Dynamic risk early warning system based on intelligent situation awareness

Abstract

The invention provides a dynamic risk early warning system based on intelligent situation awareness, and relates to the technical field of situation awareness data risk early warning. The dynamic risk early warning system based on intelligent situation awareness comprises a data acquisition module; a real-time monitoring module; a situation awareness module; and a risk early warning module. According to the invention, the perception data of the to-be-accessed patient in the preset acquisition area is collected in real time, and the change condition of the perception data of the to-be-accessed patient in the preset time period is monitored in real time, so that the relevance index is obtained; then monitoring the change condition of situation awareness of the patient to be accessed in a preset time period in real time according to the obtained relevance index to obtain situation awareness data, and finally inputting the obtained relevance index and situation awareness data into a risk early warning model to obtain a risk early warning index. Therefore, the real-time performance of situation awareness risk early warning is improved, and the problem of low real-time performance of situation awareness abnormal data risk early warning in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of situation awareness data risk warning, and in particular to a dynamic risk warning system based on intelligent situation awareness. Background Art

[0002] With the continuous advancement of medical technology and the in-depth promotion of medical informatization, the amount of medical data has shown an explosive growth trend. These data include multi-dimensional information such as patients' clinical information, test results, medical imaging data, genetic information, and lifestyle. The accumulation of these data provides a rich data source for intelligent situational awareness technology, making disease prediction and early warning based on big data analysis possible. Secondly, with the improvement of medical level and the enhancement of patients' health awareness, precision medicine and personalized treatment have become important development directions in the medical field. The dynamic risk warning system based on intelligent situational awareness can timely discover potential disease risks through comprehensive analysis and real-time monitoring of individual patient data, and provide patients with personalized health management and diagnosis and treatment suggestions, thereby achieving early prevention and treatment of diseases.

[0003] The existing dynamic risk warning system integrates data from preset data sources to form a security situation view, and uses big data analysis technology to process the acquired data in real time to identify abnormal patterns, and automatically triggers corresponding security protection measures based on the warning information to achieve risk extraction, warning and effective response. However, in the existing technology, the collection of medical data often relies on various medical devices and sensors. The real-time performance of these devices may be affected by network delays and device performance, resulting in a time lag for medical data to reach the warning system. In addition, due to the real-time update of medical data, the real-time analysis of medical data is inefficient, and there is a problem of low real-time situational awareness of abnormal data risk warning. Summary of the invention

[0004] The embodiment of the present invention solves the problem of low real-time performance of risk warning of abnormal data in situation awareness in the prior art by providing a dynamic risk warning system based on intelligent situation awareness, and improves the real-time performance of risk warning of situation awareness.

[0005] The embodiment of the present invention provides a dynamic risk warning system based on intelligent situation awareness, including:

[0006] A data acquisition module, a real-time monitoring module, a situation awareness module and a risk warning module; wherein the data acquisition module is used to collect the perception data of the patient to be visited in a preset collection area in real time, the perception data includes facial image data and physiological parameter data, the facial image data is used to reflect the facial features of the patient to be visited in real time, the facial features are used to reflect the degree of influence of facial situation awareness on the patient to be visited, the physiological parameter data is used to reflect the changes in the movement and behavior of the patient to be visited in real time, the degree of influence of facial situation awareness is used to measure the degree of influence of facial features on the facial situation awareness of the patient to be visited within a preset time period; the real-time monitoring module is used to monitor the changes in the perception data of the patient to be visited within a preset time period in real time. The situation is obtained by analyzing the correlation index, and the correlation index is used to measure the correlation degree between the perception data of the patient to be visited within a preset time period and the action behavior of the patient to be visited; the situation awareness module is used to monitor the changes of the situation awareness of the patient to be visited within a preset time period in real time according to the obtained correlation index to obtain situation awareness data, and the situation awareness data is used to reflect the situation awareness state of the patient to be visited at the current time point in real time; the risk warning module is used to input the obtained correlation index and situation awareness data into the risk warning model to obtain the risk warning index, and generate the corresponding risk level according to the risk warning index, and the risk warning index is used to measure the situation awareness risk degree of the patient to be visited at the current time point.

[0007] Optionally, the specific steps for obtaining the degree of influence of the facial situation awareness are: detecting the skin color area in the face image of the patient to be visited within a preset acquisition area, and comparing the detected skin color area with the standard skin color sample in a preset database to obtain a skin color difference value; the degree of influence of the facial situation awareness is calculated by the following formula:

[0008]

[0009] Where h is the number of the patient to be visited, h = 1, 2, ..., H, H is the total number of patients to be visited, q is the number of the preset collection area, q = 1, 2, ..., Q, Q is the total number of preset collection areas, e is a natural constant, BAI h represents the impact degree of facial situation awareness of the hth patient to be visited in the preset acquisition area, F h.q represents the skin color difference value of the hth patient to be visited in the qth preset acquisition area, ΔF0 represents the reference skin color difference value, β1 represents the skin color area contrast correction factor, L h.q represents the skin color area contrast of the hth patient's face image to be visited within the qth preset acquisition area, β2 represents the skin color area saturation correction factor, B h.qIt represents the saturation of the skin color area in the hth patient's facial image to be visited within the qth preset acquisition area.

[0010] Optionally, the correlation index is obtained by the following method: aligning the perception data within a preset time period at the same timestamp to obtain an influence rate, wherein the influence rate includes a first influence rate and a second influence rate; combining the obtained facial situation perception influence degree to obtain a correlation index, wherein the correlation index is calculated by the following formula:

[0011]

[0012] Where t is the number of the same timestamp, t = 1, 2, ..., T, T is the total number of the same timestamp, GUAN h represents the correlation index of the hth patient to be visited within the same timestamp, represents the first impact rate of the face image data of the hth patient to be visited at the same time stamp of tth time, represents the reference first impact rate, represents the second impact rate of the physiological parameter data of the hth patient to be accessed at the same time stamp of tth time, represents the reference second impact rate, δ represents the situation awareness risk impact factor, and BAI0 represents the reference impact degree of facial situation awareness.

[0013] Optionally, the situation awareness module includes a situation awareness data acquisition unit, a data preprocessing unit and a situation awareness anomaly identification unit; the situation awareness data acquisition unit is used to monitor the situation awareness changes of the patient to be visited in real time within a preset time period and record the situation awareness data of the patient to be visited; the data preprocessing unit is used to convert the format of the situation awareness data; the situation awareness anomaly identification unit is used to determine whether the situation awareness of the patient to be visited is equal to a preset situation awareness value by monitoring the fluctuation of the situation awareness data within a preset time period in real time. If not, it is recorded as situation awareness anomaly data and fed back to the preset personnel, otherwise it is stored in the preset database.

[0014] Optionally, the data is recorded as situation awareness abnormality data and fed back to a preset person, and then the situation awareness abnormality data is classified according to the acquired situation awareness abnormality degree. The specific steps for obtaining the situation awareness abnormality degree are: obtaining the abnormal deviation degree according to the degree of deviation between the situation awareness abnormality data and the situation awareness data; obtaining the abnormal duration time according to the time difference between the abnormal start time and the abnormal end time of the situation awareness abnormality data; and obtaining the situation awareness abnormality degree by combining the acquired abnormal deviation degree, abnormal duration time and the action behavior deviation of the patient to be visited within the preset time period.

[0015] Optionally, the step of inputting the acquired correlation indicators and situation awareness data into a risk warning model, and then obtaining risk warning indicators through the risk warning model, wherein the specific steps of obtaining the risk warning indicators include: obtaining risk probability values ​​based on situation awareness abnormality data and situation awareness data, and obtaining risk warning indicators by combining the acquired correlation indicators and the degree of situation awareness abnormality.

[0016] Optionally, the expression of the risk warning indicator obtained by the risk warning model is:

[0017]

[0018] Where q is the number of the preset collection area, q = 1, 2, ..., Q, Q is the total number of preset collection areas, e is a natural constant, GUAN h represents the correlation index of the hth patient to be visited within the same timestamp, GUAN0 represents the reference correlation index, and YI h represents the abnormal situation awareness level of the situation awareness abnormal data of the hth patient to be visited within the preset time period, YI0 represents the abnormal situation awareness reference level, G h.q represents the risk probability value of the hth patient to be visited in the qth preset acquisition area, and G0 represents the reference risk probability value.

[0019] Compared with the prior art, the above technical solution has at least the following beneficial effects:

[0020] The above scheme is a dynamic risk warning system based on intelligent situational awareness, which collects the perception data of the patients to be visited in a preset collection area in real time, and monitors the changes of the perception data of the patients to be visited in a preset time period in real time to obtain correlation indicators, and then monitors the changes of the situational awareness of the patients to be visited in a preset time period in real time according to the obtained correlation indicators to obtain situational awareness data, and finally inputs the obtained correlation indicators and situational awareness data into the risk warning model to obtain risk warning indicators, thereby achieving more accurate acquisition of risk warning indicators, and then achieving improved real-time performance of situational awareness risk warning, effectively solving the problem of low real-time performance of situational awareness abnormal data risk warning in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1A schematic diagram of the structure of a dynamic risk warning system based on intelligent situational awareness provided by an embodiment of the present invention;

[0023] Figure 2 A three-dimensional coordinate analysis diagram of the degree of influence of facial situation awareness provided by an embodiment of the present invention;

[0024] Figure 3 A schematic diagram of the structure of a situation awareness module in a dynamic risk warning system based on intelligent situation awareness provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] Unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, words such as "one", "one" or "the" do not indicate a quantitative limitation, but indicate the existence of at least one. Words such as "include" or "comprise" mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Words such as "connect" or "connected" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect.

[0027] It should be noted that the terms "up", "down", "left", "right", "front", "back", etc. used in the present invention are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0028] The present invention aims at the fact that the real-time collection of medical data may be affected by network delays and equipment performance, resulting in a time lag for the medical data to reach the early warning system. In addition, due to the real-time update of medical data, the efficiency of real-time analysis of medical data is low, thereby reducing the real-time risk warning of situational awareness abnormal data. A dynamic risk early warning system based on intelligent situational awareness is provided, and the real-time risk warning of situational awareness abnormal data is effectively improved.

[0029] like Figure 1As shown, it is a structural schematic diagram of a dynamic risk warning system based on intelligent situational awareness provided by an embodiment of the present invention. A dynamic risk warning system based on intelligent situational awareness provided by an embodiment of the present invention includes: a data acquisition module, a real-time monitoring module, a situational awareness module and a risk warning module; wherein the data acquisition module is used to collect perception data of the patient to be visited in a preset collection area in real time, the perception data includes facial image data and physiological parameter data, the facial image data is used to reflect the facial features of the patient to be visited in real time, the facial features are used to reflect the degree of influence of facial situational awareness of the patient to be visited, the physiological parameter data is used to reflect the changes in the movement and behavior of the patient to be visited in real time, and the degree of influence of facial situational awareness is used to measure the degree of influence of facial features on the facial situational awareness of the patient to be visited within a preset time period; the real-time monitoring module It is used to monitor in real time the changes in the perception data of the patient to be visited within a preset time period to obtain correlation indicators, and the correlation indicators are used to measure the degree of correlation between the perception data of the patient to be visited within the preset time period and the action behavior of the patient to be visited; the situation awareness module is used to monitor in real time the changes in the situation awareness of the patient to be visited within the preset time period according to the obtained correlation indicators to obtain situation awareness data, and the situation awareness data is used to reflect in real time the situation awareness status of the patient to be visited at the current time point; the risk warning module is used to input the obtained correlation indicators and situation awareness data into the risk warning model to obtain risk warning indicators, and at the same time generate corresponding risk levels according to the risk warning indicators. The risk warning model is used to obtain risk warning indicators, and the risk warning indicators are used to measure the situation awareness risk level of the patient to be visited at the current time point.

[0030] In this embodiment, in actual applications, although the degree of facial pallor is usually directly related to health problems such as anemia and hypotension, and can be preliminarily judged by visual observation, in many cases, such judgment may be affected by multiple factors such as light, skin type, makeup, etc., and is not accurate or comprehensive enough. Therefore, the degree of facial pallor is usually replaced by the degree of influence of the patient's facial situation perception, wherein the degree of influence of facial situation perception is not limited to the single dimension of skin color, but comprehensively considers the overall changes in the face and the impact of these changes on the patient's health status. This indicator can be obtained through advanced image processing technology and biosensors, which can more accurately capture subtle changes in the face and infer the patient's health status based on this; physiological parameter data usually includes blood circulation status data and blood loss degree data, wherein the blood circulation status data is used to It reflects the patient's blood circulation status, such as blood flow velocity and vascular resistance, to evaluate whether the patient's circulatory system function is normal; in the application scenario of hemorrhagic shock, the blood loss degree data is used to evaluate the patient's blood loss degree; when the patient is in the early stage of hemorrhagic shock, he usually has abnormal expressions due to emotions such as pain, tension, and fear. These abnormal expressions may appear as frowning, closing eyes, clenching teeth, etc., reflecting the patient's discomfort and pain. As hemorrhagic shock develops, the patient usually becomes confused, indifferent, or even comatose. Therefore, the degree of abnormal expression can be used as a preliminary indicator for assessing the patient's risk of hemorrhagic shock, especially in the early stages of shock; in hemorrhagic shock, due to the sharp decrease in blood circulation, the patient's face often appears obviously pale, and the degree of facial pallor is positively correlated with the risk of hemorrhagic shock, that is, the more severe the pallor, the higher the risk of hemorrhagic shock.

[0031] It is worth noting that the situation awareness module plays a core role in the dynamic risk warning system. It monitors the changes in the situation awareness of the patient to be visited within a preset time period in real time based on the acquired correlation indicators (including facial image data and physiological parameter data). The situation awareness data is usually collected in real time through medical sensor equipment. Among them, these situation awareness data usually include heart rate, blood pressure, and blood oxygen saturation. They can reflect the patient's situation awareness state at the current time point in real time. For example, in hemorrhagic shock, due to reduced blood volume and compensatory sympathetic nerve excitement, the patient's heart rate usually increases (increased heart rate is an early manifestation of hemorrhagic shock), which improves the accuracy of real-time monitoring and data processing of medical sensors; for patients with chronic diseases such as diabetes and hypertension, the situation awareness module can continuously monitor the patient's physiological indicators, detect abnormal conditions in time, and prevent the occurrence of complications; for elderly people living alone, the situation awareness module can monitor their daily activities, sleep quality, etc., evaluate their independent living ability, and detect potential health risks in time; in summary, situation awareness data can not only reflect the patient's current health status in real time, but also predict health risks, thereby improving the real-time nature of situation awareness risk warning.

[0032] Optionally, the specific steps for obtaining the degree of influence of facial situation awareness are: detecting the skin color area in the facial image of the patient to be visited within a preset acquisition area, comparing the detected skin color area with the standard skin color sample in the preset database to obtain a skin color difference value, the facial image is collected by an image sensor in the preset acquisition area, the skin color area represents the facial area of ​​the patient to be visited during the face detection process, and the skin color difference value is used to measure the degree of deviation between the skin color in the skin color area of ​​the patient to be visited and the standard skin color sample in the preset database; the degree of influence of facial situation awareness is calculated by the following formula:

[0033]

[0034] Where h is the number of the patient to be visited, h = 1, 2, ..., H, H is the total number of patients to be visited, q is the number of the preset collection area, q = 1, 2, ..., Q, Q is the total number of preset collection areas, e is a natural constant, BAI h represents the impact degree of facial situation awareness of the hth patient to be visited in the preset acquisition area, F h.q represents the skin color difference value of the hth patient to be visited in the qth preset acquisition area, ΔF0 represents the reference skin color difference value, β1 represents the skin color area contrast correction factor, L h.q represents the skin color area contrast of the hth patient's face image to be visited within the qth preset acquisition area, β2 represents the skin color area saturation correction factor, B h.q It represents the saturation of the skin color area in the hth patient's facial image to be visited within the qth preset acquisition area.

[0035] Among them, the skin color area contrast correction factor and the skin color area saturation correction factor are obtained from a preset database. The skin color area contrast correction factor is used to correct the deviation between the skin color area contrast in the facial image of the patient to be visited and the degree of influence on facial situation perception. The skin color area saturation correction factor is used to correct the deviation between the skin color area saturation in the facial image of the patient to be visited and the degree of influence on facial situation perception.

[0036] In this embodiment, a face detection algorithm is usually used to locate the face area in the image, the image is cropped according to the detected face area, and scaled to a suitable size for subsequent processing, the image is converted from the RGB color space to a color space more suitable for skin color detection (such as YCrCb), YCrCb is commonly used in skin color detection because the Cr and Cb components are relatively insensitive to changes in illumination, and a machine learning-based method (such as SVM, decision tree) is integrated to segment the skin color area in the converted color space, and the color histogram, average color, and color moment features are extracted from the detected skin color area. In the preset database, there should be multiple standard skin color samples, which may come from different races, genders, and age groups. For the skin color features of the patient to be visited, the difference value is calculated with each standard skin color sample in the database, which is usually obtained by calculating the Euclidean distance between the features.

[0037] Specifically, based on the standard skin color samples in the preset database, a series of thresholds for evaluating the degree of impact of facial situation awareness are set. These thresholds can be based on the numerical range of skin color characteristics, which are used to distinguish different degrees of facial situation awareness. For example, the following thresholds can be set: normal skin color range: represents the range of skin color characteristics in a healthy state; mild pallor threshold: slightly deviates from the normal skin color range, but is still a lower value within an acceptable range; moderate pallor threshold: significantly lower than the normal skin color range, but has not yet reached the limit of severe pallor; severe pallor threshold: significantly lower than the normal skin color range, usually indicating serious problems with health status; if the skin color difference value is within the normal skin color range, it is considered that the patient's current face has no significant pallor; if the skin color difference value exceeds the mild pallor threshold but does not reach the moderate pallor threshold, it is considered that the patient's face is mildly pale; if the skin color difference value exceeds the moderate pallor threshold but does not reach the severe pallor threshold, it is considered that the patient's face is moderately pale; if the skin color difference value exceeds the severe pallor threshold, it is considered that the patient's face is severely pale and should seek medical attention immediately.

[0038] Specifically, to simplify the analysis, we define B1=β1*L h.q +β2*B h.q , where F1 represents the skin color difference value coefficient of the hth patient to be visited in the qth preset acquisition area, and B1 represents the skin color area influence coefficient of the hth patient to be visited in the qth preset acquisition area. The simplified calculation formula for the influence degree of facial situation awareness is: The reference skin color difference value is usually represented by the sum and average of the skin color data in the preset database, such as Figure 2As shown, it is a three-dimensional coordinate analysis diagram of the influence degree of facial situation awareness provided by an embodiment of the present invention. It can be seen from the figure that the influence degree of facial situation awareness increases with the increase of the skin color difference value coefficient and the skin color area influence coefficient, and the skin color difference coefficient has a more obvious influence on the influence degree of facial situation awareness than the skin color area influence coefficient.

[0039] It should be understood that the skin color area contrast correction factor is obtained from a preset database. In a specific embodiment, the relationship between the skin color area contrast and the degree of influence of facial situation awareness is fitted by the historical skin color image contrast data and the historical situation awareness data in the preset database to obtain a fitting curve, the relationship between the skin color area contrast and the degree of influence of facial situation awareness is determined according to the fitting curve, and the real-time skin color area contrast is substituted into the fitting curve to obtain the corresponding skin color area contrast correction factor.

[0040] The skin color area saturation correction factor is obtained from a preset database. In a specific embodiment, the relationship between the skin color area saturation and the degree of influence of facial situation awareness is fitted by the historical skin color image saturation data and the historical situation awareness data in the preset database to obtain a fitting curve, the relationship between the skin color area saturation and the degree of influence of facial situation awareness is determined according to the fitting curve, and the real-time skin color area saturation is substituted into the fitting curve to obtain the corresponding skin color area saturation correction factor, thereby improving the accuracy and reliability of obtaining the degree of influence of facial situation awareness, and further improving the real-time performance of situation awareness risk warning, effectively solving the problem of low real-time performance of situation awareness abnormal data risk warning in the prior art.

[0041] Optionally, the correlation index is obtained by the following method: aligning the perception data within a preset time period at the same timestamp to obtain an influence rate, the influence rate includes a first influence rate and a second influence rate, the first influence rate is used to measure the influence of the face image data on the situation awareness risk at the same timestamp, and the second influence rate is used to measure the influence of the physiological parameter data on the situation awareness risk at the same timestamp; combining the obtained facial situation awareness influence degree to obtain the correlation index, and the correlation index is calculated by the following formula:

[0042]

[0043] Where t is the number of the same timestamp, t = 1, 2, ..., T, T is the total number of the same timestamp, GUAN h represents the correlation index of the hth patient to be visited within the same timestamp, represents the first impact rate of the face image data of the hth patient to be visited at the same time stamp of tth time, represents the reference first impact rate, represents the second impact rate of the physiological parameter data of the hth patient to be accessed at the same time stamp of tth time, represents the reference second impact rate, δ represents the situation awareness risk impact factor, and BAI0 represents the reference impact degree of facial situation awareness.

[0044] Among them, the situation awareness risk impact factor is obtained from a preset database, and the situation awareness risk impact factor is used to measure the impact of the situation awareness changes of the patient to be visited within a preset time period on the situation awareness impact.

[0045] In this embodiment, the same timestamp is a timestamp shared by the facial image data and the physiological parameter data within a preset time period, and features related to situational awareness risks are extracted from the preprocessed facial image, such as the degree of skin pallor and changes in facial vascular distribution. These features can be monitored in real time by medical sensors, and physiological parameter features can be directly extracted from the physiological parameter data. Features related to situational awareness risks, such as heart rate variability and blood pressure drop rate, can usually be used directly for analysis; in the field of medical applications, the first impact rate is usually obtained by the ratio of the total number of abnormal facial situation awareness data of the patient to the total number of facial situation awareness data, and the second impact rate is obtained by real-time monitoring of the patient's heart rate fluctuation frequency in a preset time period and the fluctuation frequency of each timestamp in the preset time period by a medical sensor device, wherein the second impact rate represents the ratio of the patient's fluctuation frequency at each timestamp to the patient's fluctuation frequency in the preset time period.

[0046] Specifically, the reference first influence rate is usually represented by the sum and average of the facial image data in the preset database, the reference second influence rate is usually represented by the sum and average of the physiological parameter data in the preset database, and the facial situation awareness reference influence degree is usually represented by the sum and average of the facial situation awareness data in the preset database.

[0047] Specifically, the situation awareness risk impact factor is obtained from a preset database. In a specific embodiment, the relationship between the facial situation awareness impact degree and the correlation index is fitted by the historical facial situation awareness data and the historical situation awareness abnormal data in the preset database to obtain a fitting curve, the relationship between the facial situation awareness impact degree and the correlation index is determined according to the fitting curve, and the real-time facial situation awareness data is substituted into the fitting curve to obtain the corresponding situation awareness risk impact factor.

[0048] It should be understood that the algorithm of this embodiment combines the first influence rate, the second influence rate and the facial situation awareness influence degree factors, and comprehensively analyzes to obtain the correlation index. The first influence rate and the facial situation awareness influence degree in this formula not only unilaterally affect the value of the correlation index, but the first influence rate also indirectly affects the value of the facial situation awareness influence degree. When medical interventions such as drug treatment or surgery effectively improve the patient's physiological condition (that is, the first influence rate increases), the patient's emotional state will often improve accordingly, thereby showing more positive postures (such as relaxation and smiling) in facial expressions, that is, the amplitude of the patient's movements and behaviors increases, which is the improvement of the facial situation awareness influence degree. Conversely, if the patient expresses a positive attitude towards treatment through non-verbal means such as facial expressions (such as actively cooperating with treatment and staying optimistic), this psychological state may in turn promote physiological recovery, thereby further improving the first influence rate, achieving the improvement of the accuracy and efficiency of obtaining the facial situation awareness influence degree, and then achieving the improvement of the real-time performance of the situation awareness risk warning, effectively solving the problem of low real-time performance of the situation awareness abnormal data risk warning in the prior art.

[0049] Alternatively, if Figure 3 As shown, it is a structural schematic diagram of a situation awareness module in a dynamic risk warning system based on intelligent situation awareness provided by an embodiment of the present invention, the situation awareness module includes a situation awareness data acquisition unit, a data preprocessing unit and a situation awareness anomaly recognition unit; the situation awareness data acquisition unit: used to monitor the situation awareness changes of the patient to be visited in real time within a preset time period and record the situation awareness data of the patient to be visited; the data preprocessing unit: used to perform format conversion on the situation awareness data, and the format conversion is used to convert the non-numerical data in the situation awareness data into numerical data; the situation awareness anomaly recognition unit: used to determine whether the situation awareness of the patient to be visited is equal to a preset situation awareness value by monitoring the fluctuation of the situation awareness data in real time within a preset time period, if not, it is recorded as situation awareness anomaly data and fed back to the preset personnel, otherwise it is stored in the preset database, and the situation awareness anomaly data is situation awareness data that exceeds the preset situation awareness threshold.

[0050] In this embodiment, the situation awareness data acquisition unit is responsible for real-time monitoring of the situation awareness of the user to be visited within a preset time period, such as heart rate, blood pressure, respiratory rate, and body temperature. This is usually achieved by connecting to the patient's medical monitoring equipment (such as an electrocardiogram monitor, a sphygmomanometer, and a blood oxygen saturation monitor). These devices can continuously and automatically collect the patient's situation awareness data, thereby improving the accuracy of situation awareness data acquisition. In actual applications, a sphygmomanometer is usually used to measure the patient's blood pressure within a preset time period in real time, that is, dynamic blood pressure monitoring. Dynamic blood pressure monitoring can continuously record blood pressure changes within a preset time period (such as 24 hours), avoiding the randomness and instability of a single measurement. Secondly, dynamic blood pressure monitoring helps to detect latent hypertension, that is, the patient's blood pressure is normal when measured in the hospital, but the blood pressure rises in daily life. It should be noted that two types of hypertension, postural hypertension and postprandial hypotension, are easily overlooked in routine blood pressure monitoring, but dynamic blood pressure monitoring can accurately identify them.

[0051] Therefore, in actual applications, the patient's blood pressure within a preset time period is usually used as situational awareness data, and it can also be selected according to the specific application scenario. The situational awareness data in the following embodiments are all the patient's blood pressure within the preset time period, and the corresponding situational awareness abnormal data are all the patient's abnormal blood pressure within the preset time period. The patient's blood pressure within the preset time period is usually obtained by real-time monitoring with a sphygmomanometer. At this time, the preset situational awareness value is the preset blood pressure value, which can accurately reflect the changes in the patient's health status, and is usually represented by the result of summing and averaging the historical blood pressure data in the preset database. If the patient's blood pressure is greater than the preset blood pressure value, it is marked as abnormal blood pressure. These abnormal blood pressure data may indicate that the patient has some health problems or risks (such as cerebral infarction caused by hypertension), which requires timely attention and treatment by medical personnel, thereby improving the real-time and accuracy of patient situational awareness monitoring.

[0052] Optionally, it is recorded as situation awareness abnormality data and fed back to a preset person, and then the situation awareness abnormality data is classified according to the acquired situation awareness abnormality level. The situation awareness abnormality level is used to measure the abnormality level of situation awareness within a preset time period. The level classification includes mild abnormality, moderate abnormality and severe abnormality. The specific steps for obtaining the situation awareness abnormality level are: obtaining the abnormal deviation degree according to the degree of deviation between the situation awareness abnormal data and the situation awareness data, and the abnormal deviation degree represents the absolute value of the difference between the situation awareness data and the situation awareness abnormal data; obtaining the abnormal duration according to the time difference between the abnormal start time and the abnormal end time of the situation awareness abnormal data, and the abnormal duration is the duration of the abnormal state within the preset time period; combining the acquired abnormal deviation degree, abnormal duration and the action behavior deviation of the patient to be visited within the preset time period to obtain the situation awareness abnormality level, and the action behavior deviation is used to measure the deviation degree between the action behavior of the patient to be visited within the preset time period and the preset behavior threshold.

[0053] In this embodiment, the content is the same as that of the previous embodiment. The situational awareness abnormal data is usually the abnormal blood pressure of the patient within a preset time period. The normal range of blood pressure (i.e., the normal range of situational awareness) is usually obtained by summing and averaging the historical blood pressure data in the preset database. When the patient's blood pressure deviates from the normal range of blood pressure, the patient needs to be treated, such as cardiopulmonary resuscitation and the use of emergency drugs. For each situational awareness abnormal data point (such as blood pressure), its deviation from the normal range of blood pressure is calculated. The deviation can be an absolute difference (i.e., the abnormal value minus the center value or lower limit / upper limit of the normal range) or a deviation percentage (i.e., the deviation value divided by half of the normal range or a fixed value, and then multiplied by 100%). According to the calculated deviation, the abnormal deviation is determined. This usually requires mapping the deviation value to a corresponding grade classification, such as "mild deviation", "moderate deviation" and "severe deviation". In actual applications, it is assumed that the normal heart rate range of a patient is 60-100 beats / minute, and the real-time monitored heart rate is 120 beats / minute. The absolute deviation of the heart rate is 120-100=20 beats / minute, and the deviation percentage is (20 / 50)*100%=40%. According to the preset deviation level classification standard, this heart rate value may be regarded as "moderate deviation"; usually, a speed sensor worn on the patient can record the patient's movement speed (such as the speed of lying down, getting up, and walking) in real time to reflect the patient's exercise intensity, which is then compared with the preset movement speed to obtain the patient's movement behavior deviation. In actual applications, the patient's movement behavior within a preset time period is usually the walking speed. At this time, the preset movement speed is the preset walking speed, wherein the preset walking speed is usually represented by the result of summing and averaging the historical walking speed data in a preset database. Therefore, the movement behavior deviation in this embodiment represents the difference between the patient's walking speed within the preset time period and the preset walking speed.

[0054] Specifically, the degree of situation awareness anomaly is calculated by the following formula:

[0055]

[0056] In the formula, h is the number of the patient to be visited, h = 1, 2,..., H, where H is the total number of patients to be visited; t is the number of the same time stamp in the preset time period, t = 1, 2,..., T, where T is the total number of the same time stamps in the preset time period; m is the number of the situation awareness anomaly data, m = 1, 2,..., M, where M is the total number of the situation awareness anomaly data, and YI h represents the degree of situation awareness anomaly of the h-th patient to be visited, γ1 represents the weight factor of anomaly deviation degree, D h.m represents the anomaly deviation degree of the m-th situation awareness anomaly data of the h-th patient to be visited within the preset time period, D0 represents the reference anomaly deviation degree, γ2 represents the weight factor of anomaly duration, and J h.m represents the anomaly duration of the m-th situation awareness anomaly data of the h-th patient to be visited within the preset time period, J0 represents the reference anomaly duration, γ3 represents the weight factor of action behavior deviation, and S h.t represents the action behavior deviation of the h-th patient to be visited at the t-th same time stamp, and S0 represents the reference action behavior deviation.

[0057] Among them, the weight factor of anomaly deviation degree, the weight factor of anomaly duration, and the weight factor of action behavior deviation are obtained through a preset database. The weight factor of anomaly deviation degree is used to measure the proportion of the anomaly deviation degree relative to the degree of situation awareness anomaly. The weight factor of anomaly duration is used to measure the proportion of the anomaly duration relative to the degree of situation awareness anomaly. The weight factor of action behavior deviation is used to measure the proportion of the action behavior deviation relative to the degree of situation awareness anomaly.

[0058] It should be noted that in the calculation process of the degree of situation awareness anomaly, when D h.m ≥ D0, J h.m ≥ J0 and S h.t ≥ S0, where D0 is the reference anomaly deviation degree (i.e., the minimum value of the anomaly deviation degree), J0 is the reference anomaly duration (i.e., the minimum value of the anomaly duration), and S0 is the reference action behavior deviation (i.e., the minimum value of the action behavior deviation). When D h.m < D0 or J h.m < J0 or S h.t < S0, then there is no need to calculate the degree of situation awareness anomaly, indicating that the situation awareness data is normal at this time.

[0059] Specifically, action behavior deviation is used to measure the degree of deviation between the action behavior of the patient to be interviewed within a preset time period and the preset behavioral threshold. This includes the patient's daily activity patterns, sleep quality, eating habits and other aspects of behavior. It can assess whether the patient's lifestyle is healthy and whether there are behavioral deviations that may lead to health problems.

[0060] Among them, the preset behavioral threshold is usually represented by the sum and average of historical patient behavior data in the preset medical database; the reference abnormal deviation is usually represented by the sum and average of historical situation awareness data in the preset medical database, the reference duration is usually represented by the sum and average of historical duration data in the preset database, and the action behavior reference deviation is usually represented by the sum and average of historical patient behavior data in the preset medical database.

[0061] It should be understood that the algorithm of this embodiment combines the abnormal deviation, abnormal duration and action behavior deviation factors to comprehensively analyze and obtain the degree of situation awareness abnormality. The abnormal deviation and action behavior deviation in this formula not only unilaterally affect the value of the situation awareness abnormality, but the action behavior deviation also indirectly affects the value of the situation awareness abnormality. When it is found that the patient's action behavior deviation is large (such as long-term sitting and frequent intake of high-sugar foods), and is accompanied by abnormal deviations in physiological indicators (such as continuous increase in blood pressure and large fluctuations in blood sugar), the algorithm will comprehensively evaluate and obtain a higher degree of situation awareness abnormality. At this time, the dynamic risk warning system will automatically send health reminders to the patient, such as recommending increased exercise and adjustment of diet structure, and at the same time send the evaluation results to the doctor so that the doctor can adjust the treatment plan in time or perform optional medical intervention.

[0062] The abnormal deviation weight factor is obtained from a preset database. In a specific embodiment, the relationship between the abnormal deviation and the situation awareness abnormality is fitted to the historical situation awareness data and the historical situation awareness abnormal data in the preset database to obtain a fitting curve, the relationship between the abnormal deviation and the situation awareness impact is determined according to the fitting curve, and the real-time abnormal deviation is substituted into the fitting curve to obtain the corresponding abnormal deviation weight factor, wherein the abnormal deviation represents the ratio of the total number of situation awareness abnormal data to the total number of situation awareness data.

[0063] The abnormal duration weight factor is obtained from a preset database. In a specific embodiment, the relationship between the abnormal duration and the situational awareness abnormality degree is fitted by the historical situational awareness data duration and the historical situational awareness abnormal data duration in the preset database to obtain a fitting curve, the relationship between the abnormal duration and the situational awareness impact degree is determined according to the fitting curve, and the abnormal duration of the real-time situational awareness abnormal data is substituted into the fitting curve to obtain the corresponding abnormal duration weight factor.

[0064] The action behavior deviation weight factor is obtained from a preset database. In a specific embodiment, the relationship between the action behavior deviation and the situation awareness abnormality is fitted by the historical facial situation awareness data and the historical action behavior deviation data in the preset database to obtain a fitting curve, the relationship between the action behavior deviation and the situation awareness impact is determined according to the fitting curve, and the real-time patient's action behavior deviation is substituted into the fitting curve to obtain the corresponding action behavior deviation weight factor, so as to achieve more accurate acquisition of the situation awareness abnormality, thereby achieving the improvement of the real-time performance of the situation awareness risk warning, and effectively solving the problem of low real-time performance of the situation awareness abnormality data risk warning in the prior art.

[0065] Optionally, the acquired correlation indicators and situational awareness data are input into a risk warning model, which includes preprocessing the acquired situational awareness data. The preprocessing includes data deduplication, data normalization and data smoothing. Data deduplication is used to remove duplicate situational awareness data records. Data normalization is used to divide the time series data in the situational awareness data by timestamps. The timestamp division is used to ensure that the situational awareness data is within the same timestamp. Data smoothing is used to eliminate noise data and short-term fluctuations in the situational awareness data.

[0066] In this embodiment, a database query statement (such as the DISTINCT keyword in SQL) is used to identify and delete duplicate records. For time series data, it may be necessary to consider the combination of timestamp and specific numerical value to determine whether it is a duplicate record; each feature value in the situation awareness data is scaled to the same dimension to eliminate the impact of dimensional differences between different features on model training. For example, the data is converted into a distribution with a mean of 0 and a standard deviation of 1. For data collected by different devices or at different time frequencies, interpolation or resampling operations are performed so that all data points are at a unified timestamp; low-pass filter is a commonly used technology in signal processing, which can remove high-frequency components (i.e., short-term fluctuations) and retain low-frequency components (i.e., long-term trends). In situation awareness data processing, time series data can be regarded as a signal, and a low-pass filter can be applied to smooth the data, thereby improving the accuracy of low-pass filter data processing, and thereby improving the accuracy and reliability of situation awareness data acquisition.

[0067] Optionally, the obtained correlation indicators and situation awareness data are input into a risk warning model, and then the risk warning indicators are obtained through the risk warning model. The specific steps of obtaining the risk warning indicators include: obtaining risk probability values ​​based on situation awareness abnormal data and situation awareness data, and combining the obtained correlation indicators and the degree of situation awareness abnormality to obtain risk warning indicators. The risk probability value is used to measure the proportion of situation awareness abnormal data in the situation awareness data.

[0068] In this embodiment, in the application scenario of patient visits, the risk probability value is usually obtained by the ratio of the abnormal blood pressure of the patient to the blood pressure within a preset time period; in addition to the risk probability value, it is also necessary to evaluate the severity of the situation awareness anomaly, which can be achieved by comparing the deviation degree of the abnormal data from the normal range, the duration of the abnormal data, and the frequency of the abnormal data. The risk probability value, the correlation index, and the degree of situation awareness anomaly are subjected to fuzzy comprehensive evaluation to generate a risk warning index, and at the same time, the warning mechanism is triggered to remind medical staff to pay attention to the health status of the patient, thereby improving the accuracy of obtaining the risk warning index.

[0069] Optionally, the expression for obtaining the risk warning index through the risk warning model is:

[0070]

[0071] In the formula, q is the number of the preset acquisition area, q = 1, 2,..., Q, where Q is the total number of the preset acquisition areas, e is the natural constant, GUAN h represents the correlation index of the hth patient to be visited within the same time stamp, GUAN0 represents the reference correlation index, YI h represents the degree of situation awareness anomaly of the situation awareness abnormal data of the hth patient to be visited within the preset time period, YI0 represents the reference situation awareness abnormal degree, G h.q represents the risk probability value of the hth patient to be visited within the qth preset acquisition area, and G0 represents the reference risk probability value.

[0072] In this embodiment, it should be noted that during the calculation process of the risk warning index, when GUAN h ≤GUAN0, G h.q ≥G0 and YI h ≥YI0, where GUAN0 is the reference correlation index (i.e., the maximum value of the correlation index), G0 is the reference risk probability value (i.e., the minimum value of the risk probability), and YI0 is the degree of situation awareness anomaly (i.e., the minimum value of the degree of situation awareness anomaly). When GUAN h >GUAN0 or G h.q <G0 or YI h <YI0, then there is no need to calculate the risk warning index, and at this time, there is no need to issue a risk warning.

[0073] For the sake of simplified analysis, define In the formula, GU1 represents the correlation coefficient of the situation awareness abnormal data of the hth patient to be visited within the preset time period, G1 represents the risk probability coefficient of the situation awareness abnormal data of the hth patient to be visited within the preset time period, and Y1 represents the situation awareness abnormal coefficient of the situation awareness abnormal data of the hth patient to be visited within the preset time period. The simplified expression of the risk warning index is: The statistical table of changes in risk warning indicators is shown in Table 1:

[0074] Table 1 Statistics of changes in risk warning indicators

[0075]

[0076] It should be understood that the reference correlation index is usually represented by the result of summing and averaging the situation awareness correlation data in the preset database, the reference risk probability value is usually represented by the result of summing and averaging the risk probability data in the preset database, and the situation awareness reference abnormality degree is usually represented by the result of summing and averaging the abnormal data in the preset situation awareness database. The risk warning index decreases with the increase of the correlation index, and increases with the increase of the situation awareness abnormality degree and the risk probability value. h =GUAN0,G h.q =G0 and YI h =YI0, the situation awareness risk level of the patient to be visited at the current time point is the lowest, which improves the accuracy and real-time performance of risk warning indicators, and further improves the real-time performance of situation awareness risk warning, effectively solving the problem of low real-time performance of situation awareness abnormal data risk warning in the prior art.

[0077] Optionally, the risk level is determined by mapping the obtained risk warning indicator with a preset threshold interval; the threshold interval includes a low value interval, a median value interval and a high value interval, and the preset threshold interval is set by a risk level classification standard; the risk level classification standard is represented by the result of summing and averaging the risk level data in a preset database.

[0078] In this embodiment, historical risk level data are collected from a preset database. These data usually come from multiple cases, patients or time periods to ensure their representativeness and extensiveness. The collected data are cleaned to remove outliers and missing values ​​to ensure the accuracy and completeness of the data. Then, the cleaned data are summed and the average value is calculated. This average value can be used as a benchmark point or reference line for risk level classification (i.e., the threshold interval corresponding to the risk level). Among them, the low value interval indicates a lower risk, and the upper limit of this interval should be lower than the average value of the risk level classification standard. The specific value can be determined according to actual conditions and needs; the median interval is between the low value interval and the high value interval, indicating a medium risk. The upper and lower limits of this interval can be determined according to the risk level, etc. The high value interval indicates a higher risk, and the lower limit of this interval should be higher than the average value of the risk level classification standard. The specific value also needs to be determined according to actual conditions and needs; the calculated risk warning indicator is compared with the set threshold interval to determine which risk level it belongs to. If the risk warning indicator falls within the low value interval, it is judged to be a low risk level; if the risk warning indicator falls within the median value interval, it is judged to be a medium risk level; if the risk warning indicator falls within the high value interval, or exceeds the upper limit of the high value interval, it is judged to be a high risk level; this helps to provide medical personnel with timely and accurate risk assessment and warning information, and realizes a more accurate setting of risk warning indicators corresponding to risk levels.

[0079] Optionally, a corresponding risk level is generated according to the risk warning indicator, and then a monitoring and warning rainbow map is generated. The specific generation steps include: mapping the real-time situation awareness data and the corresponding risk level to the basic framework of the monitoring and warning rainbow map, and rendering with preset colors and color levels to generate a monitoring and warning rainbow map. The basic framework includes coordinate axes, legends and titles. The monitoring and warning rainbow map represents the real-time changes of the risk levels corresponding to the situation awareness data through graphical visualizations containing preset colors and color levels.

[0080] In this embodiment, the basic framework of the monitoring and early warning rainbow chart usually includes a coordinate axis, a legend and a title, wherein the coordinate axis includes a horizontal coordinate axis and a vertical coordinate axis, the horizontal coordinate axis can represent time (such as hours, minutes), and is used to display the real-time changes of the situation awareness data; the vertical coordinate axis represents the specific value of the situation awareness or the processed risk warning indicator value; the legend usually clearly marks the risk level corresponding to different colors or color levels (such as green for low risk, yellow for medium risk, and red for high risk); the theme of the chart is marked, such as "Situation Awareness Risk Monitoring and Early Warning Rainbow Chart"; according to the calculated risk level, the preset color and color level are selected to render the situation awareness data.

[0081] The rendered data points (or data lines, if it is time series data) are plotted at the corresponding positions of the monitoring and early warning rainbow chart to ensure that the chart can be updated in real time to reflect the latest changes in situational awareness data and risk levels; the generated monitoring and early warning rainbow chart is transmitted to the medical staff's interactive interface (such as a computer screen, tablet computer or smart phone) through the network or other communication methods, which helps the medical staff to quickly understand the patient's situational awareness risk level, enabling the medical staff to have a deeper and more comprehensive analysis of the patient's situational awareness risk level, thereby achieving improved accuracy and real-time performance in the generation of the monitoring and early warning rainbow chart.

[0082] There are a few points to note:

[0083] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention, and other structures may refer to the general design.

[0084] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of the layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0085] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0086] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A dynamic risk warning system based on intelligent situational awareness, characterized in that: include: Data acquisition module, real-time monitoring module, situation awareness module and risk warning module; Wherein, the data acquisition module is used to collect the perception data of the patient to be visited in a preset collection area in real time, and the perception data includes facial image data and physiological parameter data, the facial image data is used to reflect the facial features of the patient to be visited in real time, the facial features are used to reflect the degree of influence of facial situation perception of the patient to be visited, the physiological parameter data is used to reflect the changes in the movement and behavior of the patient to be visited in real time, and the degree of influence of facial situation perception is used to measure the degree of influence of facial features on facial situation perception of the patient to be visited within a preset time period; The real-time monitoring module is used to monitor in real time the changes in the perception data of the patient to be visited within a preset time period to obtain a correlation index, and the correlation index is used to measure the correlation between the perception data of the patient to be visited within the preset time period and the action behavior of the patient to be visited; The situation awareness module is used to monitor the changes in the situation awareness of the patient to be visited within a preset time period in real time according to the acquired correlation index to obtain situation awareness data, and the situation awareness data is used to reflect the situation awareness state of the patient to be visited at the current time point in real time; The risk warning module is used to input the acquired correlation indicators and situation awareness data into the risk warning model to obtain risk warning indicators, and generate corresponding risk levels according to the risk warning indicators. The risk warning indicators are used to measure the situation awareness risk level of the patient to be visited at the current time point.

2. The dynamic risk warning system based on intelligent situational awareness according to claim 1 is characterized in that: The specific steps for obtaining the influence degree of facial situation awareness are as follows: Detecting the skin color area in the face image of the patient to be visited within the preset acquisition area, and comparing the detected skin color area with the standard skin color sample in the preset database to obtain a skin color difference value; The facial situation awareness impact degree is calculated by the following formula: Where h is the number of the patient to be visited, h = 1, 2, ..., H, H is the total number of patients to be visited, q is the number of the preset collection area, q = 1, 2, ..., Q, Q is the total number of preset collection areas, e is a natural constant, BAI h represents the impact degree of facial situation awareness of the hth patient to be visited in the preset acquisition area, F h. represents the skin color difference value of the hth patient to be visited in the qth preset acquisition area, ΔF0 represents the reference skin color difference value, β1 represents the skin color area contrast correction factor, L h. represents the skin color area contrast of the hth patient's face image to be visited within the qth preset acquisition area, β2 represents the skin color area saturation correction factor, B h. It represents the saturation of the skin color area in the hth patient's facial image to be visited within the qth preset acquisition area.

3. The dynamic risk warning system based on intelligent situational awareness according to claim 2 is characterized in that: The correlation index is obtained by the following method: aligning the sensing data within a preset time period at the same timestamp to obtain an influence rate, wherein the influence rate includes a first influence rate and a second influence rate; The obtained facial situation awareness influence degree is combined to obtain a correlation index, which is calculated using the following formula: Where t is the number of the same timestamp, t = 1, 2, ..., T, T is the total number of the same timestamp, GUAN h represents the correlation index of the hth patient to be visited within the same timestamp, represents the first impact rate of the face image data of the hth patient to be visited at the same time stamp of tth time, represents the reference first impact rate, represents the second impact rate of the physiological parameter data of the hth patient to be accessed at the same time stamp of tth time, represents the reference second impact rate, δ represents the situation awareness risk impact factor, and BAI0 represents the reference impact degree of facial situation awareness.

4. The dynamic risk warning system based on intelligent situational awareness according to claim 1 is characterized in that: The situation awareness module includes a situation awareness data acquisition unit, a data preprocessing unit and a situation awareness anomaly recognition unit; The situation awareness data acquisition unit is used to monitor the situation awareness changes of the patient to be visited in real time within a preset time period and record the situation awareness data of the patient to be visited; The data preprocessing unit is used to convert the format of the situation awareness data; The situation awareness anomaly identification unit is used to determine whether the situation awareness of the patient to be visited is equal to a preset situation awareness value by real-time monitoring of the fluctuation of the situation awareness data within a preset time period. If not, it is recorded as situation awareness anomaly data and fed back to the preset personnel, otherwise it is stored in the preset database.

5. The dynamic risk warning system based on intelligent situational awareness according to claim 4 is characterized in that: The situation awareness abnormality data is recorded and fed back to the preset personnel, and then the situation awareness abnormality data is classified according to the acquired situation awareness abnormality degree. The specific steps for acquiring the situation awareness abnormality degree are: Obtaining an abnormal deviation degree according to the degree of deviation between the situation awareness abnormal data and the situation awareness data; Obtain the abnormal duration based on the time difference between the abnormal start time and the abnormal end time of the situation awareness abnormal data; The abnormal degree of situation awareness is obtained by combining the acquired abnormal deviation, abnormal duration and the action behavior deviation of the patient to be visited within a preset time period.

6. The dynamic risk warning system based on intelligent situational awareness according to claim 1 is characterized by: The step of inputting the acquired correlation index and situation awareness data into the risk warning model also includes preprocessing the acquired situation awareness data, wherein the preprocessing includes data deduplication, data normalization and data smoothing.

7. The dynamic risk warning system based on intelligent situational awareness according to claim 5 is characterized by: The step of inputting the acquired correlation index and situation awareness data into the risk warning model further includes obtaining the risk warning index through the risk warning model. The specific steps of obtaining the risk warning index include: The risk probability value is obtained based on the situation awareness abnormal data and the situation awareness data, and the risk warning indicator is obtained by combining the obtained correlation index and the situation awareness abnormality degree.

8. The dynamic risk warning system based on intelligent situational awareness according to claim 7 is characterized in that: The expression of the risk warning index obtained by the risk warning model is: Where q is the number of the preset collection area, q = 1, 2, ..., Q, Q is the total number of preset collection areas, e is a natural constant, GUAN h represents the correlation index of the hth patient to be visited within the same timestamp, GUAN0 represents the reference correlation index, and YI h represents the abnormal situation awareness level of the situation awareness abnormal data of the hth patient to be visited within the preset time period, YI0 represents the abnormal situation awareness reference level, G h. represents the risk probability value of the hth patient to be visited in the qth preset acquisition area, and G0 represents the reference risk probability value.

9. The dynamic risk warning system based on intelligent situational awareness according to claim 1 is characterized in that: The risk level is determined by mapping the obtained risk warning indicator with a preset threshold range; The preset threshold interval is set by a risk level classification standard, and the risk level classification standard is represented by a result of summing and averaging the risk level data in a preset database.

10. The dynamic risk warning system based on intelligent situational awareness according to claim 1 is characterized in that: The corresponding risk level is generated according to the risk warning indicator, and then the monitoring and warning rainbow map is generated. The specific generation steps include: The real-time situational awareness data and the corresponding risk levels are mapped to the basic framework of the monitoring and early warning rainbow map, and are rendered using preset colors and color levels to generate a monitoring and early warning rainbow map. The monitoring and early warning rainbow map represents the real-time changes in the risk levels corresponding to the situational awareness data through a graphic visualization containing preset colors and color levels.

Images