A low-temperature explosion shock pressure multi-dimensional error correction method based on medium impedance characteristics

Abstract

This invention belongs to the interdisciplinary field of explosion mechanics testing and data processing. Specifically, it relates to a multi-dimensional error correction method for cryogenic explosion impact pressure based on the impedance characteristics of the medium. It can run on computing units with data processing capabilities, such as RK3588, Jetson series embedded platforms, and industrial computers. By integrating the physical properties of the medium, ambient temperature, and sensor response characteristics, a multi-dimensional nonlinear error correction model is constructed. This effectively solves the problems of large pressure measurement errors and poor versatility caused by medium impedance mismatch, sensor low-temperature nonlinear drift, and coupling interference between temperature and medium parameters in cryogenic environments ranging from -40℃ to 0℃. By constructing a correction model based on the medium type, multi-dimensional and accurate correction of the original pressure data is achieved, providing highly reliable pressure data support for cryogenic explosion impact testing.

Description

Technical Field

[0001] This invention belongs to the interdisciplinary field of explosion mechanics testing and data processing, specifically relating to a multidimensional error correction method for cryogenic explosion impact pressure based on the dielectric impedance characteristics. Background Technology

[0002] In fields such as cold-region engineering construction, defense equipment research and development, and safety assessment of blasting projects, accurately obtaining the explosion impact pressure parameters inside solid media is a core prerequisite for analyzing the mechanical response characteristics of the media, optimizing the design of protective structures, and evaluating the damage effects of weapons. However, in low-temperature environments (-40℃ to 0℃), the physical properties of solid media and the working performance of sensors undergo significant changes, leading to fundamental errors in traditional pressure testing methods that are difficult to resolve. These errors manifest themselves as follows: 1. Systematic measurement distortion caused by impedance mismatch: The impedance of a solid medium is determined by its physical parameters such as density, Young's modulus, and porosity. The acoustic impedance difference between different types of media (hard media such as concrete and rock; loose media such as sand and frozen soil) can reach 1-2 orders of magnitude. Traditional testing methods rely solely on a single type of pressure sensor for data acquisition, without considering the acoustic impedance matching relationship between the sensor and the medium. When the sensor impedance is much greater than that of the loose medium, the shock wave will be strongly reflected at the sensor-medium interface, forming a "hard core effect," resulting in the measured value being 15%-30% higher than the true value. When the sensor impedance is less than that of the hard medium, the high-frequency pressure signal will be severely attenuated, making it impossible to capture the peak characteristics and rising edge details of the shock wave, resulting in a peak loss of 8%-15%. This error caused by the mismatch of physical characteristics cannot be fundamentally eliminated simply by changing the sensor model or adjusting the installation method. 2. Sensor Nonlinear Drift and Coupling Interference Caused by Low Temperature Environment: In the low temperature range of -40℃ to 0℃, the performance of the core sensitive element of the pressure sensor undergoes nonlinear changes: the piezoelectric constant of the piezoelectric ceramic material in piezoelectric sensors decreases linearly with decreasing temperature, with a decay rate of 0.1%-0.3% / ℃. Simultaneously, the transient thermal shock during an explosion triggers a pyroelectric effect, generating false charge signals and causing pressure baseline drift. The sensitivity of the silicon microstructure in piezoresistive sensors decreases by 10%-25% due to low-temperature hardening, and the substrate noise increases significantly with decreasing temperature, leading to a decrease in the signal-to-noise ratio. Existing technologies mostly use static calibration coefficients at room temperature for single temperature drift correction, without considering the coupling effect between the dynamic hardening of the medium during the explosion (low temperature increases Young's modulus by 5%-20%) and sensor drift. The correction effect is limited, and the final measurement error remains as high as 10%-25%. 3. Limitations of existing correction methods: Current error correction methods in the industry are mainly divided into two categories: one is hardware optimization solutions, such as using low-temperature resistant packaging materials and designing special coupling agents, but these solutions are costly, have poor versatility, and cannot be adapted to various media types; the other is data processing solutions, such as using filtering algorithms to remove noise and temperature-based single-factor linear correction, but these solutions do not establish a coupling correction model between the physical parameters of the medium (Young's modulus, porosity) and temperature and sensor response, and cannot decouple the complex interaction between "temperature-medium-sensor", making it difficult to achieve high-precision error correction.

[0003] In summary, existing technologies lack a systematic error correction method that balances versatility and accuracy and is based on physical mechanisms. This makes it impossible to meet the high-precision testing requirements of explosion impact pressure in different media under low-temperature environments. There is an urgent need for a technical solution that comprehensively eliminates multi-dimensional errors through data algorithms. Summary of the Invention

[0004] The purpose of this invention is to provide a multi-dimensional error correction method for cryogenic explosion impact pressure based on the dielectric impedance characteristics. This method can solve the problems of large pressure measurement errors and poor versatility caused by dielectric impedance mismatch, sensor low-temperature nonlinear drift, and coupling interference between temperature and dielectric parameters in cryogenic environments ranging from -40℃ to 0℃. By constructing a dedicated correction model based on the dielectric type, it can achieve multi-dimensional and accurate correction of the original pressure data, providing highly reliable pressure data support for cryogenic explosion impact testing.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A multidimensional error correction method for cryogenic explosion impact pressure based on dielectric impedance characteristics includes the following steps: S1, Media characteristic identification and correction strategy matching S1.1 Establish a media characteristics database; S1.2 Media type identification; S1.3 Modify model activation; S2, Multidimensional Data Synchronization Acquisition S2.1 Data Acquisition Configuration; S2.2 Synchronous data acquisition control; S2.3 Data preprocessing; S3, Impedance Coupling Correction in Hard Media S3.1 Calculation of cryogenic drift; S3.2 Impedance matching compensation calculation; S3.3 Pyroelectric compensation calculation; S3.4 Multidimensional error correction; S4. Pore attenuation correction in loose media S4.1 Calculation of low-temperature substrate noise; S4.2 Sensitivity attenuation factor calculation; S4.3 Calculation of pore pressure loss rate; S4.4 Amplitude recovery correction; S5, Dynamic Output and Model Optimization S5.1 data output; S5.2 Model Iterative Optimization.

[0006] Furthermore, S1, the matching of media characteristic identification and correction strategies includes the following: S1.1 Establish a medium property database: Pre-store the classification criteria and impedance characteristic parameter ranges of common solid media in the calculation unit. The criteria for hard media is Young's modulus E≥10GPa, and the criteria for loose media is porosity θ≥0.15. S1.2 Media type identification: S1.3 Correction Model Activation: Based on the identification results, the corresponding preset correction model is automatically activated—for hard media, the “Stress Transmission Correction Model” (Model A) is activated, and for loose media, the “Stress Dispersion Correction Model” (Model B) is activated.

[0007] Furthermore, in S1.2 media type identification, media characteristic identification is achieved in two ways: the first way is for the user to input the type and core parameters of the medium to be tested, and the second way is for the computing unit to collect media parameters in real time through auxiliary sensors and match them with the database.

[0008] Furthermore, S2 and multidimensional data synchronization acquisition include the following: S2.1 Data Acquisition Configuration: The computing unit establishes a data communication link with the pressure sensor and temperature sensor, and sets the sampling frequency of the pressure sensor to ≥1MHz and the sampling frequency of the temperature sensor to ≥1kHz; S2.2 Synchronous Acquisition Control: Through the synchronous trigger interface of the computing unit, the pressure sensor and temperature sensor are controlled to start data acquisition simultaneously, ensuring that the synchronization accuracy of the original pressure time series Praw(t) and the real-time ambient temperature T timestamp is ≤1μs; S2.3 Data Preprocessing: The collected raw data are subjected to preliminary noise reduction. The moving average filtering algorithm is used to remove high-frequency random noise in the pressure data, and the median filtering algorithm is used to remove abnormal peaks in the temperature data, so as to obtain the preprocessed pressure data Praw'(t) and temperature data T'(t).

[0009] Furthermore, S3, impedance coupling correction under hard dielectrics includes the following: S3.1 Calculation of low temperature drift: Based on the real-time temperature T'(t), the low temperature drift D1=k1×(T'(t)+40) is calculated through the preset low temperature drift calibration curve of the piezoelectric sensor, where k1 is the low temperature drift coefficient, unit: MPa / ℃, obtained through a gradient temperature calibration experiment from -40℃ to 0℃. S3.2 Impedance Matching Compensation Calculation: Introducing the Young's modulus E of the medium, the impedance coupling error D2=k2×E related to the interface stress transmission coefficient is calculated, where k2 is the impedance coupling coefficient, unit: MPa / GPa, which characterizes the influence of the medium stiffness on the measurement error. S3.3 Pyroelectric Compensation Calculation: Considering the pyroelectric effect caused by the transient thermal shock of the explosion, the pyroelectric compensation term ΔPpyro=k3×dT / dt is calculated, where k3 is the pyroelectric coefficient, unit: MPa·s / ℃, and dT / dt is the temperature change rate, which is obtained through experimental calibration; S3.4 Multidimensional Error Correction: Substituting the above correction terms into model A, we obtain the corrected pressure value: Pcorr(t)=Praw'(t)-D1-D2+ΔPpyro That is: Pcorr(t)=Praw'(t)-k1×(T'(t)+40)-k2×E + k3×(dT / dt).

[0010] Furthermore, S4, the porosity reduction correction under loose media includes the following: S4.1 Low-temperature substrate noise calculation: Based on the real-time temperature T'(t), the low-temperature substrate noise Nnoise(T'(t)) is obtained through the low-temperature noise calibration curve of the piezoresistive sensor. This noise increases as the temperature decreases, and the typical value at -40℃ is ≤0.3MPa. S4.2 Sensitivity Attenuation Factor Calculation: Calculate the low-temperature sensitivity attenuation factor Ksense(T'(t)) of the piezoresistive sensor using the formula Ksense(T'(t))=K0×[1 - k4×(40+T'(t))], where K0 is the sensor sensitivity at room temperature of 25℃, and k4 is the sensitivity attenuation coefficient, unit: 1 / ℃; S4.3 Calculation of pore pressure loss rate: Introducing the porosity θ of the medium, the energy dissipation rate of the loose medium to the shock wave is calculated as η(θ) = k5 × θ, where k5 is the pore dissipation coefficient, with a value range of 0.4-0.6, which is obtained through explosion calibration experiments of media with different porosities. S4.4 Amplitude Recovery Correction: Substitute the above parameters into Model B to obtain the corrected pressure value: Pcorr(t)=[Praw'(t)-Nnoise(T'(t))] / [Ksense(T'(t))×(1-η(θ))] That is: Pcorr(t)=[Praw'(t)-Nnoise(T'(t))] / {K0×[1-k4×(40+T'(t))]×(1-k5×θ)}.

[0011] Furthermore, S5, dynamic output, and model optimization include the following: S5.1 Data Output: The calculation unit outputs the corrected and accurate pressure value Pcorr(t) in real time. The output format supports CSV and TDMS industrial standard formats. At the same time, it outputs the key parameters in the correction process, such as temperature T'(t) and correction terms D1 / D2 / ΔPpyro. S5.2 Model Iterative Optimization.

[0012] Furthermore, in the S5.2 model iterative optimization, after each test, the computing unit stores the original data, correction parameters, and test results into the historical database. When the amount of test data of the same type of medium and the same temperature range in the database is ≥50 sets, the model parameters are iteratively updated through the multiple linear regression algorithm to improve the correction accuracy.

[0013] The beneficial effects of this invention are as follows: The multi-dimensional error correction method for low-temperature explosion impact pressure based on the dielectric impedance characteristics of this invention can run in computing units with data processing capabilities (such as RK3588, Jetson series embedded platforms, industrial computers, etc.). By integrating the physical properties of the medium, ambient temperature and sensor response characteristics, a multi-dimensional nonlinear error correction model is constructed, which effectively solves the problems of large pressure measurement errors and poor versatility caused by dielectric impedance mismatch, sensor low-temperature nonlinear drift, and coupling interference between temperature and medium parameters in low-temperature environments from -40℃ to 0℃. By constructing a dedicated correction model based on the medium type, multi-dimensional accurate correction of the original pressure data is achieved, providing highly reliable pressure data support for explosion impact testing in cold regions. Detailed Implementation

[0014] The technical solutions of the present invention will be clearly and completely described below with reference to embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. It should be noted that, unless otherwise specified, all embodiments and preferred methods mentioned herein can be combined to form new technical solutions. Unless otherwise specified, all technical features and preferred features mentioned herein can be combined to form new technical solutions. Unless otherwise stated, the professional and scientific terms used herein have the same meaning as those familiar with the art. Furthermore, any methods or materials similar to or equivalent to the described content can also be applied to the present invention.

[0015] To address the current lack of a systematic error correction method based on physical mechanisms that balances versatility and accuracy, thus failing to meet the high-precision testing requirements of explosion impact pressure in different media at low temperatures, this embodiment presents a multi-dimensional error correction method for cryogenic explosion impact pressure based on the impedance characteristics of the medium. This method operates within a computing unit with data processing capabilities (such as RK3588, Jetson series embedded platforms, industrial computers, etc.). By integrating the physical properties of the medium, ambient temperature, and sensor response characteristics, this method constructs a multi-dimensional nonlinear error correction model, specifically including the following steps: S1, Media characteristic identification and correction strategy matching S1.1 Establish a medium property database: Pre-store the classification criteria and impedance characteristic parameter ranges of common solid media in the calculation unit. The criterion for hard media (concrete, rock, frozen soil blocks) is Young's modulus E≥10GPa, and the criterion for loose media (sand, gravel, porous frozen soil) is porosity θ≥0.15. S1.2 Medium type identification: Medium characteristics are identified in two ways. The first way is for the user to input the type and core parameters (Young's modulus E or porosity θ) of the medium to be tested. The second way is for the calculation unit to collect medium parameters in real time through auxiliary sensors (such as ultrasonic thickness gauges and density sensors) and match them with the database. S1.3, Correction Model Activation: Based on the identification results, the corresponding preset correction model is automatically activated—for hard media, the “Stress Transmission Correction Model” (Model A) is activated, and for loose media, the “Stress Dispersion Correction Model” (Model B) is activated. S2, Multidimensional Data Synchronization Acquisition S2.1 Data Acquisition Configuration: The computing unit establishes a data communication link with the pressure sensor (piezoelectric / piezoresistive, choose one to adapt) and the temperature sensor (accuracy ≤ ±0.5℃, response time ≤ 10ms), and sets the sampling frequency of the pressure sensor to ≥ 1MHz and the sampling frequency of the temperature sensor to ≥ 1kHz. S2.2 Synchronous Acquisition Control: Through the synchronous trigger interface of the computing unit, the pressure sensor and temperature sensor are controlled to start data acquisition simultaneously, ensuring that the synchronization accuracy of the original pressure time series Praw(t) and the real-time ambient temperature T timestamp is ≤1μs; S2.3 Data Preprocessing: The collected raw data are subjected to preliminary noise reduction. The moving average filtering algorithm is used to remove high-frequency random noise in the pressure data, and the median filtering algorithm is used to remove abnormal peaks in the temperature data, so as to obtain the preprocessed pressure data Praw'(t) and temperature data T'(t). S3, Impedance Coupling Correction in Hard Media (Model A) S3.1 Calculation of low temperature drift: Based on the real-time temperature T'(t), the low temperature drift D1=k1×(T'(t)+40) is calculated through the preset low temperature drift calibration curve of the piezoelectric sensor, where k1 is the low temperature drift coefficient (unit: MPa / ℃), which is obtained through a gradient temperature calibration experiment from -40℃ to 0℃. S3.2 Impedance Matching Compensation Calculation: Introducing the Young's modulus E of the medium, the impedance coupling error D2=k2×E related to the interface stress transmission coefficient is calculated, where k2 is the impedance coupling coefficient (unit: MPa / GPa), which characterizes the influence of the medium stiffness on the measurement error. S3.3 Pyroelectric Compensation Calculation: Considering the pyroelectric effect caused by the transient thermal shock of the explosion, the pyroelectric compensation term ΔPpyro=k3×dT / dt is calculated, where k3 is the pyroelectric coefficient (unit: MPa·s / ℃), and dT / dt is the temperature change rate, which is obtained through experimental calibration. S3.4 Multidimensional Error Correction: Substituting the above correction terms into model A, the corrected pressure value is obtained: Pcorr(t)=Praw'(t)-D1-D2+ΔPpyro That is: Pcorr(t)=Praw'(t)-k1×(T'(t)+40)-k2×E + k3×(dT / dt) S4. Pore attenuation correction in loose media (Model B) S4.1 Calculation of low temperature substrate noise: Based on the real-time temperature T'(t), the low temperature substrate noise Nnoise(T'(t)) is obtained through the low temperature noise calibration curve of the piezoresistive sensor. This noise increases as the temperature decreases, and the typical value at -40℃ is ≤0.3MPa. S4.2 Calculation of sensitivity attenuation factor: Calculate the low-temperature sensitivity attenuation factor Ksense(T'(t)) of the piezoresistive sensor using the formula Ksense(T'(t))=K0×[1 - k4×(40+T'(t))], where K0 is the sensor sensitivity at room temperature (25℃) and k4 is the sensitivity attenuation coefficient (unit: 1 / ℃). S4.3 Calculation of pore pressure loss rate: Introducing the porosity θ of the medium, the energy dissipation rate of the loose medium to the shock wave is calculated as η(θ) = k5 × θ, where k5 is the pore dissipation coefficient, with a value range of 0.4-0.6, which is obtained through explosion calibration experiments of media with different porosities. S4.4 Amplitude Recovery Correction: Substitute the above parameters into Model B to obtain the corrected pressure value: Pcorr(t)=[Praw'(t)-Nnoise(T'(t))] / [Ksense(T'(t))×(1-η(θ))] That is: Pcorr(t)=[Praw'(t)-Nnoise(T'(t))] / {K0×[1-k4×(40+T'(t))]×(1-k5×θ)} S5, Dynamic Output and Model Optimization S5. Data Output: The calculation unit outputs the corrected and accurate pressure value Pcorr(t) in real time. The output format supports industrial standard formats such as CSV and TDMS. At the same time, it outputs key parameters in the correction process (temperature T'(t), correction terms D1 / D2 / ΔPpyro, etc.). 5.2 Model Iterative Optimization: After each test, the computing unit stores the original data, correction parameters, and test results into the historical database. When the amount of test data of the same type of medium and the same temperature range in the database is ≥50 sets, the model parameters (k1, k2, k3, k4, k5) are iteratively updated through the multiple linear regression algorithm to further improve the correction accuracy.

[0016] Example 1: This example uses the RK3588 embedded platform (running Linux 5.10 operating system) as the computing unit to illustrate the specific implementation process of the present invention. The model parameters were determined through a gradient calibration experiment from -40℃ to 0℃: k1=0.05MPa / ℃, k2=0.005MPa / GPa, k3=0.02MPa·s / ℃, k4=0.01 / ℃, k5=0.5, the piezoelectric sensor's room temperature sensitivity K0=10mV / MPa, sampling frequency=1MHz, and temperature sensor accuracy=±0.3℃.

[0017] -40℃ granite (hard medium) explosion impact test: Step S1: Medium property identification and model activation S1.1 Users input the medium type "granite" and the core parameter Young's modulus E=60GPa through the human-computer interaction interface of the RK3588 platform; The S1.2 platform matches the medium type as a hard medium and automatically activates the "stress transmission correction model" (Model A).

[0018] Step S2: Multidimensional data synchronization acquisition After the S2.1 explosion is triggered, the pressure sensor collects the original pressure time series Praw(t), and the temperature sensor collects the real-time temperature T(t), with a synchronization accuracy of 0.8μs; S2.2 Select the preprocessed data at time t=30μs: Praw'(t)=32.5MPa, T'(t)=-40℃, temperature change rate dT / dt=5℃ / ms.

[0019] Step S3: Impedance Coupling Correction Calculation S3.1 Low-temperature drift D1 = k1 × (T'(t) + 40) = 0.05 × (-40 + 40) = 0 MPa; S3.2 Impedance coupling error D2=k2×E=0.005×60=0.3MPa; S3.3 Pyroelectric compensation term ΔPpyro=k3×dT / dt=0.02×5=0.1MPa; Substituting S3.4 into model A, we calculate: Pcorr(t) = 32.5 - 0 - 0.3 + 0.1 = 32.3 MPa.

[0020] Step S4: Data Output and Storage The platform outputs a precise pressure value of 32.3 MPa at t=30μs, and stores parameters such as temperature -40℃, correction terms D1=0MPa, D2=0.3MPa, and ΔPpyro=0.1MPa for subsequent analysis.

[0021] Example 2: Explosion impact test of silty sand (loose medium) at -20℃: Step S1: Medium property identification and model activation S1.1 User inputs the medium type as "silty sand" and the core parameter porosity θ = 0.25; The S1.2 platform is matched as a loose medium, and the "stress dispersion correction model" (Model B) is activated.

[0022] Step S2: Multidimensional data synchronization acquisition After the explosion of S2.1 was triggered, the piezoresistive sensor collected pre-processed pressure data Praw'(t)=12.8MPa (t=60μs), and the temperature sensor collected T'(t)=-20℃; S2.2 From the calibration curve, we get: Nnoise (-20℃) = 0.2MPa.

[0023] Step S3: Pore attenuation correction calculation S3.1 Sensitivity Attenuation Factor Ksense(T'(t))=1-k4×(40+T'(t))=1-0.01×(40-20)]=0.8; S3.2 Pore pressure loss rate η(θ)=k5×θ=0.5×0.25=0.125; S3.3 Substitute into model B for calculation: Pcorr(t)=[12.8-0.2] / [0.8×(1-0.125)]=18.0MPa.

[0024] Step S4: Data Output and Storage The platform outputs a precise pressure value of 18.0 MPa and stores key parameters: Nnoise=0.2 MPa, Ksense=0.8, η=0.125, ensuring data traceability.

[0025] The above embodiments demonstrate that the present invention improves the measurement accuracy of explosion impact pressure for different types of solid media in low-temperature environments ranging from -40℃ to 0℃, effectively solving the measurement error problems caused by impedance mismatch and low-temperature drift in existing technologies, and exhibiting significant practicality and reliability. The basic principles, main features, and advantages of the present invention have been shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the present invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed.

Claims

1. A multidimensional error correction method for cryogenic explosion impact pressure based on dielectric impedance characteristics, characterized in that, Includes the following steps: S1, Media characteristic identification and correction strategy matching S1.1 Establish a media characteristics database; S1.2 Media type identification; S1.3 Modify model activation; S2, Multidimensional Data Synchronization Acquisition S2.1 Data Acquisition Configuration; S2.2 Synchronous data acquisition control; S2.3 Data preprocessing; S3, Impedance Coupling Correction in Hard Media S3.1 Calculation of cryogenic drift; S3.2 Impedance matching compensation calculation; S3.3 Pyroelectric compensation calculation; S3.4 Multidimensional error correction; S4. Pore attenuation correction in loose media S4.1 Calculation of low-temperature substrate noise; S4.2 Sensitivity attenuation factor calculation; S4.3 Calculation of pore pressure loss rate; S4.4 Amplitude recovery correction; S5, Dynamic Output and Model Optimization S5.1 data output; S5.2 Model Iterative Optimization.

2. The method for multidimensional error correction of cryogenic explosion impact pressure based on dielectric impedance characteristics according to claim 1, characterized in that, S1. Medium characteristic identification and correction strategy matching includes the following: S1.1 Establish a medium property database: Pre-store the classification criteria and impedance characteristic parameter ranges of common solid media in the calculation unit. The criteria for hard media is Young's modulus E≥10GPa, and the criteria for loose media is porosity θ≥0.

15. S1.2 Media type identification: S1.3 Correction Model Activation: Based on the identification results, the corresponding preset correction model is automatically activated—for hard media, the "stress transmission correction model" (i.e., model A) is activated, and for loose media, the "stress dispersion correction model" (i.e., model B) is activated.

3. A multidimensional error correction method for cryogenic explosion impact pressure based on dielectric impedance characteristics according to claim 2, characterized in that, In S1.2 media type identification, media characteristic identification is achieved in two ways: the first way is for the user to input the type and core parameters of the medium to be tested, and the second way is for the computing unit to collect media parameters in real time through auxiliary sensors and match them with the database.

4. The method for multidimensional error correction of cryogenic explosion impact pressure based on dielectric impedance characteristics according to claim 3, characterized in that, S2. Multidimensional data synchronization acquisition includes the following: S2.1 Data Acquisition Configuration: The computing unit establishes a data communication link with the pressure sensor and temperature sensor, and sets the sampling frequency of the pressure sensor to ≥1MHz and the sampling frequency of the temperature sensor to ≥1kHz; S2.2 Synchronous Acquisition Control: Through the synchronous trigger interface of the computing unit, the pressure sensor and temperature sensor are controlled to start data acquisition simultaneously, ensuring that the synchronization accuracy of the original pressure time series Praw(t) and the real-time ambient temperature T timestamp is ≤1μs; S2.3 Data Preprocessing: The collected raw data are subjected to preliminary noise reduction. The moving average filtering algorithm is used to remove high-frequency random noise in the pressure data, and the median filtering algorithm is used to remove abnormal peaks in the temperature data, so as to obtain the preprocessed pressure data Praw'(t) and temperature data T'(t).

5. The method for multidimensional error correction of cryogenic explosion impact pressure based on dielectric impedance characteristics according to claim 4, characterized in that, S3. Impedance coupling correction under hard dielectrics includes the following: S3.1 Calculation of low temperature drift: Based on the real-time temperature T'(t), the low temperature drift D1=k1×(T'(t)+40) is calculated through the preset low temperature drift calibration curve of the piezoelectric sensor, where k1 is the low temperature drift coefficient, unit: MPa / ℃, obtained through a gradient temperature calibration experiment from -40℃ to 0℃. S3.2 Impedance Matching Compensation Calculation: Introducing the Young's modulus E of the medium, the impedance coupling error D2=k2×E related to the interface stress transmission coefficient is calculated, where k2 is the impedance coupling coefficient, unit: MPa / GPa, which characterizes the influence of the medium stiffness on the measurement error. S3.3 Pyroelectric Compensation Calculation: Considering the pyroelectric effect caused by the transient thermal shock of the explosion, the pyroelectric compensation term ΔPpyro=k3×dT / dt is calculated, where k3 is the pyroelectric coefficient, unit: MPa·s / ℃, and dT / dt is the temperature change rate, which is obtained through experimental calibration; S3.4 Multidimensional Error Correction: Substituting the above correction terms into model A, we obtain the corrected pressure value: Pcorr(t)=Praw'(t)-D1-D2+ΔPpyro That is: Pcorr(t)=Praw'(t)-k1×(T'(t)+40)-k2×E + k3×(dT / dt).

6. The method for multidimensional error correction of cryogenic explosion impact pressure based on dielectric impedance characteristics according to claim 5, characterized in that, S4. Pore attenuation correction in loose media includes the following: S4.1 Low-temperature substrate noise calculation: Based on the real-time temperature T'(t), the low-temperature substrate noise Nnoise(T'(t)) is obtained through the low-temperature noise calibration curve of the piezoresistive sensor. This noise increases as the temperature decreases, and the typical value at -40℃ is ≤0.3MPa. S4.2 Sensitivity Attenuation Factor Calculation: Calculate the low-temperature sensitivity attenuation factor Ksense(T'(t)) of the piezoresistive sensor using the formula Ksense(T'(t))=K0×[1 - k4×(40+T'(t))], where K0 is the sensor sensitivity at room temperature of 25℃, and k4 is the sensitivity attenuation coefficient, unit: 1 / ℃; S4.3 Calculation of pore pressure loss rate: Introducing the porosity θ of the medium, the energy dissipation rate of the loose medium to the shock wave is calculated as η(θ) = k5 × θ, where k5 is the pore dissipation coefficient, with a value range of 0.4-0.6, which is obtained through explosion calibration experiments of media with different porosities. S4.4 Amplitude Recovery Correction: Substitute the above parameters into Model B to obtain the corrected pressure value: Pcorr(t)=[Praw'(t)-Nnoise(T'(t))] / [Ksense(T'(t))×(1-η(θ))] That is: Pcorr(t)=[Praw'(t)-Nnoise(T'(t))] / {K0×[1-k4×(40+T'(t))]×(1-k5×θ)}.

7. The method for multidimensional error correction of cryogenic explosion impact pressure based on dielectric impedance characteristics according to claim 6, characterized in that, S5. Dynamic output and model optimization include the following: S5.1 Data Output: The calculation unit outputs the corrected and accurate pressure value Pcorr(t) in real time. The output format supports CSV and TDMS industrial standard formats. At the same time, it outputs the key parameters in the correction process, such as temperature T'(t) and correction terms D1 / D2 / ΔPpyro. S5.2 Model Iterative Optimization.

8. A multidimensional error correction method for cryogenic explosion impact pressure based on dielectric impedance characteristics according to claim 7, characterized in that, In the S5.2 model iterative optimization, after each test, the computing unit stores the original data, correction parameters, and test results into the historical database. When the amount of test data of the same type of medium and the same temperature range in the database is ≥50 sets, the model parameters are iteratively updated through the multiple linear regression algorithm to improve the correction accuracy.