Multi-dimensional device dismantling risk identification, prevention and control method

Abstract

The invention discloses a multi-dimensional device dismantling risk identification and prevention and control method, and relates to the field of risk identification systems. Comprising the steps of constructing a device information quantum database, formulating a risk prevention and control scheme by using a risk assessment model, and monitoring quantum state change of the device, dynamic evolution of a microstructure and quantum parameter fluctuation in the environment in real time by means of a quantum sensor network. By constructing a quantum database, applying a quantum optimization algorithm, deploying a quantum sensor network and other multi-dimensional quantum technology applications, atomic-scale accurate identification and dynamic prevention and control of the device dismantling risk are realized. Specifically, atomic-scale structure details are obtained by using a quantum imaging technology, a risk assessment model is optimized in combination with a quantum annealing algorithm, and the accuracy and efficiency of risk prediction are significantly improved; based on a protective barrier of a quantum regulation and control principle and a pollutant treatment technology, the quantum tunneling risk is effectively inhibited, and quantum-level decomposition of pollutants is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of risk identification systems, in particular to a multi-dimensional device dismantling risk identification and prevention method. BACKGROUND

[0002] In the process of continuous development in the industrial field, the dismantling operation of various old devices has become a key link to ensure production safety and promote industrial upgrading. With the increasing complexity of device structure and the diversification of its functions, the dismantling process is no longer a simple physical disassembly, but a systematic engineering involving multi-disciplinary knowledge integration and multi-dimensional risk interweaving, which puts unprecedented high requirements on risk identification and prevention technology.

[0003] Currently, the traditional device dismantling risk control means mainly relies on macro-structure analysis and experiential judgment. In terms of data collection, only the macro-geometric parameters and conventional material information of the device can be obtained, and there is a lack of effective means to obtain key data such as micro-structure evolution and quantum-level material property changes. In the risk assessment stage, the model used is difficult to accurately capture potential risks caused by quantum effects in the dismantling process, such as material leakage caused by quantum tunneling. In terms of monitoring and control, the real-time and precision cannot meet the needs of complex dismantling scenarios. SUMMARY

[0004] Therefore, the purpose of the present application is to provide a multi-dimensional device dismantling risk identification and prevention method to solve the technical problem that the general device dismantling risk control means mainly relies on macro-structure analysis and experiential judgment.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical scheme: a multi-dimensional device dismantling risk identification and prevention method, comprising:

[0006] S100: Constructing a device information quantum database to comprehensively and accurately collect the micro-structure, quantum-level material properties, quantum state change history during use, and quantum field data of the surrounding environment of the device to be dismantled;

[0007] S200: Using a risk assessment model that can perform ultra-fine analysis on the safety, environmental and other risks that may occur during the dismantling process;

[0008] S300: Developing a risk prevention and control scheme, which includes intelligent safety protection equipment controlled by quantum encryption communication technology and environmental intervention measures based on quantum control principles;

[0009] S400: During the dismantling process, real-time monitoring of the quantum state changes of the device, the dynamic evolution of the micro-structure, and the quantum parameter fluctuations in the environment is performed with the aid of a quantum sensor network. Once the monitoring data is abnormal, the risk prevention and control scheme is automatically adjusted through a quantum feedback mechanism.

[0010] The present invention is further configured such that, in step S100, quantum imaging technology is used to perform three-dimensional imaging of the microstructure of the device to obtain atomic-level structural details and material distribution information.

[0011] The present invention is further configured such that, in step S200, the neural network weights of deep learning are optimized by a quantum annealing algorithm to improve the accuracy and efficiency of the model's risk prediction.

[0012] The present invention is further configured such that, in step S300, a protective barrier based on the quantum barrier principle is set up to address the security risks caused by possible quantum tunneling; and to address environmental risks, quantum state control technology is used to perform quantum-level decomposition and transformation of pollutants.

[0013] The present invention is further configured such that the quantum sensor network includes a quantum displacement sensor, a quantum temperature sensor, a quantum magnetic field sensor, and a quantum gas sensor, and each sensor realizes ultra-long-distance, delay-free data transmission to a quantum data processing terminal through quantum entanglement.

[0014] The present invention is further configured such that the quantum data processing terminal adopts quantum error correction coding technology to ensure the accuracy and integrity of data during processing and transmission, and prevent quantum noise from interfering with the data.

[0015] The present invention is further configured to include virtual reality training for demolition personnel assisted by quantum technology, simulating various risk scenarios during the demolition process, allowing demolition personnel to learn risk identification and prevention skills in an immersive environment, and they can only take up their posts after passing the assessment.

[0016] The present invention is further configured to use a quantum backtracking algorithm to review the quantum data during the demolition process after the demolition is completed, so as to explore potential risk factors and optimization points of the prevention and control plan.

[0017] The present invention is further configured such that the emergency plan in the risk prevention and control scheme includes an emergency rescue command system based on quantum communication.

[0018] The present invention is further configured to use quantum machine learning technology to deeply mine and analyze quantum data of historical demolition projects, and automatically optimize the parameters of the risk assessment model and the strategy of the prevention and control plan.

[0019] In summary, the present application mainly has the following beneficial effects: The present application realizes atomic-level accurate identification and dynamic prevention and control of device dismantling risks by constructing a quantum database, using quantum optimization algorithms, deploying quantum sensor networks and other multi-dimensional quantum technology applications. Specifically, atomic-level structural details are obtained using quantum imaging technology, and a risk assessment model is optimized using a quantum annealing algorithm, significantly improving the accuracy and efficiency of risk prediction; based on the quantum control principle, the protection barrier and pollutant treatment technology effectively suppresses quantum tunneling risks and realizes quantum-level decomposition of pollutants; quantum sensor networks achieve super-long distance and non-delay data transmission through quantum entanglement, and can adjust the prevention and control scheme in real time through a quantum feedback mechanism. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 The identification and prevention and control process of the present application. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. The embodiments described below with reference to the drawings are exemplary and are only used to explain the present application, and cannot be understood as a limitation of the present application.

[0022] The embodiments of the present application will be described below according to the overall structure of the present application.

[0023] A multi-dimensional device dismantling risk identification and prevention and control method, which comprises the following steps:

[0024] S100: Construct a device information quantum database, collect microstructure and material data, and use quantum imaging technology, such as a scanning tunneling microscope and an atomic force microscope combined system, to perform three-dimensional imaging on the device, and obtain atomic-level resolution structural details in a low-temperature vacuum environment. Taking a chemical reaction kettle as an example, the carbonized layer of the inner wall thereof is scanned through quantum tunneling effect, the lattice distortion degree and impurity distribution of the alloy are accurately analyzed, and a quantum state probability cloud map of material distribution is simultaneously generated. In this process, a quantum entangled probe is used to realize nanoscale positioning error control, and the integrity of the microstructure data is ensured;

[0025] Establish a quantum state file of the whole life cycle of the device, and integrate quantum transition records and environmental quantum field data during service. Through a quantum coherence analysis algorithm, the fatigue crack propagation process of a certain pressure vessel is converted into a quantum state evolution trajectory, the change of electron spin relaxation time under temperature-stress coupling is associated, and a three-dimensional dynamic database is formed.

[0026] S200: Use a risk assessment model, which can perform super-fine analysis on possible safety, environmental and other risks during the dismantling process.

[0027] Quantum annealing algorithm is used to optimize the weights of deep learning neural networks. Taking the prediction of structural instability risk in the demolition process as an example, a neural network model with 10 hidden layers is constructed, the input parameters include device micro stress distribution, material quantum state parameters, etc. The optimal weight matrix is solved by quantum annealing algorithm to improve the prediction accuracy and efficiency of the model.

[0028] Multiple neural networks with different structures are trained simultaneously using quantum parallel computing characteristics. The model structure with the highest prediction accuracy is selected through quantum tomography technology.

[0029] S300: Develop a risk prevention and control plan, which includes intelligent security protection equipment controlled by quantum encryption communication technology and environmental intervention measures based on quantum regulation principles.

[0030] In response to the security risks that may arise due to quantum tunneling, such as radioactive material leakage, a protective barrier based on quantum potential barrier principles is installed at the sealing interface of the device. A multi-layer potential barrier structure is constructed using superlattice materials, with a potential barrier height of 5eV-10eV and a width of 10nm-20nm. By adjusting the external electric field to control the potential barrier height, the quantum tunneling effect is suppressed, and the tunneling probability is ≤10 -10 .

[0031] In response to the organic pollutants that may be generated during the demolition process, such as polychlorinated biphenyls, quantum state regulation technology is used to decompose and convert the pollutant molecules at the quantum level. Through resonance-enhanced multiphoton ionization technology, the electrons of the pollutant molecules are transferred to a high-energy state, breaking the chemical bonds and ultimately converting them into harmless substances such as CO2 and H2O.

[0032] S400: During the demolition process, quantum sensor networks are used to monitor the quantum state changes of the device, the dynamic evolution of the microstructure, and the fluctuations of quantum parameters in the environment in real time. Once the monitoring data shows an anomaly, the risk prevention and control plan is automatically adjusted through a quantum feedback mechanism.

[0033] The quantum sensor network includes: quantum displacement sensor, quantum temperature sensor, quantum magnetic field sensor, and quantum gas sensor.

[0034] Quantum displacement sensor: based on SQUID principle, measures the displacement of the device structure.

[0035] Quantum temperature sensor: uses diamond NV color center optical detection magnetic resonance technology to achieve high-precision temperature measurement.

[0036] Quantum magnetic field sensor: uses atomic magnetometer to detect magnetic field changes.

[0037] Quantum gas sensor: based on quantum cascade laser photoacoustic spectroscopy technology, detects toxic gas concentration.

[0038] Each sensor achieves ultra-long distance, delay-free data transmission to the quantum data processing terminal through a quantum entanglement distribution network.

[0039] The quantum data processing terminal uses quantum error correction coding technology to correct data in real time through quantum gate operations, ensuring the accuracy and integrity of data during processing and transmission, and preventing quantum noise such as photon scattering and thermal noise from interfering with data.

[0040] When the monitoring data is abnormal, the structural displacement rate is greater than 0.1 mm / s, and the quantum feedback mechanism is automatically adjusted to adjust the risk control scheme, such as starting the standby support structure or optimizing the demolition sequence.

[0041] Quantum technology-assisted virtual reality training is conducted for demolition personnel, and an immersive virtual environment is constructed using quantum dot display technology, combined with quantum inertial measurement units to accurately capture personnel movements.

[0042] Various risk scenarios during the demolition process are simulated, such as quantum tunneling-induced explosions and structural instability, and quantum entangled haptic feedback devices are used to allow demolition personnel to experience the consequences of risks in an immersive environment, learning risk identification and control skills. The training and examination standard is: risk identification accuracy ≥ 90%, emergency response time ≤ 5s, and passing the examination allows for on-site work.

[0043] After the demolition is completed, quantum backtracking algorithms are used to review quantum data, sensor data, and risk assessment model output data during the demolition process. Quantum state tomography technology is used to reconstruct the key quantum state evolution path during the demolition process, and potential risk factors and optimization points of the control scheme are explored.

[0044] Quantum Monte Carlo simulation is used to verify the reliability of the optimized control scheme, ensuring the effectiveness of the optimization measures.

[0045] The emergency plan in the risk control scheme includes an emergency rescue command system based on quantum communication, which uses the BB84 quantum key distribution protocol to establish an encrypted communication link, ensuring the security and real-time nature of emergency rescue instructions.

[0046] The system integrates quantum positioning technology to locate rescue personnel and trapped personnel in real time, and combines quantum image transmission to achieve high-definition backhaul of the scene, assisting command decision-making.

[0047] Quantum machine learning technology is used to deeply mine and analyze quantum data from historical demolition projects. Key risk features are extracted through quantum principal component analysis, and parameters of the risk assessment model and strategies of the control scheme are automatically optimized.

[0048] A quantum data sharing platform is established, and the security and credibility of historical data are ensured through quantum secure blockchain technology, so as to realize the continuous evolution of the risk assessment model.

[0049] Although embodiments of the present application have been shown and described, the present embodiments are merely illustrative of the present application and are not intended to limit the application, and the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

Claims

1. A multi-dimensional device dismantling risk identification and prevention method, characterized in that: include: S100: Build a quantum database of device information to comprehensively and accurately collect the microstructure, quantum-level material properties, quantum state change history of the device to be dismantled during use, and quantum field data of the surrounding environment; S200: Utilizes a risk assessment model that can provide a highly detailed analysis of potential safety and environmental risks that may arise during the demolition process; S300: Develop a risk prevention and control plan that includes intelligent safety protection equipment controlled by quantum encryption communication technology and environmental intervention measures based on quantum control principles; S400: During the demolition process, the quantum sensor network monitors the quantum state changes of the device, the dynamic evolution of the microstructure, and the quantum parameter fluctuations in the environment in real time. Once the monitoring data shows an abnormality, the risk prevention and control plan will be automatically adjusted through the quantum feedback mechanism.

2. A multi-dimensional device dismantling risk identification and prevention method according to claim 1, characterized in that: In step S100, quantum imaging technology is used to perform three-dimensional imaging of the microstructure of the device to obtain atomic-level structural details and material distribution information.

3. The multi-dimensional device dismantling risk identification and prevention method according to claim 1 is characterized by: In step S200, the weights of the neural network of deep learning are optimized by the quantum annealing algorithm to improve the accuracy and efficiency of the model's risk prediction.

4. The multi-dimensional device dismantling risk identification and prevention method according to claim 1 is characterized by: In step S300, a protective barrier based on the quantum barrier principle is set up to address the security risks caused by possible quantum tunneling; and quantum state control technology is used to decompose and transform pollutants at the quantum level to address environmental risks.

5. The multi-dimensional device dismantling risk identification and prevention method according to claim 1 is characterized by: The quantum sensor network includes quantum displacement sensors, quantum temperature sensors, quantum magnetic field sensors and quantum gas sensors. Each sensor uses quantum entanglement to achieve ultra-long-distance, delay-free data transmission to a quantum data processing terminal.

6. The multi-dimensional device dismantling risk identification and prevention method according to claim 1, characterized in that: Quantum data processing terminals use quantum error correction coding technology to ensure the accuracy and integrity of data during processing and transmission, and prevent quantum noise from interfering with the data.

7. The multi-dimensional device dismantling risk identification and prevention method according to claim 1 is characterized by: It also includes virtual reality training for demolition personnel assisted by quantum technology, simulating various risk scenarios during the demolition process, allowing demolition personnel to learn risk identification and prevention skills in an immersive environment, and they can only take up their posts after passing the assessment.

8. The multi-dimensional device dismantling risk identification and prevention method according to claim 1 is characterized by: After the demolition is completed, the quantum backtracking algorithm is used to review the quantum data during the demolition process to explore potential risk factors and optimization points of the prevention and control plan.

9. The multi-dimensional device dismantling risk identification and prevention method according to claim 1, characterized in that: The emergency plan in the risk prevention and control plan includes an emergency rescue command system based on quantum communication.

10. The multi-dimensional device dismantling risk identification and prevention method according to claim 1, characterized in that: Quantum machine learning technology is used to deeply mine and analyze quantum data of historical demolition projects, automatically optimizing the parameters of risk assessment models and the strategies of prevention and control plans.

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