Multi-mode composite life detection system

Abstract

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

Description

Technical Field

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

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

[0003] In the existing technology, most life detection systems use a single mode of detection, 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 helps to identify weak sound signals of living organisms. Each detection technology has its own unique advantages.

[0004] However, the existing technology still has some shortcomings. First, the performance of a single detection mode in a complex environment is not ideal. For example, 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 an environment with small temperature differences. Second, existing systems usually lack flexible environmental adaptability and cannot adjust detection parameters in real time according to environmental changes, which makes it impossible for them to accurately determine the presence of life when the temperature changes greatly or there are many obstacles. Furthermore, traditional wireless communication systems have problems with data transmission delays and signal loss, especially in environments with weak signals or many obstacles. The stability of communication is greatly reduced, which seriously affects the efficiency of real-time data transmission and rescue decision-making. Summary of the invention

[0005] In view of the deficiencies of the prior art, the present invention 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 objectives, the present invention is implemented through the following technical solutions: a multi-mode composite life detection system, comprising: Radar detection module, used to detect the surrounding environment through radar signals and identify the location and existence of targets; Infrared sensor module, used to detect temperature changes of living organisms through temperature differences; Acoustic detection module, used to detect weak sound signals of living organisms through sound collection and analysis; The central processing unit is used to coordinate the work of various modules, receive and process signals from different detection modules, perform signal fusion and determine the existence of life forms; The data processing and signal fusion module is used to receive signals from each detection module and perform data fusion and target tracking through Bayesian inference and particle filtering; Environmental adaptability modeling module, used to correct the attenuation and interference of detection signals when propagating in different environments and optimize signal processing effects; Wireless communication module, used to remotely transmit detection results to the command center; The human-computer interaction interface is used to display detection information and provide user operation and interaction functions.

[0007] Preferably, the radar detection module includes: A transmitting unit, used for sending electromagnetic wave signals; A receiving unit, used for receiving the reflected electromagnetic wave signal; The signal processing unit is used to calculate the reflection intensity and return time of the signal and determine the position and distance of the target; Preferably, the infrared sensor module comprises: Infrared detection unit, used to capture the temperature change of the target; A signal processing unit is used to analyze and process the received infrared signal to determine the heat source information of the target; Preferably, the acoustic detection module comprises: Microphone array, used to collect sound wave signals in the environment; A sound processing unit, used to analyze the captured sound signals, including heartbeat, breathing and cries for help; Preferably, the central processing unit comprises: A signal receiving module, used for receiving real-time data from a radar detection module, an infrared sensor module and an acoustic detection module; A data analysis module is used to process, analyze and integrate the received signals to determine the existence of life forms; Control module, used to control the operation of the entire system and coordinate the work of each module; An output module, used to output the processing results through a wireless communication module or a human-computer interaction interface; Preferably, the data processing and signal fusion module includes: The Bayesian inference module is used to jointly optimize the signals from each detection module according to the Bayesian theorem to obtain the optimal target state probability; The particle filter module is used to dynamically track the target and update the target status; The fusion and correction module is used to process and optimize the fusion results of each signal source, reduce the impact of noise, and ensure the system's accurate detection of life forms; Preferably, the environmental adaptability modeling module includes: Radar signal correction module, used to correct the attenuation of radar signals in complex environments and adjust the signal strength and propagation direction; The sound wave propagation correction module is used to adjust the attenuation of the acoustic signal according to the propagation characteristics of the sound wave in different media; Infrared signal correction module, used to adjust the reading value of the infrared signal according to the temperature gradient of the target environment, so that the detection has higher sensitivity; Preferably, the wireless communication module includes: A signal transmission unit, used to transmit the processed life detection signal and data to a command center; Communication protocol module, used to ensure the communication compatibility between the system and the platform and the stability of data transmission; Preferably, the human-computer interaction interface includes: Display unit, used to display the results of life detection in real time, showing the location, status and life signs of the target; An input unit, used to receive instructions from operators, control system operation or adjust detection parameters; An alarm unit is used to send out an audible and visual alarm when signs of life are detected to alert rescue personnel; Preferably, the data processing step of the data processing and signal fusion module includes: Step 1: Receive signal data from each detection module; Step 2: Use the Bayesian inference module to perform signal fusion and obtain the optimal detection result by maximizing the posterior probability; Step 3: Use the particle filter module to track dynamic targets to ensure accurate detection of slowly moving life forms; Step 4: Output the processed data for display on the wireless communication module or human-computer interaction interface.

[0008] The present invention provides a multi-mode composite life detection system. It has the following beneficial effects: 1. The present invention combines radar, infrared and acoustic detection modules and adopts multi-mode composite detection technology, which 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 capability in multi-obstacle environments.

[0009] 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 solution of fixed signal processing model in the prior art, the present invention effectively overcomes the problems of signal attenuation and environmental interference, and improves the adaptability and accuracy of life detection.

[0010] 3. The wireless communication module of the present invention adopts efficient transmission technology to ensure that the detection data can be stably and real-time transmitted in complex environments. Compared with traditional signal transmission schemes, the present invention significantly improves the stability of communication and the reliability of remote data transmission, avoiding the common delay and packet loss problems in traditional wireless communications.

[0011] 4. The human-computer interaction interface is intuitive and concise, which enhances the real-time response capability of operators. Compared with the cumbersome operation and unfriendly interface system in the prior art, the present invention improves the user's operating efficiency in emergency situations through a more efficient interaction method, allowing rescuers to quickly understand the detection results and make decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 is a system structure diagram of the present invention; Figure 2 The module architecture diagram of the radar detection module of the present invention; Figure 3 The module architecture diagram of the infrared sensor module of the present invention; Figure 4 This is a module architecture diagram of the acoustic detection module of the present invention; Figure 5 The module architecture diagram of the central processing unit of the present invention; Figure 6 This is a module architecture diagram of the data processing and signal fusion module of the present invention; Figure 7 The module architecture diagram of the environmental adaptability modeling module of the present invention; Figure 8 FIG. 4 is a module architecture diagram of the wireless communication module of the present invention. DETAILED DESCRIPTION

[0013] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0014] Please see attached Figure 1 -Attached Figure 8 The embodiment of the present invention provides a multi-mode composite life detection system, including: Radar detection module, used to detect the surrounding environment through radar signals and identify the location and existence of targets; Radar detection module Radar has an unparalleled advantage in detecting life in complex environments. By emitting electromagnetic waves, radar can penetrate obstacles and reflect signals back to identify the presence of life. In order for this technical module to achieve efficient detection, the propagation model of radar signals, target positioning methods, and dynamic tracking technology require detailed mathematical models and physical formulas to support.

[0015] In radar detection, the attenuation of signal propagation is a key factor. The propagation attenuation coefficient of radar signals in different media Different, so the signal strength must be corrected according to the actual environment. The radar signal propagation process is usually described using the free space propagation model. The correction formula of this model is as follows: ; in: is the power of the received signal (in watts), received by the receiving unit; is the transmission power (unit: watt), which indicates the intensity of electromagnetic waves emitted by the radar; is the transmit antenna gain (dimensionless), which represents the effectiveness of the transmit antenna in the direction of radar signal propagation; is the receiving antenna gain (dimensionless), which represents the receiving effect of the receiving antenna; is the signal wavelength (unit: meter), which is determined by the frequency of the radar signal; is the distance of signal propagation (unit: meter), which indicates the propagation distance of radar signal from transmission to target and then to reception; is the attenuation coefficient (unit: per meter), which indicates the attenuation rate of the signal in the medium, and is mainly affected by the target environment and the medium. Common attenuation media include buildings, soil, air, etc. is the base of the natural logarithm (unit: dimensionless).

[0016] The function of this formula is to calculate the strength of the received echo. The strength of the radar signal decays as the distance increases. For radar applications in complex environments, any obstacles on the signal propagation path will cause the signal strength to further decrease. Therefore, the system needs to correct the attenuation coefficient. To adapt to different environmental conditions.

[0017] The basic function of the radar detection module is to locate the target and estimate the distance between the target and the radar. The distance between the target and the radar is measured using the time difference of the echo signal. The calculation formula is as follows: ; in: is the distance between the target and the radar (unit: meter), which means the distance from the radar transmitting signal to the target reflecting the signal and then returning to the receiving unit; is the propagation speed of electromagnetic waves in the air (unit: m / s); It is the round trip time of the echo signal (unit: seconds), which is the time difference between the radar signal being transmitted and reflected and received.

[0018] Formula explanation: Signal propagation speed and echo time The relationship between is linear. By measuring the round-trip time , the distance between the target and the radar can be obtained.

[0019] 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 change in frequency is related to the speed of the target. By measuring the change in frequency, the relative speed of the target can be determined. The formula is: ; in: is the frequency offset (unit: Hz), which indicates the difference between the reflected signal and the transmitted signal frequency; is the relative speed of the target relative to the radar (unit: m / s); is the original frequency of the radar signal (unit: Hz), that is, the operating frequency of the radar signal; is the speed of light (unit: m / s).

[0020] Formula explanation: The Doppler effect causes frequency shift, and the speed of the target relative to the radar can be measured by changing the frequency This method is very suitable for the detection of dynamic targets, especially when the target is moving, and can provide information about the target's speed, which helps to further analyze the target's behavior.

[0021] During the propagation process, radar signals may be interfered by noise from different sources, such as environmental noise, multipath propagation of reflected signals, etc. Therefore, after the signal is received, filtering must be performed to reduce noise and improve signal quality.

[0022] A common method of noise suppression is to use signal filtering technology, such as Kalman Filter, to optimize signal estimation. In complex environments, Kalman Filter can effectively predict and correct the deviation of noise signals, further improving the accuracy of target positioning.

[0023] The radar detection module can not only detect the static position of the target, but also dynamically track the movement of the target. Tracking dynamic targets is crucial for rescue missions, especially when living things move slowly in complex environments. The radar tracking function can ensure the precise positioning of the target.

[0024] The system can track the target in real time through the particle filter algorithm. The particle filter estimates the state of the target based on each time step and optimizes the tracking effect through weight adjustment. This method can handle complex nonlinear and non-Gaussian noise environments and is very suitable for tracking dynamic targets. The core of the particle filter is to correct the predicted state of the target through multiple resampling and weight updates, and ensure the high accuracy of the tracking results.

[0025] The radar detection module works together with other modules (such as the infrared sensor module and the acoustic detection module) to further improve the accuracy of life detection. For example, after the radar locates a target, the infrared sensor can quickly capture the temperature changes in the area and confirm the heat source of the target. At the same time, the acoustic detection module can identify possible sound signals of life.

[0026] Through the combination of Bayesian inference and particle filtering algorithm, the detection data of multiple signal sources are effectively integrated to maximize the accuracy of each detection signal and reduce the impact of errors and noise, thereby ensuring the accurate determination of the location of living organisms.

[0027] The radar detection module can effectively detect and locate the position of living things by utilizing technical principles such as electromagnetic wave reflection, propagation time, and Doppler effect, combined with signal processing and noise suppression methods, thus providing reliable detection capabilities.

[0028] Infrared sensor module, used to detect temperature changes of living organisms through temperature differences; The infrared sensor module of the present invention is used to detect the location and existence of life by capturing infrared radiation from the target and its surrounding environment. Together with the radar detection module and the acoustic detection module, the infrared sensor module constitutes the 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 complements the deficiencies of other signal detection modules through accurate temperature change detection, especially in environments with many obstructions, such as ruins, underground spaces, etc.

[0029] The core working principle of the infrared sensor module is to capture temperature changes through thermal imaging technology. Infrared sensors mainly detect infrared radiation radiated by living organisms, especially living organisms with higher body temperatures (such as humans and animals), whose radiation signals are significantly higher than the surrounding environment.

[0030] In general, infrared sensor modules work based on the blackbody radiation model, which describes the intensity of infrared radiation radiated by 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 radiation radiated by an object varies at different wavelengths. It can be expressed by the following formula: ; in: The wavelength The radiation intensity at (unit: watts per square meter per meter) indicates 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, which indicates the speed at which electromagnetic waves propagate; is the Boltzmann constant, describing the relationship between temperature and energy; is the temperature of the target (unit: Kelvin), indicating the body temperature of the life form or target; is the wavelength of the radiation (in meters), which is related to the infrared band of the target, usually the wavelength range in which humans and animals radiate heat.

[0031] 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 location of the target object and the heat source information.

[0032] The infrared sensor module converts the infrared signal received from the target into an electrical signal through the detection unit, and then enters the signal processing unit. The function of the signal processing unit is to filter, enhance and calibrate the received signal to ensure that the system can correctly identify the temperature change of the target.

[0033] In some embodiments, the calibration of the infrared signal can be optimized by background temperature correction to avoid the interference of the ambient temperature on the detection results. The background temperature calibration formula is as follows: ; in: is the calibrated target temperature (unit: Celsius), which eliminates the interference of ambient temperature after correction; is the measured target temperature (in degrees 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 being affected by living organisms.

[0034] The purpose of this formula is to eliminate the influence of the ambient background temperature and accurately capture the thermal radiation emitted by living organisms, thereby ensuring that the system's detection results only reflect the actual heat source of the living organism.

[0035] 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 the spatial positioning and distance estimation of the target. Collaboration with the radar module can further improve the accuracy of positioning.

[0036] In collaborative work, the Bayesian inference module can jointly optimize the data from the infrared, radar and acoustic modules, and obtain the posterior probability of life by calculating the joint probability of these signals. The introduction of Bayesian inference not only improves the reliability of a single detection result, but also effectively integrates the advantages of different mode signals, reduces the impact of errors in each module, and ultimately improves the accuracy of target positioning.

[0037] For dynamic targets, the infrared sensor module can be combined with the particle filter algorithm for real-time tracking. The particle filter algorithm can effectively track the movement of the target, especially slow-moving life forms, by dynamically updating the target's state. The core idea of ​​the particle filter is to optimize the estimate of the target's position by resampling the target's state multiple times.

[0038] In the infrared sensor module of the present invention, in order to cope with environmental changes and different detection requirements, the system can adjust the working band of the infrared sensor according to the temperature gradient of the target environment. For example, in a cold environment, the sensor may choose a band that is more suitable for low temperatures for detection. By dynamically adjusting the working band, the system can adapt to the detection requirements under different temperature conditions and ensure that it can work effectively in various environments.

[0039] In addition, the performance of the infrared sensor module can be further optimized through advanced filtering technology to reduce errors caused by environmental noise and background interference and improve the accuracy of the system. Specifically, the efficient noise filtering algorithm can identify and remove irrelevant signals to ensure that only temperature changes related to living organisms are responded to.

[0040] The infrared sensor module provides strong support for the positioning of living organisms through precise temperature change detection. In addition, the optimization and performance improvement of the infrared sensor module ensure the system's adaptability under different environmental conditions and ensure that the system can always provide reliable detection data.

[0041] Acoustic detection module, used to detect weak sound signals of living organisms through sound collection and analysis; The acoustic detection module assists the system in accurately detecting life forms by capturing and analyzing sound signals in the surrounding environment. Through the use of microphone arrays, the system can collect sound information from different directions, especially low-frequency and high-frequency sound waves generated by life forms, such as heartbeats, breathing, or weak cries for help. The application of this module in complex environments, especially in environments with unclear vision (such as ruins or confined spaces), has unique advantages and can provide the system with additional life detection information.

[0042] 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 from the surrounding environment, and the sound processing unit further processes these sound signals, including denoising, feature extraction, signal analysis, etc. Through the processed signals, the system can identify the faint sounds emitted by living organisms, such as heartbeats, breathing, speaking or cries for help. This module plays a vital role in multi-mode composite life detection systems, especially providing an effective detection method in environments that cannot be detected visually.

[0043] The acoustic detection module enhances the signal reception range and accuracy by arranging multiple microphone arrays. Generally, a microphone array uses multiple microphone sensors evenly distributed in a certain area to receive sound signals from all 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.

[0044] The system uses spectrum analysis methods such as short-time Fourier transform (STFT) to convert sound signals from the time domain to the frequency domain to obtain the frequency information of the signal. By analyzing the frequency spectrum of the sound signal, the system can extract the frequency range of the sound characteristics of the living organism and further confirm the nature of the sound source.

[0045] formula: ; in: It is the time frequency representation, which means at time and frequency Signal strength on It is the time domain representation of the original sound signal; It is a window function, which is used to limit the range of the signal in the time domain; is a time variable; is a frequency variable; is a complex exponential function used to convert from the time domain to the frequency domain.

[0046] Through this conversion, the system can accurately extract the characteristic sounds of living things, such as specific frequency ranges of heartbeat or breathing, to help determine the presence of the target.

[0047] The sound processing unit is not only responsible for converting the collected sound signals into frequency spectra, but also for extracting features from the signals. In general, the system determines whether there is a living being by identifying different sound features. When performing sound analysis, the system pays special attention to the physiological sounds of living beings, such as heartbeats, breathing, coughs, or weak cries for help, which have specific frequency and time domain characteristics.

[0048] As an option, when processing the signal, the system will filter the acoustic signal to remove irrelevant background noise. By using a bandpass filter, the system can selectively retain the sound frequency band related to the living body and ignore the interference signals in other frequency ranges. The mathematical expression of the bandpass filter is as follows: ; in: is the frequency response function of the filter; and These are the lowest and highest frequencies of the desired signal, respectively, and are usually set within the frequency range of heartbeat or breathing sounds; is the frequency of the signal.

[0049] Through this frequency filtering, the system can accurately identify the sound characteristics of living organisms and effectively filter out environmental noise and irrelevant signals.

[0050] The microphone array in the acoustic detection module can also be used to locate the sound source. By calculating the time difference of the signals from different microphones, the system can determine the location of the sound source. In some embodiments, the system estimates the source location of the sound signal by using the time difference localization method (TDOA). Specifically, if there are multiple microphone arrays in the system and they receive signals from the same sound source at the same time, the system can calculate the location of the sound source by calculating the time difference of the signals received by each microphone.

[0051] formula: ; in: It is The time difference between the microphones receiving the sound wave (in seconds); It is The distance from the microphone to the sound source (in meters); is the speed of sound waves in air (in meters per second).

[0052] Through this method, the system can calculate the location of the sound source in real time, ensuring that the sound signal from the living body is accurately located, thereby improving the efficiency and accuracy of detection.

[0053] In order 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 the 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 the faint sounds of life can be accurately captured. When the environment is relatively quiet, the system reduces the sensitivity to avoid excessive collection of environmental noise.

[0054] In some embodiments, the system can automatically adjust the parameters of the sound signal processing unit according to changes in environmental noise, thereby optimizing the signal quality and detection range.

[0055] Through spectrum analysis, feature extraction and sound source location, the acoustic detection module enables the system to accurately identify sound signals such as heartbeat and breathing emitted by living organisms and track the target position in real time.

[0056] The central processing unit is used to coordinate the work of various modules, receive and process signals from different detection modules, perform signal fusion and determine the existence of life forms; The central processing unit receives data from radar, infrared, acoustic and other modules, performs data analysis, fusion and judgment, and generates the final detection results to provide rescuers with real-time information on the location of life. The module's efficient processing capabilities and flexible scheduling mechanism are the key to the system's efficient operation.

[0057] 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 the original 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 life detection results through the wireless communication module or the human-computer interaction interface for reference by rescue personnel.

[0058] 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 speaking, the signal receiving module can process concurrent data inputs from multiple detection modules at the same time and has 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.

[0059] Specifically, the signal receiving module uses high-speed data acquisition and transmission technology to quickly transmit data from different sources to the central processing unit in rescue missions with high real-time requirements. This module is connected to other modules through high-speed communication interfaces (such as high-speed data buses, wireless communications, etc.) to ensure that the system can stably receive and process large amounts of data from multiple detection modules.

[0060] The data analysis module is responsible for processing, analyzing and fusing the received signals, mainly through algorithms such as Bayesian inference and particle filtering to improve the accuracy and robustness of target detection. Specifically, the data analysis module first pre-processes each signal, removes noise and extracts useful features, and then uses the Bayesian inference method to fuse the signals to determine the probability of the existence of the target organism.

[0061] The formula for Bayesian inference is as follows: ; in: The target life state The posterior probability of a given radar signal , infrared signal Harmonic signal The probability of the target existing in the case of For a given target state When , the joint likelihood function of the signals of each detection module; is the prior probability of the state of the target life form, reflecting the prior knowledge of the existence of the target; is the marginal probability of the observed signal data.

[0062] Through Bayesian inference, the data analysis module can weight the signals from the radar, infrared and acoustic modules according to the credibility of each detection signal to infer the most likely target state. This enables the system to effectively process signals from different detection modules while eliminating noise interference and improving the accuracy of life detection.

[0063] As an option, particle filtering technology can be used to track the target dynamically. Particle filtering updates the estimate of the target position by resampling the target state multiple times, thereby tracking the target's movement. The formula for particle filtering is as follows: ; ; in: : No. The target state at the moment (e.g. position, speed, etc.); : State transfer function, which represents the state transfer of the target from the previous moment to the current moment; : Process noise, describing the uncertainty in the state transition process; : No. Observation data at all times (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.

[0064] Through particle filtering, the data analysis module can continuously update the status of the target and track dynamic targets in real time. Especially when the target may move slowly during the rescue process, particle filtering can ensure that the system continues to accurately identify the target.

[0065] The control module is responsible for coordinating the operation of each detection module in the central processing unit to ensure that the entire system works efficiently and stably. Generally, the control module dynamically adjusts the working parameters of each detection module based on the feedback from the signal receiving module and the data analysis module to ensure that the system can always provide accurate data support under different environmental conditions.

[0066] Specifically, the control module is responsible for monitoring the operating status of the system and adjusting the detection strategy as needed. For example, when encountering more complex environmental conditions (such as areas with high noise or severe signal attenuation), the control module can optimize the detection performance of 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 giving priority to radar signals or infrared signals to ensure that life forms are located as quickly as possible.

[0067] The output module is responsible for outputting the detection results processed by the data analysis module through the wireless communication module or the 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.

[0068] Specifically, in an emergency, the system will use the wireless communication module to feed back the detected life information to the rescue command center in real time. The output module can also intuitively display the detection data through the human-computer interaction interface to help operators quickly identify the location, status and other key information of the life. In addition, when the system detects the presence of a life form, the output module will send out an alarm signal to promptly notify rescuers to take further action.

[0069] The central processing unit can efficiently fuse multi-mode detection signals, eliminate errors, and improve the detection accuracy of the system through the application of technologies such as Bayesian inference and particle filtering. The control module ensures the adaptability of the system in complex environments, and the output module provides rescuers with real-time information on the location of life forms.

[0070] The data processing and signal fusion module is used to receive signals from each detection module and perform data fusion and target tracking through Bayesian inference and particle filtering; The data processing and signal fusion module can not only process the original signals from the radar, infrared sensor and acoustic detection module, but also use algorithms such as Bayesian inference and particle filtering for signal fusion to obtain the most accurate detection results of life. Through this module, the system can effectively integrate information from multiple signal sources, eliminate noise in the signals of each module, and update the target status in real time through dynamic tracking technology to ensure high accuracy and high robustness of detection.

[0071] In this embodiment, the data processing and signal fusion module processes and optimizes the signals of different detection modules through Bayesian inference and particle filtering technology. Specifically, the module combines the characteristics of the signals of different modules, weights and fuses the signals, and then obtains the optimal state estimation of the target and performs dynamic tracking. This process ensures the real-time update of the target state and improves the system's adaptability in complex environments.

[0072] Bayesian inference is one of the core algorithms of the data processing and signal fusion module, which is used to perform weighted fusion based on the signals of different detection modules. In general, 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.

[0073] 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 it with the prior probability of the target state to derive the posterior probability of the target state. In this way, the system can weight the signals of each detection module, making information fusion more reliable.

[0074] Through Bayesian inference, the system can perform weighted fusion of data from different signal sources to generate a more accurate target state estimate. For example, when the radar signal is clearer and the infrared signal is of poor quality, the system will tend to rely more on the radar signal, reducing the impact of the infrared signal on the result, ensuring that the final target state estimate is more accurate.

[0075] The particle filter algorithm is used for real-time tracking of dynamic targets, especially when the target moves slowly or the environment is complex. The particle filter can effectively update the target's position estimate. Specifically, the particle filter updates the weight of each particle by resampling the target state multiple times, and uses particles with larger weights for state estimation, ensuring that the system tracks the dynamic changes of the target in real time.

[0076] In general, the particle filter algorithm can gradually correct the target's state estimation by resampling the target state multiple times, thereby ensuring accurate tracking of the target position. In dynamic environments, the advantages of particle filtering are particularly significant, especially when the target may be temporarily obscured or in complex terrain. Particle filtering can effectively predict the target's future position and maintain continuous tracking of the target.

[0077] In the data processing and signal fusion module, signal fusion is not only the weighting of the signals of different detection modules, but also the correction of 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 improves the robustness and accuracy of the system by correcting these errors.

[0078] For example, for radar signals, since the reflected waves may be interfered by obstacles, resulting in inaccurate echo signals, the system can optimize the radar signals through correction algorithms; for infrared signals, due to changes in temperature gradients, there may be measurement errors in infrared sensors, and the system will make corrections based on changes in ambient temperature; for acoustic signals, the system can use noise suppression technology to reduce the interference of ambient noise on sound signals.

[0079] 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 status of the target.

[0080] Environmental adaptability modeling module, used to correct the attenuation and interference of detection signals when propagating in different environments and optimize signal processing effects; The environmental adaptability modeling module is used to dynamically model the propagation environment of the detection signal and to correct the attenuation, scattering, refraction and interference of the signal in different media. Since the life detection system needs to operate in different environments, various signals will be affected to varying degrees. Therefore, the core task of the environmental adaptability modeling module is to establish a mathematical model that adapts to different detection environments and improve the detection accuracy of the system in complex environments through real-time parameter adjustment.

[0081] In this embodiment, the environmental adaptability modeling module includes a radar signal correction unit, a sound wave propagation correction unit, and an infrared signal correction unit. These submodules are responsible for analyzing the signal propagation path, calculating attenuation and error, and making corrections through the environmental adaptability adjustment algorithm to ensure the robustness and adaptability of the system in various environments.

[0082] When radar signals propagate in the air, they are affected by multipath effects, obstacles and medium attenuation, which can cause errors in the received signal strength and time information. In general, the propagation path of radar signals can be described by the free space propagation model. However, in complex environments (such as buildings, underground passages, etc.), additional correction models need to be introduced to optimize signal propagation calculations.

[0083] Specifically, the path loss of the radar signal can be described using the following model: ; in: is the power of the received signal (in watts), received by the receiving unit; is the transmission power (unit: watt), which indicates the intensity of electromagnetic waves emitted by the radar; is the transmit antenna gain (dimensionless), which represents the effectiveness of the transmit antenna in the direction of radar signal propagation; is the receiving antenna gain (dimensionless), which represents the receiving effect of the receiving antenna; is the signal wavelength (unit: meter), which is determined by the frequency of the radar signal; is the distance of signal propagation (unit: meter), which indicates the propagation distance of radar signal from transmission to target and then to reception; is the attenuation coefficient (unit: per meter), which indicates the attenuation rate of the signal in the medium, and is mainly affected by the target environment and the medium. Common attenuation media include buildings, soil, air, etc. is the phase delay factor (unit: radians per meter), which is used to describe the phase change of radar waves during propagation.

[0084] During the propagation of sound waves, they are affected by air temperature, humidity, obstacles and media, and their propagation speed and attenuation characteristics will change. In general, the propagation speed of sound waves can be determined by temperature, and the calculation formula is as follows: ; in: : speed of sound in air (unit: m / s); : Ambient temperature (unit: one degree Celsius).

[0085] Specifically, when the system is in different environments (such as underground, underwater, confined spaces, 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.

[0086] Furthermore, the attenuation of sound waves can be described by the following formula: ; in: is the power of the received signal (in watts), received by the receiving unit; is the transmission power (unit: watt), which indicates the intensity of electromagnetic waves emitted by the radar; is the attenuation coefficient (unit: per meter), which indicates the attenuation rate of the signal in the medium, and is mainly affected by the target environment and the medium. Common attenuation media include buildings, soil, air, etc. is the distance the sound wave travels (unit: meter); is the base of the natural logarithm (unit: dimensionless).

[0087] The detection accuracy of infrared signals is affected by the ambient temperature gradient, air humidity and thermal radiation from the surface of the object. In general, the target temperature measured by the infrared sensor is May be affected by ambient temperature interference, so the system needs to compensate.

[0088] Specifically, the correction formula for the infrared signal is as follows: ; in: is the calibrated target temperature (unit: Celsius), which eliminates the interference of ambient temperature after correction; is the measured target temperature (in degrees 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 organisms; is the current ambient temperature (unit: Celsius); is the temperature correction coefficient (dimensionless), which is used to adjust the measurement error and depends on the sensitivity and measurement accuracy of the infrared sensor.

[0089] In some embodiments, the infrared signal correction unit uses multiple thermal sensors to model the ambient temperature and uses an adaptive temperature compensation algorithm to dynamically adjust the temperature correction coefficient according to the change of the ambient temperature gradient. , ensuring measurement accuracy.

[0090] Another key function of the environmental adaptability modeling module is to integrate multiple environmental factors and establish an adaptive model to adjust the system's detection strategy in real time. Specifically, the system completes environmental modeling through the following steps: Environmental parameter acquisition: Obtain environmental parameters (such as signal attenuation, noise level, temperature change) from radar, acoustic, and infrared sensors.

[0091] Multi-sensor fusion: Use multi-sensor data to model environmental characteristics and establish a dynamic environmental model.

[0092] Adaptive adjustment: Dynamically adjust the signal processing algorithm based on the environmental model to optimize the accuracy and robustness of target detection.

[0093] The environmental adaptability modeling module calculates parameters such as signal attenuation, propagation speed, and temperature compensation in real time. This module ensures that the system can operate stably under different environmental conditions and optimizes detection accuracy. Through adaptive adjustment and intelligent optimization, the system can adapt to complex environments to the maximum extent and improve the reliability and accuracy of life detection.

[0094] Wireless communication module, used to remotely transmit detection results to the command center; The wireless communication module is not only responsible for transmitting data between the various detection modules in the system, but also for transmitting the detection results back to the command center or the rescue personnel's handheld terminal in real time to support the efficient execution of rescue decisions and actions. Since the system may be used in a variety of environments, such as ruins, underground or mountainous areas, the signal propagation path is complex, and the system must have a high degree of 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.

[0095] In this embodiment, the wireless communication module transmits data through a radio frequency band (such as Wi-Fi, LTE, 5G or a dedicated frequency band). The module uses adaptive modulation and coding technology, advanced error correction mechanism and spectrum resource management strategy to ensure stable wireless connection and efficient data return in different environments.

[0096] 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.

[0097] Generally, when the wireless communication module selects the modulation mode, it takes into account the anti-interference ability and bandwidth efficiency of the signal. For high-speed data transmission, in some embodiments, the wireless communication module may use orthogonal frequency division multiplexing (OFDM) technology. OFDM improves spectrum efficiency and reduces interference caused by multipath effects by decomposing a high-speed data stream into multiple low-speed data streams and allocating them to different subcarriers for parallel transmission.

[0098] Specifically, OFDM modulation is based on the following formula: ; in: For the signal at time Complex number representation at (unit: volt or dimensionless); is the subcarrier index, indicating the The number of subcarriers (integer); is the total number of subcarriers (dimensionless), representing the number of spectrum allocations for OFDM (orthogonal frequency division multiplexing) or other multi-carrier signals; For the modulation symbols on subcarriers (units: dimensionless or volts); For the The frequency of a subcarrier (in Hertz), which indicates the center frequency occupied by the subcarrier; is the time variable (unit: seconds), indicating the change of the signal over time; Imaginary units satisfy =-1, used to describe the phase change of the signal.

[0099] Through OFDM, the wireless communication module can effectively reduce interference in a multipath environment and 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, where OFDM can significantly improve the communication quality of the system.

[0100] As an option, the wireless communication module can use adaptive modulation and coding (AMC) technology in environments with multipath propagation, signal attenuation and high noise. AMC adjusts the modulation mode and coding rate of the signal according to the current signal-to-noise ratio (SNR) to optimize the reliability and efficiency of data transmission. The system measures the SNR of the wireless signal and selects the appropriate modulation mode to ensure the best communication performance in complex environments.

[0101] Specifically, the system selects the most suitable modulation method based on the channel state information (CSI). The channel capacity can be expressed by the following formula: ; in: is the channel capacity (unit: bit / s), which indicates the maximum data transmission rate under given channel conditions; is the channel bandwidth (unit: Hz), which indicates the frequency range of the channel; is the average power of the signal (unit: watt); is the noise power (unit: watt); The signal-to-noise ratio (SNR) represents the ratio of signal power to noise power and is an important indicator for measuring signal quality; is the binary logarithmic function, which means The binary logarithm of is used to calculate the number of bits that can be transmitted per unit time.

[0102] In some embodiments, when the SNR is low, the system will select a lower modulation method (such as BPSK or QPSK) to improve noise resistance; in an environment with a higher SNR, the system will select a more efficient modulation method (such as 64-QAM or 256-QAM) to increase the data transmission rate.

[0103] Since the data transmitted by the life detection system may involve sensitive information, it is crucial to ensure the security of data transmission. When transmitting data, the wireless communication module uses encryption technology to protect the data to prevent data leakage or tampering. Generally, the system uses symmetric encryption (such as AES) or asymmetric encryption (such as RSA) technology to ensure the confidentiality of communication.

[0104] Specifically, the AES encryption algorithm is used to encrypt data to ensure that the data cannot be stolen during transmission. The encryption and decryption process can be expressed by the following formula: ; ; in: is the ciphertext, which means the encrypted data (unit: bit); For encryption functions, use the key For the original data Encryption is performed; To decrypt the function, use the key Ciphertext Decrypt and restore the original data.

[0105] In some embodiments, the data encryption process is performed using the AES-256 algorithm with a key length of 256 bits to ensure that the encryption strength is sufficient to cope with possible security threats. The wireless communication module also periodically updates the key to prevent the key from being leaked or cracked.

[0106] 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 the characteristics of 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 delays and avoid network congestion.

[0107] For example, when interference or blockage occurs in the signal transmission path, the system can prioritize data through priority queuing algorithms or QoS (Quality of Service) management mechanisms, giving priority to the transmission of urgent life detection information, such as target location or health status data. The system will also automatically adjust the transmission rate according to the real-time network conditions to avoid bandwidth overload or communication interruption.

[0108] The wireless communication module can also make adaptive adjustments based on changes in environmental conditions. Specifically, when the system detects a weak wireless signal 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.

[0109] For example, underground or inside buildings, the system can switch to a frequency band that supports a wider range based on the existing communication infrastructure, or choose to use relay nodes for data forwarding. In addition, the system will enhance the signal transmission power or adjust the signal transmission frequency in harsh environments to maximize the signal penetration and transmission stability.

[0110] The wireless communication module adopts efficient modulation and coding technology, data encryption mechanism, delay optimization and congestion control algorithm, which can provide reliable communication support in complex environments.

[0111] Human-computer interaction interface, used to display detection information and provide user operation and interaction functions; The human-computer interaction interface module provides users with an intuitive, easy-to-use and efficient operating interface so that users can quickly understand and analyze the detection results of the life detection system. Life detection tasks usually occur in complex and urgent environments. Therefore, the display and interaction methods of the interface must not only be efficient and clear, but also must be able to support users to make decisions quickly in a rapidly changing environment. The design focus of the present invention is to enable users to easily process detection results and take appropriate actions through a graphical interface, real-time data updates and dynamic interactions.

[0112] In this embodiment, the human-computer interaction interface presents various detection information by combining graphical display with real-time data interaction, and supports users to adjust parameters and settings according to the detection environment and task requirements for personalized operations. The interface display includes but is not limited to real-time detection data, the dynamic position of the target, the target's vital signs data, etc., and provides users with interactive functions such as parameter setting and alarm response through the operation control area.

[0113] The core part of the human-computer interaction interface is the real-time data display area, which carries the display of life detection results, target information and the output results of each detection module. Generally, in this area, users can see the results of multiple detection modules (such as radar, infrared, and acoustics), usually using icons, text boxes, and dynamic charts to display the target's location, status, vital signs, and other information.

[0114] Specifically, the system converts the detection results into graphical information to provide a clear target display on the interface. For example, the current location of the target is displayed through a marker on the map, and combined with heat maps, markings, etc., the changing trend of the target's vital signs is displayed in real time. Vital signs may include temperature, heart rate, breathing, etc. The system will use symbols of different colors to indicate the intensity and changing trend of these physiological information.

[0115] For example, in a typical scenario, the location of the target is displayed on a map, and the color and size of the markers reflect the strength of the vital signs (for example, red indicates high temperature, blue indicates lower vital signs). The data display 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.

[0116] In the human-computer interaction interface, the operation control area provides users with interactive functions, enabling them to adjust system parameters according to the current detection task requirements. Generally, users can control the working parameters of different detection modules by adjusting the sliders, input boxes or buttons on the interface to optimize the detection results.

[0117] Specifically, users can adjust the following key parameters: Radar Sensitivity: Adjust the detection sensitivity of the radar module to adapt to different environments.

[0118] Infrared sensor threshold: Set the temperature threshold of the infrared module to detect qualified life forms.

[0119] Acoustic Module Frequency Range: Controls the frequency range of the Acoustic Detection Module to capture weak sound signals emitted by living organisms.

[0120] For example, in the user interface, the radar sensitivity slider can be used to control the range of signal strength and adjust its sensitivity. In the "Mode Selection" area, users can select different detection modes (such as "Full Mode" or "Single Module Mode"), and the system will adjust the working status of all modules accordingly.

[0121] Target positioning is an important function of the human-computer interaction interface. Specifically, by updating the target's location information in real time, the interface can display the dynamic changes of the target in the detection area. The system uses a map or heat map to represent the target location and can adjust the display content in real time to reflect the changes in the target location.

[0122] For example, during the detection process, if the target moves within a certain period of time, the system will adjust the position of the marker according to the latest detection data of the target, so that the target's movement trajectory can be presented to the operator in real time. By combining the target position and vital signs data, the system provides a dynamic and comprehensive information display platform to help users quickly understand the target status and respond.

[0123] In the human-computer interaction interface, the emergency alarm system is a crucial component. Generally speaking, when the system detects that the detection results of a living being exceed the set alarm threshold, the system will immediately display a warning message through the interface and remind the user by sound, flashing light, etc.

[0124] Specifically, on the interface, when the target's vital signs (such as temperature, heart rate) exceed the set safety range, the system will automatically trigger an alarm, display a red warning box, and be accompanied by sound or vibration prompts. The alarm information not only includes the location of the life form, but also displays other related vital sign data (such as current temperature, breathing, heart rate, etc.) and emergency response suggestions.

[0125] For example, when the target's body temperature is detected to be above the threshold, the interface will highlight the target's location and pop up a window to remind rescuers that the target's body temperature is abnormal. Users can respond quickly according to the prompt and start the rescue process by clicking the "Respond Now" button on the interface.

[0126] To support rescuers in making decisions, the human-computer interaction interface also includes a series of data visualization and statistical analysis functions. In some embodiments, the interface will dynamically display various detection data in the form of line graphs, pie charts, heat maps, etc., so that users can quickly obtain key information.

[0127] Specifically, the system can generate the following types of charts: Line chart: used to show the changing trend of the target's vital signs, such as temperature changes, heart rate, etc.

[0128] Heat map: Displays the distribution of target locations and the intensity of their life signs. Color represents intensity, allowing users to intuitively see high-risk areas within the detection area.

[0129] Statistical analysis: Graphs are used to display information such as the number of targets detected over a period of time and changes in their status, helping rescuers evaluate the progress and effectiveness of rescue operations.

[0130] For example, through the heat map, users can see the distribution of targets in the entire detection area. The depth of color can help users determine which area has more signs of life, thereby guiding the rescue team to focus their efforts on searching in these areas first.

[0131] The human-machine interaction interface module of the present invention helps users quickly understand the target status and vital signs information through graphical display and data visualization. The alarm and response mechanism ensures that users can take quick action in emergency situations and optimize rescue decisions.

[0132] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-mode composite life detection system, characterized in that: include: Radar detection module, used to detect the surrounding environment through radar signals and identify the location and existence of targets; Infrared sensor module, used to detect temperature changes of living organisms through temperature differences; Acoustic detection module, used to detect weak sound signals of living organisms through sound collection and analysis; The central processing unit is used to coordinate the work of various modules, receive and process signals from different detection modules, perform signal fusion and determine the existence of life forms; The data processing and signal fusion module is used to receive signals from each detection module and perform data fusion and target tracking through Bayesian inference and particle filtering; Environmental adaptability modeling module, used to correct the attenuation and interference of detection signals when propagating in different environments and optimize signal processing effects; Wireless communication module, used to remotely transmit detection results to the command center; The human-computer interaction interface is used to display detection information and provide user operation and interaction functions.

2. A multi-mode composite life detection system according to claim 1, characterized in that: The radar detection module comprises: A transmitting unit, used for sending electromagnetic wave signals; A receiving unit, used for receiving the reflected electromagnetic wave signal; The signal processing unit is used to calculate the reflection intensity and return time of the signal to determine the position and distance of the target.

3. A multi-mode composite life detection system according to claim 1, characterized in that: The infrared sensor module comprises: Infrared detection unit, used to capture the temperature change of the target; The signal processing unit is used to analyze and process the received infrared signal to determine the heat source information of the target.

4. A multi-mode composite life detection system according to claim 1, characterized in that: The acoustic detection module comprises: Microphone array, used to collect sound wave signals in the environment; The sound processing unit is used to analyze the captured sound signals, including heartbeat, breathing and cries for help.

5. A multi-mode composite life detection system according to claim 1, characterized in that: The central processing unit comprises: A signal receiving module, used for receiving real-time data from a radar detection module, an infrared sensor module and an acoustic detection module; A data analysis module is used to process, analyze and integrate the received signals to determine the existence of life forms; Control module, used to control the operation of the entire system and coordinate the work of each module; The output module is used to output the processing results through the wireless communication module or the human-computer interaction interface.

6. A multi-mode composite life detection system according to claim 1, characterized in that: The data processing and signal fusion module includes: The Bayesian inference module is used to jointly optimize the signals from each detection module according to the Bayesian theorem to obtain the optimal target state probability; The particle filter module is used to dynamically track the target and update the target status; The fusion and correction module is used to process and optimize the fusion results of each signal source, reduce the impact of noise, and ensure the system's accurate detection of living organisms.

7. A multi-mode composite life detection system according to claim 1, characterized in that: The environmental adaptability modeling module includes: Radar signal correction module, used to correct the attenuation of radar signals in complex environments and adjust the signal strength and propagation direction; The sound wave propagation correction module is used to adjust the attenuation of the acoustic signal according to the propagation characteristics of the sound wave in different media; The infrared signal correction module is used to adjust the reading value of the infrared signal according to the temperature gradient of the target environment to make the detection more sensitive.

8. The multi-mode composite life detection system according to claim 1, characterized in that: The wireless communication module comprises: A signal transmission unit, used to transmit the processed life detection signal and data to a command center; The communication protocol module is used to ensure the communication compatibility and data transmission stability between the system and the platform.

9. A multi-mode composite life detection system according to claim 1, characterized in that: The human-computer interaction interface comprises: Display unit, used to display the results of life detection in real time, showing the location, status and life signs of the target; An input unit, used to receive instructions from operators, control system operation or adjust detection parameters; The alarm unit is used to send out audible and visual alarms when signs of life are detected to alert rescue personnel.

10. A multi-mode composite life detection system according to claim 6, characterized in that: The data processing steps of the data processing and signal fusion module include: Step 1: Receive signal data from each detection module; Step 2: Use the Bayesian inference module to perform signal fusion and obtain the optimal detection result by maximizing the posterior probability; Step 3: Use the particle filter module to track dynamic targets to ensure accurate detection of slowly moving life forms; Step 4: Output the processed data for display on the wireless communication module or human-computer interaction interface.

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