Dangerous state judgment method and device based on physical sign data and electronic equipment

Abstract

The invention discloses a dangerous state judgment method and device based on physical sign data and electronic equipment, and relates to the field of data processing. The method comprises the following steps: acquiring physical sign data corresponding to a target user, wherein the physical sign data comprises heart rate data, blood pressure data and blood sugar data; preprocessing the physical sign data, wherein the preprocessing operation is used for obtaining physical sign danger features in the physical sign data; obtaining a physical sign state corresponding to the target user according to the physical sign danger characteristics through a user state judgment model; if it is confirmed that the physical sign state is the dangerous physical sign state, a target management scheme corresponding to the dangerous physical sign state is obtained from a preset physical sign management database; and sending the target management scheme to the target user, so that the target user performs corresponding operation according to the target management scheme. According to the technical scheme, the problem that the probability of safety accidents of related workers in dangerous environments such as a construction site is greatly increased due to the fact that potential health problems cannot be found in time is solved.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a method, device and electronic equipment for judging a dangerous state based on vital sign data. Background Art

[0002] As people pay more and more attention to their health, more and more people hope to monitor their own vital signs data to understand their physical condition in a timely manner and prevent potential health problems.

[0003] Nowadays, in dangerous environments such as construction sites, relevant workers usually go to relevant medical institutions for health checks on a regular basis to obtain their own physical sign data. However, regular health checks are usually conducted every few months or once a year, and it is impossible to monitor and detect sudden health problems in a timely manner. If potential health problems are not discovered in time, the probability of safety accidents involving relevant workers in dangerous environments such as construction sites will be greatly increased.

[0004] Therefore, there is an urgent need for a method, device and electronic equipment for judging a dangerous state based on vital sign data. Summary of the invention

[0005] The present application provides a method, device and electronic equipment for judging a dangerous state based on vital sign data, which solves the problem that potential health problems cannot be discovered in time, resulting in a greatly increased probability of safety accidents occurring to relevant workers in dangerous environments such as construction sites.

[0006] In a first aspect of the present application, a method for determining a dangerous state based on vital sign data is provided, the method comprising: obtaining vital sign data corresponding to a target user, the vital sign data comprising heart rate data, blood pressure data and blood sugar data; performing a preprocessing operation on the vital sign data, the preprocessing operation being used to obtain vital sign danger features in the vital sign data; obtaining the vital sign state corresponding to the target user according to the vital sign danger features through a user state judgment model; if the vital sign state is confirmed to be a dangerous vital sign state, obtaining a target management plan corresponding to the dangerous vital sign state from a preset vital sign management database; and sending the target management plan to the target user so that the target user can perform corresponding operations according to the target management plan.

[0007] By adopting the above technical solution, the physical sign data corresponding to the target user is obtained, and the physical sign data includes heart rate data, blood pressure data and blood sugar data, so as to monitor the health status of the user in real time and continuously, instead of relying solely on regular health checks. The physical sign data is preprocessed, and the preprocessing operation is used to obtain the physical sign danger characteristics in the physical sign data. Through the user state judgment model, the physical sign state corresponding to the target user is obtained according to the physical sign danger characteristics, so that when the physical sign state is confirmed to be a dangerous physical sign state, the target management plan corresponding to the dangerous physical sign state is obtained in the preset physical sign management database, and the target management plan is sent to the target user, so as to provide personalized health management suggestions for the target user, and the target user performs corresponding operations according to the target management plan, and the target user can timely understand his or her potential health and safety risks, and carry out relevant treatment and adjustments according to the target management plan, which significantly reduces the probability of safety accidents in dangerous environments such as construction sites.

[0008] Optionally, obtaining the target user's vital sign data specifically includes: obtaining the target user's vital sign data through preset devices at preset time intervals, the preset devices including mobile devices and fixed devices, the mobile devices including smart watch devices, smart bracelet devices and smart phone devices, and the fixed devices including pulmonary function tester devices, electrocardiograph devices and smart body fat scale devices.

[0009] By adopting the above technical solution, at preset time intervals, and using a variety of preset devices, including mobile devices and fixed devices, the target user's vital sign data is obtained. The preset devices include but are not limited to: smart watch devices, smart bracelet devices, smart phone devices, lung function tester devices, electrocardiograph devices, and smart body fat scale devices. The combination of multiple preset devices makes the collection of vital sign data more comprehensive and diversified.

[0010] Optionally, a preprocessing operation is performed on the vital sign data, and the preprocessing operation is used to obtain the vital sign risk characteristics in the vital sign data, specifically including: obtaining multiple vital sign characteristics in the vital sign data, the multiple vital sign characteristics include heart rate characteristics, blood pressure characteristics and blood sugar characteristics; obtaining a target vital sign characteristic, the target vital sign characteristic is any one of the multiple vital sign characteristics; obtaining a vital sign risk association value corresponding to the target vital sign characteristic; determining whether the vital sign risk association value is greater than or equal to a preset vital sign risk association value; if the vital sign risk association value is greater than or equal to the preset vital sign risk association value, confirming that the target vital sign characteristic is a vital sign risk characteristic.

[0011] By adopting the above technical solution, multiple physical sign characteristics in the physical sign data are obtained, and the multiple physical sign characteristics include heart rate characteristics, blood pressure characteristics and blood sugar characteristics, and a target physical sign characteristic is obtained, and the target physical sign characteristic is any one of the multiple physical sign characteristics, so as to obtain a physical sign risk association value corresponding to the target physical sign characteristic according to the target physical sign characteristic, so as to judge whether the physical sign risk association value is greater than or equal to the preset physical sign risk association value, so as to confirm that the target physical sign characteristic is a physical sign risk characteristic when the physical sign risk association value is greater than or equal to the preset physical sign risk association value.

[0012] Optionally, before obtaining the physical sign status corresponding to the target user according to the physical sign risk characteristics through the user status judgment model, it is necessary to construct a user status judgment model, specifically including: obtaining multiple historical physical sign risk characteristics, the multiple historical physical sign risk characteristics including historical abnormal heart rate characteristics, historical abnormal blood pressure characteristics and historical abnormal blood sugar characteristics; constructing a user status judgment model according to the multiple historical physical sign risk characteristics.

[0013] By adopting the above technical solution, multiple historical vital sign danger characteristics are obtained, including historical abnormal heart rate characteristics, historical abnormal blood pressure characteristics and historical abnormal blood sugar characteristics, so as to construct a user status judgment model based on multiple historical vital sign danger characteristics, so that the user status judgment model can more accurately identify the dangerous vital sign status of the target user.

[0014] Optionally, if the physical sign status is confirmed to be a dangerous physical sign status, before obtaining the target management plan corresponding to the dangerous physical sign status in the preset physical sign management database, it is necessary to construct a preset physical sign management database, specifically including: obtaining multiple preset dangerous physical sign statuses and multiple preset management plans, the multiple preset dangerous physical sign statuses include diabetes risk status, hypertension risk status, comprehensive metabolic abnormality status and high fatigue risk status, the multiple preset management plans include medical advice plan, notification plan for relevant medical personnel and alarm information sending plan; constructing a correspondence between multiple preset dangerous physical sign statuses and multiple preset management plans, one preset dangerous physical sign status corresponds to one or more preset management plans; storing the correspondence between multiple preset dangerous physical sign statuses and multiple preset management plans in the preset physical sign management database.

[0015] By adopting the above technical scheme, multiple preset dangerous sign states and multiple preset management schemes are obtained, the multiple preset dangerous sign states include diabetes risk state, hypertension risk state, comprehensive metabolic abnormality state and high fatigue risk state, the multiple preset management schemes include medical advice plan, notification plan for relevant medical personnel and alarm information sending plan, and a correspondence between multiple preset dangerous sign states and multiple preset management schemes is established, one preset dangerous sign state corresponds to one or more preset management schemes, so that the correspondence between multiple preset dangerous sign states and multiple preset management schemes is stored in a preset sign management database, so that according to the correspondence in the preset sign management database, after confirming that the sign state is a dangerous sign state, the corresponding target management plan can be quickly obtained to reduce the response time, thereby responding to health risks in a timely manner.

[0016] Optionally, confirming that the physical sign status is a diabetes risk status among the dangerous physical sign states specifically includes: obtaining the abnormal fasting blood glucose characteristics, abnormal postprandial blood glucose characteristics and abnormal glycosylated hemoglobin characteristics among the dangerous physical sign states; obtaining the weight coefficients corresponding to the abnormal fasting blood glucose characteristics, abnormal postprandial blood glucose characteristics and abnormal glycosylated hemoglobin characteristics; obtaining the diabetes risk status score corresponding to the physical sign status according to the abnormal fasting blood glucose characteristics, abnormal postprandial blood glucose characteristics, abnormal glycosylated hemoglobin characteristics and their corresponding weight coefficients; determining whether the diabetes risk status score is greater than or equal to a preset diabetes risk status score; if the diabetes risk status score is greater than or equal to the preset diabetes risk status score, confirming that the physical sign status is a diabetes risk state.

[0017] By adopting the above technical scheme, when it is confirmed that the physical sign state is a diabetes risk state among the dangerous physical sign states, by obtaining the abnormal fasting blood glucose characteristics, abnormal postprandial blood glucose characteristics and abnormal glycosylated hemoglobin characteristics, and obtaining the weight coefficients corresponding to the abnormal fasting blood glucose characteristics, abnormal postprandial blood glucose characteristics and abnormal glycosylated hemoglobin characteristics, the diabetes risk state score corresponding to the physical sign state is obtained according to the abnormal fasting blood glucose characteristics, abnormal postprandial blood glucose characteristics, abnormal glycosylated hemoglobin characteristics and their corresponding weight coefficients, so as to judge whether the diabetes risk state score is greater than or equal to the preset diabetes risk state score. When it is confirmed that the diabetes risk state score is greater than or equal to the preset diabetes risk state score, it is convenient to confirm that the physical sign state is a diabetes risk state, and then by comprehensively considering multiple indicators such as abnormal fasting blood glucose characteristics, abnormal postprandial blood glucose characteristics, abnormal glycosylated hemoglobin characteristics, the limitations of a single indicator are avoided, and the accuracy and reliability of risk state assessment are improved.

[0018] Optionally, according to the abnormal fasting blood glucose characteristics, abnormal postprandial blood glucose characteristics, abnormal glycosylated hemoglobin characteristics and their corresponding weight coefficients, a diabetes risk status score corresponding to the physical sign state is obtained, specifically including: calculating the diabetes risk status score according to the following formula: ; Among them, Sdiabetes is the diabetes risk status score, Fasting Glucose is the characteristic of abnormal fasting blood glucose, Postprandial Glucose is the characteristic of abnormal postprandial blood glucose, HbA1c is the characteristic of abnormal glycated hemoglobin, α1 is the weight coefficient corresponding to the characteristic of abnormal fasting blood glucose, α2 is the weight coefficient corresponding to the characteristic of abnormal postprandial blood glucose, and α3 is the weight coefficient corresponding to the characteristic of abnormal glycated hemoglobin.

[0019] By adopting the above technical solution and the above formula, and according to the abnormal fasting blood glucose characteristics, abnormal postprandial blood glucose characteristics, abnormal glycosylated hemoglobin characteristics and their corresponding weight coefficients, the diabetes risk status score is calculated. The key indicators such as abnormal fasting blood glucose characteristics, abnormal postprandial blood glucose characteristics and abnormal glycosylated hemoglobin characteristics are comprehensively considered, and different weight coefficients are assigned to each of them, so that the evaluation result can comprehensively reflect whether the user has the risk of diabetes.

[0020] In a second aspect of the present application, a device for determining a dangerous state based on vital sign data is provided, the device comprising an acquisition module and a processing module, wherein: The acquisition module is used to obtain the vital sign data corresponding to the target user, and the vital sign data includes heart rate data, blood pressure data and blood sugar data.

[0021] The processing module is used to perform preprocessing operations on the vital sign data, and the preprocessing operations are used to obtain the vital sign danger characteristics in the vital sign data; through the user state judgment model, the vital sign state corresponding to the target user is obtained according to the vital sign danger characteristics; if the vital sign state is confirmed to be a dangerous vital sign state, the target management plan corresponding to the dangerous vital sign state is obtained from the preset vital sign management database; the target management plan is sent to the target user so that the target user can perform corresponding operations according to the target management plan.

[0022] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes any one of the methods described above.

[0023] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to perform any of the above methods.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Obtain the vital sign data corresponding to the target user, including heart rate data, blood pressure data and blood sugar data, so as to monitor the user's health status in real time and continuously, rather than relying solely on regular health checks. Perform preprocessing operations on the vital sign data, which are used to obtain the vital sign danger features in the vital sign data, and obtain the vital sign status corresponding to the target user according to the vital sign danger features through the user status judgment model. When the vital sign status is confirmed to be a dangerous vital sign status, obtain the target management plan corresponding to the dangerous vital sign status in the preset vital sign management database, and send the target management plan to the target user, so as to provide the target user with personalized health management suggestions, and the target user performs corresponding operations according to the target management plan. The target user can timely understand his or her potential health and safety risks, and carry out relevant treatment and adjustments according to the target management plan, which significantly reduces the probability of safety accidents in dangerous environments such as construction sites.

[0025] 2. Obtain the target user's vital sign data at preset time intervals and using a variety of preset devices, including mobile devices and fixed devices. Preset devices include but are not limited to: smart watch devices, smart bracelet devices, smart phone devices, lung function tester devices, electrocardiograph devices, and smart body fat scale devices. The combination of multiple preset devices makes the collection of vital sign data more comprehensive and diversified.

[0026] 3. Obtain multiple physical sign characteristics in the physical sign data, wherein the multiple physical sign characteristics include heart rate characteristics, blood pressure characteristics, and blood sugar characteristics, and obtain a target physical sign characteristic, wherein the target physical sign characteristic is any one of the multiple physical sign characteristics, so as to obtain a physical sign risk association value corresponding to the target physical sign characteristic according to the target physical sign characteristic, so as to determine whether the physical sign risk association value is greater than or equal to a preset physical sign risk association value, and when the physical sign risk association value is greater than or equal to the preset physical sign risk association value, the target physical sign characteristic is confirmed to be a physical sign risk characteristic. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flowchart of a method for determining a dangerous state based on vital sign data provided in an embodiment of the present application; Figure 2 It is a module schematic diagram of a dangerous state judgment device based on vital sign data provided in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0028] Explanation of the reference numerals: 21, acquisition module; 22, processing module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0029] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0030] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to and includes any or all possible combinations of one or more listed items.

[0031] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0032] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0033] As people pay more and more attention to their health, more and more people hope to monitor their own vital signs data to understand their physical condition in a timely manner and prevent potential health problems.

[0034] Nowadays, in dangerous environments such as construction sites, relevant workers usually go to relevant medical institutions for health checks on a regular basis to obtain their own physical sign data. However, regular health checks are usually conducted every few months or once a year, and it is impossible to monitor and detect sudden health problems in a timely manner. If potential health problems are not discovered in time, the probability of safety accidents involving relevant workers in dangerous environments such as construction sites will be greatly increased.

[0035] Therefore, there is an urgent need for a method, device and electronic equipment for judging a dangerous state based on vital sign data.

[0036] Please refer to Figure 1, which shows a flow chart of a method for determining a dangerous state based on vital sign data provided in an embodiment of the present application. The method is applied to a server. The flow chart mainly includes the following steps: S101 to S105.

[0037] Step S101, obtaining the physical sign data corresponding to the target user, wherein the physical sign data includes heart rate data, blood pressure data and blood sugar data.

[0038] Specifically, when the target user is working in a dangerous environment such as a construction site, a mine site, a chemical plant, etc., the server obtains the corresponding vital sign data of the user in real time, including but not limited to: heart rate data, blood pressure data, blood sugar data. The target user can be any user in a dangerous environment such as a construction site, a mine site, a chemical plant, etc., including but not limited to: construction workers, mine workers, chemical plant workers, etc.

[0039] In a possible implementation, step S101 further includes: obtaining the vital sign data of the target user through a preset device at intervals of a preset time period, the preset device including a mobile device and a fixed device, the mobile device including a smart watch device, a smart bracelet device and a smart phone device, and the fixed device including a pulmonary function tester device, an electrocardiograph device and a smart body fat scale device.

[0040] Specifically, the server can obtain the target user's corresponding vital sign data by setting a preset time period, and every preset time period, thereby obtaining the target user's vital sign data in real time. The preset time period can be set to one day, three days, one week, etc. according to actual needs. This embodiment does not limit the setting of the preset time period. In addition, the server can obtain the target user's vital sign data through mobile devices and fixed devices at the same time. The mobile devices include but are not limited to smart watch devices, smart bracelet devices, smart phone devices, etc. These devices collect heart rate, blood pressure, blood sugar and other data through built-in sensors. The mobile devices transmit the data to the server in real time through wireless communication (such as Bluetooth, WiFi, etc.); in addition to mobile devices, the target user can also use fixed devices to collect vital sign data regularly. The fixed devices include but are not limited to pulmonary function testers, electrocardiographs, smart body fat scales, etc. The fixed devices are installed in the target user's working environment, collect vital sign data regularly or when the user is using it, and transmit the data to the server through a wired or wireless network.

[0041] Step S102, performing a preprocessing operation on the vital sign data, wherein the preprocessing operation is used to obtain vital sign risk features in the vital sign data.

[0042] Specifically, the server performs preprocessing operations on the acquired vital sign data, and obtains the vital sign risk features in the vital sign data. It should be noted that this embodiment does not limit the number of vital sign risk features that can be obtained in the vital sign data. The vital sign risk features in step S102 are actually a collection of multiple vital sign risk features.

[0043] In a possible implementation, step S102 also includes: obtaining multiple physical sign characteristics in the physical sign data, the multiple physical sign characteristics include heart rate characteristics, blood pressure characteristics and blood sugar characteristics; obtaining a target physical sign characteristic, the target physical sign characteristic is any one of the multiple physical sign characteristics; obtaining a physical sign risk association value corresponding to the target physical sign characteristic; determining whether the physical sign risk association value is greater than or equal to a preset physical sign risk association value; if the physical sign risk association value is greater than or equal to the preset physical sign risk association value, confirming that the target physical sign characteristic is the physical sign risk characteristic.

[0044] Specifically, multiple physical sign features in the physical sign data are obtained, and the physical sign features include heart rate features, blood pressure features, and blood sugar features. The target physical sign features are any one of the multiple physical sign features, and the physical sign features include but are not limited to heart rate features, blood pressure features, blood sugar features, and other physical sign features. For the determined target physical sign features, the server obtains the corresponding physical sign risk association value. The physical sign risk association value is a numerical value that measures the relationship between the target physical sign feature and the health risk. It can be set through historical data analysis, medical literature research, or expert experience. If the physical sign risk association value of the target physical sign feature is greater than or equal to the preset physical sign risk association value, the server confirms that the target physical sign feature is a physical sign risk feature; if the physical sign risk association value of the target physical sign feature is less than the preset physical sign risk association value, the server does not use the target physical sign feature as a physical sign risk feature.

[0045] For example, for the target physical sign feature, taking systolic blood pressure as an example, its physical sign risk association value is set based on historical data analysis and medical research. For example, the physical sign risk association value of systolic blood pressure can be set to 140 mmHg. The server determines the current systolic blood pressure value of the target user. If the systolic blood pressure value reaches or exceeds 140 mmHg, the systolic blood pressure is confirmed as a physical sign risk feature.

[0046] Step S103, using a user status judgment model, obtaining the physical status corresponding to the target user according to the physical risk characteristics.

[0047] Specifically, the server inputs the physical sign risk characteristics corresponding to the user into the user status judgment model, and obtains the physical sign status corresponding to the target user through the physical sign risk characteristics according to the user status judgment model. According to a certain physical sign risk characteristic, it is not possible to accurately judge whether the target user has a health abnormality risk. Therefore, in this embodiment, it is necessary to judge whether the user has a health abnormality risk through the user status judgment model and multiple physical sign risk characteristics.

[0048] In a possible implementation, step S102 also includes obtaining multiple historical vital sign risk characteristics, wherein the multiple historical vital sign risk characteristics include historical abnormal heart rate characteristics, historical abnormal blood pressure characteristics, and historical abnormal blood sugar characteristics; and constructing the user status judgment model based on the multiple historical vital sign risk characteristics.

[0049] Specifically, the user status judgment model is a learning model built in advance, which is constructed through a large number of historical vital signs and risk features. The steps for constructing the user status judgment model are as follows: the server extracts a large number of historical vital signs and risk features of users from the historical vital signs and risk features database. The historical vital signs and risk features are not limited to: historical abnormal heart rate features (such as continuously too high or too low heart rate), historical abnormal blood pressure features (such as records of high blood pressure or low blood pressure), historical abnormal blood sugar features (such as records of high blood sugar or low blood sugar), etc.; the extracted historical vital signs and risk features are preprocessed, including data cleaning, noise removal and outlier processing, to ensure the accuracy and consistency of the data. The processed data will be marked and classified for subsequent model training. For example, abnormal heart rate features are labeled as "abnormal heart rate", abnormal blood pressure features are labeled as "abnormal blood pressure", and abnormal blood sugar features are labeled as "abnormal blood sugar". During the model training process, the input features include vital signs such as heart rate, blood pressure, and blood sugar, and the output result is the user's health status (normal or abnormal). The preprocessed data is divided into a training set and a validation set. The training set is used to train the user state judgment model, and the validation set is used to evaluate the performance of the user state judgment model. By adjusting the parameters of the user state judgment model and optimizing the algorithm, it is ensured that the user state judgment model has high accuracy and reliability when evaluating the user's vital sign status.

[0050] Step S104: if it is confirmed that the physical sign state is a dangerous physical sign state, a target management plan corresponding to the dangerous physical sign state is obtained from a preset physical sign management database.

[0051] Specifically, if the physical sign status is confirmed to be a dangerous physical sign status, the server confirms that the user has a health risk, that is, the user may have various diseases. At this time, the server obtains the target management plan corresponding to the dangerous physical sign status in the preset physical sign management database. If the user has multiple dangerous physical sign states, the server obtains the management plans corresponding to the multiple dangerous physical sign states, and uses all the management plans as the target management plans.

[0052] In a possible implementation, step S104 also includes: obtaining multiple preset danger sign states and multiple preset management plans, the multiple preset danger sign states include diabetes risk state, hypertension risk state, comprehensive metabolic abnormality state and high fatigue risk state, and the multiple preset management plans include medical advice plan, notification plan for relevant medical personnel and alarm information sending plan; constructing a correspondence between the multiple preset danger sign states and the multiple preset management plans, one preset danger sign state corresponds to one or more preset management plans; storing the correspondence between the multiple preset danger sign states and the multiple preset management plans in the preset sign management database.

[0053] Specifically, the preset physical sign management database is a data set constructed in advance, which is used to store the correspondence between multiple preset dangerous physical sign states and multiple preset management schemes. The construction steps are as follows: collect a large amount of medical data and literature on dangerous physical sign states and their management schemes. These data include but are not limited to: diabetes risk state, hypertension risk state, comprehensive metabolic abnormality state and high fatigue risk state, as well as corresponding management schemes such as medical advice schemes, notification schemes for relevant medical personnel and sending alarm information schemes; sort and classify the collected data. Classify dangerous physical sign states into different health risk categories, such as diabetes risk, hypertension risk, comprehensive metabolic abnormality and high fatigue risk. Classify management schemes into different countermeasures, such as medical advice schemes, notification schemes for medical personnel and sending alarm information schemes. Establish a correspondence between preset dangerous physical sign states and preset management schemes. Each dangerous physical sign state can correspond to one or more management schemes. For example: 1. Diabetes risk state: The corresponding management schemes include medical advice schemes (such as adjusting diet and exercise schemes), notification schemes for relevant medical personnel and sending alarm information schemes. 2. Hypertension risk state: The corresponding management plan includes a medical advice plan (such as reducing salt intake and controlling weight), a plan for notifying relevant medical personnel, and a plan for sending alarm information. 3. Hypotension risk state: The corresponding management plan includes a medical advice plan (such as increasing fluid intake and adjusting medications), a plan for notifying relevant medical personnel, and a plan for sending alarm information. 4. Heart disease risk state: The corresponding management plan includes a medical advice plan (such as controlling emotions and reducing stress), a plan for notifying relevant medical personnel, and a plan for sending alarm information. 5. High fatigue state: The corresponding management plan includes a medical advice plan (such as increasing rest time and improving sleeping environment), a plan for notifying relevant medical personnel, and a plan for sending alarm information. 6. Comprehensive metabolic abnormality state: The corresponding management plan includes a medical advice plan (such as comprehensive adjustment of diet, exercise and medication), a plan for notifying relevant medical personnel, and a plan for sending alarm information. It should be noted that the preset physical sign management database not only stores the above-mentioned correspondence, but also includes a large number of correspondences between preset dangerous physical sign states and preset management plans, which can be set according to actual needs. The structure of the preset vital signs management database is designed, which includes the following main parts: dangerous signs status table: records the detailed information of various dangerous signs status; management plan table: records the detailed information of various management plans; correspondence table: records the correspondence between dangerous signs status and management plans. At the same time, the preset vital signs management database also supports data update. The server regularly collects new medical data and literature through relevant websites, and updates the dangerous signs status and management plans in the database. Relevant websites include but are not limited to: medical literature database, official website of the World Health Organization, medical journals, etc.

[0054] In a possible implementation, step S104 also includes: confirming that the physical sign state is the diabetes risk state in the dangerous physical sign state, specifically including: obtaining the fasting blood glucose abnormality characteristics, postprandial blood glucose abnormality characteristics and glycated hemoglobin abnormality characteristics in the dangerous physical sign state; obtaining the weight coefficients corresponding to the fasting blood glucose abnormality characteristics, the postprandial blood glucose abnormality characteristics and the glycated hemoglobin abnormality characteristics; obtaining the diabetes risk state score corresponding to the physical sign state according to the fasting blood glucose abnormality characteristics, the postprandial blood glucose abnormality characteristics, the glycated hemoglobin abnormality characteristics and the corresponding weight coefficients; judging whether the diabetes risk state score is greater than or equal to the preset diabetes risk state score; if the diabetes risk state score is greater than or equal to the preset diabetes risk state score, confirming that the physical sign state is the diabetes risk state.

[0055] Specifically, the server needs to determine one by one which dangerous physical sign state the user's physical sign status is in the preset physical sign management database. If the user's physical sign status is an abnormal physical sign, taking the confirmation of the user's physical sign status as the diabetes risk state in the dangerous physical sign state as an example, the diabetes risk state listed in the above-mentioned preset physical sign management database is used for explanation. The method for confirming that the physical sign status is the diabetes risk state in the dangerous physical sign state is as follows: obtain physical sign risk characteristics related to the diabetes risk state in the dangerous physical sign state, including but not limited to: abnormal fasting blood sugar characteristics, abnormal postprandial blood sugar characteristics, abnormal glycosylated hemoglobin characteristics, etc., and then obtain the abnormal fasting blood sugar characteristics, the abnormal postprandial blood sugar characteristics, The abnormal glucose feature and the weight coefficient corresponding to the abnormal glycated hemoglobin feature are obtained according to the abnormal fasting blood glucose feature, the abnormal postprandial blood glucose feature, the abnormal glycated hemoglobin feature and the corresponding weight coefficients, and the diabetes risk state score corresponding to the physical sign state is obtained. The server determines whether the diabetes risk state score is greater than or equal to the preset diabetes risk state score. If the diabetes risk state score is greater than or equal to the preset diabetes risk state score, it is confirmed that the diabetes risk state exists in the physical sign state; if the diabetes risk state score is less than the preset diabetes risk state score, it is confirmed that the diabetes risk state does not exist in the physical sign state. The diabetes risk state score can be calculated according to the following formula: ; Among them, the S diabetesis the diabetes risk status score, the Fasting Glucose is the abnormal fasting blood glucose feature, the Postprandial Glucose is the abnormal postprandial blood glucose feature, the HbA1c is the abnormal glycated hemoglobin feature, the α1 is the weight coefficient corresponding to the abnormal fasting blood glucose feature, the α2 is the weight coefficient corresponding to the abnormal postprandial blood glucose feature, and the α3 is the weight coefficient corresponding to the abnormal glycated hemoglobin feature. Each weight coefficient can be adjusted according to actual medical experience and statistical data to reflect the importance of each sign risk feature. It should be noted that the above-mentioned features are merely three examples given for the convenience of explanation of this embodiment, and do not mean that the calculation parameters corresponding to the target user only include the above-mentioned three. When the user's dangerous physical sign status is a diabetes risk state, the user's physical sign risk characteristics also include but are not limited to the following characteristics: blood pressure characteristics, body mass index (BMI) characteristics, waist circumference characteristics, and family history characteristics. The more types of physical sign risk characteristics used to calculate the diabetes risk state score, the more accurate the calculation of the user state judgment model. If the server determines that the diabetes risk state score is less than the preset diabetes risk state score, it is confirmed that the target user's dangerous physical sign state is not a diabetes risk state.

[0056] Step S105: sending the target management solution to the target user so that the target user can perform corresponding operations according to the target management solution.

[0057] Specifically, the server sends the target management plan to the target user so that the target user can perform corresponding operations according to the target management plan. For example, when the target user is at risk of diabetes, the user can adjust the diet and exercise plan according to the medical advice in the target management plan. When the target user is in a state of high fatigue, the user can increase rest time and improve the sleeping environment according to the medical advice in the target management plan.

[0058] The present application adopts the above method to obtain the physical sign data corresponding to the target user, and the physical sign data includes heart rate data, blood pressure data and blood sugar data, so as to monitor the health status of the user in real time and continuously, instead of relying solely on regular health checks. The physical sign data is preprocessed, and the preprocessing operation is used to obtain the physical sign danger characteristics in the physical sign data, and the physical sign state corresponding to the target user is obtained according to the physical sign danger characteristics through the user state judgment model, so that when the physical sign state is confirmed to be a dangerous physical sign state, the target management plan corresponding to the dangerous physical sign state is obtained in the preset physical sign management database, and the target management plan is sent to the target user, so as to provide personalized health management suggestions for the target user, and the target user performs corresponding operations according to the target management plan, and the target user can timely understand his or her potential health and safety risks, and carry out relevant treatment and adjustments according to the target management plan, which significantly reduces the probability of safety accidents in dangerous environments such as construction sites.

[0059] Please refer to Figure 2 , which shows a module schematic diagram of a dangerous state judgment device based on vital sign data provided in an embodiment of the present application, the device is a server, and the server includes an acquisition module 21 and a processing module 22, wherein, The acquisition module 21 is used to acquire the physical sign data corresponding to the target user, and the physical sign data includes heart rate data, blood pressure data and blood sugar data.

[0060] The processing module 22 is used to perform preprocessing operations on the vital sign data, and the preprocessing operations are used to obtain the vital sign danger characteristics in the vital sign data; through the user state judgment model, the vital sign state corresponding to the target user is obtained according to the vital sign danger characteristics; if the vital sign state is confirmed to be a dangerous vital sign state, the target management plan corresponding to the dangerous vital sign state is obtained in the preset vital sign management database; the target management plan is sent to the target user, so that the target user can perform corresponding operations according to the target management plan.

[0061] In a possible implementation, the acquisition module 21 is used to acquire the vital sign data of the target user, specifically including: acquiring the vital sign data of the target user through a preset device at intervals of a preset time period, the preset device including a mobile device and a fixed device, the mobile device including a smart watch device, a smart bracelet device and a smart phone device, and the fixed device including a pulmonary function tester device, an electrocardiograph device and a smart body fat scale device.

[0062] In a possible implementation, the processing module 22 is used to perform preprocessing operations on the vital sign data, and the preprocessing operations are used to obtain the vital sign risk characteristics in the vital sign data, specifically including: obtaining multiple vital sign characteristics in the vital sign data, the multiple vital sign characteristics include heart rate characteristics, blood pressure characteristics and blood sugar characteristics; obtaining a target vital sign characteristic, the target vital sign characteristic is any one of the multiple vital sign characteristics; obtaining a vital sign risk association value corresponding to the target vital sign characteristic; determining whether the vital sign risk association value is greater than or equal to a preset vital sign risk association value; if the vital sign risk association value is greater than or equal to the preset vital sign risk association value, confirming that the target vital sign characteristic is a vital sign risk characteristic.

[0063] In a possible implementation, the processing module 22 is used to construct a user status judgment model before obtaining the physical sign status corresponding to the target user according to the physical sign risk characteristics through the user status judgment model, specifically including: obtaining multiple historical physical sign risk characteristics, the multiple historical physical sign risk characteristics including historical abnormal heart rate characteristics, historical abnormal blood pressure characteristics and historical abnormal blood sugar characteristics; constructing a user status judgment model according to the multiple historical physical sign risk characteristics.

[0064] In a possible implementation, the processing module 22 is used to construct a preset vital sign management database before obtaining the target management plan corresponding to the dangerous sign state in the preset vital sign management database if the vital sign state is confirmed to be a dangerous sign state, specifically including: obtaining multiple preset dangerous sign states and multiple preset management plans, the multiple preset dangerous sign states include diabetes risk state, hypertension risk state, comprehensive metabolic abnormality state and high fatigue risk state, the multiple preset management plans include medical advice plan, notification plan for relevant medical personnel and sending alarm information plan; constructing a correspondence between multiple preset dangerous sign states and multiple preset management plans, one preset dangerous sign state corresponds to one or more preset management plans; storing the correspondence between multiple preset dangerous sign states and multiple preset management plans in the preset vital sign management database.

[0065] In a possible implementation, the processing module 22 is used to confirm that the physical sign state is a diabetes risk state among the dangerous physical sign states, specifically including: obtaining the characteristics of abnormal fasting blood glucose, abnormal postprandial blood glucose and abnormal glycosylated hemoglobin in the dangerous physical sign state; obtaining the weight coefficients corresponding to the characteristics of abnormal fasting blood glucose, abnormal postprandial blood glucose and abnormal glycosylated hemoglobin; obtaining the diabetes risk state score corresponding to the physical sign state according to the characteristics of abnormal fasting blood glucose, abnormal postprandial blood glucose, abnormal glycosylated hemoglobin and their corresponding weight coefficients; determining whether the diabetes risk state score is greater than or equal to a preset diabetes risk state score; if the diabetes risk state score is greater than or equal to the preset diabetes risk state score, confirming that the physical sign state is a diabetes risk state.

[0066] In a possible implementation, the processing module 22 is used to obtain a diabetes risk status score corresponding to the physical sign state according to the fasting blood glucose abnormality characteristics, the postprandial blood glucose abnormality characteristics, the glycated hemoglobin abnormality characteristics and their corresponding weight coefficients, specifically including: calculating the diabetes risk status score according to the following formula: ; Among them, Sdiabetes is the diabetes risk status score, Fasting Glucose is the characteristic of abnormal fasting blood glucose, Postprandial Glucose is the characteristic of abnormal postprandial blood glucose, HbA1c is the characteristic of abnormal glycated hemoglobin, α1 is the weight coefficient corresponding to the characteristic of abnormal fasting blood glucose, α2 is the weight coefficient corresponding to the characteristic of abnormal postprandial blood glucose, and α3 is the weight coefficient corresponding to the characteristic of abnormal glycated hemoglobin.

[0067] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0068] The present application also provides an electronic device. Figure 3 , Figure 3 30 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include: at least one processor 301, at least one communication bus 302, a user interface 303, at least one network interface 304, and a memory 305.

[0069] The communication bus 302 is used to realize the connection and communication between these components.

[0070] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0071] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0072] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0073] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 The memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a dangerous state judgment application based on vital sign data.

[0074] exist Figure 3In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the dangerous state judgment application based on the vital sign data stored in the memory 305. When executed by one or more processors 301, the electronic device executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0075] The present application also provides a computer-readable storage medium, which stores instructions. When executed by one or more processors, the electronic device executes one or more of the methods described in the above embodiments.

[0076] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0077] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0078] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0079] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0080] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0081] The above are only exemplary embodiments disclosed in this application, and the scope of the disclosure of this application cannot be limited by this. That is, any equivalent changes and modifications made according to the teachings disclosed in this application are still within the scope of the disclosure of this application. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other implementation schemes disclosed in this application.

[0082] This application is intended to cover any modifications, uses or adaptive changes disclosed in this application, which follow the general principles disclosed in this application and include common knowledge or customary technical means in the technical field not recorded in this application.

Claims

1. A method for judging a dangerous state based on vital sign data, characterized in that: The method comprises: Acquire physical sign data corresponding to the target user, wherein the physical sign data includes heart rate data, blood pressure data, and blood sugar data; Performing a preprocessing operation on the vital sign data, wherein the preprocessing operation is used to obtain a vital sign risk feature in the vital sign data; By using a user status judgment model, the physical status of the target user is obtained according to the physical risk characteristics; If the physical sign status is confirmed to be a dangerous physical sign status, obtaining a target management plan corresponding to the dangerous physical sign status in a preset physical sign management database; The target management scheme is sent to the target user so that the target user can perform corresponding operations according to the target management scheme.

2. The method according to claim 1, characterized in that The obtaining of the target user's vital sign data specifically includes: At preset time intervals, the vital sign data of the target user is obtained through preset devices, the preset devices include mobile devices and fixed devices, the mobile devices include smart watch devices, smart bracelet devices and smart phone devices, and the fixed devices include pulmonary function tester devices, electrocardiograph devices and smart body fat scale devices.

3. The method according to claim 1, characterized in that The preprocessing operation is performed on the vital sign data, and the preprocessing operation is used to obtain the vital sign risk characteristics in the vital sign data, specifically including: Acquire multiple physical sign features in the physical sign data, wherein the multiple physical sign features include heart rate features, blood pressure features, and blood sugar features; Acquire a target physical sign feature, where the target physical sign feature is any one of the multiple physical sign features; Obtaining a physical sign risk association value corresponding to the target physical sign feature; Determining whether the physical sign risk association value is greater than or equal to a preset physical sign risk association value; If the physical sign risk association value is greater than or equal to the preset physical sign risk association value, the target physical sign feature is confirmed to be the physical sign risk feature.

4. The method according to claim 1, characterized in that: Before obtaining the physical sign state corresponding to the target user according to the physical sign danger characteristics through the user state judgment model, it is necessary to construct the user state judgment model, which specifically includes: Acquire a plurality of historical physical sign risk characteristics, wherein the plurality of historical physical sign risk characteristics include historical abnormal heart rate characteristics, historical abnormal blood pressure characteristics, and historical abnormal blood sugar characteristics; The user status judgment model is constructed based on the plurality of historical vital sign risk characteristics.

5. The method according to claim 1, characterized in that If the physical sign state is confirmed to be a dangerous physical sign state, before obtaining the target management plan corresponding to the dangerous physical sign state in the preset physical sign management database, it is necessary to construct the preset physical sign management database, which specifically includes: Acquire multiple preset dangerous physical sign states and multiple preset management plans, wherein the multiple preset dangerous physical sign states include a diabetes risk state, a hypertension risk state, a comprehensive metabolic abnormality state, and a high fatigue risk state, and the multiple preset management plans include a medical advice plan, a plan for notifying relevant medical personnel, and a plan for sending alarm information; Establishing a correspondence between a plurality of the preset dangerous physical sign states and a plurality of the preset management schemes, wherein one of the preset dangerous physical sign states corresponds to one or more of the preset management schemes; The correspondence between the plurality of preset dangerous vital sign states and the plurality of preset management schemes is stored in the preset vital sign management database.

6. The method according to claim 5, characterized in that Confirming that the physical sign state is the diabetes risk state in the dangerous physical sign state specifically includes: Acquiring abnormal fasting blood glucose characteristics, abnormal postprandial blood glucose characteristics, and abnormal glycosylated hemoglobin characteristics in the dangerous physical sign state; Obtaining weight coefficients corresponding to the abnormal fasting blood glucose feature, the abnormal postprandial blood glucose feature, and the abnormal glycosylated hemoglobin feature; According to the abnormal fasting blood glucose characteristics, the abnormal postprandial blood glucose characteristics, the abnormal glycosylated hemoglobin characteristics and the corresponding weight coefficients, obtaining a diabetes risk status score corresponding to the physical sign status; Determining whether the diabetes risk status score is greater than or equal to a preset diabetes risk status score; If the diabetes risk status score is greater than or equal to the preset diabetes risk status score, the physical sign status is confirmed to be the diabetes risk status.

7. The method according to claim 6, characterized in that The step of obtaining the diabetes risk status score corresponding to the physical sign state according to the abnormal fasting blood glucose characteristics, the abnormal postprandial blood glucose characteristics, the abnormal glycosylated hemoglobin characteristics and the corresponding weight coefficients specifically includes: The diabetes risk status score is calculated according to the following formula: ； Among them, the S diabetes is the diabetes risk status score, the Fasting Glucose is the abnormal fasting blood glucose characteristic, the Postprandial Glucose is the abnormal postprandial blood glucose characteristic, the HbA1c is the abnormal glycosylated hemoglobin characteristic, the α1 is the weight coefficient corresponding to the abnormal fasting blood glucose characteristic, the α2 is the weight coefficient corresponding to the abnormal postprandial blood glucose characteristic, and the α3 is the weight coefficient corresponding to the abnormal glycosylated hemoglobin characteristic.

8. A device for judging dangerous status based on vital sign data, characterized in that: The device comprises an acquisition module (21) and a processing module (22), wherein: The acquisition module (21) is used to acquire the physical sign data corresponding to the target user, wherein the physical sign data includes heart rate data, blood pressure data and blood sugar data; The processing module (22) is used to perform a preprocessing operation on the vital sign data, wherein the preprocessing operation is used to obtain the vital sign danger characteristics in the vital sign data; obtain the vital sign state corresponding to the target user according to the vital sign danger characteristics through a user state judgment model; if the vital sign state is confirmed to be a dangerous vital sign state, obtain a target management plan corresponding to the dangerous vital sign state in a preset vital sign management database; and send the target management plan to the target user so that the target user can perform corresponding operations according to the target management plan.

9. An electronic device, characterized in that: The electronic device comprises a processor (301), a communication bus (302), a user interface (303), a network interface (304) and a memory (305), wherein the memory (305) is used to store instructions, the user interface (303) and the network interface (304) are used to communicate with other devices, and the processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is performed.

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