Health monitoring method based on Internet of Things and related equipment

Abstract

The invention discloses a health monitoring method based on the Internet of Things and related equipment. The method comprises the following steps: acquiring multi-source health monitoring data acquired by at least two types of heterogeneous monitoring equipment; the health monitoring data comprises any multiple of sleep duration, respiratory rate, heart rate, body temperature, oxyhemoglobin saturation or step number; preprocessing the multi-source health monitoring data, and performing data fusion on the multi-source health monitoring data of the same index to obtain target health monitoring data; performing anomaly detection on the target health monitoring data according to a preset health rule, and if an abnormal index is detected, automatically triggering alarm information; and performing risk assessment on the target health monitoring data and generating a diagnosis report. According to the embodiment of the invention, the accuracy of health monitoring data and detection results can be improved. The method can be widely applied to the technical field of health monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of health monitoring, and particularly relates to a health monitoring method based on an Internet of Things and related equipment. BACKGROUND

[0002] The prior art has the following disadvantages: health monitoring data and detection results are inaccurate. SUMMARY

[0003] The main purpose of the embodiments of the present application is to provide a health monitoring method based on an Internet of Things and related equipment, aiming to improve the accuracy of health monitoring data and detection results.

[0004] To achieve the above-mentioned purpose, one aspect of the embodiments of the present application provides a health monitoring method based on an Internet of Things, applied to an Internet of Things platform, and the method comprises the following steps. Obtaining multi-source health monitoring data collected by at least two types of heterogeneous monitoring devices; the health monitoring data comprises any one or more of sleep duration, respiratory rate, heart rate, body temperature, blood oxygen saturation, or step count; Preprocessing the multi-source health monitoring data, and performing data fusion on the multi-source health monitoring data of the same index to obtain target health monitoring data; Performing anomaly detection on the target health monitoring data according to a preset health rule, and automatically triggering an alarm information if an abnormal index is detected; Performing risk assessment on the target health monitoring data and generating a diagnosis report.

[0005] In some embodiments, the preprocessing of the multi-source health monitoring data comprises the following steps. The multi-source health monitoring data is subjected to outlier rejection by using a preset time window and a preset time step.

[0006] In some embodiments, the data fusion on the multi-source health monitoring data of the same index to obtain the target health monitoring data comprises the following steps. Determining a data weight of each health monitoring data according to the signal quality of the multi-source health monitoring data of the same index and the calibration error of the corresponding monitoring device; Performing weighted calculation according to the multi-source health monitoring data of the same index and the corresponding data weight to obtain the target health monitoring data.

[0007] In some embodiments, the calculation formula of the data weight of each health monitoring data is as follows.

[0008] wherein, represents the data weight of the i-th health monitoring data of the same index, j and the data weight of the j-th health monitoring data of the same index. Indicates the first of the same indicators j Signal quality of individual health monitoring data Indicates the first of the same indicators j The calibration error of each acquisition device.

[0009] In some embodiments, the step of performing a risk assessment on the target health monitoring data and generating a diagnostic report includes: Feature vectors are obtained by extracting features from the target health monitoring data; The feature vector is input into the trained learning model to generate a risk level and a diagnostic report.

[0010] In some embodiments, the method further includes: The risk level and diagnostic report are matched with a preset knowledge graph to generate intervention recommendations.

[0011] To achieve the above objectives, another aspect of this application proposes a health monitoring device based on the Internet of Things (IoT), applied to an IoT platform, the device comprising: The data acquisition module is used to acquire multi-source health monitoring data collected by at least two types of heterogeneous monitoring devices; the health monitoring data includes any combination of sleep duration, respiratory rate, heart rate, body temperature, blood oxygen saturation, or steps. The data processing module is used to preprocess the multi-source health monitoring data and fuse the multi-source health monitoring data of the same indicator to obtain the target health monitoring data. The anomaly detection module is used to perform anomaly detection on the target health monitoring data according to preset health rules. If an abnormal indicator is detected, an alarm message is automatically triggered. The health assessment module is used to perform risk assessment on the target health monitoring data and generate a diagnostic report.

[0012] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the methods described above.

[0013] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods described above.

[0014] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer program product, including a computer program that, when executed by a processor, implements the methods described above.

[0015] To achieve the above objectives, another aspect of this application proposes an Internet of Things (IoT)-based health monitoring system, comprising at least two types of heterogeneous monitoring devices, an IoT platform, and a terminal. The IoT platform connects the monitoring devices and the terminal. The monitoring device is used to collect health monitoring data of the target object and send the verified health monitoring data to the Internet of Things platform; The IoT platform is used to execute the above methods; The terminal is used to receive the alarm information and the diagnostic report.

[0016] The embodiments of this application include at least the following beneficial effects: This application provides a health monitoring method and related equipment based on the Internet of Things. This solution acquires multi-source health monitoring data collected by at least two types of heterogeneous monitoring devices, reducing the limited dimensions of data collected by a single monitoring device, which are easily affected by wearing status and environmental noise. It forms complementary dimensions, improves the accuracy of data sources, preprocesses multi-source health monitoring data, and fuses multi-source health monitoring data of the same indicator to obtain target health monitoring data, reducing errors caused by individual differences. It performs anomaly detection on target health monitoring data according to preset health rules. If an abnormal indicator is detected, an alarm is automatically triggered, and abnormal indicators are monitored and warned in a timely manner. It performs risk assessment on target health monitoring data and generates a diagnostic report. By fusing multiple data for risk assessment, the accuracy of detection results is improved. Attached Figure Description

[0017] Figure 1 This is a flowchart of the IoT-based health monitoring system provided in the embodiments of this application; Figure 2 This is a flowchart of the IoT-based health monitoring method provided in the embodiments of this application; Figure 3 This is a flowchart illustrating the determination of target health monitoring data provided in an embodiment of this application; Figure 4 This is a flowchart illustrating the generation of risk levels and diagnostic reports provided in an embodiment of this application; Figure 5 This is a schematic diagram of the structure of the IoT-based health monitoring device provided in the embodiments of this application; Figure 6 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0019] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0020] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0022] The IoT-based health monitoring method provided in this application relates to the field of information technology. This IoT-based health monitoring method can be applied to a terminal, a server, or software running on a terminal or server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or in-vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the IoT-based health monitoring method, but is not limited to the above forms.

[0023] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0024] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.

[0025] See Figure 1An IoT-based health monitoring system includes at least two types of heterogeneous monitoring devices, an IoT platform, and terminals. The IoT platform communicates with the monitoring devices and terminals. The monitoring devices send the collected health monitoring data to the IoT platform, which processes the data and assesses the health status. Finally, the IoT platform sends the health status assessment results to the terminals. Terminals refer to the terminal devices used by the monitored individual or their associated persons.

[0026] See Figure 2 This application provides a health monitoring method based on the Internet of Things (IoT), applied to an IoT platform, and the method includes steps S101 to S104.

[0027] Step S101: Acquire multi-source health monitoring data collected by at least two types of heterogeneous monitoring devices; the health monitoring data includes any combination of sleep duration, respiratory rate, heart rate, body temperature, blood oxygen saturation, or steps. Step S102: Preprocess the multi-source health monitoring data and fuse the multi-source health monitoring data of the same indicator to obtain the target health monitoring data; Step S103: Perform anomaly detection on the target health monitoring data according to preset health rules. If an abnormal indicator is detected, an alarm message is automatically triggered. Step S104: Conduct a risk assessment on the target health monitoring data and generate a diagnostic report.

[0028] First, multi-source health monitoring data is acquired from at least two types of heterogeneous monitoring devices. Heterogeneous monitoring devices refer to different types of health monitoring equipment that can monitor the same health indicator. The type of heterogeneous monitoring device is determined based on the type of health monitoring data. Next, the multi-source health monitoring data is preprocessed. Preprocessing includes, but is not limited to, identifying outliers and fusing multi-source health monitoring data for the same indicator. Data fusion refers to comprehensively analyzing the data of the same indicator at the same time to obtain the target health monitoring data, which is used for subsequent abnormal health monitoring. Then, anomaly detection is performed on the target health monitoring data according to preset health rules. If an abnormal indicator is detected, an alarm is automatically triggered. The preset health rules are determined based on medically relevant indicators. For example, indicator data exceeding a preset range is considered abnormal, and alarm information includes, but is not limited to, smart phone or SMS alarms. Finally, a risk assessment is performed on the target health monitoring data, and a diagnostic report is generated. The risk assessment includes, but is not limited to, risk level, and the diagnostic report includes, but is not limited to, health analysis and precautions.

[0029] In some embodiments, preprocessing of multi-source health monitoring data includes: Step S201: Use a preset time window and preset time step to remove outliers from the multi-source health monitoring data.

[0030] It should be noted that the preset time window and preset time step are determined according to the actual application, and this embodiment does not impose specific limitations. For example, the preset time window is 20 seconds, and the preset time step is 5 seconds. The preset time window slides from left to right on the data sequence at the preset time step. For the data within the window, outliers such as the center point or newly added points are identified and removed using statistics calculated based on the window itself. Then the window moves forward one step, and this process is repeated.

[0031] In some embodiments, see Figure 3 Data fusion is performed on multi-source health monitoring data for the same indicator to obtain target health monitoring data, including: Step S301: Determine the data weight of each health monitoring data based on the signal quality of multi-source health monitoring data for the same indicator and the calibration error of the corresponding monitoring equipment; Step S302: Perform weighted calculations based on multi-source health monitoring data of the same indicator and their corresponding data weights to obtain the target health monitoring data.

[0032] The calibration error of the monitoring equipment is determined at the factory. Signal quality and the calibration error of the monitoring equipment are used together to evaluate the accuracy of the health monitoring data. The data weight of each health monitoring data point is dynamically updated over time, rather than statically fixed. The target health monitoring data is obtained by weighted summation of multi-source health monitoring data for the same indicator and their corresponding data weights.

[0033] In some embodiments, the formula for calculating the data weight of each health monitoring data point is as follows:

[0034] in, Indicates the first of the same indicators j The data weight of each health monitoring data point Indicates the first of the same indicators j Signal quality of individual health monitoring data Indicates the first of the same indicators j The calibration error of each acquisition device.

[0035] The calculation formula for fused data is as follows:

[0036] in, express t Data after real-time fusion Indicates the first i The first monitoring devicej The data weight of each health monitoring data point express t Time of the first i The first monitoring device j Health monitoring data.

[0037] In some embodiments, see Figure 4 The system performs risk assessments on target health monitoring data and generates diagnostic reports, including: Step S401: Extract features from the target health monitoring data to obtain a feature vector; Step S402: Input the feature vector into the trained learning model to generate the risk level and diagnostic report.

[0038] The dimension of the feature vector is determined based on the actual application; this embodiment does not impose specific limitations. The diagnostic report includes, but is not limited to, diagnostic results and explanatory text. Before making predictions, the learning model needs to collect sample data for training. This sample data includes health monitoring data samples and risk level samples. During training, training stops when the accuracy of the risk level prediction results reaches a preset requirement.

[0039] In some embodiments, the method further includes: Step S501: Match the risk level and diagnostic report with the preset knowledge graph to generate intervention recommendations.

[0040] The pre-built knowledge graph utilizes large-scale model knowledge extraction, fusion, and dynamic updating technologies to extract structured knowledge from multimodal medical big data, including text, images, signals, and omics. Combined with an expert review mechanism, it ultimately constructs a trusted, collaboratively shared medical knowledge system across the entire "knowledge-technology-reasoning" chain for clinical diagnosis and treatment. During its development, the knowledge graph successfully overcame several key technological breakthroughs, including intelligent medical literature parsing, multi-source heterogeneous data fusion, and knowledge extraction and reasoning, achieving automatic construction and dynamic updates. The knowledge graph covers various clinical diseases, multiple medical entity types, various medical relationship types, and various triples (note: the basic unit of knowledge representation), featuring full disease spectrum coverage, real-time dynamic updates, and traceable reasoning throughout the entire chain.

[0041] The solutions of the embodiments of the present invention will be described in detail and explained below with reference to specific application examples: The scenario is set as follows: Elderly patient: Zhang San, 80 years old, with a history of atrial fibrillation. Health monitoring data was collected using two devices: Device A: Smart bracelet (wrist-mounted PPG, calibration error ε=1.8 bpm); Device B: Smartwatch (chest patch ECG, calibration error ε=0.6 bpm). Sampling: Both devices simultaneously reported heart rate, blood oxygen, and steps every 30 seconds → MQTT → SaaS.

[0042] The data fusion process (30-second window) is as follows: Raw health monitoring data collected over 30 seconds: Device A: hr=78, spo2=94; Device B: hr=76, spo2=97. Device A signal quality: Q_A=0.88, Q_B=0.96.

[0043] Data weights for device A (w_A) and device B (w_B): w_A = (1 / 1.8) * 0.88 = 0.489 w_B = (1 / 0.6) * 0.96 = 1.600 Σw=2.089 The fusion results of the health monitoring data collected by device A and device B are as follows: HR_fused=(0.489×78+1.600×76) / 2.089≈76.5bpm SpO2_fused=(0.489×94+1.600×97) / 2.089≈96.2% Heart rate irregularity alarm rule (SQL rule table driven): Starting at 09:17:30, Zhang San's fused heart rate of 105 bpm for 3 consecutive times → trigger level-2.

[0044] Alarm SMS: Zhang San (device last four digits 1234) has a heart rate of 105 bpm, which is high and has lasted for 1.5 minutes. Please take immediate action! Health reports generated (batch at 09:20:00) are as follows: Feature vector: [HR_fused,SpO2_fused,step,sleep_hours,arr_flag]=[105,96,110,5.2,true] The XGBoost model outputs a risk level of 3 (high risk).

[0045] Knowledge graph matching: atrial fibrillation + tachycardia, recommended to "rest immediately, monitor pulse, and contact a cardiologist".

[0046] Automatically generate health reports and push them to family members' mobile devices with a delay of less than 1 second.

[0047] Monitoring results: The abnormality was detected 3 minutes in advance, and the family took him to the hospital in time. The atrial fibrillation medication was adjusted, and Zhang San's vital signs returned to stability.

[0048] See Figure 5 This application provides a health monitoring device based on the Internet of Things (IoT), applied to an IoT platform. The device includes: The data acquisition module is used to acquire multi-source health monitoring data collected by at least two types of heterogeneous monitoring devices; the health monitoring data includes any combination of sleep duration, respiratory rate, heart rate, body temperature, blood oxygen saturation, or steps. The data processing module is used to preprocess multi-source health monitoring data and fuse multi-source health monitoring data of the same indicator to obtain target health monitoring data. The anomaly detection module is used to detect anomalies in target health monitoring data according to preset health rules. If an abnormal indicator is detected, an alarm message will be automatically triggered. The health assessment module is used to conduct risk assessments on target health monitoring data and generate diagnostic reports.

[0049] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0050] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0051] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0052] Please see Figure 6 , Figure 6 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 601 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 602 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 602 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 602 and is called and executed by the processor 601 using the methods described in the embodiments of this application. The input / output interface 603 is used to implement information input and output; The communication interface 604 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 605 transmits information between various components of the device (e.g., processor 601, memory 602, input / output interface 603, and communication interface 604); The processor 601, memory 602, input / output interface 603, and communication interface 604 are connected to each other within the device via bus 605.

[0053] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0054] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0055] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0056] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0057] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0058] See Figure 1 This application also provides an Internet of Things (IoT)-based health monitoring system, including at least two types of heterogeneous monitoring devices, an IoT platform, and terminals. The IoT platform connects the monitoring devices and terminals. Monitoring equipment is used to collect health monitoring data of target objects and send the accurate health monitoring data to the Internet of Things platform. An IoT platform is used to execute the above methods; The terminal is used to receive alarm information and diagnostic reports.

[0059] IoT-based health monitoring systems can also include devices such as edge gateways, which perform some data processing, such as data fusion and feature extraction, thereby saving uplink bandwidth and reducing the computing power of the IoT platform.

[0060] The embodiments of this application include at least the following beneficial effects: This application provides a health monitoring method and related equipment based on the Internet of Things. This solution acquires multi-source health monitoring data collected by at least two types of heterogeneous monitoring devices, reducing the limited dimensions of data collected by a single monitoring device, which are easily affected by wearing status and environmental noise. It forms complementary dimensions, improves the accuracy of data sources, preprocesses multi-source health monitoring data, and fuses multi-source health monitoring data of the same indicator to obtain target health monitoring data, reducing errors caused by individual differences. It performs anomaly detection on target health monitoring data according to preset health rules. If an abnormal indicator is detected, an alarm is automatically triggered, and abnormal indicators are monitored and warned in a timely manner. It performs risk assessment on target health monitoring data and generates a diagnostic report. By fusing multiple data for risk assessment, the accuracy of detection results is improved.

[0061] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0062] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0063] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0064] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0065] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0066] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0067] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0068] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0069] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0070] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, 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. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0071] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A health monitoring method based on the Internet of Things, characterized in that, Applied to an Internet of Things (IoT) platform, the method includes: Acquire multi-source health monitoring data collected by at least two types of heterogeneous monitoring devices; the health monitoring data includes any combination of sleep duration, respiratory rate, heart rate, body temperature, blood oxygen saturation, or steps. The multi-source health monitoring data is preprocessed, and the multi-source health monitoring data for the same indicator is fused to obtain the target health monitoring data; The target health monitoring data is subjected to anomaly detection according to preset health rules. If an abnormal indicator is detected, an alarm message is automatically triggered. A risk assessment is performed on the target health monitoring data, and a diagnostic report is generated.

2. The method according to claim 1, characterized in that, The preprocessing of the multi-source health monitoring data includes: Outlier removal is performed on the multi-source health monitoring data using a preset time window and a preset time step.

3. The method according to claim 1, characterized in that, The process of fusing multi-source health monitoring data for the same indicator to obtain target health monitoring data includes: The data weight of each health monitoring data point is determined based on the signal quality of multi-source health monitoring data for the same indicator and the calibration error of the corresponding monitoring equipment. The target health monitoring data is obtained by weighting the multi-source health monitoring data of the same indicator and the corresponding data weights.

4. The method according to claim 3, characterized in that, The formula for calculating the data weight of each of the aforementioned health monitoring data is as follows: in, Indicates the first of the same indicators j The data weight of each health monitoring data point Indicates the first of the same indicators j Signal quality of individual health monitoring data Indicates the first of the same indicators j The calibration error of each acquisition device.

5. The method according to claim 1, characterized in that, The step of conducting a risk assessment of the target health monitoring data and generating a diagnostic report includes: Feature vectors are obtained by extracting features from the target health monitoring data; The feature vector is input into the trained learning model to generate a risk level and a diagnostic report.

6. The method according to claim 5, characterized in that, The method further includes: The risk level and diagnostic report are matched with a preset knowledge graph to generate intervention recommendations.

7. A health monitoring device based on the Internet of Things, characterized in that, The device, applied to an Internet of Things (IoT) platform, includes: The data acquisition module is used to acquire multi-source health monitoring data collected by at least two types of heterogeneous monitoring devices; the health monitoring data includes any combination of sleep duration, respiratory rate, heart rate, body temperature, blood oxygen saturation, or steps. The data processing module is used to preprocess the multi-source health monitoring data and fuse the multi-source health monitoring data of the same indicator to obtain the target health monitoring data. The anomaly detection module is used to perform anomaly detection on the target health monitoring data according to preset health rules. If an abnormal indicator is detected, an alarm message is automatically triggered. The health assessment module is used to perform risk assessment on the target health monitoring data and generate a diagnostic report.

8. An electronic device, characterized in that, include: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method as described in any one of claims 1-6.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

10. A health monitoring system based on the Internet of Things, characterized in that, It includes at least two types of heterogeneous monitoring devices, an IoT platform, and terminals, wherein the IoT platform connects the monitoring devices and the terminals. The monitoring device is used to collect health monitoring data of the target object and send the verified health monitoring data to the Internet of Things platform; The IoT platform is used to perform the method according to any one of claims 1-6; The terminal is used to receive the alarm information and the diagnostic report.

Images