User sign information monitoring and early warning method based on body monitoring system

Abstract

The embodiment of the invention discloses a user physical sign information monitoring and early warning method based on a body monitoring system. A specific implementation mode of the method comprises a body information monitoring module and a physical sign data processing module, through a physical sign data processing module, the following steps are executed to carry out user physical sign information monitoring and early warning on a user: carrying out data integration on user physical sign data and pre-stored historical physical sign data to generate a transverse physical sign index sequence set and a longitudinal physical sign index sequence set; body data feature extraction is carried out on the transverse sign index sequence set and the longitudinal sign index sequence set; constructing an initial physical sign data chain of the user; implicit feature extraction is carried out on the longitudinal index features to obtain implicit features of the body of the user; and in response to determining that the implicit features of the user body meet the preset sign early warning condition, executing a second sign early warning operation of the user. According to the embodiment, the physical sign data of the patient can be monitored in real time, so that early warning can be carried out in time under the abnormal condition.

Description

Technical Field

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

[0002] During the hospitalization, in order to ensure the life safety of each patient, medical staff often need to conduct ward patient inspections, pay attention to the patient's physical condition at any time, and ensure the steady implementation of the treatment plan during hospitalization. In case of emergencies, timely response and handling are required. When inspecting patients, in order not to affect the patient's rest and avoid accidentally touching the treatment tubes inserted into the patient's body, try not to make unnecessary touches on the patient. However, in special circumstances, some patients are prone to sudden death, but it is difficult to find abnormalities without touching them. Especially when inspecting patients at night, the lighting in the room is weak, and it is difficult to check the patient's abnormal conditions without affecting the patient's rest. As a result, it is easy for patients to be in danger without signs.

[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 introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[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 warning user physical sign information based on a body monitoring system. The body monitoring system includes: a body information monitoring module and a physical sign data processing module, wherein: through the body information detection module, user physical sign data of a user is monitored and the user physical sign data is sent to the physical sign data processing module; through the physical sign data processing module, the following steps are performed to monitor and warn the user of user physical sign information: integrating the user physical sign data with pre-stored historical physical sign data to generate a horizontal physical sign index sequence set and a vertical physical sign index sequence set; extracting physical data features from the horizontal physical sign index sequence set and the vertical physical sign index sequence set to obtain horizontal index features and vertical index features, and performing a first warning operation for user physical signs according to the horizontal index features and the vertical index features; using the historical physical sign data to construct an initial physical sign data chain of the user, wherein the initial physical sign data chain represents the physical sign data of the user at a specified time point; performing implicit feature extraction on the vertical index features based on the initial physical sign data chain of the user to obtain implicit features of the user's body; and in response to determining that the implicit features of the user's body meet a preset physical sign warning condition, performing a second warning operation for user physical signs.

[0007] In a second aspect, some embodiments of the present disclosure provide a device for monitoring and warning user physical sign information based on a body monitoring system. The device includes: a monitoring and sending unit configured to monitor user physical sign data of a user through a body information detection module and send the user physical sign data to a physical sign data processing module; a data processing and warning unit configured to perform the following steps to monitor and warn the user of user physical sign information through the physical sign data processing module: integrating the user physical sign data with pre-stored historical physical sign data to generate a horizontal physical sign index sequence set and a vertical physical sign index sequence set; extracting physical data features from the horizontal physical sign index sequence set and the vertical physical sign index sequence set to obtain horizontal index features and vertical index features, and performing a first warning operation for user physical signs according to the horizontal index features and the vertical index features; using the historical physical sign data to construct an initial physical sign data chain of the user, wherein the initial physical sign data chain represents the physical sign data of the user at a specified time point; performing implicit feature extraction on the horizontal index features and the vertical index features based on the initial physical sign data chain of the user to obtain implicit features of the user's body; and in response to determining that the implicit features of the user's body meet a preset physical sign warning condition, performing a second warning operation for user physical signs.

[0008] In a third aspect, some embodiments of the present disclosure provide an electronic device, including: one or more processors; a storage device storing one or more programs thereon, which, when executed by the one or more processors, cause the one or more processors to implement the method described in any implementation manner of the first aspect above.

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

[0010] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: Through the user physical sign information monitoring and warning method based on the body monitoring system in some embodiments of the present disclosure, it can be used to monitor the physical sign data of patients in real time for timely warning in case of abnormalities, thus greatly ensuring the life safety of patients. Specifically, the reason for the patient to be in danger without warning is that in order not to affect the patient's rest and avoid accidentally touching the treatment pipeline inserted into the patient's body, unnecessary touches to the patient are minimized. However, in special cases, some patients are prone to sudden sudden death. But it is difficult to detect abnormalities without touching and checking. Especially when making night rounds of patients, the indoor lighting is weak, and it is difficult to check the patient's abnormalities well without disturbing the patient's rest. Based on this, the user physical sign information monitoring and warning method based on the body monitoring system in some embodiments of the present disclosure takes the above situation into account and thus introduces a body monitoring system for automatically monitoring the physical sign data of patients. Here, the body monitoring system includes: a body information monitoring module and a physical sign data processing module. Specifically, through the above-mentioned body information detection module, the user's physical sign data is monitored and the above-mentioned user's physical sign data is sent to the above-mentioned physical sign data processing module. Through the above-mentioned physical sign data processing module, the following steps are performed to monitor and warn the user's physical sign information: First, the above-mentioned user's physical sign data is integrated with the pre-stored historical physical sign data to generate a horizontal physical sign index sequence set and a vertical physical sign index sequence set. Then, physical data feature extraction is performed on the above-mentioned horizontal physical sign index sequence set and the above-mentioned vertical physical sign index sequence set to obtain horizontal index features and vertical index features, and a first user physical sign warning operation is performed according to the above-mentioned horizontal index features and vertical index features. Here, by distinguishing horizontal physical sign indexes and vertical physical sign indexes, it is convenient to extract user data features from different data structures. Secondly, using the above-mentioned historical physical sign data, a user initial physical sign data chain is constructed, where the above-mentioned initial physical sign data chain represents the physical sign data of the user at a specified time point. Here, by constructing a user initial physical sign data chain, it can be used as a reference for the user's physical sign data. Thus, the characteristic data relationship of the user's body in the short term can be located to facilitate the prevention of sudden changes in various physical indicators. After that, based on the above-mentioned user initial physical sign data chain, implicit feature extraction is performed on the above-mentioned horizontal index features and the above-mentioned vertical index features to obtain the user's body implicit features. Here, through implicit feature extraction, it can be used to further mine the features of the user's physical sign data. Thus, quickly predict the data change situation that causes danger to the patient. Finally, in response to determining that the above-mentioned user's body implicit features meet the preset physical sign warning conditions, a second user physical sign warning operation is performed. Thus, it can be used to monitor the physical sign data of patients in real time for timely warning in case of abnormalities. Furthermore, the life safety of patients is greatly ensured. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0012] Figure 1 is a schematic diagram of an application scenario of a method for monitoring and warning user physical sign information based on a body monitoring system according to some embodiments of the present disclosure.

[0013] Figure 2 is a flowchart according to some embodiments of the method for monitoring and warning user physical sign information based on a body monitoring system of the present disclosure; Figure 3 is a schematic diagram of a mattress structure according to some embodiments of the method for monitoring and warning user physical sign information based on a body monitoring system of the present disclosure; Figure 4 is a schematic diagram of a structure according to some embodiments of the device for monitoring and warning user physical sign information based on a body monitoring system of the present disclosure; Figure 5 is a schematic diagram of a structure of an electronic device suitable for implementing some embodiments of the present disclosure. Specific Embodiments

[0014] The embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not used to limit the protection scope of the present disclosure.

[0015] In addition, it should be noted that for the sake of convenience of description, only the parts related to the relevant invention are shown in the drawings. Without conflict, the embodiments in the present disclosure and the features in the embodiments can be combined with each other.

[0016] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or mutual dependence relationship of the functions performed by these devices, modules or units.

[0017] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly stated in the context, it should be understood as "one or more".

[0018] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are for illustrative purposes only and are not used to limit the scope of these messages or information.

[0019] Regarding the operations of collecting, storing, using, etc. of the user's personal information (such as user physical sign data) involved in the present disclosure, before performing the corresponding operations, relevant organizations or individuals shall fulfill obligations including conducting a personal information security impact assessment, fulfilling the obligation of notification to the personal information subject, and obtaining the prior authorization and consent of the personal information subject. The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0020] Figure 1 It is a schematic diagram of an application scenario of a method for monitoring and warning user physical sign information based on a body monitoring system according to some embodiments of the present disclosure.

[0021] In Figure 1 the application scenario, first, the body monitoring system 101 may include a body information monitoring module 102 and a physical sign data processing module 103. Among them: Through the above-mentioned body information detection module 102, the user's physical sign data 1021 is monitored, and the above-mentioned user physical sign data 1021 is sent to the above-mentioned physical sign data processing module 103. Through the above-mentioned physical sign data processing module 103, the following steps are performed to monitor and warn the user's physical sign information: Integrate the above-mentioned user physical sign data with the pre-stored historical physical sign data to generate a horizontal physical sign index sequence set and a vertical physical sign index sequence set. Extract physical data features from the above-mentioned horizontal physical sign index sequence set and the above-mentioned vertical physical sign index sequence set to obtain horizontal index features and vertical index features, and perform a first warning operation for user physical signs according to the above-mentioned horizontal index features and vertical index features. Use the above-mentioned historical physical sign data to construct an initial physical sign data chain of the user. Among them, the above-mentioned initial physical sign data chain represents the physical sign data of the user at a specified time point. Based on the above-mentioned initial physical sign data chain of the user, perform implicit feature extraction on the above-mentioned horizontal index features and the above-mentioned vertical index features 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, perform a second warning operation for user physical signs.

[0022] It should be noted that the above-mentioned body monitoring system 101 may be hardware or software. When the computing device is hardware, it can be implemented as a distributed cluster composed of multiple servers or terminal devices, or as a single server or a single terminal device. When the computing device is embodied as software, it can be installed in the above-mentioned listed hardware devices. It can be implemented as, for example, multiple software or software modules for providing distributed services, or as a single software or software module. No specific limitation is made here. It should be understood, Figure 1The number of computing devices in [it] can be any number according to implementation requirements. The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0023] Figure 2 Flow 200 of some embodiments of a method for monitoring and warning user physical sign information based on a body monitoring system according to the present disclosure is shown. The method for monitoring and warning user physical sign information based on the body monitoring system includes the following steps: Step 201, monitor user physical sign data of a user through a body information detection module, and send the user physical sign data to a physical sign data processing module.

[0024] In some embodiments, the execution subject of the method for monitoring and warning user physical sign information based on the body monitoring system can monitor the user physical sign data of the user through the body information detection module in a wired or wireless manner, and send the user physical sign data to the physical sign data processing module. Among them, the user physical sign data can include, but is not limited to, at least one of the following: user body temperature value, user electrocardiogram value, user breathing rate, user blood pressure, etc.

[0025] In some alternative implementation manners of some embodiments, the step of the execution subject monitoring the user physical sign data of the user through the body information detection module can include the following steps: The first step, receive user physical sign sensing information monitored by a physical sign data sensing mattress used by the user. Among them, a plurality of pressure sensing pads and a plurality of temperature sensing pads are arranged inside the physical sign data sensing mattress. The plurality of pressure sensing pads and the plurality of temperature sensing pads are evenly laid in the physical sign data sensing mattress to sense the pressure of the user body on the mattress and the user body temperature. The user physical sign sensing information includes: an array of pressure analog voltage values and an array of temperature analog voltage values.

[0026] As an example, see Figure 3 . Such as Figure 3As shown in the figure, multiple pressure sensing pads 302 (the number in the figure is schematic) and multiple temperature sensing pads 303 (the number in the figure is schematic) are provided inside the above-mentioned physical sign data sensing mattress 301. The multiple pressure sensing pads and the multiple temperature sensing pads are evenly and cross-laid inside the above-mentioned physical sign data sensing mattress to sense the pressure of the user's body on the mattress and the user's body temperature. At the same time, signals are transmitted to the body information detection module through a circuit. Here, the data arrangement methods of the pressure analog voltage value array and the temperature analog voltage value array are arranged corresponding to the positions where the pressure sensing pads or the temperature sensing pads are laid. Therefore, the pressure analog voltage value array and the temperature analog voltage value array can also be used to characterize the pressure distribution and temperature distribution of the user on the mattress respectively. In addition, the pressure sensing pad can be a diaphragm type pressure sensor. The temperature sensing pad can be a sensor for sensing temperature. For example, the types of sensors for sensing temperature can include but are not limited to thermocouples, thermistors, integrated temperature sensors, etc.

[0027] Second, through the above-mentioned body information detection module, data conversion is performed on the above-mentioned user physical sign sensing information to generate user physical sign data. Among them, data conversion is performed through the following steps: Optionally, the pressure analog voltage value array and the temperature analog voltage value array included in the above-mentioned user physical sign sensing information can be subjected to noise reduction processing through the following steps to obtain the noise-reduced user physical sign sensing information. Specifically, the noise reduction processing can be used to remove the data in the above-mentioned user physical sign sensing information that exceeds the preset data range. In response to the number of data in the above-mentioned user physical sign sensing information that exceeds the preset data range being greater than the preset number threshold, a first warning operation for user physical signs is executed.

[0028] As an example, considering the laying situation of the sensors in the mattress, each data in the collected array should change continuously. Therefore, for a certain pressure analog voltage value in the pressure analog voltage value array, it can be determined whether the above-mentioned pressure analog voltage value is greater than or less than 3 times the value of the adjacent pressure analog voltage value. If it is greater, it is determined that the pressure analog voltage value belongs to a mutation value and can be removed as noise data. In addition, an interpolation algorithm can also be used to perform interpolation according to the adjacent data to complete the data.

[0029] Step 1: Based on the above pressure simulation voltage value array, construct a user posture perception map. Among them, the user posture perception map includes a sequence of user posture boundary coordinates. Secondly, pressure simulation voltage values greater than a preset pressure threshold can be selected from the above pressure simulation voltage value array as user position simulation voltage values to obtain a sequence of user position simulation voltage values. Then, data in the above pressure simulation voltage value array except for the positions where each user position simulation voltage value is located can be set to zero to obtain an initial posture perception data matrix. Next, the above initial posture perception data matrix can be converted into a binary matrix. After that, through the dilation and erosion algorithm, the binary matrix corresponding to the above initial posture perception data matrix is first dilated and then eroded to obtain a processed posture perception data matrix. Finally, the boundary coordinates of the data area in the above processed posture perception data matrix can be used as user posture boundary coordinates to obtain a sequence of user posture boundary coordinates. The processed posture perception data matrix is the user posture perception map.

[0030] Step 2: Perform user dynamic posture analysis on the above user posture perception map to generate a dynamic posture analysis result. Among them, in response to the above dynamic posture analysis result including an identifier indicating an abnormal user posture, perform a first warning operation on user physical signs. Here, through a preset human key point detection algorithm, human key point detection can be performed on the above user posture perception map to generate a set of human key point detection coordinates. Secondly, the pressure simulation voltage values corresponding to the positions centered on each human key point detection coordinate in the above set of human key point detection coordinates can be converted into user body pressure values. Here, the user body pressure value represents the force exerted by the user's body joint part on the mattress. Finally, in response to determining that the user body pressure value is greater than a preset body part weight threshold, an identifier indicating an abnormal user posture is generated.

[0031] In practice, the user body pressure value being greater than the preset body part weight threshold can represent abnormal behaviors shown by the user's limb posture in the case of sudden cardiac arrest, such as unconsciously patting the mattress or excessive local pressure on the mattress caused by abnormal movement amplitude. Therefore, the first warning operation on user physical signs can be directly performed. For example, the first warning operation on user physical signs can be to notify medical staff of the user's physical abnormality (through a medical care indicator light and an alarm).

[0032] Step 3: According to the above sequence of user posture boundary coordinates, perform analog-to-digital conversion on each temperature simulation voltage value in the above temperature simulation voltage value array to generate a sequence of user body temperature values. Among them, each user posture boundary coordinate in the above sequence of user posture boundary coordinates can represent the position where the user is located. Secondly, through the analog-to-digital converter set in the body information monitoring module, analog-to-digital conversion is performed on the temperature simulation voltage value corresponding to the position where the user is located in the above temperature simulation voltage value array to generate a sequence of user body temperature values.

[0033] Specifically, the above analog-to-digital converter can be designed corresponding to the sensor type of the temperature sensing gasket, and can convert the temperature analog voltage value into a specific temperature voltage value. Then, the preset transfer function is used to convert the temperature voltage value into a specific temperature value. Here, the transfer function can be a formula for converting the temperature analog voltage value into a temperature voltage value. Specifically, the sensor output has a linear relationship with the temperature. For example, the temperature analog voltage value = coefficient × temperature value + constant. Thus, the temperature analog voltage value can be converted into a temperature voltage value.

[0034] Step four, use the pressure analog voltage value array in the above user body sign sensing information to update the historical user electrocardiogram waveform data to obtain the current user electrocardiogram waveform data. Among them, the above pressure analog voltage value array can be band-pass filtered (for example, set 0.8 - 3 Hz (i.e., corresponding to a heart rate value of 48 - 180)) and adaptively filtered (0.1 - 0.3 Hz as the noise reference for the respiratory signal) to filter out the heartbeat signal. Add the heartbeat signal to the historical heartbeat signal sequence to obtain the current heartbeat signal sequence. Perform a short-time Fourier transform on the above current heartbeat signal sequence to locate the heartbeat cycle. According to the above heartbeat cycle, generate a heart rate value. In addition, through the above analog-to-digital conversion method, each pressure analog voltage value in the above pressure analog voltage value array can be analog-to-digital converted to obtain a user heartbeat pressure value sequence. Then, according to the mapping relationship table of the electrocardiogram signal and the heartbeat signal established in advance for the above user, determine the current user electrocardiogram waveform data corresponding to each user heartbeat pressure value. Here, the mapping relationship table can be generated by using the Dynamic Time Warping (DTW) algorithm to map the relationship between the pressure values of the user on the mattress and the measured electrocardiogram waveform data obtained in advance. In addition, the mapping relationship table can be updated daily to better conform to the user characteristics.

[0035] Step five, extract the R-wave peaks from the above current user electrocardiogram waveform data to obtain an instantaneous heart rate value sequence. Among them, the data in the current user electrocardiogram waveform data that exceeds the preset signal threshold can be used as the R-wave peak. Secondly, the instantaneous heart rate value can be determined by the time interval between adjacent R-wave peak values.

[0036] Step six, determine the above dynamic posture analysis result, the above user body temperature value sequence, the above current user electrocardiogram waveform data, and the above instantaneous heart rate value sequence as user body sign data.

[0037] In practice, the above-mentioned "in some alternative implementation manners of some embodiments" and its related steps are taken as an inventive point. When there is no other device to measure the indicators, by introducing a mattress to extract the user's physiological sign data, it can be used to sense the actual physical condition of the patient without contacting the patient. For example, there are some patients who will have an inflammatory reaction with an increase in body temperature or a shock reaction with a decrease in body temperature before sudden death. Therefore, through body temperature monitoring, heart rate monitoring, user movement monitoring, heartbeat monitoring, etc., risk warnings can be given in a timely manner.

[0038] Step 202, through the physiological sign data processing module, perform the following steps to monitor and warn the user's physiological sign information: Step 2021, integrate the user's physiological sign data with the pre-stored historical physiological sign data to generate a horizontal physiological sign index sequence set and a vertical physiological sign index sequence set.

[0039] In some embodiments, the above-mentioned execution entity can integrate the above-mentioned user's physiological sign data with the pre-stored historical physiological sign data to generate a horizontal physiological sign index sequence set and a vertical physiological sign index sequence set.

[0040] In some alternative implementation manners of some embodiments, the historical physiological sign data may include a historical index horizontal physiological sign data sequence set and a historical index vertical physiological sign sequence set. Each piece of data in the above-mentioned user's physiological sign data corresponds to a physiological sign index. And the above-mentioned execution entity integrating the above-mentioned user's physiological sign data with the pre-stored historical physiological sign data to generate a horizontal physiological sign index sequence set and a vertical physiological sign index sequence set may include the following steps: The first step is to screen out the historical index horizontal physiological sign data sequences and historical index vertical physiological sign sequences that match each physiological sign index in the above-mentioned user's physiological sign data from the above-mentioned historical physiological sign data to obtain a matched horizontal physiological sign data sequence set and a matched vertical physiological sign sequence set. Among them, the matching may be for the same physiological sign index.

[0041] The second step is to add the above-mentioned historical physiological sign data to the above-mentioned matched horizontal physiological sign data sequence set and the above-mentioned matched vertical physiological sign sequence set to obtain a horizontal physiological sign index sequence set and a vertical physiological sign index sequence set. Among them, each horizontal physiological sign index sequence in the above-mentioned horizontal physiological sign index sequence set is composed of multiple physiological sign indexes at the same moment, and each vertical physiological sign index sequence in the above-mentioned vertical physiological sign index sequence set is composed of a single physiological sign index at consecutive moments.

[0042] Step 2022, extract the body data features from the horizontal physiological sign index sequence set and the vertical physiological sign index sequence set to obtain horizontal index features and vertical index features, and perform the first warning operation for the user's physiological signs according to the horizontal index features and the vertical index features.

[0043] In some embodiments, the above-mentioned execution entity may extract physical data features from the above-mentioned horizontal physical sign index sequence set and the above-mentioned vertical physical sign index sequence set to obtain horizontal index features and vertical index features, and perform a first warning operation on the user's physical signs according to the above-mentioned horizontal index features and vertical index features.

[0044] In some alternative implementation manners of some embodiments, the above-mentioned execution entity extracts physical data features from the above-mentioned horizontal physical sign index sequence set and the above-mentioned vertical physical sign index sequence set to obtain horizontal index features and vertical index features, including: First step, extract eigenvalue from each vertical physical sign index sequence in the above-mentioned vertical physical sign index sequence set to obtain a set of vertical index eigenvalues. Among them, the eigenvalue of each vertical physical sign index sequence in the above-mentioned vertical physical sign index sequence set can be extracted by a preset mutation point detection algorithm to obtain a set of vertical index eigenvalues. Here, each vertical index eigenvalue can be the time point where the vertical physical sign value with a mutation exists in the corresponding vertical physical sign index sequence, which is used to represent the time point of the mutation abnormality in a single index in the time dimension.

[0045] As an example, the above-mentioned 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.

[0046] Second step, allocate a sliding window step length dynamically for a preset data detection sliding window according to each vertical index eigenvalue in the above-mentioned set of vertical index eigenvalues. Among them, each of the above-mentioned vertical index eigenvalues can be extended to a time period with the central time point to obtain multiple characteristic time periods. Here, when each vertical index eigenvalue is extended as the central time point, the extended time length can be the preset time length multiplied by the mutation degree of the vertical index eigenvalue. The mutation degree can be the ratio between the corresponding vertical physical sign index and the mean value of the vertical physical sign index sequence (here, the absolute value can be taken). Thus, the corresponding time periods can be extended according to the mutation degrees of different indexes. Finally, the intersection of each time period can be taken as the dynamically allocated sliding window step length of the above-mentioned data detection sliding window.

[0047] In practice, although the vertical physical sign indexes correspond to continuous single indexes, considering the correlation existing in the change of the user's physical data, even when extracting features using the individual features of each index in the time dimension, this correlation needs to be considered. Therefore, the sliding window step length is dynamically allocated with correlation.

[0048] Step 3: Based on the allocated sliding window step size, perform synchronous sliding window detection on each longitudinal physical sign index sequence in the above longitudinal physical sign index sequence set to generate longitudinal index features. Among them, the above longitudinal index features may include a set of longitudinal index anomaly information corresponding to each longitudinal physical sign index sequence. Each longitudinal index anomaly information includes anomaly index data and anomaly time periods in the longitudinal physical sign index sequence. Here, the above data detection sliding window can be used to perform synchronous sliding window detection on each longitudinal physical sign index sequence according to the allocated sliding window step size to generate longitudinal index features. Here, the anomaly time period may be the time period corresponding to multiple anomaly index data. Thus, the anomaly points in the longitudinal physical sign index sequence can be further located. Therefore, more accurate early warnings can be issued.

[0049] Step 4: Standardize the data of the above transverse physical sign index sequence set to generate a standardized data matrix. Among them, the data of the above transverse physical sign index sequence set can be standardized by the Z-score standardization algorithm to generate a standardized data matrix.

[0050] Step 5: Determine the covariance matrix of the above standardized data matrix and perform principal component analysis on the above covariance matrix to generate a principal component data matrix after analysis. Among them, the principal component analysis algorithm can be used to perform principal component analysis on the above covariance matrix to generate a principal component data matrix after analysis.

[0051] Step 6: Perform local outlier factor detection on the above principal component data matrix after analysis to generate transverse index features. Among them, the above transverse index features correspond to a set of transverse index anomaly information for each transverse physical sign index sequence, and each transverse index anomaly information includes anomaly index data and anomaly time periods. Among them, the local outlier factor detection algorithm (Local Outlier Factor, LOF) can be used to perform local outlier factor detection on the above principal component data matrix after analysis to generate transverse index features.

[0052] In practice, the above "in some optional implementation manners of some embodiments" and its related steps, as an inventive point, can be used to preliminarily determine the principal component indexes that can largely reflect the user's physical data characteristics at present by performing principal component analysis on the standardized data matrix in multiple dimensions (i.e., composed of multiple indexes) in the transverse direction. Then, by performing local outlier factor detection, it can be used to further locate the abnormal positions of the principal component indexes at different times. Thus, the time points that can reflect the user's physical sign anomalies can be extracted from the features of the multi-dimensional data. Furthermore, it can also be used to issue more accurate early warnings.

[0053] In some optional implementation manners of some embodiments, the above execution subject performs the first early warning operation for the user's physical signs according to the above transverse index features and longitudinal index features, including: First step, perform data fusion on the above-mentioned horizontal index features and vertical index features according to each abnormal time period included, to obtain the fused abnormal index information. Among them, data fusion can be arranging data in chronological order and removing index data at the same moment, as the fused abnormal index information.

[0054] Second step, in response to determining that the above-mentioned fused abnormal index information meets the first preset warning condition, perform the first warning operation on the user's physical signs. Among them, the first preset warning condition can be that there is abnormal data in the fused index information.

[0055] Step 2023, use historical physical sign data to construct an initial physical sign data chain of the user.

[0056] In some embodiments, the above-mentioned execution entity can use the above-mentioned historical physical sign data to construct an initial physical sign data chain of the user. Among them, the above-mentioned initial physical sign data chain can represent the physical sign data of the above-mentioned user at a specified time point. Here, the specified time point can be a preset time point.

[0057] In some optional implementation manners of some embodiments, the above-mentioned execution entity uses the above-mentioned historical physical sign data to construct an initial physical sign data chain of the user, including: First step, select the user's physical sign data corresponding to the preset label field and within the preset time period from the above-mentioned historical physical sign data, to obtain the extracted user physical sign data sequence set. Considering that the sudden death of the user is a sudden situation, it is necessary to extract features from short-term data. Therefore, select the user's physical sign data corresponding to the preset label field (for example, body temperature field, body pressure field, heart rate field, electrocardiogram field, etc.) and within the preset time period (for example, one hour) from the above-mentioned historical physical sign data, as the extracted user physical sign data sequence set. Here, each extracted user feature data sequence can correspond to a preset label field.

[0058] Second step, perform fitting processing on each extracted user physical sign data in each extracted user physical sign data sequence in the above-mentioned extracted user physical sign data sequence set, to generate an initial physical sign data curve set of the user. Here, fitting processing can be performed by the least squares method.

[0059] Third step, for each initial physical sign data curve in the above-mentioned initial physical sign data curve set of the user, determine the data fluctuation range corresponding to the above-mentioned initial physical sign data curve. Among them, the curve expansion of each initial physical sign data curve can be performed according to the preset fluctuation threshold set for each preset label field at the specified time point of the user, to obtain the data fluctuation range centered on the initial physical sign data curve of the user. Here, the data fluctuation range can represent the data and safety range corresponding to each preset label field.

[0060] Step 4: Construct a user initial physical sign data chain based on the above user initial physical sign data curve set and the corresponding data fluctuation range set. Among them, each user initial physical sign data curve and the data within the corresponding data fluctuation range can be spread into a multi-dimensional matrix in the order of time and index labels as the user initial physical sign data chain.

[0061] Step 2024: Based on the user initial physical sign data chain, perform implicit feature extraction on the horizontal index features and vertical index features to obtain user body implicit features.

[0062] In some embodiments, the above execution entity can perform implicit feature extraction on the above horizontal index features and the above vertical index features based on the above user initial physical sign data chain to obtain user body implicit features.

[0063] In some optional implementation manners of some embodiments, the above execution entity performs implicit feature extraction on the above horizontal index features and the above vertical index features based on the above user initial physical sign data chain to obtain user body implicit features, including: Step 1: Construct a current physical sign data curve set corresponding to the above horizontal index features and the above vertical index features according to the user initial physical sign data curve set in the above user initial physical sign data chain. Among them, the implementation manner of constructing the current physical sign data curve set can refer to the construction steps of the above user initial physical sign data chain and will not be specifically described.

[0064] Step 2: Determine the physical sign data similarity between the user initial physical sign data curve and the current physical sign data curve corresponding to the same preset label field in the above user initial physical sign data curve set and the above current physical sign data curve set to obtain a physical sign data similarity vector set. Among them, the Euclidean distance value between the user initial physical sign data curve and the current physical sign data curve corresponding to the same preset label field can be determined as the physical sign data similarity vector.

[0065] Step 3: Based on the above physical sign data similarity vector set, perform implicit feature extraction on the above vertical index features to obtain user body implicit features. Among them, the data matrix corresponding to the above vertical index features can be input into a preset implicit feature extraction network to generate user body implicit features. Here, the data matrix can be a matrix with a dimension of T×N formed by combining the vertical index features and the set of user physical sign data sequences after extraction. T can be a time series. N can be a sequence of preset label fields.

[0066] As an example, the above implicit feature extraction network can be a graph neural network. Then the above physical sign data similarity vector set can be used as the attention weights of each node corresponding to the same index in the graph neural network.

[0067] As another example, the above-mentioned implicit feature extraction network may 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 above-mentioned longitudinal index features and the above-mentioned physical sign data similarity vector set may be input into the above-mentioned input layer. Then, the above-mentioned feature aggregation layer may perform the following steps: normalize the physical sign data similarity vectors corresponding to each preset label field through the Softmax function to obtain attention weight vectors; perform feature extraction on the data matrix corresponding to the above-mentioned longitudinal index features through a sliding window, and perform weighted summation on the features within the window through similarity weights to obtain an aggregated feature matrix; thereafter, the above-mentioned feature extraction layer may perform state feature extraction on the above-mentioned aggregated feature matrix through a bidirectional LSTM (Bidirectional Long Short - Term Memory, Bi-LSTM) layer to obtain a hidden state feature matrix; then, input the above-mentioned hidden state feature matrix into a multi-layer perceptron through the above-mentioned implicit feature extraction layer (i.e., a fully connected layer) to obtain a set of implicit feature vectors; finally, input each implicit feature vector into the corresponding linear layer and normalization layer in the output layer to output a label feature anomaly score value, and obtain a set of label feature anomaly score values. Finally, the preset label fields corresponding to the label feature anomaly score values greater than the preset anomaly value in the set of label feature anomaly score values may be determined as the implicit features of the user's body.

[0068] In practice, the above-mentioned "in some optional implementation manners of some embodiments" and its related steps, as an inventive point, considering the situation that only mutation features can be extracted when performing feature extraction on individual features separately and the associated features between multiple features, it is still difficult to extract the slow changes in the user's physical signs from short-term data. Therefore, by introducing the above-mentioned implicit feature extraction network, it can be used to extract implicit features from longitudinal index features. Specifically, considering that implicit feature extraction has coupling, different feature similarities between different moments are introduced as a reference. Specifically, since a single physical sign data similarity vector is generated for implicit feature extraction, it can be used to add the associated changes of single-dimensional index data at different time points in multi-dimensional data. Thus, single-dimensional features are introduced into multi-dimensions, and the degree of change in the user's physical sign data between different time points is also introduced synchronously. Thereby, it is used in the attention mechanism of the above-mentioned implicit feature extraction network to facilitate the extraction of more meaningful implicit features. Thus, it can be used to reduce coupling. Then, by introducing bidirectional LSTM, it can be used to extract the time series features in the data matrix, and thus, it can be used to extract the slow changes in each label data from the time series. Furthermore, the accuracy of the generated implicit features of the user's body can be improved.

[0069] Step 2025, in response to determining that the implicit features of the user's body meet the preset physical sign warning conditions, perform the second warning operation for the user's physical signs.

[0070] In some embodiments, the above-mentioned execution entity may, in response to determining that the implicit features of the user's body meet the preset physical sign warning conditions, perform the second warning operation for the user's physical signs. Among them, the above-mentioned physical sign warning conditions may be that there are at least two preset index fields in the implicit features of the user's body. The second warning operation for the user's physical signs may be to send the above-mentioned implicit features of the user's body to the medical care terminal for prompt warning.

[0071] Optionally, the above-mentioned execution entity may further include: First step, in response to determining that the implicit features of the user's body do not meet the preset physical sign warning conditions, add the above-mentioned user physical sign data to the above-mentioned historical physical sign data to obtain the added historical physical sign data.

[0072] Second step, use the above-mentioned added historical physical sign data to locate the next specified time point for storage. Wherein, the fact that the implicit features of the user's body do not meet the preset physical sign warning conditions may indicate that there is no abnormality in the user's body data, so it can be used as a reference for subsequent monitoring for data storage. In addition, the current detected time point may be determined as the next specified time point indicating that the user's physical sign data is in a normal state. Thus, it is convenient for subsequent short-term data matching of the user's physical sign data to adapt to the scenario where the user suddenly dies.

[0073] The above-mentioned embodiments of the present disclosure have the following beneficial effects: through the user vital sign information monitoring and early warning method based on the body monitoring system of some embodiments of the present disclosure, it can be used to monitor the patient's vital sign data in real time, so as to timely warn in the event of abnormalities, thereby greatly ensuring the patient's life safety. Specifically, the reason why the patient is in danger without signs is that in order not to affect the patient's rest and avoid accidentally touching the treatment pipeline inserted into the patient's body, try not to make unnecessary touches on the patient. However, in special circumstances, some patients are prone to sudden death, but it is difficult to find abnormalities without touching the inspection. Especially when patrolling patients at night, the lighting in the room is weak, and it is difficult to query the patient's abnormal situation without affecting the patient's rest. Based on this, the user vital sign information monitoring and early warning method based on the body monitoring system of some embodiments of the present disclosure takes into account the above situation, so a body monitoring system is introduced to automatically monitor the patient's vital sign data. Here, the body monitoring system includes: a body information monitoring module and a vital sign data processing module. Specifically, through the above-mentioned body information detection module, the user's user vital sign data is monitored, and the above-mentioned user vital sign data is sent to the above-mentioned vital sign data processing module. Through the above-mentioned vital sign data processing module, the following steps are performed to monitor and warn the user's vital sign information of the above-mentioned user: first, 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 vertical vital sign indicator sequence set. Then, the body data feature extraction is performed on 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 the user's vital sign first warning operation is performed according to the above-mentioned horizontal indicator features and vertical indicator features. Here, by distinguishing between horizontal vital sign indicators and vertical vital sign indicators, it is convenient to extract user data features from different data structures. Secondly, using the above-mentioned historical vital sign data, the user's initial vital sign data chain is constructed, wherein the above-mentioned initial vital sign data chain represents the vital sign data of the above-mentioned user at a specified time point. Here, by constructing the user's initial vital sign data chain, it can be used as a reference for the user's vital sign data. Thereby, the characteristic data relationship of the user's body in a short period of time can be located to prevent sudden changes in various physical indicators. Afterwards, based on the above-mentioned user's initial vital sign data chain, the above-mentioned horizontal indicator features and the above-mentioned vertical indicator features are implicitly extracted to obtain the user's body implicit features. Here, through implicit feature extraction, it can be used to further explore the features of the user's vital sign data. Thus, the data changes that cause danger to the patient can be quickly predicted. Finally, in response to determining that the above-mentioned user's body implicit features meet the preset vital sign warning conditions, the user's vital sign second warning operation is executed. Thus, it can be used to monitor the patient's vital sign data in real time, so as to provide timely warnings in the event of abnormalities. Furthermore, the patient's life safety is greatly ensured. Further references Figure 4, as an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a user physical sign information monitoring and warning device based on a body monitoring system, and these device embodiments correspond to Figure 2 the method embodiments shown. The user physical sign information monitoring and warning device based on the body monitoring system can be specifically applied to various electronic devices.

[0074] As Figure 4 shown, a user physical sign information monitoring and warning device 400 based on a body monitoring system in some embodiments includes: a monitoring and sending unit 401 and a data processing and warning unit 402. Among them, the monitoring and sending unit 401 is configured to monitor the user's physical sign data through a body information detection module and send the above-mentioned user physical sign data to a physical sign data processing module; the data processing and warning unit 402 is configured to perform the following steps to monitor and warn the user's physical sign information through the physical sign data processing module: integrate the above-mentioned user physical sign data with pre-stored historical physical sign data to generate a horizontal physical sign index sequence set and a vertical physical sign index sequence set; extract physical data features from the above-mentioned horizontal physical sign index sequence set and the above-mentioned vertical physical sign index sequence set to obtain horizontal index features and vertical index features, and perform a first user physical sign warning operation according to the above-mentioned horizontal index features and the above-mentioned vertical index features; use the above-mentioned historical physical sign data to construct an initial user physical sign data chain, where the above-mentioned initial physical sign data chain represents the physical sign data of the user at a specified time point; based on the above-mentioned initial user physical sign data chain, perform implicit feature extraction on the above-mentioned horizontal index features and the above-mentioned vertical index features to obtain implicit user body features; in response to determining that the above-mentioned implicit user body features meet a preset physical sign warning condition, perform a second user physical sign warning operation.

[0075] It can be understood that the various units described in the user physical sign information monitoring and warning device 400 based on the body monitoring system correspond to the respective steps in the method described with reference to Figure 2 Therefore, the operations, features, and beneficial effects described above for the method also apply to the user physical sign information monitoring and warning device 400 based on the body monitoring system and the units included therein, and will not be repeated here. Next, refer to Figure 5 , which shows a schematic structural 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 bring any limitations to the functions and usage scopes of the embodiments of the present disclosure. As Figure 5As shown, the computer device includes a processor, a memory, and a network interface connected via a system bus. Among them, the memory may include a non-volatile storage medium and an internal memory. The non-volatile storage medium can store an operating system and computer programs. The computer programs include program instructions, which, when executed, can cause the processor to execute any one of the front-end page monitoring methods. 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 programs in the non-volatile storage medium. When the computer programs are executed by the processor, the processor can be caused to execute any one of the front-end page monitoring methods. The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art can understand that Figure 5 the structure shown in is only a block diagram of some structures 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 those shown in the figure, or combine certain components, or have different component arrangements.

[0076] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0077] Among them, in one embodiment, the above-mentioned processor is used to run a computer program stored in a memory to implement the following steps: through the above-mentioned body information detection module, monitor the user's physical sign data of the user, and send the above-mentioned user's physical sign data to the above-mentioned physical sign data processing module; through the above-mentioned physical sign data processing module, perform the following steps to monitor and warn the user's physical sign information: integrate the above-mentioned user's physical sign data with the pre-stored historical physical sign data to generate a horizontal physical sign index sequence set and a vertical physical sign index sequence set; extract physical data features from the above-mentioned horizontal physical sign index sequence set and the above-mentioned vertical physical sign index sequence set to obtain horizontal index features and vertical index features, and perform a first warning operation on the user's physical signs according to the above-mentioned horizontal index features and the above-mentioned vertical index features; use the above-mentioned historical physical sign data to construct an initial physical sign data chain of the user, where the above-mentioned initial physical sign data chain represents the physical sign data of the user at a specified time point; based on the above-mentioned initial physical sign data chain of the user, perform implicit feature extraction on the above-mentioned vertical index features 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, perform a second warning operation on the user's physical signs.

[0078] The embodiments of the present disclosure also provide a computer-readable storage medium, on which a computer program is stored, and the computer program includes program instructions. The method implemented when the program instructions are executed can refer to the various embodiments of the method for monitoring and warning the user's physical sign information based on the body monitoring system of the present disclosure.

[0079] Among them, the above-mentioned computer-readable storage medium may be an internal storage unit of the above-mentioned computer device in the foregoing embodiment, such as the hard disk or memory of the above-mentioned computer device. The above-mentioned computer-readable storage medium may also be an external storage device of the above-mentioned computer device, such as a plug-in hard disk equipped on the above-mentioned computer device, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc.

[0080] It should be noted that in this article, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such a process, method, article or system. Without further limitations, an element defined by the statement "including a..." does not exclude the existence of another identical element in the process, method, article or system including that element.

[0081] The above description is only some preferred embodiments of the present disclosure and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) disclosed in the embodiments of the present disclosure that have similar functions.

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 comprises: a body information monitoring module and a physical sign data processing module, wherein: Monitoring the user's vital sign data through the body information detection 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 warn the user's vital sign information: Integrating the user's physical sign data with pre-stored historical physical sign data to generate a transverse physical sign indicator sequence set and a longitudinal physical sign indicator sequence set; Extracting 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, and performing a first warning operation of a user's physical sign according to the transverse indicator features and the longitudinal indicator features; Using the historical vital sign data, constructing a user's initial vital sign data chain, wherein the 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, extract implicit features of the longitudinal indicator features to obtain implicit features of the user's body; 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 post-addition 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 physical sign data by the physical information detection module includes: Receiving user vital sign sensing information monitored by the 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 in the vital sign data sensing mattress to sense the pressure of the user's body on the mattress and the user's body temperature, and the user vital sign sensing information includes: a pressure simulation voltage value array and a temperature simulation voltage value array; The user's vital sign sensing information is converted into data by the body information detection module to generate user vital sign data, wherein the data conversion is performed by the following steps: Based on the pressure simulation voltage value array, construct a user posture perception map, wherein the user posture perception map includes a user posture boundary coordinate sequence; Performing user dynamic posture analysis on the user posture perception graph to generate a dynamic posture analysis result, wherein in response to the dynamic posture analysis result including an identification characterizing an abnormal user posture, performing a first user vital sign warning operation; According to the user posture boundary coordinate sequence, performing analog-to-digital conversion on each temperature analog voltage value in the temperature analog voltage value array 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 ECG waveform data to obtain the current user's ECG waveform data; Extracting R wave peaks from the electrocardiogram waveform data of the current user to obtain an instantaneous heart rate value sequence; 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, characterized in that: 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 item of data 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 transverse vital sign indicator sequence set and a longitudinal vital sign indicator sequence set includes: Filter out historical indicator transverse physical sign data sequences and historical indicator longitudinal physical sign sequences that match each physical sign indicator in the user physical sign data from the historical physical sign data, and obtain a matched transverse physical sign data sequence set and a matched longitudinal 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, 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.

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; According to each longitudinal indicator characteristic value in the longitudinal indicator characteristic value set, dynamically allocating a sliding window step size for a preset data detection sliding window; Based on the allocated sliding window step length, performing synchronous sliding window detection on each longitudinal physical sign indicator sequence in the longitudinal physical sign indicator sequence set to generate a longitudinal indicator feature, wherein the longitudinal indicator feature includes a longitudinal indicator abnormality information set corresponding to each longitudinal physical sign indicator sequence, and each longitudinal indicator abnormality information includes abnormal indicator data and abnormal time period in the longitudinal physical sign indicator sequence; 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 a principal component data matrix after analysis; The analyzed principal component data matrix is ​​subjected to local outlier factor detection to generate transverse indicator features, wherein the transverse indicator features correspond to transverse indicator abnormality information sets of each transverse vital sign indicator sequence, and each transverse indicator abnormality information includes abnormal indicator data and abnormal time period.

6. The method according to claim 5, characterized in that The performing a first warning operation of a user's vital signs according to the horizontal indicator feature and the vertical indicator feature 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 a 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 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, respectively, to generate a user initial vital sign data curve set; For each user's initial vital sign data curve in the user's initial vital sign data curve set, determining a data jump range corresponding to the user's initial vital sign data curve; According to the user's initial vital sign data curve set and the corresponding data jump range set, a user's initial vital sign data chain is constructed.

8. A user's vital signs information monitoring and early warning device based on a body monitoring system, comprising: A monitoring and sending unit, configured to monitor the user's vital sign data through the body information detection 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 physical sign data with pre-stored historical physical sign data to generate a transverse physical sign indicator sequence set and a longitudinal physical sign indicator sequence set; Extracting 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, and performing a first warning operation of a user's physical sign according to the transverse indicator features and the longitudinal indicator features; Using the historical vital sign data, constructing a user's initial vital sign data chain, wherein the 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 features; 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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