User vital signs information monitoring and early warning method based on body monitoring system

Abstract

The embodiment of the present disclosure discloses a user vital sign information monitoring and early warning method based on a body monitoring system. A specific implementation of the method includes: a body information monitoring module and a vital sign data processing module; through the vital sign data processing module, the following steps are executed to monitor and early warning the user's vital sign information: integrate the user's vital sign data with the pre-stored historical vital sign data to generate a horizontal vital sign indicator sequence set and a vertical vital sign indicator sequence set; extract body data features from the horizontal vital sign indicator sequence set and the vertical vital sign indicator sequence set; construct a user's initial vital sign data chain; extract implicit features from the longitudinal indicator features to obtain the user's body implicit features; in response to determining that the user's body implicit features meet the preset vital sign early warning conditions, execute the user's vital sign second early warning operation. This embodiment can be used to monitor the patient's vital sign data in real time, so as to provide timely early warnings in the event of an abnormality.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of information monitoring, and more particularly to a method for monitoring and early warning user vital sign information based on a body monitoring system. Background Art

[0002] To ensure the safety of every inpatient during their hospital stay, medical staff frequently conduct ward rounds, keeping tabs on their physical condition and ensuring the smooth implementation of their treatment plan. They must also promptly respond to emergencies. When making rounds, medical staff avoid unnecessary contact with patients, ensuring they don't disturb their rest and avoid accidentally touching any treatment tubes inserted into their bodies. However, in exceptional circumstances, some patients are prone to sudden death, and it can be difficult to detect abnormalities without physical examination. This is especially true during nighttime rounds, where dim lighting makes it difficult to detect abnormalities without disturbing the patient's rest. This can easily lead to patients being in danger without warning.

[0003] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art. Summary of the Invention

[0004] The content of this disclosure is used to briefly introduce concepts that will be described in detail in the detailed description section below. The content of this disclosure is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0005] Some embodiments of the present disclosure propose a user vital sign information monitoring and early warning method based on a body monitoring system to solve one or more of the technical problems mentioned in the above background technology section.

[0006] In a first aspect, some embodiments of the present disclosure provide a method for monitoring and early warning of user vital signs information based on a body monitoring system, wherein the body monitoring system comprises: a body information monitoring module and a vital signs data processing module, wherein: the user's vital signs data of the user is monitored by the body information monitoring module, and the user's vital signs data is sent to the vital signs data processing module; the vital signs data processing module performs the following steps to monitor and early warning of the user's vital signs information: the user's vital signs data is integrated with pre-stored historical vital signs data to generate a horizontal vital signs indicator sequence set and a vertical vital signs indicator sequence set; the horizontal vital signs indicator sequence set is processed by the body information monitoring module; the user's vital signs data is processed by the body information processing module; the user's vital signs data ... The physical sign indicator sequence set and the above-mentioned longitudinal physical sign indicator sequence set are subjected to body data feature extraction to obtain horizontal indicator features and vertical indicator features, and a first user physical sign warning operation is performed based on the above-mentioned horizontal indicator features and the above-mentioned vertical indicator features; the user's initial physical sign data chain is constructed using the above-mentioned historical physical sign data, wherein the above-mentioned initial physical sign data chain represents the physical sign data of the above-mentioned user at a specified time point; based on the above-mentioned user's initial physical sign data chain, the above-mentioned longitudinal indicator features are subjected to implicit feature extraction to obtain implicit features of the user's body; in response to determining that the above-mentioned implicit features of the user's body meet the preset physical sign warning conditions, a second user physical sign warning operation is performed.

[0007] In a second aspect, some embodiments of the present disclosure provide a user vital sign information monitoring and early warning device based on a body monitoring system, the device comprising: a monitoring and sending unit, configured to monitor the user's user vital sign data through a body information monitoring module, and send the above user vital sign data to a vital sign data processing module; a data processing and early warning unit, configured to perform the following steps to monitor and early warning the user's user vital sign information through the vital sign data processing module: integrating the above user vital sign data with pre-stored historical vital sign data to generate a horizontal vital sign indicator sequence set and a vertical vital sign indicator sequence set; processing the above horizontal vital sign indicator sequence; The method comprises the following steps: extracting body data features from the above-mentioned set and the above-mentioned longitudinal vital sign indicator sequence set to obtain transverse indicator features and longitudinal indicator features, and performing a first warning operation of the user's vital signs based on the above-mentioned transverse indicator features and the above-mentioned longitudinal indicator features; constructing a user's initial vital sign data chain using the above-mentioned historical vital sign data, wherein the above-mentioned initial vital sign data chain represents the vital sign data of the above-mentioned user at a specified time point; extracting implicit features from the above-mentioned transverse indicator features and the above-mentioned longitudinal indicator features based on the above-mentioned user's initial vital sign data chain to obtain implicit features of the user's body; and performing a second warning operation of the user's vital signs in response to determining that the above-mentioned implicit features of the user's body meet the preset vital sign warning conditions.

[0008] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0009] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation of the first aspect is implemented.

[0010] The above-described embodiments of the present disclosure have the following beneficial effects: The user vital sign information monitoring and early warning methods based on a body monitoring system, as described in some embodiments of the present disclosure, can be used to monitor patient vital sign data in real time, providing timely early warnings in the event of anomalies, thereby significantly ensuring patient safety. Specifically, the reason patients may be in danger without warning is that, in order to avoid disturbing their rest and accidentally touching the therapeutic tubes inserted into their bodies, unnecessary contact with the patient is avoided as much as possible. However, in special circumstances, some patients are prone to sudden death, and abnormalities are difficult to detect without physical examination. This is especially true during nighttime patient rounds, where indoor lighting is dim, making it difficult to accurately detect abnormalities without disturbing the patient's rest. Based on this, the user vital sign information monitoring and early warning methods based on a body monitoring system, as described in some embodiments of the present disclosure, take this into account and introduce a body monitoring system to automatically monitor patient vital sign data. The body monitoring system includes a body information monitoring module and a vital sign data processing module. Specifically, the body information monitoring module monitors the user's vital sign data and transmits the user's vital sign data to the vital sign data processing module. The vital sign data processing module performs the following steps to monitor and provide early warnings for the user's vital sign information: First, the user's vital sign data is integrated with pre-stored historical vital sign data to generate a transverse vital sign indicator sequence set and a longitudinal vital sign indicator sequence set. Then, body data feature extraction is performed on the transverse vital sign indicator sequence set and the longitudinal vital sign indicator sequence set to obtain transverse indicator features and longitudinal indicator features, and a first user vital sign early warning operation is performed based on the transverse indicator features and longitudinal indicator features. Distinguishing between transverse vital sign indicators and longitudinal vital sign indicators facilitates the extraction of user data features from different data structures. Next, utilizing the historical vital sign data, an initial user vital sign data chain is constructed, wherein the initial user vital sign data chain represents the user's vital sign data at a specified time point. Constructing the initial user vital sign data chain can serve as a reference for the user's vital sign data. This allows the short-term relationship between the user's body's characteristic data to be identified, thereby preventing sudden changes in various physical indicators. Subsequently, based on the initial user vital sign data chain, implicit feature extraction is performed on the horizontal and vertical indicator features to obtain the user's implicit body features. This implicit feature extraction can be used to further explore the characteristics of the user's vital sign data. This allows for rapid prediction of data changes that could lead to dangerous outcomes for the patient. Finally, in response to determining that the user's implicit body features meet pre-set vital sign warning conditions, a second user vital sign warning operation is executed. This allows for real-time monitoring of patient vital sign data, enabling timely warnings in the event of anomalies. This significantly ensures patient safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0012] Figure 1 1 is a schematic diagram of an application scenario of a method for monitoring and early warning of user vital signs information based on a body monitoring system in some embodiments of the present disclosure.

[0013] Figure 2 is a flow chart of some embodiments of a method for monitoring and early warning user vital signs information based on a body monitoring system according to the present disclosure;

[0014] Figure 3 is a schematic diagram of a mattress structure according to some embodiments of the user vital sign information monitoring and early warning method based on the body monitoring system of the present disclosure;

[0015] Figure 4 is a schematic structural diagram of some embodiments of a user vital sign information monitoring and early warning device based on a body monitoring system according to the present disclosure;

[0016] Figure 5 is a schematic structural diagram of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0017] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0018] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other.

[0019] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0020] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0021] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0022] With regard to the collection, storage, and use of user personal information (such as user vital signs data) involved in this disclosure, before performing the corresponding operations, the relevant organizations or individuals shall fulfill their obligations, including conducting personal information security impact assessments, fulfilling the obligation to inform the personal information subjects, and obtaining the authorization and consent of the personal information subjects in advance.

[0023] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0024] Figure 1 Schematic diagram of application scenarios of a method for monitoring and early warning of user vital signs information based on a body monitoring system in some embodiments of the present disclosure.

[0025] exist Figure 1 In the application scenario, first, the body monitoring system 101 may include a body information monitoring module 102 and a vital sign data processing module 103. Among them: through the above-mentioned body information monitoring module 102, the user's user vital sign data 1021 is monitored, and the above-mentioned user vital sign data 1021 is sent to the above-mentioned vital sign data processing module 103. Through the above-mentioned vital sign data processing module 103, the following steps are performed to monitor and warn the user's vital sign information of the above-mentioned user: the above-mentioned user vital sign data is integrated with the pre-stored historical vital sign data to generate a horizontal vital sign indicator sequence set and a longitudinal vital sign indicator sequence set. The body data feature extraction is performed on the above-mentioned horizontal vital sign indicator sequence set and the above-mentioned longitudinal vital sign indicator sequence set to obtain horizontal indicator features and longitudinal indicator features, and the first user vital sign warning operation is performed according to the above-mentioned horizontal indicator features and longitudinal indicator features. The above-mentioned historical vital sign data is used to construct the user's initial vital sign data chain. Among them, the above-mentioned initial vital sign data chain represents the vital sign data of the above-mentioned user at a specified time point. Based on the initial user vital sign data chain, implicit feature extraction is performed on the horizontal indicator feature and the vertical indicator feature to obtain the user's body implicit feature. In response to determining that the user's body implicit feature meets the preset vital sign warning condition, a second user vital sign warning operation is executed.

[0026] It should be noted that the above-mentioned body monitoring system 101 can be hardware or software. When the computing device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or it can be implemented as a single server or a single terminal device. When the computing device is embodied as software, it can be installed in the hardware devices listed above. It can be implemented as multiple software or software modules for providing distributed services, for example, or it can be implemented as a single software or software module. No specific limitation is made here. It should be understood that Figure 1 The number of computing devices in can be any number according to implementation requirements.

[0027] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0028] Figure 2 The process 200 of some embodiments of the method for monitoring and early warning of user vital signs based on a body monitoring system according to the present disclosure is shown. The method for monitoring and early warning of user vital signs based on a body monitoring system includes the following steps:

[0029] Step 201 : Monitor the user's vital sign data through the body information monitoring module, and send the user's vital sign data to the vital sign data processing module.

[0030] In some embodiments, the user vital sign information monitoring and early warning method based on the body monitoring system may be implemented by a subject to monitor the user's vital sign data via the body information monitoring module via a wired or wireless method, and transmit the user vital sign data to the vital sign data processing module. The user vital sign data may include, but is not limited to, at least one of the following: user body temperature, user electrocardiogram (ECG) value, user respiratory rate, user blood pressure, etc.

[0031] In some optional implementations of some embodiments, the execution subject monitors the user's vital sign data through the body information monitoring module, which may include the following steps:

[0032] The first step is to receive user vital sign sensing information collected by the user's vital sign data sensing mattress. The mattress is internally provided with a plurality of pressure sensing pads and a plurality of temperature sensing pads. These pressure sensing pads and temperature sensing pads are evenly distributed within the mattress to sense the user's body pressure on the mattress and the user's body temperature. The user vital sign sensing information includes an array of pressure analog voltage values ​​and an array of temperature analog voltage values.

[0033] For example, see Figure 3 .like Figure 3As shown, the vital sign data sensing mattress 301 is internally provided with multiple pressure sensing pads 302 (the number shown in the figure is schematic) and multiple temperature sensing pads 303 (the number shown in the figure is schematic). These pressure sensing pads and temperature sensing pads are evenly and cross-laid within the mattress to sense the user's body pressure on the mattress and the user's body temperature. The signals are simultaneously transmitted to the body information monitoring module via a circuit. The data in the pressure simulation voltage value array and the temperature simulation voltage value array are arranged according to the placement of the pressure sensing pads or temperature sensing pads. Therefore, the pressure simulation voltage value array and the temperature simulation voltage value array can also be used to represent the pressure distribution and temperature distribution of the user on the mattress, respectively. Furthermore, the pressure sensing pads can be diaphragm-type pressure sensors, and the temperature sensing pads can be temperature sensors. For example, temperature sensors may include, but are not limited to, thermocouples, thermistors, and integrated temperature sensors.

[0034] The second step is to convert the user's vital signs sensing information into data through the body information monitoring module to generate user vital signs data. The data conversion is performed by the following steps:

[0035] Optionally, the pressure analog voltage value array and the temperature analog voltage value array included in the user vital sign sensing information may be subjected to noise reduction processing in the following steps to obtain noise-reduced user vital sign sensing information. Specifically, the noise reduction processing may be used to remove data in the user vital sign sensing information that exceeds a preset data range. In response to the number of data in the user vital sign sensing information that exceeds the preset data range being greater than a preset threshold, a first user vital sign warning operation is executed.

[0036] For example, considering the placement of sensors in a mattress, the data collected in the array should be continuously changing. Therefore, for a particular pressure simulation voltage value in the pressure simulation voltage array, it can be determined whether it is greater than or less than three times the value of the adjacent pressure simulation voltage value. If it is greater, the pressure simulation voltage value is determined to be a sudden change and can be removed as noise data. Furthermore, an interpolation algorithm can be used to interpolate adjacent data points to complete the data.

[0037] Step one, constructing a user posture perception map based on the above-mentioned pressure simulation voltage value array. The above-mentioned user posture perception map includes a user posture boundary coordinate sequence. Secondly, the pressure simulation voltage value greater than the preset pressure threshold can be selected from the above-mentioned pressure simulation voltage value array as the user position simulation voltage value to obtain a user position simulation voltage value sequence. Then, the data in the above-mentioned pressure simulation voltage value array except for the position of each user position simulation voltage value can be set to zero to obtain an initial posture perception data matrix. Next, the above-mentioned initial posture perception data matrix can be converted into a binary matrix. Afterwards, the binary matrix corresponding to the above-mentioned initial posture perception data matrix can be first expanded and then corroded by the expansion and corrosion algorithm to obtain a processed posture perception data matrix. Finally, the boundary coordinates of the data area in the above-mentioned processed posture perception data matrix can be used as the user posture boundary coordinates to obtain a user posture boundary coordinate sequence. The processed posture perception data matrix is ​​the user posture perception map.

[0038] Step 2: Perform dynamic posture analysis on the user posture perception map to generate a dynamic posture analysis result. In response to the dynamic posture analysis result including an indicator indicating abnormal user posture, a first warning operation of user vital signs is performed. Here, human key point detection can be performed on the user posture perception map using a preset human key point detection algorithm to generate a human key point detection coordinate group. Secondly, the pressure simulation voltage value corresponding to the position where the center coordinates of each human key point detection coordinate in the human key point detection coordinate group are located can be converted into a user body pressure value. Here, the user body pressure value represents the force exerted by the user's body joints on the mattress. Finally, in response to determining that the user's body pressure value is greater than a preset body part weight threshold, an indicator indicating abnormal user posture is generated.

[0039] In practice, a user's body pressure value exceeding a preset body part weight threshold can indicate abnormal body posture behavior during a sudden death attack, such as unconsciously slapping the mattress or excessive localized pressure on the mattress due to abnormal movement amplitude. Therefore, a first user vital sign warning operation can be directly executed. For example, the first user vital sign warning operation can be to notify medical personnel (via a medical indicator light and alarm) of the user's physical abnormality.

[0040] Step 3: Based on the user posture boundary coordinate sequence, each temperature analog voltage value in the temperature analog voltage value array is analog-to-digital converted to generate a user body temperature value sequence. Each user posture boundary coordinate in the user posture boundary coordinate sequence can represent the user's location. Next, an analog-to-digital converter provided in the body information monitoring module can be used to perform analog-to-digital conversion on the temperature analog voltage value corresponding to the user's location in the temperature analog voltage value array to generate a user body temperature value sequence.

[0041] Specifically, the analog-to-digital converter can be designed based on the sensor type of the temperature sensing pad and can convert the temperature analog voltage value into a specific temperature voltage value. The temperature voltage value can then be converted into a specific temperature value using a preset transfer function. Here, the transfer function can be a formula for converting the temperature analog voltage value into the temperature voltage value. Specifically, the sensor output has a linear relationship with temperature, for example, temperature analog voltage value = coefficient × temperature value + constant. Thus, the temperature analog voltage value can be converted into the temperature voltage value.

[0042] Step 4: Using the pressure analog voltage value array in the user's vital sign sensing information, update the historical user ECG waveform data to obtain the current user's ECG waveform data. The pressure analog voltage value array can be subjected to bandpass filtering (for example, setting 0.8-3 Hz (corresponding to heart rate values ​​of 48-180)) and adaptive filtering (0.1-0.3 Hz as a noise reference for the respiratory signal) to filter out the heartbeat signal. The heartbeat signal is added to the historical heartbeat signal sequence to obtain the current heartbeat signal sequence. A short-time Fourier transform is performed on the current heartbeat signal sequence to locate the heartbeat cycle. Based on the heartbeat cycle, a heart rate value is generated. Alternatively, each pressure analog voltage value in the pressure analog voltage value array can be converted to a digital form using the analog-to-digital conversion method to obtain a user heartbeat pressure value sequence. Subsequently, the current user ECG waveform data corresponding to each user's heartbeat pressure value is determined based on a pre-established mapping table of ECG signals and heartbeat signals for the user. Here, the mapping table can be generated by mapping the pre-acquired user mattress pressure values ​​and the measured ECG waveform data using a dynamic time warping (DTW) algorithm. Furthermore, the mapping table can be updated daily to better reflect user characteristics.

[0043] Step 5: Extract R-wave peaks from the current user's ECG waveform data to obtain a sequence of instantaneous heart rate values. Data in the current user's ECG waveform data that exceeds a preset signal threshold can be considered R-wave peaks. Secondly, the instantaneous heart rate value can be determined by the time interval between adjacent R-wave peak values.

[0044] Step 6: Determine the dynamic posture analysis result, the user's body temperature value sequence, the current user's electrocardiogram waveform data, and the instantaneous heart rate value sequence as user vital sign data.

[0045] In practice, the aforementioned "some optional implementations in some embodiments" and their related steps serve as an inventive point. By extracting user vital sign data from the mattress, when no other equipment is available, this can be used to perceive a patient's actual physical condition without physical contact. For example, some patients experience an inflammatory response with elevated body temperature or a shock response with hypothermia before sudden death. Therefore, by monitoring body temperature, heart rate, user movement, and heartbeat, timely risk warnings can be issued.

[0046] Step 202: The vital sign data processing module performs the following steps to monitor the user's vital sign information and provide early warning:

[0047] Step 2021 : Integrate the user's vital sign data with pre-stored historical vital sign data to generate a transverse vital sign indicator sequence set and a longitudinal vital sign indicator sequence set.

[0048] In some embodiments, the execution entity may integrate the user's vital sign data with pre-stored historical vital sign data to generate a horizontal vital sign indicator sequence set and a vertical vital sign indicator sequence set.

[0049] In some optional implementations of some embodiments, the historical vital sign data may include a historical indicator horizontal vital sign data sequence set and a historical indicator vertical vital sign sequence set. Each item of data in the user vital sign data corresponds to a vital sign indicator. The execution entity integrates the user vital sign data with pre-stored historical vital sign data to generate the horizontal vital sign indicator sequence set and the vertical vital sign indicator sequence set, which may include the following steps:

[0050] In the first step, the historical vital sign data is filtered to identify the historical indicator horizontal vital sign data sequences and the historical indicator vertical vital sign sequences that match the individual vital sign indicators in the user's vital sign data, thereby obtaining a set of matched horizontal vital sign data sequences and a set of matched vertical vital sign sequences. The matches may correspond to the same vital sign indicator.

[0051] The second step is to add the historical vital sign data to the matched transverse vital sign data sequence set and the matched longitudinal vital sign sequence set to obtain a transverse vital sign indicator sequence set and a longitudinal vital sign indicator sequence set. Each transverse vital sign indicator sequence in the transverse vital sign indicator sequence set is composed of multiple vital sign indicators at the same moment, and each longitudinal vital sign indicator sequence in the longitudinal vital sign indicator sequence set is composed of single vital sign indicators at consecutive moments.

[0052] Step 2022: extract body data features from the transverse vital sign indicator sequence set and the longitudinal vital sign indicator sequence set to obtain transverse indicator features and longitudinal indicator features, and execute the first warning operation of the user's vital signs based on the transverse indicator features and the longitudinal indicator features.

[0053] In some embodiments, the above-mentioned execution entity can extract body data features from the above-mentioned horizontal vital sign indicator sequence set and the above-mentioned vertical vital sign indicator sequence set to obtain horizontal indicator features and vertical indicator features, and execute the user's vital sign first warning operation based on the above-mentioned horizontal indicator features and vertical indicator features.

[0054] In some optional implementations of some embodiments, the execution entity extracts body data features from the transverse vital sign indicator sequence set and the longitudinal vital sign indicator sequence set to obtain transverse indicator features and longitudinal indicator features, including:

[0055] The first step is to extract feature values ​​from each longitudinal sign indicator sequence in the longitudinal sign indicator sequence set to obtain a longitudinal indicator feature value set. Feature value extraction can be performed on each longitudinal sign indicator sequence in the longitudinal sign indicator sequence set using a preset mutation point detection algorithm to obtain a longitudinal indicator feature value set. Each longitudinal indicator feature value can be the time point at which a mutation occurs in the corresponding longitudinal sign indicator sequence, representing the time point at which a mutation occurs in a single indicator in the time dimension.

[0056] As an example, the mutation point detection algorithm may include but is not limited to at least one of the following: PELT (Pruned Exact Linear Time) efficient time series analysis and change point detection algorithm, Binary Segmentation (binary segmentation algorithm), etc.

[0057] In the second step, according to each longitudinal indicator characteristic value in the above longitudinal indicator characteristic value set, a sliding window step size is dynamically allocated for the preset data detection sliding window. Among them, each longitudinal indicator characteristic value can be extended to a time period as the central time point to obtain multiple characteristic time periods. Here, when each longitudinal indicator characteristic value is extended as the central time point, the extended time length can be a preset time length multiplied by the degree of mutation of the longitudinal indicator characteristic value. The degree of mutation can be the ratio between the corresponding longitudinal sign indicator and the mean of the longitudinal sign indicator sequence (here, the absolute value can be taken). In this way, the corresponding time period can be extended according to the degree of mutation of different indicators. Finally, the sum of the time periods can be taken as the sliding window step size for the dynamic allocation of the above data detection sliding window.

[0058] In practice, although longitudinal vital signs correspond to continuous individual indicators, given the correlation between changes in user physical data, this correlation must be considered even when extracting features using the individual features of each indicator in the time dimension. Therefore, the window step size is dynamically allocated based on correlation.

[0059] The third step is to perform synchronous sliding window detection on each longitudinal vital sign indicator sequence in the above longitudinal vital sign indicator sequence set based on the allocated sliding window step size to generate a longitudinal indicator feature. The above longitudinal indicator feature may include a longitudinal indicator abnormality information set corresponding to each longitudinal vital sign indicator sequence. Each longitudinal indicator abnormality information includes abnormal indicator data and abnormal time period in the longitudinal vital sign indicator sequence. Here, the above data detection sliding window can be used to perform synchronous sliding window detection on each longitudinal vital sign indicator sequence according to the allocated sliding window step size to generate a longitudinal indicator feature. Here, the abnormal time period can be a time period corresponding to multiple abnormal indicator data. In this way, the abnormal points in the longitudinal vital sign indicator sequence can be further located. Thus, a more accurate early warning can be provided.

[0060] The fourth step is to perform data standardization on the transverse vital sign indicator sequence set to generate a standardized data matrix. The data standardization on the transverse vital sign indicator sequence set to generate a standardized data matrix can be performed using a Z-score standardization algorithm.

[0061] The fifth step is to determine the covariance matrix of the standardized data matrix and perform principal component analysis on the covariance matrix to generate a principal component data matrix after analysis. The principal component analysis can be performed on the covariance matrix using a principal component analysis algorithm to generate a principal component data matrix after analysis.

[0062] The sixth step is to perform local outlier factor detection on the principal component data matrix after analysis to generate horizontal indicator features. These horizontal indicator features correspond to the horizontal indicator abnormality information set for each horizontal physical sign indicator sequence. Each horizontal indicator abnormality information includes abnormal indicator data and abnormal time period. Local outlier factor detection (LOF) can be performed on the principal component data matrix after analysis to generate horizontal indicator features.

[0063] In practice, the aforementioned "some optional implementations of some embodiments" and their related steps serve as an inventive point. By performing principal component analysis on a horizontal, multi-dimensional (i.e., composed of multiple indicators) standardized data matrix, it can be used to preliminarily determine the principal component indicators that currently best reflect the user's physical data characteristics. Subsequently, by performing local outlier detection, it can be used to further locate abnormalities in the principal component indicators at different times. Thus, the time points that indicate abnormalities in the user's vital signs can be extracted from the characteristics of the multi-dimensional data. Furthermore, this can be used to provide more accurate early warnings.

[0064] In some optional implementations of some embodiments, the execution subject performs the first warning operation of the user's vital signs based on the horizontal indicator characteristics and the vertical indicator characteristics, including:

[0065] The first step is to fuse the horizontal and vertical indicator features according to the included abnormal time periods to obtain fused abnormal indicator information. Data fusion can be performed by arranging the data in chronological order and removing indicator data at the same time to obtain the fused abnormal indicator information.

[0066] In the second step, in response to determining that the fused abnormal indicator information meets the first preset warning condition, a first warning operation of the user's vital signs is executed. The first preset warning condition may be the presence of abnormal data in the fused indicator information.

[0067] Step 2023: Utilize historical vital sign data to construct the user's initial vital sign data chain.

[0068] In some embodiments, the execution entity may utilize the historical vital sign data to construct an initial vital sign data chain of the user. The initial vital sign data chain may represent the vital sign data of the user at a specified time point. The specified time point may be a pre-set time point.

[0069] In some optional implementations of some embodiments, the execution entity uses the historical vital sign data to construct an initial vital sign data chain of the user, including:

[0070] The first step is to select the user vital sign data corresponding to the preset tag field and within the preset time period from the above-mentioned historical vital sign data to obtain a set of extracted user vital sign data sequences. Considering that sudden death of a user is an emergency, it is necessary to extract features from short-term data. Therefore, the user vital sign data corresponding to the preset tag field (for example, body temperature field, body pressure field, heart rate field, electrocardiogram field, etc.) and within a preset time period (for example, one hour) are selected from the above-mentioned historical vital sign data as the set of extracted user vital sign data sequences. Here, each extracted user feature data sequence can correspond to a preset tag field.

[0071] In the second step, each extracted user vital sign data in each extracted user vital sign data sequence in the above extracted user vital sign data sequence set is fitted to generate a user initial vital sign data curve set. Here, the fitting process can be performed using the least squares method.

[0072] The third step is to determine the data jitter range corresponding to each user's initial vital sign data curve in the aforementioned user initial vital sign data curve set. Each user's initial vital sign data curve can be curve-expanded according to the jitter threshold pre-set by the user for each preset tag field at a specified time point to obtain a data jitter range centered on the user's initial vital sign data curve. The data jitter range can represent the data and safety range corresponding to each preset tag field.

[0073] The fourth step is to construct a user initial vital sign data chain based on the aforementioned user initial vital sign data curve set and the corresponding data jump range set. The initial vital sign data curves and the data within the corresponding data jump range can be spread into a multidimensional matrix in the order of time and indicator labels to serve as the user initial vital sign data chain.

[0074] Step 2024: Based on the user's initial vital sign data chain, implicit feature extraction is performed on the horizontal indicator features and the vertical indicator features to obtain the user's body implicit features.

[0075] In some embodiments, the execution entity may perform implicit feature extraction on the horizontal indicator features and the vertical indicator features based on the initial vital sign data chain of the user to obtain implicit features of the user's body.

[0076] In some optional implementations of some embodiments, the execution entity extracts implicit features from the horizontal indicator features and the vertical indicator features based on the initial vital sign data chain of the user to obtain implicit features of the user's body, including:

[0077] The first step is to construct a current set of vital sign data curves corresponding to the aforementioned transverse and longitudinal indicator features based on the user's initial vital sign data curve set in the aforementioned initial vital sign data chain. The implementation method for constructing the current set of vital sign data curves can refer to the steps for constructing the aforementioned initial vital sign data chain and will not be further described.

[0078] The second step is to determine the physical sign data similarity between the initial user physical sign data curve set and the current physical sign data curve set corresponding to the same preset tag field, thereby obtaining a physical sign data similarity vector set. The physical sign data similarity vector can be determined as the Euclidean distance between the initial user physical sign data curve and the current physical sign data curve corresponding to the same preset tag field.

[0079] The third step is to perform implicit feature extraction on the longitudinal indicator features based on the aforementioned vital sign data similarity vector set to obtain the user's implicit body features. The data matrix corresponding to the longitudinal indicator features can be input into a preset implicit feature extraction network to generate the user's implicit body features. Here, the data matrix can be a T×N matrix composed of the longitudinal indicator features combined with the extracted user vital sign data sequence set. T can be a time series, and N can be a sequence of preset label fields.

[0080] As an example, the implicit feature extraction network can be a graph neural network. Then, the set of similarity vectors of the vital sign data can be used as the attention weights of each node corresponding to the same indicator in the graph neural network.

[0081] As another example, the implicit feature extraction network can also include the following structure: an input layer, a feature aggregation layer, a feature extraction layer, an implicit feature extraction layer, and an output layer. Specifically, the data matrix corresponding to the longitudinal indicator features and the set of vital sign data similarity vectors can be input into the input layer. The feature aggregation layer can then perform the following steps: normalizing the vital sign data similarity vectors corresponding to each preset label field using a Softmax function to obtain an attention weight vector. Feature extraction can be performed on the data matrix corresponding to the longitudinal indicator features using a sliding window, and weighted summing the features within the window using the similarity weights to obtain an aggregated feature matrix. The feature extraction layer can then perform state feature extraction on the aggregated feature matrix using a bidirectional LSTM (Bidirectional Long Short-Term Memory) layer to obtain a hidden state feature matrix. The hidden state feature matrix can then be input into a multilayer perceptron through the implicit feature extraction layer (i.e., a fully connected layer) to obtain a set of implicit feature vectors. Finally, each implicit feature vector is input into the corresponding linear layer and normalization layer in the output layer, and the label feature anomaly score value is output to obtain a label feature anomaly score value group. Finally, the preset label field corresponding to the label feature anomaly score value greater than the preset anomaly value in the label feature anomaly score value group can be determined as the user's implicit body feature.

[0082] In practice, the aforementioned "some optional implementations in some embodiments" and their related steps serve as an inventive point. Considering that feature extraction of individual features and correlation between multiple features can only extract mutational features, it remains difficult to extract slowly changing user vital signs from short-term data. Therefore, by introducing the aforementioned implicit feature extraction network, implicit features can be extracted from longitudinal indicator features. Specifically, considering the coupled nature of implicit feature extraction, the similarity of features between different moments is introduced as a reference. Furthermore, because a single vital sign data similarity vector is generated for implicit feature extraction, it can be used to incorporate the correlated changes of single-dimensional indicator data at different time points into multidimensional data. This introduces single-dimensional features into the multidimensional world, simultaneously also incorporating the degree of change in the user's vital sign data at different time points. This is then used in the attention mechanism within the aforementioned implicit feature extraction network to extract more meaningful implicit features. This can thus reduce coupling. Subsequently, by introducing a bidirectional LSTM, time series features can be extracted from the data matrix, thereby extracting slowly changing data for each label from the time series. Furthermore, the accuracy of the generated implicit features of the user's body can be improved.

[0083] Step 2025: In response to determining that the user's body implicit characteristics meet the preset physical sign warning condition, a second user physical sign warning operation is performed.

[0084] In some embodiments, the execution entity may execute a second user vital sign warning operation in response to determining that the user's implicit physical characteristics meet a preset vital sign warning condition. The vital sign warning condition may be the presence of at least two preset indicator fields in the user's implicit physical characteristics. The second user vital sign warning operation may be transmitting the user's implicit physical characteristics to a healthcare terminal for prompting a warning.

[0085] Optionally, the above-mentioned execution entities may also include:

[0086] In the first step, in response to determining that the user's implicit body features do not meet a preset physical sign warning condition, the user's physical sign data is added to the historical physical sign data to obtain added historical physical sign data.

[0087] The second step is to use the added historical vital sign data to locate the next designated time point for storage. The fact that the user's implicit body features do not meet the preset vital sign warning conditions indicates that the user's body data is normal, and thus can be used as a reference for subsequent monitoring and data storage. Furthermore, the current detection time point can be determined as the next designated time point indicating that the user's vital sign data is in a normal state. This facilitates short-term data matching of subsequent user vital sign data to accommodate scenarios in which the user suffers sudden death.

[0088] The above-described embodiments of the present disclosure have the following beneficial effects: The user vital sign information monitoring and early warning methods based on a body monitoring system, as described in some embodiments of the present disclosure, can be used to monitor patient vital sign data in real time, providing timely early warnings in the event of anomalies, thereby significantly ensuring patient safety. Specifically, the reason patients may be in danger without warning is that, in order to avoid disturbing their rest and accidentally touching the therapeutic tubes inserted into their bodies, unnecessary contact with the patient is avoided as much as possible. However, in special circumstances, some patients are prone to sudden death, and abnormalities are difficult to detect without physical examination. This is especially true during nighttime patient rounds, where indoor lighting is dim, making it difficult to accurately detect abnormalities without disturbing the patient's rest. Based on this, the user vital sign information monitoring and early warning methods based on a body monitoring system, as described in some embodiments of the present disclosure, take this into account and introduce a body monitoring system to automatically monitor patient vital sign data. The body monitoring system includes a body information monitoring module and a vital sign data processing module. Specifically, the body information monitoring module monitors the user's vital sign data and transmits the user's vital sign data to the vital sign data processing module. The vital sign data processing module performs the following steps to monitor and provide early warnings for the user's vital sign information: First, the user's vital sign data is integrated with pre-stored historical vital sign data to generate a transverse vital sign indicator sequence set and a longitudinal vital sign indicator sequence set. Then, body data feature extraction is performed on the transverse vital sign indicator sequence set and the longitudinal vital sign indicator sequence set to obtain transverse indicator features and longitudinal indicator features, and a first user vital sign early warning operation is performed based on the transverse indicator features and longitudinal indicator features. Distinguishing between transverse vital sign indicators and longitudinal vital sign indicators facilitates the extraction of user data features from different data structures. Next, utilizing the historical vital sign data, an initial user vital sign data chain is constructed, wherein the initial user vital sign data chain represents the user's vital sign data at a specified time point. Constructing the initial user vital sign data chain can serve as a reference for the user's vital sign data. This allows the short-term relationship between the user's body's characteristic data to be identified, thereby preventing sudden changes in various physical indicators. Subsequently, based on the initial user vital sign data chain, implicit feature extraction is performed on the horizontal and vertical indicator features to obtain the user's implicit body features. This implicit feature extraction can be used to further explore the characteristics of the user's vital sign data. This allows for rapid prediction of data changes that could lead to dangerous outcomes for the patient. Finally, in response to determining that the user's implicit body features meet pre-set vital sign warning conditions, a second user vital sign warning operation is executed. This allows for real-time monitoring of patient vital sign data, enabling timely warnings in the event of anomalies. This significantly ensures patient safety.

[0089] Further references Figure 4 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a user vital sign information monitoring and early warning device based on a body monitoring system. These device embodiments are similar to Figure 2 Corresponding to the method embodiments shown, the user vital sign information monitoring and early warning device based on the body monitoring system can be specifically applied to various electronic devices.

[0090] like Figure 4 As shown, some embodiments of the user vital sign information monitoring and early warning device 400 based on the body monitoring system include: a monitoring and sending unit 401 and a data processing and early warning unit 402. The monitoring and sending unit 401 is configured to monitor the user's user vital sign data through the body information monitoring module and send the above user vital sign data to the vital sign data processing module; the data processing and early warning unit 402 is configured to perform the following steps to monitor the user's user vital sign information and early warning through the vital sign data processing module: integrate the above user vital sign data with the pre-stored historical vital sign data to generate a horizontal vital sign indicator sequence set and a longitudinal vital sign indicator sequence set; perform body data characteristic processing on the above horizontal vital sign indicator sequence set and the above longitudinal vital sign indicator sequence set. The method comprises the following steps: extracting the horizontal indicator features and the vertical indicator features, and performing a first warning operation of the user's vital signs according to the horizontal indicator features and the vertical indicator features; constructing an initial vital sign data chain of the user using the historical vital sign data, wherein the initial vital sign data chain represents the vital sign data of the user at a specified time point; performing implicit feature extraction on the horizontal indicator features and the vertical indicator features based on the initial vital sign data chain of the user, and obtaining implicit features of the user's body; and performing a second warning operation of the user's vital signs in response to determining that the implicit features of the user's body meet a preset vital sign warning condition.

[0091] It is understandable that the various units recorded in the user vital signs information monitoring and early warning device 400 based on the body monitoring system and the reference Figure 2 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the user vital sign information monitoring and early warning device 400 based on the body monitoring system and the units included therein, and will not be repeated here.

[0092] Reference below Figure 5 , which shows a structural schematic diagram of an electronic device suitable for implementing some embodiments of the present disclosure. Figure 5 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure. Figure 5As shown, the computer device includes a processor, a memory and a network interface connected via a system bus, wherein the memory may include a non-volatile storage medium and an internal memory. The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions, which, when executed, can enable the processor to execute any front-end page monitoring method. The processor is used to provide computing and control capabilities to support the operation of the entire computer device. The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium, which, when executed by the processor, can enable the processor to execute any front-end page monitoring method. The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 5 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present disclosure, and does not constitute a limitation on the computer device to which the solution of the present disclosure is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0093] It should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0094] In one embodiment, the processor is configured to execute a computer program stored in a memory to implement the following steps: monitoring user vital sign data of the user through the body information monitoring module, and sending the user vital sign data to the vital sign data processing module; performing the following steps to monitor and warn the user's vital sign information through the vital sign data processing module: integrating the user vital sign data with pre-stored historical vital sign data to generate a transverse vital sign indicator sequence set and a longitudinal vital sign indicator sequence set; extracting body data features from the transverse vital sign indicator sequence set and the longitudinal vital sign indicator sequence set to obtain transverse indicator features and longitudinal indicator features, and performing a first user vital sign warning operation based on the transverse indicator features and the longitudinal indicator features; constructing an initial user vital sign data chain using the historical vital sign data, wherein the initial vital sign data chain represents the vital sign data of the user at a specified time point; performing implicit feature extraction on the longitudinal indicator features based on the initial user vital sign data chain to obtain implicit body features of the user; and performing a second user vital sign warning operation in response to determining that the implicit body features of the user meet preset vital sign warning conditions.

[0095] An embodiment of the present disclosure also provides a computer-readable storage medium, on which a computer program is stored. The computer program includes program instructions. The method implemented when the program instructions are executed can refer to the various embodiments of the user vital sign information monitoring and early warning method based on the body monitoring system disclosed in the present disclosure.

[0096] The computer-readable storage medium may be an internal storage unit of the computer device described in the aforementioned embodiment, such as a hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a SmartMediaCard (SMC), a Secure Digital (SD) card, a flash memory card, etc., provided on the computer device.

[0097] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0098] The above descriptions are merely some preferred embodiments of the present disclosure and illustrate the underlying technical principles. Those skilled in the art should understand that the scope of the invention encompassed by the embodiments of the present disclosure is not limited to technical solutions formed by specific combinations of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the aforementioned inventive concept. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A user vital sign information monitoring and early warning method based on a body monitoring system, characterized in that: The body monitoring system includes: a body information monitoring module and a vital sign data processing module, wherein: Monitoring the user's vital sign data through the body information monitoring module, and sending the user's vital sign data to the vital sign data processing module; The vital sign data processing module performs the following steps to monitor and issue early warnings on the user's vital sign information: Integrating the user's vital sign data with pre-stored historical vital sign data to generate a transverse vital sign indicator sequence set and a longitudinal vital sign indicator sequence set, wherein each transverse vital sign indicator sequence in the transverse vital sign indicator sequence set is composed of multiple vital sign indicators at the same moment, and each longitudinal vital sign indicator sequence in the longitudinal vital sign indicator sequence set is composed of single vital sign indicators at consecutive moments; Performing body data feature extraction on the transverse vital sign indicator sequence set and the longitudinal vital sign indicator sequence set to obtain transverse indicator features and longitudinal indicator features, and executing a first user vital sign warning operation based on the transverse indicator features and the longitudinal indicator features, wherein the longitudinal indicator features include a longitudinal indicator abnormality information set corresponding to each longitudinal vital sign indicator sequence, each longitudinal indicator abnormality information includes abnormal indicator data and an abnormal time period in the longitudinal vital sign indicator sequence, and the transverse indicator features correspond to a transverse indicator abnormality information set for each transverse vital sign indicator sequence, each transverse indicator abnormality information includes abnormal indicator data and an abnormal time period in the transverse vital sign indicator sequence; Using the historical vital sign data, constructing a user initial vital sign data chain, wherein the user initial vital sign data chain represents the user's vital sign data at a specified time point; Based on the user's initial vital sign data chain, performing implicit feature extraction on the horizontal indicator feature and the vertical indicator feature to obtain the user's body implicit feature, wherein the implicit feature extraction takes the user's initial vital sign data chain, the horizontal indicator feature and the vertical indicator feature as input and outputs the user's body implicit feature; In response to determining that the user's body implicit feature meets a preset physical sign warning condition, a second user physical sign warning operation is performed.

2. The method according to claim 1, characterized in that The method further comprises: In response to determining that the user's implicit body feature does not meet a preset physical sign warning condition, adding the user's physical sign data to the historical physical sign data to obtain added historical physical sign data; The added historical vital sign data is used to locate the next designated time point for storage.

3. The method according to claim 1, characterized in that The monitoring of the user's vital signs data by the body information monitoring module includes: receiving user vital sign sensing information collected by a vital sign data sensing mattress used by the user, wherein the vital sign data sensing mattress is provided with a plurality of pressure sensing pads and a plurality of temperature sensing pads, and the plurality of pressure sensing pads and the plurality of temperature sensing pads are evenly laid out in the vital sign data sensing mattress for sensing pressure exerted by the user's body on the mattress and the user's body temperature, the user vital sign sensing information comprising: a pressure simulation voltage value array and a temperature simulation voltage value array; The body information monitoring module performs data conversion on the user's vital sign sensing information to generate user vital sign data, wherein the data conversion is performed by the following steps: constructing a user posture perception map based on the pressure simulation voltage value array, wherein the user posture perception map includes a user posture boundary coordinate sequence; performing a user dynamic posture analysis on the user posture perception map to generate a dynamic posture analysis result, wherein, in response to the dynamic posture analysis result including an indicator indicating abnormal user posture, executing a first user vital sign warning operation; performing analog-to-digital conversion on each temperature analog voltage value in the temperature analog voltage value array according to the user posture boundary coordinate sequence to generate a user body temperature value sequence; Using the pressure simulation voltage value array in the user's vital sign sensing information, updating the historical user's electrocardiogram waveform data to obtain the current user's electrocardiogram waveform data; Extracting R-wave peaks from the current user's ECG waveform data to obtain a sequence of instantaneous heart rate values; The dynamic posture analysis result, the user body temperature value sequence, the current user electrocardiogram waveform data and the instantaneous heart rate value sequence are determined as user vital sign data.

4. The method according to claim 1, wherein The historical vital sign data includes a historical indicator horizontal vital sign data sequence set and a historical indicator vertical vital sign sequence set, and each data item in the user's vital sign data corresponds to a vital sign indicator; as well as The step of integrating the user's vital sign data with pre-stored historical vital sign data to generate a horizontal vital sign indicator sequence set and a vertical vital sign indicator sequence set includes: Screening out historical indicator horizontal physical sign data sequences and historical indicator vertical physical sign sequences that match each physical sign indicator in the user's physical sign data from the historical physical sign data, to obtain a matched horizontal physical sign data sequence set and a matched vertical physical sign sequence set; The historical vital sign data is added to the matched transverse vital sign data sequence set and the matched longitudinal vital sign sequence set to obtain a transverse vital sign indicator sequence set and a longitudinal vital sign indicator sequence set.

5. The method according to claim 3, characterized in that The extracting of body data features from the transverse physical sign indicator sequence set and the longitudinal physical sign indicator sequence set to obtain transverse indicator features and longitudinal indicator features includes: Extracting characteristic values ​​from each longitudinal physical sign indicator sequence in the longitudinal physical sign indicator sequence set to obtain a longitudinal indicator characteristic value set; Dynamically allocating a sliding window step size for a preset data detection sliding window according to each longitudinal indicator characteristic value in the longitudinal indicator characteristic value set; Based on the allocated sliding window step size, performing synchronous sliding window detection on each longitudinal vital sign indicator sequence in the longitudinal vital sign indicator sequence set to generate a longitudinal indicator feature; performing data standardization on the transverse physical sign indicator sequence set to generate a standardized data matrix; Determining a covariance matrix of the standardized data matrix, and performing principal component analysis on the covariance matrix to generate an analyzed principal component data matrix; The analyzed principal component data matrix is ​​subjected to local outlier detection to generate horizontal indicator features.

6. The method according to claim 5, characterized in that The performing of the first warning operation of the user's vital signs according to the horizontal indicator characteristics and the vertical indicator characteristics includes: Performing data fusion on the horizontal indicator features and the vertical indicator features according to each abnormal time period included to obtain fused abnormal indicator information; In response to determining that the fused abnormal indicator information meets a first preset warning condition, a first warning operation of the user's vital signs is performed.

7. The method according to claim 6, characterized in that The method of using the historical vital sign data to construct the user's initial vital sign data chain includes: Selecting user vital sign data corresponding to a preset tag field and within a preset time period from the historical vital sign data to obtain an extracted user vital sign data sequence set; performing fitting processing on each extracted user vital sign data in each extracted user vital sign data sequence in the extracted user vital sign data sequence set to generate a user initial vital sign data curve set; For each user initial vital sign data curve in the user initial vital sign data curve set, determining a data jitter range corresponding to the user initial vital sign data curve; A user initial vital sign data chain is constructed according to the user initial vital sign data curve set and the corresponding data jump range set.

8. A user vital sign information monitoring and early warning device based on a body monitoring system, comprising: A monitoring and sending unit is configured to monitor the user's vital sign data through the body information monitoring module and send the user's vital sign data to the vital sign data processing module; The data processing and early warning unit is configured to perform the following steps to monitor the user's vital sign information and provide early warning via the vital sign data processing module: Integrating the user's vital sign data with pre-stored historical vital sign data to generate a transverse vital sign indicator sequence set and a longitudinal vital sign indicator sequence set, wherein each transverse vital sign indicator sequence in the transverse vital sign indicator sequence set is composed of multiple vital sign indicators at the same moment, and each longitudinal vital sign indicator sequence in the longitudinal vital sign indicator sequence set is composed of single vital sign indicators at consecutive moments; Performing body data feature extraction on the transverse vital sign indicator sequence set and the longitudinal vital sign indicator sequence set to obtain transverse indicator features and longitudinal indicator features, and executing a first user vital sign warning operation based on the transverse indicator features and the longitudinal indicator features, wherein the longitudinal indicator features include a longitudinal indicator abnormality information set corresponding to each longitudinal vital sign indicator sequence, each longitudinal indicator abnormality information includes abnormal indicator data and an abnormal time period in the longitudinal vital sign indicator sequence, and the transverse indicator features correspond to a transverse indicator abnormality information set for each transverse vital sign indicator sequence, each transverse indicator abnormality information includes abnormal indicator data and an abnormal time period in the transverse vital sign indicator sequence; Using the historical vital sign data, constructing a user initial vital sign data chain, wherein the user initial vital sign data chain represents the user's vital sign data at a specified time point; Based on the user's initial vital sign data chain, performing implicit feature extraction on the horizontal indicator feature and the vertical indicator feature to obtain the user's body implicit feature, wherein the implicit feature extraction takes the user's initial vital sign data chain, the horizontal indicator feature and the vertical indicator feature as input and outputs the user's body implicit feature; In response to determining that the user's body implicit feature meets a preset physical sign warning condition, a second user physical sign warning operation is performed.

9. An electronic device comprising: one or more processors; a storage device having one or more programs stored thereon, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

10. A computer-readable medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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