Plateau hypoxic encephaledema early warning system

Abstract

The invention provides a plateau hypoxic encephaledema early warning system, which comprises a first acquisition module configured to emit ultrasonic waves to at least two tangent planes of a first area to obtain an ultrasonic image reflected after being transmitted by an eyeball, and determining the diameter of an optic nerve sheath after first preprocessing; the second acquisition module is configured to emit a near-infrared signal source to a second area to obtain a spectral image reflected after transcranial transmission, and absorption spectrums of oxyhemoglobin and deoxidized hemoglobin are obtained after second preprocessing; the data processing module is configured to obtain the optic nerve sheath diameter and the absorption spectrum and determine a first influence factor and a second influence factor about the optic nerve sheath diameter and the absorption spectrum; and the early warning module is configured to give an alarm if the first influence factor and the second influence factor exceed a preset threshold value under a first preset condition. Early warning and monitoring of encephaledema are achieved through multi-mode monitoring.

Description

Technical Field

[0001] This invention belongs to the field of medical auxiliary monitoring equipment technology, specifically relating to an early warning system for high-altitude hypoxic cerebral edema. Background Technology

[0002] Under hypoxic conditions at high altitudes, multiple organs in the human body can develop hypoxic lesions. Among these, cerebral edema caused by hypoxia poses the greatest threat to life. Brain tissue swells under hypoxic conditions, and early symptoms are often discomfort such as headache and nausea, which can be relieved with routine treatment such as oxygen inhalation and rest. However, for some patients, when cerebral edema reaches a certain "threshold," they may suddenly fall into a coma, their condition deteriorates rapidly, and they may die quickly and irreversibly. Therefore, it is crucial to provide early warning before cerebral edema reaches the irreversible threshold, to quickly implement emergency measures for these patients, and to transport them to lower altitudes. This is essential to avoid death due to high-altitude hypoxia.

[0003] In the existing technology, cerebral edema monitoring is generally carried out by cranial CT to assess the degree of cerebral edema and brain damage. There are other single-modal monitoring methods, such as magnetic resonance imaging (MRI), invasive intracranial pressure monitoring (ICP), and transcranial Doppler ultrasound (TCD) to monitor cerebral edema caused by diseases such as brain trauma and cerebral hemorrhage.

[0004] Therefore, there is an urgent need to develop an early warning system for high-altitude hypoxic cerebral edema. Summary of the Invention

[0005] Therefore, the present invention provides an early warning system for high-altitude hypoxic cerebral edema, which provides early warning of high-altitude hypoxic cerebral edema.

[0006] A first aspect of the present invention provides an early warning system for high-altitude hypoxic cerebral edema, comprising: The first acquisition module is configured to emit ultrasound waves to at least two sections of the first region to obtain an ultrasound image reflected after transmission through the eyeball, and to determine the diameter of the optic nerve sheath after a first preprocessing step. The second acquisition module is configured to emit a near-infrared signal source into the second region to obtain a spectral image of transcranial transmission and reflection, and obtain the absorption spectra of oxyhemoglobin and deoxyhemoglobin after the second preprocessing. The data processing module is configured to acquire the optic nerve sheath diameter and the absorption spectrum, and determine a first influencing factor and a second influencing factor related to both. Specifically, the optic nerve sheath diameter is segmented and mapped to construct structural parameters reflecting the spatial expansion characteristics of the optic nerve sheath; the absorption spectra of oxyhemoglobin and deoxyhemoglobin are spectrally normalized to obtain spectral parameters reflecting changes in brain oxygen saturation; and the first influencing factor and the second influencing factor are generated based on the structural parameters and the spectral parameters, respectively. The early warning module is configured to issue an alarm if the first influence factor and the second influence factor exceed a preset threshold under a first preset condition.

[0007] As a preferred embodiment, the first acquisition module is further configured to... Multiple sampling points are set in a first direction toward the first region, and ultrasonic waves are emitted from each sampling point along at least two cross-sections formed in a second direction.

[0008] As a further preferred embodiment, the first acquisition module is also configured to... In the first cross section of any of the sampling points, multiple cross sections formed by the first cross section from the first direction to the second direction are continuously sampled; At the same sampling point, a second cross section is obtained, and multiple cross sections formed by the second cross section from the first direction to the second direction are continuously sampled. Repeat the steps performed on the previous sampling point for each of the other sampling points.

[0009] As a preferred embodiment, the first preprocessing includes the following steps: S1. Determine the first dataset formed by continuous sampling of the first cross-section at any sampling point, determine multiple first boundaries of the first dataset determined by the first gray level continuous threshold at the first cross-section, and determine the maximum fitting boundary among the first boundaries based on the average change value of gray level. S2. Determine the first diameter in the orthogonal direction corresponding to the first tangent based on the maximum fitting boundary; S3. Determine the second dataset formed by continuous sampling of the second cross-section at the same sampling point, determine multiple second boundaries using the first grayscale continuous threshold as the fitting reference value, and determine the maximum fitting boundary among the second boundaries; S4. Determine the second diameter in the orthogonal direction corresponding to the second tangent based on the fitted boundary; S5. Based on the first diameter, the second diameter, and the maximum fitting boundary corresponding to both, obtain the fitting curvature and construct a closed fitting projection from the first tangent to the second tangent. S6. Repeat steps S1-S5 at other sampling points to obtain a continuous closed fitting projection between each sampling point; S7. Obtain the diameter of the optic nerve sheath.

[0010] As a further preferred embodiment, the first preprocessing between S6 and S7 further includes the following steps: S601. Perform iterative training to obtain a closed fitting projection as the fitting boundary line; S602. Using the fitted boundary line as a reference value, and based on the first grayscale threshold, the grayscale change value in the obtained first dataset and second dataset is reduced or increased by a preset value. S603. Obtain the grayscale change value after iteration, determine the second grayscale threshold, and determine the iterative fitting boundary within the closed fitting projection range based on the second grayscale threshold. S604. Determine the closed fitting projection after iteration based on the iterative fitting boundary; S605. Repeat the above steps until the grayscale change value is trained to the preset value.

[0011] As a further preferred embodiment, the first acquisition module includes an ultrasonic patch, an ultrasonic transmitting module, and an ultrasonic receiving module. The ultrasonic patch is a single, adhesive circular or bowl-shaped piece, and the ultrasonic transmitting module and the ultrasonic receiving module are located on the adhesive side of the ultrasonic patch.

[0012] As a further preferred embodiment, the second acquisition module includes a transmission grating, a diode array detector, and a near-infrared spectrometer.

[0013] A second aspect of the present invention provides a method for early warning of hypoxic cerebral edema at high altitudes, comprising the following steps: S101. Obtain the diameter of the optic nerve sheath in the first region of the target area; S102. Obtain the absorption spectra of oxyhemoglobin and deoxyhemoglobin in the second region of the target area; S103. Determine the first influence factor and the second influence factor based on the optic nerve sheath diameter and the absorption spectrum; S104. Determine whether the first influence factor and the second influence factor exceed a preset threshold under preset conditions.

[0014] The above-described technical solution of the present invention has the following advantages compared with the prior art: 1. This invention achieves multimodal monitoring of optic nerve sheath, oxyhemoglobin and deoxyhemoglobin, which plays a predictive and monitoring role in the occurrence of high-altitude cerebral edema. Compared with single-modal monitoring, it improves the accuracy of monitoring and enhances the early warning effect.

[0015] 2. This invention obtains a more accurate optic nerve sheath by continuously sampling multiple cross-sections, thereby obtaining cross-sectional images of the optic nerve sheath in multiple projection directions and reconstructing them into a more complete local image through a pixel neural network, providing more accurate data for monitoring. Attached Figure Description

[0016] Figure 1 This is a structural block diagram of the system provided in the embodiments of the present invention; Figure 2This is a schematic diagram of continuous sampling of the first sampling point along the first tangent plane provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of continuous sampling of the first and second sampling points along the first tangent plane provided in an embodiment of the present invention; Among them, 1. eyeball; 2. optic nerve; 3. first sampling point; 4. second sampling point; 3a. continuous sampling of section one; 3b. continuous sampling of section two; 3c. continuous sampling of section three; 4a. continuous sampling of section four. Detailed Implementation

[0017] To provide a more detailed understanding of the features and technical content of the embodiments of this disclosure, the implementation of the embodiments of this disclosure 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 disclosure. 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.

[0018] This disclosure provides an early warning system for high-altitude hypoxic cerebral edema, such as... Figure 1 As shown, it includes a first acquisition module, a second acquisition module, a data processing module, and an early warning module.

[0019] In this embodiment, the first acquisition module is configured to emit ultrasound waves to at least two sections of the first region to obtain an ultrasound image emitted after transmission through the eyeball. The diameter of the optic nerve sheath is then determined after a first preprocessing step. Since ultrasound cannot penetrate the skull to directly detect the brain, assessing cerebral edema via ultrasound is difficult. However, in the orbital region, the optic nerve sheath behind the eyeball is connected to the brain. Ultrasound waves can directly detect the width of the optic nerve sheath through the eyeball, and the width of the optic nerve sheath can, to some extent, reflect the degree of cerebral edema.

[0020] As a further preferred embodiment of this disclosure, please refer to Figure 2 As shown, the first acquisition module is further configured to obtain, in a first cross-section of any of the sampling points, a plurality of continuous cross-sections formed by the first direction to the second direction from the first direction; and in a second cross-section of the same sampling point, to obtain a plurality of continuous cross-sections formed by the second direction from the first direction to the second direction from the second direction; and to repeat the steps performed at the previous sampling point for each of the other sampling points. This disclosure provides... Figure 2In this process, sampling is performed from the first sampling point toward the optic nerve to obtain continuous sampling of sections 1, 2, and 3 as shown in Figures 3a, 3b, and 3c. It should be noted that the number of continuous sampling sections provided in the figures is only for illustration and does not limit the specific number of continuous sampling sections actually obtained. By obtaining the above sections, images of the optic nerve in multiple directions are obtained after ultrasound passes through the eyeball and through the optic nerve at different angles. Furthermore, multiple sets of images of the optic nerve sheath in non-single projection directions can be obtained based on the images between continuous sampling sections.

[0021] See also Figure 3 As shown, a schematic diagram of obtaining four consecutive cross-sectional samples at the second sampling point is presented. Similar to the first sampling point, it also obtains multiple consecutive cross-sectional samples, which will not be described in detail here.

[0022] Furthermore, in this embodiment, the sampling of other cross-sections is configured in the same way as the first acquisition module in this embodiment, and will not be repeated here. It should be further explained that the cross-section defined in this embodiment is the cross-section formed by the line connecting the first direction to the second direction of the sampling point on the eyeball. Obviously, in this case, there should be multiple sets of cross-sections to obtain a complete image of the optic nerve sheath as in this embodiment, i.e., as shown in the example. Figure 3 The sampling overlap is shown.

[0023] Specifically, in this embodiment, the sclera region of the eyeball is used as the reference sampling area. Each sampling point is set along the sclera position, and the continuous cross-sectional sampling formed by each sampling point includes a gray-level overlap region. Based on the gray-level overlap region, partial data overlap of the dataset between different sampling points is obtained, and then a complete image is fitted to obtain the diameter of the optic nerve sheath shown in multiple cross-sections. Furthermore, the detection distance is determined based on the properties of ultrasound detection to obtain the length of the optic nerve sheath, and then the complete image is determined based on the overlap region. In this art, determining the detection distance based on the properties of ultrasound detection is a conventional technical means in this art. How to determine the detection distance will not be described in this embodiment.

[0024] As a preferred embodiment of this disclosure, the first preprocessing includes the following steps: S1. Determine the first dataset formed by continuous sampling of the first cross-section at any sampling point, determine multiple first boundaries of the first dataset determined by the first gray level continuous threshold at the first cross-section, and determine the maximum fitting boundary among the first boundaries based on the average change value of gray level. S2. Determine the first diameter in the orthogonal direction corresponding to the first sectional plane according to the maximum fitting boundary; the orthogonal direction referred to in this embodiment is the perpendicular line of the line connecting the first direction to the second direction, and the first diameter is the diameter of the obtained continuous sampling view of the sectional plane in the direction of the perpendicular line. It should be noted that the view obtained by continuous sampling of the sectional plane is not necessarily perpendicular to the line connecting the first direction to the second direction. In this case, the first diameter is obtained by splicing the adjacent or non-adjacent views of the view obtained by continuous sampling of the sectional plane at the sectional position.

[0025] S3. Determine the second dataset formed by continuous sampling of the second cross-section at the same sampling point, determine multiple second boundaries using the first grayscale continuous threshold as the fitting reference value, and determine the maximum fitting boundary among the second boundaries; S4. Determine the second diameter in the orthogonal direction corresponding to the second tangent based on the fitted boundary; S5. Based on the first diameter, the second diameter, and the maximum fitting boundary corresponding to both, obtain the fitting curvature and construct a closed fitting projection from the first tangent to the second tangent. S6. Repeat steps S1-S5 at other sampling points to obtain a continuously closed fitted projection between each sampling point. S7. Obtain the diameter of the optic nerve sheath. It should be noted that, in this embodiment of the present disclosure, to improve the accuracy of obtaining the diameter of the optic nerve sheath, the following step is further included between S6 and S7 provided in this embodiment of the present disclosure: S601. Perform iterative training to obtain a closed fitting projection as the fitting boundary line; S602. Using the fitted boundary line as a reference value, and based on the first grayscale threshold, the grayscale change value in the obtained first dataset and second dataset is reduced or increased by a preset value. S603. Obtain the grayscale change value after iteration, determine the second grayscale threshold, and determine the iterative fitting boundary within the closed fitting projection range based on the second grayscale threshold. S604. Determine the closed fitting projection after iteration based on the iterative fitting boundary; S605. Repeat the above steps until the grayscale change value is trained to the preset value.

[0026] In this embodiment of the disclosure, the second acquisition module is configured to emit a near-infrared signal source to the second region to obtain a spectral image of transcranial transmission and reflection, and obtain the absorption spectra of oxyhemoglobin and deoxyhemoglobin after a second preprocessing.

[0027] As a preferred embodiment of this disclosure, the first acquisition module includes an ultrasonic patch, an ultrasonic transmitting module, and an ultrasonic receiving module. In this embodiment, the ultrasonic patch is a single, adhesive circular or bowl-shaped piece. The ultrasonic transmitting module and the ultrasonic receiving module are disposed on the adhesive side of the ultrasonic patch. As an optional embodiment of this disclosure, the adhesive side of the ultrasonic patch is covered by a peelable adhesive film, or the adhesive side of the ultrasonic patch is suction cup shaped. In the art, the structure of attaching to the sampled target is a common technical means in the art, and will not be described in detail here for the sake of simplicity.

[0028] In a preferred embodiment of this disclosure, the second acquisition module includes a transmission grating, a diode array detector, and a near-infrared spectrometer. In this embodiment, the transcranial transmissive reflectance spectrum is obtained using Beer-Lambert's law, thereby acquiring brain oxygen saturation.

[0029] As a further preferred embodiment of this disclosure, the contrast ratio of the transmission grating is greater than 1:50000. At this contrast ratio, superior sensitivity and extremely low scattered light can be achieved when monitoring brain oxygen saturation. It is suitable for uniform dispersion in the spectral range of 850-1650 nm. Correspondingly, the diode array detector adopts a resolution of 1024 pixels. It is suitable for measuring the spectrum in the wavelength range of 850-1650 nm. Further adapted, the optical spectrometer is equipped with two-stage light-shielding plates. The first-stage light-shielding plate can block all the light entering the diode array detector and is used to detect the dark current background noise of the spectrometer, which can be subtracted in subsequent spectral calculations, thereby improving the accuracy and reliability of spectral calculations. The second-stage light-shielding plate can reflect all the light transmitted from the spectrometer to the sensor probe back into the spectrometer, which is used as the reference background spectrum of the spectrometer. The introduction of the reference background spectrum can greatly improve the accuracy and reliability of subsequent spectral calculations. In this embodiment of the disclosure, the spectrum obtained based on Beer-Lambert's law is a common spectral measurement method in the art, and for the sake of simplicity, it will not be described in detail here.

[0030] In this embodiment of the disclosure, the data processing module is configured to acquire the optic nerve sheath diameter and the absorption spectrum, and determine a first influencing factor and a second influencing factor related to both; wherein, the optic nerve sheath diameter is segmented and mapped to construct structural parameters reflecting the spatial expansion characteristics of the optic nerve sheath; and the absorption spectra of oxyhemoglobin and deoxyhemoglobin are subjected to spectral normalization processing to obtain spectral parameters reflecting changes in brain oxygen saturation; and the first influencing factor and the second influencing factor are generated based on the structural parameters and the spectral parameters, respectively.

[0031] In this embodiment of the disclosure, the early warning module is configured to issue an alarm if the first influence factor and the second influence factor exceed a preset threshold under a first preset condition.

[0032] The first preset condition in this embodiment is the probability of cerebral edema calculated by the data processing module. Specifically, the alarm value in this embodiment is:

[0033] Where P(Y=1) represents the warning value for irreversible cerebral edema, W represents the non-zero matrix formed by the optic nerve sheath in each region, a is the first influencing factor, and b is the second influencing factor for calculating cerebral oxygen saturation.

[0034] In this embodiment, the matrix is ​​constructed as follows: based on the optic nerve sheath diameter obtained in S7 of this embodiment, multiple intersecting units are constructed according to the sampling points and the cross-sectional positions of each sampling point. The intersecting units are used as matrix units, and the value of the matrix unit is the expansion ratio between the projection corresponding to the cross-section and the reference value of the optic nerve sheath at that location, which is Nn / n, where N is the monitored optic nerve sheath diameter and n is the reference value of the optic nerve sheath. Then, other values ​​of the intersection points of adjacent cross-sections are obtained according to one of the cross-sectional directions to construct the optic nerve sheath expansion matrix.

[0035] The following example is a matrix W in an embodiment of this disclosure:

[0036] This matrix can be used to determine the expansion of each sampled section. The rank of the matrix can be used to obtain the degree of expansion uniformity of the sampled optic nerve sheath. The higher the rank, the lower the degree of expansion uniformity.

[0037] The first influence factor is calculated based on the uniformity of the expansion of the optic nerve sheath and the values ​​in the corresponding matrix of the optic nerve sheath:

[0038] Where f is the number of elements in matrix W with a value of 0; The second influencing factor is an empirical influencing factor, whose value is positively correlated with brain oxygen saturation. The value range is (0, 1]. The higher the brain oxygen saturation, the lower the value range. When the brain oxygen saturation is lower than the preset value, the second influencing factor is set to 1.

[0039] In this embodiment of the present disclosure, when the alarm module obtains that the cerebral edema alarm value calculated by the data processing module exceeds a preset threshold, the alarm module initiates an alarm. In this embodiment of the present disclosure, the preset threshold is set to 0.3.

[0040] As a preferred embodiment of this disclosure, the alarm module provides alarms through sound, light, or a combination of both. Providing alarm sound and light prompts is not limited to displaying warning text. It should be noted that for the alarms provided in this disclosure, corresponding methods can also be provided to disable the warnings. Controlling alarms through buttons is a common technical means in the art and will not be described in detail here.

[0041] In this embodiment of the disclosure, the data processing module consists of a cloud and edge nodes. The edge nodes are deployed in the first acquisition module and the second acquisition module. The first edge node in the first acquisition module is used to acquire the diameter of the optic nerve sheath and calculate the first influence factor. The second edge node in the second acquisition module is used to acquire the absorption spectrum and calculate the second influence factor. After the early warning module initiates an alarm, the edge node uploads the corresponding first and second influencing factors to the cloud and records them as alarm data.

[0042] In this embodiment of the disclosure, the system further includes an appearance detection module, which is used to collect the patient's appearance physiological information, including at least blood oxygen saturation, blood pressure, and heart rate. The cloud-based system has a built-in neural network model, which is trained based on the alarm data and the apparent physiological information. The trained neural network model is used to predict whether a patient is in a high-risk state, and to mark the patient and notify medical staff based on the prediction results.

[0043] As a preferred embodiment of this disclosure, a neural network model is trained to predict the health risks of patients, particularly for an early warning system for high-altitude hypoxic cerebral edema. The specific process is as follows: The physiological information of appearance is collected through the appearance detection module. The physiological information of appearance includes: blood oxygen saturation (SpO2), blood pressure (such as systolic blood pressure and diastolic blood pressure), and heart rate. In this embodiment of the disclosure, the methods for obtaining blood oxygen saturation (SpO2), blood pressure (such as systolic blood pressure and diastolic blood pressure), and heart rate are common medical methods, which will not be described in detail here. Each record in the alarm data is accompanied by a label indicating whether the patient was in a high-risk state when the event occurred. For example, the label can be 0 (low risk) or 1 (high risk). These labels will serve as the target for the model to learn during the training process.

[0044] The neural network architecture is a fully connected neural network. The number of neurons in the input layer of the neural network corresponds to the number of features of the physiological information. The neural network uses a binary cross-entropy loss function to evaluate the classification effect of the model and optimizes it through the SGD algorithm. In this embodiment, the fully connected neural network structure, the binary cross-entropy loss function, and the SGD algorithm are all technical solutions that can be found in the art. For the sake of brevity, they will not be described in detail here. After training and verification, the neural network model is deployed to the cloud. When the patient is under real-time monitoring, the system inputs the patient's physiological information into the trained neural network. The neural network will output a high-risk prediction result, thereby alerting medical staff.

[0045] This disclosure also provides a method for early warning of hypoxic cerebral edema at high altitudes, including the following steps: S101. Obtain the diameter of the optic nerve sheath in the first region of the target area; S102. Obtain the absorption spectra of oxyhemoglobin and deoxyhemoglobin in the second region of the target area; S103. Determine the first influence factor and the second influence factor based on the optic nerve sheath diameter and the absorption spectrum; S104. Determine whether the first influence factor and the second influence factor exceed a preset threshold under preset conditions.

[0046] The steps of the method provided in the embodiments of this disclosure have been described in detail in the system section of the embodiments of this disclosure, and will not be repeated here.

[0047] The foregoing description and accompanying drawings fully illustrate embodiments of this disclosure 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. Similarly, the term “and / or” as used in this application means including one or more of the associated listed items and all possible combinations thereof. Additionally, when used in this application, the term "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. Without further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element. In this document, each embodiment may focus on 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, the relevant parts can be referred to the description of the method section.

Claims

1. A high-altitude hypoxic cerebral edema early warning system, characterized in that, include: The first acquisition module is configured to emit ultrasound waves to at least two sections of the first region to obtain an ultrasound image reflected after transmission through the eyeball, and to determine the diameter of the optic nerve sheath after a first preprocessing step. The second acquisition module is configured to emit a near-infrared signal source into the second region to obtain a spectral image of transcranial transmission and reflection, and obtain the absorption spectra of oxyhemoglobin and deoxyhemoglobin after the second preprocessing. The data processing module is configured to acquire the optic nerve sheath diameter and the absorption spectrum, and determine a first influencing factor and a second influencing factor related to both. Specifically, the optic nerve sheath diameter is segmented and mapped to construct structural parameters reflecting the spatial expansion characteristics of the optic nerve sheath; the absorption spectra of oxyhemoglobin and deoxyhemoglobin are spectrally normalized to obtain spectral parameters reflecting changes in brain oxygen saturation; and the first influencing factor and the second influencing factor are generated based on the structural parameters and the spectral parameters, respectively. The early warning module is configured to issue an alarm if the first influence factor and the second influence factor exceed a preset threshold under a first preset condition.

2. The high-altitude hypoxic cerebral edema early warning system according to claim 1, characterized in that, The first acquisition module is also configured to Multiple sampling points are set in a first direction toward the first region, and ultrasonic waves are emitted from each sampling point along at least two cross-sections formed in a second direction.

3. The high-altitude hypoxia-induced edema early warning system according to claim 2, characterized in that, The first acquisition module is also configured to In the first cross section of any of the sampling points, multiple cross sections formed by the first cross section from the first direction to the second direction are continuously sampled; At the same sampling point, a second cross section is obtained, and multiple cross sections formed by the second cross section from the first direction to the second direction are continuously sampled. Repeat the steps performed on the previous sampling point for each of the other sampling points.

4. The high-altitude hypoxia-induced edema early warning system according to claim 3, characterized in that, The first preprocessing includes the following steps: S1. Determine the first dataset formed by continuous sampling of the first cross-section at any sampling point, determine multiple first boundaries of the first dataset at the first gray level continuous threshold of the first cross-section, and determine the maximum fitting boundary among the first boundaries based on the average change value of gray level. S2. Determine the first diameter in the orthogonal direction corresponding to the first tangent based on the maximum fitting boundary; S3. Determine the second dataset formed by continuous sampling of the second cross-section at the same sampling point, determine multiple second boundaries using the first grayscale continuous threshold as the fitting reference value, and determine the maximum fitting boundary among the second boundaries. S4. Determine the second diameter in the orthogonal direction corresponding to the second tangent based on the fitted boundary; S5. Based on the first diameter, the second diameter, and the maximum fitting boundary corresponding to both, obtain the fitting curvature and construct a closed fitting projection from the first tangent to the second tangent. S6. Repeat steps S1-S5 at other sampling points to obtain a continuous closed fitting projection between each sampling point; S7. Obtain the diameter of the optic nerve sheath.

5. The high-altitude hypoxia-induced edema early warning system according to claim 4, characterized in that, The first preprocessing, which takes place between S6 and S7, further includes the following steps: S601. Perform iterative training to obtain a closed fitting projection as the fitting boundary line; S602. Using the fitted boundary line as a reference value, and based on the first grayscale threshold, the grayscale change value in the obtained first dataset and second dataset is reduced or increased by a preset value. S603. Obtain the grayscale change value after iteration, determine the second grayscale threshold, and determine the iterative fitting boundary within the closed fitting projection range based on the second grayscale threshold. S604. Determine the closed fitting projection after iteration based on the iterative fitting boundary; S605. Repeat the above steps until the grayscale change value is trained to the preset value.

6. The high-altitude hypoxia-induced edema early warning system according to claim 1, characterized in that, The first acquisition module includes an ultrasonic patch, an ultrasonic transmitting module, and an ultrasonic receiving module. The ultrasonic patch is a single, adhesive circular or bowl-shaped piece, and the ultrasonic transmitting module and ultrasonic receiving module are located on the adhesive side of the ultrasonic patch.

7. The high-altitude hypoxia-induced edema early warning system according to claim 1, characterized in that, The second acquisition module includes a transmission grating, a diode array detector, and a near-infrared spectrometer.

8. The high-altitude hypoxia-induced edema early warning system according to claim 1, characterized in that, The data processing module consists of a cloud and edge nodes. The edge nodes are deployed in the first acquisition module and the second acquisition module. The first edge node in the first acquisition module is used to acquire the diameter of the optic nerve sheath and calculate the first influence factor. The second edge node in the second acquisition module is used to acquire the absorption spectrum and calculate the second influence factor. After the early warning module initiates an alarm, the edge node uploads the corresponding first and second influencing factors to the cloud and records them as alarm data.

9. The high-altitude hypoxia-induced edema early warning system according to claim 8, characterized in that, The system also includes an appearance detection module, which is used to collect the patient's appearance physiological information, including at least blood oxygen saturation, blood pressure, and heart rate. The cloud-based system has a built-in neural network model, which is trained based on the alarm data and the apparent physiological information. The trained neural network model is used to predict whether a patient is in a high-risk state, and to mark the patient and notify medical staff based on the prediction results.

10. A method for early warning of hypoxic edema at high altitudes, characterized in that, Includes the following steps: S101. Obtain the diameter of the optic nerve sheath in the first region of the target area; S102. Obtain the absorption spectra of oxyhemoglobin and deoxyhemoglobin in the second region of the target area; S103. Determine the first influence factor and the second influence factor based on the optic nerve sheath diameter and the absorption spectrum; S104. Determine whether the first influence factor and the second influence factor exceed a preset threshold under preset conditions.

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