Multi-mode self-positioning networking radar life detection method

Abstract

The invention discloses a multi-mode self-positioning networking radar life detection method, and relates to the technical field of radar life detection, and the method comprises the following steps: firstly, dividing a to-be-detected region into a plurality of sub-regions; according to the invention, the to-be-detected region is divided into a plurality of sub-regions and the dynamic change of the signal is analyzed, so that the system can recognize abnormal signals in time in complex environments such as debris flow and the like. A signal change coefficient is generated by utilizing characteristic parameters such as a phase deviation index and a spectrum spreading index and combining a machine learning model, and the signal quality is accurately judged. The method effectively avoids missing detection and improves the rescue success rate. Meanwhile, by comparing the signal change coefficient with a reference threshold value, the system optimizes resource configuration, improves rescue efficiency, ensures that trapped people can be quickly positioned, and saves more lives.

Description

Technical Field

[0001] The present invention relates to the technical field of radar life detection, and in particular to a multi-mode self-positioning networking radar life detection method. Background Art

[0002] Multi-mode self-positioning networked radar life detection is an advanced detection technology that is mainly used in disaster relief and emergency response scenarios to search and identify organisms. The technology combines multiple radar modes to enhance the recognition of biological signals through different frequency bands and detection methods. The self-positioning function enables the radar system to automatically adjust its position and angle in a dynamic environment to optimize the detection effect; while the networking capability allows multiple radar devices to collaborate with each other to form a detection network with wide coverage and information sharing, which is crucial to improving the accuracy and real-time performance of detection. During the life detection process, the radar equipment can penetrate obstacles (such as ruins or snow) and use the weak signals generated by the movement and vital signs (such as breathing, heartbeat, etc.) of the organism for identification. This non-contact and efficient technology enables rescuers to quickly locate the trapped. In addition, the networked radar system can also use data fusion and machine learning technology to improve the recognition rate of vital signs in different environments and conditions, further improving the success rate and efficiency of rescue. This detection method that combines multiple technologies not only improves the life detection capability, but also provides more accurate and efficient support for future post-disaster rescue operations.

[0003] Multi-mode self-positioning networked radar life detection technology is widely used in drones. This combination improves the efficiency and effectiveness of drones in disaster relief. After the drone is equipped with this radar system, it can fly autonomously in complex and dangerous environments, automatically adjust its position and angle, and thus optimize the detection effect. By penetrating obstacles (such as ruins and snow), drones can monitor the vital signs of organisms (such as breathing and heartbeat) in real time and quickly identify the location of trapped people. At the same time, the networking capability allows multiple drones to work together to form a widely covered detection network, enhance information sharing and data fusion, and improve the accuracy and real-time nature of life detection. The application of this technology can not only greatly improve the success rate of rescue operations, but also reduce rescue time while ensuring the safety of rescuers, thereby saving lives more effectively.

[0004] The prior art has the following deficiencies: In the prior art, the life detection radar equipped by the drone usually covers a fixed detection area for vital sign detection when working, which is effective in most application scenarios. However, after a debris flow disaster occurs, water may accumulate in the area, especially a mud-water mixture, which will cause strong attenuation and scattering of the radar signal, especially the high-frequency signal. If the radar continues to cover a fixed detection area for detection, serious consequences may occur. First, the high-frequency signal will experience significant attenuation in the mud-water mixture, resulting in the inability to capture the vital signs of the trapped person, thereby missing the detection. This may cause the loss of life in emergency rescue situations and significantly affect the efficiency of rescue. Secondly, the fixed detection area may receive a large amount of background noise and artifact signals, leading to misjudgment. This will cause rescuers to misjudge the location of the trapped person and waste time and resources on ineffective searches.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a multi-mode self-positioning networked radar life detection method. By dividing the area to be detected into multiple sub-areas and analyzing the dynamic changes of the signal, the system can promptly identify abnormal signals in complex environments such as mudslides. By using characteristic parameters such as phase shift index and spectrum expansion index, combined with a machine learning model to generate a signal variation coefficient, the signal quality can be accurately judged. This method effectively avoids missed detection and improves the success rate of rescue. At the same time, the system optimizes resource allocation and improves rescue efficiency by comparing the signal variation coefficient with the reference threshold, ensuring that the trapped person can be quickly located and more lives can be saved to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a multi-mode self-positioning networked radar life detection method, comprising the following steps: First, the area to be detected is divided into multiple sub-areas; The drone equipped with life detection radar collects signal data from each sub-area according to the set fixed detection area to obtain the dynamic change data of the signal in the entire area to be detected; Extract features from the collected signal dynamic change data. Based on the extracted features, use the pre-trained machine learning model within the detection window to analyze and evaluate the extracted feature data to identify abnormal changes in the signal data. Based on the evaluation results of the machine learning model, all sub-areas are divided into areas with strong signal attenuation and areas with normal signal acquisition; Mark all sub-regions that are classified as areas with strong signal attenuation; For the sub-area of ​​the area where the signal is normally acquired, continue to detect vital signs with the initially set fixed detection area; For the sub-region marked as the area with strong signal attenuation, the detection area is reset based on the evaluation result of the signal data change information of the sub-region.

[0008] Preferably, feature extraction is performed on the collected signal dynamic change data, and the extracted features include signal phase change and signal spectrum expansion degree. Under the detection window, after analyzing the signal phase change and signal spectrum expansion degree, a phase shift index and a spectrum expansion index are generated respectively. The generated phase shift index and spectrum expansion index are input into a pre-trained machine learning model, and a signal change coefficient is generated by the machine learning model. The signal dynamic change data is analyzed and evaluated by the signal change coefficient to identify abnormal changes in the signal data.

[0009] Preferably, in the detection window, the specific steps of analyzing the signal phase change and generating the phase shift index are as follows: Under the detection window, first obtain the time domain waveform of the radar signal and calibrate the time domain waveform of the radar signal as , preprocess the time domain waveform of the acquired radar signal, remove the background noise, extract the phase of the preprocessed signal, calculate the instantaneous phase of the signal, and obtain the instantaneous phase by the analytical signal method. The calculation expression is as follows: , where is the instantaneous phase of the signal, is the time domain waveform of the received radar signal, is the Hilbert transform of the signal, is an imaginary unit; Calculate time points and time point The instantaneous phase difference at time is calculated as follows: , where is the phase difference, at the time point and time point The instantaneous phase change between The phase difference at each time point, It's at the time The instantaneous phase difference at time, It's at the time The instantaneous phase difference at time, is the phase adjustment factor, where is any integer, express is an element of the set of integers; The phase difference is converted into a phase shift index through a nonlinear mapping function, and the calculation expression is as follows: , where is the phase shift index, is the total number of time points, that is, the number of phase differences obtained at all time points, is the natural base, is the attenuation factor, is the nonlinear coefficient, is the normalized time constant.

[0010] Preferably, in the detection window, the signal spectrum spread degree is analyzed and the specific steps of generating the spectrum spread index are as follows: Under the detection window, obtain the frequency distribution data of the signal and The amplitude at the moment is calibrated as , that is, the signal changes with time The change of the time domain signal is converted into a frequency domain signal through Fourier transform, and the calculation expression is as follows: , where is the frequency domain signal, that is, the amplitude of the signal in the frequency domain, is a complex exponential term, is an imaginary unit; In the frequency domain, the instantaneous frequency offset function is calculated by the offset of the center frequency of the spectrum. The calculation expression is as follows: , where is the instantaneous frequency offset, is the center frequency of the signal; In order to more accurately capture the characteristics of spectrum expansion, the frequency weight function is defined, and the calculation expression is as follows: , where is the frequency weight function, is a decay factor; By transforming the frequency domain signal With frequency weight function Combined with the degree of deviation of different frequencies, the expansion characteristics of the spectrum energy are obtained, and the expression is as follows: , where is the spectrum energy spread function; Spectral Energy Spread Function Integrate to get the total energy of spectrum expansion, the calculation expression is as follows: , where is the total energy of spectrum expansion, which represents the total energy in the entire frequency range. It is calculated by the spectrum energy expansion function In the frequency range Integrate within to get, and are the upper and lower limits of the integral, respectively, indicating the frequency range of our analysis; Total energy of comprehensive spectrum expansion and instantaneous frequency deviation Generate the spectrum spreading index, the calculation expression is as follows: , where is the spectrum spreading index.

[0011] Preferably, the signal variation coefficient generated after analyzing the signal dynamic variation data collected from the sub-regions is compared and analyzed with a preset signal variation coefficient reference threshold, and the sub-regions are divided. The division process is as follows: If the signal variation coefficient is greater than or equal to a preset signal variation coefficient reference threshold, the sub-region is divided into a strong signal attenuation region; If the signal variation coefficient is less than a preset signal variation coefficient reference threshold, the sub-region is divided into a normal signal acquisition region.

[0012] Preferably, for the sub-region marked as the strong signal attenuation region, based on the signal data change information evaluation result of the sub-region, the specific steps of resetting the detection area are as follows: First, the initial fixed detection area is calibrated as , evaluate the signal data change information of the marked signal strong attenuation area, and use the signal change coefficient generated by the machine learning model Recalibrated to signal variation coefficient ; The recalibrated signal variation coefficient Compare with the preset signal change coefficient reference threshold. If it meets the requirement, , it indicates that the signal attenuation in this area is serious and the detection area needs to be reset; According to the comparison results, the new detection area is reset and the calculation expression is as follows: , where To reset the detection area, is the maximum value of the signal change, is the environmental complexity coefficient; Finally, within the new detection area, signal data is collected again and the signal strength and clarity are evaluated. The calculation expression is as follows: , where This is the latest test result. is the sum of the signal strengths successfully detected within the new detection area, It is The strength of an effective detection signal, is the number of valid signals detected.

[0013] In the above technical solution, the technical effects and advantages provided by the present invention are: The present invention divides the area to be detected into multiple sub-areas, and collects and analyzes the dynamic change data of the signal in each sub-area, so as to timely identify abnormal changes in the signal in a complex debris flow environment. In particular, in the case of signal attenuation caused by accumulated water and mud-water mixtures, by extracting uncommon characteristic parameters such as phase shift index and spectrum expansion index, combined with a pre-trained machine learning model, the system can generate a signal variation coefficient, thereby accurately judging the signal quality of each sub-area. This method effectively avoids the risk of missed detection under a fixed detection area, ensures that the vital signs of the trapped person can be accurately captured at critical moments, and significantly improves the success rate of rescue operations.

[0014] By comparing and analyzing the signal variation coefficient with a preset reference threshold, the system can quickly and intelligently divide sub-areas into areas with strong signal attenuation and areas with normal signal acquisition. This process allows the rescue team to focus resources and energy on deep detection in areas with severe signal attenuation, while continuing to use the initially set fixed detection area for effective detection in areas with normal signals, thereby achieving dynamic adjustment. This not only reduces the waste of resources caused by misjudgment, but also greatly improves the rescue efficiency, ensuring that trapped persons can be located more quickly in emergency situations, reducing rescue response time, and thus saving more lives. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0016] Figure 1 The present invention is a method flow chart of a multi-mode self-positioning networked radar life detection method. DETAILED DESCRIPTION

[0017] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0018] The present invention provides Figure 1 A multi-mode self-positioning networked radar life detection method is shown, comprising the following steps: First, the area to be detected is divided into multiple sub-areas; GIS technology is used to integrate the terrain, vegetation and infrastructure information of the region to generate a detailed geographic model. Secondly, high-resolution images of the region are obtained through remote sensing data (such as satellite images or drone aerial photography) to analyze the distribution of landform features and potential obstacles. Finally, based on this information and the actual situation after the debris flow disaster, sub-regions are reasonably divided to ensure that the size and shape of each sub-region can adapt to the detection capability of the radar, thereby improving the pertinence and effectiveness of subsequent detection; The drone equipped with life detection radar collects signal data from each sub-area according to the set fixed detection area to obtain the dynamic change data of the signal in the entire area to be detected; The drone equipped with life detection radar collects signal data from each sub-area according to the set fixed detection area, aiming to obtain the dynamic change data of the signal in the entire area to be detected. Specifically, the drone flies in each sub-area and emits electromagnetic waves to detect weak vital signs signals (such as breathing and heartbeat) generated by organisms. In this way, the radar can record information such as signal strength, frequency changes, and environmental interference in each sub-area, thereby forming a detailed database of dynamic signal changes. These data provide a basis for subsequent feature extraction and analysis, helping to identify the location and status of trapped people and improve rescue efficiency.

[0019] Extract features from the collected signal dynamic change data. Based on the extracted features, use the pre-trained machine learning model within the detection window to analyze and evaluate the extracted feature data to identify abnormal changes in the signal data. Extract features from the collected signal dynamic change data. The extracted features include signal phase change and signal spectrum expansion degree. Under the detection window, after analyzing the signal phase change and signal spectrum expansion degree, generate a phase shift index and a spectrum expansion index respectively. Input the generated phase shift index and spectrum expansion index into a pre-trained machine learning model. Generate a signal change coefficient through the machine learning model. Analyze and evaluate the signal dynamic change data through the signal change coefficient to identify abnormal changes in the signal data. When the signal frequently shows significant phase shift, this usually indicates that there are factors in the sub-area that cause strong attenuation and scattering of the radar signal. During the propagation of radar signals, phase shift is caused by the signal passing through different media or encountering obstacles. If the phase shift amplitude is large, it may indicate that the signal encountered significant obstacles when penetrating mud and water mixtures, ruins or other complex environments. This phase change not only affects the propagation speed and path of the signal, but may also cause the reflection and scattering of the signal to increase, further reducing the received signal strength. When the signal strength decays to a certain extent, the radar may not be able to effectively identify the weak vital signs of the organism, resulting in missed detection. Therefore, a significant change in phase shift is an important indicator that there are environmental factors (such as humidity, soil or other materials) in the sub-area that affect the propagation characteristics of the radar signal, which in turn leads to rapid attenuation and scattering of the signal.

[0020] Under the detection window, the specific steps for analyzing the signal phase change and generating the phase shift index are as follows: Under the detection window, first obtain the time domain waveform of the radar signal and calibrate the time domain waveform of the radar signal as , preprocess the time domain waveform of the acquired radar signal, remove the background noise, extract the phase of the preprocessed signal, calculate the instantaneous phase of the signal, and obtain the instantaneous phase through the analytical signal method (Hilbert transform). The calculation expression is as follows: , where is the instantaneous phase of the signal, is the time domain waveform of the received radar signal, is the Hilbert transform of the signal, is an imaginary unit; Phase is a quantity that describes the instantaneous state of a periodic signal at a certain point in a cycle, usually expressed as an angle (radians or degrees). In a complete cycle, the phase reflects the position of the signal in time and can be used to describe the starting point and time delay of the signal. For periodic signals such as sine waves, changes in phase directly affect the waveform and spectral characteristics of the signal. For example, in electrical signals or sound waves, changes in phase difference may affect the interference and superposition effects of the signal, thereby changing its amplitude and frequency characteristics. In signal processing and communication systems, understanding and controlling phase is crucial because phase information can reveal the transmission characteristics, processing effects, and relationships between signals.

[0021] The analytical signal method is a technique in signal processing that generates a complex signal by applying the Hilbert transform to a real-valued signal. The real part of this complex signal is the original signal, and the imaginary part is the expression of its instantaneous phase. Through this transformation, the instantaneous amplitude and instantaneous phase of the signal can be obtained, thereby more comprehensively analyzing the characteristics of the signal.

[0022] The main function of Hilbert transform is to extract the phase information and amplitude change of the signal, making its analysis in the frequency domain and time domain more efficient and intuitive. Specifically, it can help identify the frequency components and change trends of the signal. Especially when it is necessary to monitor the phase change of the signal, Hilbert transform can provide accurate phase information. This method is widely used in radar signal processing, communication system analysis, and biomedical signal processing, improving the understanding and processing capabilities of complex signals.

[0023] Calculate time points and time point The instantaneous phase difference at time is calculated as follows: , where is the phase difference, at the time point and time point The instantaneous phase change between The phase difference at each time point, It's at the time The instantaneous phase difference at time, It's at the time The instantaneous phase difference at time, is the phase adjustment factor, where is an arbitrary integer. Since the phase is periodic, Used to adjust the phase difference range to ensure that it is within arrive within the range of express is an element of the set of integers, this constraint states that Can be any integer, including positive integers, negative integers and zero, by choosing different The phase difference can be adjusted to the required range to ensure the accuracy of phase analysis; Instantaneous phase difference refers to the amount of signal phase change between two consecutive time points, and is usually used to describe the phase change trend of a signal at a specific moment. Instantaneous phase difference can reflect the phase shift caused by environmental changes (such as obstacles, changes in media, etc.) during the propagation of a signal. Its role is to help identify the dynamic characteristics of the signal, especially when detecting the vital signs of an organism or monitoring interference in the environment, the instantaneous phase difference can provide important information about signal strength, interference sources, and changes in signal paths.

[0024] The phase adjustment factor is an integer correction term used to solve the problem of signal phase jumps in periodic changes. arrive or 0 to When the phase difference is discontinuous, a phase "jump" will occur at the boundary of The phase difference is adjusted to the specified interval (for example arrive ), so that the phase difference remains continuous and accurately reflects the real changes of the signal. This factor is crucial in phase difference calculation, especially in signal processing, communication and radar systems, to ensure the accuracy and stability of phase information.

[0025] The phase difference is converted into a phase shift index through a nonlinear mapping function, and the calculation expression is as follows: , where is the phase shift index, is the total number of time points, that is, the number of phase differences obtained at all time points, is the natural base, is the attenuation factor, is the nonlinear coefficient, is the normalized time constant; Normalized time constant is a time parameter used to standardize the phase difference change, which helps eliminate the impact caused by different sampling frequencies or time scales. In the formula, Used to square the phase difference Normalize so that the phase shift index at different time scales ( ) calculation results are consistent. This means that no matter how often the signal is sampled or how long the different monitoring windows are, the normalized time constant This ensures that the results are comparable under different conditions, thus guaranteeing the robustness and applicability of the phase shift index. This consistency is particularly important in signal processing and comparison in complex environments.

[0026] The nonlinear coefficient is a parameter used to control the strength and influence of the nonlinear term in the formula. ) in the calculation of the nonlinear coefficient Adjusted the phase difference square term Contribution to the phase shift index value. When the phase difference is large, the nonlinear coefficient amplifies its influence, making the phase difference have a more significant impact on the result. The value of can control the strength of the nonlinear effect, thereby weighting phase changes of different amplitudes in the signal differently. This is particularly important in complex environments with significant signal fluctuations, because it makes the formula more sensitive to abnormal or extreme phase changes, helping to more accurately reflect the attenuation and scattering of the signal.

[0027] In the detection window, the larger the phase shift index value generated after analyzing the signal phase change, the more significant the signal phase change is in the sub-area, which usually means that the signal has encountered strong attenuation and scattering during propagation, resulting in a decrease in the quality of the received signal. This may be caused by obstacles in a complex environment (such as mud and water mixtures, ruins or trees, etc.). When the phase shift index is small, it means that the phase change of the signal is not obvious, indicating that the environmental conditions in the sub-area are relatively good, the signal propagation is relatively smooth, and it will not cause strong attenuation and scattering.

[0028] When the signal spectrum width increases significantly, it usually indicates that the sub-area may cause strong attenuation and scattering of radar signals. This is because the expansion of the signal spectrum means that the frequency components of the signal have undergone significant changes during propagation, which may be caused by stray objects, obstacles or medium inhomogeneity in the environment. In complex environments such as muddy water mixtures, when electromagnetic waves encounter different media, multipath propagation and reflection will occur, causing the frequency and phase of the signal to change, thereby expanding the spectrum width. The expansion of the spectrum means that the clarity of the signal is reduced and the transmitted information becomes blurred, which directly affects the ability to detect vital signs. In addition, the expansion of the spectrum may also lead to the enhancement of background noise, which further masks weak biological signals. In this case, the strong attenuation of the signal not only affects the detection range, but also leads to misjudgment and missed detection, seriously affecting the rescue efficiency. Therefore, the significant increase in the spectrum width is an important indicator for judging the degradation of signal quality and potential strong attenuation in this area.

[0029] In the detection window, the signal spectrum spread degree is analyzed and the specific steps of generating the spectrum spread index are as follows: Under the detection window, obtain the frequency distribution data of the signal and The amplitude at the moment is calibrated as , that is, the signal changes with time The change of the time domain signal is converted into a frequency domain signal through Fourier transform, and the calculation expression is as follows: , where It is a frequency domain signal, that is, the amplitude of the signal in the frequency domain, indicating that the signal The expression in the frequency domain after Fourier transform indicates that the signal is at frequency The amplitude and phase of It is a complex exponential term, which is a key term in Fourier transform and represents a rotational complex exponential function. Decompose into different frequencies The ingredients, is an imaginary unit; Complex exponential terms is a complex number (imaginary unit ), which decomposes the real signal into a rotated complex form. In Fourier transform, the role of the complex exponential term is to convert the time domain signal Convert to frequency domain signal , so that the signal can be decomposed into sine and cosine wave components of different frequencies. Specifically, the complex exponential term converts each frequency component into its corresponding amplitude and phase, thereby revealing the frequency structure of the signal in the frequency domain. In this way, the complex exponential term can help us analyze the energy distribution of the signal at different frequencies, allowing us to identify the characteristic frequencies in the signal.

[0030] In the frequency domain, the instantaneous frequency offset function is calculated by the offset of the center frequency of the spectrum. The calculation expression is as follows: , where is the instantaneous frequency offset, indicating the signal at frequency With center frequency The offset between is the center frequency of the signal; In order to more accurately capture the characteristics of spectrum expansion, the frequency weight function is defined, and the calculation expression is as follows: , where is the frequency weight function, which is used to adjust the weights of different frequency components in the calculation of the spectrum spreading index. is a decay factor that controls the speed at which the frequency weight function changes; The frequency weight function is a function used to adjust the influence of different frequency components in the overall analysis. By assigning different weights to each frequency component, the contribution of certain frequency components to the result can be enhanced or suppressed. Here, the frequency weight function is used to adjust the influence of different frequency components on the signal. The frequency components with different degrees of deviation are weighted, and the greater the deviation, the higher the weight. In this way, the components in the spectrum that deviate more from the center frequency can be highlighted to reflect the changing characteristics of these components after the signal is affected by the environment (such as attenuation and scattering). Through this weighting, the frequency weight function helps us capture the subtle characteristics of spectrum spread more sensitively, thereby improving the analysis accuracy of the spectrum spread index in complex environments.

[0031] By transforming the frequency domain signal With frequency weight function Combined with the degree of deviation of different frequencies, the expansion characteristics of the spectrum energy are obtained, and the expression is as follows: , where is the spectrum energy spread function; Spectral Energy Spread Function Integrate to get the total energy of spectrum expansion. The total energy of spectrum expansion describes the expansion intensity of the entire spectrum. The calculation expression is as follows: , where is the total energy of spectrum expansion, which represents the total energy in the entire frequency range. It is calculated by the spectrum energy expansion function In the frequency range Integrate within to get, and are the upper and lower limits of the integral, respectively, indicating the frequency range of our analysis; Total energy of comprehensive spectrum expansion and instantaneous frequency deviation Generate the spectrum spreading index, the calculation expression is as follows: , where is the spectrum spreading index; Under the detection window, the signal spectrum expansion degree is analyzed, and the larger the spectrum expansion index is, the more it usually indicates that there is significant signal attenuation and scattering in the sub-area. This expansion means that the signal is affected by multipath effects, obstacle reflections or medium inhomogeneity during propagation, resulting in changes and blurring of the signal frequency components, thereby reducing the clarity and recognizability of the signal, and causing strong signal attenuation. On the contrary, if the spectrum expansion index is smaller, it means that the signal can propagate more stably in the sub-area, with less interference and attenuation, which means that the area will not significantly affect the quality of the radar signal.

[0032] The machine learning model is not limited here and can realize the phase shift index and Spectrum Spread Index Perform comprehensive analysis to generate signal variation coefficient In order to realize the technical solution of the present invention, the present invention provides a specific implementation method; Signal Variation Coefficient The calculation formula is as follows: , where , is the phase shift index and Spectrum Spread Index The preset scaling factor of , Both are greater than 0.

[0033] It can be seen from the calculation expression of the signal variation coefficient that, under the detection window, the larger the phase shift index expression value generated after analyzing the signal phase change, that is, the larger the signal variation coefficient expression value generated after analyzing the signal dynamic change data collected in the sub-area under the detection window, it indicates that the signal in the sub-area has encountered strong attenuation and scattering during the propagation process, resulting in a decrease in the quality of the received signal. On the contrary, it indicates that the environmental conditions in the sub-area are relatively good, the signal propagation is relatively smooth, and it will not cause strong attenuation and scattering.

[0034] Based on the evaluation results of the machine learning model, all sub-areas are divided into areas with strong signal attenuation and areas with normal signal acquisition; The signal change coefficient generated by analyzing the signal dynamic change data collected in the sub-area is compared and analyzed with the preset signal change coefficient reference threshold, and the sub-area is divided. The division process is as follows: If the signal variation coefficient is greater than or equal to a preset signal variation coefficient reference threshold, the sub-region is divided into a strong signal attenuation region; If the signal variation coefficient is less than a preset signal variation coefficient reference threshold, the sub-area is divided into a normal signal acquisition area; The area of ​​strong signal attenuation refers to the area where the signal is significantly reduced when passing through the area during radar detection, and its strength and clarity are seriously affected. The area of ​​normal signal acquisition refers to the area where the signal strength and quality are high during radar detection, and the vital signs of organisms can be effectively captured.

[0035] Mark all sub-regions that are classified as areas with strong signal attenuation; This step ensures that rescuers can quickly identify areas with poor signal quality so that they can pay special attention to these areas in subsequent surveys. Tag information can also be updated in real time through the drone system to facilitate sharing of information among team members.

[0036] For the sub-area of ​​the area where the signal is normally acquired, continue to detect vital signs with the initially set fixed detection area; This process ensures that vital signs data can still be effectively captured in areas with better signal quality, maximizing the coverage and effectiveness of detection.

[0037] For the sub-region marked as the area with strong signal attenuation, based on the evaluation result of the signal data change information of the sub-region, the detection area is reset; For the sub-region marked as the area with strong signal attenuation, the specific steps of resetting the detection area based on the evaluation result of the signal data change information of the sub-region are as follows: First, the initial fixed detection area is calibrated as , evaluate the signal data change information of the marked signal strong attenuation area, and use the signal change coefficient generated by the machine learning model Recalibrated to signal variation coefficient ; The recalibrated signal variation coefficient Compare with the preset signal change coefficient reference threshold. If it meets the requirement, , it indicates that the signal attenuation in this area is serious and the detection area needs to be reset; According to the comparison results, the new detection area is reset and the calculation expression is as follows: , where To reset the detection area, is the maximum value of the signal change, is the environmental complexity coefficient, which represents the adjustment factor for the impact of signal attenuation; Finally, within the new detection area, signal data is collected again and the signal strength and clarity are evaluated. The calculation expression is as follows: , where It is the latest detection result, which reflects the strength of the effective signal per square meter in the new detection area. is the sum of the signal strengths successfully detected within the new detection area, It is The strength of an effective detection signal, is the number of valid signals detected; Through the latest test results , we can quantify the relationship between the signal strength effectively detected within the re-set detection area and the detection area. This result provides an important basis for evaluating the effectiveness of the detection strategy and can intuitively show the performance of the new detection area in capturing vital signs. A higher value indicates that the newly set detection area can more effectively identify the vital signs of the trapped persons, thereby improving the success rate and efficiency of the rescue operation, ensuring that the trapped persons can be quickly and accurately located in an emergency and saving lives to the greatest extent.

[0038] The present invention divides the area to be detected into multiple sub-areas, and collects and analyzes the dynamic change data of the signal in each sub-area, so as to timely identify abnormal changes in the signal in a complex debris flow environment. In particular, in the case of signal attenuation caused by accumulated water and mud-water mixtures, by extracting uncommon characteristic parameters such as phase shift index and spectrum expansion index, combined with a pre-trained machine learning model, the system can generate a signal variation coefficient, thereby accurately judging the signal quality of each sub-area. This method effectively avoids the risk of missed detection under a fixed detection area, ensures that the vital signs of the trapped person can be accurately captured at critical moments, and significantly improves the success rate of rescue operations.

[0039] By comparing and analyzing the signal variation coefficient with a preset reference threshold, the system can quickly and intelligently divide sub-areas into areas with strong signal attenuation and areas with normal signal acquisition. This process allows the rescue team to focus resources and energy on deep detection in areas with severe signal attenuation, while continuing to use the initially set fixed detection area for effective detection in areas with normal signals, thereby achieving dynamic adjustment. This not only reduces the waste of resources caused by misjudgment, but also greatly improves the rescue efficiency, ensuring that trapped persons can be located more quickly in emergency situations, reducing rescue response time, and thus saving more lives.

[0040] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A multi-mode self-positioning networked radar life detection method, characterized in that: The following steps are involved: First, the area to be detected is divided into multiple sub-areas; The drone equipped with life detection radar collects signal data from each sub-area according to the set fixed detection area to obtain the dynamic change data of the signal in the entire area to be detected; Extract features from the collected signal dynamic change data. Based on the extracted features, use the pre-trained machine learning model within the detection window to analyze and evaluate the extracted feature data to identify abnormal changes in the signal data. Based on the evaluation results of the machine learning model, all sub-areas are divided into areas with strong signal attenuation and areas with normal signal acquisition; Mark all sub-regions that are classified as areas with strong signal attenuation; For the sub-area of ​​the area where the signal is normally acquired, continue to detect vital signs with the initially set fixed detection area; For the sub-region marked as the area with strong signal attenuation, the detection area is reset based on the evaluation result of the signal data change information of the sub-region.

2. A multi-mode self-positioning networked radar life detection method according to claim 1, characterized in that: The collected signal dynamic change data is subjected to feature extraction, and the extracted features include signal phase change and signal spectrum expansion degree. Under the detection window, the signal phase change and signal spectrum expansion degree are analyzed to generate a phase shift index and a spectrum expansion index respectively. The generated phase shift index and spectrum expansion index are input into a pre-trained machine learning model, and a signal change coefficient is generated by the machine learning model. The signal dynamic change data is analyzed and evaluated by the signal change coefficient to identify abnormal changes in the signal data.

3. A multi-mode self-positioning networked radar life detection method according to claim 2, characterized in that: Under the detection window, the specific steps for analyzing the signal phase change and generating the phase shift index are as follows: Under the detection window, first obtain the time domain waveform of the radar signal and calibrate the time domain waveform of the radar signal as , preprocess the time domain waveform of the acquired radar signal, remove the background noise, extract the phase of the preprocessed signal, calculate the instantaneous phase of the signal, and obtain the instantaneous phase by the analytical signal method. The calculation expression is as follows: , where is the instantaneous phase of the signal, is the time domain waveform of the received radar signal, is the Hilbert transform of the signal, is an imaginary unit; Calculate time points and time point The instantaneous phase difference at time is calculated as follows: , where is the phase difference, at the time point and time point The instantaneous phase change between The phase difference at each time point, It's at the time The instantaneous phase difference at time, It's at the time The instantaneous phase difference at time, is the phase adjustment factor, where is any integer, express is an element of the set of integers; The phase difference is converted into a phase shift index through a nonlinear mapping function, and the calculation expression is as follows: , where is the phase shift index, is the total number of time points, that is, the number of phase differences obtained at all time points, is the natural base, is the attenuation factor, is the nonlinear coefficient, is the normalized time constant.

4. A multi-mode self-positioning networked radar life detection method according to claim 2, characterized in that: Under the detection window, the specific steps of analyzing the signal spectrum spread degree and generating the spectrum spread index are as follows: Under the detection window, obtain the frequency distribution data of the signal and The amplitude at the moment is calibrated as , that is, the signal changes with time The change of the time domain signal is converted into a frequency domain signal through Fourier transform, and the calculation expression is as follows: , where is the frequency domain signal, that is, the amplitude of the signal in the frequency domain, is a complex exponential term, is an imaginary unit; In the frequency domain, the instantaneous frequency offset function is calculated by the offset of the center frequency of the spectrum. The calculation expression is as follows: , where is the instantaneous frequency offset, is the center frequency of the signal; In order to more accurately capture the characteristics of spectrum expansion, the frequency weight function is defined, and the calculation expression is as follows: , where is the frequency weight function, is a decay factor; By transforming the frequency domain signal With frequency weight function Combined with the degree of deviation of different frequencies, the expansion characteristics of the spectrum energy are obtained, and the expression is as follows: , where is the spectrum energy spread function; Spectral Energy Spread Function Integrate to get the total energy of spectrum expansion, the calculation expression is as follows: , where is the total energy of spectrum expansion, which represents the total energy in the entire frequency range. It is calculated by the spectrum energy expansion function In the frequency range Integrate within to get, and are the upper and lower limits of the integral, respectively, indicating the frequency range of our analysis; Total energy of comprehensive spectrum expansion and instantaneous frequency deviation Generate the spectrum spreading index, the calculation expression is as follows: , where is the spectrum spreading index.

5. The multi-mode self-positioning networked radar life detection method according to claim 2, characterized in that: The signal change coefficient generated by analyzing the signal dynamic change data collected in the sub-area is compared and analyzed with the preset signal change coefficient reference threshold, and the sub-area is divided. The division process is as follows: If the signal variation coefficient is greater than or equal to a preset signal variation coefficient reference threshold, the sub-region is divided into a strong signal attenuation region; If the signal variation coefficient is less than a preset signal variation coefficient reference threshold, the sub-region is divided into a normal signal acquisition region.

6. A multi-mode self-positioning networked radar life detection method according to claim 5, characterized in that: For the sub-region marked as the area with strong signal attenuation, the specific steps of resetting the detection area based on the evaluation result of the signal data change information of the sub-region are as follows: First, the initial fixed detection area is calibrated as , evaluate the signal data change information of the marked signal strong attenuation area, and use the signal change coefficient generated by the machine learning model Recalibrated to signal variation coefficient ; The recalibrated signal variation coefficient Compare with the preset signal change coefficient reference threshold. If it meets the requirement, , it indicates that the signal attenuation in this area is serious and the detection area needs to be reset; According to the comparison results, the new detection area is reset and the calculation expression is as follows: , where To reset the detection area, is the maximum value of the signal change, is the environmental complexity coefficient; Finally, within the new detection area, signal data is collected again and the signal strength and clarity are evaluated. The calculation expression is as follows: , where This is the latest test result. is the sum of the signal strengths successfully detected within the new detection area, It is The strength of an effective detection signal, is the number of valid signals detected.

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