Firefighter firefighting protective clothing data collection method and system based on biosensor

Abstract

The present invention belongs to the field of fire safety technology. The present invention discloses a data collection method and system for firefighter firefighting protective clothing based on biosensing, including: collecting fire environment data and personnel physiological data, performing sensor enhancement on the personnel physiological data to obtain enhanced physiological data; performing data decoupling on the enhanced physiological data to obtain physiological clear data; performing environmental temperature calibration on the physiological clear data based on the fire environment data to obtain physiological calibration data, performing micro-motion compensation on the physiological calibration data to obtain physiological compensation data; performing physiological evaluation on the physiological compensation data based on a constructed physiological evaluation model to obtain a health evaluation label of the firefighter, and making corresponding safety warnings to the firefighter based on the health label; significantly improving the intelligence level and safety performance of the firefighter protective clothing.

Description

Technical Field

[0001] The present invention relates to the field of fire safety technology, and more particularly to a method and system for collecting data on firefighters' firefighting protective clothing based on biosensing. Background Art

[0002] When performing firefighting tasks, firefighters often need to enter extreme fire scenes. These environments are full of multiple dangerous factors such as high temperature, thick smoke and toxic gases. Prolonged exposure to such environments poses a great threat to the life safety of firefighters. Ensuring the physical health of firefighters and timely understanding the changes in their vital signs are crucial to ensuring the safety of firefighters. For this reason, biological data collection systems have emerged and become an important means of real-time monitoring of the physiological status of firefighters.

[0003] However, during firefighting, firefighters often need to enter extremely hot environments. High temperatures not only pose a threat to the human body, but also pose challenges to the sensor systems in protective clothing. In particular, sensors based on traditional technologies are prone to quantum tunneling in high-temperature environments. Quantum tunneling refers to the quantum mechanical ability of particles (such as electrons) to pass through energy barriers that they normally cannot pass through. This phenomenon can cause distortion or increased noise in sensor signals, thereby affecting data accuracy. In extreme environments, firefighters' physiological states are often in drastic changes. Changes in multiple physiological parameters such as heart rate, body temperature, and respiratory rate generate complex chaotic signals. These signals are coupled and difficult to separate and process. Traditional biodata acquisition systems are unable to effectively separate and extract individual physiological signals, resulting in inaccurate monitoring results, which in turn affects the assessment of the firefighter's physical condition. Firefighters often face extremely high temperatures and intense exercise in fires, which causes tiny relative motions between the sensor and the skin, generating micro-currents. These micro-currents can cause signal distortion, affecting the accuracy and reliability of the data acquisition system.

[0004] In view of this, the present invention proposes a firefighter firefighting protective clothing data collection method and system based on biosensing to solve the above problems. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned objectives, the present invention provides the following technical solution: a method for collecting data of firefighters' firefighting protective clothing based on biosensing, comprising:

[0006] S1. Collect fire environment data and personnel physiological data, perform sensor enhancement on the personnel physiological data, and obtain enhanced physiological data;

[0007] S2. Decouple the enhanced physiological data to obtain clear physiological data;

[0008] S3. Performing environmental temperature calibration on the physiological clarity data based on the fire environment data to obtain physiological calibration data, and performing micro-motion compensation on the physiological calibration data to obtain physiological compensation data;

[0009] S4. Perform physiological evaluation on the physiological compensation data based on the constructed physiological evaluation model to obtain the health evaluation label of the firefighter, and issue corresponding safety warnings to the firefighter based on the health evaluation label;

[0010] Furthermore, the firefighting environment data includes: ambient temperature; the personnel physiological data includes: respiratory rate, body temperature data, heart rate data and blood oxygen data.

[0011] Furthermore, the method of enhancing the sensing of the physiological data of the personnel includes:

[0012] Each type of data in the physiological data is taken as the data to be enhanced, and the normalization algorithm is used to normalize the data to be enhanced to obtain the normalized data to be enhanced; a low-pass filter is preset, and low-frequency denoising is performed on the normalized data to be enhanced based on the low-pass filter to obtain denoised data; an initial enhancement scale factor is preset, and an enhancement transformation function is preset. The formula of the enhancement transformation function is:

[0013] ;in, Represents the denoised data in The enhanced signal at all times, represents pi, Represents the denoised data in The value of the moment, represents the initial enhancement scale factor, represents the imaginary unit, Represents the time scale, represents infinity; for the enhanced signal under the initial enhancement scale factor, the signal amplitude and signal phase of the enhanced signal are statistically analyzed, and an enhancement evaluation function is constructed based on the signal amplitude and signal phase. The formula of the enhancement evaluation function is:

[0014] ;in, represents the enhanced evaluation value, represents the signal amplitude, represents the imaginary unit, represents the signal phase, represents the denoised signal amplitude;

[0015] The enhancement evaluation value under the initial enhancement scale factor is recorded as the initial evaluation, the enhancement disturbance factor is preset, the disturbance multiple is selected by a random selection algorithm, the initial disturbance factor is disturbed based on the disturbance multiple, and the product of the disturbance multiple and the enhancement disturbance factor is added to the initial enhancement scale factor to obtain the disturbance enhancement factor; the denoised data is enhanced by the disturbance enhancement factor, and the enhancement evaluation value corresponding to the disturbance enhancement factor is obtained based on the enhancement evaluation formula. When the enhancement evaluation value corresponding to the disturbance enhancement factor is greater than the initial evaluation, the disturbance enhancement factor is used as the new initial enhancement scale factor, and the enhancement evaluation value corresponding to the disturbance enhancement factor is used as the new initial evaluation. Repeat until the value of the initial evaluation no longer changes, and output the initial enhancement scale factor at this time as the optimal enhancement scale; substitute the optimal enhancement scale into the enhancement transformation function to perform enhancement transformation on the denoised data to obtain the optimal enhancement signal. All optimal enhancement signals constitute enhanced physiological data.

[0016] Furthermore, the method of decoupling the enhanced physiological data includes:

[0017] The spline analysis method is used to perform functional analysis on each type of data in the enhanced physiological data to obtain the signal function of each type of data. A standard coordinate system is preset, and different signal functions are plotted in the standard coordinate system. The common intersection of all signal functions is the coupling point. The signal function is cut with the coupling point as the cutting point to obtain the function segment. The starting point and end point of each function segment are both coupling points; for each function segment, the different signal functions in the function segment are coupled and simulated to obtain the coupling operator; for the type A signal function, the coupling operator of other types of signal functions to the type A signal function is calculated, and a coupling influence set is formed. Based on the coupling influence set, the coupling point at the end of each function segment is decoupled to obtain the decoupling value; for the coupling point of each type of data, the value at the coupling point is replaced by the decoupling value to obtain the complete data of each type of data. All complete data constitute the clear physiological data.

[0018] Furthermore, the formula for coupling simulation of different signal functions in the function segment is: ;in, Representative Class signal function for the Coupling operators of signal-like functions, represents the coupling parameter, Represents the time scale, Represents the time span of the function segment, Representative Signal-like functions in The value of the moment, Represents the d-type signal function in The value of the moment;

[0019] The formula for signal decoupling at the coupling point at the end of each function segment is:

[0020] ;in, Representative Decoupled values ​​of signal-like functions, represents the signal conversion function, represents the size of the coupling influence set, Representative The coupled influence set of signal-like functions, Representative A coupling operator, Represents the value of the coupling point.

[0021] Furthermore, the method of performing environmental temperature calibration on physiological clarity data includes:

[0022] Based on the constructed temperature calibration model, physiological clarity data and fire environment data are used as inputs of the temperature calibration model for calibration to obtain physiological calibration data.

[0023] Furthermore, the method of performing micro-motion compensation on the physiological calibration data includes:

[0024] An energy level radius is preset, and a circle is drawn with the data collection point of each type of data in the physiological calibration data as the center and the energy level radius as the radius of the circle to obtain the energy level area. The energy level area is used as the data energy point, and an initial energy evaluation is performed on the data energy point to obtain the initial energy level; a vibration sensor is preset to collect vibration data in the energy level area, and the vibration data is aligned with the physiological calibration data in time series to obtain synchronized vibration data; based on the synchronized vibration data, the initial energy level of each type of data in the physiological calibration data is evolved to obtain the time series energy level; based on the time series energy level, compensation calculation is performed on each type of data in the physiological calibration data to obtain the micro-motion compensation factor; based on the micro-motion compensation factor, micro-motion compensation is eliminated for the physiological calibration data, and the value of the physiological calibration data at each moment is subtracted from the corresponding micro-motion compensation factor to obtain the physiological compensation data.

[0025] Furthermore, the formula for performing energy level evolution on the initial energy level of each type of data in the physiological calibration data is: ;in, Representative Physiological calibration data in The temporal energy level of the moment, Representative Initial energy levels of physiological calibration data, represents the attenuation coefficient, Represents the synchronous vibration data in The value of the moment, Representative Physiological calibration data in The value of the moment, Represents the frequency of the synchronized vibration data, Represents the time scale, Represents the phase of synchronous vibration data;

[0026] The formula for performing compensation calculation on each type of data in the physiological calibration data is:

[0027] ;in Representative Physiological calibration data in Moment micro-motion compensation factor, represents the integral weight, represents the differential weight, Represents the time derivative of the temporal energy level.

[0028] Furthermore, the physiological assessment model is constructed in the following manner:

[0029] The linear regression model is used as the initial model of the physiological assessment model, and E groups of training data are collected in advance. The training data includes historical physiological data and historical health labels. The training data is used as the training sample set, and the linear regression model is trained using the training sample set. The historical physiological data and historical health labels are used as the input data of the physiological assessment model, and the predicted health assessment labels are used as the output data of the physiological assessment model; minimizing the error between the actual historical health labels and the health assessment labels predicted by the physiological assessment model is used as the training goal, and the recall rate function is used as the loss function of the physiological assessment model. When the loss function reaches convergence, the training is stopped to obtain the physiological assessment model.

[0030] The firefighter firefighting protective clothing data acquisition system based on biosensing includes:

[0031] Data acquisition and processing module: collects fire environment data and personnel physiological data, performs sensor enhancement on personnel physiological data, and obtains enhanced physiological data;

[0032] Coupling and separation module: decouples enhanced physiological data to obtain clear physiological data;

[0033] Interference correction module: including a temperature calibration unit and a micro-motion compensation unit. The temperature calibration unit performs environmental temperature calibration on the physiological clarity data based on the fire environment data to obtain physiological calibration data. The micro-motion compensation unit performs micro-motion compensation on the physiological calibration data to obtain physiological compensation data.

[0034] Health assessment module: Perform physiological assessment on physiological compensation data based on the constructed physiological assessment model, obtain the health assessment label of the firefighter, and make corresponding safety warnings to the firefighter based on the health assessment label.

[0035] The technical effects and advantages of the biosensor-based firefighting protective clothing data collection method and system of the present invention are as follows:

[0036] By enhancing the sensory perception of personnel physiological data, the present invention can effectively overcome the signal attenuation problem caused by the obstruction of multi-layer protective clothing, further improve the strength and stability of sensor signals, thereby enhancing the data's anti-interference ability, reducing sensor errors and signal interference, and ensuring the accuracy and reliability of data acquisition. Data decoupling of the enhanced physiological data can effectively separate the independent change trends of each type of physiological signal from the complex mixed signal, minimizing interference between different types of physiological signals and improving the independence and clarity of the data. A temperature calibration model is used to calibrate the clear physiological data to ambient temperature in real time, eliminating interference from ambient temperature changes on the physiological data and making the evaluation results more accurate. Micro-motion compensation of the physiological calibration data effectively reduces signal distortion caused by tiny displacements and vibrations between the skin and the sensor, ensuring accurate collection and real-time feedback of physiological data and avoiding the impact of micro-motion on the signal. Through the physiological assessment model, the system can monitor the physiological status of firefighters in real time and provide timely safety warnings, reducing the risk of accidents caused by health problems and providing scientific decision-making support for firefighters' safety protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a schematic diagram of the data collection method for firefighters' firefighting protective clothing based on biosensing of the present invention;

[0038] Figure 2 This is a schematic diagram of the biosensor-based firefighting protective clothing data acquisition system for firefighters of the present invention. DETAILED DESCRIPTION

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0040] Example 1

[0041] See also Figure 1As shown, the method for collecting data of firefighting protective clothing for firefighters based on biosensing in this embodiment includes: S1, collecting firefighting environment data and personnel physiological data, performing sensor enhancement on the personnel physiological data, and obtaining enhanced physiological data;

[0042] S2. Decouple the enhanced physiological data to obtain clear physiological data;

[0043] S3. Performing environmental temperature calibration on the physiological clarity data based on the fire environment data to obtain physiological calibration data, and performing micro-motion compensation on the physiological calibration data to obtain physiological compensation data;

[0044] S4. Perform physiological evaluation on the physiological compensation data based on the constructed physiological evaluation model to obtain the health evaluation label of the firefighter, and make corresponding safety warnings to the firefighter based on the health evaluation label.

[0045] Firefighting environment data includes: ambient temperature; personnel physiological data includes: respiratory rate, body temperature data, heart rate data and blood oxygen data; ambient temperature is obtained through a temperature sensor installed on the outside of the firefighter's protective clothing; respiratory rate is obtained through an airflow sensor installed in the mouth and nose area of ​​the firefighter's protective clothing, body temperature data is obtained through a body temperature sensor, heart rate data is obtained through an optical heart rate sensor close to the firefighter's chest, and blood oxygen data is obtained through a blood oxygen sensor.

[0046] Ways to enhance sensory data of personnel include:

[0047] Each type of data in the physiological data is taken as the data to be enhanced, and the normalization algorithm is used to normalize the data to be enhanced to obtain the normalized data to be enhanced. Common normalization algorithms include the maximum and minimum normalization algorithm and the zero score normalization algorithm; a low-pass filter is preset, and low-frequency denoising is performed on the normalized data to be enhanced based on the low-pass filter to obtain denoised data; an initial enhancement scale factor is preset, and the value range of the initial enhancement scale factor is , preset enhanced transformation function, the formula of enhanced transformation function is:

[0048] ;in, Represents the denoised data in The enhanced signal at all times, represents pi, Represents the denoised data in The value of the moment, Represents the initial enhancement scale factor, which is used to control the degree of enhancement transformation. represents the imaginary unit, Represents the time scale, represents infinity; for the enhanced signal under the initial enhancement scale factor, the signal amplitude and signal phase of the enhanced signal are statistically analyzed, and an enhancement evaluation function is constructed based on the signal amplitude and signal phase. The formula of the enhancement evaluation function is: ;in, represents the enhanced evaluation value, represents the signal amplitude, represents the signal phase, represents the denoised signal amplitude (the signal amplitude of the denoised data);

[0049] Record the enhancement evaluation value under the initial enhancement scale factor as the initial evaluation, preset the enhancement disturbance factor, select the disturbance multiple using a random selection algorithm, disturb the initial disturbance factor based on the disturbance multiple, increase the product of the disturbance multiple and the enhancement disturbance factor to obtain the disturbance enhancement factor; perform enhancement transformation on the denoised data using the disturbance enhancement factor, and obtain the enhancement evaluation value corresponding to the disturbance enhancement factor based on the enhancement evaluation formula; when the enhancement evaluation value corresponding to the disturbance enhancement factor is greater than the initial evaluation, use the disturbance enhancement factor as the new initial enhancement scale factor, and use the enhancement evaluation value corresponding to the disturbance enhancement factor as the new initial evaluation, repeat until the value of the initial evaluation no longer changes, and output the initial enhancement scale factor at this time as the optimal enhancement scale; substitute the optimal enhancement scale into the enhancement transformation function to perform enhancement transformation on the denoised data to obtain the optimal enhancement signal, and all optimal enhancement signals constitute enhanced physiological data;

[0050] The sensor signal is attenuated due to the obstruction of multiple layers of protective clothing. By sensing the physiological data of the personnel, the strength of the sensor signal is restored, the robustness of the sensor signal is enhanced, the data is made more resistant to interference, and the accuracy and reliability of the data are improved.

[0051] Methods for data decoupling of augmented physiological data include:

[0052] The spline analysis method is used to perform functional analysis on each type of data in the enhanced physiological data to obtain the signal function of each type of data. A standard coordinate system is preset and different signal functions are plotted into the standard coordinate system. The common intersection of all signal functions is the coupling point. The signal function is cut using the coupling point as the cutting point to obtain function segments. The starting point and end point of each function segment are both coupling points. For each function segment, the different signal functions in the function segment are coupled and simulated. The formula for coupling simulation is: ;in, Representative Class signal function for the Coupling operators of signal-like functions, represents the coupling parameter, Represents the time scale, Represents the time span of the function segment, Representative Signal-like functions in The value of the moment, Represents the d-type signal function in The value at the moment; for the type A signal function, calculate the coupling operators of other types of signal functions on the type A signal function, and form a coupling influence set. Based on the coupling influence set, perform signal decoupling on the coupling point at the end of each function segment. The formula for signal decoupling is: ;in, Representative Decoupled values ​​of signal-like functions, Represents the signal conversion function. Common signal conversion functions include Hilbert transform function, represents the size of the coupling influence set, Representative The coupled influence set of signal-like functions, Representative A coupling operator, Represents the value of the coupling point; for each type of data at the coupling point, the value at the coupling point is replaced by the decoupling value to obtain the complete data of each type of data, and all complete data constitute physiological clear data;

[0053] By decoupling the enhanced physiological data, these complex chaotic signals can be effectively separated, making the changing trend of each type of physiological signal clearer and independent, significantly improving the accuracy and efficiency of signal processing, ensuring the reliability of monitoring data, and thus improving the accuracy of firefighters' physical condition assessment.

[0054] Methods for calibrating physiological clarity data to ambient temperature include:

[0055] Collect historical physiological data, historical calibration data, and historical fire environment data of group B, where the data type of the historical physiological data is consistent with that of the personnel physiological data, and the historical calibration data is based on data obtained by technicians in this field through corresponding experiments; based on the historical physiological data, historical calibration data, and historical fire environment data, use the historical physiological data and historical fire environment data as inputs of a temperature calibration model, use the predicted physiological calibration data as outputs of the temperature calibration model, and use the actual historical calibration data as a prediction target for the temperature calibration model to perform model training, thereby constructing a temperature calibration model, which is a ridge regression model. The target loss function of the temperature calibration model is a mean square error function, and minimizing the value of the target loss function is used as the training goal to obtain a temperature calibration model with the minimum value of the target loss function;

[0056] Based on the constructed temperature calibration model, physiological clarity data and fire environment data are used as inputs of the temperature calibration model for calibration to obtain physiological calibration data.

[0057] Methods for compensating for micro-motion in physiological calibration data include:

[0058] The energy level radius is preset, and the data collection point of each type of data in the physiological calibration data is used as the center. The energy level radius is used as the radius of the circle to draw a circle to obtain the energy level area. The energy level area is used as the data energy point, and the initial energy evaluation of the data energy point is performed. The formula for the initial energy evaluation is:

[0059] ;in, Representative Initial energy levels of physiological calibration data, represents Planck's constant, The effective mass of electrons representing the energy level region is an inherent property of the material and is obtained through the Hall effect experiment. Represents the energy level radius; a preset vibration sensor collects vibration data in the energy level area, and the vibration data is aligned with the physiological calibration data in time series to obtain synchronized vibration data; based on the synchronized vibration data, the initial energy level of each type of data in the physiological calibration data is evolved, and the formula for energy level evolution is: ;in, Representative Physiological calibration data in The temporal energy level of the moment, Representative Initial energy levels of physiological calibration data, Represents the attenuation coefficient, which is used to simulate the signal attenuation caused by micro-motion. Represents the synchronous vibration data in The value of the moment, Representative Physiological calibration data in The value of the moment, Represents the frequency of the synchronized vibration data, Represents the time scale, Represents the phase of the synchronous vibration data; based on the time series energy level, each type of data in the physiological calibration data is compensated and calculated. The compensation calculation formula is: ;in Representative Physiological calibration data in Moment micro-motion compensation factor, Represents the integral weight, which is used to balance the proportion of the integral term to the micro-motion compensation factor. Represents the differential weight, which is used to balance the proportion of the differential term to the micro-compensation factor. Represents the derivative of the time series energy level with respect to time, indicating the rate at which the time series energy level changes over time, and is used to capture rapidly changing signal components; based on the micro-motion compensation factor, the physiological calibration data is subjected to micro-motion compensation elimination, and the corresponding micro-motion compensation factor is subtracted from the value of the physiological calibration data at each moment to obtain the physiological compensation data;

[0060] By performing micro-motion compensation on physiological calibration data, signal distortion caused by micro-motion current is effectively reduced, and signal deviations caused by tiny displacements between the skin and the sensor are accurately identified and compensated, thereby improving the accuracy and stability of physiological data, quickly responding to and correcting signal errors caused by micro-motion, reducing delays and errors, and ensuring that the sensor can still stably output reliable data in extreme environments.

[0061] The physiological assessment model is constructed in the following ways:

[0062] A linear regression model is used as the initial model of the physiological assessment model. E sets of training data are collected in advance. The training data include historical physiological data and historical health labels. The training data is used as a training sample set. The linear regression model is trained using the training sample set. The historical physiological data and historical health labels are used as input data of the physiological assessment model, and the predicted health assessment labels are used as output data of the physiological assessment model. Minimizing the error between the actual historical health labels and the health assessment labels predicted by the physiological assessment model is used as the training goal. The recall function is used as the loss function of the physiological assessment model. When the loss function reaches convergence, the training is stopped to obtain the physiological assessment model.

[0063] Based on the trained physiological assessment model, physiological compensation data is physiologically assessed to obtain the firefighter's health assessment label, and corresponding safety warnings are issued to the firefighter based on the health assessment label.

[0064] This embodiment enhances the sensory perception of physiological data, effectively overcoming the signal attenuation problem caused by the obstruction of multi-layer protective clothing. It further improves the strength and stability of sensor signals, thereby enhancing the data's anti-interference capability, reducing sensor errors and signal interference, and ensuring the accuracy and reliability of data acquisition. Data decoupling of the enhanced physiological data effectively separates the independent change trends of each type of physiological signal from the complex mixed signal, minimizing interference between different types of physiological signals and improving the independence and clarity of the data. A temperature calibration model is used to calibrate the clear physiological data to ambient temperature in real time, eliminating interference from ambient temperature changes on the physiological data and making the assessment results more accurate. Micro-motion compensation of the physiological calibration data effectively reduces signal distortion caused by tiny displacements and vibrations between the skin and the sensor, ensuring accurate collection and real-time feedback of physiological data and avoiding the impact of micro-motion on the signal. Through the physiological assessment model, the system can monitor the physiological status of firefighters in real time and provide timely safety warnings, reducing the risk of accidents caused by health problems and providing scientific decision-making support for firefighter safety protection.

[0065] Example 2

[0066] See also Figure 2 As shown, for the parts not described in detail in this embodiment, please refer to the description of Example 1. The firefighter firefighting protective clothing data acquisition system based on biosensors includes:

[0067] Data acquisition and processing module: collects fire environment data and personnel physiological data, performs sensor enhancement on personnel physiological data, and obtains enhanced physiological data;

[0068] Coupling and separation module: decouples enhanced physiological data to obtain clear physiological data;

[0069] Interference correction module: including a temperature calibration unit and a micro-motion compensation unit. The temperature calibration unit performs environmental temperature calibration on the physiological clarity data based on the fire environment data to obtain physiological calibration data. The micro-motion compensation unit performs micro-motion compensation on the physiological calibration data to obtain physiological compensation data.

[0070] Health assessment module: Based on the established physiological assessment model, the physiological compensation data is physiologically assessed to obtain the firefighter's health assessment label, and corresponding safety warnings are issued to the firefighter based on the health assessment label;

[0071] The modules are connected via wired and / or wireless means to achieve data transmission between modules.

[0072] Example 3

[0073] This embodiment discloses an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the operation mode of the above-mentioned biosensor-based firefighting protective clothing data collection method for firefighters is implemented.

[0074] Since the electronic device described in this embodiment is used to implement the biosensor-based data collection method for firefighter firefighting protective clothing in the embodiments of this application, those skilled in the art will be able to understand the specific implementation and various variations of the electronic device of this embodiment based on the biosensor-based data collection method for firefighter firefighting protective clothing described in the embodiments of this application. Therefore, how the electronic device implements the method in the embodiments of this application will not be described in detail here. As long as those skilled in the art implement the electronic device used in the biosensor-based data collection method for firefighter firefighting protective clothing in the embodiments of this application, it falls within the scope of protection of this application.

[0075] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0076] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the principles of the present invention are within the scope of protection of the present invention. It should be noted that for users of ordinary skill in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A firefighter firefighting protective clothing data collection method based on biosensing, characterized in that: include: S1. Collect fire environment data and personnel physiological data, perform sensor enhancement on the personnel physiological data, and obtain enhanced physiological data; S2. Decouple the enhanced physiological data to obtain clear physiological data; S3. Performing environmental temperature calibration on the physiological clarity data based on the fire environment data to obtain physiological calibration data, and performing micro-motion compensation on the physiological calibration data to obtain physiological compensation data; S4. Perform physiological evaluation on the physiological compensation data based on the constructed physiological evaluation model to obtain the health evaluation label of the firefighter, and issue corresponding safety warnings to the firefighter based on the health evaluation label; The method of decoupling the enhanced physiological data includes: The spline analysis method is used to perform function analysis on each type of data in the enhanced physiological data to obtain the signal function of each type of data. A standard coordinate system is preset, and different signal functions are plotted in the standard coordinate system. The common intersection of all signal functions is the coupling point. The signal function is cut with the coupling point as the cutting point to obtain the function segment. The starting point and end point of each function segment are both coupling points; for each function segment, the different signal functions in the function segment are coupled and simulated to obtain the coupling operator; for the type A signal function, the coupling operator of other types of signal functions to the type A signal function is calculated, and a coupling influence set is formed. Based on the coupling influence set, the coupling point at the end of each function segment is decoupled to obtain the decoupling value; for the coupling point of each type of data, the value at the coupling point is replaced by the decoupling value to obtain the complete data of each type of data. All complete data constitute the clear physiological data; The formula for coupling simulation of different signal functions in the function segment is: ;in, Representative Class signal function for the Coupling operators of signal-like functions, represents the coupling parameter, Represents the time scale, Represents the time span of the function segment, Representative Signal-like functions in The value of the moment, Represents the d-type signal function in The value of the moment; The formula for signal decoupling at the coupling point at the end of each function segment is: ;in, Representative Decoupled values ​​of signal-like functions, represents the signal conversion function, represents the size of the coupling influence set, Representative The coupled influence set of signal-like functions, Representative A coupling operator, Represents the value of the coupling point.

2. The method for collecting data of firefighters' firefighting protective clothing based on biosensing according to claim 1 is characterized in that: The firefighting environment data includes: ambient temperature; the personnel physiological data includes: respiratory rate, body temperature data, heart rate data and blood oxygen data.

3. The method for collecting data of firefighters' firefighting protective clothing based on biosensing according to claim 2 is characterized in that: The method of enhancing the sensing of personnel physiological data includes: Each type of data in the physiological data is taken as the data to be enhanced, and the normalization algorithm is used to normalize the data to be enhanced to obtain the normalized data to be enhanced; a low-pass filter is preset, and low-frequency denoising is performed on the normalized data to be enhanced based on the low-pass filter to obtain denoised data; an initial enhancement scale factor is preset, and an enhancement transformation function is preset. The formula of the enhancement transformation function is: ;in, Represents the denoised data in The enhanced signal at all times, represents pi, Represents the denoised data in The value of the moment, represents the initial enhancement scale factor, represents the imaginary unit, Represents the time scale, represents infinity; for the enhanced signal under the initial enhancement scale factor, the signal amplitude and signal phase of the enhanced signal are statistically analyzed, and an enhancement evaluation function is constructed based on the signal amplitude and signal phase. The formula of the enhancement evaluation function is: ;in, represents the enhanced evaluation value, represents the signal amplitude, represents the signal phase, represents the denoised signal amplitude; The enhancement evaluation value under the initial enhancement scale factor is recorded as the initial evaluation, the enhancement disturbance factor is preset, the disturbance multiple is selected by a random selection algorithm, the initial disturbance factor is disturbed based on the disturbance multiple, and the product of the disturbance multiple and the enhancement disturbance factor is added to the initial enhancement scale factor to obtain the disturbance enhancement factor; the denoised data is enhanced by the disturbance enhancement factor, and the enhancement evaluation value corresponding to the disturbance enhancement factor is obtained based on the enhancement evaluation formula. When the enhancement evaluation value corresponding to the disturbance enhancement factor is greater than the initial evaluation, the disturbance enhancement factor is used as the new initial enhancement scale factor, and the enhancement evaluation value corresponding to the disturbance enhancement factor is used as the new initial evaluation. Repeat until the value of the initial evaluation no longer changes, and output the initial enhancement scale factor at this time as the optimal enhancement scale; substitute the optimal enhancement scale into the enhancement transformation function to perform enhancement transformation on the denoised data to obtain the optimal enhancement signal. All optimal enhancement signals constitute enhanced physiological data.

4. The method for collecting data of firefighters' firefighting protective clothing based on biosensing according to claim 3 is characterized in that: The method of performing environmental temperature calibration on physiological clarity data includes: Based on the constructed temperature calibration model, physiological clarity data and fire environment data are used as inputs of the temperature calibration model for calibration to obtain physiological calibration data.

5. The method for collecting data of firefighters' firefighting protective clothing based on biosensing according to claim 4 is characterized in that: The method of performing micro-motion compensation on physiological calibration data includes: An energy level radius is preset, and a circle is drawn with the data collection point of each type of data in the physiological calibration data as the center and the energy level radius as the radius of the circle to obtain the energy level area. The energy level area is used as the data energy point, and an initial energy evaluation is performed on the data energy point to obtain the initial energy level; a vibration sensor is preset to collect vibration data in the energy level area, and the vibration data is aligned with the physiological calibration data in time series to obtain synchronized vibration data; based on the synchronized vibration data, the initial energy level of each type of data in the physiological calibration data is evolved to obtain the time series energy level; based on the time series energy level, compensation calculation is performed on each type of data in the physiological calibration data to obtain the micro-motion compensation factor; based on the micro-motion compensation factor, micro-motion compensation is eliminated for the physiological calibration data, and the value of the physiological calibration data at each moment is subtracted from the corresponding micro-motion compensation factor to obtain the physiological compensation data.

6. The method for collecting data of firefighters' firefighting protective clothing based on biosensing according to claim 5 is characterized in that: The formula for energy level evolution of the initial energy level of each type of data in the physiological calibration data is: ;in, Representative Physiological calibration data in The temporal energy level of the moment, Representative Initial energy levels of physiological calibration data, represents the attenuation coefficient, Represents the synchronous vibration data in The value of the moment, Representative Physiological calibration data in The value of the moment, Represents the frequency of the synchronized vibration data, Represents the time scale, Represents the phase of synchronous vibration data; The formula for performing compensation calculation on each type of data in the physiological calibration data is: ;in Representative Physiological calibration data in Moment micro-motion compensation factor, represents the integral weight, represents the differential weight, Represents the time derivative of the temporal energy level.

7. The method for collecting data of firefighters' firefighting protective clothing based on biosensing according to claim 6 is characterized in that: The physiological assessment model is constructed in the following manner: The linear regression model is used as the initial model of the physiological assessment model, and E groups of training data are collected in advance. The training data includes historical physiological data and historical health labels. The training data is used as the training sample set, and the linear regression model is trained using the training sample set. The historical physiological data and historical health labels are used as the input data of the physiological assessment model, and the predicted health assessment labels are used as the output data of the physiological assessment model; minimizing the error between the actual historical health labels and the health assessment labels predicted by the physiological assessment model is used as the training goal, and the recall rate function is used as the loss function of the physiological assessment model. When the loss function reaches convergence, the training is stopped to obtain the physiological assessment model.

8. A firefighter firefighting protective clothing data acquisition system based on biosensing, which is used to implement the firefighter firefighting protective clothing data acquisition method based on biosensing according to any one of claims 1 to 7, characterized in that: include: Data acquisition and processing module: collects fire environment data and personnel physiological data, performs sensor enhancement on personnel physiological data, and obtains enhanced physiological data; Coupling and separation module: decouples enhanced physiological data to obtain clear physiological data; Interference correction module: including a temperature calibration unit and a micro-motion compensation unit. The temperature calibration unit performs environmental temperature calibration on the physiological clarity data based on the fire environment data to obtain physiological calibration data. The micro-motion compensation unit performs micro-motion compensation on the physiological calibration data to obtain physiological compensation data. Health assessment module: Perform physiological assessment on physiological compensation data based on the constructed physiological assessment model, obtain the health assessment label of the firefighter, and make corresponding safety warnings to the firefighter based on the health assessment label.

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