CN112132719B - Personnel search and rescue method, device and system for emergency rescue and storage medium - Google Patents
Personnel search and rescue method, device and system for emergency rescue and storage medium Download PDFInfo
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Abstract
The invention discloses a personnel search and rescue method, device and system for emergency rescue and a storage medium. Wherein the method comprises the following steps: comprising the following steps: acquiring reflected long-range radio (LoRa) signal data during the process of scanning a target area by a LoRa transceiver; extracting features of the LoRa signal data to obtain a first signal for reflecting vital activity features of a human body; performing personnel detection on the first signal based on a pre-constructed feature set, and determining whether trapped personnel exist in the target area; wherein the feature set is determined based on the first signals acquired in an unmanned environment, a manned environment, respectively. The embodiment of the invention can realize the detection of the trapped personnel in a non-invasive mode in an emergency rescue scene, has a large detection range, and is particularly suitable for detecting the trapped personnel in a fire rescue scene of a high-rise building.
Description
Technical Field
The invention relates to the field of emergency rescue, in particular to a personnel search and rescue method, device and system for emergency rescue and a storage medium.
Background
With the high-speed development of economy, the urban scale is continuously enlarged, high-rise buildings are increasingly increased, urban building structures are increasingly complex, people's mouth density in the buildings is increased, unsafe factors such as overload, overheat, short circuit and aging of electrical equipment are increased, the probability of occurrence of a heavy and extremely large fire disaster is in a year-by-year rising trend, the harm and property loss to people are also increasingly greater, and the emergency rescue problem facing the fire scene is increasingly valued by people.
Existing wire-based fire alarm systems and fire protection linkages have tended to mature. However, when a fire occurs, the smoke concentration is high, the environment is complex and severe, and the scheme still has the following characteristics and disadvantages: on one hand, the wired communication mode has poor expansion performance, severe requirements on pipelines, large investment, complicated wiring, difficult maintenance and easy damage; on the other hand, the traditional wired fire alarm system and the fire-fighting linkage system do not have the functions of positioning and tracking rescue workers and trapped workers. Therefore, trapped people can escape from the fire only by means of the exit indicator lamp in the building, and firefighters can search and find the fire only by means of the firefighter self during rescue, so that the rescue efficiency is low.
The non-invasive personnel detection technology is utilized to improve the fire rescue efficiency and reduce the casualties, and is one of the problems of important research in the current fire rescue field. Because the smoke concentration in the fire scene is high, the environment is complex and severe, the traditional non-invasive personnel detection technology such as infrared rays, ultrasonic waves, computer vision and the like has poor penetrability, weak anti-interference capability and detection dead angles, and the requirements of fire rescue scenes are difficult to meet; in addition, non-invasive personnel detection technologies based on wireless signals are rapidly developed and rise, however, the technologies depend on existing equipment, the sensing distance is very limited, the equipment is large in size, and the technology cannot be directly applied to fire rescue of high-rise buildings.
Disclosure of Invention
In view of the above, the embodiment of the invention provides a personnel search and rescue method, device, system and storage medium for emergency rescue, aiming at accurately identifying trapped personnel so as to improve the rescue efficiency of the trapped personnel.
The technical scheme of the embodiment of the invention is realized as follows:
in a first aspect, an embodiment of the present invention provides a method for searching and rescuing a person for emergency rescue, including:
obtaining the reflected LoRa signal data in the process of scanning a target area by a LoRa (Long Range Radio) transceiver;
extracting features of the LoRa signal data to obtain a first signal for reflecting vital activity features of a human body;
performing personnel detection on the first signal based on a pre-constructed feature set, and determining whether trapped personnel exist in the target area; wherein the feature set is determined based on the first signals acquired in an unmanned environment, a manned environment, respectively.
In some embodiments, the detecting the first signal based on a pre-constructed feature set, determining whether a trapped person is present in the target area, comprises:
converting the first signal to a frequency domain signal based on a fourier transform;
Calculating a variance probability distribution of a power spectral density of the first signal based on the frequency domain signal;
calculating a first distance between the variance probability distribution and a variance probability distribution of the power spectral density of the first signal corresponding to the unmanned environment in a pre-constructed feature set, and a second distance between the variance probability distribution and a variance probability distribution of the power spectral density of the first signal corresponding to the manned environment in the pre-constructed feature value;
if the first distance is smaller than the second distance, determining that no trapped person exists in the target area;
and if the first distance is greater than or equal to the second distance, determining that trapped people exist in the target area.
In some embodiments, the feature extracting the LoRa signal data to obtain a first signal for reflecting vital signs of a human body includes:
performing data compression on the LoRa signal data;
and filtering the compressed data based on a high-pass filter and a low-pass filter to obtain the first signal.
In some embodiments, before the person detecting the first signal based on the pre-constructed feature set, the method further comprises:
Acquiring the first signals respectively corresponding to the unmanned environment and the manned environment;
converting the first signal corresponding to the unmanned environment into a frequency domain signal corresponding to the unmanned environment based on Fourier transform, and calculating a variance probability distribution of the power spectral density of the first signal corresponding to the unmanned environment based on the frequency domain signal corresponding to the unmanned environment;
converting the first signal corresponding to the manned environment into a frequency domain signal corresponding to the manned environment based on Fourier transform, and calculating a variance probability distribution of the power spectral density of the first signal corresponding to the manned environment based on the frequency domain signal corresponding to the manned environment;
wherein the pre-constructed feature set comprises: the variance probability distribution of the power spectral density of the first signal corresponding to the unmanned environment and the variance probability distribution of the power spectral density of the first signal corresponding to the manned environment.
In some embodiments, if there is a trapped person in the target area, the method further comprises:
detecting the motion state of trapped personnel on the basis of a pre-constructed personnel state detection model for the first signal; the personnel state detection model is generated based on the first signal training respectively corresponding to the static personnel and the moving personnel.
In some embodiments, detecting the motion state of the trapped person based on the pre-constructed person state detection model for the first signal comprises:
obtaining time-frequency data corresponding to the first signal based on short-time Fourier transform;
and inputting the time-frequency data into a pre-constructed personnel state detection model to obtain a detection result that the trapped personnel are static personnel or moving personnel.
In some embodiments, if the trapped person is a stationary person, the method further comprises:
determining a respiratory frequency of the stationary person based on peaks in a power spectral density of the first signal;
a risk level of the stationary person is determined based on the respiratory rate of the stationary person.
In some embodiments, if the trapped person is a sportsman, the method further comprises:
an escape path for guiding the sportsman to escape is generated and transmitted.
In a second aspect, an embodiment of the present invention further provides a personnel search and rescue device for emergency rescue, including:
the acquisition module is used for acquiring the data of the LoRa signal reflected in the process of scanning the target area by the LoRa transceiver;
the feature extraction module is used for carrying out feature extraction on the LoRa signal data to obtain a first signal for reflecting vital activity features of a human body;
The personnel detection module is used for detecting personnel of the first signal based on a pre-constructed feature set and determining whether trapped personnel exist in the target area; wherein the feature set is determined based on the first signals acquired in an unmanned environment, a manned environment, respectively.
In a third aspect, an embodiment of the present invention further provides a personal search and rescue apparatus for emergency rescue, including: the unmanned aerial vehicle comprises a processor and a memory for storing a computer program capable of running on the processor, wherein the processor is in communication connection with the unmanned aerial vehicle and is used for executing the steps of the method according to the first aspect of the embodiment of the invention when the computer program is run.
In a fourth aspect, an embodiment of the present invention further provides a personnel search and rescue system for emergency rescue, including:
the unmanned aerial vehicle is provided with a LoRa transceiver;
the personnel search and rescue equipment of the embodiment of the invention is in communication connection with the unmanned aerial vehicle and is used for executing the steps of the method of the first aspect of the embodiment of the invention when running a computer program.
In a fifth aspect, an embodiment of the present invention further provides a storage medium, where a computer program is stored, where the computer program, when executed by a processor, implements the steps of the method according to the first aspect of the embodiment of the present invention.
According to the technical scheme provided by the embodiment of the invention, the obtained LoRa signal data reflected by the target area is subjected to characteristic extraction to obtain a first signal for reflecting vital activity characteristics of a human body; performing personnel detection on the first signal based on a pre-constructed feature set, and determining whether trapped personnel exist in the target area; the detection of trapped people can be realized in a non-invasive mode in an emergency rescue scene, the detection range is large, and the method is particularly suitable for detecting the trapped people in a fire rescue scene of a high-rise building.
Drawings
FIG. 1 is a schematic flow chart of a personnel search and rescue method for emergency rescue according to an embodiment of the invention;
FIG. 2 is a histogram of probability of variance of power spectral density of a first signal in an unmanned environment according to an embodiment of the present invention;
FIG. 3 is a histogram of variance probability of power spectral density of a first signal in a human environment in an application example of the present invention;
FIG. 4 is a schematic diagram of a network structure of a SquezeNet model in an application example of the present invention;
FIG. 5 is a schematic structural diagram of a personnel search and rescue device for emergency rescue according to an embodiment of the present invention;
FIG. 6 is a schematic structural diagram of a personal search and rescue device for emergency rescue according to an embodiment of the present invention;
Fig. 7 is a schematic structural diagram of a personnel search and rescue system for emergency rescue according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
Before describing embodiments of the present invention in further detail, the terms and terminology involved in the embodiments of the present invention will be described, and the terms and terminology involved in the embodiments of the present invention are suitable for the following explanation:
the embodiment of the invention provides a personnel search and rescue method for emergency rescue, which is shown in fig. 1 and comprises the following steps:
here, the target area may be an area where emergency rescue is required, for example, a fire rescue is taken as an example, and may be an area corresponding to different floors of a high-rise building. In other embodiments, the target area may also be an area corresponding to an emergency rescue scenario in which a mine disaster, building collapse, or other disaster occurs.
For example, the LoRa transceiver may be carried by a mobile platform to scan different target areas, for example, may be carried by an unmanned aerial vehicle, and may hover at and around different floors of a high-rise building and climb or fall to meet the detection needs of whether trapped people are present in different areas within different high-rise buildings. In other embodiments, the LoRa transceiver may also be carried by an intelligent mobile robot or a biomimetic mouse, etc. for application in personnel search and rescue in a small space in disaster environments such as mining disaster or building collapse.
102, extracting features of the LoRa signal data to obtain a first signal for reflecting vital movement features of a human body;
the obtained LoRa signal data reflected by the target area can be data in unit sampling duration, and the characteristic extraction can be performed on the LoRa signal data due to the fact that the data size is large and more interference signals exist, so that interference signal components can be removed as much as possible or reduced as much as possible, the signal-to-noise ratio of the data is improved, and a first signal for reflecting vital activity characteristics of a human body is obtained.
In some embodiments, the feature extracting the LoRa signal data to obtain a first signal for reflecting vital signs of a human body includes:
Performing data compression on the LoRa signal data;
and filtering the compressed data based on a high-pass filter and a low-pass filter to obtain the first signal.
Illustratively, data compression may be performed based on M-point mean filtering (moving average filter). Specifically, firstly, a template of M points is utilized to translate on a signal with the length of N, the acquired signal with the length of N is divided into a plurality of sections of signal sequences with the length of N and containing M sampling points, and the average value of the M points is used for replacing the middle point of the original signal, so that the compression and the smoothing of signal data are realized.
For example, for the compressed data, a high-pass filtering process is performed based on a 4-order elliptic IIR high-pass filter to filter out reflection clutter such as walls and the like, so as to improve the signal-to-noise ratio. Here, the high-pass filter can filter out the low-frequency component representing the wall reflection signal, and the embodiment of the invention adopts the 4-order elliptical high-pass IIR filter to extract the target signal component in the LoRa signal data and remove the wall echo component. The transition band of the elliptical high-pass filter is much narrower than the amplitude-frequency response transition band of a traditional two-pulse canceller, and Doppler frequencies generated by micro motion of a human target basically fall within the passband range of the filter. Therefore, the elliptical high-pass filter can keep Doppler frequency information of the target to the maximum extent while inhibiting fixed clutter, thereby effectively improving the signal-to-noise ratio. And carrying out low-pass filtering processing on the data after high-pass filtering based on the low-pass filter so as to eliminate the influence of unmanned aerial vehicle shake. In particular, the unmanned aerial vehicle has a movement frequency in the range of 60hz to 150hz, which, unlike the low frequency range (< 10 hz) of human vital signals, can have a certain effect on human movement recognition. In order to eliminate noise introduced by vibration during flight of the unmanned aerial vehicle, time domain data may be converted into a frequency domain by FFT (fast fourier transform), and a high frequency part in the frequency domain may be removed by a low pass filter (for example, a second order butterworth low pass filter with a cut-off frequency of 10 hz), and then IFFT (inverse fast fourier transform) may be performed on the filtered low frequency signal, i.e., a time domain signal may be reconstructed, to obtain a first signal for reflecting vital signs of a human body, to be processed in a next stage. The low-pass filtering can eliminate unmanned aerial vehicle jitter, and the reserved low-frequency signals are utilized to reconstruct data, so that the data is further compressed.
Here, the pre-constructed feature set may include a first feature set corresponding to the unmanned environment and a second feature set corresponding to the manned environment, and the first signal is matched based on the pre-constructed feature set, so that it may be determined whether the trapped person exists in the target area.
The personnel search and rescue method provided by the embodiment of the invention can realize the detection of trapped personnel in a non-invasive mode in an emergency rescue scene, has a large detection range, and is particularly suitable for detecting the trapped personnel in a fire rescue scene of a high-rise building.
In some embodiments, the LoRa transceiver may be onboard the drone, and the drone may be configured to scan the target area and receive reflected LoRa signal data. For example, the fire rescue site may temporarily build an edge computing node, such as a notebook computer, or a preconfigured desktop computer, etc., that may obtain the LoRa signal data forwarded by the drone or sent by the LoRa transceiver.
Illustratively, the LoRa transceiver comprises: the LoRa transmitter can be a common commercial LoRa node, the receiver can be a LimeSDR mini, the receiver can be connected with the Raspberry Pi through USB 3.0, received LoRa signal data is forwarded to the edge computing node through the Raspberry Pi in a wireless mode such as 4G or WiFi mode, so that an end-edge architecture is constructed, and data processing performance of the edge computing node is utilized, so that data processing delay can be reduced.
The traditional wireless indoor positioning technology is completely based on the existing wireless infrastructure, a user does not need any extra hardware equipment, and specific wireless positioning software can be installed on the terminal, so that the positioning can be independently realized. Most of mobile terminals are small-sized and energy-limited portable computing devices, and in order to save terminal electric quantity and computing resources, positioning systems based on a terminal-cloud server architecture are often adopted in actual deployment. In the event of a fire, location services often have the following characteristics: the indoor positioning infrastructure is extremely fragile, the indoor positioning environment changes more rapidly, the user positioning request is more frequent, the data fusion of multi-terminal combined positioning is more complex in the environment of the Internet of things, and a large amount of data transmission and data calculation brought by improving the positioning precision are characterized. In the positioning process, if the mode of uploading the cloud, analyzing and processing and returning to the equipment is adopted, signal transmission delay is caused, and in an emergency situation, field potential misjudgment and rescue scheme making errors are extremely easy to cause casualties. According to the embodiment of the invention, the edge computing node is used for extracting the characteristics of the LoRa signal data reflected by the target area to obtain the first signal, and the first signal is subjected to personnel detection based on the pre-constructed characteristic set, so that the time delay can be effectively reduced, and the urgent requirement of emergency rescue can be met. In addition, the LoRa transceiver is carried by the unmanned aerial vehicle, and can enlarge the scanning and sensing area as unified signal acquisition equipment.
In some embodiments, the detecting the first signal based on a pre-constructed feature set, determining whether a trapped person is present in the target area, comprises:
converting the first signal to a frequency domain signal based on a fourier transform;
calculating a variance probability distribution of a power spectral density of the first signal based on the frequency domain signal;
calculating a first distance between the variance probability distribution and a variance probability distribution of the power spectral density of the first signal corresponding to the unmanned environment in a pre-constructed feature set, and a second distance between the variance probability distribution and a variance probability distribution of the power spectral density of the first signal corresponding to the manned environment in the pre-constructed feature value;
if the first distance is smaller than the second distance, determining that no trapped person exists in the target area;
and if the first distance is greater than or equal to the second distance, determining that trapped people exist in the target area.
In practical applications, a feature set for personnel detection needs to be pre-constructed, based on which, in some embodiments, before the personnel detection is performed on the first signal based on the pre-constructed feature set, the method further includes:
Acquiring the first signals respectively corresponding to the unmanned environment and the manned environment;
converting the first signal corresponding to the unmanned environment into a frequency domain signal corresponding to the unmanned environment based on Fourier transform, and calculating a variance probability distribution of the power spectral density of the first signal corresponding to the unmanned environment based on the frequency domain signal corresponding to the unmanned environment;
converting the first signal corresponding to the manned environment into a frequency domain signal corresponding to the manned environment based on Fourier transform, and calculating a variance probability distribution of the power spectral density of the first signal corresponding to the manned environment based on the frequency domain signal corresponding to the manned environment;
wherein the pre-constructed feature set comprises: the variance probability distribution of the power spectral density of the first signal corresponding to the unmanned environment and the variance probability distribution of the power spectral density of the first signal corresponding to the manned environment.
When the environment is free of human targets, i.e. in an unmanned environment, clutter in the LoRa signal data can be approximately seen as white noise following a gaussian distribution, and therefore, the LoRa signal data can be approximately a superposition of a constant signal and the gaussian white noise, so that a time-invariant Power Spectral Density (PSD) is generated, i.e. the Power Spectral Density (PSD) in an unmanned environment is theoretically a constant, and the Power Spectral Density (PSD) variance values at different sampling moments are all concentrated near zero. When the environment has a human target, i.e. in the presence of a human environment, the LoRa signal data contains echoes (clutter) generated by living echoes, noise and other objects and backgrounds, which can be approximately regarded as colored noise following a gaussian distribution, the power spectral density of the clutter is gaussian, i.e. in the presence of a human environment, the Power Spectral Density (PSD) variance distribution at different sampling moments is more dispersed, and the variance value is also larger.
Based on this, in the embodiment of the present invention, the first signals corresponding to the unmanned environment and the manned environment respectively are obtained, that is, the characteristic extraction is performed on the LoRa signal data corresponding to the unmanned environment and the manned environment respectively, so as to obtain corresponding first signals, and the frequency domain signal is obtained based on the FFT for the first signals, so as to obtain the variance probability histogram of the power spectrum density of the first signals, as shown in fig. 2 and fig. 3, fig. 2 is the variance probability histogram of the power spectrum density of the first signals in the unmanned environment, most of variance values are concentrated around 0, and the probability value is larger. Fig. 3 is a histogram of variance probability of power spectral density of a first signal in a man-made environment, where the PSD variance probability is significantly reduced and the variance distribution gradually moves to the right. Thus, it can be concluded that the higher the environmental dynamics, the more the power spectral density variance distribution at different times will spread to the right of the abscissa and the closer the value is to 0.
The embodiment of the invention acquires the LoRa signal data in a static unmanned environment and a dynamic manned environment at a small number of different moments, obtains a first signal reflecting vital activity characteristics of a human body, namely an effective time domain signal, converts the time domain signal into a frequency domain by utilizing fast Fourier transform, calculates Power Spectral Density (PSD), further calculates variance probability distribution of the power spectral density PSD at different moments as an environment characteristic value, and constructs environment characteristic sets in the two environments, namely the first characteristic set and the second characteristic set.
For example, comparing the online acquired first signal with the pre-constructed feature set to detect whether the trapped person exists in the target area, the similarity matching can be performed between the variance probability distribution of the power spectrum density of the online acquired first signal and the variance probability distribution of the power spectrum density of the first signal corresponding to the unmanned environment in the feature set (i.e., the first feature set), and the variance probability distribution of the power spectrum density of the first signal corresponding to the manned environment in the feature set (i.e., the second feature set), so as to determine whether the trapped person exists in the target area.
In some embodiments, the similarity match may be determined using Hellinger Distance (sea-ringer distance) which measures similarity between different distributions. Illustratively, let the variance probability distributions of the two sets of power spectral densities be w= (W) 1 ,w 2 ,…,w k ) Sum q= (Q 1 ,q 2 ,…q k ) The Hellinger distance metric for W and Q is calculated as follows:
the above equation can be seen as the Euclidean distance of the square root vector of two discrete probability distributions, as follows:
after the distance d (0.ltoreq.d.ltoreq.1) between the online feature and the two offline features is obtained, the system determines the most likely state representing the current environmental personnel with the smallest distance. For example, if the distance between the online feature and the offline feature constructed in the dynamic manned environment is the smallest, the system judges that dynamic personnel exist and enters a personnel state detection module; if the distance between the online feature and the offline feature constructed in the static unmanned environment is minimum, the static unmanned environment is considered, and whether the first signal at the next moment has a personnel target is continuously detected.
Therefore, the personnel search and rescue method provided by the embodiment of the invention can realize detection of whether trapped personnel exist in the target area.
In some embodiments, if there is a trapped person in the target area, the method further comprises:
detecting the motion state of trapped personnel on the basis of a pre-constructed personnel state detection model for the first signal; the personnel state detection model is generated based on the first signal training respectively corresponding to the static personnel and the moving personnel.
In practical application, a personnel state detection model needs to be built in advance. In some embodiments, constructing the personnel status detection model includes: historical time-frequency feature extraction, feature set construction and lightweight CNN classification model training. And then, detecting the motion state of the trapped person on the basis of the first signal acquired online on the basis of the trained person state detection model.
The following describes the historical time-frequency feature extraction and feature set construction, lightweight CNN classification model training and the detection of the motion state of trapped personnel based on a personnel state detection model.
1. Historical time-frequency feature extraction and feature set construction
Because of the complexity of the signals, the original first signals are generally difficult to directly reflect the personnel vital signal modulation characteristics in the echo signals, and the first signals are directly used as the input of the CNN model, so that the identification accuracy is often not ideal. The time-frequency diagram obtained by time-frequency analysis shows the joint distribution information of the time domain and the frequency domain, intuitively reflects the time-varying relation of each frequency component of the signal, and contains rich environmental state information.
In order to better enable the CNN model to accurately extract rich vital signal features contained in echo signals, in the embodiment of the invention, short-time Fourier transform is firstly adopted to perform feature transformation on a first signal, so that a time-frequency diagram of the signal is obtained. The time-frequency diagram of the signal can not only show the time domain characteristics of the signal, but also show the frequency domain characteristics of the signal. The short-time fourier transform is to divide the signal into small segments that can be seen as short-lived and stationary by windowing the continuous time-domain signal R (t), and fourier transforming each small segment separately. The short-time fourier transform is defined as:
r (t) is a time domain signal; g (t- τ) is a hanning window function; τ is the center of the window function.
Thus, the first signal obtained through low-pass filtering can be converted into a time-frequency characteristic diagram by using short-time Fourier transformation.
In the embodiment of the invention, firstly, offline signal data (namely LoRa signal data) of a small number of personnel static states and personnel motion states are collected, the offline signal data are subjected to feature extraction to obtain a first signal for reflecting vital activity features of a human body, a time domain signal is converted into a time-frequency diagram by utilizing short-time Fourier transform, pixel values of the extracted time-frequency diagram are stored as 901 x 1201 x 3 three-dimensional matrixes, personnel state feature diagram sets in the two states are constructed, and the personnel state feature diagram sets are divided into a training set and a testing set to serve as input of training of a lightweight CNN classification model.
2. Lightweight CNN classification model training
In the embodiment of the invention, the lightweight CNN classification model can be a SquezeNet model, and the model has the following advantages: 1) The filter of 1*1 is used for replacing a 3×3 filter, the number of input channels input to a 3×3 convolution kernel is reduced, a global average pooling layer is used for replacing a full-connection layer, model parameters are effectively reduced, and the method has the advantages that the cost of updating a model by a client is smaller, and a smaller updated model can be downloaded from a cloud; 2) The downsampling layer is arranged at the rear part of the network as much as possible, so that the convolution layer can have a larger activation characteristic diagram, and the network cannot reduce classification accuracy due to reduction of parameters.
Illustratively, as shown in fig. 4, the entire network of the SqueezeNet model contains 10 layers, layer 1 is a 1*1 convolution layer, the input image is scaled down, and 96-dimensional features are extracted. Layers 2 through 9 are fire modules, comprising two convolutions 1*1 and 3*3, each of which is internally convolved by 1*1 to reduce the number of channels (squeeze) and then by 3*3 to increase the number of channels (expand). After every two modules, the number of channels increases. Downsampling max pulling with step size of 2 is added after 1,4,8 layers, and the size is reduced by half. Layer 10, in turn, is a convolution layer, predicting a classification score for each pixel of the panel. Finally, an average scoring is used to obtain the classification score of the graph, and the classification score is normalized to probability by using a softmax function.
Illustratively, the specific training steps for the lightweight CNN classification model are as follows:
(1) The defined network model randomly initializes the weight and bias of the network;
(2) Remolding the three-dimensional color picture pixel matrix in the training set constructed in the first step into 28 x 1 three-dimensional matrix sample data serving as input of a lightweight CNN model;
(3) The layer 1 is a convolution kernel with the size of 96 pieces of 1*1, the activation function of each layer is RELU, and after the convolution of the input time-frequency diagrams of different personnel states, a feature diagram of a series of features related to the personnel states is obtained through the activation function;
(4) Layers 2 through 9 are eight Fire layers for reducing network parameters, wherein each layer comprises an extrusion layer and an expansion layer, the convolution kernel size of the extrusion layer is 1*1, the expansion layer comprises 1*1 and 3*3 convolution kernels, the convolution kernel output of the size 1*1 is concatenated with the convolution kernel output of the size 3*3 to serve as the final output of the layer, and the personnel state feature diagram size is further reduced; adding a maximum downsampling layer with the step length of 2 after the 1,4 and 8 layers, and reducing the characteristic dimension;
(5) The 10 th layer convolution layer is a convolution kernel with the size of 1*1, outputs the finally extracted multi-dimensional personnel state characteristics, conveys the characteristics to a Softmax classifier after dimension reduction by utilizing a global average pooling layer, classifies and identifies personnel states, outputs predicted class labels, compares the predicted labels with input actual labels, calculates a prediction error, starts an error counter propagation stage, adjusts weight and bias parameters of each layer by using a gradient descent method, and then carries out forward propagation again until errors between the predicted labels and the actual labels are reduced to a set threshold value, and saves various parameters of lightweight CNN at the moment;
(6) In the model verification stage, the model formed by the parameters is used for identifying the test set, if the obtained identification accuracy rate meets the requirement, the model is considered to be the optimal model, the model can be directly used for identifying the data of unknown types, otherwise, the parameters of the model need to be further adjusted until the identification accuracy rate of the test set meets the requirement.
3. Detection of the movement state of trapped people based on a person state detection model
In the embodiment of the present invention, detecting the motion state of the trapped person based on the pre-constructed person state detection model for the first signal includes:
obtaining time-frequency data corresponding to the first signal based on short-time Fourier transform;
and inputting the time-frequency data into a pre-constructed personnel state detection model to obtain a detection result that the trapped personnel are static personnel or moving personnel.
According to the embodiment of the invention, the signal time-frequency diagram corresponding to the first signal is obtained by utilizing short-time Fourier transform, and finally, the time-frequency data of the signal time-frequency diagram is subjected to characteristic extraction and classification by utilizing the lightweight CNN classification model trained in an offline training stage to obtain the state of the personnel in the current area, namely, the trapped personnel can be determined to be static personnel or moving personnel, so that different rescue or self-rescue strategies can be formulated for the moving state of the trapped personnel conveniently.
In some embodiments, if the trapped person is a stationary person, the method further comprises:
determining a respiratory frequency of the stationary person based on peaks in a power spectral density of the first signal;
a risk level of the stationary person is determined based on the respiratory rate of the stationary person.
Here, the respiratory rate of the resting person is further determined for the resting person, and the risk level of the resting person is determined based on the respiratory rate, so that a rescue plan can be formulated more specifically.
Illustratively, the respiratory frequency of the stationary person is determined based on a peak in the power spectral density of the first signal.
Here, the fluctuation of the thoracic cavity is caused by respiration of the human body, and the fluctuation of the thoracic cavity causes fluctuation of the power spectral density of the first signal.
In the embodiment of the invention, the received LoRa signal data can be subjected to the characteristic extraction to obtain the first signal, the first signal is subjected to discrete Fourier transform to obtain the corresponding frequency domain signal, and the power spectrum density is determined.
Illustratively, the discrete fourier transform corresponds to the following transform formula:
the formula for the power spectral density is as follows:
wherein R (N) is the I/Q value of the first signal, N is the length of the first signal, k=0, 1, …, N-1.
In one example of application, the breathing frequency of a human being is in the interval of about 10 to 40 times per minute, corresponding to a frequency of 0.167Hz to 0.667Hz. Because dangerous environments such as fire sites and the like can cause the mind of the user to generate tension, anxiety and fear, or the concentration of toxic gases in the air is increased, the heart rate of personnel is accelerated, and the breathing difficulty is caused, so that the breathing frequency is increased; as the fire spreads, the concentration of toxic gases in the air increases, and the respiration of people becomes gradually slow and weak, so that the people become unconscious until the respiration stops. According to medical experience, the respiratory rate of normal people is 16-18 times/min, and if respiratory acceleration occurs, even more than 30 times/min, the respiratory rate of static people is high, so that the situation is difficult. If bradykinesia occurs, even 10 times per minute or less is considered to be a high person coma, an urgent (highest priority) rescue is required. Therefore, after the respiratory frequency and the current positions of a plurality of detection personnel are estimated, the most efficient rescue route can be planned for the firefighters according to the respiratory frequency and the current positions, so that all trapped personnel can be rescued most quickly.
In some embodiments, if the trapped person is a sportsman, the method further comprises:
An escape path for guiding the sportsman to escape is generated and transmitted.
By way of example, an escape path for guiding the sportsman to escape can be generated based on the current position of the sportsman, fire situation information, crowd density and other factors, and the escape path is pushed to a mobile terminal (such as a mobile phone) of the sportsman, so that the sportsman can save oneself conveniently.
In order to achieve the method of the embodiment of the invention, the embodiment of the invention also provides a personnel search and rescue device for emergency rescue, which corresponds to the personnel search and rescue method, and each step in the personnel search and rescue method embodiment is also completely applicable to the personnel search and rescue device embodiment for emergency rescue.
As shown in fig. 5, the personal search and rescue device for emergency rescue includes: the device comprises an acquisition module 501, a feature extraction module 502 and a personnel detection module 503, wherein the acquisition module 501 is used for acquiring the reflected LoRa signal data in the process of scanning a target area by a LoRa transceiver; the feature extraction module 502 is configured to perform feature extraction on the LoRa signal data to obtain a first signal that is used to reflect vital signs of a human body; the person detection module 503 is configured to perform person detection on the first signal based on a pre-constructed feature set, and determine whether a trapped person exists in the target area; wherein the feature set is determined based on the first signals acquired in an unmanned environment, a manned environment, respectively.
In some embodiments, the person detection module 503 is specifically configured to:
converting the first signal to a frequency domain signal based on a fourier transform;
calculating a variance probability distribution of a power spectral density of the first signal based on the frequency domain signal;
calculating a first distance between the variance probability distribution and a variance probability distribution of the power spectral density of the first signal corresponding to the unmanned environment in a pre-constructed feature set, and a second distance between the variance probability distribution and a variance probability distribution of the power spectral density of the first signal corresponding to the manned environment in the pre-constructed feature value;
if the first distance is smaller than the second distance, determining that no trapped person exists in the target area;
and if the first distance is greater than or equal to the second distance, determining that trapped people exist in the target area.
In some embodiments, feature extraction module 502 is specifically configured to:
performing data compression on the LoRa signal data;
and filtering the compressed data based on a high-pass filter and a low-pass filter to obtain the first signal.
In some embodiments, the personal search and rescue device for emergency rescue further includes:
The feature set construction module 504 is configured to obtain the first signals respectively corresponding to the unmanned environment and the manned environment;
converting the first signal corresponding to the unmanned environment into a frequency domain signal corresponding to the unmanned environment based on Fourier transform, and calculating a variance probability distribution of the power spectral density of the first signal corresponding to the unmanned environment based on the frequency domain signal corresponding to the unmanned environment;
converting the first signal corresponding to the manned environment into a frequency domain signal corresponding to the manned environment based on Fourier transform, and calculating a variance probability distribution of the power spectral density of the first signal corresponding to the manned environment based on the frequency domain signal corresponding to the manned environment;
wherein the pre-constructed feature set comprises: the variance probability distribution of the power spectral density of the first signal corresponding to the unmanned environment and the variance probability distribution of the power spectral density of the first signal corresponding to the manned environment.
In some embodiments, if there is a trapped person in the target area, the person detection module 503 is further configured to: detecting the motion state of trapped personnel on the basis of a pre-constructed personnel state detection model for the first signal; the personnel state detection model is generated based on the first signal training respectively corresponding to the static personnel and the moving personnel.
In some embodiments, the person detection module 503 is specifically configured to:
obtaining time-frequency data corresponding to the first signal based on short-time Fourier transform;
and inputting the time-frequency data into a pre-constructed personnel state detection model to obtain a detection result that the trapped personnel are static personnel or moving personnel.
In some embodiments, if the trapped person is a stationary person, the person detection module 503 is further configured to:
determining a respiratory frequency of the stationary person based on peaks in a power spectral density of the first signal;
a risk level of the stationary person is determined based on the respiratory rate of the stationary person.
In some embodiments, the personal search and rescue device for emergency rescue further includes:
a path planning module 505 for generating and transmitting an escape path for guiding the sportsman to escape.
In practical application, the acquiring module 501, the feature extracting module 502, the personnel detecting module 503, the feature set constructing module 504 and the path planning module 505 may be implemented by a processor in a personnel search and rescue device for emergency rescue. Of course, the processor needs to run a computer program in memory to implement its functions.
It should be noted that: the personnel search and rescue device for emergency rescue provided in the above embodiment is only exemplified by the division of the above program modules when personnel search and rescue for emergency rescue is performed, and in practical application, the above processing allocation may be completed by different program modules according to needs, that is, the internal structure of the device is divided into different program modules, so as to complete all or part of the above processing. In addition, the personnel search and rescue device for emergency rescue and the personnel search and rescue method for emergency rescue provided in the foregoing embodiments belong to the same concept, and detailed implementation processes of the personnel search and rescue device for emergency rescue are shown in the method embodiments, which are not described herein.
Based on the hardware implementation of the program module, and in order to implement the method of the embodiment of the invention, the embodiment of the invention also provides personnel search and rescue equipment for emergency rescue. Fig. 6 shows only an exemplary structure of the personal search and rescue apparatus, and not all the structure, and a part or all of the structure shown in fig. 6 may be implemented as needed.
As shown in fig. 6, a personal search and rescue apparatus 600 provided in an embodiment of the present invention includes: at least one processor 601, a memory 602, a user interface 603 and at least one network interface 604. The various components in the personnel search and rescue device 600 are coupled together by a bus system 605. It is understood that the bus system 605 is used to enable connected communications between these components. The bus system 605 includes a power bus, a control bus, and a status signal bus in addition to a data bus. But for clarity of illustration the various buses are labeled as bus system 605 in fig. 6.
The user interface 603 may include, among other things, a display, keyboard, mouse, trackball, click wheel, keys, buttons, touch pad, or touch screen, etc.
The memory 602 in embodiments of the present invention is used to store various types of data to support the operation of the personnel search and rescue equipment. Examples of such data include: any computer program for operating on a personal search and rescue device.
The personnel search and rescue method for emergency rescue disclosed by the embodiment of the invention can be applied to the processor 601 or realized by the processor 601. The processor 601 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the personnel search and rescue method for emergency rescue may be accomplished by instructions in the form of integrated logic circuits or software of hardware in the processor 601. The processor 601 may be a general purpose processor, a digital signal processor (DSP, digital Signal Processor), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. Processor 601 may implement or perform the methods, steps and logic blocks disclosed in embodiments of the present invention. The general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the method disclosed in the embodiment of the invention can be directly embodied in the hardware of the decoding processor or can be implemented by combining hardware and software modules in the decoding processor. The software module may be located in a storage medium, where the storage medium is located in the memory 602, and the processor 601 reads information in the memory 602, and combines the information with hardware to implement the steps of the personnel search and rescue method for emergency rescue provided in the embodiment of the present invention.
In an exemplary embodiment, the personal search and rescue device may be implemented by one or more application specific integrated circuits (ASIC, application Specific Integrated Circuit), DSPs, programmable logic devices (PLD, programmable Logic Device), complex programmable logic devices (CPLD, complex Programmable Logic Device), FPGAs, general purpose processors, controllers, microcontrollers (MCU, micro Controller Unit), microprocessors, or other electronic elements for performing the aforementioned methods.
It is to be appreciated that the memory 602 can be either volatile memory or nonvolatile memory, and can include both volatile and nonvolatile memory. Wherein the nonvolatile Memory may be Read Only Memory (ROM), programmable Read Only Memory (PROM, programmable Read-Only Memory), erasable programmable Read Only Memory (EPROM, erasable Programmable Read-Only Memory), electrically erasable programmable Read Only Memory (EEPROM, electrically Erasable Programmable Read-Only Memory), magnetic random access Memory (FRAM, ferromagnetic random access Memory), flash Memory (Flash Memory), magnetic surface Memory, optical disk, or compact disk Read Only Memory (CD-ROM, compact Disc Read-Only Memory); the magnetic surface memory may be a disk memory or a tape memory. The volatile memory may be random access memory (RAM, random Access Memory), which acts as external cache memory. By way of example, and not limitation, many forms of RAM are available, such as static random access memory (SRAM, static Random Access Memory), synchronous static random access memory (SSRAM, synchronous Static Random Access Memory), dynamic random access memory (DRAM, dynamic Random Access Memory), synchronous dynamic random access memory (SDRAM, synchronous Dynamic Random Access Memory), double data rate synchronous dynamic random access memory (ddr SDRAM, double Data Rate Synchronous Dynamic Random Access Memory), enhanced synchronous dynamic random access memory (ESDRAM, enhanced Synchronous Dynamic Random Access Memory), synchronous link dynamic random access memory (SLDRAM, syncLink Dynamic Random Access Memory), direct memory bus random access memory (DRRAM, direct Rambus Random Access Memory). The memory described by embodiments of the present invention is intended to comprise, without being limited to, these and any other suitable types of memory.
It can be appreciated that the personnel search and rescue device in the embodiment of the invention can calculate the nodes for the edges near the emergency rescue scene. This personnel search and rescue equipment can with carry on the LoRa transceiver communication connection on unmanned aerial vehicle, acquire the echo signal (the aforesaid LoRa signal data promptly) that the LoRa transceiver received to realize online personnel detection and personnel's state's detection.
The embodiment of the invention also provides a personnel search and rescue system for emergency rescue, as shown in fig. 7, the personnel search and rescue system comprises:
unmanned aerial vehicle 701, unmanned aerial vehicle 701 carries a LoRa transceiver;
the personnel search and rescue equipment 702 is in communication connection with the unmanned aerial vehicle 701 and is used for executing the steps of the method according to the first aspect of the embodiment of the invention when running a computer program.
Here, the personnel search and rescue device 702 may be communicatively connected to the unmanned aerial vehicle 701 and configured to receive the LoRa signal data collected by the LoRa transceiver, and execute the personnel search and rescue method according to the foregoing embodiment of the present invention. Alternatively, the LoRa signal data collected by the LoRa transceiver is transmitted to the processor of the unmanned aerial vehicle 701, and the personnel search and rescue equipment 702 establishes communication connection with the unmanned aerial vehicle 701, so as to receive the LoRa signal data forwarded by the processor of the unmanned aerial vehicle 701.
In some embodiments, the personnel search and rescue system may further include a cloud server at a remote end, where the personnel search and rescue device 702 is in communication connection with the cloud server, so as to form an end-side-cloud cooperative system, where the unmanned aerial vehicle 701 and a LoRa transceiver carried by the unmanned aerial vehicle form a terminal for collecting LoRa signal data, the personnel search and rescue device 702 is used as an edge computing node, and the cloud server may perform detection on whether trapped personnel exist, a movement state of the trapped personnel, and the like, and may perform the foregoing pre-built feature set and the pre-built personnel state detection model, so as to effectively reduce resource consumption of the processor.
In an exemplary embodiment, the present invention further provides a storage medium, i.e. a computer storage medium, which may specifically be a computer readable storage medium, for example, including a memory 602 storing a computer program, where the computer program may be executed by the processor 601 of the personnel search and rescue device to perform the steps described in the method according to the embodiment of the present invention. The computer readable storage medium may be ROM, PROM, EPROM, EEPROM, flash Memory, magnetic surface Memory, optical disk, or CD-ROM.
It should be noted that: "first," "second," etc. are used to distinguish similar objects and not necessarily to describe a particular order or sequence.
In addition, the embodiments of the present invention may be arbitrarily combined without any collision.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (11)
1. A personnel search and rescue method for emergency rescue, comprising:
obtaining the reflected LoRa signal data in the process of scanning the target area by the long-distance radio LoRa transceiver;
extracting features of the LoRa signal data to obtain a first signal for reflecting vital activity features of a human body;
performing personnel detection on the first signal based on a pre-constructed feature set, and determining whether trapped personnel exist in the target area; wherein the feature set is determined based on the first signals acquired respectively in an unmanned environment and a manned environment;
the step of detecting the first signal based on a pre-constructed feature set to determine whether the trapped person exists in the target area comprises the following steps:
Converting the first signal to a frequency domain signal based on a fourier transform;
calculating a variance probability distribution of a power spectral density of the first signal based on the frequency domain signal;
calculating a first distance between the variance probability distribution and a variance probability distribution of the power spectral density of the first signal corresponding to the unmanned environment in a pre-constructed feature set, and a second distance between the variance probability distribution and a variance probability distribution of the power spectral density of the first signal corresponding to the manned environment in the pre-constructed feature value;
if the first distance is smaller than the second distance, determining that no trapped person exists in the target area;
and if the first distance is greater than or equal to the second distance, determining that trapped people exist in the target area.
2. The method of claim 1, wherein the performing feature extraction on the LoRa signal data to obtain a first signal for reflecting vital signs of a human body comprises:
performing data compression on the LoRa signal data;
and filtering the compressed data based on a high-pass filter and a low-pass filter to obtain the first signal.
3. The method of claim 1, wherein prior to the person detection of the first signal based on the pre-constructed feature set, the method further comprises:
acquiring the first signals respectively corresponding to the unmanned environment and the manned environment;
converting the first signal corresponding to the unmanned environment into a frequency domain signal corresponding to the unmanned environment based on Fourier transform, and calculating a variance probability distribution of the power spectral density of the first signal corresponding to the unmanned environment based on the frequency domain signal corresponding to the unmanned environment;
converting the first signal corresponding to the manned environment into a frequency domain signal corresponding to the manned environment based on Fourier transform, and calculating a variance probability distribution of the power spectral density of the first signal corresponding to the manned environment based on the frequency domain signal corresponding to the manned environment;
wherein the pre-constructed feature set comprises: the variance probability distribution of the power spectral density of the first signal corresponding to the unmanned environment and the variance probability distribution of the power spectral density of the first signal corresponding to the manned environment.
4. The method of claim 1, wherein if there is a trapped person in the target area, the method further comprises:
Detecting the motion state of trapped personnel on the basis of a pre-constructed personnel state detection model for the first signal; the personnel state detection model is generated based on the first signal training respectively corresponding to the static personnel and the moving personnel.
5. The method of claim 4, wherein detecting the motion state of the trapped person based on the pre-constructed person state detection model for the first signal comprises:
obtaining time-frequency data corresponding to the first signal based on short-time Fourier transform;
and inputting the time-frequency data into a pre-constructed personnel state detection model to obtain a detection result that the trapped personnel are static personnel or moving personnel.
6. The method of claim 5, wherein if the trapped person is a stationary person, the method further comprises:
determining a respiratory frequency of the stationary person based on peaks in a power spectral density of the first signal;
a risk level of the stationary person is determined based on the respiratory rate of the stationary person.
7. The method of claim 5, wherein if the trapped person is a sportsman, the method further comprises:
An escape path for guiding the sportsman to escape is generated and transmitted.
8. A personnel search and rescue device for emergency rescue, comprising:
the acquisition module is used for acquiring the data of the LoRa signal reflected in the process of scanning the target area by the LoRa transceiver;
the feature extraction module is used for carrying out feature extraction on the LoRa signal data to obtain a first signal for reflecting vital activity features of a human body;
the personnel detection module is used for detecting personnel of the first signal based on a pre-constructed feature set and determining whether trapped personnel exist in the target area; wherein the feature set is determined based on the first signals acquired respectively in an unmanned environment and a manned environment;
the personnel detection module is specifically used for:
converting the first signal to a frequency domain signal based on a fourier transform;
calculating a variance probability distribution of a power spectral density of the first signal based on the frequency domain signal;
calculating a first distance between the variance probability distribution and a variance probability distribution of the power spectral density of the first signal corresponding to the unmanned environment in a pre-constructed feature set, and a second distance between the variance probability distribution and a variance probability distribution of the power spectral density of the first signal corresponding to the manned environment in the pre-constructed feature value;
If the first distance is smaller than the second distance, determining that no trapped person exists in the target area;
and if the first distance is greater than or equal to the second distance, determining that trapped people exist in the target area.
9. A personal search and rescue device for emergency rescue, comprising:
a processor and a memory for storing a computer program capable of running on the processor, wherein,
the processor, in communication with the drone, for performing the steps of the method of any one of claims 1 to 7 when the computer program is run.
10. A personnel search and rescue system for emergency rescue, comprising:
the unmanned aerial vehicle is provided with a LoRa transceiver;
a personal search and rescue apparatus as defined in claim 9, in communication with the unmanned aerial vehicle.
11. A storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method of any of claims 1 to 7.
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