Danger early warning method, device and equipment based on monitoring shoes and medium

Abstract

The invention belongs to the technical field of intelligent wearing, and provides a danger early warning method, device and equipment based on a monitoring shoe and a medium, and the method comprises the following steps: embedding a plurality of sensors in the shoe, monitoring vital signs in real time, and giving out early warning and uploading data when the vital signs are abnormal; a 360-degree camera is arranged to be combined with fisheye splicing, environment images are obtained in real time, obstacles are detected through ultrasonic and millimeter wave radars, traffic risks are recognized through deep learning, and an alarm is given; a GPS / Beidou chip and a high-definition map are adopted for accurate positioning, dangerous areas are detected, and early warning is given out; an accelerometer, a gyroscope and a barometer are built in, the moving distance and the height change are calculated, the high-altitude falling risk is judged, and an alarm is given when a threshold value is exceeded; by fusing multi-source data including vital signs, environmental perception, positioning and motion states, comprehensive monitoring of multi-scene risks of health, traffic, environment and falling of a wearer is realized, and potential risks are warned in advance.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent wearable devices, and specifically relates to a method, device, equipment and medium for danger warning based on monitoring shoes. Background Art

[0002] With the aggravation of population aging and the increasing demand for personal safety and health monitoring, intelligent wearable devices have been widely used in the fields of health monitoring and danger warning.

[0003] However, current mainstream intelligent wearable devices, such as smart bracelets and smart watches, although they can monitor basic vital signs such as heart rate and steps, have relatively single functions, can only monitor single or a few vital sign indicators, and the monitoring accuracy is easily affected by movement interference. For example, during strenuous exercise, the heart rate monitoring data of the bracelet will show large deviations and cannot accurately reflect the true physiological state. In addition, existing devices lack the ability to fuse and analyze multi-vital sign data, making it difficult to mine potential health risks from comprehensive data and unable to provide users with comprehensive health warnings.

[0004] Moreover, the environmental monitoring ability of existing wearable devices is seriously insufficient. Most products do not have environmental perception functions, or can only detect simple environmental factors such as temperature and humidity, and cannot effectively identify complex dangerous scenarios such as traffic, water areas, and fire sources. Some wearable devices with cameras, due to limited viewing angles, cannot achieve 360-degree full-range environmental monitoring, resulting in a large number of visual blind spots; and lack a cooperative working mechanism with other sensors (such as radar), and in complex traffic scenarios, cannot accurately identify dynamic risk targets (such as fast-moving vehicles and suddenly emerging pedestrians), making it difficult to early warn of potential dangers.

[0005] At the same time, traditional fall detection devices usually only rely on a single accelerometer sensor to detect the human body's motion state, and judge whether a fall has occurred by setting a simple acceleration threshold. This method is extremely vulnerable to interference from daily movements (such as running and jumping) or accidental collisions, resulting in a very high false alarm rate; at the same time, due to the lack of comprehensive analysis of multi-dimensional data such as attitude angle and vertical height change, it is impossible to accurately distinguish normal movement from falling behavior, and it is difficult to issue an alarm in a timely and accurate manner when a high-altitude fall actually occurs, missing the best rescue opportunity.

[0006] Therefore, those skilled in the art have proposed a method, device, equipment and medium for danger warning based on monitoring shoes, aiming to integrate multi-source data of vital signs, environmental perception, positioning and motion state, and achieve comprehensive monitoring and accurate warning of multi-scenario risks to meet people's growing demand for personal safety and health monitoring. Summary of the Invention

[0007] To solve the above technical problems, the present invention provides a method, device, equipment and medium for dangerous warning based on a monitoring shoe to solve the problems raised in the background art.

[0008] According to a first aspect of the present disclosure, a method for dangerous warning based on a monitoring shoe is proposed, including the following steps:

[0009] S1. Embed a variety of vital sign sensors in the shoe to monitor the wearer's vital signs in real time and obtain vital sign data; according to the vital sign data, when it exceeds the normal physiological index range, send out an abnormal vital sign warning signal and upload the data to a designated guardian or medical institution;

[0010] S2. Install a plurality of miniature wide-angle cameras on the outside of the shoe to cover a 360-degree view, cooperate with the fish-eye lens stitching technology to obtain the surrounding environment images in real time, and be equipped with ultrasonic sensors and millimeter-wave radars to detect obstacles at close range and obtain environmental perception data; based on the environmental perception data, identify potential traffic accident risks through a deep learning algorithm, and trigger an accident alarm signal when the traffic accident risk exceeds a preset accident threshold;

[0011] S3. Use a GPS / Beidou dual-mode positioning chip combined with high-precision map data to accurately locate the position of the shoe and obtain positioning data;

[0012] S4. Based on the positioning data and the environmental perception data, integrate a variety of environmental sensors in the shoe to detect whether it is close to a dangerous area and obtain a dangerous area identification result; based on the dangerous area identification result, send out a dangerous area approaching warning signal when entering the preset dangerous area radius range;

[0013] S5. Install an accelerometer, a gyroscope and a barometer in the shoe, the accelerometer detects the change in motion acceleration, the gyroscope senses the attitude angle, and the barometer obtains the height change data according to the air pressure change; through the inertial measurement unit technology, calculate the translation distance and the vertical movement distance per unit time in real time to obtain the translation distance result and the vertical movement distance result; based on the translation distance result and the vertical movement distance result, combined with the height change data, judge whether there is a risk of falling from a height, and trigger a fall alarm signal when it exceeds the preset fall risk threshold.

[0014] Preferably, in the step S1, the vital sign sensors include a PPG sensor, an ECG electrode patch and a pressure sensor;

[0015] Use the following formula to preprocess the vital sign data:

[0016]

[0017] where x i(t) is the original data of the i-th vital sign sensor at time t, is the preprocessed vital sign data, α is the exponential smoothing coefficient, and its value range is [0, 1];

[0018] Use the following formula to establish a personalized threshold based on the wearer's basic physiological data:

[0019] T i upper (t) = μ i + k·σ i ·f(t)T i lower (t) = μ i - k·σ i ·f(t)

[0020] where, T i upper (t), T i lower (t) respectively represent the dynamic upper and lower threshold values of the i-th vital sign at time t, μ i , σ i respectively represent the mean value and standard deviation of the i-th vital sign of the wearer in the normal state, k is the confidence coefficient, and f(t) is a time function used to adjust the physiological fluctuations in different time periods;

[0021] By calculating the degree to which the current vital sign data deviates from the normal range:

[0022]

[0023] where, D i (t) represents the degree of abnormality of the i-th vital sign at time t, and its value range is [0, +∞];

[0024] According to the degree of abnormality, use the weighted summation method to calculate the comprehensive abnormality index:

[0025]

[0026] where, AI(t) is the comprehensive abnormality index, ω i is the weight coefficient of the i-th vital sign, and n is the number of monitored vital signs; trigger different levels of warnings according to the comprehensive abnormality index, send out vital sign abnormality warning signals, and upload the data to the designated guardian or medical institution.

[0027] Preferably, in the step S2, the ultrasonic sensor is used to detect the distance and azimuth of the nearby obstacle, and output the discrete point cloud data D ul = {d1, d2,..., d n}, where d iThe distance measured by the i-th ultrasonic sensor;

[0028] Obtain the speed, angle, and distance information of medium- and long-distance targets through a millimeter-wave radar, and output point cloud data D radar ={v j , θ j , r j}, where v j is the target speed, θ j is the azimuth angle, and r j is the distance;

[0029] Extract target features from the surrounding environment images obtained in real time through a convolutional neural network, and output the target detection result as O image ={(x k , y k , w k , h k , c k )}, where (x k , y k ) is the target center coordinate, w k , h k are the dimensions, and c k is the class label, including cars and pedestrians;

[0030] Based on the ultrasonic data, millimeter-wave radar data, and environmental image target detection results, unify them to the same spatio-temporal coordinate system through timestamps and spatial coordinates to form the fused environmental perception data D fu ={O image , D ul , D radar};

[0031] Based on the environmental perception data, identify the potential probability of a traffic accident as P risk (t), and P risk (t) ∈ [0, 1]; when P risk (t) > τ, trigger an accident alarm signal and upload the environmental perception data to the specified terminal at the same time, where τ is the preset accident risk threshold.

[0032] Preferably, in step S4, the environmental sensors include a water quality sensor, a combustible gas sensor, a temperature sensor, and a smoke sensor; the real-time coordinates (x, y, z) obtained through a GPS / Beidou dual-mode positioning chip are combined with high-precision map data to match whether the current position is in a predefined dangerous area;

[0033] Based on the environmental perception data combined with the positioning data, divide the dangerous area into a static dangerous area and a dynamic dangerous area;

[0034] Set the geographical fence coordinate set of the static danger area as {(x i , y i , r i ), where r i is the warning radius of the static danger area;

[0035] Based on the environmental perception data, dynamically adjust the warning radius r(t) of the dynamic danger area through the following formula:

[0036] r(t) = r0 + v · t

[0037] where r0 is the initial radius, v is the danger diffusion speed, and t is the time;

[0038] For the static danger area, calculate the distance d between the current position and the center of the danger area:

[0039]

[0040] When d ≤ r i Judge that the warning radius is entered, and trigger the warning signal for approaching the danger area;

[0041] For the dynamic danger area, calculate the correction distance r 动态 :

[0042] r 动态 = r0 + v 车 · t 预测

[0043] where t 预测 is the predicted time for the vehicle to reach the current position; combined with the environmental data obtained by the environmental sensor, when the preset environmental threshold is exceeded, even if the positioning data does not enter the r 动态 warning radius, also trigger the warning signal for approaching the danger area.

[0044] Preferably, through the inertial measurement unit technology, use the following formula to calculate the translational distance and vertical movement distance per unit time in real time:

[0045]

[0046] where s 平移 is the horizontal displacement within the unit time Δt, s 垂直 is the vertical displacement within the unit time Δt, a′ z is the true linear acceleration value obtained by removing the gravitational acceleration component in the vertical direction;

[0047] Calculate the height change rate through the barometer as When where v 阈值If it is the vertical speed threshold, there is a risk of falling;

[0048] By detecting the continuous t 阈值 The vertical acceleration a′ z ≈-g, and the vertical velocity v z When it continues to increase, it is judged as a free fall state; when a momentary high acceleration occurs after a free fall, it is judged as a landing impact;

[0049] The following formula is used to combine multiple characteristics of the height fall risk index:

[0050] R=ω1·f 自由落体 +ω2·f 冲击 +ω3·f 高度

[0051] Among them, ω1, ω2, ω3 are the corresponding weight coefficients; f 自由落体 is the confidence level of the free fall state, f 冲击 is the confidence level of the impact event, which is determined by the magnitude of the impact acceleration, f 高度 is the height change risk factor, which is composed of the current height h and the height change rate Determine; R is the risk index of falling from height;

[0052] When the high altitude falling risk index R exceeds the preset falling risk threshold R 阈值 , and satisfy h>h 安全 or The alarm is triggered when h 安全 is a high risk threshold.

[0053] According to a second aspect of the present disclosure, a danger warning device based on monitoring shoes is proposed, comprising:

[0054] A vital sign monitoring module is used to monitor the wearer's vital signs in real time and obtain vital sign data; based on the vital sign data, when it exceeds the normal physiological index range, it issues a vital sign abnormality warning signal and uploads the data to a designated guardian or medical institution;

[0055] An environmental perception module is used to acquire images of the surrounding environment in real time and obtain environmental perception data; based on the environmental perception data, a deep learning algorithm is used to identify potential traffic accident risks, and when the traffic accident risk exceeds a preset accident threshold, an accident alarm signal is triggered;

[0056] The positioning module is used to use the GPS / Beidou dual-mode positioning chip combined with high-precision map data to accurately locate the position of the shoes and obtain positioning data;

[0057] A danger area recognition module, configured to detect whether it is approaching a danger area according to the positioning data and environmental perception data, and obtain a danger area recognition result; based on the danger area recognition result, when entering the preset radius range of the danger area, send out a danger area approaching warning signal;

[0058] A moving distance detection module, configured to calculate the translational distance and vertical moving distance per unit time in real time through inertial measurement unit technology, and obtain a translational distance result and a vertical moving distance result; based on the translational distance result and the vertical moving distance result, combined with the height change data, determine whether there is a risk of falling from a height, and when exceeding a preset falling risk threshold, trigger a falling alarm signal.

[0059] According to the third aspect of the present disclosure, a computer device is further provided, characterized in that the device includes:

[0060] One or more processors;

[0061] A storage device, configured to store one or more programs;

[0062] When the one or more programs are executed by the one or more processors, the one or more processors implement any of the danger warning methods based on the guardianship shoes in the first aspect.

[0063] According to the fourth aspect of the present disclosure, a storage medium is further provided, on which a computer program is stored, characterized in that when the program is executed by a processor, it implements any of the danger warning methods based on the guardianship shoes in the first aspect.

[0064] Compared with the prior art, the present invention has the following beneficial effects:

[0065] 1. By integrating multi-source data including vital signs, environmental perception, positioning, and motion state, the present invention realizes comprehensive monitoring of multi-scenario risks of the wearer's health, traffic, environment, and falling, adopts 360-degree environmental perception and intelligent risk recognition, breaks through the vision limitation of traditional wearable devices, and combines millimeter-wave radar and deep learning to realize dynamic risk assessment in complex traffic scenarios.

[0066] 2. By detecting changes in motion acceleration and the gyroscope sensing the attitude angle, the present invention calculates the translational distance and vertical moving distance per unit time in real time, accurately monitors the falling from a height, and improves the accuracy of falling recognition. Description of the Drawings

[0067] Figure 1 It is a flowchart of the danger warning method based on the guardianship shoes of the present invention;

[0068] Figure 2Block diagram of the hazard warning device based on a monitoring shoe according to the present invention. Detailed implementation manners

[0069] The following further describes in detail the implementation manners of the present invention in conjunction with the drawings and embodiments. The following embodiments are used to illustrate the present invention, but cannot be used to limit the scope of the present invention.

[0070] Embodiment 1: As shown in the appended Figure 1 figures, the present invention provides a hazard warning method based on a monitoring shoe, including the following steps:

[0071] S1. Embed a variety of vital sign sensors in the shoe to continuously monitor the vital signs of the wearer and obtain vital sign data; when the vital sign data exceeds the normal physiological index range, send out a vital sign abnormality warning signal and upload the data to a designated guardian or medical institution; the vital sign sensors include a PPG sensor, an ECG electrode patch, and a pressure sensor;

[0072] Use the following formula to preprocess the vital sign data:

[0073]

[0074] where, x i (t) is the original data of the i-th vital sign sensor at time t, is the preprocessed vital sign data, α is the exponential smoothing coefficient, and its value range is [0, 1];

[0075] Use the following formula to establish a personalized threshold based on the basic physiological data of the wearer:

[0076] T i upper (t) = μ i + k·σ i ·f(t)T i lower (t) = μ i - k·σ i ·f(t)

[0077] where, T i upper (t), T i lower (t) respectively represent the dynamic upper and lower threshold values of the i-th vital sign at time t, μ i , σ i respectively represent the mean and standard deviation of the i-th vital sign of the wearer in the normal state, k is the confidence coefficient, and f(t) is a time function used to adjust the physiological fluctuations in different time periods;

[0078] By calculating the degree to which the current vital sign data deviates from the normal range:

[0079]

[0080] where D i (t) represents the degree of abnormality of the i-th vital sign at time t, and the value range is [0, +∞];

[0081] According to the degree of abnormality, the weighted summation method is used to calculate the comprehensive abnormality index:

[0082]

[0083] where AI(t) is the comprehensive abnormality index, ω i is the weight coefficient of the i-th vital sign, and n is the number of monitored vital signs; Different levels of warnings are triggered according to the comprehensive abnormality index, and an abnormal warning signal of vital signs is sent, and the data is uploaded to the designated guardian or medical institution.

[0084] By collecting the vital sign data of the wearer such as heart rate, blood oxygen saturation, and body temperature in real time, the physiological abnormalities of the wearer can be detected in time, the risk of sudden diseases can be warned in advance, and the data can be synchronized to the guardian or medical institution, providing a basis for remote first aid and shortening the rescue response time.

[0085] S2. Install multiple miniature wide-angle cameras on the outside of the shoes to cover a 360-degree view, cooperate with the fisheye lens stitching technology to obtain the surrounding environment images in real time, and be equipped with ultrasonic sensors and millimeter-wave radars to detect short-distance obstacles to obtain environmental perception data; Based on the environmental perception data, potential traffic accident risks are identified through deep learning algorithms, and when the traffic accident risk exceeds the preset accident threshold, an accident alarm signal is triggered; The distance and azimuth of short-distance obstacles are detected by ultrasonic sensors, and discrete point cloud data D ul ={d1, d2,..., d n} is output, where d i is the distance measured by the i-th ultrasonic sensor;

[0086] The speed, angle, and distance information of medium- and long-distance targets are obtained through millimeter-wave radars, and point cloud data D radar ={v j , θ j , r j} is output, where v j is the target speed, θ j is the azimuth angle, and r j is the distance;

[0087] Target feature extraction is performed on the surrounding environment images obtained in real time through a convolutional neural network, and the target detection result is output as O image= {(x k , y k , w k , h k , c k )}, where (x k , y k ) is the target center coordinate, w k , h k are the dimensions, and c k is the class label, including cars and pedestrians;

[0088] Based on the ultrasonic data, millimeter-wave radar data, and environmental image target detection results, the data is unified to the same spatio-temporal coordinate system through timestamps and spatial coordinates to form the fused environmental perception data D fu = {O image , D ul , D radar};

[0089] Based on the environmental perception data, the probability of potential traffic accident risks is identified as P risk (t) through a deep learning algorithm, and P risk (t) ∈ [0, 1]; when P risk (t) > τ, an accident alarm signal is triggered, and at the same time, the environmental perception data is uploaded to the specified terminal, where τ is the preset accident risk threshold.

[0090] By capturing information such as road vehicles and obstacles in real time, using a deep learning model to identify risk targets in the traffic scene and calculate the collision probability, potential traffic accidents can be warned in advance, reducing the casualty risk of the wearer in the traffic scene.

[0091] S3. Use a GPS / Beidou dual-mode positioning chip combined with high-precision map data to accurately locate the position of the shoes and obtain the positioning data; by obtaining the real-time position coordinates of the wearer, it can ensure that the guardian or rescue personnel can quickly lock the position, especially improving the positioning accuracy in complex environments.

[0092] S4. Based on the positioning data and environmental perception data, integrate multiple environmental sensors in the shoes to detect whether it is approaching a dangerous area and obtain the dangerous area recognition result; based on the dangerous area recognition result, when entering the preset dangerous area radius range, a dangerous area approach warning signal is issued; the environmental sensors include water quality sensors, combustible gas sensors, temperature sensors, and smoke sensors;

[0093] Through the real-time coordinates (x, y, z) obtained by the GPS / Beidou dual-mode positioning chip, combined with high-precision map data, match whether the current position is in a predefined dangerous area;

[0094] Based on environmental perception data combined with positioning data, the dangerous area is divided into a static dangerous area and a dynamic dangerous area;

[0095] The preset geographical fence coordinate set of the static dangerous area is {(x i , y i , r i ), where r i is the warning radius of the static dangerous area;

[0096] Based on environmental perception data, the warning radius r(t) of the dynamic dangerous area is dynamically adjusted through the following formula:

[0097] r(t) = r0 + v · t

[0098] where r0 is the initial radius, v is the dangerous diffusion speed, and t is the time;

[0099] For the static dangerous area, by calculating the distance d between the current position and the center of the dangerous area:

[0100]

[0101] When d ≤ r i It is judged that the warning radius is entered, and a warning signal for approaching the dangerous area is triggered;

[0102] For the dynamic dangerous area, the corrected distance r 动态 is calculated through environmental perception data:

[0103] r 动态 = r0 + v 车 · t 预测

[0104] where t 预测 is the predicted time for the vehicle to reach the current position; combined with the environmental data obtained by the environmental sensor, when the preset environmental threshold is exceeded, even if the positioning data does not enter the r 动态 warning radius, a warning signal for approaching the dangerous area is also triggered.

[0105] Combined with positioning data and environmental sensors, it is judged whether it is close to the preset dangerous area. When the wearer enters the warning radius of the dangerous area, a warning signal is sent in real time to prevent entering the dangerous area by mistake.

[0106] S5. An accelerometer, a gyroscope, and a barometer are built into the shoes. The accelerometer detects changes in motion acceleration, the gyroscope senses the attitude angle, and the barometer obtains height change data based on air pressure changes. Through inertial measurement unit technology, the translational distance and vertical movement distance per unit time are calculated in real time to obtain the translational distance result and the vertical movement distance result. Based on the translational distance result and the vertical movement distance result, combined with the height change data, it is determined whether there is a risk of high-altitude fall. When the preset fall risk threshold is exceeded, a fall alarm signal is triggered.

[0107] Through inertial measurement unit technology, using the following formula, the translational distance and vertical movement distance per unit time are calculated in real time:

[0108]

[0109] where s 平移 is the horizontal displacement within the unit time Δt, and s 垂直 is the vertical displacement within the unit time Δt. a′ z is the true linear acceleration value obtained by removing the gravitational acceleration component in the vertical direction;

[0110] The height change rate calculated by the barometer is When where v 阈值 is the vertical velocity threshold, then there is a fall risk;

[0111] By detecting the continuous vertical acceleration a′ 阈值 lasting for t z ≈ -g, and the vertical velocity v z continuously increasing, it is determined to be in a free fall state; when there is an instantaneous high acceleration after free fall, it is determined to be a landing impact;

[0112] Using the following formula, the high-altitude fall risk index integrating multiple features:

[0113] R = ω1·f 自由落体 + ω2·f 冲击 + ω3·f 高度

[0114] where ω1, ω2, and ω3 are the corresponding weight coefficients respectively; f 自由落体 is the confidence level of the free fall state, f 冲击 is the confidence level of the impact event, determined by the magnitude of the impact acceleration, and f 高度 is the height change risk factor, determined by the current height h and the height change rate ; R is the high-altitude fall risk index;

[0115] When the high-altitude fall risk index R exceeds the preset fall risk threshold R 阈值and satisfy h > h 安全 or to trigger an alarm; where h 安全 is the height risk threshold.

[0116] An inertial measurement unit is constructed through an accelerometer, a gyroscope, and a barometer to monitor the motion state and height change in real time, thereby calculating the translational distance and vertical movement distance per unit time. Combining with barometric height data, it determines whether a high-altitude fall has occurred, quickly identifies the fall event and issues an alarm, so as to strive for the golden time for rescue.

[0117] Embodiment 2: As shown in the appendix Figure 2 The present invention provides a dangerous early warning device based on a guardianship shoe, including:

[0118] A vital sign monitoring module for real-time monitoring of the wearer's vital signs to obtain vital sign data; according to the vital sign data, when it exceeds the normal physiological index range, it issues a vital sign abnormality warning signal and uploads the data to the designated guardian or medical institution;

[0119] An environmental perception module for real-time acquisition of the surrounding environment image to obtain environmental perception data; based on the environmental perception data, it identifies potential traffic accident risks through a deep learning algorithm, and when the traffic accident risk exceeds the preset accident threshold, it triggers an accident alarm signal;

[0120] A positioning module for accurately positioning the shoe position by using a GPS / Beidou dual-mode positioning chip combined with high-precision map data to obtain positioning data;

[0121] A dangerous area identification module for detecting whether it is close to a dangerous area according to the positioning data and environmental perception data to obtain a dangerous area identification result; based on the dangerous area identification result, when entering the preset dangerous area radius range, it issues a dangerous area approaching warning signal;

[0122] A moving distance detection module for real-time calculation of the translational distance and vertical movement distance per unit time through inertial measurement unit technology to obtain a translational distance result and a vertical movement distance result; based on the translational distance result and the vertical movement distance result, combining with height change data, it determines whether there is a high-altitude fall risk, and when it exceeds the preset fall risk threshold, it triggers a fall alarm signal.

[0123] As can be seen from the above, by integrating vital signs, visual / radar environmental perception, positioning, and motion sensors into shoes, an innovative form of "footwear + full-scenario monitoring" is achieved. The use of a micro wide-angle camera + fisheye lens stitching technology breaks through the field of view limitations of traditional wearable devices. Combining millimeter-wave radar and deep learning enables dynamic risk assessment in complex traffic scenarios. Moreover, traditional fall detection relies on a single acceleration threshold and is vulnerable to motion interference, while this solution combines a multi-parameter fusion algorithm of acceleration, attitude angle, and barometric altitude to improve the accuracy of fall recognition. At the same time, by combining high-precision positioning and environmental sensors, different dangerous areas can be dynamically adapted instead of fixed geographical fences, enhancing scene adaptability.

[0124] Embodiment 3: The present invention also provides a hazard warning and monitoring device based on a monitoring shoe, and the device includes:

[0125] One or more processors;

[0126] A storage device for storing one or more programs;

[0127] When the one or more programs are executed by the one or more processors, the one or more processors implement any of the hazard warning methods in the above embodiments.

[0128] Embodiment 4: The present invention also provides a hazard warning medium based on a monitoring shoe, on which a computer program is stored, and when the program is executed by a processor, it implements any of the hazard warning methods in the above embodiments.

[0129] The computer storage medium of the embodiments of the present invention can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium includes, but is not limited to: electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage medium include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROMD), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, the computer-readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, device, or component.

[0130] A computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, carrying computer-readable program code. Such a propagated data signal may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0131] The program code contained on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0132] The computer program code for performing the operations of the present invention may be written in one or more programming languages or combinations thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0133] It is important to note that the construction and arrangement of the present application shown in multiple different exemplary embodiments are merely illustrative. Although only a few embodiments are described in detail in this disclosure, those skilled in the art who refer to this disclosure should readily understand that many modifications are possible without materially departing from the novel teachings and advantages of the subject matter described in this application. Other substitutions, modifications, changes, and omissions may be made in the design, operating conditions, and arrangement of the exemplary embodiments without departing from the scope of the present invention. Therefore, the present invention is not limited to a particular embodiment, but extends to various modifications that still fall within the scope of the appended claims.

[0134] In addition, to provide a concise description of the exemplary embodiments, all features of the actual embodiments may not be described (i.e., those features that are not relevant to the currently contemplated best mode of carrying out the present invention or those features that are not relevant to implementing the present invention).

[0135] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A danger warning method based on a monitoring shoe, characterized in that, It includes the following steps: S1. Embed a variety of vital sign sensors in the shoes to continuously monitor the wearer's vital signs and obtain vital sign data; according to the vital sign data, when it exceeds the normal physiological index range, send out a vital sign abnormality warning signal and upload the data to the designated guardian or medical institution; S2. Install multiple miniature wide-angle cameras on the outside of the shoes to cover a 360-degree view, cooperate with the fish-eye lens stitching technology to continuously obtain the surrounding environment images, and be equipped with ultrasonic sensors and millimeter-wave radars to detect short-distance obstacles and obtain environmental perception data; based on the environmental perception data, identify potential traffic accident risks through deep learning algorithms, and trigger an accident alarm signal when the traffic accident risk exceeds the preset accident threshold; S3. Use a GPS / Beidou dual-mode positioning chip combined with high-precision map data to accurately locate the position of the shoes and obtain positioning data; S4. Based on the positioning data and environmental perception data, integrate a variety of environmental sensors in the shoes to detect whether it is close to a dangerous area and obtain a dangerous area recognition result; based on the dangerous area recognition result, send out a dangerous area approaching warning signal when entering the preset dangerous area radius range; S5. Install an accelerometer, a gyroscope and a barometer in the shoes. The accelerometer detects changes in motion acceleration, the gyroscope senses the attitude angle, and the barometer obtains altitude change data based on air pressure changes; through inertial measurement unit technology, calculate the translational distance and vertical movement distance per unit time in real time to obtain a translational distance result and a vertical movement distance result; based on the translational distance result and the vertical movement distance result, combined with the altitude change data, judge whether there is a risk of high-altitude fall, and trigger a fall alarm signal when it exceeds the preset fall risk threshold.

2. The method for danger warning based on a monitoring shoe according to claim 1, wherein In the step S1, the vital sign sensors include a PPG sensor, an ECG electrode patch and a pressure sensor; Use the following formula to preprocess the vital sign data: where x i (t) is the raw data of the i-th vital sign sensor at time t, is the preprocessed vital sign data, α is the exponential smoothing coefficient, and its value range is [0, 1]; Use the following formula to establish a personalized threshold based on the wearer's basic physiological data: Among them, respectively represent the dynamic upper and lower threshold values of the i-th vital sign at time t, μ i , σ i respectively represent the mean value and standard deviation of the i-th vital sign of the wearer in the normal state, k is the confidence coefficient, and f(t) is a time function used to adjust the physiological fluctuations in different time periods; By calculating the degree to which the current vital sign data deviates from the normal range: Among them, D i (t) represents the degree of abnormality of the i-th vital sign at time t, and the value range is [0, +∞]; According to the degree of abnormality, use the weighted summation method to calculate the comprehensive abnormality index: where AI(t) is the comprehensive anomaly index, ω i is the weight coefficient of the i-th vital sign, and n is the number of monitored vital signs; different levels of warnings are triggered based on the comprehensive anomaly index to issue a vital sign anomaly warning signal and upload the data to the designated guardian or medical institution.

3. The method for danger warning based on a monitoring shoe according to claim 1, wherein, In the step S2, the distance and azimuth of the nearby obstacle are detected by the ultrasonic sensor, and the discrete point cloud data D ul ={d1, d2,..., d n}, where d i is the distance measured by the i-th ultrasonic sensor; Obtain the speed, angle, and distance information of medium- and long-distance targets through a millimeter-wave radar, and output point cloud data D radar ={v j , θ j , r j}, where v j is the target speed, θ j is the azimuth angle, and r j is the distance; Extract the target features from the real-time acquired surrounding environment images through a convolutional neural network, and output the target detection result as O image ={(x k ,y k ,w k ,h k ,c k )}, where (x k ,y k ) is the target center coordinate, w k ,h k are the dimensions, and c k is the class label, including cars and pedestrians; Based on the target detection results of ultrasonic data, millimeter-wave radar data, and environmental images, the data is unified into the same spatio-temporal coordinate system through timestamps and spatial coordinates to form the fused environmental perception data D fu ={O image , D ul , D radar}; Based on the environmental perception data, the probability of potential traffic accident risks is identified as P risk (t) through a deep learning algorithm, and P risk (t) ∈ [0, 1]; when P risk (t) > τ, an accident alarm signal is triggered, and at the same time, the environmental perception data is uploaded to a specified terminal, where τ is a preset accident risk threshold.

4. The dangerous warning method based on a guardian shoe according to claim 1, wherein In the step S4, the environmental sensors include a water quality sensor, a combustible gas sensor, a temperature sensor, and a smoke sensor; match whether the current position is in a predefined dangerous area by combining the real-time coordinates (x, y, z) obtained by the GPS / Beidou dual-mode positioning chip with high-precision map data; Based on the environmental perception data combined with the positioning data, divide the dangerous area into a static dangerous area and a dynamic dangerous area; Preset the set of geofence coordinates of the static hazard area as \(\{(x i ,y i ,r i )\}\), where \(r i is the warning radius of the static hazard area; Based on the environmental perception data, dynamically adjust the warning radius r(t) of the dynamic dangerous area through the following formula: r(t) = r0 + v·t where r0 is the initial radius, v is the dangerous diffusion speed, and t is the time; For the static dangerous area, calculate the distance d between the current position and the center of the dangerous area: When d ≤ r i It is determined that the warning radius is entered, and a warning signal for approaching the dangerous area is triggered; For the dynamic danger area, a corrected distance r is calculated based on the environmental perception data 动态 : r 动态 = r0 + v 车 ·t 预测 where t 预测 is the predicted time for the vehicle to reach the current position; combining the environmental data obtained by the environmental sensor, when the preset environmental threshold is exceeded, even if the positioning data does not enter the r 动态 warning radius, a warning signal for approaching a dangerous area is also triggered.

5. The method for danger warning based on a monitoring shoe according to claim 1, wherein In the step S5, through inertial measurement unit technology, use the following formula to calculate the translational distance and vertical movement distance per unit time in real time: where s 平移 is the horizontal displacement within the unit time Δt, and s 垂直 is the vertical displacement within the unit time Δt, and a′ z is the true linear acceleration value obtained by removing the gravitational acceleration component in the vertical direction; The altitude change rate calculated by the barometer is When where v 阈值 is the vertical velocity threshold, there is a risk of falling; By detecting a continuous vertical acceleration a′ 阈值 lasting for t z ≈ -g, and when the vertical velocity v z continues to increase, it is determined to be in a free-fall state; when there is an instantaneous high acceleration after free fall, it is determined to be a landing impact; Using the following formula, the high-altitude fall risk index integrating multiple features: R = ω1·f 自由落体 + ω2·f 冲击 + ω3·f 高度 where ω1, ω2, and ω3 are the corresponding weight coefficients; f 自由落体 is the confidence level in the free-fall state, and f 冲击 is the confidence level in the impact event, which is determined by the magnitude of the impact acceleration, and f 高度 is the height change risk factor, which is determined by the current height h and the height change rate ; R is the high-altitude fall risk index; When the high-altitude fall risk index R exceeds the preset fall risk threshold R 阈值 , and satisfies h > h 安全 or when the alarm is triggered; where h 安全 is the height risk threshold.

6. The danger warning device based on the monitoring shoes is characterized in that, Including: A life feature monitoring module for real-time monitoring of the wearer's vital signs to obtain vital sign data; According to the vital sign data, when it exceeds the normal physiological index range, an abnormal vital sign warning signal is issued, and the data is uploaded to the designated guardian or medical institution; An environmental perception module for real-time acquisition of surrounding environment images to obtain environmental perception data; based on the environmental perception data, potential traffic accident risks are identified through a deep learning algorithm, and when the traffic accident risk exceeds a preset accident threshold, an accident alarm signal is triggered; A positioning module for accurately positioning the shoe position using a GPS / Beidou dual-mode positioning chip combined with high-precision map data to obtain positioning data; A dangerous area identification module for detecting whether it is close to a dangerous area according to the positioning data and the environmental perception data to obtain a dangerous area identification result; based on the dangerous area identification result, when entering the preset dangerous area radius range, a dangerous area approaching warning signal is issued; A moving distance detection module for real-time calculation of the translational distance and the vertical moving distance per unit time through inertial measurement unit technology to obtain a translational distance result and a vertical moving distance result; based on the translational distance result and the vertical moving distance result, combined with the height change data, it is judged whether there is a high-altitude fall risk, and when it exceeds the preset fall risk threshold, a fall alarm signal is triggered.

7. A computer device, characterized in that, The device includes: One or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the dangerous warning method based on the guardianship shoe as described in any one of claims 1-5.

8. A storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the dangerous warning method based on the guardianship shoe as described in any one of claims 1-5.

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