Nanoscale fault early warning and self-repairing method based on quantum sensing and material gene

Abstract

This invention relates to the field of intelligent fault early warning technology, and discloses a nanoscale fault early warning and self-repair method based on quantum sensing and materials genome. The method includes: a detection layer: using a diamond NV color center quantum sensor to capture nanoscale lattice distortion, and fusing multimodal data through federated learning; an evaluation layer: dynamically updating the probability of fault tree nodes, combining pre-set SSC critical threshold conditions from a materials genome library, establishing a remaining lifetime prediction model, and simulating the remaining lifetime; and a repair layer: automatically triggering a repair mechanism when the quantum signal offset and AI confidence level > 90%, and verifying the repair effect. This application, through the interdisciplinary integration of quantum sensing, materials genome engineering, and industrial AI, constructs a closed-loop system of nanoscale damage detection, dynamic fault tree evaluation, and autonomous repair, solving the technical challenges of difficult nanoscale damage monitoring, poor dynamic evaluation adaptation, and delayed repair response in traditional turbochargers under extreme conditions of high sulfur, high pressure, and high volatility.

Description

Technical Field

[0001] This invention relates to the field of intelligent fault early warning and repair technology, specifically to a nanoscale fault early warning and self-repair method based on quantum sensing and materials genes. Background Technology

[0002] In the fields of oil and gas field development and gas storage operation and maintenance, booster compressors, as key power equipment, are directly related to energy extraction efficiency and safe production due to their operational reliability. Currently, the fault monitoring technology commonly used in the industry is still based on traditional vibration sensing and periodic maintenance. Although these solutions can capture vibration signals at the micron level, they have gradually revealed systemic defects when faced with extreme operating conditions such as hydrogen embrittlement corrosion in ultra-high sulfur gas wells and high-frequency pressure fluctuations in gas storage facilities.

[0003] The core contradiction of the existing technology system lies in the dual deficiencies of monitoring accuracy and adaptability to operating conditions. On the one hand, the detection resolution of 1μm of traditional sensors cannot identify material lattice distortion, which leads to fault warnings often lagging behind the actual damage development. According to industry statistics, about 68% of sudden failures of booster compressors are due to early nanoscale defects that were not detected in time. On the other hand, the evaluation model based on static fault trees relies too much on historical experience data and is difficult to dynamically adapt to complex operating conditions such as changes in sulfur-containing medium concentration and pressure pulse impact. In ultra-high sulfur-containing gas wells, the fault misjudgment rate is as high as 23%.

[0004] This technological bottleneck directly triggered a chain reaction: the average unplanned downtime of equipment exceeded 48 hours, and the average daily production capacity loss per well reached 32,000 cubic meters. Furthermore, during personnel repairs, the H2S corrosive environment further accelerated the aging of seals, creating a vicious cycle of "failure-repair-failure." In-depth analysis revealed that the root cause lies in the physical limitations of traditional monitoring technologies; mechanical sensors are constrained by the elastic-optical effect threshold and cannot achieve atomic-level strain sensing. Simultaneously, the multi-scale characteristics of material damage evolution make it difficult for experience-based static models to construct universally applicable predictive patterns. Summary of the Invention

[0005] The present invention aims to provide a nanoscale fault early warning and self-repair method based on quantum sensing and material genes, which solves the problems that traditional monitoring cannot capture the initiation of nanoscale microcracks, is difficult to adapt to dynamic working conditions, and causes early warning failure and high false alarm rate.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a nanoscale fault early warning and self-repair method based on quantum sensing and materials genes, comprising, Detection layer: Employs a diamond NV color center quantum sensor to capture nanoscale lattice distortions and fuses multimodal data through federated learning; Evaluation layer: Based on the GNN algorithm, the probability of fault tree nodes is dynamically updated. Combined with the SSC critical threshold conditions preset in the material gene library, a Monte Carlo method is used to establish a remaining lifetime prediction model and simulate and calculate the remaining lifetime. Repair layer: When the quantum signal offset And AI confidence At 90%, the repair mechanism is automatically triggered; and the chemical bond reconstruction process is monitored simultaneously by Raman spectroscopy to verify the repair effect.

[0007] The principles and advantages of this scheme are: In existing technologies, oil and gas field booster pumps generally face systemic technical challenges in three major scenarios: the mid-to-late stage of single-well boosting, ultra-high sulfur gas wells, and dynamic injection and production in gas storage facilities. Firstly, traditional vibration sensors (1μm accuracy) cannot capture the initial lattice distortion (0.1pm-level microcracks) in the hydrogen embrittlement stage, resulting in 68% of sudden failures originating from undetected early nanoscale defects; under minute-level pressure fluctuations (cycle < 5 minutes) in gas storage facilities, the conventional 10kHz sampling frequency misses 5ms-level transient anomalies, resulting in blind spots in detection.

[0008] Secondly, static fault trees rely on historical experience and cannot dynamically adapt to the critical threshold of sulfide stress cracking (SSC) in high sulfur environments, with a misjudgment rate as high as 23%; they are also difficult to cope with the overload operation in the middle and late stages of single-well pressurization, resulting in an evaluation bottleneck.

[0009] Third, relying on operator response, the cycle is generally longer than 48 hours, and unplanned downtime results in a daily production loss of 32,000 cubic meters per well. The corrosive environment further exacerbated the secondary failure of the equipment after repair.

[0010] Therefore, this solution constructs a closed-loop intelligent operation and maintenance system throughout the entire lifecycle by integrating quantum sensing, materials genome engineering, and industrial AI.

[0011] For the detection layer, a diamond NV color center quantum sensor is used to achieve 0.1 pm lattice strain monitoring and a 1 MHz sampling frequency to capture transient anomalies, realizing nanoscale detection; combined with federated learning of vibration and acoustic emission signals, the anomaly recognition rate is improved.

[0012] For the evaluation layer, a dynamic fault tree is constructed based on a graph neural network (GNN) to correct node probabilities in real time and achieve dynamic evaluation; and the SSC critical thresholds pre-set in the material gene library are integrated to achieve remaining lifetime prediction through Monte Carlo simulation.

[0013] For the repair layer, when the quantum signal offset Furthermore, when the AI ​​confidence level is >90%, the release of borate microcapsules is automatically triggered (pH < 4 response), and the chemical bond reconstruction is monitored in situ by Raman spectroscopy, resulting in a 72% reduction in the corrosion rate.

[0014] This solution establishes a quantitative mapping relationship from "nanolattice distortion to macroscopic faults" for the first time, breaking through cross-scale monitoring and solving the "monitoring chain break" problem of traditional technologies. It provides fatigue crack early warning for single wells up to 240 hours in advance. Furthermore, for ultra-high sulfur-containing wells, by introducing a corrosion-hydrogen embrittlement synergistic factor, the false alarm rate is reduced from 50% to <5%, achieving dynamic adaptability; response time is shortened from 48 hours to <1ms, unplanned downtime is reduced by more than 60%, single-well operation and maintenance costs are reduced by 35%, and annual natural gas production is increased by over 200 million cubic meters, significantly improving the efficiency of closed-loop repair. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the nanoscale fault early warning and self-repair method based on quantum sensing and materials genes of the present invention. Figure 2 This is a closed-loop structural diagram of the nanoscale fault early warning and self-repair method based on quantum sensing and materials genes of the present invention. Detailed Implementation

[0016] The following detailed description illustrates the specific implementation method: The nanoscale fault early warning and self-repair method based on quantum sensing and materials genome in this embodiment addresses three key scenarios: the mid-to-late stage of single-well pressurization in oil and gas field booster turbines, pressurization and development of ultra-high sulfur gas wells, and dynamic pressurization during the injection and production process of gas storage facilities. It constructs an interdisciplinary intelligent operation and maintenance system of "quantum sensing + AI computing power + materials genome engineering", which solves problems such as insufficient pressure, corrosion resistance, hydrogen embrittlement resistance, and insufficient equipment reliability under high-frequency pressure fluctuations. It realizes closed-loop management of the entire life cycle from nanoscale damage detection to macroscopic fault early warning and molecular-level autonomous repair.

[0017] Provides a nanoscale fault early warning and self-healing method based on quantum sensing and materials genes, as shown in the appendix. Figure 1 and attached Figure 2 As shown, including, Detection layer: Employs a diamond NV color center quantum sensor to capture nanoscale lattice distortions and fuses multimodal data through federated learning.

[0018] In this embodiment, a diamond NV color center quantum sensor is used to realize the correlation mapping between nanoscale lattice strain (accuracy 0.1pm) and macroscopic faults, directly linking quantum sensing (nanoscale) with industrial equipment faults (macroscale), realizing cross-scale monitoring, and converting the initial lattice distortion of hydrogen embrittlement into a quantitative mapping of macroscopic vibration anomalies.

[0019] By embedding diamond NV color center quantum sensors into critical components, such as the root of compressor blades or within sealing rings, a resolution of 0.1 pm and a sampling frequency of 1 MHz can capture transient anomaly signals at the 5 ms level. By monitoring lattice distortion, early warning of nanoscale damage is achieved, providing core data support for equipment fault prediction and autonomous repair. Early warning of lattice distortion through quantum sensing addresses the sudden failure problem caused by material fatigue accumulation in the later stages of single-well pressurization. For minute-level pressure fluctuations in gas storage facilities, a 1 MHz sampling frequency quantum sensor has been developed to capture transient anomaly signals, achieving high-frequency dynamic adaptation. It also possesses resistance to... Its corrosion-resistant and high-pressure-resistant properties make it suitable for harsh working conditions such as ultra-high sulfur gas wells, and the false alarm rate can be controlled below 5%.

[0020] By fusing multimodal data through federated learning, the federated learning framework uses quantum signal, vibration and acoustic emission data fusion to improve the accuracy of anomaly identification, break through the blind spot of traditional micron-level monitoring, and achieves an F1-score of 0.94 compared to traditional anomaly identification (traditional is 0.82).

[0021] Evaluation layer: Based on the GNN algorithm, the probability of fault tree nodes is dynamically updated. Combined with the pre-set SSC critical threshold conditions in the material gene library, a Monte Carlo method is used to establish a remaining lifetime prediction model and simulate and calculate the remaining lifetime.

[0022] In this embodiment, a dynamic fault tree is constructed based on a graph neural network (GNN), and physical constraints provided by a materials genome database are integrated to correct the failure probability in real time. AI computing power (Graph Neural Network GNN) is used to update the failure probability of the fault tree in real time, solving the problem of condition drift in traditional static fault trees under high sulfur content environments.

[0023] Wherein, the basic failure rate is expressed as (1) The dynamic correction term is represented as (2) Among them, the weighting coefficient Dynamic updates through federated learning are represented as (3) In equation (1), The baseline failure rate; , , This is the operating condition influence coefficient; It is the stress cracking factor for sulfides; This represents the cumulative runtime since the last major overhaul. This represents the cumulative number of start-stop cycles. This refers to the proportion of remaining effective components in the lubricant. By integrating equipment operating conditions with extreme environmental factors, dynamic prediction of failure rates is achieved, overcoming the technical bottleneck that traditional static models cannot adapt to high sulfur and variable operating conditions.

[0024] In equation (2), , , These are weighting coefficients, corresponding to the contributions of quantum sensing, vibration, and acoustic emission signals, respectively. For strain readings from quantum sensors; The amplitude of the vibration; Using acoustic emission energy, the failure rate is dynamically corrected through weighted fusion of multimodal data (quantum + vibration + acoustic emission), thereby improving the anomaly identification accuracy under extreme conditions (such as high sulfur and high frequency fluctuations).

[0025] In equation (3), The learning rate is used to control the step size of weight updates and avoid overfitting. The actual fault labels are provided by historical data or personnel annotations; The model predicts the failure probability, which is output by the dynamic fault tree. Let i be the input feature value of the i-th sensor, i.e. , or The weights of each sensor are dynamically adjusted based on the prediction error to achieve adaptive optimization within the federated learning framework.

[0026] By providing fundamental data such as fatigue constants and corrosion rates through a materials genome library, a physical constraint layer is constructed. This materials genome data serves as the physically interpretable layer for the AI ​​model, preventing overfitting. In this embodiment, the materials genome constraint adopts a physical limit, specifically the SSC critical threshold condition, which is the critical condition for sulfide stress cracking, expressed as... (4) In the formula, The sulfide stress cracking risk index; This refers to the concentration of hydrogen sulfide. Ambient temperature; Local stress in the material; , , For environmental coupling coefficients, respectively corresponding to The influence weights of concentration, temperature, and stress; The critical strain threshold for SSC resistance of the material is provided by the materials gene library.

[0027] When calculated greater than the material's critical strain threshold When the equipment is deemed to have a risk of sulfide stress cracking, an early warning or repair mechanism needs to be triggered. This is achieved through quantification. The synergistic effect of concentration, temperature, and stress enables dynamic assessment of the risk of sulfide stress cracking in booster materials in ultra-high sulfur gas wells, overcoming the technical bottleneck that traditional experience-based judgments cannot quantify critical thresholds.

[0028] In this embodiment, outlier analysis based on Mahalanobis distance is used to detect anomalies, and the remaining lifetime is calculated using Monte Carlo simulation. Specifically, the remaining lifetime prediction model includes a fatigue accumulation model based on quantum sensing and a multi-parameter coupled correction model, wherein the fatigue accumulation model is expressed as follows: (5) The multi-parameter coupled correction model is expressed as follows: (6) In equation (5), The material fatigue constant, provided by the Materials Gene Database, reflects the material's ability to resist fatigue damage; for The strain reading of the quantum sensor at a given moment; The lattice strain safety threshold is the critical value at which a material begins to suffer irreversible damage. By capturing nanoscale lattice distortion through quantum sensing, the accumulation of microscopic damage is directly correlated with the macroscopic remaining lifetime, overcoming the technical bottleneck of traditional vibration monitoring's inability to quantify early fatigue damage.

[0029] In equation (6), This is a correction factor for hydrogen sulfide concentration, used for quantification. The impact of corrosion on lifespan; This is a temperature correction factor, reflecting the accelerating effect of temperature on the fatigue rate of materials. This is a pressure correction factor that considers the impact of pressure fluctuations on stress accumulation in the equipment. In this embodiment, the correction factor is updated in real time using a graph neural network (GNN), and the input parameters include real-time... Concentration, temperature, and pressure data, along with a corrosion-stress coupling model from a materials gene library, are used. Through multi-parameter coupling correction, the limitations of a single quantum sensing model in adapting to extreme operating conditions (high sulfur, high temperature, and high pressure) are overcome, allowing the remaining life prediction error to be controlled within ±8 hours, providing a precise decision-making basis for "predictive maintenance".

[0030] It also includes adjusting the fault tree model according to the operating conditions; among them, for the middle and late stages of single-well pressurization, a power-fatigue coupling model is introduced; parameters are dynamically adjusted according to the formation pressure drop rate; and for ultra-high sulfur wells, the SSC critical threshold model is activated.

[0031] Specifically, for the mid-to-late stage of single-well pressurization, the introduced power-fatigue coupling model is expressed as follows: (7) In the formula, Material fatigue failure rate; is a proportionality coefficient, an empirical constant related to equipment type and material properties; Let t be the outlet pressure of the booster compressor; Let be the gas flow rate at time t; The speed of the booster compressor; This represents the cumulative operating time since the last major overhaul, reflecting the degree of accumulated fatigue damage. This represents the material's characteristic lifespan parameter. In this embodiment, the nonlinear effect of runtime on fatigue accumulation is quantified using an exponential term. When near When the exponential term grows rapidly, it indicates that the material is approaching its fatigue limit and the failure rate increases sharply. By coupling real-time operating parameters (pressure, flow rate, rotational speed) with cumulative operating time, the dynamic adaptation problem of "overload-fatigue accumulation" caused by formation pressure drop in the middle and late stages of single-well pressurization is solved. This provides a quantitative basis for the "power-material fatigue" branch in the dynamic fault tree, enabling fault warnings to be issued more than 240 hours in advance.

[0032] against Concentration > 30g / m 3 Under extreme operating conditions, a corrosion-hydrogen embrittlement synergistic factor is introduced to dynamically assess the risk of synergistic failure of materials due to corrosion and hydrogen embrittlement. This factor serves as a correction factor for the remaining life prediction model, improving the accuracy of fault early warning in high-sulfur environments and reducing false alarm rates. This can be expressed as... (8) In the formula, This represents the volume percentage of carbon dioxide. The relative humidity coefficient is used. Through multi-parameter coupling, the technical gap in quantifying the corrosion-hydrogen embrittlement synergistic effect under extreme sulfur-containing environments is filled.

[0033] For dynamic injection and production in gas storage facilities, the targeted release of a repair agent is triggered by pressure fluctuation cycles. A 1MHz sampling frequency is triggered when the pressure fluctuation cycle is less than 5 minutes, achieving a transient anomaly detection rate of 98% and resolving seal wear issues. The transient anomaly detection criterion can be expressed as follows: (9) In the formula, Score transient anomalies; The pressure fluctuation term characterizes the degree of pressure change during the gas storage injection and production process. For strain readings from quantum sensors; The dynamic anomaly threshold is adjusted in real time through a sliding window based on historical data and current operating conditions (such as gas composition and temperature).

[0034] By coupling the dynamic characteristics of pressure with the response of micromaterials, the technology gap of missed transient anomalies under high-frequency pressure fluctuations in gas storage has been filled, and the anomaly detection rate has been increased from 60% to 98%, providing early warning for faults such as seal wear and valve impact.

[0035] Repair layer: When the quantum signal offset And AI confidence At 90%, the repair mechanism is automatically triggered; and the chemical bond reconstruction process is monitored simultaneously by Raman spectroscopy to verify the repair effect.

[0036] In this embodiment, a self-healing trigger condition is set to automatically trigger the repair mechanism, i.e., release microcapsules (such as borate repair agents). The chemical bond reconstruction process in the repair region is then monitored in situ using Raman spectroscopy to verify the repair effect.

[0037] Among them, the borate microcapsules release at pH < 4, reducing the corrosion rate by 72%, and the in-situ verification of chemical bond reconstruction by Mann spectroscopy avoids the downtime losses of traditional disassembly and testing.

[0038] This embodiment also includes a parameter update mechanism, which updates the parameters by learning and correcting formulas online, as shown below. (10) In the formula, These are the updated model parameters; The old parameter values ​​before the update; The learning rate controls the step size of parameter updates, balancing convergence speed and stability; For loss function For parameters The gradient reflects the direction of the parameter's influence on the error; For materials genome physical constraints; The interpretability adjustment coefficient controls the weight of the physical constraint term and avoids overfitting. It realizes the integration of data-driven learning and physical law constraints, solves the overfitting problem of traditional AI models under extreme conditions (such as high sulfur and high frequency pressure fluctuations), and keeps the probability correction error of dynamic fault tree nodes within ±5%.

[0039] Meanwhile, in this embodiment, when non-gradual changes occur in operating conditions (such as switching between gas storage injection and production modes or sudden changes in the composition of high-sulfur gas well media), parameter fine-tuning alone cannot adapt to the distribution shift; drift detection is required to identify such changes. In this case, operating condition drift detection quantitatively determines whether the current operating condition deviates from the historical benchmark. When the difference exceeds a threshold, a model reconstruction mechanism is triggered to ensure the adaptability of core algorithms such as dynamic fault tree and remaining lifetime prediction under extreme operating conditions (such as a sudden increase in sulfur concentration or aggravated pressure fluctuations). This can be expressed as follows: (11) In the formula, The operating condition drift index; Data distribution for the new operating conditions; Distribution of historical operating data; This is the drift threshold. By calculating the KL divergence of the distribution of new and old operating condition data, we can quantitatively determine whether the current operating condition deviates from the historical benchmark.

[0040] When model reconstruction is triggered, i.e. At that time, the system calls new operating condition samples (such as material fatigue data under high sulfur content environment) from the material gene database, retrains the GNN model of dynamic fault tree, and updates the node probability correction rules (such as the operating condition adaptation coefficient of seal failure probability) to ensure the accuracy of fault warning. This solves the adaptability defects of traditional static models under sudden operating condition scenarios. Through distribution difference quantification, it realizes the intelligent switching from "passive parameter update" to "active model reconstruction", so that the fault prediction error of the system under extreme operating conditions such as sulfur content fluctuation and pressure change is controlled within ±8 hours, supporting the reliable operation of the booster throughout its entire life cycle.

[0041] The following is an explanation using a practical application example.

[0042] In this embodiment, taking the data of Jiangyou DTY1400 compressor unit as an example, a generalized fault prediction framework is first constructed, with the following input parameters: runtime =12,000h; Start-stop count =85 times; Lubricant active ingredients =0.65; sulfur content =25g / m 3 ; Quantum strain readings =0.8pm.

[0043] Take the baseline failure rate The basic failure rate can then be calculated using equation (1). This indicates that sulfur-containing environments increase the failure rate to 42 times the baseline value, with quantum strain contributing a 24% increment.

[0044] By selecting a crankshaft wear scenario and predicting the remaining life, we can obtain the following result according to equation (5): The lifespan is approximately 148 days, but high-sulfur environments shorten it to 66% of the theoretical value.

[0045] Set the critical criterion for sulfide stress cracking (SSC), and input the following parameters: Crankshaft stress =120MPa; Materials gene library data .

[0046] Then, according to equation (4), it can be calculated that .

[0047] The following will further illustrate this with specific scenario-based examples. Taking the lubrication system failure rate as an example, under summer operating conditions, the oil temperature T=89.14℃, and the vibration value... =7.2mm / s. The specific calculation result is output from equation (1). This indicates that the cooling system optimization needs to be initiated immediately. The "quantum sensing + dynamic fault tree" system's early warning effect in real-world scenarios was verified (fault warning 240 hours in advance). Simultaneously, the computational efficiency of the simplified model was improved (single execution time <100ms).

[0048] For piston seal leakage early warning scenarios, specifically targeting the DTY1400 compressor unit's piston seal system, the probability of seal failure is calculated and an early warning is triggered by quantifying input parameters such as quantum strain, pressure fluctuations, and operating time. For example, if the input parameters are... Packing ring strain ; Damage rate .

[0049] The triggering condition is: .

[0050] Based on the preceding calculations, and using the vibration interlocking event that occurred in January 2018 (subsequent time period) as the result for verification, the input parameters for the vibration abrupt change event are: ; By comparison, it can be seen that this method accurately predicts crankshaft wear.

[0051] Data from before and after the application of the DTY1400 compressor unit shows that this solution significantly improves operational efficiency: the fault warning lead time has been extended from the traditional ≤24 hours to 240 hours, a 10-fold improvement; the false alarm rate has decreased from 50% to 5%, a 90% reduction; and the number of unplanned shutdowns has decreased from 18 times / 3 years to ≤5 times / 3 years, a 72% reduction. These improvements effectively enhance the reliability and stability of equipment operation, significantly reducing operational costs and the risk of production interruption. Through quantum sensing and AI calculations constrained by materials genes, the DTY1400 unit can achieve a remaining life error of ±8 hours for key components, a 72% reduction in corrosion rate in high-sulfur environments, and 100% early warning of abnormal vibration events.

[0052] In this embodiment, the detection layer achieves early warning through nanoscale damage detection. Quantum sensing breaks through the blind zone of nanoscale damage monitoring, improving resolution by tens of millions of times and surpassing the limits of traditional physical monitoring. The evaluation layer, combining dynamic fault trees with materials genome data, enhances decision interpretability and achieves intelligent evaluation of dynamic fault trees. The repair layer, through a set automatic triggering repair mechanism, achieves millisecond-level response speeds for molecular-level self-healing materials, with a corrosion inhibition rate exceeding 70%, realizing a closed-loop autonomous repair system and reducing reliance on manual labor and unplanned downtime. This solution, through the interdisciplinary integration of quantum sensing, materials genome engineering, and industrial AI, systematically solves the three major industry challenges of "difficult detection, difficult evaluation, and difficult repair" for oil and gas field booster compressors under extreme operating conditions, providing original technical equipment support for unconventional gas field development.

[0053] The above descriptions are merely embodiments of the present invention, and common knowledge such as specific technical solutions and / or characteristics are not described in detail here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the technical solutions of the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A method for nanoscale fault early warning and self-repairing based on quantum sensing and material gene, characterized in that, include, Detection layer: Employs a diamond NV color center quantum sensor to capture nanoscale lattice distortions and fuses multimodal data through federated learning; Evaluation layer: Based on the GNN algorithm, the probability of fault tree nodes is dynamically updated. Combined with the SSC critical threshold conditions preset in the material gene library, a Monte Carlo method is used to establish a remaining lifetime prediction model and simulate and calculate the remaining lifetime. Repair layer: when the quantum signal offset And AI confidence > 90%, automatically trigger repair mechanism; and through the Raman spectrum monitoring chemical bond reconstruction process, verify the repair effect.

2. The method of claim 1, wherein the method is based on quantum sensing and material genomics for nanoscale failure early warning and self-repair. The diamond NV color center quantum sensor is implanted in the root of the blade or inside the sealing ring, with a resolution of 0.1 pm and a sampling frequency of 1 MHz.

3. The nanoscale fault early warning and self-repair method based on quantum sensing and materials genes according to claim 1, characterized in that: The federated learning framework employs the fusion of three sources of data: quantum signals, vibrations, and acoustic emissions.

4. The nanoscale fault early warning and self-repair method based on quantum sensing and materials genes according to claim 3, characterized in that: In the evaluation layer, the basic failure rate is expressed as: ； The dynamic correction term is represented as ； Among them, the weighting coefficient Dynamic updates through federated learning are represented as ； In the formula, The baseline failure rate; , , This is the operating condition influence coefficient; It is the stress cracking factor for sulfides; This represents the cumulative runtime since the last major overhaul. This represents the cumulative number of start-stop cycles. This refers to the percentage of remaining effective components in the lubricant. , , These are the weighting coefficients; For strain readings from quantum sensors; The amplitude of the vibration; For acoustic emission energy; The learning rate; Labels for actual faults; Predict the failure probability for the model; Let be the input feature value of the i-th sensor.

5. The nanoscale fault early warning and self-repair method based on quantum sensing and materials genes according to claim 1, characterized in that: The preset SSC critical threshold condition is the critical condition for sulfide stress cracking, expressed as: ； In the formula, This refers to the concentration of hydrogen sulfide. Ambient temperature; Local stress in the material; , , The environmental coupling coefficient; This represents the critical strain threshold for the material's resistance to SSC.

6. The nanoscale fault early warning and self-repair method based on quantum sensing and materials genes according to claim 5, characterized in that: The remaining lifetime prediction model includes a fatigue accumulation model based on quantum sensing and a multi-parameter coupled correction model, wherein the fatigue accumulation model is expressed as follows: ； The multi-parameter coupled correction model is expressed as follows: ； In the formula, The fatigue constant of the material; for The strain reading of the quantum sensor at a given moment; The lattice strain safety threshold; This is the correction factor for hydrogen sulfide concentration; This is a temperature correction factor; This is the pressure correction factor.

7. The nanoscale fault early warning and self-repair method based on quantum sensing and materials genes according to claim 1, characterized in that: The evaluation layer also includes adjusting the fault tree model according to the operating conditions; among them, for the middle and late stages of single-well pressurization, a power-fatigue coupling model is introduced; parameters are dynamically adjusted according to the formation pressure drop rate; and for ultra-high sulfur wells, the SSC critical threshold model is activated.

8. The nanoscale fault early warning and self-repair method based on quantum sensing and materials genes according to claim 7, characterized in that: The power-fatigue coupling model is expressed as follows: ； In the formula, This is the proportionality coefficient; Let t be the outlet pressure of the booster compressor; Let be the gas flow rate at time t; The speed of the booster compressor; This represents the cumulative runtime since the last major overhaul. These are the characteristic lifetime parameters of the material.

9. The nanoscale fault early warning and self-repair method based on quantum sensing and materials genes according to claim 7, characterized in that: against Concentration > 30g / m 3 Under extreme operating conditions, a corrosion-hydrogen embrittlement synergistic factor is introduced to dynamically assess the risk of synergistic failure of materials due to corrosion and hydrogen embrittlement. This factor serves as a correction factor for the remaining life prediction model, expressed as: ； In the formula, This represents the volume percentage of carbon dioxide. This is the relative humidity coefficient.

10. The nanoscale fault early warning and self-repair method based on quantum sensing and materials genes according to claim 1, characterized in that: It also includes a parameter update mechanism that updates parameters by learning and correcting formulas online, as shown below. ； In the formula, The learning rate; For loss function For parameters The gradient; For materials genome physical constraints; This is the interpretability adjustment coefficient.

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