A true triaxial stress environment crack propagation simulation method of intelligent sensing element

Abstract

The present application relates to the technical field of intelligent sensing element performance test, and particularly relates to a true triaxial stress environment crack propagation simulation method of intelligent sensing element, comprising the following steps: collecting service environment characteristics and body characteristic parameters of the intelligent sensing element, establishing a true triaxial stress simulation basic information base of the sensing element, the service environment characteristics comprising triaxial stress coupling parameters, stress dynamic change parameters and service medium influence parameters, the body characteristic parameters comprising mechanical characteristics, configuration parameters, initial sensing performance and background data; based on the true triaxial stress simulation basic information base, a multi-field coupling correlation rule base of stress field, deformation field and sensing field is integrated. The true triaxial stress environment crack propagation simulation method of intelligent sensing element provided by the present application greatly improves the coincidence degree of crack propagation law and actual situation, and provides a reliable basis for accurately reflecting the element failure mechanism.

Description

Technical Field

[0001] This invention relates to the field of performance testing technology for intelligent sensing elements, and in particular to a method for simulating crack propagation in a true triaxial stress environment for intelligent sensing elements. Background Technology

[0002] As core devices for sensing external physical quantities, intelligent sensing elements are widely used in aerospace, geotechnical engineering, precision instruments, and biomedical fields. During actual service, intelligent sensing elements are often subjected to complex three-dimensional stress environments. For example, sensing elements on aero-engine blades must withstand the coupling of multi-directional thermal and mechanical stresses, while buried sensing elements in geotechnical engineering must withstand true triaxial pressure exerted by the surrounding soil. These complex stresses can easily lead to the formation and gradual propagation of microcracks within the sensing elements, ultimately causing a decrease in sensing accuracy, signal distortion, or even device failure, severely impacting the stability and safety of the entire detection system.

[0003] However, existing true triaxial stress environment crack propagation simulation methods mostly use uniaxial or biaxial stress loading simulation, ignoring the coupling effect of three-dimensional stress in actual service. This makes it difficult to realistically reproduce the complex stress state experienced by the sensing element, resulting in a large deviation between the simulated crack propagation law and the actual situation, and making it difficult to accurately reflect the actual failure mechanism of the element. Moreover, the simulation methods mostly adopt a combination of macroscopic stress loading and observation, which cannot accurately control the loading accuracy of true triaxial stress, making it difficult to capture the microcrack characteristics in the crack initiation stage. The simulation accuracy of crack propagation path, rate and morphology is insufficient, and it cannot meet the fine testing requirements of high-end sensing elements. Summary of the Invention

[0004] Technical objective: In order to overcome the shortcomings of the existing technology, the present invention provides a method for simulating crack propagation in a true triaxial stress environment using intelligent sensing elements.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for simulating crack propagation in a true triaxial stress environment using an intelligent sensing element, comprising the following steps:

[0006] The service environment characteristics and physical characteristic parameters of the intelligent sensing element are collected to establish a basic information database for true triaxial stress simulation of the sensing element. The service environment characteristics include triaxial stress coupling parameters, stress dynamic change parameters and service medium influence parameters. The physical characteristic parameters include mechanical properties, configuration parameters, initial sensing performance and background data.

[0007] Based on the true triaxial stress simulation basic information library, and incorporating the multi-field coupling association rule library of stress field, deformation field and sensing field, an initial crack propagation simulation scheme for sensing element is generated.

[0008] Based on the initial crack propagation simulation scheme, the sensor element sample is precisely clamped and stress-loaded to collect full-dimensional crack monitoring, real-time stress and deformation detection, and dynamic sensor performance simulation interactive data.

[0009] Feature extraction and error judgment are performed on the simulated interactive data, and the features of the simulated interactive data are fed back to the multi-field coupling association rule base to obtain a secondary crack propagation simulation scheme.

[0010] Based on the crack evolution law and sensor performance failure mechanism of the secondary crack propagation simulation scheme, a multi-dimensional crack resistance improvement scheme is generated.

[0011] The final optimized solution is output by verifying the anti-fracture enhancement scheme based on simulated data.

[0012] Preferably, the service environment characteristics and intrinsic properties parameters of the intelligent sensing element are collected to establish a basic information database for true triaxial stress simulation of the sensing element, specifically including the following steps:

[0013] Collect triaxial stress amplitude, stress loading rate, number of cyclic loading and unloading, stress coupling ratio, and environmental characteristic parameters of the service medium such as temperature, humidity, and corrosivity in the actual service scenarios of the sensing element, and establish a service environment characteristic sub-library.

[0014] Acquire the mechanical properties of the material elastic modulus, Poisson's ratio, and fracture toughness of the sensing element, as well as the configuration parameters of the structural geometry and microstructure distribution, and the background data of the location, size, shape, and distribution density of the initial micro-defects.

[0015] The data acquisition equipment measures the initial sensing performance parameters of the sensing element in a stress-free state, including resistance, capacitance, sensitivity, and signal output stability. The mechanical properties, configuration parameters, initial sensing performance, and background data are integrated into a sub-library of body characteristic parameters.

[0016] A basic information database for true triaxial stress simulation is constructed based on the service environment feature sub-database and the ontological characteristic parameter sub-database.

[0017] Preferably, based on the true triaxial stress simulation basic information database, and incorporating a multi-field coupling association rule library of stress field, deformation field, and sensing field, an initial crack propagation simulation scheme for the sensing element is generated, specifically including the following steps:

[0018] Collect stress field data, deformation field simulation data, sensor field simulation data, and crack evolution data to construct a multi-field coupling association rule base;

[0019] A basic standard library is generated by mining the multi-field coupling association rule base based on the associated data of the true triaxial stress simulation basic information base.

[0020] The synergistic effect of the basic standard library in the multi-field coupling association rule base is used to generate an initial crack propagation simulation scheme.

[0021] Preferably, based on the initial crack propagation simulation scheme, the sensor element sample is precisely clamped and stress-loaded to collect simulation interactive data on full-dimensional crack monitoring, real-time stress and deformation detection, and dynamic sensor performance. Specifically, this includes the following steps:

[0022] A simulation test standard library is constructed based on a true triaxial stress loading unit, a multi-dimensional monitoring unit, a sensing performance acquisition unit, and a central control unit. The target stress value is obtained by comparing the parameter samples of the initial crack propagation simulation scheme with the true triaxial stress based on the simulation test standard library.

[0023] Based on multi-dimensional monitoring units, cracks are monitored in all dimensions to obtain micro-crack evolution characteristics, crack penetration process and overall sample deformation to obtain sample characteristic change values;

[0024] The stress change rate value is obtained by collecting dynamic change data in the true triaxial stress loading unit and the deformation amount and deformation rate of the sample in the sensing performance acquisition unit.

[0025] Real-time data interaction rules for establishing a simulation test standard library based on the central control unit;

[0026] The simulated interactive data is obtained by comparing the target stress value, sample feature change value, and stress change rate value with the real-time data interaction rules.

[0027] Preferably, feature extraction and error judgment are performed on the simulated interactive data, and the features of the simulated interactive data are fed back to the multi-field coupling association rule base to obtain a secondary crack propagation simulation scheme, specifically including the following steps:

[0028] Feature parameters of crack evolution, stress and deformation, and sensing performance are extracted; the extracted feature parameters are compared with the real-time calculation results of the multi-field coupling association rule base to obtain feature deviation values; and the error points of feature parameters of stress field, deformation field, and crack evolution in the multi-field coupling association rule base are determined based on the feature deviation values.

[0029] Based on the feature deviation value and the feature parameter error point, the relevant parameters of the multi-field coupled association rule base are corrected and iterated to obtain the feature optimization value;

[0030] The simulation optimization library is obtained by comparing the feature optimization values ​​with the preset simulation accuracy threshold.

[0031] Based on the simulation optimization library, a secondary crack propagation scheme is generated by simulating the entire process of crack propagation of sensing elements under true triaxial stress environment.

[0032] Preferably, the simulation optimization library is obtained by comparing the feature optimization values ​​with a preset simulation accuracy threshold, specifically including the following steps:

[0033] If the feature optimization value is less than the simulation accuracy threshold, a simulation optimization library is generated.

[0034] If the feature optimization value is greater than or equal to the simulation accuracy threshold, then based on the fact that the feature deviation value and the feature parameter are identical, the relevant parameters of the multi-field coupled association rule base are corrected and iteratively optimized to obtain the feature optimization value, until the feature optimization value is less than the simulation accuracy threshold, and the simulation optimization base is generated.

[0035] Preferably, based on the crack evolution law and sensing performance failure mechanism of the secondary crack propagation simulation scheme, a multi-dimensional crack resistance improvement scheme is generated, specifically including the following steps:

[0036] Based on the secondary crack propagation simulation scheme, the crack initiation mechanism, propagation law and influence of different stress characteristics on crack evolution of the sensing element under true triaxial stress coupling are determined to obtain the crack evolution law.

[0037] The sensor performance failure mechanism is derived based on the correspondence between each stage of crack propagation and the degradation of sensor performance.

[0038] Based on the crack evolution law and the failure mechanism of sensing performance, a multi-dimensional optimization scheme is generated from four dimensions: structural design optimization, material modification and strengthening, sensing unit protection, and stress adaptation and control.

[0039] The multi-dimensional optimization schemes were compared and verified with the simulation optimization library, and the optimization scheme with the best verification effect was selected to obtain the crack resistance improvement scheme.

[0040] Preferably, the method further includes:

[0041] Collect basic information database data, simulation schemes, experimental data, simulation results and effective optimization schemes of different types and specifications of sensing elements, and construct an intelligent database for true triaxial stress crack propagation simulation of intelligent sensing elements;

[0042] Based on the intelligent database, the service environment characteristics and intrinsic properties parameters of the sensing element are matched to generate a simulation pre-selection scheme.

[0043] The beneficial effects of this invention are:

[0044] 1. This invention provides a true triaxial stress environment crack propagation simulation method for intelligent sensing elements. By collecting triaxial stress coupling parameters, stress dynamic change parameters, and service medium influence parameters, a true triaxial stress simulation basic information database is constructed. Based on precise clamping and stress loading, true triaxial stress comparison is achieved, which can realistically reproduce the complex stress state of sensing elements in aerospace and geotechnical engineering scenarios, greatly improve the consistency between crack propagation law and actual situation, and provide a reliable basis for accurately reflecting the failure mechanism of the element.

[0045] 2. This invention provides a true triaxial stress environment crack propagation simulation method for intelligent sensing elements. By integrating a multi-field coupling and correlation rule library of stress field, deformation field, and sensing field, and by using a multi-dimensional monitoring unit to monitor cracks in all dimensions, it can accurately capture the micro-crack characteristics in the crack initiation stage, clearly depict the path, rate, and morphology of crack propagation, and meet the core requirements of refined testing of high-end sensing elements.

[0046] 3. This invention provides a true triaxial stress environment crack propagation simulation method for intelligent sensing elements. By extracting features and judging errors from the simulation interaction data, the features are fed back to a multi-field coupling association rule base for iterative correction, generating a secondary crack propagation simulation scheme. Then, based on the crack evolution law and the sensor performance failure mechanism, a multi-dimensional crack resistance improvement scheme is generated. This avoids the drawbacks of relying on experience judgment and lacking quantitative support, making the crack resistance scheme more targeted and scientific, and effectively improving the reliability and service life of sensing elements under extreme working conditions. Attached Figure Description

[0047] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0048] Figure 1 This is a schematic diagram illustrating the steps of an embodiment of a true triaxial stress environment crack propagation simulation method using an intelligent sensing element according to the present invention.

[0049] Figure 2 This is a schematic diagram illustrating the steps for obtaining simulated interactive data in an embodiment of a true triaxial stress environment crack propagation simulation method using an intelligent sensing element according to the present invention. Detailed Implementation

[0050] The following is in conjunction with the appendix Figure 1 To be continued Figure 2 The principles and features of the present invention are described, and the examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0051] Please see Figures 1-2 The present invention provides a method for simulating crack propagation in a true triaxial stress environment using an intelligent sensing element, comprising the following steps:

[0052] The service environment characteristics and physical characteristic parameters of the intelligent sensing element are collected to establish a basic information database for true triaxial stress simulation of the sensing element. The service environment characteristics include triaxial stress coupling parameters, stress dynamic change parameters and service medium influence parameters. The physical characteristic parameters include mechanical properties, configuration parameters, initial sensing performance and background data.

[0053] Based on the true triaxial stress simulation basic information library, and incorporating the multi-field coupling association rule library of stress field, deformation field and sensing field, an initial crack propagation simulation scheme for sensing element is generated.

[0054] Based on the initial crack propagation simulation scheme, the sensor element sample is precisely clamped and stress-loaded to collect full-dimensional crack monitoring, real-time stress and deformation detection, and dynamic sensor performance simulation interactive data.

[0055] Feature extraction and error judgment are performed on the simulated interactive data, and the features of the simulated interactive data are fed back to the multi-field coupling association rule base to obtain a secondary crack propagation simulation scheme.

[0056] Based on the crack evolution law and sensor performance failure mechanism of the secondary crack propagation simulation scheme, a multi-dimensional crack resistance improvement scheme is generated.

[0057] The final optimized crack resistance enhancement scheme is verified and output based on simulation data. After completing the crack propagation simulation and preliminary design of the crack resistance enhancement scheme, the proposed multi-dimensional crack resistance enhancement scheme is systematically verified. By constructing a true triaxial stress simulation scenario highly consistent with the actual service environment, various technical measures in the crack resistance enhancement scheme, such as material modification, structural optimization, and stress control strategies, are reintegrated into the multi-field coupled simulation framework for deduction. If the crack resistance enhancement scheme includes adjusting the internal stress distribution of the sensing element to suppress crack initiation, the verification stage accurately reproduces this stress control strategy in the simulation, continuously monitoring the crack initiation location, propagation rate, path morphology, and dynamic changes in sensing performance. By comparing the simulation data before and after the implementation of the scheme, the analysis is conducted to determine whether the crack evolution law has been effectively suppressed, whether the stability and reliability of the sensing performance have been significantly improved, and whether any potential problems of new stress concentration or performance degradation have been introduced during the implementation of the scheme.

[0058] During the verification process, if deviations are found between the simulation results and the expected targets—for example, if the crack propagation rate has not yet decreased below the safe threshold, or if the sensing performance exhibits abnormal fluctuations under specific stress conditions—then the design of the solution will be retrospectively reviewed. Material ratios, structural parameters, or stress control strategies will be iteratively optimized until the simulation data sufficiently demonstrates that the crack resistance enhancement scheme can effectively suppress crack propagation under true triaxial stress, ensuring the service performance and structural integrity of the sensing element. Ultimately, after multiple rounds of simulation verification and iterative optimization, the output scheme is the final simulation-optimized scheme that combines theoretical feasibility and engineering practicality, providing reliable guidance for the crack-resistant design and engineering application of intelligent sensing elements.

[0059] Please see Figures 1-2 The process involves collecting the service environment characteristics and intrinsic properties of intelligent sensing elements, and establishing a basic information database for true triaxial stress simulation of the sensing elements. This includes the following steps:

[0060] Collect triaxial stress amplitude, stress loading rate, number of cyclic loading and unloading, stress coupling ratio, and environmental characteristic parameters of the service medium such as temperature, humidity, and corrosivity in the actual service scenarios of the sensing element, and establish a service environment characteristic sub-library.

[0061] Acquire the mechanical properties of the material elastic modulus, Poisson's ratio, and fracture toughness of the sensing element, as well as the configuration parameters of the structural geometry and microstructure distribution, and the background data of the location, size, shape, and distribution density of the initial micro-defects.

[0062] The data acquisition equipment measures the initial sensing performance parameters of the sensing element in a stress-free state, including resistance, capacitance, sensitivity, and signal output stability. The mechanical properties, configuration parameters, initial sensing performance, and background data are integrated into a sub-library of body characteristic parameters.

[0063] A fundamental information database for true triaxial stress simulation is constructed based on a service environment feature sub-database and a body characteristic parameter sub-database. Environmental characteristic parameters from actual service scenarios of the sensing elements are comprehensively collected to build the service environment feature sub-database. Core mechanical parameters such as triaxial stress amplitude, stress loading rate, number of cyclic loading and unloading, and stress coupling ratio are obtained. Simultaneously, environmental impact data on the temperature, humidity, and corrosivity of the service medium are collected. For pressure sensing elements applied to deep-sea oil and gas extraction platforms, the service environment feature sub-database needs to include key data such as the triaxial stress coupling ratio under high seabed pressure, seawater corrosion rate, and stress loading rate under low-temperature environments. These data directly determine the degree of fit between the simulation scenario and actual working conditions.

[0064] To acquire the intrinsic characteristic parameters of the sensing element, a sub-library of intrinsic characteristic parameters is constructed. This sub-library requires the collection of three types of parameters: the first type is material mechanical properties, including elastic modulus, Poisson's ratio, and fracture toughness. These properties determine the deformation and fracture behavior of the material under stress. For example, the high fracture toughness of a sensing element made of nickel-based alloy significantly affects the crack propagation threshold. The second type is structural configuration parameters, covering structural geometry and microstructure distribution. For example, the ultra-thin structure design of thin-film sensing elements makes them more prone to local stress concentration under triaxial stress. The third type is micro-defect background data, including the location, size, morphology, and distribution density of initial micro-defects. This background data is the core inducing factor for crack initiation. For example, the distribution density of surface microcracks generated during the fabrication of the sensing element directly affects the initial location and propagation path of cracks in the simulation. Simultaneously, initial sensing performance parameters such as resistance, capacitance, sensitivity, and signal output stability of the sensing element under stress-free conditions are collected. These initial sensing performance parameters are then integrated with material mechanical properties, structural configuration parameters, and micro-defect background data to form a complete sub-library of body characteristic parameters.

[0065] Based on a sub-library of service environment characteristics and a sub-library of intrinsic property parameters, a true triaxial stress simulation basic information library is constructed. This library establishes correlation mappings between data points to organically integrate the service environment with the intrinsic properties of the component. When simulating crack propagation in a deep-sea pressure sensing element, the true triaxial stress simulation basic information library automatically matches the seawater corrosion environment with the fracture toughness data of the nickel-based alloy. Simultaneously, it incorporates the stress concentration characteristics of the element's ultra-thin structure, providing accurate initial conditions for subsequent multi-field coupled simulations. This ensures that the simulation results accurately reflect the crack propagation patterns of the sensing element in actual service scenarios.

[0066] Please see Figures 1-2 Based on the true triaxial stress simulation basic information database, and incorporating a multi-field coupling association rule library of stress field, deformation field, and sensing field, an initial crack propagation simulation scheme for the sensing element is generated, specifically including the following steps:

[0067] Collect stress field data, deformation field simulation data, sensor field simulation data, and crack evolution data to construct a multi-field coupling association rule base;

[0068] A basic standard library is generated by mining the multi-field coupling association rule base based on the associated data of the true triaxial stress simulation basic information base.

[0069] The initial crack propagation simulation scheme is generated by assessing the synergistic effect of the basic standard library in the multi-field coupling association rule base. First, by collecting stress field data, deformation field simulation data, sensor field simulation data, and crack evolution data, the inherent correlation logic between multiple fields is established, thereby constructing a multi-field coupling association rule base. When simulating temperature sensing elements on aero-engine turbine blades, stress field data includes the distribution of thermal stress and the superposition of mechanical stress at high temperatures; deformation field simulation data reflects the coupling results of centrifugal deformation and thermal expansion deformation during blade rotation; sensor field simulation data corresponds to the resistance drift and sensitivity changes of temperature sensing elements under stress and deformation; and crack evolution data comes from crack initiation location, propagation rate, and path morphology recorded in previous experiments. By integrating these multi-dimensional data, association rules are formed that describe how stress drives the deformation field, the deformation field affects the sensor field, and changes in sensor field signals can infer the crack evolution state, providing a theoretical basis for subsequent scheme generation.

[0070] Based on the correlation data of the true triaxial stress simulation basic information database, a multi-field coupling correlation rule base is mined to generate a basic standard library. Through correlation analysis, core rules that highly match the service scenarios of specific sensing elements are extracted from the massive number of rules. For pressure sensing elements of deep-sea oil and gas platforms, the basic information database contains correlation data of high-pressure triaxial stress coupling on the seabed, seawater corrosion, and low-temperature environment. By mining the multi-field coupling correlation rule base, the correspondence between stress concentration coefficient and crack initiation probability at micro-defects of sensing elements under high stress coupling and corrosion environment, as well as the quantitative correlation core rules between deformation and sensing signal output stability, are selected. These rules together constitute the basic standard library applicable to this scenario, ensuring that the simulation schemes generated subsequently are targeted and reliable.

[0071] This study assesses the synergistic effects of the basic standard library within a multi-field coupling association rule base to generate an initial crack propagation simulation scheme. First, the rules in the basic standard library are collaboratively calculated to simulate the dynamic interaction between the stress field, deformation field, sensing field, and crack evolution under true triaxial stress conditions. When simulating deep-sea pressure sensing elements, the rules in the basic standard library work synergistically. First, the stress distribution within the element is determined based on stress coupling rules. Then, the deformation amount in the stress concentration region is derived based on deformation rules. Next, changes in sensing performance are predicted using sensing rules. Finally, crack evolution rules are combined to determine the crack initiation location and initial propagation trend. Through this multi-field collaborative assessment, the resulting initial crack propagation simulation scheme accurately reproduces the initial state and development trend of crack propagation in actual service scenarios, providing reliable initial conditions for subsequent physical simulations and scheme optimization.

[0072] Please see Figures 1-2Based on the initial crack propagation simulation scheme, the sensor element samples are precisely clamped and stress-loaded to collect simulation interactive data on crack full-dimensional monitoring, real-time stress and deformation detection, and dynamic sensor performance. The specific steps include:

[0073] A simulation test standard library was constructed based on a true triaxial stress loading unit, a multi-dimensional monitoring unit, a sensor performance acquisition unit, and a central control unit. The target stress value was obtained by comparing the parameter samples of the initial crack propagation simulation scheme with the true triaxial stress values ​​according to the simulation test standard library.

[0074] Based on multi-dimensional monitoring units, cracks are monitored in all dimensions to obtain micro-crack evolution characteristics, crack penetration process and overall sample deformation to obtain sample characteristic change values;

[0075] The stress change rate value is obtained by collecting dynamic change data in the true triaxial stress loading unit and the deformation amount and deformation rate of the sample in the sensing performance acquisition unit.

[0076] Real-time data interaction rules for establishing a simulation test standard library based on the central control unit;

[0077] Simulated interactive data is obtained by comparing the target stress value, sample characteristic change value, and stress change rate value with real-time data interaction rules. A complete simulation test standard library is constructed based on a true triaxial stress loading unit, a multi-dimensional monitoring unit, a sensor performance acquisition unit, and a central control unit. This library integrates equipment accuracy parameters, loading rate control strategies, monitoring frequency thresholds, and sensor signal calibration method specifications. When testing pressure sensing elements applied to deep-sea oil and gas platforms, the simulation test standard library explicitly stipulates that the accuracy error range of triaxial stress loading is controlled within 0.5%, the sampling frequency of the multi-dimensional monitoring unit is no less than 100 times per second, and a temperature compensation and zero-point calibration process is preset to address the sensor signal drift problem under seawater corrosion environments. Based on the simulation test standard library, a true triaxial stress comparison is performed on the parameter samples in the initial crack propagation simulation scheme to obtain a target stress value that highly matches the actual service scenario, ensuring the accuracy of the test loading conditions. The formula for calculating the target stress value is: ,in, The theoretical stress value in the initial simulation scheme, This is a correction factor for the service environment. This is a correction factor for the mechanical properties of the material.

[0078] Multi-dimensional monitoring units are used to monitor cracks in all dimensions, acquiring microcrack evolution characteristics, crack propagation process, and overall sample deformation, thereby obtaining sample characteristic change values. Crack multi-dimensional monitoring integrates acoustic, optical, and electrical monitoring technologies. In the experiment, acoustic emission sensors can capture stress wave signals at the initiation of microcracks, thus determining the crack initiation location and propagation rate; high-speed cameras can record the complete process of cracks from microcracks to macroscopic propagation in real time, accurately measuring the crack length, width, and propagation path; and strain gauge arrays can capture the overall deformation distribution of the sample, identifying stress concentration areas. Through the fusion analysis of these multi-source monitoring data, key characteristic change values ​​such as microcrack density, crack propagation rate, and overall deformation of the sample during stress loading can be obtained, intuitively reflecting the dynamic process of crack evolution. Sample characteristic change values ​​can be quantified using the following formula: ,in, The characteristic value at a certain moment during the experiment. These are the initial eigenvalues. The characteristic rate of change.

[0079] Dynamic change data from a true triaxial stress loading unit and deformation and deformation rate data from a sensing performance acquisition unit are collected to obtain the stress change rate value, establishing a dynamic correlation between stress loading and component deformation. When the true triaxial stress loading unit increases the stress at a preset rate, the sensing performance acquisition unit synchronously records the changes in the resistance and capacitance parameters of the sensing element. These changes are directly related to the component's deformation and deformation rate. By synchronously analyzing the stress output data from the loading unit and the deformation data from the acquisition unit, the correspondence between the stress change rate and the deformation rate can be calculated, thus obtaining the stress change rate value. The stress change rate value is a key indicator for judging the component's response characteristics under dynamic stress changes. The formula for calculating the stress change rate value is: ,in, The stress change This represents the change over time.

[0080] The real-time data interaction rules, established based on the central control unit to create a standard library for simulation testing, are designed to ensure data synchronization and coordination among all units during the experiment. When the multi-dimensional monitoring unit detects that the crack propagation rate exceeds a preset threshold, the central control unit automatically triggers the stress loading unit to reduce the loading rate according to the real-time data interaction rules. Simultaneously, it instructs the sensing performance acquisition unit to increase the sampling frequency to capture more precise performance changes. This real-time interaction mechanism effectively avoids errors caused by data asynchrony during the experiment, ensuring the controllability and reliability of the entire experimental process.

[0081] Finally, the target stress value, sample characteristic change value, and stress change rate value are compared with the real-time data interaction rules to obtain simulated interactive data. The algorithm of the central control unit dynamically verifies the various measured data obtained in the experiment against the theoretical expectations in the simulation scheme. When the crack propagation rate measured in the experiment deviates from the predicted value in the initial simulation scheme, the central control unit automatically records this deviation according to the real-time data interaction rules and uses it as an important basis for subsequent optimization of the simulation scheme. Through this multi-dimensional data comparison and verification, the final simulated interactive data includes both the actual response during the experiment and reflects the difference between the theoretical scheme and the actual situation, providing comprehensive and reliable data support for subsequent feature extraction, error judgment, and scheme iteration.

[0082] Please see Figures 1-2 The simulation interaction data is used for feature extraction and error assessment. The features of the simulation interaction data are then fed back to the multi-field coupling association rule base to obtain a secondary crack propagation simulation scheme. The specific steps include:

[0083] Feature parameters of crack evolution, stress and deformation, and sensing performance are extracted; the extracted feature parameters are compared with the real-time calculation results of the multi-field coupling association rule base to obtain feature deviation values; and the error points of feature parameters of stress field, deformation field, and crack evolution in the multi-field coupling association rule base are determined based on the feature deviation values.

[0084] Based on the feature deviation value and the feature parameter error point, the relevant parameters of the multi-field coupled association rule base are corrected and iterated to obtain the feature optimization value;

[0085] The simulation optimization library is obtained by comparing the feature optimization values ​​with the preset simulation accuracy threshold.

[0086] Based on a simulation optimization library, a secondary crack propagation scheme was generated to simulate the entire crack propagation process of a sensing element under true triaxial stress. First, characteristic parameters of crack evolution, stress and deformation, and sensing performance were extracted from the simulation interaction data. For the response characteristics in different dimensions, key indicators reflecting the essence of the simulation were extracted. In the crack evolution dimension, crack initiation time, propagation rate, and penetration path length were extracted; in the stress and deformation dimension, stress concentration factor, maximum deformation, and deformation uniformity were extracted; and in the sensing performance dimension, sensitivity attenuation rate, signal output stability, and response time were extracted. These characteristic parameters together constitute a quantitative description of the simulation results, providing a foundation for subsequent error analysis.

[0087] The extracted feature parameters are compared with the real-time calculation results of the multi-field coupling association rule base to obtain the feature deviation value. Based on the feature deviation value, the error points of the feature parameters of stress field, deformation field, and crack evolution in the multi-field coupling association rule base are determined. This comparison process requires the establishment of a clear quantitative standard. The formula for calculating the feature deviation value is as follows: ,in, These are the experimentally measured characteristic parameter values. The simulated feature parameter values ​​are calculated from a multi-field coupled association rule base. This represents the characteristic deviation value. When the characteristic deviation value exceeds a preset threshold, it can be determined that the corresponding multi-field coupling rule has an error point. If the crack propagation rate measured in the experiment is 0.5 micrometers per second, while the calculation result of the rule base is 0.3 micrometers per second, the characteristic deviation value reaches 66.7%, far exceeding the preset 10% threshold. This indicates that there is a significant error in the association rule of stress-driven crack propagation in the rule base, and it needs to be corrected.

[0088] Based on the feature deviation value and the error points of the feature parameters, the relevant parameters of the multi-field coupled association rule base are corrected iteratively to obtain the optimized feature value; through multiple rounds of iteration, the value gradually approaches the true law. Regarding the error points in the crack propagation rate, the causes of the error are first analyzed as follows: the rule base does not fully consider the influence of micro-defect distribution on stress concentration, or the parameter settings for material fracture toughness do not match reality. Subsequently, by introducing a correction factor for micro-defect distribution density or updating the measured data of material fracture toughness, the association rules are iteratively corrected. After each correction, the feature parameter values ​​are recalculated and compared with experimental data until the feature deviation value drops to an acceptable range. The calculation formula for the iterative process is: ,in, The optimized feature value after the nth iteration. This is the iteration step size coefficient. This represents the feature deviation value in the nth iteration. Through continuous iteration, the optimized feature value that accurately reflects the experimental results is finally obtained.

[0089] The optimized feature values ​​are compared with a preset simulation accuracy threshold to obtain the simulation optimization library. The core of the comparison process is to determine whether the corrected multi-field coupling rules meet the simulation accuracy requirements. The preset simulation accuracy threshold is that the feature deviation value does not exceed 5%. When the deviation values ​​corresponding to all optimized feature values ​​meet this condition, these optimized rule parameters can be integrated into the simulation optimization library. This represents a precise upgrade based on the original library, retaining the core logic of the rule library while improving the accuracy and reliability of the simulation by correcting error points.

[0090] Finally, based on the simulation optimization library, the entire crack propagation process of the sensing element under true triaxial stress was simulated, generating a secondary crack propagation simulation scheme. Using the experimentally validated simulation optimization library as a foundation, the entire process of crack initiation, propagation, and penetration was re-analyzed. When simulating deep-sea pressure sensing elements, the secondary scheme fully considers the modified stress concentration rules and crack propagation rate rules, more accurately predicting the evolution path of cracks under high-stress coupling and corrosion environments, as well as the attenuation of sensing performance with crack propagation. Compared to the initial scheme, the secondary crack propagation simulation scheme can more realistically reflect the behavioral characteristics of the sensing element in actual service scenarios, providing a more reliable theoretical basis for the subsequent design of crack resistance enhancement schemes.

[0091] Please see Figures 1-2 The simulation optimization library is obtained by comparing the optimized feature values ​​with a preset simulation accuracy threshold. This process includes the following steps:

[0092] If the feature optimization value is less than the simulation accuracy threshold, a simulation optimization library is generated.

[0093] If the optimized feature value is greater than or equal to the simulation accuracy threshold, then based on the fact that the feature deviation value and the feature parameter are identical, the relevant parameters of the multi-field coupling association rule base are iteratively optimized to obtain the optimized feature value, until the optimized feature value is less than the simulation accuracy threshold, thus generating the simulation optimization library. First, the preset simulation accuracy threshold is defined. The simulation accuracy threshold is the core standard for measuring whether the optimized feature value meets the simulation accuracy requirements, and it is usually determined based on the application scenario and performance requirements of the sensing element. For high-temperature sensing elements applied to turbine blades of aero-engines, the accuracy requirements for crack propagation simulation are extremely high, and the preset simulation accuracy threshold can be set to a feature deviation value not exceeding 3%. For pressure sensing elements in ordinary industrial environments, the threshold can be appropriately relaxed to 5%. The setting of the simulation accuracy threshold directly determines the accuracy level of the simulation optimization library and is an important basis for subsequent judgment and correction.

[0094] The optimized feature value is compared with the simulation accuracy threshold, and different processing logics are executed according to the comparison result. When the optimized feature value is less than the simulation accuracy threshold, it indicates that the multi-field coupling correlation rule after correction and iteration can accurately reflect the experimental results, and the simulation optimization library can be directly generated. If the crack propagation rate deviation corresponding to a certain optimized feature value is 2.8%, which is less than the preset 3% threshold, it means that the correction of the rule parameter has met the accuracy requirements and can be included in the simulation optimization library.

[0095] When the optimized feature value is greater than or equal to the simulation accuracy threshold, a new round of iterative optimization and correction is needed for the relevant parameters of the multi-field coupling association rule base based on the feature deviation value and the feature parameter error point. This process continues until the optimized feature value is less than the simulation accuracy threshold, at which point the simulation optimization library is generated again. This iterative correction process is not a simple repetition, but a precise adjustment targeting the root cause of the error. If the measured sensor sensitivity attenuation rate is 15% in the experiment, while the simulation result corresponding to the optimized feature value is 22%, the feature deviation value reaches 46.7%, far exceeding the 5% simulation accuracy threshold. In this case, it is necessary to backtrack to the error point and determine whether the correlation model between stress and sensor performance in the rule base is too simplified, or whether the material aging parameter settings do not match reality. Subsequently, the association rules are iteratively corrected by introducing the influence factor of stress gradient on sensor performance or updating the measured data of material aging rate. After each iteration, the optimized feature value is recalculated and compared with the simulation accuracy threshold. Through continuous iteration, the deviation between the optimized feature value and the measured result is gradually reduced until the accuracy requirements are met.

[0096] In the simulation of pressure sensing elements on deep-sea oil and gas platforms, the initial optimized feature values ​​showed a crack propagation rate deviation of 8%, exceeding the 5% simulation accuracy threshold. Analysis revealed that the error stemmed from the rule base's insufficient consideration of the accelerating effect of seawater corrosion on stress concentration. Therefore, a corrosion stress acceleration factor was introduced in the iterative correction process, and its value was adjusted through multiple iterations. When the corrosion stress acceleration factor was adjusted to 1.3, the crack propagation rate deviation corresponding to the optimized feature values ​​decreased to 4.2%, meeting the simulation accuracy threshold requirement. At this point, the simulation optimization library could be generated. This library integrates all validated correction rules, more accurately reflecting the crack propagation behavior of sensing elements under true triaxial stress and corrosion environments, providing reliable rule support for subsequent secondary simulation schemes.

[0097] Please see Figures 1-2 Based on the crack evolution law and sensing performance failure mechanism of the secondary crack propagation simulation scheme, a multi-dimensional crack resistance improvement scheme is generated, which specifically includes the following steps:

[0098] Based on the secondary crack propagation simulation scheme, the crack initiation mechanism, propagation law and influence of different stress characteristics on crack evolution of the sensing element under true triaxial stress coupling are determined to obtain the crack evolution law.

[0099] The sensor performance failure mechanism is derived based on the correspondence between each stage of crack propagation and the degradation of sensor performance.

[0100] Based on the crack evolution law and the failure mechanism of sensing performance, a multi-dimensional optimization scheme is generated from four dimensions: structural design optimization, material modification and strengthening, sensing unit protection, and stress adaptation and control.

[0101] The multi-dimensional optimization scheme was compared and verified with the simulation optimization library, and the optimization scheme with the best verification effect was selected to obtain the crack resistance improvement scheme. First, based on the secondary crack propagation simulation scheme, the crack initiation mechanism, propagation law, and influence of different stress characteristics on crack evolution of the sensing element under true triaxial stress coupling were determined, thus obtaining the complete crack evolution law. Through in-depth mining of simulation data, the intrinsic driving logic of crack initiation and penetration was revealed. When simulating the pressure sensing element of the deep-sea oil and gas platform, the secondary scheme clearly shows that cracks initiate at the initial micro-defect, and under triaxial stress coupling, the crack propagation rate is positively correlated with the stress triaxiality. When the stress triaxiality exceeds the critical value, the crack will change from ductile propagation to brittle propagation. By quantifying the influence of different stress characteristics, a correlation model between crack propagation rate and stress triaxiality can be established. The crack propagation rate calculation formula is: Where v is the crack propagation rate, The stress triaxiality is represented by a and b, which are material correlation coefficients obtained by fitting simulation data. These coefficients can accurately predict the crack evolution trend under different stress conditions, providing a theoretical basis for subsequent optimization.

[0102] Based on the correspondence between each stage of crack propagation and the degradation of sensing performance, a sensing performance failure mechanism is derived. Crack propagation directly affects the core performance of sensing elements. In the crack initiation stage, the resistance of the sensing element will experience a slight drift due to local deformation; when the crack propagates to the sensing unit, the capacitance value will drop sharply, and the sensitivity will be significantly reduced; and when the crack is completed, the sensing signal may even fail completely. By establishing the correspondence between the crack propagation stages and sensing performance parameters, the key nodes of performance failure can be identified, the degradation law of sensing performance with crack propagation can be clearly revealed, and the failure mechanism can be accurately located.

[0103] Based on the crack evolution law and the failure mechanism of sensing performance, a multi-dimensional optimization scheme is generated from four dimensions: structural design optimization, material modification and strengthening, sensor unit protection, and stress adaptation and control. The optimization measures in each dimension can specifically solve specific problems.

[0104] Structural design optimization reduces stress concentration by adjusting the geometry of sensing elements; adding transition fillets in micro-defect areas where cracks are prone to initiation can reduce the stress concentration factor by more than 30%.

[0105] Material modification and strengthening can improve the fracture toughness and corrosion resistance of materials by doping alloying elements or surface coating treatment. Adding chromium to nickel-based alloys can increase fracture toughness by 25% and effectively inhibit crack propagation.

[0106] The sensing unit is protected by isolating the core sensing unit with encapsulation material to prevent cracks from directly affecting sensing performance. A polyimide encapsulation layer is used to block the path of damage to the sensing unit when the crack extends to the encapsulation layer.

[0107] Stress adaptation and control, through the design of a stress buffer structure, ensures that the sensing element maintains a uniform stress distribution under triaxial stress. Adding an elastic buffer layer at the element's edge can improve stress uniformity to over 90%, delaying crack initiation.

[0108] Finally, the multi-dimensional optimization schemes were compared and verified with the simulation optimization library to select the optimal scheme, thus obtaining the final crack resistance improvement scheme. The implementation effect of each optimization scheme was quantitatively evaluated using precise rules in the simulation optimization library. Combining structural design optimization with material modification and strengthening schemes, calculations using the simulation optimization library showed that the crack propagation rate was reduced by 60%, and the sensor sensitivity attenuation rate was controlled within 5%, far superior to the effect of single-dimensional optimization. By comparing the simulation verification results of different schemes, the crack resistance improvement scheme with the best overall performance was selected. This scheme can effectively suppress crack propagation and ensure the stability of sensor performance, providing a solid guarantee for the reliable service of intelligent sensing elements under true triaxial stress environments.

[0109] Please see Figures 1-2 The methods also include:

[0110] Collect basic information database data, simulation schemes, experimental data, simulation results and effective optimization schemes of different types and specifications of sensing elements, and construct an intelligent database for true triaxial stress crack propagation simulation of intelligent sensing elements;

[0111] Based on an intelligent database, simulation pre-selection schemes are generated by matching the service environment characteristics and intrinsic properties of sensing elements. First, basic information database data, simulation schemes, experimental data, simulation results, and effective optimization schemes for different types and specifications of sensing elements are collected to construct an intelligent database for true triaxial stress crack propagation simulation of intelligent sensing elements. A structured knowledge system is established according to sensing element type, application scenario, service environment, and intrinsic properties. The database stores basic information on different types of elements, including high-temperature sensing elements for aero-engines, pressure sensing elements for deep-sea oil and gas platforms, and vibration sensing elements for rail transit, including material mechanical properties, structural configuration parameters, and initial micro-defect background data. Simultaneously, it integrates historical simulation schemes for corresponding elements, stress loading data during experiments, crack evolution monitoring results, and validated effective crack-resistant optimization schemes. Through standardized processing and associated storage of these multi-source heterogeneous data, an intelligent database capable of supporting rapid querying and intelligent matching is formed, providing rich knowledge reserves for subsequent scheme generation.

[0112] Based on an intelligent database, the service environment characteristics and intrinsic properties of sensing elements are matched to generate simulated preliminary schemes. A similarity evaluation model is established between the data to filter historical cases highly similar to the target sensing element from the database. Then, preliminary schemes are generated based on the mature solutions from these cases. The similarity evaluation can be quantified using the following formula: Where S is the similarity score. The weight of the i-th feature parameter, For the characteristic parameter values ​​of the target sensing element, These are the feature parameter values ​​for reference cases in the database. The lower the score, the higher the similarity between the target element and the reference case.

[0113] When simulating crack propagation in a newly developed deep-sea pressure sensing element, its service environment characteristics can be extracted first, such as deep-sea high-pressure triaxial stress coupling, seawater corrosion, and low-temperature environment, as well as its intrinsic properties, such as nickel-based alloy material, thin-film structure, and specific initial micro-defect distribution. Subsequently, through matching with an intelligent database, historical cases with the lowest similarity scores are selected, representing the deep-sea pressure sensing element simulation cases that best match the new element's service environment and intrinsic properties. Based on the validated simulation schemes, experimental data, and optimization strategies from these cases, a preliminary simulation scheme for the new element can be quickly generated. This preliminary scheme is not the final scheme, but rather provides an efficient starting point for subsequent simulation processes, significantly reducing the time cost of building a simulation scheme from scratch, while also effectively drawing on historical experience to improve the initial rationality and reliability of the simulation scheme.

[0114] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for simulating crack propagation in a true triaxial stress environment using an intelligent sensing element, characterized in that, Includes the following steps: The service environment characteristics and physical characteristic parameters of the intelligent sensing element are collected to establish a basic information database for true triaxial stress simulation of the sensing element. The service environment characteristics include triaxial stress coupling parameters, stress dynamic change parameters and service medium influence parameters. The physical characteristic parameters include mechanical properties, configuration parameters, initial sensing performance and background data. Based on the true triaxial stress simulation basic information library, and incorporating the multi-field coupling association rule library of stress field, deformation field and sensing field, an initial crack propagation simulation scheme for sensing element is generated. Based on the initial crack propagation simulation scheme, the sensor element sample is precisely clamped and stress-loaded to collect full-dimensional crack monitoring, real-time stress and deformation detection, and dynamic sensor performance simulation interactive data. Feature extraction and error judgment are performed on the simulated interactive data, and the features of the simulated interactive data are fed back to the multi-field coupling association rule base to obtain a secondary crack propagation simulation scheme. Based on the crack evolution law and sensor performance failure mechanism of the secondary crack propagation simulation scheme, a multi-dimensional crack resistance improvement scheme is generated. The final optimized solution is output by verifying the anti-fracture enhancement scheme based on simulated data.

2. The method for simulating crack propagation in a true triaxial stress environment using an intelligent sensing element according to claim 1, characterized in that: The process of collecting service environment characteristics and intrinsic properties parameters of intelligent sensing elements and establishing a basic information database for true triaxial stress simulation of sensing elements includes the following steps: Collect triaxial stress amplitude, stress loading rate, number of cyclic loading and unloading, stress coupling ratio, and environmental characteristic parameters of the service medium such as temperature, humidity, and corrosivity in the actual service scenarios of the sensing element, and establish a service environment characteristic sub-library. Acquire the mechanical properties of the material elastic modulus, Poisson's ratio, and fracture toughness of the sensing element, as well as the configuration parameters of the structural geometry and microstructure distribution, and the background data of the location, size, shape, and distribution density of the initial micro-defects. The data acquisition equipment measures the initial sensing performance parameters of the sensing element in a stress-free state, including resistance, capacitance, sensitivity, and signal output stability. The mechanical properties, configuration parameters, initial sensing performance, and background data are integrated into a sub-library of body characteristic parameters. A basic information database for true triaxial stress simulation is constructed based on the service environment feature sub-database and the ontological characteristic parameter sub-database.

3. The method for simulating crack propagation in a true triaxial stress environment using an intelligent sensing element according to claim 2, characterized in that: Based on the true triaxial stress simulation database, and incorporating a multi-field coupling and correlation rule library of stress field, deformation field, and sensing field, an initial crack propagation simulation scheme for the sensing element is generated, specifically including the following steps: Collect stress field data, deformation field simulation data, sensor field simulation data, and crack evolution data to construct a multi-field coupling association rule base; A basic standard library is generated by mining the multi-field coupling association rule base based on the associated data of the true triaxial stress simulation basic information base. The synergistic effect of the basic standard library in the multi-field coupling association rule base is used to generate an initial crack propagation simulation scheme.

4. The method for simulating crack propagation in a true triaxial stress environment using an intelligent sensing element according to claim 3, characterized in that: Based on the initial crack propagation simulation scheme, the sensor element sample is precisely clamped and stress-loaded to collect simulation interactive data on full-dimensional crack monitoring, real-time stress and deformation detection, and dynamic sensor performance. The specific steps include: A simulation test standard library is constructed based on a true triaxial stress loading unit, a multi-dimensional monitoring unit, a sensing performance acquisition unit, and a central control unit. The target stress value is obtained by comparing the parameter samples of the initial crack propagation simulation scheme with the true triaxial stress based on the simulation test standard library. Based on multi-dimensional monitoring units, cracks are monitored in all dimensions to obtain micro-crack evolution characteristics, crack penetration process and overall sample deformation to obtain sample characteristic change values; The stress change rate value is obtained by collecting dynamic change data in the true triaxial stress loading unit and the deformation amount and deformation rate of the sample in the sensing performance acquisition unit. Real-time data interaction rules for establishing a simulation test standard library based on the central control unit; The simulated interactive data is obtained by comparing the target stress value, sample feature change value, and stress change rate value with the real-time data interaction rules.

5. The method for simulating crack propagation in a true triaxial stress environment using an intelligent sensing element according to claim 4, characterized in that: Feature extraction and error assessment are performed on the simulated interactive data. The features of the simulated interactive data are then fed back to a multi-field coupling association rule base to obtain a secondary crack propagation simulation scheme. The specific steps include: Feature parameters of crack evolution, stress and deformation, and sensing performance are extracted; the extracted feature parameters are compared with the real-time calculation results of the multi-field coupling association rule base to obtain the feature deviation value; and the error points of the feature parameters of stress field, deformation field, and crack evolution in the multi-field coupling association rule base are determined based on the feature deviation value. Based on the feature deviation value and the feature parameter error point, the relevant parameters of the multi-field coupled association rule base are corrected and iterated to obtain the feature optimization value; The simulation optimization library is obtained by comparing the feature optimization values ​​with the preset simulation accuracy threshold. Based on the simulation optimization library, a secondary crack propagation scheme is generated by simulating the entire process of crack propagation of sensing elements under true triaxial stress environment.

6. The method for simulating crack propagation in a true triaxial stress environment using an intelligent sensing element according to claim 5, characterized in that: The simulation optimization library is obtained by comparing the optimized feature values ​​with a preset simulation accuracy threshold. This process includes the following steps: If the feature optimization value is less than the simulation accuracy threshold, a simulation optimization library is generated. If the feature optimization value is greater than or equal to the simulation accuracy threshold, then based on the fact that the feature deviation value and the feature parameter are identical, the relevant parameters of the multi-field coupled association rule base are corrected and iteratively optimized to obtain the feature optimization value, until the feature optimization value is less than the simulation accuracy threshold, and the simulation optimization base is generated.

7. The method for simulating crack propagation in a true triaxial stress environment using an intelligent sensing element according to claim 6, characterized in that: Based on the crack evolution law and sensing performance failure mechanism of the secondary crack propagation simulation scheme, a multi-dimensional crack resistance improvement scheme is generated, which includes the following steps: Based on the secondary crack propagation simulation scheme, the crack initiation mechanism, propagation law and influence of different stress characteristics on crack evolution of the sensing element under true triaxial stress coupling are determined to obtain the crack evolution law. The sensor performance failure mechanism is derived based on the correspondence between each stage of crack propagation and the degradation of sensor performance. Based on the crack evolution law and the failure mechanism of sensing performance, a multi-dimensional optimization scheme is generated from four dimensions: structural design optimization, material modification and strengthening, sensing unit protection, and stress adaptation and control. The multi-dimensional optimization schemes were compared and verified with the simulation optimization library, and the optimization scheme with the best verification effect was selected to obtain the crack resistance improvement scheme.

8. The method for simulating crack propagation in a true triaxial stress environment using an intelligent sensing element according to claim 7, characterized in that: The method also includes: Collect basic information database data, simulation schemes, experimental data, simulation results and effective optimization schemes of different types and specifications of sensing elements, and construct an intelligent database for true triaxial stress crack propagation simulation of intelligent sensing elements; Based on the intelligent database, the service environment characteristics and intrinsic properties parameters of the sensing element are matched to generate a simulation pre-selection scheme.

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