoxia. This represents the current injury severity index.

[0096] This application also provides specific methods for obtaining the current injury index, current altitude sickness index, and current duration index. The following are exemplary descriptions of these methods.

[0097] First, we will provide an example of how to obtain the current injury index.

[0098] The number of deaths directly caused by the accident Number of seriously injured and number of minor injuries Among them, the number of deaths It does not participate in the calculation of the current injury index, but directly participates in the calculation of the severity of the accident.

[0099] For example, the number of victims can be determined by the maximum and minimum number of deaths in a historical accident sample. Normalization is performed: ; in, The death toll index This refers to the number of people killed in this traffic accident. This represents the minimum number of fatalities in a historical accident sample. This represents the maximum number of fatalities in a historical accident sample.

[0100] ; in, Depending on the severity of the accident, The sigma activation function. , , , The parameters are for linear fitting. This is an index representing the current inherent deterioration of outcomes. This is an index indicating the current deterioration of hypoxia. The current injury index, This represents the number of deaths.

[0101] The following continues with the analysis based on the number of seriously injured. and number of minor injuries The process of determining the current injury level will be explained.

[0102] Number of seriously injured and number of minor injuries Normalization is performed: ; ; in, The severity index. This refers to the number of people seriously injured in this traffic accident. This represents the minimum number of seriously injured individuals in a historical accident sample. This represents the maximum number of seriously injured individuals in a historical accident sample. The minor injury index is... This refers to the number of people who sustained minor injuries in this traffic accident. This represents the minimum number of minor injuries in a historical accident sample. This represents the maximum number of minor injuries in a historical accident sample.

[0103] Current injury index includes severe injury index and minor injury index Or, the current injury index is a severe injury index. and minor injury index The weighted sum.

[0104] Secondly, an example is provided to illustrate the current method for obtaining the altitude sickness index.

[0105] The current altitude sickness index is used to indicate the degree of altitude sickness in injured personnel.

[0106] The specific calculation method is as follows: enter: Passengers' basic physiological data (age, medical history, experience of traveling to Tibet); the acquisition of basic physiological data requires the passenger's consent; Environmental data: altitude of the accident site (obtained through GPS data of the vehicles involved in the accident), real-time temperature, and oxygen content (estimated by pressure sensors or calculated directly based on altitude). Physiological monitoring data: Blood oxygen saturation (SpO2), heart rate, etc. can be obtained through portable devices (such as smart bracelets).

[0107] Quantification methods: Due to the complexity of altitude sickness, its severity can be estimated using pre-trained predictive models.

[0108] If real-time data is unavailable, a simplified weighted scoring method can be used.

[0109] ; Altitude (km); Temperature (°C), lower temperatures receive higher scores; Average age of drivers and passengers; The proportion of people visiting Tibet for the first time; , , , : , , , Each corresponds to a weight; : A quantitative indicator of the severity of altitude sickness output by a simplified evaluation method.

[0110] If historical data is available, a lightweight XGBoost classifier can be trained to predict the average probability of moderate to severe acute mountain sickness (AMS) in the event of the accident.

[0111] Model input feature system: Basic characteristic variables: Altitude (meters); Blood oxygen saturation SpO2 (%) Heart rate (beats / minute); Age (years); Gender (0 = female, 1 = male); Experiences in Tibet (0 = none, 1 = yes); Real-time temperature (°C); Atmospheric pressure (hPa); Derived characteristic variables: ; ; ; Altitude Adaptation Index; Age-altitude risk index; Oxygenation index.

[0112] Complete feature vector: ; : Complete feature vector.

[0113] Output target definition: Classified output: ; Altitude sickness level ( =None, = Mild, =Moderate (severe); Probability output: ; The probability of experiencing moderate altitude sickness; The probability of experiencing severe altitude sickness; : The probability of experiencing moderate to severe altitude sickness.

[0114] Output : ; The total number of people involved in the accident; that is, the sum of those with minor injuries and those with serious injuries. The first in the accident personal; : No. 1 in the accident The probability of an individual experiencing moderate to severe altitude sickness; : A quantitative indicator of the severity of altitude sickness obtained using XGBoost.

[0115] Normalization is performed: ; Raw values ​​of quantitative indicators for altitude sickness; The smallest in history value; The largest in history value; Standardized The value is the current altitude sickness index.

[0116] Finally, an example is provided to illustrate how the current duration index is obtained.

[0117] enter: The path distance (km) from the accident site to the nearest rescue station; Estimated response time (hours); Weather deterioration coefficient (categorical variable); Weighting coefficients for each factor.

[0118] Quantification formula: ; : The path distance from the accident site to the nearest rescue station ( ); : Estimated rescue response time (hours); Weather index (sunny) Light rain and snow Heavy rain and snow Extreme weather ); , , The weighting functions for each factor can be obtained using the APH method, such as... , , ; Maximum rescue distance within the area (e.g.) ); Maximum acceptable response time (e.g.) Hour); The maximum value of the weather deterioration coefficient. ; : The output of quantitative indicators of rescue difficulty The larger the value, the more difficult the rescue operation.

[0119] Normalization is performed: ; : The output of quantitative indicators of rescue difficulty The larger the value, the more difficult the rescue operation. The smallest in history value; The largest in history value; Standardized The value is the current duration index.

[0120] After obtaining the current injury index, current altitude sickness index, and current duration index through the above methods, the severity of this accident can be assessed using the two methods for assessing the impact of highway traffic accidents in high-altitude areas provided in the aforementioned embodiments.

[0121] In addition, the aforementioned embodiments distinguish between hypoxic medical models and non-hypoxic medical models, as well as the first statistical model and the second statistical model.

[0122] In some other embodiments, the first statistical model and the second statistical model are combined into a single statistical model; Historical accident samples from high-altitude areas were used as fusion samples of hypoxia injury samples and non-hypoxia injury samples; The hypoxic medical model and the non-hypoxic medical model are combined into a medical model of injury evolution. The basic form of the medical model of injury evolution is: ; in, This is the injury deterioration outcome index, derived from the inherent deterioration outcome index. and hypoxia worsening outcome index It is formed through interaction; Initial injury index; The exponent is a base of the natural constant; The rate at which the inherent injury worsens; The factor representing the severity of injury due to hypoxia; Altitude sickness index; Duration index; The logarithmic form of the medical model of injury evolution is: ; The linear regression model is as follows: ; The dependent variable; The set of independent variables is , , , , , , ; , , , , , , is the regression coefficient.

[0123] Among them, the symbol " " indicates multiplication and is only used to distinguish between them" The '+' symbol has no other special meaning, and its omission does not affect the actual calculation process.

[0124] The above-mentioned medical model of injury evolution will be further illustrated below.

[0125] This application's embodiments construct an injury evolution medical model based on the following three types of injuries that occur in traffic accidents: Traumatic brain injury (TBI) includes mild head injuries, contusions, and concussions; a hypoxic environment can lead to brain hypoxia, which in turn causes faster deterioration of neurological function. Bleeding injuries indicate damage to internal blood vessels that require hemostasis or blood transfusion; Fractures / soft tissue contusions include open wounds, distal ischemia, and the risk of infection.

[0126] Among them, the medical model for the evolution of traumatic brain injury is as follows: ; in, The evolution results of a medical model for the evolution of traumatic brain injury. The inherent medical rate of deterioration of traumatic brain injury, The medical hypoxia severity factor in traumatic brain injury. The altitude sickness index. For time index, This is the initial injury index for traumatic brain injury.

[0127] The medical model for the evolution of bleeding injuries is as follows: like ,but: ; like ,but: ; in, As for blood volume saturation, Current blood volume This is the critical value for blood volume. , The evolution results of the medical model for the evolution of injury corresponding to bleeding injuries. The inherent medical rate of deterioration of bleeding injuries. The medical hypoxia severity factor for bleeding injuries. The altitude sickness index. For time index, The initial injury index for bleeding injuries. The rate of injury deterioration caused by low blood perfusion.

[0128] The medical model for the evolution of injuries corresponding to fractures / soft tissue contusions is as follows: like ,but: ; like ,but: ; in, The evolution results of medical models for injury evolution corresponding to fractures / soft tissue contusions. The inherent medical rate of deterioration of bleeding injuries. The medical hypoxia severity factor for bleeding injuries. The altitude sickness index. For time index, The initial injury index for fractures / soft tissue contusions. Infection duration index, This refers to a rapid deterioration of the injury caused by infection.

[0129] In addition, in cases of minor injuries, the probabilities of the above three types of injuries occurring in traffic accidents are as follows: Traumatic brain injury: 15%–40%, with an average of 27.5% used in the calculation process; Bleeding injury rate: 2%–10%, with an average of 6% used in the calculation process; Fractures / soft tissue contusions: 30%–60%, with an average of 45% used in the calculation process.

[0130] In cases of serious injury, the probabilities of the above three types of injuries occurring in a traffic accident are as follows: Traumatic brain injury: 40%–75%, with an average value of 57.5% used in the calculation process; Bleeding injury rate: 20%–50%, with an average of 35% used in the calculation process; Fractures / soft tissue contusions: 25%–60%, with an average of 42.5% used in the calculation process.

[0131] When calculating the injury deterioration outcome index, it can be calculated separately for each type of injury. The initial injury index for each type of injury can be obtained by multiplying the original initial injury index by the corresponding injury probability, for example: The third product is obtained by multiplying the minor injury index by 6%, and the fourth product is obtained by multiplying the serious injury index by 35%. The sum of the third and fourth products is used as the initial injury severity index for bleeding injuries. ; The fifth product is obtained by multiplying the minor injury index by 45%, and the sixth product is obtained by multiplying the severe injury index by 42.5%. The sum of the fifth and sixth products is used as the initial injury severity index for fractures / soft tissue contusions. .

[0132] That is, for any type of injury, the current injury index is the weighted sum of the minor injury index and the serious injury index. The weight of the minor injury index is the probability of any type of injury occurring in a minor injury scenario, and the weight of the serious injury index is the probability of any type of injury occurring in a serious injury scenario.

[0133] In the process of performing regression processing on the linear regression model to obtain regression coefficients, the interaction terms are reconstructed in the following manner. : ; in, For reconstructed interactive items , This serves as the index for the historical accident samples. , , The total number of the historical accident samples; Standardize each regression coefficient: , , ; For interactive items Perform multiple linear diagnosis: ; This represents the 6th independent variable. The regression coefficients are influenced by the regression processes of other independent variables; if This indicates that it is acceptable; if This indicates the existence of some collinearity; if This indicates severe multicollinearity, particularly in the interaction terms of some samples. Perform the deletion operation and fill in the missing values ​​using the interpolation method.

[0134] Among them, for interactive items Performing a deletion operation refers to deleting interaction items from a subset of samples. And use interpolation to fill in the missing values.

[0135] Optionally, the hypoxia-induced injury deterioration coefficient and the intrinsic injury deterioration rate are obtained as follows: ; in, To statistically analyze the rate of deterioration of the inherent injury, To calculate the severity of injuries due to hypoxia; , , This represents the total number of historical accident samples.

[0136] Optionally, the fusion-based intrinsic injury deterioration rate and the fusion-based hypoxic injury deterioration coefficient are obtained in the following ways: ; ; or, ; ; in, To integrate the rate at which the existing injury deteriorates, To integrate the hypoxia-induced injury deterioration coefficient, Due to the inherent rate of deterioration of medical conditions, The medical hypoxia injury deterioration coefficient, To statistically analyze the rate of deterioration of the inherent injury, To calculate the severity of injuries due to hypoxia, The first influence weight of the statistically inherent rate of injury deterioration on the injury deterioration outcome index, calculated based on historical accident samples. This represents the second influence weight of the statistical hypoxia injury deterioration coefficient, calculated based on historical accident samples, on the injury deterioration outcome index.

[0137] Optionally, the first influence weight Second influence weight The methods for obtaining these variables include: determining the set of independent variables based on the regression coefficients of the linear regression model. The weight corresponding to each element; based on the set of independent variables. middle , and The sum of the corresponding weights determines the first influence weight. According to the set of independent variables middle , and The sum of the corresponding weights determines the second influence weight. .

[0138] After obtaining the regression coefficients, calculate the standard deviations of the independent and dependent variables in the historical accident sample: ; ; ; Standardization coefficient: ; in, For regression coefficients, ; Use absolute values ​​to measure importance.

[0139] Normalized to standard weights: ; ; ; .

[0140] Optionally, the set of independent variables can be determined based on the fit coefficients of the linear regression model. The objective weight corresponding to each element in the set of independent variables Perform APH analysis to obtain the variable set The subjective weight corresponding to each element is assigned using game theory based on the objective and subjective weights of all elements to obtain the set of independent variables. The comprehensive weight corresponding to each element in the set of independent variables is determined based on the set of independent variables. middle , and The sum of the corresponding comprehensive weights determines the first influence weight. According to the set of independent variables middle , and The sum of the corresponding comprehensive weights determines the second influence weight. .

[0141] Optionally, the medical model for injury evolution that incorporates both the inherent injury deterioration rate and the hypoxia injury deterioration coefficient is as follows: ; in, for The matrix formed , For the first The first sample One independent variable, , for Construct a matrix, For the first The coefficients obtained by performing univariate regression on 1 independent variable. The elements in the middle are the set of independent variables. The weight of each element in the equation.

[0142] When calculating the overall weight, this Set of independent variables The overall weight of each element; without calculating the overall weight, this... That is, the aforementioned .

[0143] Figure 3 This is a modular schematic diagram of a highway traffic accident impact assessment system in high-altitude areas provided in this application embodiment, wherein each module can be implemented by software, hardware or a combination of both.

[0144] Combination Figure 3 As shown, the high-altitude highway traffic accident impact assessment system includes a first acquisition module 31, a second acquisition module 32, a first determination module 33, and a second determination module 34.

[0145] The first acquisition module 31 is used to acquire the current injury index, current altitude sickness index, and current time index of rescue units arriving at the accident site in the current traffic accident. The second acquisition module 32 is used to acquire the hypoxia medical model after parameter update; wherein, parameter update refers to replacing the original medical oxygen injury deterioration coefficient in the hypoxia medical model with the fused hypoxia injury deterioration coefficient; the fused hypoxia injury deterioration coefficient is formed by fusing the medical oxygen injury deterioration coefficient and the statistical hypoxia injury deterioration coefficient. The medical hypoxia injury deterioration coefficient is the injury deterioration coefficient caused by hypoxia determined according to medical theory, and the statistical hypoxia injury deterioration coefficient is the injury deterioration coefficient caused by hypoxia determined by statistical hypoxia injury samples. The first statistical model used for statistical hypoxia injury samples can fit the original hypoxia medical model; The first determining module 33 is used to use the updated hypoxia medical model as a calculation framework to determine the current hypoxia deterioration result index corresponding to the current injury index based on the current altitude reaction index and the current duration index; wherein, the current hypoxia deterioration result index is positively correlated with the current injury index, positively correlated with the current altitude reaction index, and positively correlated with the current duration index. The second determining module 34 is used to determine the severity of the current traffic accident based on the current hypoxia deterioration result index; wherein the severity is positively correlated with the current hypoxia deterioration result index.

[0146] Figure 4 This is a hardware schematic diagram of a highway traffic accident impact assessment system provided in an embodiment of this application.

[0147] Combination Figure 4 As shown, the road traffic accident impact assessment system for high-altitude areas includes: The processor 41 and memory 42 may also include a communication interface 43 and a bus 44. The processor 41, communication interface 43, and memory 42 can communicate with each other via the bus 44. The communication interface 43 can be used for information transmission. The processor 41 can call logical instructions in the memory 42 to execute the high-altitude highway traffic accident impact assessment method provided in the foregoing embodiments.

[0148] Furthermore, the logical instructions in the aforementioned memory 42 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0149] The memory 42, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of this application. The processor 41 executes functional applications and data processing by running the software programs, instructions, and modules stored in the memory 42, thereby implementing the methods in the above-described method embodiments.

[0150] The memory 42 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 42 may include high-speed random access memory and may also include non-volatile memory.

[0151] This application provides a computer-readable storage medium storing computer-executable instructions configured to execute the high-altitude highway traffic accident impact assessment method provided in the foregoing embodiments.

[0152] This application provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions, which, when executed by a computer, cause the computer to perform the high-altitude highway traffic accident impact assessment method provided in the aforementioned embodiments.

[0153] The aforementioned computer-readable storage medium may be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.

[0154] The technical solutions of this application embodiment can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes one or more instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods in this application embodiment. The aforementioned storage medium can be a non-transitory storage medium, including: USB flash drive, portable hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, and other media capable of storing program code; it can also be a transient storage medium.

[0155] The foregoing description and accompanying drawings fully illustrate embodiments of this application to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operations may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terminology used in this application is for describing embodiments only and is not intended to limit the claims. As used in the description of embodiments and claims, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Additionally, when used in this application, the terms “comprise” and its variations “comprises” and / or “comprising” refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Unless otherwise specified, an element defined by the phrase "comprising a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes that element. In this document, each embodiment may focus on describing the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, then the relevant parts can be referred to the description of the method section.

[0156] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0157] The methods and products (including but not limited to devices and equipment) disclosed in the embodiments herein can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units may be merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed units may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms. Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to implement this embodiment according to actual needs. Furthermore, the functional units in the embodiments of this application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0158] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. A method for assessing the impact of highway traffic accidents in high-altitude areas, characterized in that, include: Obtain the current injury index, current altitude sickness index, and current time index of rescue units arriving at the accident site in the current traffic accident; Obtain the hypoxia medical model with updated parameters; wherein, parameter update refers to replacing the original medical hypoxia injury deterioration coefficient in the hypoxia medical model with a fused hypoxia injury deterioration coefficient; the fused hypoxia injury deterioration coefficient is formed by fusing the medical hypoxia injury deterioration coefficient and the statistical hypoxia injury deterioration coefficient, the medical hypoxia injury deterioration coefficient is the injury deterioration coefficient caused by hypoxia determined according to medical theory, and the statistical hypoxia injury deterioration coefficient is the injury deterioration coefficient caused by hypoxia determined by statistically analyzing hypoxia injury samples, and the first statistical model used to statistically analyze the hypoxia injury samples can fit the original hypoxia medical model; Using the updated hypoxia medical model as the calculation framework, the current hypoxia deterioration result index corresponding to the current injury index is determined based on the current altitude reaction index and the current duration index; wherein, the current hypoxia deterioration result index is positively correlated with the current injury index, positively correlated with the current altitude reaction index, and positively correlated with the current duration index; The severity of the current traffic accident is determined based on the current hypoxia deterioration result index; wherein the severity is positively correlated with the current hypoxia deterioration result index.

2. The method for assessing the impact of highway traffic accidents in high-altitude areas according to claim 1, characterized in that, After obtaining the current injury index, current altitude sickness index, and current time elapsed since the rescue unit arrived at the accident site, the following information is also included: Obtain a non-hypoxic medical model with updated parameters; wherein, parameter update refers to replacing the medically inherent injury deterioration rate in the non-hypoxic medical model with the fusion inherent injury deterioration rate; the fusion inherent injury deterioration rate is formed by fusing the medically inherent injury deterioration rate and the statistically inherent injury deterioration rate, the medically inherent injury deterioration rate is the injury deterioration rate in a non-hypoxic environment determined according to medical theory, and the statistically inherent injury deterioration rate is the injury deterioration rate in a non-hypoxic environment determined by statistically analyzing non-hypoxic injury samples, and the second statistical model used to statistically analyze the non-hypoxic injury samples can fit the original non-hypoxic medical model; Using the updated non-hypoxic medical model, the current inherent deterioration outcome index corresponding to the current injury index is determined based on the current duration index; wherein, the current inherent deterioration outcome index is positively correlated with the current duration index and positively correlated with the current injury index; Determining the severity of the current traffic accident based on the current hypoxia deterioration result index includes: determining the severity based on the current inherent deterioration result index and the current hypoxia deterioration result index; wherein the severity is positively correlated with the current inherent deterioration result index.

3. The method for assessing the impact of highway traffic accidents in high-altitude areas according to claim 1 or 2, characterized in that, In the hypoxia medical model, the effects of duration index and altitude sickness index on the initial injury index are amplified exponentially, and the medical hypoxia injury deterioration coefficient affects the rate of exponential amplification. The first statistical model used to statistically analyze the hypoxia injury samples can fit the original hypoxia medical model, including: The first statistical model is a linear regression model, wherein the first independent variable of the linear regression model is any one and / or the interaction term of the logarithm of the initial injury index, the duration index, and the altitude reaction index in the hypoxia injury sample; and the first dependent variable of the linear regression model is the logarithm of the hypoxia deterioration result index in the hypoxia injury sample. Logarithmic operations were performed on the original hypoxia medical model to transform the initial injury index, altitude sickness index, and duration index into linear terms and / or interaction terms. The linear regression model can fit the first-order terms and / or interaction terms corresponding to the initial injury index, altitude sickness index, and duration index; And / or, In the case of a non-hypoxic medical model with updated parameters, the duration index in the non-hypoxic medical model has an exponentially amplified effect on the initial injury index, and the inherent medical injury deterioration rate affects the exponential amplification rate. The second statistical model used to statistically analyze non-hypoxic injury samples can fit the original non-hypoxic medical model, including: The second statistical model is a linear regression model, wherein the second independent variable of the linear regression model is either the logarithm of the initial injury index or the duration index in the non-hypoxic injury sample, or the interaction term thereof; and the second dependent variable of the linear regression model is the logarithm of the inherent deterioration outcome index in the non-hypoxic injury sample. Logarithmic operation was performed on the original non-hypoxic medical model to convert the initial injury index and duration index into linear terms and / or interaction terms. The linear regression model can fit the first-order terms and / or interaction terms corresponding to the initial injury index and the duration index.

4. The method for assessing the impact of highway traffic accidents in high-altitude areas according to claim 3, characterized in that, In the case of obtaining a non-hypoxic medical model with updated parameters, the first statistical model and the second statistical model are merged into a single statistical model. Historical accident samples from high-altitude areas were used as fusion samples for hypoxic injury samples and non-hypoxic injury samples; The hypoxia medical model and the non-hypoxia medical model are combined into a trauma evolution medical model; The basic form of the medical model for injury evolution is as follows: ; in, This is the injury deterioration outcome index, derived from the inherent deterioration outcome index. and hypoxia worsening outcome index It is formed through interaction; Initial injury index; The exponent is a base of the natural constant; The rate at which the inherent injury worsens; The factor representing the severity of injury due to hypoxia; Altitude sickness index; Duration index; The logarithmic form of the injury evolution medical model is: ; The linear regression model is as follows: ; The dependent variable; The set of independent variables is , , , , , , ; , , , , , , is the regression coefficient.

5. The method for assessing the impact of highway traffic accidents in high-altitude areas according to claim 4, characterized in that, The statistical hypoxia injury deterioration coefficient and the statistical intrinsic injury deterioration rate were obtained as follows: ; in, The statistically inherent rate of injury deterioration, The statistical hypoxia injury deterioration coefficient is used for the above purposes; , , This refers to the total number of historical accident samples. And / or, The rate of deterioration of the fused inherent injury and the coefficient of deterioration of the fused hypoxic injury were obtained in the following manner: ; ; or, ; ; in, The rate of deterioration of the inherent injury due to fusion. The coefficient for worsening of hypoxic injury is denoted as . Due to the inherent rate of deterioration of medical conditions, The coefficient for worsening of the aforementioned medical hypoxic injury. The statistically inherent rate of injury deterioration, The statistical hypoxia injury deterioration coefficient is used. The first influence weight of the statistically inherent injury deterioration rate, calculated based on the historical accident samples, on the injury deterioration outcome index. This is the second influence weight of the statistical hypoxia injury deterioration coefficient, calculated based on the historical accident samples, on the injury deterioration outcome index.

6. The method for assessing the impact of highway traffic accidents in high-altitude areas according to claim 5, characterized in that, First Influence Weight Second influence weight The acquisition methods include: determining the set of independent variables based on the regression coefficients of the linear regression model. The weight corresponding to each element in the set of independent variables; middle , and The sum of the corresponding weights determines the first influence weight. According to the set of independent variables middle , and The sum of the corresponding weights determines the second influence weight. ; or, The set of independent variables is determined based on the fit coefficients of the linear regression model. The objective weight corresponding to each element in the set of independent variables Perform AHP analysis to obtain the set of variables. The subjective weight corresponding to each element is assigned using game theory based on the objective and subjective weights of all elements to obtain the set of independent variables. The comprehensive weight corresponding to each element in the set of independent variables is determined based on the set of independent variables. middle , and The first influence weight is determined by the sum of the corresponding comprehensive weights. According to the set of independent variables middle , and The second influence weight is determined by the sum of the corresponding comprehensive weights. .

7. The method for assessing the impact of highway traffic accidents in high-altitude areas according to claim 5, characterized in that, The medical model for injury evolution, which includes the fusion-inherent injury deterioration rate and the fusion-hypoxic injury deterioration coefficient, is as follows: ； in, for The matrix formed , For the first The first sample One independent variable, , for Construct a matrix, For the first The coefficients obtained by performing univariate regression on 1 independent variable. The elements in the middle are the set of independent variables. The weight of each element in the equation.

8. The method for assessing the impact of highway traffic accidents in high-altitude areas according to claim 4, characterized in that, In the process of performing regression processing on the linear regression model to obtain regression coefficients, the interaction terms are reconstructed in the following manner. : ; in, For reconstructed interactive items , This serves as the index for the historical accident samples. For the first The deviation of the first independent variable in a sample. For the first The deviation of the third independent variable in a sample. , , The total number of the historical accident samples. For the first In the nth sample The deviation of each independent variable, For the first The first sample There are several independent variables for calculation. and , Take 1 and take 3 respectively; Standardize each variable: ， , ; in, For the first The standard score of the third independent variable in the sample. For the first sample in the entire sample The standard deviation of each independent variable For the first Standard scores of the dependent variable in each sample For the first The dependent variable for each sample The mean of the dependent variable across all samples. The standard deviation of the dependent variable across all samples; For interactive items Perform multiple linear diagnosis: ; This represents the 6th independent variable. variance expansion factor This represents the 6th independent variable. The regression coefficients are influenced by the regression processes of other independent variables; if This indicates that it is acceptable; if This indicates the existence of some collinearity; if This indicates severe multicollinearity, particularly in the interaction terms of some samples. Perform the deletion operation and fill in the missing values ​​using the interpolation method.

9. The method for assessing the impact of highway traffic accidents in high-altitude areas according to claim 1 or 2, characterized in that, The severity of the current traffic accident is determined based on the current hypoxia worsening result index, including: ;in, As to the degree of severity, The sigma activation function. and This is a linear adjustment factor. This refers to the current index indicating worsening hypoxia. And / or, In the high-altitude highway traffic accident impact assessment method, where the current inherent deterioration outcome index is determined, the severity is determined based on the current inherent deterioration outcome index and the current hypoxia deterioration outcome index, including: ; in, As to the degree of severity, The sigma activation function. , The parameters are for linear fitting. The current inherent deterioration index, This is the index indicating the current deterioration of hypoxia. This represents the current injury severity index.

10. A system for assessing the impact of highway traffic accidents in high-altitude areas, characterized in that, include: The first acquisition module is used to acquire the current injury index, current altitude sickness index, and current time index of rescue units arriving at the accident site in the current traffic accident. The second acquisition module is used to acquire the hypoxia medical model after parameter updates. Parameter updates refer to replacing the original medical hypoxia injury deterioration coefficient in the hypoxia medical model with a fused hypoxia injury deterioration coefficient. The fused hypoxia injury deterioration coefficient is formed by fusing the medical hypoxia injury deterioration coefficient and the statistical hypoxia injury deterioration coefficient. The medical hypoxia injury deterioration coefficient is the injury deterioration coefficient caused by hypoxia determined according to medical theory, while the statistical hypoxia injury deterioration coefficient is the injury deterioration coefficient caused by hypoxia determined through statistical analysis of hypoxia injury samples. The first statistical model used to statistically analyze the hypoxia injury samples can fit the original hypoxia medical model. The first determining module is used to use the updated hypoxia medical model as a calculation framework to determine the current hypoxia deterioration result index corresponding to the current injury index based on the current altitude reaction index and the current duration index; wherein, the current hypoxia deterioration result index is positively correlated with the current injury index, positively correlated with the current altitude reaction index, and positively correlated with the current duration index; The second determining module is used to determine the severity of the current traffic accident based on the current hypoxia deterioration result index; wherein the severity is positively correlated with the current hypoxia deterioration result index.

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