Healthcare Abnormality Response System and Method Based on Internet of Things Technology

Abstract

The present invention belongs to the technical field of health monitoring, and provides a health care anomaly response system and method based on Internet of Things technology. The method includes: receiving the first real-time physiological signal of a target health care object and retrieving the corresponding normal threshold range for the target health care object, and when the first real-time physiological signal exceeds the normal threshold range, obtaining the first positioning information of the target health care object; obtaining the second real-time physiological signals of other health care objects within a specified range, and using a deep analysis model to analyze each second real-time physiological signal to obtain a physiological feature matching probability; if the physiological feature matching probability is higher than a first threshold, sending a reminder signal to the first terminal of the target health care object; otherwise, sending a reminder signal to both the first terminal and the first monitoring terminal of the target health care object. By comprehensively analyzing the real-time physiological signal, positioning information, and data of surrounding health care objects, the present invention can significantly improve the accuracy of anomaly judgment.

Description

Technical Field

[0001] The present invention relates to the technical field of health monitoring, and more specifically, to a health care anomaly response system and method based on Internet of Things technology. Background Art

[0002] With the intensification of the global population aging, the importance of the health care industry has become increasingly prominent. When facing the increasing number of elderly people and diverse health needs, the traditional health care service model has gradually revealed many deficiencies. For example, in terms of daily health monitoring, it often relies on manual regular inspections, which is not only inefficient but also difficult to achieve real-time and comprehensive tracking of the physical conditions of health care objects. Once a health anomaly occurs, such as an emergency situation like a sudden illness or a fall, it is impossible to detect it in time and take effective measures, which may lead to serious consequences.

[0003] At the same time, the Internet of Things technology has achieved rapid development in recent years. Through various sensors, intelligent devices and network connections, the Internet of Things can realize information interaction and communication between objects and between people and objects. Applying the Internet of Things technology to the health care field can provide new solutions for health care anomaly response. With the help of various wearable devices and environmental sensors, physiological data (such as heart rate, blood pressure, sleep quality, etc.) and environmental data (such as temperature, humidity, air quality, etc.) of health care objects can be collected in real time. These data are transmitted to the data processing center through the network and are analyzed in real time using big data analysis and artificial intelligence algorithms. Once an abnormal situation is detected, the system can quickly issue an alarm and notify relevant medical staff or family members in time to take response measures.

[0004] However, the current health care anomaly recognition strategy is mainly based on a preset physiological signal threshold range, that is, when the measured physiological signal exceeds this threshold range, it is determined that the health care object has an abnormal physical state. However, since various normal situations can also cause the physiological characteristics of the health care object to exceed the above threshold range. For example, when a health care object participates in group activities (such as running), the heart rate will increase, but at this time, the health care object does not have an abnormal physical state. Therefore, the accuracy of the existing health care anomaly response method based on a fixed threshold range still needs to be further improved. Summary of the Invention

[0005] In order to solve the technical problems existing in the above background art, the present invention provides a health care anomaly response method, system, electronic device, computer storage medium and computer program product based on Internet of Things technology.

[0006] The present invention provides a method for coping with abnormal health care based on Internet of Things technology, which is applied to a cloud platform. The method includes the following steps: receiving the first real-time physiological signal of a target health care object and retrieving the corresponding normal threshold range for the target health care object. When the first real-time physiological signal exceeds the normal threshold range, further obtaining the first positioning information of the target health care object; obtaining the second real-time physiological signals of other health care objects within a specified range, and using a deep analysis model to analyze each of the second real-time physiological signals to obtain a physiological feature matching probability; wherein the specified range is a circular range delimited by a preset distance with the first positioning information as the reference; if the physiological feature matching probability is higher than a first threshold, sending a reminder signal to the first terminal of the target health care object; otherwise, sending a reminder signal to both the first terminal and the first guardian terminal of the target health care object.

[0007] In some embodiments, the normal threshold range is determined in the following manner: retrieving the historical physiological signals of the target health care object, and classifying the historical physiological signals into a first group of historical physiological signals and a second group of historical physiological signals according to whether there is an abnormal confirmation signal; wherein each historical physiological signal in the first group of historical physiological signals has no abnormal confirmation signal, and each historical physiological signal in the second group of historical physiological signals has an abnormal confirmation signal; clustering the first group of historical physiological signals and the second group of historical physiological signals to respectively obtain a first physiological signal equivalent value and a second physiological signal equivalent value at the clustering center; calculating the difference between the second physiological signal equivalent value and the first physiological signal equivalent value, and respectively determining an upper floating value and a lower floating value according to an upper adjustment coefficient, a lower adjustment coefficient and the difference, and performing addition and subtraction operations on the first physiological signal equivalent value with the upper floating value and the lower floating value respectively to obtain the normal threshold range; wherein the upper adjustment coefficient is greater than the lower adjustment coefficient.

[0008] In some embodiments, the preset distance is determined in the following manner: determining the scene attribute information of the geographical area where the target health care object is located according to the first positioning information, and the scene attribute information corresponds to the enclosed area of the geographical area; determining the preset distance according to the size of the enclosed area.

[0009] In some embodiments, analyzing each of the second real-time physiological signals using a depth analysis model to obtain a physiological feature matching probability includes: analyzing the distance stability between the second positioning information corresponding to each of the second real-time physiological signals and the first positioning information, classifying the second real-time physiological signals with a distance stability higher than a first preset value into a participation group, and classifying the second real-time physiological signals with a distance stability not higher than the first preset value into a non-participation group; using a first depth analysis model to perform feature extraction and depth analysis on each of the second real-time physiological signals in the participation group to obtain a first physiological feature matching probability; and using a second depth analysis model to perform feature extraction and depth analysis on each of the second real-time physiological signals in the non-participation group to obtain a second physiological feature matching probability; and performing weighted calculation on the first physiological feature matching probability and the second physiological feature matching probability respectively based on corresponding weighting values to obtain the physiological feature matching probability.

[0010] In some embodiments, sending a reminder signal to the first terminal and the first monitoring terminal of the target health care object simultaneously includes: if the first real-time physiological signal exceeds the normal threshold range by a second preset value, and the distance between the first monitoring terminal of the target health care object and the first terminal is greater than a third preset value, then sending a reminder signal to the second terminals of multiple other health care objects within the specified range and their corresponding second monitoring terminals simultaneously.

[0011] The present invention also provides a health care anomaly response system based on Internet of Things technology, which is applied to a cloud platform. The system includes a first receiving and processing unit, a second receiving and processing unit, and a response processing unit. The first receiving and processing unit is configured to receive the first real-time physiological signal of the target health care object and retrieve the corresponding normal threshold range of the target health care object, and further obtain the first positioning information of the target health care object when the first real-time physiological signal exceeds the normal threshold range. The second receiving and processing unit is configured to obtain the second real-time physiological signals of other health care objects within the specified range, and analyze each of the second real-time physiological signals using a depth analysis model to obtain a physiological feature matching probability. Wherein, the specified range is a circular range delimited by a preset distance with the first positioning information as a reference. The response processing unit is configured to send a reminder signal to the first terminal of the target health care object if the physiological feature matching probability is higher than a first threshold; otherwise, send a reminder signal to the first terminal and the first monitoring terminal of the target health care object simultaneously.

[0012] In some embodiments, the normal threshold range is determined in the following manner: retrieve the historical physiological signals of the target health care object, and classify the historical physiological signals into a first group of historical physiological signals and a second group of historical physiological signals according to whether there is an abnormal confirmation signal; wherein, each historical physiological signal in the first group of historical physiological signals has no abnormal confirmation signal, and each historical physiological signal in the second group of historical physiological signals has an abnormal confirmation signal; cluster the first group of historical physiological signals and the second group of historical physiological signals to respectively obtain a first physiological signal equivalent value and a second physiological signal equivalent value at the cluster center; calculate the difference between the second physiological signal equivalent value and the first physiological signal equivalent value, and respectively determine an upper floating value and a lower floating value according to an upper adjustment coefficient, a lower adjustment coefficient and the difference, and perform addition and subtraction operations on the first physiological signal equivalent value with the upper floating value and the lower floating value respectively to obtain the normal threshold range; wherein, the upper adjustment coefficient is greater than the lower adjustment coefficient.

[0013] In some embodiments, the preset distance is determined in the following manner: determine the scene attribute information of the geographical area where the target health care object is located according to the first positioning information, and the scene attribute information corresponds to the enclosed area of the geographical area; determine the preset distance according to the size of the enclosed area.

[0014] In some embodiments, the second receiving and processing unit is specifically configured to: analyze the distance stability between the second positioning information corresponding to each second real-time physiological signal and the first positioning information, classify the second real-time physiological signals with a distance stability higher than a first preset value into a participating group, and classify the second real-time physiological signals with a distance stability not higher than the first preset value into a non-participating group; use a first deep analysis model to perform feature extraction and deep analysis on each second real-time physiological signal in the participating group to obtain a first physiological feature matching probability; and use a second deep analysis model to perform feature extraction and deep analysis on each second real-time physiological signal in the non-participating group to obtain a second physiological feature matching probability; perform weighted calculation on the first physiological feature matching probability and the second physiological feature matching probability respectively based on corresponding weighting values to obtain the physiological feature matching probability.

[0015] In some embodiments, the response processing unit is specifically configured to: if the first real-time physiological signal exceeds the normal threshold range by a second preset value, and the distance between the first monitoring terminal of the target health care object and the first terminal is greater than a third preset value, then simultaneously send a reminder signal to the second terminals and the corresponding second monitoring terminals of multiple other health care objects within the specified range.

[0016] The present invention also provides an electronic device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor. When the processor executes the computer program, the steps of any one of the above methods are implemented.

[0017] The present invention also provides a computer storage medium storing a computer program, and when the computer program is executed by a processor, the steps of any one of the above methods can be implemented.

[0018] The present invention also provides a computer program product, which contains computer program code. The computer program code is executed by a processor of an electronic device to implement the steps of any one of the above methods.

[0019] The beneficial effects of the present invention are as follows: By comprehensively analyzing real-time physiological signals, positioning information, and surrounding health care object data, the present invention can significantly improve the accuracy of abnormal judgment. For example, when judging the abnormal heart rate of an elderly person, it can distinguish whether it is caused by individual health problems or group exercise behaviors, avoiding unnecessary panic and waste of medical resources. At the same time, the present invention can also adopt different coping strategies according to the relative situation between the physiological feature matching probability obtained by analysis and the first threshold, thereby significantly improving the efficiency of coping with health care abnormalities. Description of the Drawings

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0021] Figure 1 It is a schematic flowchart of a method for coping with health care abnormalities based on Internet of Things technology disclosed in an embodiment of the present invention.

[0022] Figure 2 It is a schematic structural diagram of a deep analysis model disclosed in an embodiment of the present invention.

[0023] Figure 3 It is a schematic structural diagram of a system for coping with health care abnormalities based on Internet of Things technology disclosed in an embodiment of the present invention. Detailed Embodiments

[0024] The following specific embodiments illustrate the implementation manners of the present application. Those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts belong to the scope protected by the present application.

[0025] In addition, the technical features involved in different implementation manners of the present application described below can be combined with each other as long as they do not conflict with each other.

[0026] In traditional health care abnormality identification, only the preset physiological signal threshold is used to judge whether the physical state of the health care object is abnormal. For example, the normal range of heart rate is set to 60 - 100 beats per minute. When the measured heart rate exceeds this range, it is determined that the physical state is abnormal. However, when a health care object participates in group activities such as running, the heart rate may reach 130 beats per minute, but this is a normal exercise response and not a physical abnormality. This leads to the existing health care abnormality response method based on a fixed threshold range having low accuracy in abnormality identification.

[0027] In view of the above technical problems, as Figure 1 shown, an embodiment of the present invention discloses a health care abnormality response method based on Internet of Things technology, which is applied to a cloud platform. The method includes the following steps: S1, receiving the first real-time physiological signal of a target health care object and retrieving the corresponding normal threshold range for the target health care object. When the first real-time physiological signal exceeds the normal threshold range, further obtaining the first location information of the target health care object.

[0028] Specifically: The target health care object wears a wearable device or other monitoring devices on the body. These devices can collect the first real-time physiological signals of the target health care object, including but not limited to heart rate, blood pressure, skin electrical signal, body temperature, etc., and the cloud platform receives this signal. At the same time, the cloud platform retrieves the corresponding normal threshold range for the target health care object pre-stored in the cloud, which is a personalized index set according to comprehensive factors such as the personal physical condition and past health data of the target health care object. When the first real-time physiological signal exceeds the normal threshold range, it indicates that there may be an abnormal situation. At this time, the cloud platform further obtains the first location information of the target health care object.

[0029] For example, if the smart bracelet worn by the target health care object monitors the heart rate in real time and uploads it to the cloud, and the cloud platform retrieves its normal heart rate threshold range as 60 - 100 beats per minute. When the monitored heart rate is 120 beats per minute, which exceeds the above threshold range, the current first location information of the elderly person is obtained immediately through the positioning device (such as a smart bracelet with built-in GPS) carried by the elderly person.

[0030] S2. Obtain the second real-time physiological signals of other health care objects within a specified range, and use a deep analysis model to analyze each of the second real-time physiological signals to obtain a physiological feature matching probability; wherein, the specified range is a circular range delimited by a preset distance with the first positioning information as the reference.

[0031] Specifically: With the first positioning information as the reference, delimit a circular range according to a preset distance (such as 50 meters), and search for other health care objects within this specified range. The cloud platform obtains the second real-time physiological signals of these other health care objects, and these signals also come from their respective wearable devices or other monitoring devices. Then, use a deep analysis model to analyze each second real-time physiological signal to obtain the physiological feature matching probability between the other health care objects and the target health care object. This physiological feature matching probability refers to the probability that the target health care object and other health care objects are jointly carrying out group activities (such as running together, playing badminton, dancing square dance, etc.).

[0032] S3. If the physiological feature matching probability is higher than the first threshold, send a reminder signal to the first terminal of the target health care object; otherwise, send a reminder signal to both the first terminal and the first guardianship terminal of the target health care object.

[0033] Specifically: Compare the obtained physiological feature matching probability with the first threshold. If the physiological feature matching probability is higher than the first threshold, it indicates that the abnormal physiological signal of the target health care object may be caused by normal group activities. For example, when exercising collectively in the park, the heart rate generally increases. At this time, only send a reminder signal to the first terminal of the target health care object (such as the old person's own mobile phone), inform its current physiological signal status, and remind it to pay attention to its own situation. However, if the physiological feature matching probability is lower than the first threshold, it means that the abnormal physiological signal of the target health care object may be caused by individual health problems. To ensure timely handling, send a reminder signal to both the first terminal and the first guardianship terminal of the target health care object (such as the mobile phones of the old person's children and family doctor) at the same time, so that the guardians can quickly take measures to ensure the health and safety of the health care object.

[0034] The present invention can significantly improve the accuracy of abnormal judgment by comprehensively analyzing real-time physiological signals, positioning information, and data of surrounding health care objects. For example, when judging the abnormal heart rate of an old person, it can distinguish whether it is caused by individual health problems or normal participation in group activities, avoiding unnecessary panic and waste of medical resources. At the same time, the present invention can also adopt different coping strategies according to the relative situation between the physiological feature matching probability obtained by analysis and the first threshold, thereby significantly improving the efficiency of coping with health care abnormalities.

[0035] In some embodiments, the normal threshold range is determined as follows: retrieve the historical physiological signals of the target health care object, and divide the historical physiological signals into a first group of historical physiological signals and a second group of historical physiological signals according to whether there is an abnormal confirmation signal; wherein, each historical physiological signal in the first group of historical physiological signals has no abnormal confirmation signal, and each historical physiological signal in the second group of historical physiological signals has an abnormal confirmation signal; cluster the first group of historical physiological signals and the second group of historical physiological signals to respectively obtain a first physiological signal equivalent value and a second physiological signal equivalent value at the cluster center; calculate the difference between the second physiological signal equivalent value and the first physiological signal equivalent value, and respectively determine an upper floating value and a lower floating value according to an upper adjustment coefficient, a lower adjustment coefficient and the difference, and perform addition and subtraction operations on the first physiological signal equivalent value with the upper floating value and the lower floating value respectively to obtain the normal threshold range; wherein, the upper adjustment coefficient is greater than the lower adjustment coefficient.

[0036] In this embodiment, the historical physiological signals of the target health care object are stored in a cloud platform or other storage devices, which cover the data collected by various monitoring devices over a period of time in the past, such as heart rate, blood pressure, blood oxygen saturation, etc. Then, these historical physiological signals are classified according to whether there is an abnormal confirmation signal. The abnormal confirmation signal mainly comes from the diagnosis of medical staff, the feedback of the health care object itself or other abnormal event records. If a historical physiological signal has no associated abnormal confirmation signal, it is classified into the first group of historical physiological signals; if a historical physiological signal is associated with an abnormal confirmation signal, it is classified into the second group of historical physiological signals. For example, for the blood pressure data of a certain health care object in the past six months, the blood pressure data in the daily stable state is classified into the first group, while the blood pressure data with abnormal fluctuations during hospitalization due to illness is marked as the second group.

[0037] For the above two different groups of historical physiological signals, clustering analysis technology is respectively used to find the core trend of each group of data, that is, the cluster center. For the first group of historical physiological signals representing the normal state, the physiological signal value corresponding to the cluster center obtained after clustering (usually the mean value, weighted mean value, etc. of multiple physiological signals) is the first physiological signal equivalent value, which centrally reflects the typical signal characteristics of the health care object in the normal physiological state. Similarly, for the second group of historical physiological signals representing the abnormal state, clustering analysis is performed, and the corresponding cluster center is the second physiological signal equivalent value, which reflects the physiological signal characteristics in the abnormal state. For example, after clustering the first group of normal blood pressure data, the average normal blood pressure value is obtained as 120 / 80 mmHg (the first physiological signal equivalent value), and after clustering the second group of abnormal blood pressure data, the average abnormal blood pressure value is obtained as 160 / 100 mmHg (the second physiological signal equivalent value).

[0038] Calculate the difference between the equivalent value of the second physiological signal and the equivalent value of the first physiological signal. This difference characterizes the degree of difference in physiological signals of the target health care object in the normal state and the abnormal state. At the same time, in order to more carefully judge abnormal situations and prevent misjudging normal physiological fluctuations as abnormal, the present invention further sets an upper adjustment coefficient and a lower adjustment coefficient both less than 0.5 (for 1 / 2 of the difference), and the two coefficients are used to appropriately reduce the above-mentioned difference respectively. Usually, the physiological signals of health care objects are more likely to show an upward float within the normal range and less likely to show a downward float within the normal range. Therefore, the present invention sets the upper adjustment coefficient to be greater than the lower adjustment coefficient. For example, the upper adjustment coefficient is 0.45 and the lower adjustment coefficient is 0.4. The upper adjustment coefficient and the lower adjustment coefficient are respectively multiplied by the above-mentioned difference to obtain the upper floating value and the lower floating value, and then the normal threshold range can be determined.

[0039] The normal threshold range determined in this way closely fits the actual physiological conditions of the health care object and can significantly improve the accuracy of abnormal judgment.

[0040] In some embodiments, the preset distance is determined in the following manner: Determine the scene attribute information of the geographical area where the target health care object is located according to the first positioning information, and the scene attribute information corresponds to the enclosed area of this geographical area; Determine the preset distance according to the size of the enclosed area.

[0041] In this embodiment, the cloud platform analyzes the relevant characteristics of the geographical area where the target health care object is located based on the first positioning information, so as to determine its scene attribute information. The scene attribute information includes the enclosed area of this geographical area. For example, if the target health care object is located in a certain building unit of a community, this belongs to a closed space. Through geographical information system (GIS) data or pre-entered site information, it can be known that the enclosed area of this building unit is 100 square meters; If it is located in a fitness venue of a certain nursing home, although the fitness venue is not completely closed, a relatively closed activity range can be divided according to its boundary and functional areas. Suppose its enclosed area is measured to be 500 square meters.

[0042] Since there are differences in the personnel distribution and activity ranges in geographical areas with different enclosed areas, different preset distances are required to reasonably collect the physiological signals of surrounding health care objects. In areas with a small enclosed area, generally only a small number of people can be accommodated to carry out group sports at the same time. For example, doing fitness exercises following a video blogger at home in a building unit. At this time, the preset distance can be set shorter, such as 5 meters, to ensure that the surrounding data collected is relevant and effective. For a fitness venue with an enclosed area of 500 square meters, large group activities are often held there, such as square dancing, fun sports, etc. In order to obtain sufficient and representative physiological signals of surrounding health care objects, the preset distance may be set to a radius of 50 meters.

[0043] In this way of dynamically adjusting the preset distance according to the enclosed area, valuable peripheral data for judging the physiological state of the target elderly care object can be collected more accurately in different scenarios, thereby improving the accuracy and reliability of abnormal judgment based on the peripheral data.

[0044] In some embodiments, analyzing each of the second real-time physiological signals using the depth analysis model to obtain the physiological feature matching probability includes: analyzing the distance stability between the second positioning information corresponding to each of the second real-time physiological signals and the first positioning information, classifying the second real-time physiological signals with a distance stability higher than a first preset value into the participation group, and classifying the second real-time physiological signals with a distance stability not higher than the first preset value into the non-participation group; using a first depth analysis model to perform feature extraction and depth analysis on each of the second real-time physiological signals in the participation group to obtain a first physiological feature matching probability; and using a second depth analysis model to perform feature extraction and depth analysis on each of the second real-time physiological signals in the non-participation group to obtain a second physiological feature matching probability; and performing weighted calculation on the first physiological feature matching probability and the second physiological feature matching probability respectively based on the corresponding weighting values to obtain the physiological feature matching probability.

[0045] In this embodiment, first, the distance stability between the second positioning information corresponding to each second real-time physiological signal and the first positioning information of the target elderly care object is analyzed. The distance stability reflects the change in the relative position between other elderly care objects and the target elderly care object. For example, if the distance between a certain elderly care object and the target elderly care object remains within a small fluctuation range for a period of time, it indicates a high distance stability, and they may be running or dancing together; conversely, if the distance fluctuates greatly, the distance stability is low, and this other elderly care object may be an audience. According to the preset first preset value, the selected other elderly care objects are classified into the participation group and the non-participation group respectively. Among them, the second real-time physiological signals in the participation group come from the surrounding elderly care objects with a relatively stable relative position to the target elderly care object, and their physiological signals may be more closely related to the physiological state of the target elderly care object; while the second real-time physiological signals in the non-participation group are less closely related to the physiological state of the target elderly care object, but can be used to indirectly analyze whether the target elderly care object is performing group exercise in a normal physical state. For example, the audience will also be excited due to group activities, resulting in an increase in their physiological signals (the increase amplitude is generally weaker than that of the group activity participants).

[0046] For the signals of the above different groups, such as Figure 2As shown in the figure, the present invention constructs a first deep analysis model and a second deep analysis model based on a convolutional neural network (CNN) or a recurrent neural network (RNN) respectively. The two models can respectively extract features from the second real-time physiological signals of the above-mentioned participating group and non-participating group to obtain key information related to the physiological characteristics of the target health care object, and then perform deep analysis processing on it to obtain the first physiological characteristic matching probability and the second physiological characteristic matching probability. Among them, the first physiological characteristic matching probability directly reflects the similarity degree of the physiological characteristics between the surrounding health care objects in the participating group and the target health care object, while the second physiological characteristic matching probability indirectly reflects the similarity degree of the physiological characteristics between the surrounding health care objects in the non-participating group and the target health care object. It should be noted that the first deep analysis model is trained based on the physiological signals of each health care object directly participating in group activities, while the second deep analysis model is trained based on the physiological signals of each health care object directly participating in group activities and watching group activities. That is to say, the second deep analysis model can analyze the internal correlation between the physiological signals of viewers and the physiological signals of participants, and then be used to indirectly analyze whether the target health care object is conducting group activities.

[0047] In order to comprehensively consider the influence of the two groups of signals on the physiological characteristic matching probability of the target health care object, the first physiological characteristic matching probability and the second physiological characteristic matching probability are respectively weighted and calculated based on the corresponding weighted values, and the weighted value of the first physiological characteristic matching probability is higher than the weighted value of the second physiological characteristic matching probability.

[0048] In some embodiments, the sending of the reminder signal to the first terminal and the first monitoring terminal of the target health care object includes: if the first real-time physiological signal exceeds the normal threshold range by a second preset value, and the distance between the first monitoring terminal of the target health care object and the first terminal is greater than a third preset value, then a reminder signal is sent to the second terminals of multiple other health care objects within the specified range and the corresponding second monitoring terminals simultaneously.

[0049] In this embodiment, when the system monitors that the first real-time physiological signal of the target health care object exceeds the normal threshold range and the degree of this exceedance reaches the second preset value, it indicates that the physiological state of the target health care object is very likely to have a serious abnormality. At the same time, if it is found that the distance between the first monitoring terminal of the target health care object and the first terminal (i.e., the device carried by the health care object himself, such as a mobile phone) is greater than the third preset value, this means that family members, family doctors, etc. carrying the first monitoring terminal cannot directly monitor, intervene and rescue the target health care object in time.

[0050] At this time, the cloud platform not only sends reminder signals to the first terminal and the first monitoring terminal of the target health care object, but also simultaneously sends reminder signals to the second terminals of multiple other health care objects within a specified range and the second monitoring terminals corresponding to these second terminals. In this way, when the target health care object is in an emergency abnormal situation and the main caregiver cannot arrive in time, the power of other surrounding health care objects can be utilized to provide help to the target health care object more quickly.

[0051] As Figure 3 shown, an embodiment of the present invention also discloses a health care abnormality response system based on Internet of Things technology, which is applied to a cloud platform. The system includes a first receiving and processing unit, a second receiving and processing unit, and a response processing unit. The first receiving and processing unit is configured to receive the first real-time physiological signal of the target health care object and retrieve the corresponding normal threshold range of the target health care object. When the first real-time physiological signal exceeds the normal threshold range, further obtain the first positioning information of the target health care object. The second receiving and processing unit is configured to obtain the second real-time physiological signals of other health care objects within a specified range, and use a deep analysis model to analyze each of the second real-time physiological signals to obtain a physiological feature matching probability. Wherein, the specified range is a circular range delimited by a preset distance with the first positioning information as a reference. The response processing unit is configured to, if the physiological feature matching probability is higher than a first threshold, send a reminder signal to the first terminal of the target health care object; otherwise, send reminder signals to the first terminal and the first monitoring terminal of the target health care object simultaneously.

[0052] In some embodiments, the normal threshold range is determined by the following method: retrieve the historical physiological signals of the target health care object, and divide the historical physiological signals into a first group of historical physiological signals and a second group of historical physiological signals according to whether there is an abnormal confirmation signal. Wherein, each historical physiological signal in the first group of historical physiological signals has no abnormal confirmation signal, and each historical physiological signal in the second group of historical physiological signals has an abnormal confirmation signal. Cluster the first group of historical physiological signals and the second group of historical physiological signals to respectively obtain a first physiological signal equivalent value and a second physiological signal equivalent value at the cluster center. Calculate the difference between the second physiological signal equivalent value and the first physiological signal equivalent value, and respectively determine an upper floating value and a lower floating value according to an upper adjustment coefficient, a lower adjustment coefficient, and the difference. Perform addition and subtraction operations on the first physiological signal equivalent value with the upper floating value and the lower floating value respectively to obtain the normal threshold range. Wherein, the upper adjustment coefficient is greater than the lower adjustment coefficient.

[0053] In some embodiments, the preset distance is determined in the following manner: determining the scene attribute information of the geographical area where the target healthcare object is located according to the first positioning information, and the scene attribute information corresponds to the enclosed area of the geographical area; determining the preset distance according to the size of the enclosed area.

[0054] In some embodiments, the second receiving and processing unit is specifically configured to: analyze the distance stability between the second positioning information corresponding to each second real-time physiological signal and the first positioning information, classify the second real-time physiological signals with a distance stability higher than a first preset value into the participation group, and classify the second real-time physiological signals with a distance stability not higher than the first preset value into the non-participation group; use a first deep analysis model to perform feature extraction and deep analysis on each second real-time physiological signal in the participation group to obtain a first physiological feature matching probability; and use a second deep analysis model to perform feature extraction and deep analysis on each second real-time physiological signal in the non-participation group to obtain a second physiological feature matching probability; perform weighted calculation on the first physiological feature matching probability and the second physiological feature matching probability respectively based on the corresponding weighting values to obtain the physiological feature matching probability.

[0055] In some embodiments, the response processing unit is specifically configured to: if the first real-time physiological signal exceeds the normal threshold range by a second preset value and the distance between the first monitoring terminal of the target healthcare object and the first terminal is greater than a third preset value, send a reminder signal to the second terminals and the corresponding second monitoring terminals of multiple other healthcare objects within the specified range simultaneously.

[0056] The system in this embodiment adopts all the technical solutions of the above embodiments, and thus at least has all the beneficial effects brought by the technical solutions of the above embodiments, which will not be elaborated herein one by one.

[0057] An embodiment of the present invention also discloses an electronic device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, and when the processor executes the computer program, the steps of any one of the above methods are implemented.

[0058] An embodiment of the present invention also discloses a computer storage medium, which stores a computer program, and when the computer program is executed by a processor, the steps of any one of the above methods can be implemented.

[0059] An embodiment of the present invention also discloses a computer program product, which includes computer program code, and when the computer program code is executed by a processor of an electronic device, the steps of any one of the above methods are implemented.

[0060] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices produce means for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0061] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0062] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operating steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0063] As described above, it is only a preferred embodiment of the present invention and is not used to limit the protection scope of the present invention.

Claims

1. A health care abnormality response method based on Internet of Things technology, applied to a cloud platform, characterized by: The method comprises the following steps: Receiving a first real-time physiological signal of a target health care object and retrieving a normal threshold range corresponding to the target health care object, and when the first real-time physiological signal exceeds the normal threshold range, further acquiring first positioning information of the target health care object; Acquire second real-time physiological signals of other health care objects within a specified range, and use a deep analysis model to analyze each of the second real-time physiological signals to obtain a physiological feature matching probability; wherein the specified range is a circular range defined by a preset distance based on the first positioning information; If the physiological feature matching probability is higher than a first threshold, a reminder signal is sent to the first terminal of the target health care object; otherwise, a reminder signal is sent to the first terminal and the first monitoring terminal of the target health care object at the same time; The preset distance is determined by: Determine the scene attribute information of the geographical area where the target health care object is located according to the first positioning information, wherein the scene attribute information corresponds to the closed area of ​​the geographical area; determine the preset distance according to the size of the closed area; and the preset distance is positively correlated with the size of the closed area; The using the deep analysis model to analyze each of the second real-time physiological signals to obtain a physiological feature matching probability includes: Analyze the distance stability between the second positioning information corresponding to each of the second real-time physiological signals and the first positioning information, classify the second real-time physiological signals whose distance stability is higher than a first preset value into a participating group, and classify the second real-time physiological signals whose distance stability is not higher than the first preset value into a non-participating group; Using the first deep analysis model to perform feature extraction and deep analysis on each of the second real-time physiological signals in the participating group to obtain a first physiological feature matching probability; and using the second deep analysis model to perform feature extraction and deep analysis on each of the second real-time physiological signals in the non-participating group to obtain a second physiological feature matching probability; The first physiological feature matching probability and the second physiological feature matching probability are weightedly calculated based on corresponding weighted values ​​to obtain the physiological feature matching probability.

2. According to claim 1, a method for dealing with health care anomalies based on Internet of Things technology is characterized by: The normal threshold range is determined as follows: Retrieving the historical physiological signals of the target health care object, and dividing the historical physiological signals into a first group of historical physiological signals and a second group of historical physiological signals according to whether there is an abnormal confirmation signal; wherein each historical physiological signal in the first group of historical physiological signals has no abnormal confirmation signal, and each historical physiological signal in the second group of historical physiological signals has an abnormal confirmation signal; Clustering the first group of historical physiological signals and the second group of historical physiological signals to obtain a first physiological signal equivalent value and a second physiological signal equivalent value located at a cluster center, respectively; Calculate the difference between the equivalent value of the second physiological signal and the equivalent value of the first physiological signal, determine the upper floating value and the lower floating value according to the upper adjustment coefficient, the lower adjustment coefficient and the difference, respectively, add and subtract the upper floating value and the lower floating value from the equivalent value of the first physiological signal, respectively, to obtain the normal threshold range; wherein the upper adjustment coefficient is greater than the lower adjustment coefficient.

3. According to claim 1, a method for dealing with health care anomalies based on Internet of Things technology is characterized by: The sending of a reminder signal to the first terminal and the first monitoring terminal of the target health care object at the same time includes: If the first real-time physiological signal exceeds the normal threshold range and reaches a second preset value, and the distance between the first monitoring terminal of the target health care object and the first terminal is greater than a third preset value, a reminder signal is simultaneously sent to the second terminals of multiple other health care objects within the specified range and the corresponding second monitoring terminals.

4. A health care abnormality response system based on Internet of Things technology, the system is based on the method according to any one of claims 1 to 3, applied to a cloud platform, characterized in that: The system comprises a first receiving and processing unit, a second receiving and processing unit, and a response processing unit; The first receiving and processing unit is used to receive a first real-time physiological signal of a target health care object and retrieve a normal threshold range corresponding to the target health care object, and further obtain first positioning information of the target health care object when the first real-time physiological signal exceeds the normal threshold range; The second receiving and processing unit is used to obtain second real-time physiological signals of other health care objects within a specified range, and analyze each of the second real-time physiological signals using a deep analysis model to obtain a physiological feature matching probability; wherein the specified range is a circular range defined by a preset distance based on the first positioning information; The response processing unit is used to send a reminder signal to the first terminal of the target health care object if the physiological feature matching probability is higher than a first threshold; otherwise, send a reminder signal to the first terminal and the first monitoring terminal of the target health care object at the same time.

5. According to claim 4, a health care abnormality response system based on Internet of Things technology is characterized by: The normal threshold range is determined as follows: Retrieving the historical physiological signals of the target health care object, and dividing the historical physiological signals into a first group of historical physiological signals and a second group of historical physiological signals according to whether there is an abnormal confirmation signal; wherein each historical physiological signal in the first group of historical physiological signals has no abnormal confirmation signal, and each historical physiological signal in the second group of historical physiological signals has an abnormal confirmation signal; Clustering the first group of historical physiological signals and the second group of historical physiological signals to obtain a first physiological signal equivalent value and a second physiological signal equivalent value located at a cluster center, respectively; Calculate the difference between the equivalent value of the second physiological signal and the equivalent value of the first physiological signal, determine the upper floating value and the lower floating value according to the upper adjustment coefficient, the lower adjustment coefficient and the difference, respectively, add and subtract the upper floating value and the lower floating value from the equivalent value of the first physiological signal, respectively, to obtain the normal threshold range; wherein the upper adjustment coefficient is greater than the lower adjustment coefficient.

6. An electronic device, comprising: At least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor implements the method according to any one of claims 1 to 3 when executing the computer program.

7. A computer storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

8. A computer program product, comprising computer program code, characterized in that: The computer program code is executed by a processor of an electronic device to implement the method according to any one of claims 1 to 3.

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