A safety assessment method and system for radioactive source equipment based on multi-factor analysis

Abstract

The present invention discloses a radioactive source equipment safety assessment method and system based on multi-factor analysis, which relates to the technical field of equipment safety assessment. A radioactive source equipment safety assessment system based on multi-factor analysis includes: a data acquisition module, a data analysis module, a cognitive intelligence assessment module, a self-organizing management module, an environmental adaptability simulation module, and a decision optimization module. The present invention adopts a Bayesian causal network combined with neural symbolic artificial intelligence, which can not only automatically analyze the operating status of radioactive source equipment, but also combine expert knowledge to achieve higher interpretability and prediction accuracy, thereby optimizing safety decisions; adopts blockchain combined with decentralized autonomous organizations to ensure the non-tamperability of radioactive source equipment data, and realizes automatic tracking of equipment status, authority management, and security policy execution through smart contracts, thereby improving management transparency and security and reducing risks caused by human intervention.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment safety assessment, and in particular to a method and system for safety assessment of radioactive source equipment based on multi-factor analysis. Background Art

[0002] Currently, safety assessment methods for radioactive source equipment mainly rely on traditional methods such as regular testing, manual inspections, and historical data analysis. These methods have many limitations in data collection, intelligent analysis, risk prediction, and adaptability to extreme environments:

[0003] On the one hand, existing safety assessment methods primarily rely on single data sources, such as equipment operating parameters and historical maintenance records. They lack comprehensive analysis of factors such as environmental variables, operator behavior, and long-term equipment operating trends, making it difficult to form a complete safety assessment system. Furthermore, traditional detection methods typically rely on periodic inspections, resulting in delayed data updates and an inability to achieve real-time status monitoring, making it difficult to provide timely warnings before potential equipment failures occur.

[0004] Furthermore, the operation of radioactive source equipment involves high-dimensional, nonlinear data. Existing data analysis methods suffer from low accuracy and high computational complexity when processing complex data. Furthermore, most machine learning-based risk prediction methods rely on pattern matching, lacking causal reasoning capabilities and unable to accurately analyze the causes of risk. Furthermore, current time series analysis models used for radioactive source equipment status prediction struggle to strike a balance between short-term changes and long-term trend prediction, impacting prediction accuracy.

[0005] In addition, with the increasing application of radioactive source equipment in extreme environments such as deep space exploration and deep-sea nuclear energy, existing safety assessment methods lack adaptive modeling for special environments such as high pressure, microgravity, and strong radiation, and cannot effectively predict the operating status and potential risks of radioactive source equipment under extreme conditions. Summary of the Invention

[0006] This invention aims to address the limitations of current safety assessment methods for radioactive source equipment, primarily addressing issues such as a single data source, insufficient real-time monitoring capabilities, low risk prediction accuracy, and poor adaptability to extreme environments. By combining Bayesian causal networks, neural symbolic artificial intelligence, blockchain, and decentralized autonomous organization technologies, this invention enables multidimensional data collection, intelligent analysis, adaptive decision optimization, and safety management for radioactive source equipment. This improves the accuracy, transparency, and predictability of equipment operating status monitoring, effectively reduces safety hazards caused by human intervention, and enhances the equipment's adaptability and management efficiency in extreme environments.

[0007] A safety assessment method for radioactive source equipment based on multi-factor analysis, including:

[0008] Data collection: Acquire multi-dimensional data from radioactive source equipment, use a distributed sensor network to monitor the equipment's operating status in real time, and share data from different radioactive source equipment through federated learning;

[0009] Data dimensionality reduction and analysis: Reduce the dimensionality of multidimensional data, extract characteristic variables, combine quantum computing to optimize data processing efficiency, and use multi-scale analysis models to predict trends in time series data;

[0010] Cognitive intelligence risk assessment: Build a Bayesian causal network, use causal reasoning methods to analyze the operating status of radioactive source equipment, and combine expert knowledge and deep learning with a neural symbolic artificial intelligence model to improve the accuracy of risk prediction;

[0011] Self-organizing management: A decentralized autonomous organization based on blockchain enables automatic tracking of radioactive source equipment status, data sharing, and permission management, combined with smart contracts to execute security policies;

[0012] Extreme Environment Adaptability Modeling: Simulates the operating status of radioactive source equipment in special environments and combines it with generative adversarial networks to optimize risk assessment strategies;

[0013] Assessment result output and decision optimization: Input the risk assessment results into the adaptive decision-making system, combine reinforcement learning to adjust the equipment maintenance strategy, and form a five-level risk warning mechanism.

[0014] As a preferred technical solution of the present invention, the data collection adopts Bayesian causal reasoning combined with a distributed sensor network, performs real-time causal analysis during the data collection phase, automatically screens key influencing factors, and combines federated learning to achieve data collaborative training between different devices;

[0015] The causal reasoning results of data collection are directly used as input for neural symbolic artificial intelligence analysis. The results of data collection are stored in the blockchain network; device status data is encrypted and stored using a zero-knowledge proof mechanism to improve data privacy.

[0016] As a preferred technical solution of the present invention, the risk assessment combines Bayesian causal reasoning with neural symbolic artificial intelligence, adopts a causal graph modeling method to analyze the key failure factors of radioactive source equipment, and makes inferences through symbolic logic reasoning based on an expert knowledge base. The result of causal reasoning serves as the trigger condition of the smart contract. When the assessment result exceeds the set risk threshold, the smart contract on the blockchain will automatically trigger the security policy and make decisions and approvals through self-organizing management; the neural symbolic artificial intelligence is combined with a reinforcement learning mechanism to automatically adjust the weights of the causal reasoning model during the assessment process.

[0017] As a preferred technical solution of the present invention, the self-organizing management uses blockchain combined with smart contracts and the evaluation results of causal reasoning to dynamically manage the status and authority allocation of radioactive source equipment and automatically perform the following operations:

[0018] When the assessment result is an abnormal risk, the smart contract automatically triggers the device self-check program and adjusts the device's operating permissions based on the scope of the fault impact; combined with the self-organizing management mechanism, the smart contract automatically notifies the relevant responsible persons and confirms key decisions through the multi-party consensus mechanism on the blockchain; all decisions, status updates and security policy executions are stored in the blockchain ledger to prevent human tampering.

[0019] As a preferred technical solution of the present invention, the smart contract implements the following security policies:

[0020] Smart contracts predict equipment failures through Bayesian causal reasoning, and automatically trigger maintenance instructions and adjust maintenance plans based on evaluation results optimized by neural symbolic artificial intelligence; when radioactive source equipment enters a high-risk state, the smart contract automatically switches the equipment to a safe operating mode and restricts unauthorized operations through decentralized permission management; when the system determines a high-risk event, the smart contract automatically triggers the DAO mechanism, and a multi-party consensus review is conducted to determine whether to execute major maintenance or shutdown strategies.

[0021] As a preferred technical solution of the present invention, the extreme environment adaptability modeling is combined with digital twin technology and Bayesian causal reasoning to establish a simulation of the radioactive source equipment in extreme environments, and perform state analysis through computational fluid dynamics, specifically including:

[0022] During the extreme environment simulation process, Bayesian causal networks are used to analyze the impact of different environmental variables on equipment safety and automatically adjust the evaluation model; neural symbolic artificial intelligence is used to dynamically adjust the parameters of the digital twin model; in extreme environments, when causal reasoning detects that the equipment has operating parameters that exceed the safety threshold, the smart contract automatically executes emergency response instructions.

[0023] As a preferred technical solution of the present invention, the decision optimization adopts reinforcement learning combined with causal reasoning analysis, and the reinforcement learning optimization includes:

[0024] The reinforcement learning model uses causal reasoning results as input to gradually optimize the equipment's operating strategy; it combines neural symbolic artificial intelligence to provide expert knowledge constraints; and all decision adjustments are managed in real time by smart contracts on the blockchain.

[0025] A radioactive source equipment safety assessment system based on multi-factor analysis, including:

[0026] Data acquisition module: used to obtain multi-dimensional data of radioactive source equipment and use distributed sensor networks for real-time monitoring;

[0027] Data analysis module: reduces the dimensionality of multidimensional data, optimizes data processing with quantum computing, and uses multi-scale analysis models to predict trends in time series data;

[0028] Cognitive intelligence assessment module: Builds a Bayesian causal network to analyze the status of radioactive source equipment through causal reasoning methods, and combines neural symbolic artificial intelligence to optimize risk prediction;

[0029] Self-organizing management module: A blockchain-based decentralized autonomous organization manages the status, data sharing, and permissions of radioactive source equipment, and implements maintenance strategies in conjunction with smart contracts;

[0030] Environmental adaptability simulation module: This module simulates the operating status of radioactive source equipment in special environments, optimizes risk assessment strategies through generative adversarial networks, uses digital twin technology for simulation, and integrates computational fluid dynamics analysis equipment.

[0031] Decision optimization module: Receives evaluation results and dynamically optimizes equipment maintenance strategies using reinforcement learning.

[0032] The present invention has the following advantages:

[0033] This invention uses a distributed sensor network to collect multi-dimensional data such as the operating parameters, environmental variables, historical maintenance records, and operator behavior of radioactive source equipment, and realizes cross-device data sharing through federated learning to ensure data integrity and privacy protection; compared with traditional methods that only rely on a single data source, this invention integrates multiple factors for analysis, constructs a more accurate safety assessment system, and improves the reliability of the assessment results.

[0034] This invention introduces topological data analysis to reduce the dimensionality of multidimensional data. Combined with quantum computing, it optimizes data processing and improves computational efficiency, enabling the system to rapidly process large-scale data from radioactive source equipment, enhancing real-time performance and accuracy. It also employs a multi-scale analytical model to predict the time series trends of the operating status of radioactive source equipment, analyzing both short-term changes and long-term trends to optimize prediction accuracy. Compared to traditional time series analysis methods, this invention can more accurately predict the likelihood of equipment failure, providing early warning of potential risks and improving equipment operational safety.

[0035] This invention uses Bayesian causal networks combined with neural symbolic artificial intelligence, which can not only automatically analyze the operating status of radioactive source equipment, but also combine expert knowledge to achieve higher interpretability and prediction accuracy, thereby optimizing safety decisions; it uses blockchain combined with decentralized autonomous organizations to ensure the non-tamperability of radioactive source equipment data, and realizes automatic tracking of equipment status, authority management and security policy execution through smart contracts, thereby improving management transparency and security and reducing risks caused by human intervention.

[0036] This invention combines digital twin technology to construct a virtual mapping of the radioactive source equipment, which can simulate the equipment status in real time, and combines it with the generative adversarial network to optimize the evaluation strategy to improve the adaptability of the radioactive source equipment in extreme environments such as microgravity, high pressure, and strong radiation, thereby ensuring the safety of the equipment in special environments.

[0037] This invention uses reinforcement learning to optimize equipment maintenance strategies and forms a five-level risk warning mechanism. Based on the equipment's operating status, historical data, and prediction results, it intelligently adjusts maintenance plans to achieve predictive maintenance, reduce unnecessary maintenance costs, and ensure the long-term stable operation of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only schematic diagrams of the present invention. Those skilled in the art can also derive other drawings based on the provided drawings without inventive effort.

[0039] Figure 1 This is a schematic diagram of the structure of a radioactive source equipment safety assessment system based on multi-factor analysis adopted in an embodiment of the present invention. DETAILED DESCRIPTION

[0040] To make the objectives, technical solutions, and advantages of the present invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0041] Example 1, a method for safety assessment of radioactive source equipment based on multi-factor analysis, comprising the following steps:

[0042] Step S1, data collection: obtain multi-dimensional data of radioactive source equipment, use a distributed sensor network to monitor the equipment operation status in real time, and share data from different radioactive source equipment through federated learning to ensure a balance between data integrity, privacy protection and computing efficiency.

[0043] Specifically include:

[0044] S101: Multidimensional data collection and data sources;

[0045] Equipment operating parameters: including temperature, pressure, vibration, radiation dose, energy consumption, operating time and other information.

[0046] Environmental variables: including external temperature, humidity, air quality, radiation background value, electromagnetic interference and other environmental factors that may affect the status of radiation source equipment.

[0047] Historical maintenance records: including equipment overhaul time, maintenance type, failure records, parts replacement records, repair logs, and other information to predict possible equipment failure modes.

[0048] Operator behavior data: including equipment usage logs, operator identity, operation history, and emergency response records. This data is used to analyze the impact of human factors on equipment safety and to prevent risks caused by incorrect operations.

[0049] S102: Use edge computing combined with cloud computing to improve the real-time performance of data processing;

[0050] By deploying edge computing nodes near radioactive source equipment, real-time preprocessing, data compression, and preliminary analysis of local data can be achieved, reducing data transmission delays and improving response speed.

[0051] Through cloud computing, data from multiple radioactive source devices are aggregated and deeply processed, and large-scale computing resources are used for complex modeling and analysis to ensure data fusion and intelligent evaluation across devices.

[0052] Combined with a layered computing architecture, high-priority tasks (real-time anomaly detection) are processed at the edge, while low-priority tasks (historical data modeling) are performed in the cloud, improving the overall operating efficiency of the system.

[0053] S103: Use privacy-preserving computing, including differential privacy and secure multi-party computing, to ensure the security of radioactive source data sharing;

[0054] Differential privacy: When sharing data, perturb the data to avoid leaking key device status information while ensuring the effectiveness of the overall statistical analysis of the data.

[0055] Secure Multi-Party Computation (MPC): When performing data calculations between multiple radioactive source devices or management agencies, there is no need to directly exchange original data. Instead, risk assessment is completed through encrypted calculations to ensure that the data is secure and available but not visible.

[0056] Homomorphic encryption: used to keep data encrypted during data processing, preventing cloud computing or third-party computing nodes from accessing original sensitive data, and enhancing data privacy protection capabilities.

[0057] S104: Use decentralized identity authentication technology to ensure the security and traceability of data access.

[0058] The use of decentralized identity authentication (DID) technology enables each radioactive source equipment, operation and maintenance personnel, and management agency to have independent identity credentials, ensuring the secure management of data access rights.

[0059] Authentication and access logs are stored through blockchain technology to ensure that access records cannot be tampered with and data traceability is ensured.

[0060] Combined with zero-knowledge proof (ZKP), it allows users or devices to prove their access rights without exposing their identity data, further enhancing data security.

[0061] Step S2, data dimensionality reduction and analysis: Reduce the dimensionality of multidimensional data, extract characteristic variables, combine quantum computing to optimize data processing efficiency, and use multi-scale analysis models to predict trends in time series data to improve the real-time, accuracy, and adaptability of data analysis.

[0062] Specifically include:

[0063] S201: Use quantum computing technology to increase data processing speed and reduce computing resource consumption;

[0064] High-dimensional matrix operation acceleration based on quantum computing: Utilizing the super-parallel characteristics of quantum computing, matrix operations are performed on high-dimensional data to improve the computational efficiency of data dimensionality reduction and pattern recognition, and reduce computing resource consumption.

[0065] Using quantum Fourier transform (QFT) to optimize signal processing: For the time series data of radioactive source equipment, QFT is used to enhance frequency domain analysis capabilities and improve the accuracy of identifying changes in equipment status.

[0066] Using Quantum Support Vector Machine (QSVM) for feature selection: Combined with QSVM to classify and extract features from data, it improves the ability to detect abnormal data, reduces redundant calculations, and improves the quality of data dimensionality reduction.

[0067] S202: Using topological data analysis methods to extract key features from multidimensional data and optimize the data structure;

[0068] Feature extraction based on topological data analysis (TDA): TDA can identify the hidden topological structure of multidimensional data, extract global and local features, and ensure the feature integrity of equipment operation data.

[0069] Use persistent homology algorithms to reduce the dimensionality of high-dimensional data: Persistent homology can detect key patterns and topological structures in data, identify important state transitions, and improve the accuracy of equipment failure prediction.

[0070] Multi-scale dimensionality reduction optimization: Multi-scale topological dimensionality reduction is used for different time scales of equipment status data to improve the analysis capabilities of short-term fluctuations and long-term trends.

[0071] S203: Adopting an adaptive dimensionality reduction method, the analysis model is adjusted according to real-time data to ensure the stability of the evaluation system.

[0072] Adaptive dimensionality reduction algorithm optimizes data accuracy: Dynamically adjusts the dimensionality reduction model based on the real-time status of the radioactive source equipment to ensure the accuracy and stability of feature extraction.

[0073] Dimensionality reduction model optimization based on reinforcement learning: Combined with reinforcement learning, dimension reduction parameters are adjusted in real time so that the dimension reduction method can be optimized as the device status changes, improving system adaptability.

[0074] Abnormal data detection and adaptive filtering: In response to possible data anomalies, adaptive filtering methods (autoregressive moving average model) are used to improve data quality and reduce error accumulation during the dimensionality reduction process.

[0075] Step S3, cognitive intelligent risk assessment: Based on the multi-dimensional data analysis results of radioactive source equipment, a Bayesian causal network is constructed, and the causal reasoning method is used to analyze the equipment operation status. Combined with the neural symbolic artificial intelligence model, expert knowledge and deep learning are integrated to improve the accuracy and interpretability of radioactive source equipment anomaly detection, fault prediction and safety assessment.

[0076] Specifically include:

[0077] S301: Using reinforcement learning models combined with expert systems to optimize risk prediction strategies;

[0078] Build a dynamic risk assessment model based on reinforcement learning: Using deep reinforcement learning (DRL), we continuously optimize risk assessment strategies based on equipment operating status and historical failure modes, thereby improving the predictive maintenance capabilities of radioactive source equipment.

[0079] Improve the interpretability of prediction strategies by combining expert systems: Combine the knowledge base of nuclear safety experts with machine learning models, and optimize equipment anomaly identification and early warning mechanisms through rule engines combined with data-driven analysis.

[0080] Adaptive optimization of risk thresholds: Based on the feedback mechanism of reinforcement learning, the risk assessment threshold is dynamically adjusted to ensure that the system can adapt to changes in different devices and environments, thereby improving assessment accuracy.

[0081] S302: Using inverse reinforcement learning to simulate expert decision-making to improve prediction accuracy;

[0082] Construct an expert behavior dataset: Collect historical decision-making data of radioactive source equipment managers and learn the expert judgment methods through behavioral analysis models.

[0083] Inverse reinforcement learning (IRL) is used to train intelligent evaluation models: Based on the actual decision-making patterns of experts, the intelligent system is trained to make reasonable evaluation decisions even in the absence of a clear reward function, thereby improving prediction accuracy.

[0084] Adaptively learn from expert experience to optimize incident response plans: The inverse reinforcement learning model can automatically adjust security assessment strategies based on the characteristics of different devices and environments, enabling the system to simulate the best decisions of experts when facing emergencies and improve incident response efficiency.

[0085] S303: Use neuro-symbolic artificial intelligence, combined with expert knowledge and deep learning, to improve the reliability of causal reasoning.

[0086] Build a hybrid model based on neural symbolic AI: combining symbolic logic reasoning (expert rules) and deep learning (data-driven) to improve the ability to model complex causal relationships.

[0087] Causal reasoning improves anomaly detection accuracy: Using Bayesian causal networks, we deduce the possible causes of anomalies in radioactive source equipment, avoiding the problems of deep learning black box models and improving the credibility of risk predictions.

[0088] Causal reasoning optimization based on graph neural networks (GNNs): Graph neural networks are used to analyze the causal relationship between equipment operating states, optimize safety assessment processes, and improve early warning capabilities for potential failures.

[0089] Step S4, Self-Organization Management: Build a decentralized autonomous organization (DAO) based on blockchain technology to automatically track the status of radioactive source equipment, share data, and manage permissions. Combined with smart contracts to enforce security policies, this ensures transparency, security, and cross-institutional collaboration in equipment management, improving the management of radioactive source equipment throughout its lifecycle.

[0090] Specifically include:

[0091] S401: Use blockchain technology to manage the status of radioactive source equipment to ensure data transparency and immutability;

[0092] Distributed ledger storage device status data: Blockchain decentralized storage is used to ensure that the operating status, maintenance records, permission changes and other data of radioactive source equipment cannot be tampered with, thus ensuring data integrity.

[0093] Device identity authentication based on hash encryption: Assign a unique blockchain identity (DID) to each radioactive source device to ensure the authenticity of the device status and prevent illegal tampering and data forgery.

[0094] Use a timestamp mechanism to improve data traceability: All equipment status change records are timestamped to ensure data source traceability and enhance accident investigation and compliance supervision capabilities.

[0095] S402: Using smart contracts to automatically perform maintenance and status updates of radioactive source equipment;

[0096] Automated maintenance plans: Smart contracts can automatically trigger maintenance plans based on parameters such as device operating status, historical maintenance records, and environmental variables to ensure that the device is in a safe operating state.

[0097] Abnormal status warning and processing: If the device status is abnormal, the smart contract can automatically generate an alarm and notify relevant managers to ensure timely response.

[0098] Automatic execution of device permission changes: When device permissions change due to maintenance, transfer, or elimination, the smart contract automatically updates the permission management policy to prevent unauthorized access or operation.

[0099] S403: Use decentralized autonomous organizations to manage cross-institutional collaboration on radioactive source equipment to ensure the security of data sharing.

[0100] Decentralized governance mechanism: DAO allows different institutions (such as government regulatory agencies, research institutions, and equipment manufacturers) to participate in the management of radioactive source equipment without trust, avoiding the drawbacks of single-point management.

[0101] Cross-institutional data sharing based on alliance chain: Using the alliance chain model, secure and controllable data sharing is achieved among multiple institutions, ensuring that all parties can access necessary device status information while ensuring data privacy.

[0102] Equipment operation approval mechanism based on multi-party consensus: During key operations of radioactive source equipment (such as permission changes, maintenance execution, and equipment transfer), a multi-party consensus mechanism is introduced to ensure that all operations are legally approved and improve the security of equipment management.

[0103] Step S5, extreme environment adaptability modeling: Simulate the operating status of radioactive source equipment in special environments (such as deep space, deep sea, high temperature and high pressure, high radiation, etc.), analyze the performance changes of the equipment under extreme conditions, and combine with generative adversarial networks (GAN) to optimize risk assessment strategies and improve the accuracy and adaptability of assessment results.

[0104] Specifically include:

[0105] S501: Use digital twin technology to simulate the operation of radioactive source equipment in different environments;

[0106] Build a digital twin model: Based on the historical data, operating parameters, and structural model of the radioactive source equipment, establish a real-time digital twin system to simulate the state changes of the equipment in different environments.

[0107] Environmental simulation and parameter adjustment: In the digital twin system, environmental variables such as temperature, pressure, humidity, radiation dose, and vibration amplitude are adjusted to test the adaptability and stability of the equipment under different working conditions.

[0108] Real-time status feedback and prediction: Digital twin technology combines sensor data to achieve real-time feedback on equipment operating status and predict possible failure modes in extreme environments, thereby improving equipment safety.

[0109] S502: Use computational fluid dynamics methods to analyze the heat conduction and operational safety of equipment in high-pressure environments;

[0110] Thermodynamic modeling in high-pressure environments: Based on computational fluid dynamics (CFD) methods, this tool analyzes the heat conduction, heat dissipation efficiency, and convective heat transfer of radioactive source equipment in deep-sea high-pressure or enclosed chambers, and optimizes the equipment's heat dissipation design.

[0111] Material stability analysis in radiation environments: Simulate changes in material properties of radioactive source equipment in high-radiation environments, analyze the impact of radiation on core components such as equipment casings, shielding layers, and circuits, and optimize the use of radiation-resistant materials.

[0112] Operational stability assessment in a vacuum environment: Based on the operating characteristics of radioactive source equipment in the vacuum environment of outer space, analyze the equipment's cooling method, thermal expansion and contraction effects, electromagnetic interference impact, etc., to improve the equipment's adaptability in space missions.

[0113] S503: Generative adversarial networks are used to optimize risk prediction in different environments to ensure the accuracy of assessment results.

[0114] Generate extreme environment sample data using GAN: Based on existing environmental datasets, Generative Adversarial Networks (GANs) are used to create device operation data in different environments to enhance the training dataset and improve the generalization ability of the evaluation model.

[0115] Simulate the impact of different environmental factors on equipment status: Through virtual data generated by GAN, test the operating status of equipment in extreme environments such as high temperature, high humidity, strong radiation, and microgravity to improve the comprehensiveness of risk prediction.

[0116] Comparing with real data for model optimization: Using GAN to generate extreme environment data and compare it with real equipment operation data, we can optimize the risk assessment model, reduce prediction errors, and improve the reliability of the assessment system.

[0117] Step S6, evaluation result output and decision optimization: The risk assessment results are input into the adaptive decision-making system, combined with reinforcement learning to optimize the equipment maintenance strategy, and form a five-level risk warning mechanism to ensure that the safety management of radioactive source equipment is more intelligent, precise and dynamically optimized.

[0118] Specifically include:

[0119] S601: Using reinforcement learning to optimize equipment maintenance strategies and achieve predictive maintenance;

[0120] Intelligent maintenance model based on deep reinforcement learning (DRL): Utilizes reinforcement learning algorithms (policy gradient PG) to autonomously optimize maintenance strategies and improve maintenance efficiency based on the real-time operating status of the equipment, historical fault records, and environmental variables.

[0121] Adaptive adjustment of maintenance cycles: Unlike traditional fixed-periodic maintenance, this method combines reinforcement learning to achieve on-demand maintenance. That is, maintenance is performed in advance when the equipment's operating status approaches the failure threshold, avoiding problems such as over-maintenance or under-maintenance.

[0122] Resource optimization based on predictive maintenance: The intelligent system can dynamically adjust the spare parts supply chain, maintenance personnel scheduling, maintenance time windows, etc. according to equipment maintenance needs to ensure the accuracy and efficiency of maintenance work.

[0123] S602: Use expert systems to build a multi-level risk warning mechanism to improve the response speed of abnormal conditions;

[0124] Construct a five-level risk early warning system:

[0125] Level 1 warning (low risk): The equipment is operating normally with only slight parameter fluctuations. No intervention is required, and only the data change trend is recorded.

[0126] Level 2 warning (medium-low risk): There is a slight abnormality in the equipment status, but it does not affect normal operation. The system automatically adjusts the operating parameters and reminds maintenance personnel to observe.

[0127] Level 3 Warning (Medium Risk): Abnormal equipment data exceeds the set safety range but is still within the controllable range. The system recommends arranging maintenance inspections and recording detailed logs.

[0128] Level 4 warning (high risk): If the equipment shows a fault signal that may affect operation, the system will automatically generate a maintenance plan and send an emergency notice to the relevant responsible persons.

[0129] Level 5 Warning (Extremely High Risk): A serious abnormality occurs in the equipment, which may cause radiation leakage or equipment damage. The system immediately triggers the safety shutdown procedure and activates the emergency response mechanism.

[0130] Multi-level expert system optimizes early warning response: Combined with a rule-based expert system, it utilizes multi-dimensional data analysis results to improve the accuracy of abnormal state determination and optimize the early warning response process to ensure that key risk events are handled quickly.

[0131] S603: Combined with the adaptive optimization algorithm, the security policy is dynamically adjusted according to the historical operating data and real-time status of the radioactive source equipment.

[0132] Safety strategy optimization based on genetic algorithm (GA): Use genetic algorithm to optimize equipment operating parameters to ensure that the equipment can achieve the optimal operating state under different working conditions and reduce safety risks.

[0133] Dynamically adjust equipment operation strategies: Adjust equipment operation modes based on historical data of radioactive source equipment, environmental change trends, and risk prediction results to improve equipment stability and safety.

[0134] Adaptive optimization of equipment status: Based on Bayesian optimization and combined with the latest evaluation data, we continuously optimize equipment safety management strategies to ensure that equipment is always in the best operating state.

[0135] In Example 2, the multi-factor analysis-based safety assessment method for radioactive source equipment provided by this invention is applicable to a variety of scenarios and can be widely used in fields such as nuclear power plants, medical radiation equipment, deep space exploration, deep-sea nuclear energy systems, and industrial non-destructive testing. Through multi-dimensional data collection, intelligent analysis, risk prediction, self-organizing management, and extreme environment adaptability modeling, the safety management and intelligent maintenance capabilities of radioactive source equipment are improved.

[0136] 1. Safety assessment and intelligent management of radioactive source equipment in nuclear power plants, applicable scenarios:

[0137] The radioactive source equipment in a nuclear power plant mainly includes nuclear fuel assemblies, reactor control rods, radiation monitoring instruments, spent fuel storage devices, etc. Their safety is crucial to the operation of the entire nuclear power plant. This method can be used for:

[0138] Safety monitoring of radioactive source equipment in the core area of the reactor to detect abnormal conditions such as radioactive source leakage and excessive radiation dose;

[0139] Spent fuel storage management, ensuring the safe storage of discarded radioactive materials and anticipating risks during long-term storage;

[0140] Remote monitoring and intelligent early warning utilize blockchain technology combined with distributed sensors to achieve security data sharing between different nuclear power plants and improve the overall safety management level.

[0141] Case:

[0142] Safety assessment based on Bayesian causal networks: Deploy multiple sensors in nuclear reactors to monitor radiation doses, and combine Bayesian causal reasoning to predict the decay risk of fuel assemblies and provide early warning of possible failures.

[0143] Intelligent maintenance strategy optimization: Optimize the maintenance plan of nuclear power plants through reinforcement learning, reduce unnecessary maintenance downtime, and improve equipment utilization.

[0144] 2. Intelligent diagnosis and maintenance of medical radiology equipment, applicable scenarios:

[0145] Medical radiation equipment is widely used in radiotherapy, nuclear medicine imaging, tumor treatment and other fields, including:

[0146] CT (Computed Tomography): Long-term operation may cause problems such as equipment overheating and X-ray tube aging. This invention can monitor the equipment status in real time, optimize scanning parameters, and increase equipment life.

[0147] PET (Positron Emission Tomography): The use of radioactive tracers in PET equipment requires strict management. This method can intelligently evaluate the dose and decay of radioactive tracers to ensure medical safety.

[0148] Linear Accelerator (LINAC): Used in cancer radiotherapy, device stability is crucial. This method can predict device failure based on historical data and real-time monitoring results, reducing the risk of treatment interruption.

[0149] Case:

[0150] Intelligent diagnostic optimization: Based on a multi-scale analysis model, it optimizes CT / PET scanning parameters, reduces radiation dose, and improves imaging quality.

[0151] Predictive maintenance: Using reinforcement learning to adjust maintenance plans for medical radiology equipment, reducing equipment downtime and improving medical service efficiency.

[0152] 3. Safety assessment and optimization of nuclear energy systems for deep space probes, applicable scenarios:

[0153] Deep space exploration missions (such as lunar and Mars probes) typically use radioisotope thermoelectric generators (RTGs) or small nuclear reactors as energy supply systems. These radioactive source devices face extreme environmental challenges:

[0154] Microgravity environment: affects the heat conduction mode of radioactive source equipment, which may cause abnormal temperature changes of the equipment.

[0155] Strong radiation environment: Cosmic rays may accelerate the decay of radioactive materials and affect the service life of equipment.

[0156] Remote maintenance is difficult: Since deep space exploration missions cannot be maintained manually, the present invention provides an intelligent evaluation method based on digital twins combined with GAN models to simulate equipment operation in extreme environments and improve the long-term adaptability of the equipment.

[0157] Case:

[0158] Application of digital twin technology: Create a digital twin model of the probe's nuclear energy system on the ground to simulate its operating status in the Martian environment, and generate extreme environment data through the GAN model to optimize the operating parameters of the nuclear energy system.

[0159] Automated health management: An intelligent assessment system based on reinforcement learning adaptively adjusts the power output of the nuclear energy system to avoid excessive fuel consumption and extend mission life.

[0160] 4. Intelligent monitoring and management of deep-sea nuclear energy systems, applicable scenarios:

[0161] Underwater nuclear energy systems, such as deep-sea probes and nuclear-powered submarines, operate in extreme environments and face challenges such as high pressure, high humidity, and severe corrosion.

[0162] Nuclear-powered submarines: Radioactive source equipment needs to operate in a closed environment for a long time, and radioactive leakage monitoring is crucial.

[0163] Deep-sea experimental station: The nuclear energy system needs to operate stably for a long time to ensure energy supply, while monitoring radiation levels and preventing environmental pollution.

[0164] Case:

[0165] Computational fluid dynamics (CFD) modeling: Analyze the heat conduction characteristics of nuclear-powered submarines in deep-sea high-pressure environments, optimize the heat dissipation system, and improve equipment stability.

[0166] Intelligent radiation monitoring: Utilize distributed sensor networks combined with blockchain technology to achieve real-time monitoring and data sharing of radioactive source equipment, and improve the level of safety management in deep-sea environments.

[0167] 5. Safety assessment of radioactive source equipment in industrial non-destructive testing, applicable scenarios:

[0168] Industrial non-destructive testing (such as X-ray and gamma-ray testing) is widely used in material testing, weld testing, oil pipeline testing and other fields:

[0169] X-ray detection equipment: Long-term high-intensity work may cause X-ray source aging, affecting detection accuracy.

[0170] Gamma ray detector: The activity of the radioactive source needs to be strictly controlled to ensure detection safety.

[0171] Case:

[0172] Intelligent dose control: Dynamically adjust the radiation dose of detection equipment based on reinforcement learning to improve detection accuracy while reducing radiation exposure risks.

[0173] Radioactive source aging prediction: Utilize causal reasoning and time series analysis models to predict the degradation trend of radioactive source equipment and replace key components in advance to ensure the stability of detection equipment.

[0174] Example 3: The radioactive source equipment safety assessment method based on multi-factor analysis provided by the present invention relies on advanced computing architecture and combines cutting-edge technologies such as federated learning, quantum computing, and decentralized data storage to improve data processing efficiency, enhance the intelligence of equipment safety assessment, and ensure data security and traceability.

[0175] The computing architecture consists of:

[0176] 1. Use federated learning to improve cross-device data collaboration capabilities and optimize device security assessment models;

[0177] Radioactive source equipment is often geographically distributed across multiple institutions and systems. Traditional centralized data processing methods pose risks to data privacy, high computational costs, and weak collaboration capabilities. This paper uses federated learning to enable collaborative data training across different radioactive source equipment without sharing raw data, thereby optimizing safety assessment models.

[0178] Specifically include:

[0179] Local model training on edge devices: Each radioactive source device independently trains a safety assessment model without uploading original data, thus preventing privacy leaks.

[0180] Encrypted model parameter aggregation: The local model parameters of each device are protected through homomorphic encryption or differential privacy mechanisms and securely aggregated in a central server or decentralized computing architecture, improving the privacy and security of collaborative training.

[0181] Adaptive model update mechanism: Using reinforcement learning combined with dynamic weight adjustment, the global model of federated learning is optimized according to the operating status and data characteristics of the radioactive source equipment, thereby improving the accuracy of equipment failure prediction.

[0182] Case:

[0183] Safety assessment of radioactive source equipment across nuclear power plants: Each nuclear power plant independently trains a safety assessment model and shares model parameters under a federated learning framework, enabling experience sharing among different nuclear power plants and improving equipment failure prediction capabilities.

[0184] Intelligent diagnostic optimization of medical radiology equipment: The hospital's CT and PET equipment share optimization models through federated learning, improving the intelligent diagnostic capabilities of equipment of different brands and models while preventing medical data leakage.

[0185] 2. Use quantum computing to accelerate large-scale data analysis and improve the efficiency of multi-dimensional data calculations;

[0186] Safety assessments of radioactive source equipment involve large amounts of multidimensional data. Traditional computational methods (such as principal component analysis (PCA) and support vector machines (SVM)) are computationally intensive and time-consuming when processing high-dimensional data, making them inadequate for real-time monitoring and prediction. This invention uses quantum computing to accelerate data processing and leverage its parallel computing capabilities to improve the speed and accuracy of high-dimensional data analysis.

[0187] Specifically include:

[0188] Quantum Fourier Transform (QFT) optimizes time series data processing: For time series signals of radioactive source equipment (such as radiation dose, temperature changes, etc.), QFT is used to accelerate frequency domain analysis to improve the accuracy of trend prediction.

[0189] Quantum Support Vector Machine (QSVM) Optimized Anomaly Detection: QSVM is used to build an intelligent anomaly detection model to improve the anomaly identification capability of radioactive source equipment.

[0190] Quantum Bayesian Network (QBN) optimizes causal reasoning: Integrating QBN with risk analysis improves the computational efficiency of causal reasoning, enabling the system to more quickly analyze the causes of equipment failures.

[0191] Case:

[0192] Optimization of nuclear energy systems for deep space probes: Using quantum computing to accelerate thermodynamic data analysis and improve the efficiency of fault diagnosis for nuclear energy systems in deep space probes.

[0193] Nuclear power plant accident emergency response: Using quantum Bayesian networks for multivariate causal reasoning, we can complete complex accident analysis in a short period of time and improve emergency response speed.

[0194] 3. Adopt a decentralized data storage architecture to ensure the security and traceability of radioactive source equipment data;

[0195] Traditional centralized data storage methods (SQL databases, cloud storage) are vulnerable to security issues such as hacker attacks, data tampering, and single points of failure. This invention adopts a decentralized data storage architecture, using blockchain and distributed storage technology to improve the security, integrity, and traceability of radioactive source equipment data.

[0196] Specifically include:

[0197] Blockchain storage device operation data and maintenance records: The operating status, maintenance logs, and permission change records of radioactive source equipment are all encrypted and stored on the blockchain to ensure that the data cannot be tampered with.

[0198] Distributed storage based on IPFS (InterPlanetary File System): Large-scale sensor data of radioactive source equipment is stored using IPFS or distributed hash tables (DHT) to improve the redundancy and security of data storage.

[0199] Data access rights management: Combined with decentralized identity authentication (DID), ensure that users at different levels (such as operators, maintenance personnel, and regulators) have reasonable data access rights to prevent unauthorized data leakage.

[0200] Case:

[0201] Data security management for nuclear energy regulatory agencies: Nuclear regulatory agencies can access safety assessment records of radioactive source equipment through blockchain without accessing the original data, ensuring the compliance and security of nuclear energy data.

[0202] Remote monitoring of deep-sea nuclear energy systems: Using IPFS to store data on deep-sea nuclear energy equipment ensures that equipment data can be fully recorded and remotely accessed even in harsh environments such as high pressure and high humidity.

[0203] Example 4, a radioactive source equipment safety assessment system based on multi-factor analysis, see Figure 1 As shown, it includes the following modules:

[0204] Data acquisition module: used to obtain multi-dimensional data of radioactive source equipment and use distributed sensor networks for real-time monitoring;

[0205] Data analysis module: reduces the dimensionality of multidimensional data, optimizes data processing with quantum computing, and uses multi-scale analysis models to predict trends in time series data;

[0206] Cognitive intelligence assessment module: Builds a Bayesian causal network to analyze the status of radioactive source equipment through causal reasoning methods, and combines neural symbolic artificial intelligence to optimize risk prediction;

[0207] Self-organizing management module: A blockchain-based decentralized autonomous organization manages the status, data sharing, and permissions of radioactive source equipment, and implements maintenance strategies in conjunction with smart contracts;

[0208] Environmental adaptability simulation module: This module simulates the operating status of radioactive source equipment in special environments, optimizes risk assessment strategies through generative adversarial networks, uses digital twin technology for simulation, and combines computational fluid dynamics to analyze the thermal conductivity characteristics and operational safety of equipment in high-pressure environments.

[0209] Decision optimization module: Receives evaluation results and combines reinforcement learning to dynamically optimize equipment maintenance strategies to achieve predictive maintenance.

[0210] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for safety assessment of radioactive source equipment based on multi-factor analysis, characterized in that: include: Data collection: Acquire multi-dimensional data from radioactive source equipment, use a distributed sensor network to monitor the equipment's operating status in real time, and share data from different radioactive source equipment through federated learning; Data dimensionality reduction and analysis: Reduce the dimensionality of multidimensional data, extract characteristic variables, combine quantum computing to optimize data processing efficiency, and use multi-scale analysis models to predict trends in time series data; Cognitive intelligence risk assessment: Constructing a Bayesian causal network, using causal reasoning methods to analyze the operating status of radioactive source equipment, and combining neural symbolic artificial intelligence models to integrate expert knowledge and deep learning to improve the accuracy of risk prediction; The risk assessment combines Bayesian causal reasoning with neural symbolic artificial intelligence, employing a causal graph modeling approach to analyze key failure factors of radioactive source equipment. This analysis is then performed through symbolic logic reasoning based on an expert knowledge base. The results of causal reasoning serve as trigger conditions for smart contracts. When the assessment result exceeds a set risk threshold, the smart contract on the blockchain automatically triggers security policies and conducts decision-making and approval through self-organizing management. The neural symbolic artificial intelligence, combined with a reinforcement learning mechanism, automatically adjusts the weights of the causal reasoning model during the assessment process. The smart contract implements the following security policies: Smart contracts predict equipment failures through Bayesian causal reasoning and automatically trigger maintenance instructions and adjust maintenance plans based on evaluation results optimized by neural symbolic artificial intelligence. When radioactive source equipment enters a high-risk state, the smart contract automatically switches the equipment to a safe operating mode and restricts unauthorized operations through decentralized permission management. When the system determines a high-risk event, the smart contract automatically triggers the DAO mechanism, and a multi-party consensus review determines whether to execute major maintenance or shutdown strategies. Self-organizing management: A decentralized autonomous organization based on blockchain enables automatic tracking of radioactive source equipment status, data sharing, and permission management, combined with smart contracts to execute security policies; The self-organizing management uses blockchain combined with smart contracts and causal reasoning assessment results to dynamically manage the status and authority allocation of radioactive source equipment and automatically perform the following operations: When the assessment result indicates an abnormal risk, the smart contract automatically triggers the device's self-check program and adjusts the device's operating permissions based on the scope of the fault. Combined with a self-organizing management mechanism, the smart contract automatically notifies relevant responsible persons and confirms key decisions through a multi-party consensus mechanism on the blockchain. All decisions, status updates, and security policy executions are stored in the blockchain ledger to prevent human tampering. Extreme Environment Adaptability Modeling: Simulates the operating status of radioactive source equipment in special environments and combines it with generative adversarial networks to optimize risk assessment strategies; Assessment result output and decision optimization: Input the risk assessment results into the adaptive decision-making system, combine reinforcement learning to adjust the equipment maintenance strategy, and form a five-level risk warning mechanism.

2. The method for safety assessment of radioactive source equipment based on multi-factor analysis according to claim 1, characterized in that: The data collection uses Bayesian causal reasoning combined with a distributed sensor network to perform real-time causal analysis during the data collection phase, automatically screen key influencing factors, and combine federated learning to achieve data collaborative training between different devices; The causal reasoning results of the data collection are directly used as input for the neural symbolic artificial intelligence analysis, and the results of the data collection are stored in the blockchain network; Device status data is encrypted and stored using a zero-knowledge proof mechanism to improve data privacy.

3. The method for safety assessment of radioactive source equipment based on multi-factor analysis according to claim 1, characterized in that: The extreme environment adaptability modeling combines digital twin technology with Bayesian causal reasoning to establish a simulation of radioactive source equipment in extreme environments and perform state analysis through computational fluid dynamics, specifically including: During the extreme environment simulation process, Bayesian causal networks are used to analyze the impact of different environmental variables on equipment safety and automatically adjust the evaluation model; neural symbolic artificial intelligence is used to dynamically adjust the parameters of the digital twin model; in extreme environments, when causal reasoning detects that the equipment has operating parameters that exceed the safety threshold, the smart contract automatically executes emergency response instructions.

4. The method for safety assessment of radioactive source equipment based on multi-factor analysis according to claim 1, characterized in that: The decision optimization adopts reinforcement learning combined with causal reasoning analysis. The reinforcement learning optimization includes: The reinforcement learning model uses causal reasoning results as input to gradually optimize the equipment's operating strategy; it combines neural symbolic artificial intelligence to provide expert knowledge constraints; and all decision adjustments are managed in real time by smart contracts on the blockchain.

5. A radioactive source equipment safety assessment system based on multi-factor analysis, characterized in that: The system applies the radioactive source equipment safety assessment method based on multi-factor analysis as described in any one of claims 1 to 4, including: Data acquisition module: used to obtain multi-dimensional data of radioactive source equipment and use distributed sensor networks for real-time monitoring; Data analysis module: reduces the dimensionality of multidimensional data, optimizes data processing with quantum computing, and uses multi-scale analysis models to predict trends in time series data; Cognitive intelligence assessment module: Builds a Bayesian causal network to analyze the status of radioactive source equipment through causal reasoning methods, and combines neural symbolic artificial intelligence to optimize risk prediction; Self-organizing management module: A blockchain-based decentralized autonomous organization manages the status, data sharing, and permissions of radioactive source equipment, and implements maintenance strategies in conjunction with smart contracts; Environmental adaptability simulation module: This module simulates the operating status of radioactive source equipment in special environments, optimizes risk assessment strategies through generative adversarial networks, uses digital twin technology for simulation, and integrates computational fluid dynamics analysis equipment. Decision optimization module: Receives evaluation results and dynamically optimizes equipment maintenance strategies using reinforcement learning.

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