Monitoring method and system, electronic equipment and storage medium

Abstract

The invention provides a monitoring method and system, electronic equipment and a storage medium. The monitoring method comprises the steps that human body state information and environment parameter information in a monitoring area are acquired; performing feature extraction on the human body state information and the environment parameter information to obtain state data and environment data; performing data fusion on the state data and the environment data to generate comprehensive event data; and determining early warning information according to the comprehensive event data. Therefore, the problem that the accuracy and the reliability of a single sensor in the prior art are possibly influenced by various factors, such as performance limitation of the sensor, interference of an external environment, human body activity and the like, so that monitoring data errors or missing is caused can be effectively solved.

Description

Technical Field

[0001] The present application relates to the technical field of health monitoring, and in particular to a monitoring method, system, electronic device and storage medium. Background Art

[0002] With the aging population and increasing awareness of health, the market demand for health monitoring devices is growing. According to China Research Network, the market size of China's smart medical and health equipment market will exceed 200 billion yuan in 2025, of which medical-grade wearable devices will account for 45%. People expect these devices to accurately monitor their health status and surrounding environmental parameters in real time, allowing them to better manage their health and promptly identify potential health issues.

[0003] The accuracy and reliability of a single sensor in existing technologies may also be affected by multiple factors, such as the performance limitations of the sensor itself, interference from the external environment, and human activities, which may lead to errors or omissions in monitoring data. Summary of the Invention

[0004] The present invention provides a monitoring method, system, electronic device, and storage medium to solve the problems existing in related technologies. The technical solutions are as follows:

[0005] In a first aspect, an embodiment of the present application provides a monitoring method, comprising:

[0006] Obtain human status information and environmental parameter information within the monitoring area;

[0007] Performing feature extraction on the human body state information and the environmental parameter information to obtain state data and environmental data;

[0008] Performing data fusion on the state data and the environment data to generate comprehensive event data;

[0009] According to the comprehensive event data, early warning information is determined.

[0010] In one embodiment, the human body state information includes infrared data obtained by an infrared sensor and radar data obtained by a millimeter wave radar, the environmental parameter information includes environmental data and sound data, and obtaining the human body state information and environmental parameter information in the monitoring area includes:

[0011] acquiring the infrared data, the radar data, the environmental data, and the sound data;

[0012] According to the PTP clock synchronization signal broadcast by the master node, the infrared data, the radar data, the environmental data and the sound data are reorganized according to the N-second time window to obtain human body status information and environmental parameter information in the detection area.

[0013] In one embodiment, obtaining human body status information and environmental parameter information within the monitoring area further includes:

[0014] According to the radar data, a radar coordinate system is established as a reference space;

[0015] determining a spatial transformation matrix of the infrared data, the environmental data, and the sound data according to the reference space;

[0016] The infrared data, the environmental data and the sound data are three-dimensionally transformed according to a spatial conversion matrix of the infrared data, the environmental data and the sound data to obtain human body status information and environmental parameter information in the monitoring area.

[0017] In one embodiment, the extracting features of the human body state information and the environmental parameter information to obtain state data and environmental data includes:

[0018] determining breathing information, heart rate information, and motion trajectory information based on the radar data;

[0019] determining existence probability information based on the infrared data;

[0020] Determining temperature, humidity, and air information based on the environmental data;

[0021] Determining voiceprint information and sound information based on the sound data;

[0022] State data and environmental data are obtained according to the breathing information, the heart rate information, the motion trajectory information, the existence probability information, the temperature and humidity information, the air information, the voiceprint information, and the sound information.

[0023] In one embodiment, fusing the state data and the environment data to generate comprehensive event data includes:

[0024] performing calculations based on the breathing information, the heart rate information, the motion trajectory information, the existence probability information, the voiceprint information, and the sound information to obtain a joint confidence level;

[0025] The joint confidence is adjusted according to the temperature and humidity information and the air information to generate comprehensive event data.

[0026] In one embodiment, the calculating based on the breathing information, the heart rate information, the motion trajectory information, the existence probability information, the voiceprint information, and the sound information to obtain the joint confidence includes:

[0027] Acquire the infrared sensor, the millimeter-wave radar, and the sound sensor;

[0028] Determining, based on the historical data, the credibility of the infrared sensor, the credibility of the millimeter-wave radar, the credibility of the sound sensor, the weight of the infrared sensor, the weight of the millimeter-wave radar, and the weight of the sound sensor;

[0029] Adjusting the existence probability information according to the credibility of the infrared sensor to obtain the confidence of the existence probability information;

[0030] Adjusting the motion trajectory information according to the credibility of the millimeter wave radar to obtain the confidence of the motion trajectory information;

[0031] Adjusting the breathing information according to the credibility of the millimeter-wave radar to obtain the confidence of the breathing information;

[0032] adjusting the heart rate information according to the credibility of the millimeter-wave radar to obtain the confidence of the heart rate information;

[0033] Adjusting the voiceprint information according to the credibility of the sound sensor to obtain the confidence of the voiceprint information;

[0034] adjusting the sound information according to the credibility of the sound sensor to obtain the confidence of the sound information;

[0035] A joint confidence is obtained according to the weight of the infrared sensor, the weight of the millimeter-wave radar, the weight of the sound sensor, the confidence of the existence probability information, the confidence of the breathing information, the confidence of the heart rate information, the confidence of the voiceprint information, and the confidence of the sound information.

[0036] In one embodiment, the warning information includes level one warning information, level two warning information, and level three warning information, and determining the warning information based on the comprehensive event data includes:

[0037] When the comprehensive event data is greater than or equal to a first specified threshold, triggering a first-level warning message;

[0038] When the comprehensive event data is less than the first specified threshold and greater than or equal to the second specified threshold, triggering a second-level warning information;

[0039] When the comprehensive event data is less than the second specified threshold and any of the breathing information, the heart rate information, the motion trajectory information, the existence probability information, the temperature and humidity information, the air information, the voiceprint information and the sound information is abnormal, a third warning signal is triggered.

[0040] In a second aspect, an embodiment of the present application provides a system, including:

[0041] An acquisition unit, used to acquire human status information and environmental parameter information within the monitoring area;

[0042] an obtaining unit, configured to extract features from the human body state information and the environmental parameter information to obtain state data and environmental data;

[0043] a generating unit, configured to perform data fusion on the state data and the environment data to generate comprehensive event data;

[0044] A determination unit is used to determine warning information based on the comprehensive event data.

[0045] In one embodiment, the human body state information includes infrared data obtained by an infrared sensor and radar data obtained by a millimeter wave radar, the environmental parameter information includes environmental data and sound data, and obtaining the human body state information and environmental parameter information in the monitoring area includes:

[0046] acquiring the infrared data, the radar data, the environmental data, and the sound data;

[0047] According to the PTP clock synchronization signal broadcast by the master node, the infrared data, the radar data, the environmental data and the sound data are reorganized according to the N-second time window to obtain human body status information and environmental parameter information in the detection area.

[0048] In one embodiment, obtaining human body status information and environmental parameter information within the monitoring area further includes:

[0049] According to the radar data, a radar coordinate system is established as a reference space;

[0050] determining a spatial transformation matrix of the infrared data, the environmental data, and the sound data according to the reference space;

[0051] The infrared data, the environmental data and the sound data are three-dimensionally transformed according to a spatial conversion matrix of the infrared data, the environmental data and the sound data to obtain human body status information and environmental parameter information in the monitoring area.

[0052] In one embodiment, the extracting features of the human body state information and the environmental parameter information to obtain state data and environmental data includes:

[0053] determining breathing information, heart rate information, and motion trajectory information based on the radar data;

[0054] determining existence probability information based on the infrared data;

[0055] Determining temperature, humidity, and air information based on the environmental data;

[0056] Determining voiceprint information and sound information based on the sound data;

[0057] State data and environmental data are obtained according to the breathing information, the heart rate information, the motion trajectory information, the existence probability information, the temperature and humidity information, the air information, the voiceprint information, and the sound information.

[0058] In one embodiment, fusing the state data and the environment data to generate comprehensive event data includes:

[0059] performing calculations based on the breathing information, the heart rate information, the motion trajectory information, the existence probability information, the voiceprint information, and the sound information to obtain a joint confidence level;

[0060] The joint confidence is adjusted according to the temperature and humidity information and the air information to generate comprehensive event data.

[0061] In one embodiment, the calculating based on the breathing information, the heart rate information, the motion trajectory information, the existence probability information, the voiceprint information, and the sound information to obtain the joint confidence includes:

[0062] Acquire the infrared sensor, the millimeter-wave radar, and the sound sensor;

[0063] Determining, based on the historical data, the credibility of the infrared sensor, the credibility of the millimeter-wave radar, the credibility of the sound sensor, the weight of the infrared sensor, the weight of the millimeter-wave radar, and the weight of the sound sensor;

[0064] Adjusting the existence probability information according to the credibility of the infrared sensor to obtain the confidence of the existence probability information;

[0065] Adjusting the motion trajectory information according to the credibility of the millimeter wave radar to obtain the confidence of the motion trajectory information;

[0066] Adjusting the breathing information according to the credibility of the millimeter-wave radar to obtain the confidence of the breathing information;

[0067] adjusting the heart rate information according to the credibility of the millimeter-wave radar to obtain the confidence of the heart rate information;

[0068] Adjusting the voiceprint information according to the credibility of the sound sensor to obtain the confidence of the voiceprint information;

[0069] adjusting the sound information according to the credibility of the sound sensor to obtain the confidence of the sound information;

[0070] A joint confidence is obtained according to the weight of the infrared sensor, the weight of the millimeter-wave radar, the weight of the sound sensor, the confidence of the existence probability information, the confidence of the breathing information, the confidence of the heart rate information, the confidence of the voiceprint information, and the confidence of the sound information.

[0071] In one embodiment, the warning information includes level one warning information, level two warning information, and level three warning information, and determining the warning information based on the comprehensive event data includes:

[0072] When the comprehensive event data is greater than or equal to a first specified threshold, triggering a first-level warning message;

[0073] When the comprehensive event data is less than the first specified threshold and greater than or equal to the second specified threshold, triggering a second-level warning information;

[0074] When the comprehensive event data is less than the second specified threshold and any of the breathing information, the heart rate information, the motion trajectory information, the existence probability information, the temperature and humidity information, the air information, the voiceprint information and the sound information is abnormal, a third warning signal is triggered.

[0075] In a third aspect, an embodiment of the present application provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor so as to enable the at least one processor to execute the above-mentioned monitoring method.

[0076] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed on a computer, the method in any one of the above-mentioned embodiments is executed.

[0077] The advantages or beneficial effects of the above technical solution include at least:

[0078] In this embodiment, the monitoring method includes: obtaining human body status information and environmental parameter information within the monitoring area; performing feature extraction on the human body status information and the environmental parameter information to obtain status data and environmental data; performing data fusion on the status data and the environmental data to generate comprehensive event data; and determining early warning information based on the comprehensive event data. This effectively solves the problem in the prior art that the accuracy and reliability of a single sensor may also be affected by various factors, such as the performance limitations of the sensor itself, interference from the external environment, and human activities, which may lead to errors or omissions in the monitoring data. The monitoring method of this embodiment has a multi-level data processing and intelligent decision-making mechanism, performs comprehensive analysis based on the movement of the person and the environment, reduces the possibility of misjudgment, can more accurately determine the health status of the person, and builds a complete technical chain from physical signals to health warnings, realizing all-weather, non-contact human body status monitoring in scenarios such as home monitoring and elderly care, and significantly improving the emergency response capability of emergencies.

[0079] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present application will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the multiple drawings represent the same or similar components or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings only depict some embodiments disclosed in this application and should not be construed as limiting the scope of this application.

[0081] Figure 1 is a schematic diagram of a monitoring method according to an embodiment of the present application;

[0082] Figure 2 This is a block diagram of an electronic device used to implement the monitoring method of an embodiment of the present application. DETAILED DESCRIPTION

[0083] Hereinafter, only certain exemplary embodiments are briefly described. As will be appreciated by those skilled in the art, the described embodiments may be modified in various ways without departing from the spirit or scope of the present application. Therefore, the drawings and description are to be regarded as illustrative in nature and not restrictive.

[0084] With the aging population and increasing awareness of health, there is a growing demand for devices capable of accurately monitoring human health and surrounding environmental parameters in real time. Existing human monitoring technologies often have limitations based on single sensors. For example, traditional accelerometers are susceptible to false alarms when used for fall detection, while camera-based monitoring methods pose privacy risks. While millimeter-wave radar offers certain advantages in detecting human motion and vital signs, its accuracy in determining the presence of a person is susceptible to environmental noise and other factors, leaving room for improvement. Infrared sensors offer simplicity and speed in detecting occupancy, but they lack the ability to accurately capture vital signs such as heart rate and respiratory rate. Furthermore, environmental factors such as light intensity, gas composition, temperature and humidity, and sound have significant impacts on human health and comfort, yet effective integrated monitoring solutions are lacking. Therefore, developing a system that leverages the strengths of multiple independent sensors, enabling them to work together through wireless connectivity to achieve high-precision, multifunctional human and environmental monitoring is of great practical significance.

[0085] Figure 1 FIG. 1 is a flow chart showing a monitoring method according to an embodiment of the present application. Figure 1-Figure 2 As shown, a monitoring method may include:

[0086] In a first aspect, an embodiment of the present application provides a monitoring method, comprising:

[0087] S110: Acquiring human status information and environmental parameter information within the monitoring area;

[0088] S120: Extracting features from the human body state information and the environmental parameter information to obtain state data and environmental data;

[0089] S130: Performing data fusion on the state data and the environment data to generate comprehensive event data;

[0090] S140: Determine warning information based on the comprehensive event data.

[0091] The monitoring method of this embodiment can be implemented by providing hardware support through the host device and executing the monitoring method in the host device.

[0092] In this embodiment, the monitoring method includes: obtaining human body status information and environmental parameter information within the monitoring area; performing feature extraction on the human body status information and the environmental parameter information to obtain status data and environmental data; performing data fusion on the status data and the environmental data to generate comprehensive event data; and determining early warning information based on the comprehensive event data. This effectively solves the problem in the prior art that the accuracy and reliability of a single sensor may also be affected by various factors, such as the performance limitations of the sensor itself, interference from the external environment, and human activities, which may lead to errors or omissions in the monitoring data. The monitoring method of this embodiment has a multi-level data processing and intelligent decision-making mechanism, performs comprehensive analysis based on the movement of the person and the environment, reduces the possibility of misjudgment, can more accurately determine the health status of the person, and builds a complete technical chain from physical signals to health warnings, realizing all-weather, non-contact human body status monitoring in scenarios such as home monitoring and elderly care, and significantly improving the emergency response capability of emergencies.

[0093] In the embodiments of this application, the monitoring method of this application is intended to provide a multifunctional human monitoring system composed of multiple independent sensors working together via wireless connections. Through the division of labor and cooperation among the sensors, the accuracy and reliability of detecting human conditions such as falls, heart rate, respiratory rate, and sleep quality are improved. The presence of people in the monitoring area can be effectively determined. In addition, the light intensity, gas concentration, temperature and humidity, and sound information in the environment can be monitored in real time, providing users with comprehensive health and environmental monitoring services.

[0094] In order to implement the monitoring method in this embodiment, the following data acquisition device can be used to transmit the collected data to the data processing device through the wireless communication unit, and the data processing device performs data processing of the monitoring method, thereby achieving

[0095] Millimeter-wave radar: A radar chip capable of transmitting and receiving millimeter-wave signals, operating in the 60-64 GHz frequency band, is used to acquire real-time information about a person's micro-movements and movement trajectories. The millimeter-wave radar has a built-in wireless communication unit that wirelessly transmits the collected data to data processing equipment.

[0096] Infrared sensing equipment: Pyroelectric infrared sensors can be used to detect changes in infrared radiation within the monitoring area to determine the presence of people. Wireless communication units are also included to wirelessly transmit detection signals to data processing equipment.

[0097] Light sensing device: Uses high-precision photosensors to monitor ambient light intensity in real time. Light intensity data is sent to data processing equipment via wireless communication units.

[0098] Gas sensing equipment: Equipped with sensors sensitive to specific gases (such as carbon dioxide), they monitor the gas concentration in the environment in real time. Using wireless communication capabilities, they transmit gas concentration data to data processing equipment.

[0099] Temperature and humidity sensing device: Integrates temperature and humidity sensors to obtain environmental temperature and humidity data in real time, and transmits the data to data processing equipment through wireless communication units.

[0100] Sound sensing equipment: uses a highly sensitive microphone to collect sound signals in the monitoring area, converts them into electrical signals, and transmits them to the data processing equipment through a wireless communication unit.

[0101] Data processing equipment: A high-performance microprocessor with powerful data calculation and processing capabilities is selected to receive and process data wirelessly transmitted from various sensor devices. The monitoring method of this embodiment is implemented through this data processing equipment.

[0102] Communication unit: It can use wireless communication technologies such as 433M, Bluetooth, Wi-Fi or ZigBee to realize data transmission between each sensor device and the data processing device, as well as data interaction between the data processing module and external devices (such as mobile phones, smart bracelets or cloud servers, etc.).

[0103] Each sensor device works independently to collect data. The infrared sensing device continuously monitors the area for people. When a person enters, it triggers the millimeter-wave radar device to start working via a wireless signal. The millimeter-wave radar transmits millimeter-wave signals, receives reflected signals, converts them into electrical signals, and transmits them wirelessly to the data processing device. The data processing device uses specific algorithms based on time-frequency analysis, etc. to process the millimeter-wave radar data and extract information about the person's heart rate and respiratory rate. At the same time, based on the human motion trajectory information obtained by the millimeter-wave radar and combined with a preset fall detection model, it determines whether a person has fallen. During sleep monitoring, the millimeter-wave radar detects micro-movements of the human body, analyzes the number of times a person turns over during sleep, body movements, etc., and assesses sleep quality.

[0104] Light sensors monitor ambient light intensity in real time and wirelessly transmit this data to a data processing device. This module can be combined with other sensor data. For example, in a sleep monitoring scenario, if light intensity suddenly increases during sleep and the millimeter-wave radar detects an increase in the frequency of human micro-movements, it can be inferred that the person may have awakened from sleep due to light disturbance, thus improving the sleep quality assessment system. In daily activity monitoring, light data helps determine the user's current situation.

[0105] The gas sensing device continuously monitors the ambient gas concentration. When it detects that the concentration of a specific gas (such as carbon dioxide) exceeds a preset threshold, it sends data to the data processing device via wireless communication. The data processing device combines the human information detected by the infrared device and sends an alert to the external device through the communication module to inform the user that the ambient gas concentration is abnormal.

[0106] The temperature and humidity sensing device wirelessly transmits the ambient temperature and humidity data to the data processing module in real time. The data processing module can provide users with environmental comfort analysis based on temperature and humidity changes and other sensor data. For example, in a sleeping scenario, the data processing module can combine the human sleep state monitored by millimeter-wave radar to determine whether temperature and humidity affect sleep quality.

[0107] The sound sensing device collects sound signals and transmits them wirelessly to the data processing device. In terms of distress sound detection, the data processing device uses sound recognition technology to perform real-time analysis of the collected sound signals and compare them with preset distress sound feature templates. If the match exceeds a certain threshold, combined with the human body position and movement status information detected by the millimeter-wave radar and infrared equipment, after confirming that someone is in an abnormal state, a distress alert is sent to the preset emergency contact or relevant rescue platform through the communication module. In terms of sleep snoring monitoring, the data processing device uses a sound recognition algorithm to identify snoring characteristics, and combined with the human respiratory micro-movement information monitored by the millimeter-wave radar, it analyzes whether the user has sleep disorders such as sleep apnea.

[0108] Infrared sensors quickly determine the presence of a person, effectively preventing misjudgments by millimeter-wave radar due to factors like ambient clutter when no one is around. This improves the accuracy of subsequent millimeter-wave radar detection of vital signs and motion. The data from each sensor complements each other, and the integration of multiple sources enhances the accuracy of judgments about human status and environmental parameters.

[0109] At the same time, it can realize multiple functions such as fall detection, heart rate monitoring, respiratory rate monitoring, sleep quality monitoring, light intensity monitoring, gas concentration monitoring, temperature and humidity monitoring, and sound monitoring, and finally form comprehensive event data to meet users' needs for comprehensive health and environmental monitoring.

[0110] Each sensor device is independent and connected wirelessly, which facilitates flexible installation and layout according to different monitoring scenarios and needs, improving the applicability of the system.

[0111] Unlike camera-based monitoring methods, this monitoring method does not involve image acquisition, fully protecting user privacy. Each sensor only collects specific types of data, and data is encrypted during transmission to ensure user data security.

[0112] In one embodiment, the human body state information includes infrared data obtained by an infrared sensor and radar data obtained by a millimeter wave radar, the environmental parameter information includes environmental data and sound data, and obtaining the human body state information and environmental parameter information in the monitoring area includes:

[0113] acquiring the infrared data, the radar data, the environmental data, and the sound data;

[0114] According to the PTP clock synchronization signal broadcast by the master node, the infrared data, the radar data, the environmental data and the sound data are reorganized according to the N-second time window to obtain human body status information and environmental parameter information in the detection area.

[0115] In this embodiment, the master node periodically broadcasts synchronization messages and calculates the clock offset Δoffset;

[0116] in:

[0117]

[0118] t1: The time when the master node sends the synchronization message;

[0119] t2: The time when the slave node receives the synchronization message;

[0120] t3: The time it takes for the slave node to send a delay request;

[0121] t4: The time it takes for the master node to receive the delayed request.

[0122] In this embodiment, the master node can be the PTP clock in the data processing device, and the slave node is each sensor device. The master node broadcasts the PTP clock synchronization signal, which is the clock offset Δoffset. N seconds can be 1 second or 1.5 seconds, etc. The infrared data, the radar data, the environmental data and the sound data are adjusted by the clock offset Δoffset within 1 second, so that the adjusted infrared data, the radar data, the environmental data and the sound data are aligned in time.

[0123] The local clock is calibrated from the sensor data of the node (the infrared data, the radar data, the environmental data and the sound data) with an alignment accuracy of ±1ms, and the data is reorganized according to a 1-second time window to eliminate timing deviation.

[0124] Interpolation can also be used to align sensor data with different sampling rates (the time and frequency of different sensor data are inconsistent, for example, radar sends data 100 times per second and infrared sends data once per second).

[0125] t aligned =interp(t raw , treference )t aligned =interp(t raw , t reference )

[0126] t raw is the original data timestamp, t reference is the reference timestamp.

[0127] This allows asynchronous data to be aligned to the same timeline for easier analysis and evaluation.

[0128] In one embodiment, obtaining human body status information and environmental parameter information within the monitoring area further includes:

[0129] According to the radar data, a radar coordinate system is established as a reference space;

[0130] determining a spatial transformation matrix of the infrared data, the environmental data, and the sound data according to the reference space;

[0131] The infrared data, the environmental data and the sound data are three-dimensionally transformed according to a spatial conversion matrix of the infrared data, the environmental data and the sound data to obtain human body status information and environmental parameter information in the monitoring area.

[0132] In this embodiment, the radar coordinate system is established as the reference space through radar data, and the conversion matrix of other sensor spaces is calculated:

[0133] Specifically:

[0134] Arrange more than three reflective marking points in the monitoring area;

[0135] Millimeter wave radar records the coordinates of the marker point p r (i) =(x r ,y r , z r );

[0136] When other sensors are triggered, the local coordinates p are recorded. l (i) =(x l ,y l );

[0137] SVD solution:

[0138]

[0139] Calculation steps:

[0140] a. Calculate the center of mass coordinates:

[0141]

[0142] b. Construct the covariance matrix:

[0143]

[0144] c.SVD decomposition: H = UΣV T

[0145] d. Rotation matrix: R = VU T

[0146] e. Translation vector:

[0147]

[0148] The infrared sensor coordinates can also be transformed according to the reference space: rotate 15° + offset 2.4m on the Z axis;

[0149] Map the 3D positions of environmental sensors according to the reference space.

[0150] Sensor space alignment is achieved through three-dimensional coordinate system transformation. Assuming the millimeter wave radar coordinate system as the reference, the coordinate transformation matrix of other sensors is:

[0151] P′=R·P+T

[0152] Where R is the rotation matrix and T is the translation vector, and the parameters are obtained through calibration experiments.

[0153] This allows the radar data, the infrared data, the environmental data, and the sound data to be aligned in time and space, thereby avoiding time and space errors and misjudgment caused by errors between the sensor data.

[0154] In one embodiment, the extracting features of the human body state information and the environmental parameter information to obtain state data and environmental data includes:

[0155] determining breathing information, heart rate information, and motion trajectory information based on the radar data;

[0156] determining existence probability information based on the infrared data;

[0157] Determining temperature, humidity, and air information based on the environmental data;

[0158] Determining voiceprint information and sound information based on the sound data;

[0159] State data and environmental data are obtained according to the breathing information, the heart rate information, the motion trajectory information, the existence probability information, the temperature and humidity information, the air information, the voiceprint information, and the sound information.

[0160] In this embodiment, radar data includes real-time capture of human chest micro-motion signals (60GHz frequency band) through millimeter-wave radar, such as FMCW radar phase differential extraction of chest movement, namely respiratory signals and heart rate signals, for example: chest displacement → respiratory waveform (0.1-0.5Hz bandpass filtering) to obtain respiratory information; through millimeter-wave radar through micro-Doppler characteristics → heart rate estimation (0.8-3Hz spectrum analysis), to obtain heart rate information.

[0161] For example: radar data is resolved by chest motion phase:

[0162]

[0163] φnoise is the phase noise;

[0164] λ: millimeter wave wavelength (5mm@60GHz);

[0165] Δd(t): chest cavity displacement

[0166] Respiration / heart rate separation: using dual-channel bandpass filter

[0167] Respiratory signal: 0.1-0.5Hz (6-30 times / minute)

[0168] Heart rate signal: 0.8-3Hz (48-180 beats / minute)

[0169] Radar data includes real-time capture of motion trajectories by millimeter-wave radar for analysis:

[0170] Extended Kalman filter (EKF) model:

[0171]

[0172] Where F is the state transfer matrix and Q is the process noise covariance.

[0173] The infrared data obtained by the infrared sensor mainly monitors whether the human body exists in the monitoring area. The human body presence state filtering (sliding window threshold method) can be used, for example: the double threshold comparison method can be used to determine whether the human body exists in the monitoring area.

[0174] It can also be determined by the existence probability calculation formula:

[0175]

[0176] V high =2.5V, V low =1.2V: empirical threshold

[0177] Thus, the existence probability information is obtained.

[0178] Extracting data from the environment:

[0179] Environmental Composite Index (ECI):

[0180] ECI=0.5·THI+0.3·GCI+0.2·LI

[0181] Temperature and humidity information (THI):

[0182] THI=(1.8T+32)-(0.55-0.0055RH)(1.8T-26)

[0183] Air Information (GCI):

[0184]

[0185] The temperature, humidity and air information can be determined by the above formula.

[0186] Sound sensor: Based on dynamic time warping (DTW), it matches abnormal voiceprints and obtains voiceprint information:

[0187]

[0188] Function: Converts sound into a machine-recognizable "fingerprint" to distinguish between cries for help, snoring, etc.

[0189] Symbol explanation:

[0190] E k : The energy output of the Mel filter bank, simulating the sensitivity of the human ear to different frequencies.

[0191] N: number of Mel filters (usually 40).

[0192] i: The serial number of the MFCC coefficient (usually the first 13).

[0193] The human ear is more sensitive to low frequencies (such as male voices) and less sensitive to high frequencies. MFCC compresses high-frequency information through a Mel filter bank, preserving key features.

[0194] The sound information can be directly detected by the sound sensor.

[0195] In one embodiment, fusing the state data and the environment data to generate comprehensive event data includes:

[0196] performing calculations based on the breathing information, the heart rate information, the motion trajectory information, the existence probability information, the voiceprint information, and the sound information to obtain a joint confidence level;

[0197] The joint confidence is adjusted according to the temperature and humidity information and the air information to generate comprehensive event data.

[0198] Through the method of this embodiment, the joint confidence can be determined first, and then the joint confidence can be adjusted by the weights of the temperature and humidity information and the air information. The environmental information is added to further analyze whether there are people in the actual monitoring area who have fallen or become unconscious, which can improve the accuracy of the analysis.

[0199] In one embodiment, the calculating based on the breathing information, the heart rate information, the motion trajectory information, the existence probability information, the voiceprint information, and the sound information to obtain the joint confidence includes:

[0200] Acquire the infrared sensor, the millimeter-wave radar, and the sound sensor;

[0201] Determining, based on the historical data, the credibility of the infrared sensor, the credibility of the millimeter-wave radar, the credibility of the sound sensor, the weight of the infrared sensor, the weight of the millimeter-wave radar, and the weight of the sound sensor;

[0202] Adjusting the existence probability information according to the credibility of the infrared sensor to obtain the confidence of the existence probability information;

[0203] Adjusting the motion trajectory information according to the credibility of the millimeter-wave radar to obtain the confidence of the motion trajectory information;

[0204] Adjusting the breathing information according to the credibility of the millimeter-wave radar to obtain the confidence of the breathing information;

[0205] Adjusting the heart rate information according to the credibility of the millimeter wave radar to obtain the confidence of the heart rate information;

[0206] adjusting the voiceprint information according to the credibility of the sound sensor to obtain the confidence of the voiceprint information;

[0207] adjusting the sound information according to the credibility of the sound sensor to obtain the confidence of the sound information;

[0208] A joint confidence is obtained according to the weight of the infrared sensor, the weight of the millimeter-wave radar, the weight of the sound sensor, the confidence of the existence probability information, the confidence of the breathing information, the confidence of the heart rate information, the confidence of the voiceprint information, and the confidence of the sound information.

[0209] In this embodiment, by analyzing historical data, for example, by inputting historical data into a trained neural network, the credibility of the infrared sensor, the credibility of the millimeter-wave radar, and the credibility of the sound sensor can be obtained. In order to make the joint confidence more reliable, historical information can be input into another trained neural network to determine the weight of the infrared sensor, the weight of the millimeter-wave radar, and the weight of the sound sensor for the entire judgment of a fall or injury, etc., so that the output accuracy is higher.

[0210] Based on the credibility of each sensor obtained above, the confidence of the corresponding information is adjusted so that the confidence of the existence probability information, the confidence of the breathing information, the confidence of the heart rate information, the confidence of the voiceprint information and the confidence of the sound information can correspond to the corresponding sensors, so that the joint confidence judgment of the current event can be more accurate.

[0211] The joint confidence can be obtained by the following formula:

[0212]

[0213] Integrate the confidence of multiple sensors to improve decision reliability.

[0214] w i is the weight of the ith sensor among the weights of the infrared sensor, the millimeter-wave radar, and the sound sensor;

[0215] acc i (j) is the confidence level of the ith one of the confidence levels of the existence probability information, the breathing information, the heart rate information, the voiceprint information, and the sound information;

[0216] mi(Aj) is the probability of the i-th event Aj occurring among the confidence of the existence probability information, the confidence of the breathing information, the confidence of the heart rate information, the confidence of the voiceprint information and the confidence of the sound information, such as a fall.

[0217] N is the number of sensors.

[0218]

[0219] m(A) is the joint confidence of event A;

[0220] B kThe set of propositions supported by the confidence level of each existence probability information, the confidence level of the breathing information, the confidence level of the heart rate information, the confidence level of the voiceprint information, and the confidence level of the sound information, for example: sensor 1 supports "fall", and sensor 2 supports "normal";

[0221] ∩B k =A is the intersection of the propositions supported by the confidence of the existence probability information, the confidence of the breathing information, the confidence of the heart rate information, the confidence of the voiceprint information and the confidence of the sound information, which is A. That is, it is included in the numerator only when all confidence evidence supports A.

[0222] K is the conflict factor, which indicates the degree of conflict between the evidence of each sensor (K = 1 for complete conflict);

[0223] ∏m i (B k ) is the product of the confidence of the existence probability information, the confidence of the breathing information, the confidence of the heart rate information, the confidence of the voiceprint information and the confidence of the sound information.

[0224] The independent evidence from multiple sensors is fused into a unified conclusion to obtain the final joint confidence.

[0225] Among them, the above method can solve the problem that a single sensor is prone to misjudgment.

[0226] For example: Radar alone: 75% likely to be a fall;

[0227] Adding infrared data: increased to 82%;

[0228] Adding sound evidence: final 89% confidence.

[0229] For the weight part,

[0230] Dynamically adjust sensor credibility based on historical accuracy:

[0231] Millimeter-wave radar weight 0.6 (high-precision vital signs)

[0232] Infrared sensor weight 0.3 (reliable presence detection)

[0233] Environmental sensor / sound sensor 0.05 each (auxiliary verification).

[0234] In one embodiment, the warning information includes level one warning information, level two warning information, and level three warning information, and determining the warning information based on the comprehensive event data includes:

[0235] When the comprehensive event data is greater than or equal to a first specified threshold, triggering a first-level warning message;

[0236] When the comprehensive event data is less than the first specified threshold and greater than or equal to the second specified threshold, triggering a second-level warning information;

[0237] When the comprehensive event data is less than the second specified threshold and any of the breathing information, the heart rate information, the motion trajectory information, the existence probability information, the temperature and humidity information, the air information, the voiceprint information and the sound information is abnormal, a third warning signal is triggered.

[0238] In this embodiment, by classifying the first-level warning information, the second-level warning information and the third-level warning information into different levels, responses can be made according to different levels so that caregivers can handle them in a timely manner.

[0239]

[0240] Obtaining vital signs data in advance can improve first aid efficiency by 30% and enable timely treatment.

[0241] In a second aspect, an embodiment of the present application provides a system, including:

[0242] An acquisition unit, used to acquire human status information and environmental parameter information within the monitoring area;

[0243] an obtaining unit, configured to extract features from the human body state information and the environmental parameter information to obtain state data and environmental data;

[0244] a generating unit, configured to perform data fusion on the state data and the environment data to generate comprehensive event data;

[0245] A determination unit is used to determine warning information based on the comprehensive event data.

[0246] In this embodiment, the monitoring method includes: obtaining human body status information and environmental parameter information within the monitoring area; performing feature extraction on the human body status information and the environmental parameter information to obtain status data and environmental data; performing data fusion on the status data and the environmental data to generate comprehensive event data; and determining early warning information based on the comprehensive event data. This effectively solves the problem in the prior art that the accuracy and reliability of a single sensor may also be affected by various factors, such as the performance limitations of the sensor itself, interference from the external environment, and human activities, which may lead to errors or omissions in the monitoring data. The monitoring method of this embodiment has a multi-level data processing and intelligent decision-making mechanism, performs comprehensive analysis based on the movement of the person and the environment, reduces the possibility of misjudgment, can more accurately determine the health status of the person, and builds a complete technical chain from physical signals to health warnings, realizing all-weather, non-contact human body status monitoring in scenarios such as home monitoring and elderly care, and significantly improving the emergency response capability of emergencies.

[0247] In one embodiment, the human body state information includes infrared data obtained by an infrared sensor and radar data obtained by a millimeter wave radar, the environmental parameter information includes environmental data and sound data, and obtaining the human body state information and environmental parameter information in the monitoring area includes:

[0248] acquiring the infrared data, the radar data, the environmental data, and the sound data;

[0249] According to the PTP clock synchronization signal broadcast by the master node, the infrared data, the radar data, the environmental data and the sound data are reorganized according to the N-second time window to obtain human body status information and environmental parameter information in the detection area.

[0250] In one embodiment, obtaining human body status information and environmental parameter information within the monitoring area further includes:

[0251] According to the radar data, a radar coordinate system is established as a reference space;

[0252] determining a spatial transformation matrix of the infrared data, the environmental data, and the sound data according to the reference space;

[0253] The infrared data, the environmental data and the sound data are three-dimensionally transformed according to a spatial conversion matrix of the infrared data, the environmental data and the sound data to obtain human body status information and environmental parameter information in the monitoring area.

[0254] In one embodiment, the extracting features of the human body state information and the environmental parameter information to obtain state data and environmental data includes:

[0255] determining breathing information, heart rate information, and motion trajectory information based on the radar data;

[0256] determining existence probability information based on the infrared data;

[0257] Determining temperature, humidity, and air information based on the environmental data;

[0258] Determining voiceprint information and sound information based on the sound data;

[0259] State data and environmental data are obtained according to the breathing information, the heart rate information, the motion trajectory information, the existence probability information, the temperature and humidity information, the air information, the voiceprint information, and the sound information.

[0260] In one embodiment, fusing the state data and the environment data to generate comprehensive event data includes:

[0261] performing calculations based on the breathing information, the heart rate information, the motion trajectory information, the existence probability information, the voiceprint information, and the sound information to obtain a joint confidence level;

[0262] The joint confidence is adjusted according to the temperature and humidity information and the air information to generate comprehensive event data.

[0263] In one embodiment, the calculating based on the breathing information, the heart rate information, the motion trajectory information, the existence probability information, the voiceprint information, and the sound information to obtain the joint confidence includes:

[0264] Acquire the infrared sensor, the millimeter-wave radar, and the sound sensor;

[0265] Determining, based on the historical data, the credibility of the infrared sensor, the credibility of the millimeter-wave radar, the credibility of the sound sensor, the weight of the infrared sensor, the weight of the millimeter-wave radar, and the weight of the sound sensor;

[0266] Adjusting the existence probability information according to the credibility of the infrared sensor to obtain the confidence of the existence probability information;

[0267] Adjusting the motion trajectory information according to the credibility of the millimeter wave radar to obtain the confidence of the motion trajectory information;

[0268] Adjusting the breathing information according to the credibility of the millimeter-wave radar to obtain the confidence of the breathing information;

[0269] adjusting the heart rate information according to the credibility of the millimeter-wave radar to obtain the confidence of the heart rate information;

[0270] adjusting the voiceprint information according to the credibility of the sound sensor to obtain the confidence of the voiceprint information;

[0271] adjusting the sound information according to the credibility of the sound sensor to obtain the confidence of the sound information;

[0272] A joint confidence is obtained according to the weight of the infrared sensor, the weight of the millimeter-wave radar, the weight of the sound sensor, the confidence of the existence probability information, the confidence of the breathing information, the confidence of the heart rate information, the confidence of the voiceprint information, and the confidence of the sound information.

[0273] In one embodiment, the warning information includes level one warning information, level two warning information, and level three warning information, and determining the warning information based on the comprehensive event data includes:

[0274] When the comprehensive event data is greater than or equal to a first specified threshold, triggering a first-level warning message;

[0275] When the comprehensive event data is less than the first specified threshold and greater than or equal to the second specified threshold, triggering a second-level warning information;

[0276] When the comprehensive event data is less than the second specified threshold and any of the breathing information, the heart rate information, the motion trajectory information, the existence probability information, the temperature and humidity information, the air information, the voiceprint information and the sound information is abnormal, a third warning signal is triggered.

[0277] The functions of each module in each device in the embodiments of the present application can be found in the corresponding description in the above method and will not be repeated here.

[0278] Figure 2 FIG. 1 shows a structural block diagram of an electronic device according to an embodiment of the present application. Figure 2As shown, the electronic device includes: a memory 410 and a processor 420, and the memory 410 stores instructions that can be run on the processor 420. When the processor 420 executes the instructions, the monitoring method in the above embodiment is implemented. The number of the memory 410 and the processor 420 can be one or more. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0279] The electronic device may also include a communication interface 430 for communicating with external devices and performing data exchange transmission. The various devices are interconnected using different buses and can be installed on a common mainboard or in other ways as needed. The processor 420 can process instructions executed in the electronic device, including instructions stored in or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In other embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple electronic devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0280] Optionally, in a specific implementation, if the memory 410, the processor 420 and the communication interface 430 are integrated on a chip, the memory 410, the processor 420 and the communication interface 430 can communicate with each other through an internal interface.

[0281] It should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or 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. It is worth noting that the processor may be a processor that supports the Advanced RISC Machines (ARM) architecture.

[0282] An embodiment of the present application provides a computer-readable storage medium (such as the memory 410 described above), which stores computer instructions. When the program is executed by a processor, the method provided in the embodiment of the present application is implemented.

[0283] Optionally, the memory 410 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and applications required for at least one function; the data storage area may store data created based on the use of the electronic device, etc. In addition, the memory 410 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 410 may optionally include a memory remotely located relative to the processor 420, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0284] In the description of this specification, the reference terms "one embodiment," "some embodiments," "example," "specific example," or "some examples" mean that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials, or characteristics described may be combined in any appropriate manner in any one or more embodiments or examples. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification, as well as features of different embodiments or examples, unless they are mutually inconsistent.

[0285] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. Throughout the description of this application, "plurality" means two or more, unless otherwise specifically defined.

[0286] Any process or method description in the flowchart or otherwise described herein can be understood to represent a module, segment or portion of code comprising one or more (two or more) executable instructions for implementing the steps of a specific logical function or process. And the scope of the preferred embodiments of the present application includes other implementations, in which the functions may not be performed in the order shown or discussed, including performing the functions in a substantially simultaneous manner or in the reverse order depending on the functions involved.

[0287] The logic and / or steps represented in the flowchart or otherwise described herein may be considered, for example, as an ordered list of executable instructions for implementing logical functions, and may be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device).

[0288] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. All or part of the steps of the above embodiment method can be completed by instructing the relevant hardware through a program, which can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0289] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the aforementioned integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium. The storage medium may be a read-only memory, a magnetic disk, or an optical disk, etc.

[0290] The above are only specific embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A monitoring method, characterized in that: include: Obtain human status information and environmental parameter information within the monitoring area; Performing feature extraction on the human body state information and the environmental parameter information to obtain state data and environmental data; Performing data fusion on the state data and the environment data to generate comprehensive event data; According to the comprehensive event data, early warning information is determined.

2. The method according to claim 1, characterized in that The human body state information includes infrared data obtained by an infrared sensor and radar data obtained by a millimeter wave radar, and the environmental parameter information includes environmental data and sound data. The human body state information and environmental parameter information obtained in the monitoring area include: acquiring the infrared data, the radar data, the environmental data, and the sound data; According to the PTP clock synchronization signal broadcast by the master node, the infrared data, the radar data, the environmental data and the sound data are reorganized according to the N-second time window to obtain human body status information and environmental parameter information in the detection area.

3. The method according to claim 2, characterized in that The obtaining of human body status information and environmental parameter information within the monitoring area further includes: According to the radar data, a radar coordinate system is established as a reference space; determining a spatial transformation matrix of the infrared data, the environmental data, and the sound data according to the reference space; The infrared data, the environmental data and the sound data are three-dimensionally transformed according to a spatial conversion matrix of the infrared data, the environmental data and the sound data to obtain human body status information and environmental parameter information in the monitoring area.

4. The method according to claim 3, characterized in that The feature extraction of the human body state information and the environmental parameter information to obtain state data and environmental data includes: determining breathing information, heart rate information, and motion trajectory information based on the radar data; determining existence probability information based on the infrared data; Determining temperature, humidity, and air information based on the environmental data; Determining voiceprint information and sound information based on the sound data; State data and environmental data are obtained according to the breathing information, the heart rate information, the motion trajectory information, the existence probability information, the temperature and humidity information, the air information, the voiceprint information, and the sound information.

5. The method according to claim 4, characterized in that The fusing the state data and the environment data to generate comprehensive event data includes: performing calculations based on the breathing information, the heart rate information, the motion trajectory information, the existence probability information, the voiceprint information, and the sound information to obtain a joint confidence level; The joint confidence is adjusted according to the temperature and humidity information and the air information to generate comprehensive event data.

6. The method according to claim 5, characterized in that The calculating based on the breathing information, the heart rate information, the motion trajectory information, the existence probability information, the voiceprint information and the sound information to obtain the joint confidence includes: Acquire the infrared sensor, the millimeter-wave radar, and the sound sensor; Determining, based on the historical data, the credibility of the infrared sensor, the credibility of the millimeter-wave radar, the credibility of the sound sensor, the weight of the infrared sensor, the weight of the millimeter-wave radar, and the weight of the sound sensor; Adjusting the existence probability information according to the credibility of the infrared sensor to obtain the confidence of the existence probability information; Adjusting the motion trajectory information according to the credibility of the millimeter wave radar to obtain the confidence of the motion trajectory information; Adjusting the breathing information according to the credibility of the millimeter-wave radar to obtain the confidence of the breathing information; adjusting the heart rate information according to the credibility of the millimeter-wave radar to obtain the confidence of the heart rate information; Adjusting the voiceprint information according to the credibility of the sound sensor to obtain the confidence of the voiceprint information; adjusting the sound information according to the credibility of the sound sensor to obtain the confidence of the sound information; A joint confidence is obtained according to the weight of the infrared sensor, the weight of the millimeter-wave radar, the weight of the sound sensor, the confidence of the existence probability information, the confidence of the breathing information, the confidence of the heart rate information, the confidence of the voiceprint information, and the confidence of the sound information.

7. The method according to claim 6, characterized in that The warning information includes level one warning information, level two warning information, and level three warning information. The warning information determined based on the comprehensive event data includes: When the comprehensive event data is greater than or equal to a first specified threshold, triggering a first-level warning message; When the comprehensive event data is less than the first specified threshold and greater than or equal to the second specified threshold, triggering a second-level warning information; When the comprehensive event data is less than the second specified threshold and any of the breathing information, the heart rate information, the motion trajectory information, the existence probability information, the temperature and humidity information, the air information, the voiceprint information and the sound information is abnormal, a third warning signal is triggered.

8. A monitoring system, characterized in that: include: An acquisition unit, used to acquire human status information and environmental parameter information within the monitoring area; an obtaining unit, configured to extract features from the human body state information and the environmental parameter information to obtain state data and environmental data; a generating unit, configured to perform data fusion on the state data and the environment data to generate comprehensive event data; A determination unit is used to determine warning information based on the comprehensive event data.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, which, when executed by a processor, implement the method according to any one of claims 1 to 7.

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