A multi-mode composite life detection system

Abstract

The application relates to the technical field of emergency rescue, and discloses a multi-mode composite life detection system, which comprises a radar detection module, an infrared sensor module, an acoustic detection module, a central processing unit and a data processing and signal fusion module; the radar detection module is used for detecting the surrounding environment through a radar signal, identifying a target position and existence; the infrared sensor module is used for detecting the temperature change of a living body through temperature difference; the acoustic detection module is used for detecting the weak sound signal of a living body through sound collection and analysis; the central processing unit is used for coordinating the work of various modules, receiving and processing signals from different detection modules, performing signal fusion and judging the existence of a living body; and the data processing and signal fusion module is used for receiving the signals of various detection modules; through multi-mode composite detection, environment adaptability modeling, wireless communication optimization and an intelligent man-machine interaction interface, the application realizes high-precision life detection in a complex environment, stable data transmission and efficient operation response, and greatly improves rescue efficiency and accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of emergency rescue, in particular to a multi-mode composite life detection system. BACKGROUND

[0002] In disaster rescue, life detection systems play a crucial role. Whether it is an earthquake, fire or other emergencies, quickly finding trapped personnel, accurately locating targets, and timely taking rescue measures are directly related to the safety of survivors.

[0003] In the prior art, life detection systems mostly use single-mode detection methods such as radar, infrared or acoustic detection. Radar systems can effectively penetrate obstacles and detect the approximate location of the target, infrared detection can capture temperature changes, and acoustic detection can help identify the weak sound signals of living beings. Each detection technology has its unique advantages.

[0004] However, the prior art also has some shortcomings; first, single detection mode performs poorly in complex environments, such as in ruins or underground scenes, radar signals will be affected by multipath propagation and signal attenuation, resulting in reduced positioning accuracy, while infrared detection cannot provide effective detection information in environments with small temperature differences; second, existing systems often lack flexible environmental adaptability and cannot adjust detection parameters in real time according to environmental changes, which makes them unable to accurately determine the presence of living beings in environments with large temperature changes or many obstacles; third, traditional wireless communication systems have data transmission delays and signal loss problems, especially in weak signal or multi-obstacle environments, the stability of communication is greatly reduced, which seriously affects the efficiency of real-time data transmission and rescue decision-making. SUMMARY

[0005] To overcome the shortcomings of the prior art, the present application provides a multi-mode composite life detection system, which solves the problems of single detection mode, poor environmental adaptability and unstable communication in the prior art.

[0006] To achieve the above purpose, the present application is implemented by the following technical scheme: a multi-mode composite life detection system, comprising:

[0007] a radar detection module for detecting the surrounding environment through radar signals, identifying the target position and existence;

[0008] an infrared sensor module for detecting the temperature changes of living beings through temperature difference;

[0009] an acoustic detection module for detecting the weak sound signals of living beings through sound collection and analysis;

[0010] A central processing unit is configured to coordinate the operation of each module, receive and process signals from different detection modules, and perform signal fusion and determine the presence of living beings.

[0011] A data processing and signal fusion module is configured to receive signals from each detection module, perform data fusion and target tracking through Bayesian inference and particle filtering.

[0012] An environmental adaptability modeling module is configured to correct the attenuation and interference of detection signals in different environments, and optimize the signal processing effect.

[0013] A wireless communication module is configured to remotely transmit detection results to a command center.

[0014] A human-computer interaction interface is configured to display detection information and provide user operation and interaction functions.

[0015] Preferably, the radar detection module comprises:

[0016] A transmitting unit is configured to send electromagnetic wave signals.

[0017] A receiving unit is configured to receive reflected electromagnetic wave signals.

[0018] A signal processing unit is configured to calculate the reflection intensity and return time of signals, and determine the position and distance of targets.

[0019] Preferably, the infrared sensor module comprises:

[0020] An infrared detection unit is configured to capture temperature changes of targets.

[0021] A signal processing unit is configured to analyze and process received infrared signals, and determine heat source information of targets.

[0022] Preferably, the acoustic detection module comprises:

[0023] A microphone array is configured to collect sound wave signals in the environment.

[0024] A sound processing unit is configured to analyze captured sound signals, including heartbeats, breathing, and distress signals.

[0025] Preferably, the central processing unit comprises:

[0026] A signal receiving module is configured to receive real-time data from the radar detection module, the infrared sensor module, and the acoustic detection module.

[0027] A data analysis module is configured to process, analyze, and integrate received signals to determine the presence of living beings.

[0028] A control module is configured to control the operation of the entire system and coordinate the operation of each module.

[0029] an output module for outputting the processing result through a wireless communication module or a human-computer interaction interface;

[0030] Preferably, the data processing and signal fusion module comprises:

[0031] a Bayesian inference module for jointly optimizing the signals from the detection modules according to the Bayesian theorem to obtain the optimal target state probability;

[0032] a particle filter module for dynamically tracking the target and updating the state of the target;

[0033] a fusion and correction module for processing and optimizing the fusion results of the signal sources, reducing the influence of noise, and ensuring accurate detection of living beings by the system;

[0034] Preferably, the environment adaptive modeling module comprises:

[0035] a radar signal correction module for correcting the attenuation of radar signals in complex environments and adjusting the intensity and propagation direction of the signals;

[0036] a sound wave propagation correction module for adjusting the attenuation of acoustic signals according to the propagation characteristics of sound waves in different media;

[0037] an infrared signal correction module for adjusting the reading value of the infrared signal according to the temperature gradient of the target environment, so that the detection has higher sensitivity;

[0038] Preferably, the wireless communication module comprises:

[0039] a signal transmission unit for transmitting the processed life detection signals and data to the command center;

[0040] a communication protocol module for ensuring the communication compatibility between the system and the platform and the stability of data transmission;

[0041] Preferably, the human-computer interaction interface comprises:

[0042] a display unit for displaying the results of life detection in real time, showing the position, state and life signs of the target;

[0043] an input unit for receiving the instructions of the operator to control the system operation or adjust the detection parameters;

[0044] an alarm unit for issuing sound and light alarms when life signs are detected to remind the rescue personnel;

[0045] Preferably, the data processing step of the data processing and signal fusion module comprises:

[0046] Step 1: receiving signal data from each detection module;

[0047] Step 2: signal fusion using the Bayesian inference module to obtain the optimal detection result by maximizing the posterior probability;

[0048] Step 3: dynamic target tracking using the particle filter module to ensure accurate detection of slowly moving living beings;

[0049] Step 4: outputting the processed data for display by the wireless communication module or the human-computer interaction interface.

[0050] The present application provides a multi-mode composite life detection system. It has the following beneficial effects:

[0051] 1. The present application combines radar, infrared and acoustic detection modules, adopts multi-mode composite detection technology, and can provide higher detection accuracy in complex environments. Compared with the existing single-mode detection method, this integrated design solves the limitations of single-mode detection and effectively improves the target positioning ability in a multi-obstacle environment.

[0052] 2. Through the environmental adaptability modeling module, the system can dynamically adjust the attenuation of the detection signal to ensure reliable operation in various complex scenarios. Compared with the existing technology of fixed signal processing model, the present application effectively overcomes the problems of signal attenuation and environmental interference, and improves the adaptability and accuracy of life detection.

[0053] 3. The wireless communication module of the present application adopts efficient transmission technology to ensure that the detection data can be stably and real-time returned in complex environments. Compared with the traditional signal transmission scheme, the present application significantly improves the stability of communication and the reliability of long-distance data transmission, avoiding the common problems of delay and packet loss in traditional wireless communication.

[0054] 4. The human-computer interaction interface is designed to be intuitive and simple, enhancing the real-time response ability of the operator. Compared with the existing technology of complicated operation and unfriendly interface, the present application improves the operation efficiency of the user in emergency through more efficient interaction, so that the rescue personnel can quickly understand the detection result and make decisions. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 is the system structure diagram of the present application;

[0056] Figure 2 is the module architecture diagram of the radar detection module of the present application;

[0057] Figure 3 is the module architecture diagram of the infrared sensor module of the present application;

[0058] Figure 4Module architecture diagram of the acoustic detection module of the present application;

[0059] Figure 5 Module architecture diagram of the central processing unit of the present application;

[0060] Figure 6 Module architecture diagram of the data processing and signal fusion module of the present application;

[0061] Figure 7 Module architecture diagram of the environment adaptability modeling module of the present application;

[0062] Figure 8 Module architecture diagram of the wireless communication module of the present application. DETAILED DESCRIPTION

[0063] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the specification of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0064] Please refer to the drawings in the specification of the present application Figure 1 - the drawings in the specification of the present application Figure 8 The embodiments of the present application provide a multi-mode composite life detection system, which comprises:

[0065] The radar detection module is used for detecting the surrounding environment through radar signals, identifying the target position and existence;

[0066] In the life detection in complex environment, the radar has incomparable advantages. By emitting electromagnetic waves, the radar can penetrate obstacles and reflect signals back, thereby identifying the existence of living beings. In order to realize efficient detection of the technical module, the propagation model of radar signals, target positioning method and dynamic tracking technology need detailed mathematical model and physical formula support.

[0067] In radar detection, the attenuation of signal propagation is a key factor. The propagation attenuation coefficient of radar signals in different media is different, so the signal strength must be corrected according to the actual environment. The propagation process of radar signals is usually described using the free space propagation model. The correction formula of the model is as follows:

[0068] ;

[0069] Wherein: is the power of the received signal (unit: watt), received by the receiving unit; is the transmission power (unit: watt), indicating the intensity of the electromagnetic wave emitted by the radar; Transmit Antenna Gain (dimensionless), representing the effectiveness of the transmit antenna in the direction of radar signal propagation; Receive Antenna Gain (dimensionless), representing the receiving effect of the receive antenna; Signal Wavelength (unit: meters), determined by the frequency of the radar signal; Signal Propagation Distance (unit: meters), representing the propagation distance of the radar signal from transmission to target and then to reception; Attenuation Coefficient (unit: per meter), representing the rate of signal attenuation in the medium, mainly affected by the target environment and medium. Common attenuation media include buildings, soil, air, etc.; Natural Logarithm Base (dimensionless).

[0070] The function of this formula is to calculate the strength of the received echo. The strength of the radar signal decreases with the increase of distance. For radar applications in complex environments, any obstacles on the signal propagation path will cause further reduction in signal strength, therefore, the system needs to correct this attenuation coefficient to adapt to different environmental conditions.

[0071] The basic function of the radar detection module is to locate the target and estimate the distance between the target and the radar. The time difference of the echo signal is used to measure the distance between the target and the radar. The calculation formula is as follows:

[0072] ;

[0073] Where: Distance between target and radar (unit: meters), representing the distance from radar signal transmission to target reflection and then back to the receiving unit; Speed of electromagnetic wave propagation in air (unit: meters / second); Round-trip time of echo signal (unit: seconds), the time difference from radar signal transmission to reflection back to reception.

[0074] Formula explanation: the relationship between the propagation speed of the signal and the echo time is linear. By measuring the round-trip time , the distance between the target and the radar can be obtained.

[0075] The radar detection module can also use the Doppler effect to estimate the relative speed of the target. When the target moves relative to the radar, the frequency of the reflected radar signal changes, and the amount of frequency change is related to the speed of the target. By measuring the amount of frequency change, the relative speed of the target can be determined. Its formula is:

[0076] ;

[0077] Where: is the frequency offset (unit: Hz), representing the difference between the reflected signal and the transmitted signal frequency; is the relative velocity of the target relative to the radar (unit: m / s); is the original frequency of the radar signal (unit: Hz), i.e. the working frequency of the radar signal; is the speed of light (unit: m / s).

[0078] Formula explanation: Doppler effect leads to frequency offset, the speed of the target relative to the radar can be inferred by measuring the frequency change . This method is very suitable for the detection of dynamic targets, especially in the case of target movement, it can provide information about the speed of the target, which helps to further analyze the behavior of the target.

[0079] During the propagation of the radar signal, it may be disturbed by noise from different sources, such as environmental noise, multi-path propagation of reflected signals, etc. Therefore, after receiving the signal, it must be filtered to reduce noise and improve signal quality.

[0080] A common method of noise suppression is to use signal filtering techniques, such as Kalman filter, to optimize signal estimation. In complex environments, Kalman filter can effectively predict and correct the deviation of noisy signals, further improving the accuracy of target positioning.

[0081] The radar detection module not only can detect the static position of the target, but also can dynamically track the movement of the target. The tracking of dynamic targets is crucial for rescue missions, especially when the life body moves slowly in a complex environment, the tracking function of the radar can ensure accurate positioning of the target position.

[0082] Through the particle filter algorithm, the system can track the target in real time. Particle filtering estimates the state of the target at each time step and optimizes the tracking effect through weight adjustment. This method can handle complex non-linear, non-Gaussian noise environments and is very suitable for tracking dynamic targets. The core of particle filtering is to correct the predicted state of the target through multiple resampling and weight updating, and ensure the high accuracy of the tracking result.

[0083] The radar detection module works with other modules (such as infrared sensor module, acoustic detection module) to further improve the accuracy of life body detection. For example, after the radar locates a target position, the infrared sensor can quickly capture the temperature change in that area to confirm the heat source of the target. At the same time, the acoustic detection module can identify possible life body sound signals.

[0084] Through the combination of Bayesian inference and particle filtering algorithm, the detection data of multiple signal sources are effectively fused, the accuracy of each detection signal is maximized, the influence of error and noise is reduced, and thus the accurate determination of the position of the living body is ensured.

[0085] The radar detection module can effectively detect and locate the position of the living body by using the technical principles of reflection, propagation time, Doppler effect, etc. of electromagnetic waves, combined with signal processing and noise suppression methods, thereby providing reliable detection capability.

[0086] The infrared sensor module is used to detect the temperature change of the living body through temperature difference;

[0087] The infrared sensor module of the present application is used to detect the position and existence of the living body by capturing the infrared radiation of the target and its surrounding environment. Together with the radar detection module and the acoustic detection module, the infrared sensor module constitutes a core component of the system, helping the system to make accurate life detection judgments in complex environments. In this multi-mode composite life detection system, the infrared sensor module supplements the shortcomings of other signal detection modules through accurate temperature change detection, especially in environments with many obstructions, such as ruins, underground spaces, etc.

[0088] The core working principle of the infrared sensor module is to capture temperature changes through thermal imaging technology. The infrared sensor mainly detects the infrared radiation emitted by the living body, especially the living bodies with higher body temperature (such as humans and animals), whose radiation signals are significantly higher than the surrounding environment.

[0089] Generally, the infrared sensor module works based on the blackbody radiation model, which describes the infrared radiation intensity of an ideal blackbody (i.e. an object that perfectly absorbs and emits radiation) according to its temperature. According to Planck's law, the intensity of the object's radiation changes at different wavelengths. The intensity of the object's radiation can be represented by the following formula:

[0090] ;

[0091] Where: is the radiation intensity at wavelength (unit: watt per square meter per meter), representing the intensity of the infrared radiation emitted by the target; is the Planck constant, a constant in quantum mechanics that describes the relationship between energy and frequency; is the speed of light, representing the speed of electromagnetic wave propagation; is the Boltzmann constant, describing the relationship between temperature and energy; is the temperature of the target (unit: Kelvin), representing the body temperature of the living body or target; λ is the wavelength of the radiation (unit: meter), which is related to the infrared band of the target, usually the wavelength range of human and animal thermal radiation.

[0092] This formula is used to determine the intensity of infrared radiation emitted by an object at different temperatures. By capturing the changes in these radiation intensities through infrared sensors, the system can calculate the position and heat source information of the target object.

[0093] The infrared sensor module converts the infrared signals received from the target into electrical signals through the detection unit, and then enters the signal processing unit. The role of the signal processing unit is to filter, enhance and calibrate the received signals to ensure that the system can correctly identify the temperature changes of the target.

[0094] In some embodiments, the calibration of the infrared signal can be optimized by background temperature correction to avoid the interference of the surrounding environment temperature on the detection results. The background temperature calibration formula is as follows:

[0095] ;

[0096] Where: T is the calibrated target temperature (unit: Celsius), which excludes the interference of environmental temperature after correction; T is the measured target temperature (unit: Celsius), which is the value directly measured from the sensor; T is the environmental background temperature (unit: Celsius), which refers to the temperature of all objects in the environment without the influence of living beings.

[0097] The purpose of this formula is to eliminate the influence of environmental background temperature and accurately capture the thermal radiation emitted by living beings, so as to ensure that the detection results of the system only reflect the true heat source of living beings.

[0098] In the infrared sensor module, the signal processing unit is not only responsible for signal filtering and calibration, but also needs to combine the infrared radiation intensity with the target position for positioning. This process involves spatial positioning and distance estimation of the target. Cooperation with the radar module can further improve the accuracy of positioning.

[0099] In the collaborative work, the Bayesian inference module can jointly optimize the data from the infrared, radar and acoustic modules, and calculate the posterior probability of living beings by calculating the joint probability of these signals. The introduction of Bayesian inference not only improves the reliability of single detection results, but also effectively integrates the advantages of different mode signals, reduces the influence of errors of each module, and finally improves the accuracy of target positioning.

[0100] For dynamic targets, the infrared sensor module can combine with particle filter algorithm for real-time tracking. Particle filter algorithm can effectively track the movement of the target by dynamically updating the state of the target, especially the slow-moving living beings. The core idea of particle filter is to optimize the estimation of target position by multiple resampling of target state.

[0101] In the infrared sensor module of the present application, in order to cope with environmental changes and different detection needs, the system can adjust the working waveband of the infrared sensor according to the temperature gradient of the target environment. For example, in cold environment, the sensor may choose a waveband more suitable for low temperature for detection. By dynamically adjusting the working waveband, the system can adapt to the detection needs under different temperature conditions, ensuring its effective work in various environments.

[0102] In addition, the performance of the infrared sensor module can be further optimized by advanced filtering technology, reducing the error caused by environmental noise and background interference, and improving the accuracy of the system. Specifically, the efficient noise filtering algorithm can identify and eliminate irrelevant signals, ensuring only the temperature changes related to living beings are responded to.

[0103] The infrared sensor module provides strong support for the positioning of living beings through accurate temperature change detection. In addition, the optimization and performance improvement of the infrared sensor module ensure the adaptability of the system under different environmental conditions, ensuring that the system can always provide reliable detection data.

[0104] The acoustic detection module is used to detect the weak sound signals of living beings through sound collection and analysis;

[0105] The acoustic detection module captures and analyzes sound signals in the surrounding environment to assist the system in accurately detecting living beings. Through the use of microphone arrays, the system can collect sound information from different directions, especially low-frequency and high-frequency sound waves produced by living beings, such as heartbeats, breathing, or faint cries for help. This module has unique advantages in complex environments, especially in environments with poor visibility (such as ruins or enclosed spaces), and can provide additional life detection information for the system.

[0106] In this embodiment, the acoustic detection module is composed of a microphone array and a sound processing unit. The microphone array is used to collect sound wave signals in the surrounding environment, and the sound processing unit further processes these sound signals, including noise reduction, feature extraction, signal analysis, etc. Through the processed signals, the system can identify weak sounds emitted by living beings, such as heartbeats, breathing, speech, or cries for help. This module plays a crucial role in the multi-module composite life detection system, especially in environments where visual detection is not possible, providing an effective means of detection.

[0107] The acoustic detection module enhances the reception range and accuracy of signals by arranging multiple microphone arrays. Generally, microphone arrays use multiple microphone sensors evenly distributed within a certain area to receive sound signals from various directions. This array structure can effectively reduce the impact of noise and improve the directionality of signal reception, helping the system obtain more accurate sound data in different environments.

[0108] The system converts the sound signal from the time domain to the frequency domain through spectral analysis methods such as short-time Fourier transform (STFT), thereby obtaining the frequency information of the signal. By analyzing the frequency spectrum of the sound signal, the system can extract the frequency range of the life sound characteristics, further confirming the nature of the sound source.

[0109] Formula:

[0110] ;

[0111] Where: is the time-frequency representation, representing the signal intensity at time and frequency ; is the time-domain representation of the original sound signal; is the window function, used to limit the effective range of the signal in the time domain; is the time variable; is the frequency variable; is the complex exponential function, used to convert from the time domain to the frequency domain.

[0112] Through this conversion, the system can accurately extract the characteristic sound of the living body, such as the specific frequency interval of the heartbeat or breathing, helping to determine the existence of the target.

[0113] The sound processing unit not only converts the collected sound signal into a spectrum, but also extracts features from the signal. Generally, the system determines whether there is a living body by recognizing different sound characteristics. When analyzing sound, the system pays special attention to the physiological sounds of living beings, such as heartbeats, breathing, coughing, or faint cries for help, which have specific frequency and time domain characteristics.

[0114] As an option, when processing signals, the system will filter acoustic signals to eliminate irrelevant background noise. By using a band-pass filter, the system can selectively retain the sound frequency band related to living beings and ignore interference signals in other frequency ranges. The mathematical expression of the band-pass filter is as follows:

[0115] ;

[0116] Where: is the frequency response function of the filter; and are the lowest and highest frequencies of the desired signal, typically set within the frequency range of a heartbeat or breathing sound; is the frequency of the signal.

[0117] Through this frequency filtering, the system can accurately identify the acoustic characteristics of living beings and effectively filter out environmental noise and irrelevant signals.

[0118] The microphone array in the acoustic detection module can also be used for sound source localization. By calculating the time difference of arrival of signals from different microphones, the system can determine the location of the sound source. In some embodiments, the system estimates the location of the sound signal source by using Time Difference of Arrival (TDOA) method. Specifically, if there are multiple microphone arrays in the system and they receive the same sound source signal at the same time, the system can calculate the time difference of receiving the signal by each microphone to estimate the location of the sound source.

[0119] Formula:

[0120] ;

[0121] Where: is the time difference (in seconds) between the reception of sound waves by the th microphone; is the distance (in meters) from the th microphone to the sound source; is the propagation speed of sound waves in air (in meters / second).

[0122] Through this method, the system can calculate the location of the sound source in real time, ensuring that the signal from the living being is accurately located, thereby improving the efficiency and accuracy of detection.

[0123] To improve the adaptability of the system, in some embodiments, the acoustic detection module can automatically adjust the working parameters according to the changes in environmental noise. For example, when the noise in the detection area is large, the system can automatically increase the sensitivity of the microphone and adjust the parameters of the filter to ensure that weak living being sounds can be accurately captured. In a quiet environment, the system reduces the sensitivity to avoid excessive collection of environmental noise.

[0124] In some embodiments, the system can automatically adjust the parameters of the sound signal processing unit according to the changes in environmental noise, thereby optimizing the quality and detection range of the signal.

[0125] Through spectral analysis, feature extraction, and sound source localization, the acoustic detection module can accurately identify the heartbeat, breathing, and other sound signals emitted by living beings and track the target location in real time.

[0126] The central processing unit is used for coordinating the work of each module, receiving and processing signals from different detection modules, and performing signal fusion and judgment of the existence of living beings.

[0127] The central processing unit receives data from radar, infrared, acoustic and other modules, performs data analysis, fusion and judgment, and generates the final detection result, providing real-time living body position information for rescue personnel. The efficient processing capability and flexible scheduling mechanism of this module are the key to the efficient work of the system.

[0128] In this embodiment, the central processing unit is composed of a signal receiving module, a data analysis module, a control module and an output module. The signal receiving module is responsible for receiving raw signals from different detection modules, the data analysis module processes the received signals in detail, the control module is used to coordinate the operation of different modules, and the output module outputs the final living body detection result through wireless communication module or human-computer interaction interface for reference by rescue personnel.

[0129] The function of the signal receiving module is to receive raw signal data from radar, infrared, acoustic and other detection modules, and transmit these signals to the data analysis module for further processing. Generally, the signal receiving module can handle concurrent data input from multiple detection modules, with good compatibility. Whether it is the echo signal received from the radar module, or the temperature change data and sound signal received from the infrared module and acoustic module, the signal receiving module can perform efficient parallel reception.

[0130] Specifically, the signal receiving module uses high-speed data acquisition and transmission technology, which can quickly transmit data from different sources to the central processing unit in rescue tasks with high real-time requirements. This module is connected to other modules through high-speed communication interfaces such as high-speed data bus, wireless communication, etc., to ensure that the system can stably receive and process a large amount of data from multiple detection modules.

[0131] The data analysis module is responsible for processing, analyzing and fusing the received signals, mainly through Bayesian inference and particle filtering algorithms to improve the accuracy and robustness of target detection. Specifically, the data analysis module first preprocesses each signal, removes noise and extracts useful features, and then uses Bayesian inference to fuse the signals to determine the existence probability of the target living being.

[0132] The formula of Bayesian inference is as follows:

[0133] ;

[0134] Where: is the posterior probability of the state of the target living being , indicating the existence probability of the target living being given the radar signal Infrared signals Harmony and acoustic signals The probability of the target existing under the given circumstances; Given a target state At that time, the joint likelihood function of the signals from each detection module; The prior probability of the target life form's state reflects prior knowledge of the target's existence; The marginal probability of the observed signal data.

[0135] Through Bayesian inference, the data analysis module can weight signals from radar, infrared, and acoustic modules based on the confidence level of each detection signal, thereby inferring the most probable target state. This enables the system to effectively process signals from different detection modules while eliminating noise interference, thus improving the accuracy of life detection.

[0136] As an alternative, particle filtering can be used for dynamic target tracking. Particle filtering updates the target's estimated position by resampling the target's state multiple times, thus enabling the tracking of the target's motion. The formula for particle filtering is as follows:

[0137] ;

[0138] ;

[0139] in: : No. The target state at any given moment (e.g., position, velocity, etc.); : State transition function, representing the state transition of the target from the previous time step to the current time step; Process noise describes the uncertainties in the state transition process; : No. Real-time observation data (such as radar echoes, infrared temperature data, etc.); : Observation function, which describes the relationship between the target state and the observed data; : Measurement noise, describing the uncertainty in the observation process.

[0140] Through particle filtering, the data analysis module can continuously update the target's status and track dynamic targets in real time. Especially when the target may move slowly during a rescue operation, particle filtering can ensure that the system can continuously and accurately identify the target.

[0141] The control module, located in the central processing unit, coordinates the operation of each detection module to ensure the efficient and stable operation of the entire system. Typically, based on feedback from the signal receiving and data analysis modules, the control module dynamically adjusts the operating parameters of each detection module to ensure the system consistently provides accurate data support under varying environmental conditions.

[0142] Specifically, the control module is responsible for monitoring the operating state of the system and adjusting the detection strategy as needed. For example, when encountering complex environmental conditions (such as high noise or severe signal attenuation areas), the control module can optimize the detection performance of the radar, infrared, and acoustic modules by adjusting parameters such as detection frequency and sensitivity. In emergency situations, the control module can also prioritize the operation of specific modules, such as prioritizing radar signals or infrared signals, to ensure the fastest possible location of living beings.

[0143] The output module is responsible for outputting the detection results processed by the data analysis module through the wireless communication module or human-computer interaction interface. As an option, the output module of the system can support real-time data transmission function, transmitting the detection results to the command center or other remote platforms for rescue personnel to make decisions.

[0144] Specifically, in emergency situations, the system feeds back the detected living body information to the rescue command center in real time through the wireless communication module. The output module can also visually display the detection data through the human-computer interaction interface, helping operators quickly identify the location, state, and other key information of living beings. In addition, when the system detects the presence of living beings, the output module will issue an alarm signal to timely notify rescue personnel to take further action.

[0145] Through the application of Bayesian inference and particle filtering technology, the central processing unit can efficiently fuse multi-modal detection signals, eliminate errors, and improve the detection accuracy of the system. The control module ensures the adaptability of the system in complex environments, and the output module provides real-time living body location information for rescue personnel.

[0146] The data processing and signal fusion module is used to receive signals from various detection modules, and performs data fusion and target tracking through Bayesian inference and particle filtering;

[0147] The data processing and signal fusion module not only processes raw signals from radar, infrared sensors, and acoustic detection modules, but also uses Bayesian inference and particle filtering algorithms for signal fusion to obtain the most accurate living body detection results. Through this module, the system can effectively integrate information from multiple signal sources, eliminate noise in each module signal, and update the target state in real time through dynamic tracking technology, ensuring high accuracy and robustness of detection.

[0148] In this embodiment, the data processing and signal fusion module processes and optimizes signals from different detection modules through Bayesian inference and particle filtering technology. Specifically, the module combines the characteristics of signals from different modules, weights and fuses the signals, and then obtains the optimal state estimation of the target, and performs dynamic tracking. This process ensures real-time updating of the target state and improves the adaptability of the system in complex environments.

[0149] Bayesian inference is one of the core algorithms of the data processing and signal fusion module, which is used to weight and fuse signals from different detection modules. Generally, the Bayesian inference method can jointly optimize multiple signal data based on the prior probability and conditional probability of each signal source, thereby deriving the optimal posterior probability of the target state.

[0150] Specifically, in this embodiment, Bayesian inference is used to comprehensively evaluate the signals of radar, infrared and acoustic modules. First, Bayesian inference calculates the likelihood of each signal source and combines the prior probability of the target state to obtain the posterior probability of the target state. In this way, the system can weight the signals of each detection module, making the information fusion more reliable.

[0151] Through Bayesian inference, the system can weight and fuse data from different signal sources to generate more accurate target state estimation. For example, when the radar signal is clear and the infrared signal quality is poor, the system will tend to rely more on radar signals and reduce the impact of infrared signals on the result, ensuring that the final target state estimation is more accurate.

[0152] Particle filtering algorithm is used for real-time tracking of dynamic targets, especially in the case of slow target movement or complex environment, particle filtering can effectively update the position estimation of the target. Specifically, particle filtering updates the weight of each particle by resampling the target state multiple times, and uses particles with larger weights for state estimation to ensure that the system can track the dynamic changes of the target in real time.

[0153] Generally, the particle filtering algorithm can gradually correct the state estimation of the target by resampling the target state multiple times, thereby ensuring accurate tracking of the target position. In dynamic environments, the advantage of particle filtering is particularly significant, especially in cases where the target may be temporarily obscured or in complex terrain, particle filtering can effectively predict the future position of the target, maintaining continuous tracking of the target.

[0154] In the data processing and signal fusion module, signal fusion is not only a weighted sum of different detection module signals, but also needs to correct the errors in the signals. Specifically, due to factors such as multipath propagation and noise, the signals of each detection module may contain certain errors. The data processing and signal fusion module corrects these errors to improve the robustness and accuracy of the system.

[0155] For example, for radar signals, due to the interference of obstacles, the reflected wave may not be accurate, and the system can optimize the radar signal through a correction algorithm; for infrared signals, due to the change of temperature gradient, the infrared sensor may have measurement errors, and the system will correct it according to the change of environmental temperature; for acoustic signals, the system can use noise suppression technology to reduce the interference of environmental noise on sound signals.

[0156] Through these error correction mechanisms, the system can effectively improve the accuracy of target detection and ensure that data from different signal sources can fully reflect the true state of the target.

[0157] The environmental adaptability modeling module is used to correct the attenuation and interference of detection signals in different environments, and to optimize the signal processing effect;

[0158] The environmental adaptability modeling module is used to dynamically model the propagation environment of detection signals and correct the attenuation, scattering, refraction and interference of signals in different media. Since the life detection system needs to operate in different environments, various signals will be affected to different degrees. Therefore, the core task of the environmental adaptability modeling module is to establish a mathematical model that adapts to different detection environments, and to improve the detection accuracy of the system in complex environments through real-time parameter adjustment.

[0159] In this embodiment, the environmental adaptability modeling module includes a radar signal correction unit, an acoustic wave propagation correction unit and an infrared signal correction unit. These sub-modules are responsible for analyzing the signal propagation path, calculating the attenuation and error, and correcting them through environmental adaptability adjustment algorithms to ensure the robustness and adaptability of the system in various environments.

[0160] When radar signals propagate in the air, they are affected by multipath effects, obstacle shielding and medium attenuation, which can cause errors in the received signal strength and time information. Generally, the propagation path of radar signals can be described by the free space propagation model. However, in complex environments (such as buildings, underground tunnels, etc.), additional correction models need to be introduced to optimize signal propagation calculations.

[0161] Specifically, the path loss of radar signals can be described using the following model:

[0162] ;

[0163] wherein: P_r is the power of the received signal (unit: watt), received by the receiving unit; P_t is the transmit power (unit: watt), representing the intensity of the electromagnetic wave emitted by the radar; G_t is the transmit antenna gain (unitless), representing the effectiveness of the transmit antenna in the direction of radar signal propagation; G_r is the receive antenna gain (unitless), representing the receiving effect of the receive antenna; λ is the signal wavelength (unit: meter), determined by the frequency of the radar signal; R is the distance of signal propagation (unit: meter), representing the propagation distance of the radar signal from transmission to target and then to reception; α is the attenuation coefficient (unit: per meter), representing the rate of signal attenuation in the medium, mainly affected by the target environment and medium. Common attenuation media include buildings, soil, air, etc; φ is the phase delay factor (unit: radian per meter), used to describe the phase change of the radar wave during propagation.

[0164] During the propagation of sound waves, the propagation speed and attenuation characteristics change due to the influence of air temperature, humidity, obstacles, and medium. Generally, the propagation speed of sound waves can be determined by temperature, and the calculation formula is as follows:

[0165] ;

[0166] wherein: c is the speed of sound in air (unit: meters / second); T is the ambient temperature (unit: degrees Celsius).

[0167] Specifically, when the system is in different environments (such as underground, underwater, enclosed space, etc.), the propagation speed of sound waves will change, and the system needs to adjust this parameter in real time to correct the target positioning calculation based on sound waves.

[0168] In addition, the attenuation of sound waves can be described by the following formula:

[0169] ;

[0170] wherein: P_r is the power of the received signal (unit: watt), received by the receiving unit; P_t is the transmit power (unit: watt), representing the intensity of the electromagnetic wave emitted by the radar; α is the attenuation coefficient (unit: per meter), representing the rate of signal attenuation in the medium, mainly affected by the target environment and medium. Common attenuation media include buildings, soil, air, etc; R is the distance of sound wave propagation (unit: meter). Base of natural logarithm (unit: dimensionless).

[0171] The detection accuracy of infrared signals is affected by environmental temperature gradient, air humidity, and object surface thermal radiation. Generally, the target temperature measured by the infrared sensor may be disturbed by the ambient temperature , so the system needs to be compensated.

[0172] Specifically, the correction formula of the infrared signal is as follows:

[0173] ;

[0174] Where: is the calibrated target temperature (unit: Celsius), which excludes the interference of ambient temperature after correction; is the measured target temperature (unit: Celsius), the value directly measured from the sensor; is the ambient background temperature (unit: Celsius), which refers to the temperature of all objects in the environment without the influence of living beings; is the current environmental temperature (unit: Celsius); is the temperature correction coefficient (dimensionless), used to adjust the measurement error, which depends on the sensitivity and measurement accuracy of the infrared sensor.

[0175] In some embodiments, the infrared signal correction unit uses multiple thermal sensors to model the ambient temperature and adopts an adaptive temperature compensation algorithm to dynamically adjust the temperature correction coefficient according to the changes in the environmental temperature gradient, ensuring the measurement accuracy.

[0176] Another key function of the environmental adaptability modeling module is to integrate various environmental factors to establish an adaptive model to adjust the detection strategy of the system in real time. Specifically, the system completes environmental modeling through the following steps:

[0177] Environmental parameter acquisition: Obtain environmental parameters (such as signal attenuation, noise level, temperature change) from radar, acoustic, and infrared sensors.

[0178] Multi-sensor fusion: Use multi-sensor data to model environmental characteristics and establish a dynamic environmental model.

[0179] Adaptive adjustment: According to the environmental model, dynamically adjust the signal processing algorithm to optimize the accuracy and robustness of target detection.

[0180] The environmental adaptability modeling module calculates parameters such as signal attenuation, propagation speed, temperature compensation in real time. This module ensures that the system can operate stably under different environmental conditions and optimizes the detection accuracy. Through adaptive adjustment and intelligent optimization, the system can maximize the adaptation to complex environments, improve the reliability and accuracy of life detection.

[0181] The wireless communication module is used for remotely transmitting the detection results to the command center.

[0182] The wireless communication module not only transmits data between various detection modules within the system, but also transmits detection results in real time to the command center or rescue personnel handheld terminals to support efficient execution of rescue decisions and actions. Since the system may be applied in various environments such as ruins, underground or mountainous areas, the signal propagation path is complex, and the system must have high adaptability, stability and real-time performance. The core task of the wireless communication module is to ensure reliable, encrypted and low-latency transmission of data.

[0183] In this embodiment, the wireless communication module transmits data through radio frequency bands such as Wi-Fi, LTE, 5G or dedicated frequency bands. This module uses adaptive modulation and coding technology, advanced error correction mechanisms and spectrum resource management strategies to ensure stable wireless connection and efficient data transmission in different environments.

[0184] The working principle of the wireless communication module is based on the transmission and reception of radio waves. In the life detection system, the communication module converts digital signals into electromagnetic waves suitable for transmission in the radio frequency band through modulation technology. These modulation methods include but are not limited to frequency modulation (FM), phase modulation (PM), orthogonal frequency division multiplexing (OFDM), etc.

[0185] In general, when selecting a modulation method for the wireless communication module, the anti-interference ability and bandwidth efficiency of the signal are considered. For high-speed data transmission, in some embodiments, the wireless communication module may use orthogonal frequency division multiplexing (OFDM) technology. OFDM divides high-speed data streams into multiple low-speed data streams and assigns them to different subcarriers for parallel transmission, thereby improving spectral efficiency and reducing interference caused by multipath effects.

[0186] Specifically, the modulation method of OFDM is based on the following formula:

[0187] ;

[0188] Where: is the complex representation of the signal at time (unit: volts or dimensionless); is the subcarrier index, which represents the number of the th subcarrier (integer); Nsc: Total number of subcarriers (dimensionless), represents the number of spectral allocations for OFDM (Orthogonal Frequency Division Multiplexing) or other multicarrier signals. Nsc: Total number of subcarriers (dimensionless), represents the number of spectral allocations for OFDM (Orthogonal Frequency Division Multiplexing) or other multicarrier signals. S: Modulation symbol (dimensionless or Volts) on the Nth subcarrier; S: Modulation symbol (dimensionless or Volts) on the Nth subcarrier; fN: Frequency (Hz) of the Nth subcarrier, represents the center frequency occupied by this subcarrier; t: Time variable (seconds), represents the variation of the signal over time; j: Imaginary unit, satisfies j = -1, used to describe the phase variation of the signal. j: Imaginary unit, satisfies j = -1, used to describe the phase variation of the signal.

[0189] Through OFDM, the wireless communication module can effectively reduce interference in a multipath environment, improve data transmission rate and reliability. This is crucial for real-time data transmission in life detection missions, especially in complex terrain or multi-obstacle environments, OFDM can significantly improve the communication quality of the system.

[0190] As an alternative, in an environment with large multi-path propagation, signal attenuation and noise, the wireless communication module can use adaptive modulation and coding (AMC) technology. AMC adjusts the modulation method and coding rate of the signal according to the current signal-to-noise ratio (SNR), thereby optimizing the reliability and efficiency of data transmission. The system measures the SNR of the wireless signal to select the appropriate modulation method to ensure the best communication performance in complex environments.

[0191] Specifically, the system selects the most suitable modulation method based on channel state information (CSI). Channel capacity can be represented by the following formula:

[0192] ;

[0193] where: C: Channel capacity (bits / second), represents the maximum data transmission rate under given channel conditions; B: Channel bandwidth (Hz), represents the frequency range of the channel; P: Average power of the signal (Watts); N: Noise power (Watts); SNR: Signal-to-noise ratio (SNR), represents the ratio of signal power to noise power, which is an important indicator of signal quality; log2: Binary logarithm function, represents the binary logarithm of , used to calculate the number of bits that can be transmitted per unit time.

[0194] In some embodiments, when the SNR is low, the system will choose a lower modulation method (such as BPSK or QPSK) to improve the anti-noise performance; while in the environment with high SNR, the system will choose a more efficient modulation method (such as 64-QAM or 256-QAM) to improve the data transmission rate.

[0195] Since the data transmitted by the life detection system may involve sensitive information, it is crucial to ensure the security of data transmission. When performing data transmission, the wireless communication module uses encryption technology to protect the data and prevent data leakage or tampering. Generally, the system will use symmetric encryption (such as AES) or asymmetric encryption (such as RSA) technology to ensure the confidentiality of communication.

[0196] Specifically, the AES encryption algorithm is used to encrypt the data, ensuring that the data is not stolen during transmission. The encryption and decryption process can be represented by the following formula:

[0197] ;

[0198] ;

[0199] Where: is the ciphertext, representing the encrypted data (unit: bit); is the encryption function, using the key to encrypt the original data ; is the decryption function, using the key to decrypt the ciphertext and recover the original data.

[0200] In some embodiments, the data encryption process is performed through the AES-256 algorithm, with a key length of 256 bits, ensuring that the encryption strength is sufficient to withstand possible security threats. The wireless communication module also regularly updates the key to prevent key leakage or cracking.

[0201] In life detection missions, the real-time performance of the system is crucial, especially in critical situations where the detection results must be quickly fed back to the command center. Therefore, the wireless communication module must have low latency and high reliability. In some embodiments, the wireless communication module introduces a delay optimization algorithm and a congestion control mechanism to reduce data transmission delay and avoid network congestion.

[0202] For example, when interference or blockage occurs in the signal transmission path, the system can prioritize the transmission of emergency life detection information such as target location or health status data through a priority queuing algorithm or QoS (Quality of Service) management mechanism. The system can also automatically adjust the transmission rate according to real-time network conditions to avoid bandwidth overload or communication interruption.

[0203] The wireless communication module can also adapt to changes in environmental conditions. Specifically, when the system detects weak wireless signals or network congestion, the communication module will automatically switch to a more reliable transmission path or frequency band to avoid signal loss or data transmission failure.

[0204] For example, in underground or building interiors, the system can switch to a frequency band that supports a larger range based on existing communication infrastructure, or choose to use relay nodes for data forwarding. In addition, the system can increase signal transmission power or adjust signal transmission frequency in harsh environments to maximize signal penetration and transmission stability.

[0205] The wireless communication module can provide reliable communication support in complex environments by using efficient modulation and coding techniques, data encryption mechanisms, delay optimization, and congestion control algorithms.

[0206] A human-machine interface for displaying detection information and providing user operation and interaction functions;

[0207] The human-machine interface module provides an intuitive, easy-to-use, and efficient operation interface for users to quickly understand and analyze the detection results of the life detection system. Life detection tasks often occur in complex and emergency environments, so the display and interaction of the interface not only need to be efficient and clear, but also must be able to support users to make quick decisions in rapidly changing environments. The design focus of the present invention is to make it easy for users to process detection results and take appropriate action through graphical interfaces, real-time data updates, and dynamic interactions.

[0208] In this embodiment, the human-machine interface presents various types of detection information through a combination of graphical display and real-time data interaction, supporting users to adjust parameters and settings according to the detection environment and task requirements for personalized operation. The interface display includes but is not limited to real-time detection data, dynamic target location, target vital sign data, etc., and provides users with interactive functions such as parameter setting, alarm response, etc. through the operation control area.

[0209] The core part of the human-machine interface is the real-time data display area, which carries the display of life detection results, target information, and the output of each detection module. Generally, in this area, users can see the results of multiple detection modules (such as radar, infrared, and acoustic), usually through icons, text boxes, and dynamic charts to display the target's location, status, vital signs, and other information.

[0210] Specifically, the system provides a clear target display on the interface by converting detection results into graphical information. For example, the current location of the target is displayed through a marker point on the map, combined with heat maps, reticles, and other forms to display the changing trend of the target's vital signs in real time. Vital signs may include temperature, heart rate, respiration, etc., and the system will use different colored symbols to represent the intensity and changing trend of these physiological information.

[0211] For example, in a typical scenario, the target's location is displayed on the map, and the color and size of the marker point reflect the strength of the vital signs (e.g., red for high temperature and blue for lower vital signs). The display of data not only reflects the spatial location of the target, but also dynamically updates the changes in the target's vital signs, so that users can monitor the target's status in real time.

[0212] In the human-machine interface, the operation control area provides users with interactive functions, allowing them to adjust system parameters according to current detection task requirements. Generally, users can adjust the working parameters of different detection modules through sliders, input boxes, or buttons on the interface to optimize detection results.

[0213] Specifically, users can adjust the following key parameters:

[0214] Radar sensitivity: adjust the detection sensitivity of the radar module to adapt to different environments.

[0215] Infrared sensor threshold: set the temperature threshold of the infrared module to detect life forms that meet the conditions.

[0216] Frequency range of acoustic module: control the frequency range of the acoustic detection module to capture the weak sound signals emitted by living beings.

[0217] For example, in the user interface, the slider for radar sensitivity can be used to control the range of signal intensity, adjusting its sensitivity. In the "mode selection" area, users can choose different detection modes (such as "full mode" or "single module mode"), and the system will adjust the working state of all modules accordingly.

[0218] Target positioning is an important function of the human-machine interface. Specifically, by updating the target's location information in real-time, the interface can show the dynamic changes of the target within the detection area. The system uses a map or heat map to represent the target's location and can adjust the display content in real-time to reflect changes in the target's location.

[0219] For example, during the detection process, if the target moves within a certain time, the system will adjust the position of the marker point based on the latest detection data of the target, so that the target's movement trajectory is presented to the operator in real-time. By combining the target's location and vital sign data, the system provides a dynamic and comprehensive information display platform to help users quickly understand the target's state and respond accordingly.

[0220] In the human-machine interface, the emergency alarm system is a crucial component. Generally, when the system detects that the detection result of a living body exceeds the set alarm threshold, the system will immediately display warning information through the interface and alert the user through sound, flashing lights, etc.

[0221] Specifically, when the target's vital signs (such as temperature, heart rate) exceed the set safety range on the interface, the system will automatically trigger an alarm, displaying a red warning box with a sound or vibration prompt. The alarm information not only includes the location of the living body, but also displays other related vital sign data (such as current temperature, whether there is breathing, heart rate, etc.) and emergency response suggestions.

[0222] For example, when the target's body temperature exceeds the threshold, the interface will highlight the target's location and pop up a window to remind the rescuer that the target's body temperature is abnormal. The user can quickly respond according to the prompt and start the rescue process through the "Immediate Response" button on the interface.

[0223] To support the decision-making of rescuers, the human-machine interface also includes a series of data visualization and statistical analysis functions. In some embodiments, the interface will dynamically display various types of detection data in the form of line charts, pie charts, heat maps, etc., to facilitate users to quickly obtain key information.

[0224] Specifically, the system can generate the following charts:

[0225] Line chart: used to show the trend of the target's vital signs, such as temperature changes, heart rate, etc.

[0226] Heat map: shows the distribution of the target's location and its vital sign intensity, with color representing intensity, so users can visually see the high-risk areas within the detection area.

[0227] Statistical analysis: through charts, it shows the number of targets detected within a certain period of time, state changes, etc., to help rescuers evaluate the progress and effectiveness of the rescue.

[0228] For example, through the heat map, the user can see the distribution of targets within the entire detection area, and the depth of color can help the user determine which area has more signs of life, thereby guiding the rescue team to focus their efforts on searching in these areas first.

[0229] The man-machine interaction interface module of the present application helps users quickly understand target status and vital sign information through graphical display and data visualization. The alarm and response mechanism ensures that users can take action quickly in emergency situations, optimizing rescue decisions.

[0230] Although embodiments of the present application have been shown and described, it will be understood by those having ordinary skill in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-mode composite life detection system, comprising: Comprise: Radar detection module, for detecting the surrounding environment by radar signal, identifying target position and existence; Infrared sensor module, for detecting the temperature change of living body by temperature difference; Acoustic detection module, for detecting the weak sound signal of living body by sound collection and analysis; Central processing unit, for coordinating the work of each module, receiving and processing signals from different detection modules, signal fusion and judging the existence of living body; Data processing and signal fusion module, for receiving signals from each detection module, data fusion and target tracking through Bayesian inference and particle filtering; Environmental adaptability modeling module, for correcting the attenuation and interference of detection signals in different environments, optimizing signal processing effect; Wireless communication module, for transmitting detection results to command center remotely; Man-machine interface, for displaying detection information, providing user operation and interaction function; The radar detection module comprises: Transmitting unit, for sending electromagnetic wave signal; Receiving unit, for receiving reflected electromagnetic wave signal; Signal processing unit, for calculating the reflection intensity and return time of signal, determining the position and distance of target; The propagation attenuation coefficient of radar signal is different in different media, so the signal strength must be corrected according to the actual environment; The propagation process of radar signal is described by using free space propagation model; The correction formula of the model is as follows: ； Wherein: P is the power of the received signal, received by the receiving unit; P is the transmit power, indicating the intensity of the electromagnetic wave emitted by the radar; G is the transmit antenna gain, indicating the effectiveness of the transmit antenna in the direction of radar signal propagation; G is the receive antenna gain, indicating the receiving effect of the receive antenna; λ is the signal wavelength, determined by the frequency of the radar signal; D is the signal propagation distance, indicating the propagation distance of the radar signal from transmission to the target and then to reception; α is the attenuation coefficient, indicating the attenuation rate of the signal in the medium, mainly affected by the target environment and the medium; e is the base of natural logarithm; The basic function of radar detection module is to locate the target and estimate the distance between the target and the radar; The time difference of echo signal is used to measure the distance between the target and the radar; The calculation formula is as follows: ； wherein: is the distance of the target from the radar, representing the distance from the radar transmitting the signal to the target reflecting the signal back to the receiving unit; is the speed of propagation of electromagnetic waves in air; is the round trip time of the echo signal, the difference in time from the radar signal being transmitted to the signal being reflected back to the receiving unit; The radar detection module also uses Doppler effect to estimate the relative speed of the target; When the target moves relative to the radar, the frequency of the reflected radar signal changes, and the change of frequency is related to the speed of the target; By measuring the change of frequency, the relative speed of the target can be determined; Its formula is: ； wherein: is the frequency offset, representing the difference in frequency between the reflected signal and the transmitted signal; is the relative velocity of the target with respect to the radar; is the original frequency of the radar signal, i.e. the operating frequency of the radar signal; is the speed of light; In the propagation process of radar signal, noise interference from different sources will be received, Kalman filter is used to optimize signal estimation; In complex environment, Kalman filter can effectively predict and correct the deviation of noise signal, improve the accuracy of target positioning; The infrared sensor module comprises: Infrared detection unit, for capturing temperature change of target; Signal processing unit, for analyzing and processing received infrared signal, determining heat source information of target; The infrared sensor module works based on blackbody radiation model, the intensity of object radiation will change at different wavelengths, the radiation intensity of object is represented by the following formula ； wherein: is the intensity of radiation at the wavelength, representing the intensity of infrared radiation emitted by the target; is Planck's constant, a constant in quantum mechanics that describes the relationship between energy and frequency; is the speed of light, representing the speed at which electromagnetic waves propagate; is the Boltzmann constant, describing the relationship between temperature and energy; is the temperature of the target, representing the body temperature of the target; is the wavelength of radiation, related to the infrared band of the target, and is the wavelength range of human and animal radiation heat; The calibration of infrared signal is optimized by background temperature correction, avoiding the interference of surrounding temperature on detection results; The background temperature calibration formula is as follows: ； wherein: Ttarget is the calibrated target temperature, which is corrected to exclude the influence of ambient temperature; Tmeasured is the measured target temperature, which is the value measured directly from the sensor; Tbackground is the ambient background temperature, which refers to the temperature of all objects in the environment without the influence of living beings; The acoustic detection module comprises: Microphone array, for collecting sound wave signal in environment; Sound processing unit, for analyzing captured sound signal, including heartbeat, breathing and distress sound; Through short time Fourier transform (STFT), the sound signal is converted from time domain to frequency domain, so as to obtain the frequency information of signal; By analyzing the frequency spectrum of sound signal, the system can extract the frequency range of life sound characteristics, and confirm the nature of sound source; The formula is as follows: ； wherein is a time frequency representation, representing the signal strength at time t and frequency f; is a time domain representation of the original sound signal; is a window function, limiting the range of action of the signal in the time domain; is a time variable; is a frequency variable; is a complex exponential function, used for the conversion from the time domain to the frequency domain; In processing signals, the system filters the acoustic signal to eliminate irrelevant background noise; by using a band-pass filter, the system can selectively retain the frequency band related to living beings and ignore interference signals in other frequency ranges; the mathematical expression of the band-pass filter is as follows: ； where: is the frequency response function of the filter; and are the lowest and highest frequencies of the desired signal, set in the frequency range of the heartbeat or breathing sound, respectively; is the frequency of the signal. The source position of the sound signal is estimated by using the time difference positioning method (TDOA); specifically, if there are multiple microphone arrays in the system and they receive the same sound source signal at the same time, the system can calculate the time difference of the signals received by each microphone to calculate the position of the sound source; the formula is: ； wherein, is the time difference in which the sound wave is received by the i-th microphone; is the distance from the i-th microphone to the sound source; is the propagation speed of the sound wave in air; The central processing unit includes: The signal receiving module receives real-time data from the radar detection module, infrared sensor module and acoustic detection module; The data analysis module processes, analyzes and integrates the received signals to determine the presence of living beings; The control module controls the operation of the entire system and coordinates the work of each module; The output module outputs the processing results through the wireless communication module or human-computer interaction interface; The data processing and signal fusion module includes: The Bayesian inference module uses Bayesian theorem to jointly optimize the signals from each detection module to obtain the optimal target state probability; The particle filter module is used to dynamically track the target and update the target state; The fusion and correction module is used to process and optimize the fusion results of each signal source, reduce noise influence and ensure accurate detection of living beings by the system; The data analysis module is responsible for processing, analyzing and fusing the received signals; the data analysis module first preprocesses each signal to remove noise and extract useful features, and then uses Bayesian inference method to fuse the signals to determine the existence probability of the target living being; The formula of Bayesian inference is as follows: ； where: is the posterior probability of the target state of life, representing the probability of the target being present given the radar signal , the infrared signal , and the acoustic signal ; is the joint likelihood function of the individual detection module signals given the target state ; is the prior probability of the target state of life, reflecting the prior knowledge of the target being present; is the marginal probability of the observed signal data; Particle filtering technology can be used for dynamic tracking of the target; particle filtering updates the estimation of the target position by resampling the target state multiple times, so as to track the motion of the target; the formula of particle filtering is as follows: ； wherein: is the target state at the kth time instant; is the state transition function, denotes the state transition of the target from the previous time instant to the current time instant; is the process noise, describing the uncertainty in the state transition process; is the observation data at the kth time instant; is the observation function, describing the relationship between the target state and the observation data; is the measurement noise, describing the uncertainty in the observation process; The environmental adaptability modeling module includes: The radar signal correction module is used to correct the attenuation of radar signals in complex environments and adjust the intensity and propagation direction of the signals; The sound wave propagation correction module adjusts the attenuation of acoustic signals according to the propagation characteristics of sound waves in different media; The infrared signal correction module adjusts the reading value of the infrared signal according to the temperature gradient of the target environment to make the detection more sensitive; When radar signals propagate in the air, they are affected by multipath effect, obstacle shielding and medium attenuation, which can cause errors in the received signal strength and time information; the path loss of radar signals is described by the following model: ； Wherein: P is the power of the received signal, received by the receiving unit; P is the transmit power, indicating the intensity of the electromagnetic wave emitted by the radar; G is the transmit antenna gain, indicating the effectiveness of the transmit antenna in the direction of radar signal propagation; G is the receive antenna gain, indicating the receiving effect of the receive antenna; λ is the signal wavelength, determined by the frequency of the radar signal; D is the signal propagation distance, indicating the propagation distance of the radar signal from transmission to the target and then to reception; α is the attenuation coefficient, indicating the attenuation rate of the signal in the medium, mainly affected by the target environment and the medium; φ is the phase delay factor, used to describe the phase change of the radar wave in the propagation process; During the propagation of sound waves, the propagation speed and attenuation characteristics change due to the influence of air temperature, humidity, obstacles and media; the propagation speed of sound waves is determined by temperature, and the calculation formula is as follows: ； wherein: is the speed of sound in air; is the ambient temperature; The attenuation of sound waves is described by the following formula: ； Where the parameters are defined as in the radar received signal power formula; The detection accuracy of the infrared signal is affected by the environmental temperature gradient, air humidity and the thermal radiation of the object surface; the target temperature measured by the infrared sensor is disturbed by the environmental temperature, so the system needs to be compensated; Specifically, the correction formula of the infrared signal is as follows: ； wherein: Ttarget is the calibrated target temperature, which is corrected to exclude the influence of ambient temperature; Tmeasured is the measured target temperature, which is directly measured from the sensor; Tbackground is the ambient background temperature, which refers to the temperature of all objects in the environment without the influence of living beings; Tcurrent is the current ambient temperature; Tcorrection is the temperature correction coefficient, which is used to adjust the measurement error and depends on the sensitivity and measurement accuracy of the infrared sensor; The wireless communication module comprises: The signal transmission unit is configured to transmit the processed life detection signal and data to the command center; The communication protocol module is configured to ensure the communication compatibility between the system and the platform and the stability of data transmission; When the wireless communication module selects the modulation mode, considering the anti-interference ability and bandwidth efficiency of the signal, the orthogonal frequency division multiplexing (OFDM) technology is used; OFDM divides the high-speed data stream into multiple low-speed data streams and distributes them to different subcarriers for parallel transmission, thereby improving the spectral efficiency and reducing the interference caused by multipath effect; the modulation mode of OFDM is based on the following formula: ； where: is the complex representation of the signal at time t; is the subcarrier index, representing the number of the th subcarrier; is the total number of subcarriers, representing the number of spectral allocations of the OFDM or other multi-carrier signal; is the modulation symbol on the kth subcarrier; is the frequency of the kth subcarrier, representing the center frequency occupied by the subcarrier; t is the time variable, representing the change of the signal over time; imaginary unit, satisfying is used to describe the phase change of the signal; The system selects the most suitable modulation mode according to the channel state information (CSI); the channel capacity is represented by the following formula: ； Where: C is the channel capacity, representing the maximum data transmission rate under given channel conditions; B is the channel bandwidth, representing the frequency range of the channel; P is the average power of the signal; N is the noise power; SNR is the signal-to-noise ratio (SNR), representing the ratio of signal power to noise power, which is an important indicator of signal quality; The human-computer interaction interface comprises: The display unit is configured to display the results of life detection in real time, show the position, state and life signs of the target; The input unit is configured to receive the instructions of the operator, control the system operation or adjust the detection parameters; The alarm unit is configured to issue sound and light alarm when the life signs are detected, reminding the rescue personnel; The data processing steps comprise: Step 1: receiving the signal data from each detection module; Step 2: using the Bayesian inference module to perform signal fusion, obtaining the optimal detection result by maximizing the posterior probability; Step 3: using the particle filtering module to perform dynamic target tracking, ensuring accurate detection of slowly moving life; Step 4: outputting the processed data for the wireless communication module or the human-computer interaction interface to display.

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