An internet-based unmanned intelligent biological threat monitoring method

Abstract

The application provides an Internet-based unmanned intelligent biological threat monitoring method, and relates to the technical field of biological threat monitoring.The physiological signal data of a human body is collected by a radar device, including a plurality of parameters of transmitted and received signals, such as transmission time, frequency, amplitude and phase, the radar signal data is analyzed, a radar transmission signal expression and a radar receiving signal expression are generated, the intermediate frequency signal is further analyzed, the relationship between the phase change of the intermediate frequency signal and the fluctuation of the chest cavity of the human body is analyzed and determined, the ambient temperature is further collected, the thermal noise signal generated in the temperature during the analysis and detection process is analyzed and detected, the temperature noise index for reflecting the change amount of the noise power caused by the change of the temperature is generated, the intermediate frequency phase jitter index for reflecting the jitter degree of the intermediate frequency signal phase caused by the temperature is generated, and the evaluation accuracy level is output.

Description

Technical Field

[0001] This invention relates to the field of biological threat monitoring technology, specifically to an internet-based unmanned intelligent biological threat monitoring method. Background Technology

[0002] Biothreat monitoring is used to assess whether a person is under a biothreat. When a person is under a biothreat, it can lead to respiratory disturbances. When a person is threatened by viruses or bacteria, changes in body temperature cause changes in the responses of various organs. Since these organs are closely connected to the heart, this can increase the burden on the heart, resulting in respiratory disturbances as an external physical manifestation. This monitoring process can use radar signals to monitor changes in chest cavity movement and further generate the degree of respiratory disturbance. Data can be shared via the internet, and equipment can be remotely controlled or the data analyzed to achieve remote monitoring of respiratory disturbances and determine whether a person is under a viral or bacterial biothreat.

[0003] However, radar signals are affected by external environmental factors during monitoring. Among them, when the temperature changes, thermal noise is generated. Thermal noise is random noise caused by the thermal motion of electrons. When the radar signal is received, it will also receive thermal noise signals caused by temperature, thus affecting the accuracy of radar signal reception. It is difficult for staff to judge the accuracy of the analysis results based on experience.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide an Internet-based unmanned intelligent biological threat monitoring method to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] An internet-based unmanned intelligent biological threat monitoring method is used to monitor and assess whether a human body is experiencing respiratory disturbances due to biological threats. The specific steps include:

[0008] S1. Transmit radar signals to the human chest cavity and collect radar signal data, wherein the radar signal data includes the transmission time, minimum frequency, maximum frequency, sweep period, amplitude, and initial phase of the transmitted signal, as well as the reception time, amplitude, and initial phase of the received signal.

[0009] S2. Perform correlation analysis on the minimum and maximum frequencies of the transmitted signal to generate the transmitted signal bandwidth. Perform correlation analysis on the transmitted signal bandwidth and radar signal data to generate the transmitted signal frequency expression.

[0010] S3. Analyze the frequency expression of the transmitted signal to generate the radar transmitted signal expression. Perform correlation analysis on the radar transmitted signal expression, the transmission time of the transmitted signal, and the reception time of the received signal to generate the radar received signal expression.

[0011] S4. After the transmitted and received signals are mixed, they are processed by a low-pass processor to generate an intermediate frequency (IF) signal. Correlation analysis is performed on the radar received signal expression and the radar transmitted signal expression to generate the phase of the IF signal. Analysis of the phase of the IF signal generates the chest cavity fluctuation change d′(t), which reflects the degree of change of the human chest cavity over time. This represents the phase change of the intermediate frequency signal;

[0012] S5. Collect the ambient temperature during measurement, perform correlation analysis between ambient temperature and transmitted signal bandwidth, generate temperature noise index, and further combine radar transmitted signal power analysis to generate intermediate frequency phase jitter index ΔΦ(t). Intermediate frequency phase jitter index ΔΦ(t) is used to reflect the degree of phase jitter of intermediate frequency signal caused by ambient temperature.

[0013] S6. Perform correlation analysis on the mid-frequency phase jitter index ΔΦ(t) and the chest cavity fluctuation change d′(t) to generate the measurement assessment accuracy index CPZ. The measurement assessment accuracy index CPZ is used to reflect the accuracy of the chest cavity fluctuation change measured by radar signal. A threshold is set, and the measurement assessment accuracy index CPZ is compared with the accuracy threshold θ to output the accuracy level. The accuracy level is used to output whether the chest cavity fluctuation change is accurate. If it is accurate, the chest cavity fluctuation change is used for biological threat assessment. If it is inaccurate, step 1 is repeated.

[0014] Furthermore, in S1, a continuous wave radar signal is transmitted through a radar transmitting device and the continuous wave radar signal is acquired through a radar receiving device.

[0015] Furthermore, for the minimum frequency f of the transmitted signal min Maximum frequency f max Correlation analysis is performed to generate the transmitted signal bandwidth B, based on the following formula:

[0016] B = f max -f min

[0017] Correlation analysis is performed on the transmitted signal bandwidth and radar signal data to generate the transmitted signal frequency expression f.(t) :

[0018]

[0019] Among them, T s The frequency sweep period is expressed as f, which represents the frequency of the transmitted signal. (t) It is used to reflect the relationship between the frequency of the transmitted signal and time. t is the time variable of the transmitted radar signal, with the starting point of the transmitted radar signal as the zero point.

[0020] Furthermore, regarding the expression for the transmitted signal frequency f... (t) The analysis is performed to generate the radar transmission signal expression x. f (t), based on the following formula:

[0021]

[0022] Where A is the amplitude of the transmitted signal. Given the initial phase of the transmitted signal, the radar transmitted signal expression is x. f (t) is used to reflect the relationship between the signal strength of the transmitted signal and time.

[0023] Furthermore, regarding the radar transmitted signal expression x f (t) and the transmission time t1 of the transmitted signal, the reception time t2 of the received signal, the amplitude A2 of the received signal, and the initial phase of the received signal. Perform correlation analysis to generate the radar received signal expression x. j (t), based on the following formula:

[0024]

[0025] Radar received signal expression x j (t) is used to reflect the relationship between the signal strength of the received signal and time, Δt=t2-t1.

[0026] Furthermore, regarding the radar received signal expression x j (t), Radar transmission signal expression x f (t) Perform correlation analysis to generate the intermediate frequency signal x. i (t), based on the following formula:

[0027]

[0028] Where s is an imaginary number, and f3 is the frequency of the intermediate frequency signal. Let A1 be the phase of the intermediate frequency (IF) signal, and A2 be the amplitude of the IF signal. Combine this with the radar received signal expression x. j (t), Radar transmission signal expression x f(t), intermediate frequency signal x i (t), the phase of the intermediate frequency signal is output through MATLAB software. Where D is the distance between the human body and the radar, d(t) is the displacement of the human chest cavity over time, and c is the speed of light.

[0029] right Taking the derivative, we get... The formula used to generate the change in chest cavity volume d′(t) is as follows: Among them, the change in chest cavity volume d′(t) is used to reflect the degree of change in the human chest cavity over time. This represents the phase change of the intermediate frequency signal.

[0030] Furthermore, ambient temperature T(t) is collected, and a correlation analysis is performed between ambient temperature T(t) and transmitted signal bandwidth B to generate the temperature noise index N(t), based on the following formula:

[0031] N(t) = k[T(t) + 273.15] * B

[0032] Where k is the Boltzmann constant, the ambient temperature T(t) specifically refers to the ambient temperature that changes over time during radar detection, and the temperature noise index N(t) is used to reflect the change in noise power caused by changes in ambient temperature.

[0033] Correlation analysis was performed on the temperature noise index N(t) to generate the mid-frequency phase jitter index ΔΦ, based on the following formula:

[0034]

[0035] Where P1(t) is the signal power of the radar transmitted signal as it changes over time during radar detection, and the intermediate frequency phase jitter index ΔΦ(t) is used to reflect the degree of phase jitter of the intermediate frequency signal caused by ambient temperature.

[0036] Furthermore, a correlation analysis was performed on the mid-frequency phase jitter index ΔΦ(t) and the change in chest cavity fluctuation d′(t) to generate the measurement and assessment accuracy index CPZ, based on the following formula:

[0037]

[0038] in, The phase change of the intermediate frequency signal The maximum value of t4 is the measurement accuracy index CPZ, which is used to reflect the accuracy of the changes in chest cavity fluctuations measured by radar signals. t4 is the measurement end time.

[0039] The accuracy threshold θ is set to 1.39. The measurement evaluation accuracy index CPZ is compared with the accuracy threshold θ. When CPZ>θ, the accuracy level of the chest cavity fluctuation change measured by the output radar signal is level two, and the chest cavity fluctuation change measured by the radar signal is inaccurate. When CPZ≤θ, the accuracy level of the chest cavity fluctuation change measured by the output radar signal is level one, and the chest cavity fluctuation change measured by the radar signal is accurate.

[0040] Compared with the prior art, the beneficial effects of the present invention are:

[0041] This invention collects physiological signal data of the human body using radar equipment, including various parameters of transmitted and received signals, such as transmission time, frequency, amplitude, and phase. It analyzes the radar signal data to generate expressions for both transmitted and received radar signals. Furthermore, it analyzes the intermediate frequency (IF) signal, determining the relationship between the phase change of the IF signal and the rise and fall of the human chest cavity during respiration. It also collects ambient temperature data, analyzes the thermal noise signal generated during the detection process, and generates a temperature noise index to reflect the change in noise power caused by temperature variations. Additionally, it generates an IF phase jitter index to reflect the degree of phase jitter in the IF signal caused by temperature. Finally, it performs correlation analysis between the IF phase jitter index and the change in chest cavity rise and fall, generating a measurement accuracy index to reflect the accuracy of the radar signal measurement of chest cavity rise and fall changes, and outputs the accuracy level of the assessment. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the overall method flow of the present invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0044] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0045] Example:

[0046] Please see Figure 1 The present invention provides a technical solution:

[0047] An internet-based unmanned intelligent biological threat monitoring method is used to monitor and assess whether a person's breathing is disturbed due to a biological threat. When a person is threatened by viruses or bacteria, changes in body temperature cause changes in the responses of various organs. Since these organs are closely connected to the heart, this increases the burden on the heart, resulting in respiratory disturbances. Monitoring respiratory disturbances helps determine whether a person is threatened by viruses or bacteria. The radar device of this invention collects data and shares it via the internet, allowing for remote data reception. The more rapid the breathing rate, the higher the degree of the person's stress response. Specific steps include:

[0048] Step 1: Transmit radar signals to the human chest cavity and collect radar signal data. The radar signal data includes the transmission time, minimum frequency, maximum frequency, sweep period, amplitude, and initial phase of the transmitted signal, as well as the reception time, amplitude, and initial phase of the received signal. Transmit continuous wave radar signals through radar transmitting equipment and collect continuous wave radar signals through radar receiving equipment.

[0049] The process involves transmitting and receiving radar signals, collecting radar signal data, and then analyzing the expressions for transmitting and receiving signals.

[0050] Step 2: Perform correlation analysis on the minimum and maximum frequencies of the transmitted signal to generate the transmitted signal bandwidth. Perform correlation analysis on the transmitted signal bandwidth and radar signal data to generate the transmitted signal frequency expression.

[0051] The minimum frequency f of the transmitted signal min Maximum frequency f max Correlation analysis is performed to generate the transmitted signal bandwidth B, based on the following formula:

[0052] B = f max -f min

[0053] Correlation analysis is performed on the transmitted signal bandwidth and radar signal data to generate the transmitted signal frequency expression f. (t) :

[0054]

[0055] Among them, T s The frequency sweep period is expressed as f, which represents the frequency of the transmitted signal. (t)It is used to reflect the relationship between the frequency of the transmitted signal and time. t is the time variable of the transmitted radar signal, with the starting point of the transmitted radar signal as the zero point.

[0056] Step 3: In order to further analyze the transmitted and received signals, it is necessary to clarify the parameters of their expressions. Therefore, the frequency expression of the transmitted signal is analyzed, frequency domain transformation is performed, and the radar transmitted signal expression is generated. Correlation analysis is performed on the radar transmitted signal expression, the transmission time of the transmitted signal, and the reception time of the received signal to generate the radar received signal expression.

[0057] To simplify calculations, a frequency domain transformation is performed on the transmitted signal frequency expression f. (t) The analysis is performed to generate the radar transmission signal expression x. f (t), based on the following formula:

[0058]

[0059] Where A is the amplitude of the transmitted signal. Given the initial phase of the transmitted signal, the radar transmitted signal expression is x. f (t) is used to reflect the relationship between the signal strength of the transmitted signal and time.

[0060] The radar transmitted signal expression x f The radar received signal is generated by analyzing (t) and the time difference, amplitude, and phase of the received signal, and by analyzing the radar transmitted signal expression x. f (t) and the transmission time t1 of the transmitted signal, the reception time t2 of the received signal, the amplitude A2 of the received signal, and the initial phase of the received signal. Perform correlation analysis to generate the radar received signal expression x. j (t), based on the following formula:

[0061]

[0062] Radar received signal expression x j (t) is used to reflect the relationship between the signal strength of the received signal and time, Δt=t2-t1.

[0063] Step 4: To determine the relationship between the change in chest cavity volume and the radar transmitted and received signals, the intermediate frequency (IF) signal after mixing the transmitted and received signals is first analyzed. After mixing, the IF signal is processed by a low-pass processor to generate an IF signal. Correlation analysis is performed on the radar received signal expression and the radar transmitted signal expression to generate the phase of the IF signal. Analysis of the phase of the IF signal generates the change in chest cavity volume d′(t). The change in chest cavity volume d′(t) reflects the degree of change in the human chest cavity over time. This represents the phase change of the intermediate frequency signal;

[0064] For the radar received signal expression x j (t), Radar transmission signal expression x f (t) Perform correlation analysis to generate the intermediate frequency signal x. i (t), based on the following formula:

[0065]

[0066] Where s is an imaginary number, and f3 is the frequency of the intermediate frequency signal. Let A1 be the phase of the intermediate frequency (IF) signal, and A2 be the amplitude of the IF signal. Combine this with the radar received signal expression x. j (t), Radar transmission signal expression x f (t), intermediate frequency signal x i (t), the phase of the intermediate frequency signal is output through MATLAB software. Where D is the distance between the human body and the radar, d(t) is the displacement of the human chest cavity over time, and c is the speed of light.

[0067] right Taking the derivative, we get... The formula used to generate the change in chest cavity volume d′(t) is as follows: Among them, the change in chest cavity volume d′(t) is used to reflect the degree of change in the human chest cavity over time. The phase change of the intermediate frequency signal is proportional to the change in chest cavity fluctuation. Once the numerical relationship is determined, the change in chest cavity fluctuation can be obtained by analyzing the phase change of the intermediate frequency signal.

[0068] Step 5: Collect the ambient temperature during measurement, perform correlation analysis between ambient temperature and transmitted signal bandwidth to generate temperature noise index, and further combine it with radar transmitted signal power analysis to generate intermediate frequency phase jitter index ΔΦ(t). Intermediate frequency phase jitter index ΔΦ(t) is used to reflect the degree of phase jitter of intermediate frequency signal caused by ambient temperature.

[0069] Because ambient temperature interferes with signal strength during radar transmission and reception, generating noise and affecting measurement results, the ambient temperature T(t) is collected to analyze its impact on the measurement. A correlation analysis is then performed between the ambient temperature T(t) and the transmitted signal bandwidth B to generate the temperature noise index N(t), based on the following formula:

[0070] N(t) = k[T(t) + 273.15] * B

[0071] The degree of temperature noise is proportional to the temperature and the bandwidth of the transmitted signal. Therefore, the above formula is used for calculation, where k is the Boltzmann constant, the ambient temperature T(t) is the ambient temperature that changes with time during radar detection, and the temperature noise index N(t) is used to reflect the amount of noise power change caused by changes in ambient temperature.

[0072] Correlation analysis was performed on the temperature noise index N(t) to generate the mid-frequency phase jitter index ΔΦ(t), based on the following formula:

[0073]

[0074] The phase jitter of the intermediate frequency signal is proportional to the temperature noise. At the same time, the greater the signal power, the smaller the phase jitter will be, because the strong signal can better mask the noise. Here, P1(t) is the signal power of the radar transmitted signal as a function of time during radar detection, and the intermediate frequency phase jitter index ΔΦ(t) is used to reflect the degree of phase jitter of the intermediate frequency signal caused by the ambient temperature.

[0075] Step 6: Perform correlation analysis on the intermediate frequency phase jitter index ΔΦ(t) and the chest cavity fluctuation change d′(t) to generate the measurement assessment accuracy index CPZ. The measurement assessment accuracy index CPZ is used to reflect the accuracy of the chest cavity fluctuation change measured by the radar signal. A threshold is set, and the measurement assessment accuracy index CPZ is compared with the accuracy threshold θ to output the accuracy level. The accuracy level is used to output whether the chest cavity fluctuation change is accurate. If it is accurate, the chest cavity fluctuation change is used for biological threat assessment. If it is inaccurate, step 1 is repeated.

[0076] A correlation analysis was performed on the mid-frequency phase jitter index ΔΦ(t) and the change in chest cavity fluctuation d′(t) to generate the measurement accuracy index CPZ, based on the following formula:

[0077]

[0078] in, The phase change of the intermediate frequency signal The maximum value of t4 is the measurement end time. The measurement accuracy index CPZ is used to reflect the accuracy of the measurement of chest cavity fluctuation changes by radar signal. The smaller the value of the measurement accuracy index CPZ, the smaller the influence of temperature on the amount of chest cavity fluctuation changes, and the higher the measurement accuracy.

[0079] The accuracy threshold θ is set to 1.39. The measurement evaluation accuracy index CPZ is compared with the accuracy threshold θ. When CPZ>θ, the accuracy level of the chest cavity fluctuation change measured by the output radar signal is level two, and the chest cavity fluctuation change measured by the radar signal is inaccurate. When CPZ≤θ, the accuracy level of the chest cavity fluctuation change measured by the output radar signal is level one, and the chest cavity fluctuation change measured by the radar signal is accurate.

[0080] The present invention also provides a module for performing an Internet-based unmanned intelligent biological threat monitoring method, comprising:

[0081] Radar signal data acquisition module, used to acquire radar signal data;

[0082] The transmit signal frequency analysis module is used to perform correlation analysis on the minimum and maximum frequencies of the transmitted signal, generate the transmit signal bandwidth, perform correlation analysis on the transmit signal bandwidth and radar signal data, and generate the transmit signal frequency expression.

[0083] The signal analysis module is used to analyze the frequency expression of the transmitted signal, generate the radar transmitted signal expression, perform correlation analysis on the radar transmitted signal expression, the transmission time of the transmitted signal, and the reception time of the received signal, and generate the radar received signal expression.

[0084] The intermediate frequency signal analysis module is used to mix the transmitted and received signals, process them through a low-pass processor, generate an intermediate frequency signal, perform correlation analysis on the radar received signal expression and the radar transmitted signal expression, generate the phase of the intermediate frequency signal, analyze the phase of the intermediate frequency signal, and generate the chest cavity fluctuation change.

[0085] The temperature analysis module is used to collect the ambient temperature during measurement, perform correlation analysis between ambient temperature and transmitted signal bandwidth, generate temperature noise index, and further combine radar transmitted signal power analysis to generate intermediate frequency phase jitter index;

[0086] The comprehensive analysis module is used to perform correlation analysis on the mid-frequency phase jitter index ΔΦ(t) and the chest cavity fluctuation change d′(t), and generate the measurement evaluation accuracy index CPZ. The measurement evaluation accuracy index CPZ is used to reflect the accuracy of the chest cavity fluctuation change measured by the radar signal. A threshold is set, and the measurement evaluation accuracy index CPZ is compared with the accuracy threshold θ to output the accuracy level.

[0087] The radar signal data collected by this invention can be processed to generate the amount of change in the human chest cavity. By identifying disease-related text on the Internet (such as pathogen names and incidence rates), the degree of threat to the human body can be analyzed. Geographic information can be automatically extracted. This information can be used to link multiple reports together. The system's dictionary can be continuously expanded, initially including Chinese and English.

[0088] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0089] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0090] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0091] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. An internet-based unmanned intelligent biological threat monitoring method for monitoring and assessing whether a human body is experiencing respiratory disturbances due to biological threats, characterized in that... The specific steps include: S1. Transmit radar signals to the human chest cavity and collect radar signal data, wherein the radar signal data includes the transmission time, minimum frequency, maximum frequency, sweep period, amplitude, and initial phase of the transmitted signal, as well as the reception time, amplitude, and initial phase of the received signal. S2. Perform correlation analysis on the minimum and maximum frequencies of the transmitted signal to generate the transmitted signal bandwidth. Perform correlation analysis on the transmitted signal bandwidth and radar signal data to generate the transmitted signal frequency expression. S3. Analyze the frequency expression of the transmitted signal to generate the radar transmitted signal expression. Perform correlation analysis on the radar transmitted signal expression, the transmission time of the transmitted signal, and the reception time of the received signal to generate the radar received signal expression. S4. After mixing the transmitted and received signals, the signals are processed by a low-pass processor to generate an intermediate frequency (IF) signal. Correlation analysis is performed on the radar received signal expression and the radar transmitted signal expression to generate the phase of the IF signal. The phase of the IF signal is then analyzed to generate the chest cavity fluctuation change d′(t). The chest cavity fluctuation change d′(t) is used to reflect the degree of change of the human chest cavity over time. This represents the phase change of the intermediate frequency signal; S5. Collect the ambient temperature during measurement, perform correlation analysis between ambient temperature and transmitted signal bandwidth, generate temperature noise index, and further combine radar transmitted signal power analysis to generate intermediate frequency phase jitter index ΔΦ(t). Intermediate frequency phase jitter index ΔΦ(t) is used to reflect the degree of phase jitter of intermediate frequency signal caused by ambient temperature. S6. Perform correlation analysis on the mid-frequency phase jitter index ΔΦ(t) and the chest cavity fluctuation change d′(t) to generate the measurement assessment accuracy index CPZ. The measurement assessment accuracy index CPZ is used to reflect the accuracy of the chest cavity fluctuation change measured by radar signal. A threshold is set, and the measurement assessment accuracy index CPZ is compared with the accuracy threshold θ to output the accuracy level. The accuracy level is used to output whether the chest cavity fluctuation change is accurate. If it is accurate, the chest cavity fluctuation change is used for biological threat assessment. If it is inaccurate, step 1 is repeated.

2. The Internet-based unmanned intelligent biological threat monitoring method according to claim 1, characterized in that: In step S1, a continuous wave radar signal is transmitted through a radar transmitting device and received through a radar receiving device.

3. The Internet-based unmanned intelligent biological threat monitoring method according to claim 2, characterized in that: The minimum frequency f of the transmitted signal min Maximum frequency f max Correlation analysis is performed to generate the transmitted signal bandwidth B, based on the following formula: B=f max -f min Correlation analysis is performed on the transmitted signal bandwidth and radar signal data to generate the transmitted signal frequency expression f. (t) : Among them, T s The frequency sweep period is expressed as f, which represents the frequency of the transmitted signal. (t) It is used to reflect the relationship between the frequency of the transmitted signal and time. t is the time variable of the transmitted radar signal, with the starting point of the transmitted radar signal as the zero point.

4. The Internet-based unmanned intelligent biological threat monitoring method according to claim 3, characterized in that: For the expression of the transmitted signal frequency f (t) The analysis is performed to generate the radar transmission signal expression x. f (t), based on the following formula: Where A is the amplitude of the transmitted signal. Given the initial phase of the transmitted signal, the radar transmitted signal expression is x. f (t) is used to reflect the relationship between the signal strength of the transmitted signal and time.

5. The Internet-based unmanned intelligent biological threat monitoring method according to claim 4, characterized in that: The expression for the radar transmitted signal x f (t) and the transmission time t1 of the transmitted signal, the reception time t2 of the received signal, the amplitude A2 of the received signal, and the initial phase of the received signal. Perform correlation analysis to generate the radar received signal expression x. j (t), based on the following formula: Radar received signal expression x j (t) is used to reflect the relationship between the signal strength of the received signal and time, Δt=t2-t1.

6. The Internet-based unmanned intelligent biological threat monitoring method according to claim 5, characterized in that: For the radar received signal expression x j (t), Radar transmission signal expression x f (t) Perform correlation analysis to generate the intermediate frequency signal x. i (t), based on the following formula: Where s is an imaginary number, and f3 is the frequency of the intermediate frequency signal. Let A1 be the phase of the intermediate frequency (IF) signal, and A2 be the amplitude of the IF signal. Combine this with the radar received signal expression x. j (t), Radar transmission signal expression x f (t), intermediate frequency signal x i (t), the phase of the intermediate frequency signal is output through MATLAB software. Where D is the distance between the human body and the radar, d(t) is the displacement of the human chest cavity over time, and c is the speed of light. right Taking the derivative, we get... The formula used to generate the change in chest cavity volume d′(t) is as follows: Among them, the change in chest cavity volume d′(t) is used to reflect the degree of change in the human chest cavity over time. This represents the phase change of the intermediate frequency signal.

7. The Internet-based unmanned intelligent biological threat monitoring method according to claim 6, characterized in that: The ambient temperature T(t) is collected, and a correlation analysis is performed between the ambient temperature T(t) and the transmitted signal bandwidth B to generate the temperature noise index N(t). The formula used is as follows: N(t) = k[T(t) + 273.15] * B Where k is the Boltzmann constant, the ambient temperature T(t) specifically refers to the ambient temperature that changes over time during radar detection, and the temperature noise index N(t) is used to reflect the change in noise power caused by changes in ambient temperature. Correlation analysis was performed on the temperature noise index N(t) to generate the mid-frequency phase jitter index ΔΦ, based on the following formula: Where P1(t) is the signal power of the radar transmitted signal as it changes over time during radar detection, and the intermediate frequency phase jitter index ΔΦ(t) is used to reflect the degree of phase jitter of the intermediate frequency signal caused by ambient temperature.

8. The Internet-based unmanned intelligent biological threat monitoring method according to claim 7, characterized in that: A correlation analysis was performed on the mid-frequency phase jitter index ΔΦ(t) and the change in chest cavity fluctuation d′(t) to generate the measurement accuracy index CPZ, based on the following formula: in, The phase change of the intermediate frequency signal The maximum value of t4 is the measurement accuracy index CPZ, which is used to reflect the accuracy of the changes in chest cavity fluctuations measured by radar signals. t4 is the measurement end time. The accuracy threshold θ is set to 1.

39. The measurement evaluation accuracy index CPZ is compared with the accuracy threshold θ. When CPZ>θ, the accuracy level of the chest cavity fluctuation change measured by the output radar signal is level two, and the chest cavity fluctuation change measured by the radar signal is inaccurate. When CPZ≤θ, the accuracy level of the chest cavity fluctuation change measured by the output radar signal is level one, and the chest cavity fluctuation change measured by the radar signal is accurate.

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