Radioactive source equipment safety assessment method and system based on multi-factor analysis

Abstract

The invention discloses a radioactive source equipment safety assessment method and system based on multi-factor analysis, and relates to the technical field of equipment safety assessment. A radioactive source equipment safety evaluation system based on multi-factor analysis comprises a data acquisition module, a data analysis module, a cognitive intelligent evaluation module, a self-organization management module, an environmental adaptability simulation module and a decision optimization module. According to the method, the Bayesian causal network is combined with neural symbol artificial intelligence, so that the operation state of radioactive source equipment can be automatically analyzed, and higher interpretability and prediction precision can be realized in combination with expert knowledge, thereby optimizing a safety decision; the block chain is combined with the decentralized autonomous organization, the non-tampering property of the radioactive source equipment data is ensured, automatic tracking, authority management and security policy execution of the equipment state are realized through the smart contract, the transparency and security of management are improved, and the risk caused by human intervention is reduced.

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] At present, the 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: On the one hand, existing safety assessment methods mainly rely on a single data source, such as equipment operating parameters and historical maintenance records, and lack comprehensive analysis of factors such as environmental variables, operator behavior, and long-term equipment operation trends, making it difficult to form a complete safety assessment system. In addition, traditional detection methods usually rely on regular inspections, with delayed data updates, and cannot achieve real-time status monitoring, making it difficult to provide timely warnings before potential equipment failures occur.

[0003] On the other hand, the operation of radioactive source equipment involves high-dimensional nonlinear data. Existing data analysis methods have problems such as low accuracy and large amount of calculation when processing complex data. At the same time, most risk prediction methods based on machine learning rely on pattern matching, lack causal reasoning capabilities, and cannot accurately analyze the causes of risks. In addition, the current time series analysis model used for radioactive source equipment status prediction is difficult to strike a balance between short-term changes and long-term trend predictions, affecting prediction accuracy.

[0004] In addition, with the increasing application of radioactive source equipment in extreme environments such as deep space exploration and deep-sea nuclear energy, the 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

[0005] The present invention aims to solve the limitations of the safety assessment method of radioactive source equipment, mainly targeting the problems of the current assessment method, such as single data source, insufficient real-time monitoring capability, low risk prediction accuracy, and poor adaptability to extreme environments. By combining Bayesian causal networks, neural symbolic artificial intelligence, blockchain, and decentralized autonomous organization technology, the present invention realizes multi-dimensional data collection, intelligent analysis, adaptive decision optimization, and safety management of radioactive source equipment, improves the accuracy, transparency, and predictability of equipment operation status monitoring, effectively reduces safety hazards caused by human intervention, and improves the adaptability and management efficiency of equipment in extreme environments.

[0006] A safety assessment method for radioactive source equipment based on multi-factor analysis, including: Data collection: Obtain multi-dimensional data of radioactive source equipment, use distributed sensor networks to monitor the operating status of equipment in real time, and share data from different radioactive source equipment through federated learning; Data dimensionality reduction and analysis: Reduce the dimensionality of multi-dimensional data, extract characteristic variables, optimize data processing efficiency with quantum computing, 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; Self-organization management: A decentralized autonomous organization based on blockchain can realize automatic tracking of the status of radioactive source equipment, data sharing and authority management, and implement security policies in combination with smart contracts; Extreme environment adaptability modeling: simulate the operating status of radioactive source equipment in special environments, and optimize risk assessment strategies in combination with generative adversarial networks; Evaluation result output and decision optimization: Input the risk assessment results into the adaptive decision-making system, adjust the equipment maintenance strategy in combination with reinforcement learning, and form a five-level risk warning mechanism.

[0007] 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 in the data collection stage, automatically screens key influencing factors, and combines federated learning to achieve data collaborative training between different devices; The causal reasoning results of data collection are directly used as input for neural symbolic artificial intelligence analysis, and the results of data collection are stored in the blockchain network; the device status data is encrypted and stored using a zero-knowledge proof mechanism to improve data privacy.

[0008] 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 is used as a trigger condition for 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.

[0009] 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: When the assessment result is an abnormal risk, the smart contract automatically triggers the equipment self-check procedure and adjusts the equipment'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.

[0010] As a preferred technical solution of the present invention, 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 the radiation source equipment enters a high-risk state, the smart contract automatically switches the equipment to a safe operation 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 is used to review whether to execute major maintenance or shutdown strategies.

[0011] 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 radiation source equipment in an extreme environment, and perform state analysis through computational fluid dynamics, specifically including: During the extreme environment simulation process, the Bayesian causal network is 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.

[0012] 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: 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; all decision adjustments are managed in real time by smart contracts on the blockchain.

[0013] A safety assessment system for radioactive source equipment based on multi-factor analysis, 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: Reduce the dimensionality of multi-dimensional data, optimize data processing with quantum computing, and use multi-scale analysis models to predict trends in time series data; Cognitive intelligence assessment module: constructs a Bayesian causal network, analyzes 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 decentralized autonomous organization based on blockchain that manages the status, data sharing and permissions of radioactive source equipment, and executes maintenance strategies in conjunction with smart contracts; Environmental adaptability simulation module: simulates the operating status of radioactive source equipment in special environments, optimizes risk assessment strategies by combining generative adversarial networks, uses digital twin technology for simulation, and combines computational fluid dynamics analysis equipment; Decision optimization module: receives the evaluation results and dynamically optimizes the equipment maintenance strategy in combination with reinforcement learning.

[0014] The present invention has the following advantages: The present invention uses a distributed sensor network to collect multi-dimensional data such as operating parameters, environmental variables, historical maintenance records, operator behavior, etc. of radiation 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, the present invention integrates multiple factors for analysis, builds a more accurate safety assessment system, and improves the reliability of the assessment results.

[0015] The present invention introduces topological data analysis to reduce the dimensionality of multidimensional data, and combines quantum computing to optimize data processing and improve computing efficiency, so that the system can quickly process large-scale radioactive source equipment data and improve real-time performance and accuracy; a multi-scale analysis model is used to predict the time series trend of the operating status of the radioactive source equipment, and short-term changes and long-term trends are analyzed at the same time to optimize the prediction accuracy. Compared with traditional time series analysis methods, the present invention can more accurately predict the possibility of equipment failure, warn of potential risks in advance, and improve the safety of equipment operation.

[0016] The present invention adopts Bayesian causal network combined with neural symbolic artificial intelligence, which can not only automatically analyze the operating status of radiation source equipment, but also combine expert knowledge to achieve higher interpretability and prediction accuracy, thereby optimizing safety decisions; blockchain is combined with decentralized autonomous organizations to ensure the non-tamperability of radiation source equipment data, and through smart contracts, automatic tracking of equipment status, authority management and security policy execution are realized, thereby improving the transparency and security of management and reducing the risks caused by human intervention.

[0017] The present invention combines digital twin technology to construct a virtual mapping of the radiation source equipment, which can simulate the equipment status in real time, and combines the generative adversarial network to optimize the evaluation strategy, thereby improving the adaptability of the radiation source equipment in extreme environments such as microgravity, high pressure, and strong radiation, and ensuring the safety of the equipment in special environments.

[0018] The present invention uses reinforcement learning to optimize equipment maintenance strategies and forms a five-level risk warning mechanism. According to the operating status, historical data and prediction results of the equipment, the maintenance plan is intelligently adjusted to achieve predictive maintenance, reduce unnecessary maintenance costs, and ensure the long-term stable operation of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art description are briefly introduced below. Obviously, the drawings in the following description are only schematic diagrams of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on the provided drawings without creative work. Figure 1 The present invention is a schematic diagram of the structure of a radiation source equipment safety assessment system based on multi-factor analysis adopted in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Embodiment 1, a method for safety assessment of radioactive source equipment based on multi-factor analysis, comprising the following steps: Step S1, data collection: obtain multi-dimensional data of radiation source equipment, use a distributed sensor network to monitor the equipment operation status in real time, and share data of different radiation source equipment through federated learning to ensure a balance between data integrity, privacy protection and computing efficiency.

[0022] Specifically include: S101: Multidimensional data collection and data sources; Equipment operating parameters: including temperature, pressure, vibration, radiation dose, energy consumption, operating time and other information.

[0023] 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.

[0024] Historical maintenance records: including equipment maintenance time, maintenance type, failure records, parts replacement records, maintenance logs and other information to predict possible failure modes of the equipment.

[0025] Operator behavior data: including equipment usage logs, operator identity, operation history, and emergency response records, to analyze the impact of human factors on equipment safety and to prevent risks caused by misoperation.

[0026] S102: Use edge computing combined with cloud computing to improve the real-time performance of data processing; By deploying edge computing nodes near radiation source equipment, real-time preprocessing, data compression, and preliminary analysis of local data can be achieved, reducing data transmission delays and improving response speed.

[0027] Through cloud computing, data from multiple radiation 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.

[0028] Combined with a layered computing architecture, it ensures that 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.

[0029] S103: Use privacy computing, including differential privacy and secure multi-party computing, to ensure the security of radioactive source data sharing; Differential privacy: When sharing data, the data is perturbed to avoid leaking key device status information while ensuring the effectiveness of the overall statistical analysis of the data.

[0030] Secure Multi-Party Computing (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.

[0031] Homomorphic encryption: used to keep data encrypted during data processing, prevent cloud computing or third-party computing nodes from accessing original sensitive data, and enhance data privacy protection capabilities.

[0032] S104: Use decentralized identity authentication technology to ensure the security and traceability of data access.

[0033] The decentralized identity authentication (DID) technology is used to enable each radiation source equipment, operation and maintenance personnel, and management agency to have independent identity credentials to ensure the secure management of data access rights.

[0034] Authentication and access logs are stored through blockchain technology to ensure that access records cannot be tampered with and data traceability is ensured.

[0035] 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.

[0036] 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.

[0037] Specifically include: S201: Use quantum computing technology to increase data processing speed and reduce computing resource consumption; Acceleration of high-dimensional matrix operations based on quantum computing: Utilize the super-parallel characteristics of quantum computing to perform matrix operations on high-dimensional data, improve the computational efficiency of data dimensionality reduction and pattern recognition, and reduce computing resource consumption.

[0038] Using quantum Fourier transform (QFT) to optimize signal processing: For the time series data of radioactive source equipment, QFT is used to enhance the frequency domain analysis capability and improve the accuracy of identifying changes in equipment status.

[0039] Using quantum support vector machine (QSVM) for feature selection: Combine QSVM to classify and extract features from data, improve the ability to detect abnormal data, reduce redundant calculations, and improve the quality of data dimensionality reduction.

[0040] S202: extract key features from multidimensional data using a topological data analysis method and optimize the data structure; 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.

[0041] Use persistent homology algorithm to reduce the dimension of high-dimensional data: Persistent homology can detect key patterns and topological structures in the data, identify important state transitions, and improve the accuracy of equipment failure prediction.

[0042] 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.

[0043] S203: Adopt an adaptive dimensionality reduction method to adjust the analysis model according to real-time data to ensure the stability of the evaluation system.

[0044] Adaptive dimensionality reduction algorithm optimizes data accuracy: dynamically adjusts the dimensionality reduction model according to the real-time status of the radiation source equipment to ensure the accuracy and stability of feature extraction.

[0045] 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, thereby improving system adaptability.

[0046] Abnormal data detection and adaptive filtering: In response to possible data anomalies, an adaptive filtering method (autoregressive moving average model) is used to improve data quality and reduce error accumulation during dimensionality reduction.

[0047] Step S3, cognitive intelligent risk assessment: Based on the multi-dimensional data analysis results of the radiation source equipment, a Bayesian causal network is constructed, and the causal reasoning method is used to analyze the equipment operation status. In combination with the neural symbolic artificial intelligence model, expert knowledge and deep learning are integrated to improve the accuracy and interpretability of anomaly detection, fault prediction and safety assessment of radiation source equipment.

[0048] Specifically include: S301: Use reinforcement learning model and expert system to optimize risk prediction strategy; Build a dynamic risk assessment model based on reinforcement learning: Use deep reinforcement learning (DRL) to continuously optimize risk assessment strategies based on equipment operating status and historical failure modes, and improve the predictive maintenance capabilities of radioactive source equipment.

[0049] Combine with expert systems to improve the interpretability of prediction strategies: 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.

[0050] Adaptive optimization of risk threshold: 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 and improve the accuracy of assessment.

[0051] S302: Using inverse reinforcement learning to simulate expert decision-making mode to improve the accuracy of prediction; Construct an expert behavior dataset: collect historical decision-making data of radioactive source equipment managers and learn the experts’ judgment methods through behavioral analysis models.

[0052] Use inverse reinforcement learning (IRL) to train the intelligent evaluation model: Based on the actual decision-making pattern of experts, the intelligent system is trained so that it can make reasonable evaluation decisions in the absence of a clear reward function, thereby improving the accuracy of predictions.

[0053] Adaptively learn expert experience and optimize accident response plans: The inverse reinforcement learning model can automatically adjust the security assessment strategy according to the characteristics of different devices and environments, so that the system can simulate the best decision of experts when facing emergencies and improve the efficiency of accident response.

[0054] S303: Use neuro-symbolic artificial intelligence, combined with expert knowledge and deep learning, to improve the reliability of causal reasoning.

[0055] Build a hybrid model based on neuro-symbolic AI: combine symbolic logic reasoning (expert rules) and deep learning (data-driven) to improve the ability to model complex causal relationships.

[0056] Causal reasoning improves anomaly detection accuracy: Use Bayesian causal networks to deduce possible causes of abnormal conditions in radioactive source equipment, avoid problems with deep learning black box models, and improve the credibility of risk predictions.

[0057] Causal reasoning optimization based on graph neural network (GNN): Use graph neural network to analyze the causal relationship between equipment operating states, optimize the safety assessment process, and improve the early warning capability of potential failures.

[0058] Step S4, self-organization management: Build a decentralized autonomous organization (DAO) based on blockchain technology to achieve automatic tracking of the status of radioactive source equipment, data sharing and authority management. Combined with smart contracts to execute security policies, ensure the transparency, security and cross-institutional collaboration of equipment management, and improve the full life cycle management level of radioactive source equipment.

[0059] Specifically include: S401: Use blockchain technology to manage the status of radioactive source equipment to ensure data transparency and immutability; 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 the radioactive source equipment cannot be tampered with, thereby ensuring data integrity.

[0060] 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.

[0061] Use 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.

[0062] S402: Using smart contracts to automatically perform maintenance and status updates of radioactive source equipment; Automated maintenance plan: Smart contracts can automatically trigger maintenance plans based on parameters such as equipment operating status, historical maintenance records, and environmental variables to ensure that the equipment is in a safe operating state.

[0063] 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.

[0064] Automatic execution of device permission changes: When the device permissions change due to maintenance, transfer or elimination, the smart contract automatically updates the permission management policy to prevent unauthorized access or operation.

[0065] S403: Use decentralized autonomous organizations to manage cross-institutional collaboration on radioactive source equipment to ensure the security of data sharing.

[0066] Decentralized governance mechanism: DAO allows different institutions (such as government regulators, research institutions, and equipment manufacturers) to participate in the management of radioactive source equipment without the need for trust, avoiding the drawbacks of single-point management.

[0067] Cross-institutional data sharing based on alliance chain: Adopting the alliance chain model, secure and controllable data sharing is achieved among multiple institutions, ensuring that all parties can access the necessary equipment status information while ensuring data privacy.

[0068] Equipment operation approval mechanism based on multi-party consensus: In the key operations of radioactive source equipment (such as authority changes, maintenance execution, equipment transfer), a multi-party consensus mechanism is introduced to ensure that all operations are legally approved and improve the security of equipment management.

[0069] Step S5, extreme environment adaptability modeling: simulate the operating status of radiation 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 the generative adversarial network (GAN) to optimize the risk assessment strategy and improve the accuracy and adaptability of the assessment results.

[0070] Specifically include: S501: Use digital twin technology to simulate the operation of radiation source equipment in different environments; Build a digital twin model: Based on the historical data, operating parameters, and structural model of the radiation source equipment, establish a real-time digital twin system to simulate the state changes of the equipment in different environments.

[0071] 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.

[0072] Real-time status feedback and prediction: Digital twin technology combines sensor data to achieve real-time feedback on the operating status of equipment and predict possible failure modes in extreme environments, thereby improving the safety of equipment.

[0073] S502: Use computational fluid dynamics methods to analyze the heat conduction and operation safety of equipment under high pressure environment; Thermodynamic modeling under high pressure environment: Based on the computational fluid dynamics (CFD) method, the heat conduction, heat dissipation efficiency, and convective heat transfer of radioactive source equipment in deep-sea high pressure or closed chambers are analyzed to optimize the heat dissipation design of the equipment.

[0074] Material stability analysis in radiation environments: simulate the changes in material properties of radioactive source equipment in high-radiation environments, analyze the impact of radiation on core components such as equipment casing, shielding layer, circuit, etc., and optimize the use of radiation-resistant materials.

[0075] Operation stability assessment in a vacuum environment: Based on the working characteristics of radiation 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.

[0076] S503: Generative adversarial networks are used to optimize risk prediction in different environments to ensure the accuracy of assessment results.

[0077] GAN generates extreme environment sample data: Based on the existing environmental data set, the generative adversarial network (GAN) is used to create equipment operation data in different environments to enhance the training data set and improve the generalization ability of the evaluation model.

[0078] Simulate the impact of different environmental factors on equipment status: Through the 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.

[0079] Compare the model with real data: Use GAN to generate extreme environment data and compare it with the real equipment operation data to optimize the risk assessment model, reduce prediction errors, and improve the reliability of the assessment system.

[0080] Step S6, evaluation result output and decision optimization: input the risk assessment results into the adaptive decision-making system, combine reinforcement learning to optimize the equipment maintenance strategy, and form a five-level risk warning mechanism to ensure that the safety management of radiation source equipment is more intelligent, precise and dynamically optimized.

[0081] Specifically include: S601: Use reinforcement learning to optimize equipment maintenance strategies and achieve predictive maintenance; Intelligent maintenance model based on deep reinforcement learning (DRL): Utilizes reinforcement learning algorithm (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.

[0082] Adaptive adjustment of maintenance cycle: Different from traditional fixed periodic maintenance, this method combines reinforcement learning to achieve on-demand maintenance, that is, maintenance is performed in advance when the equipment operating status is close to the fault threshold to avoid over-maintenance or under-maintenance.

[0083] Resource optimization based on predictive maintenance: The intelligent system can dynamically adjust the spare parts supply chain, maintenance personnel scheduling, maintenance time window, etc. according to equipment maintenance needs to ensure the accuracy and efficiency of maintenance work.

[0084] S602: Use expert systems to build a multi-level risk warning mechanism to improve the response speed of abnormal conditions; Construct a five-level risk warning system: 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] Level 5 Warning (Extremely High Risk): If a serious abnormality occurs in the equipment, which may cause radiation leakage or equipment damage, the system will immediately trigger the safety shutdown procedure and start the emergency response mechanism.

[0089] Multi-level expert system optimizes early warning response: Combined with rule-based expert system, it uses multi-dimensional data analysis results to improve the accuracy of abnormal state judgment and optimize the early warning response process to ensure that key risk events are handled quickly.

[0090] S603: Combined with the adaptive optimization algorithm, dynamically adjust the security strategy according to the historical operation data and real-time status of the radiation source equipment.

[0091] Safety strategy optimization based on genetic algorithm (GA): Use genetic algorithm to optimize equipment operating parameters to ensure that the equipment can reach the optimal operating state under different working conditions and reduce safety risks.

[0092] Dynamically adjust equipment operation strategies: According to the historical data of radiation source equipment, environmental change trends and risk prediction results, adjust the equipment operation mode to improve the stability and safety of the equipment.

[0093] Adaptive optimization of equipment status: Based on Bayesian optimization and combined with the latest evaluation data, the equipment safety management strategy is continuously optimized to ensure that the equipment is always in the best operating state.

[0094] Example 2: The radiation source equipment safety assessment method based on multi-factor analysis provided by the present invention is applicable to a variety of scenarios and can be widely used in nuclear power plants, medical radiation equipment, deep space exploration, deep-sea nuclear energy systems, industrial non-destructive testing and other fields. Through multi-dimensional data collection, intelligent analysis, risk prediction, self-organization management and extreme environment adaptability modeling, the safety management and intelligent maintenance capabilities of radiation source equipment are improved.

[0095] 1. Safety assessment and intelligent management of nuclear power plant radioactive source equipment, applicable scenarios: The radioactive source equipment of a nuclear power plant mainly includes nuclear fuel assemblies, reactor control rods, radiation monitoring instruments, spent fuel storage devices, etc., and its safety is crucial to the operation of the entire nuclear power plant. This method can be used for: 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; Spent fuel storage management, ensuring the safe storage of discarded radioactive materials and predicting risks during long-term storage; Remote monitoring and intelligent early warning use blockchain technology combined with distributed sensors to achieve security data sharing between different nuclear power plants and improve the overall safety management level.

[0096] Examples: Safety assessment based on Bayesian causal networks: Deploy multiple sensors in nuclear reactors to monitor radiation doses, combine Bayesian causal reasoning to predict the decay risk of fuel assemblies, and provide early warning of possible failures.

[0097] Intelligent maintenance strategy optimization: Optimize the maintenance plan of nuclear power plants through reinforcement learning, reduce unnecessary maintenance downtime, and improve equipment utilization.

[0098] 2. Intelligent diagnosis and maintenance of medical radiation equipment, applicable scenarios: Medical radiation equipment is widely used in radiation therapy, nuclear medicine imaging, tumor treatment and other fields, including: CT (Computed Tomography): Long-term operation may cause problems such as equipment overheating and X-ray tube aging. The present invention can monitor the equipment status in real time, optimize scanning parameters, and increase the life of the equipment.

[0099] PET (positron emission tomography): The use of radioactive tracers in PET equipment requires strict management. This method can intelligently evaluate the dosage and decay of radioactive tracers to ensure medical safety.

[0100] Linear Accelerator (LINAC): Used in cancer radiotherapy, the stability of the equipment is crucial. This method can predict equipment failure based on historical data and real-time monitoring results, reducing the risk of treatment interruption.

[0101] Examples: Intelligent diagnosis optimization: Based on a multi-scale analysis model, it optimizes CT / PET scanning parameters, reduces radiation dose, and improves imaging quality.

[0102] Predictive maintenance: Use reinforcement learning to adjust the maintenance plan of medical radiology equipment to reduce equipment downtime and improve medical service efficiency.

[0103] 3. Safety assessment and optimization of nuclear energy systems for deep space probes, applicable scenarios: Deep space exploration missions (such as lunar and Mars probes) usually use radioisotope thermoelectric generators (RTGs) or small nuclear reactors as energy supply systems. These radioactive source devices face extreme environmental challenges: Microgravity environment: affects the heat conduction mode of radiation source equipment, which may cause abnormal temperature changes of the equipment.

[0104] Strong radiation environment: Cosmic rays may accelerate the decay of radioactive materials and affect the service life of equipment.

[0105] 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 a GAN model to simulate the operation of equipment in extreme environments and improve the long-term adaptability of the equipment.

[0106] Examples: 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.

[0107] Automated health management: An intelligent assessment system based on reinforcement learning that adaptively adjusts the power output of the nuclear energy system to avoid excessive fuel consumption and extend the mission life.

[0108] 4. Intelligent monitoring and management of deep-sea nuclear energy systems, applicable scenarios: The operating environment of underwater nuclear energy systems such as deep-sea detectors and nuclear-powered submarines is extreme, and they face challenges such as high pressure, high humidity, and strong corrosion: Nuclear-powered submarines: Radioactive source equipment needs to operate in a closed environment for a long time, and radioactive leakage monitoring is crucial.

[0109] 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.

[0110] Examples: 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.

[0111] 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.

[0112] 5. Safety assessment of radioactive source equipment in industrial non-destructive testing, applicable scenarios: 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: X-ray detection equipment: Long-term high-intensity work may cause aging of the X-ray source and affect detection accuracy.

[0113] Gamma ray detector: The activity of the radioactive source needs to be strictly controlled to ensure detection safety.

[0114] Examples: Intelligent dose control: Dynamically adjust the radiation dose of detection equipment based on reinforcement learning to improve detection accuracy while reducing radiation exposure risks.

[0115] Radiation source aging prediction: Use causal reasoning and time series analysis models to predict the degradation trend of radiation source equipment and replace key components in advance to ensure the stability of detection equipment.

[0116] Example 3: The radiation 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.

[0117] The computing architecture consists of: 1. Use federated learning to improve cross-device data collaboration capabilities and optimize device security assessment models; Radioactive source equipment is usually distributed in different geographical areas, involving multiple institutions and systems. Traditional centralized data processing methods have problems such as data privacy risks, high computing costs, and weak collaboration capabilities. The present invention adopts federated learning to achieve data collaborative training between different radioactive source equipment without sharing original data, and optimize the safety assessment model.

[0118] Specifically include: Local model training on edge devices: Each radiation source device independently trains a safety assessment model without uploading original data to avoid privacy leakage.

[0119] Encrypted aggregation of model parameters: The local model parameters of each device are protected by homomorphic encryption or differential privacy mechanisms and securely aggregated in a central server or decentralized computing architecture to improve the privacy and security of collaborative training.

[0120] Adaptive model update mechanism: Reinforcement learning combined with dynamic weight adjustment is used to optimize the global model of federated learning according to the operating status and data characteristics of the radiation source equipment, thereby improving the accuracy of equipment failure prediction.

[0121] Examples: 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 to achieve experience sharing among different nuclear power plants and improve the ability to predict equipment failures.

[0122] Intelligent diagnosis and optimization of medical radiology equipment: The hospital's CT and PET equipment share optimization models through federated learning to improve the intelligent diagnosis capabilities of equipment of different brands and models, while avoiding medical data leakage.

[0123] 2. Use quantum computing to accelerate large-scale data analysis and improve the efficiency of multi-dimensional data computing; The safety assessment of radioactive source equipment involves large-scale multidimensional data. Traditional computing methods (such as principal component analysis PCA and support vector machine SVM) have large computational complexity and high time overhead when processing high-dimensional data, making it difficult to meet the needs of real-time monitoring and prediction. The present invention uses quantum computing to accelerate data processing and utilizes its parallel computing characteristics to improve the speed and accuracy of high-dimensional data analysis.

[0124] Specifically include: Quantum Fourier Transform (QFT) optimizes time series data processing: For the time series signals of radiation source equipment (such as radiation dose, temperature changes, etc.), QFT is used to accelerate frequency domain analysis to improve the accuracy of trend prediction.

[0125] Quantum support vector machine (QSVM) optimizes anomaly detection: QSVM is used to build an intelligent anomaly detection model to improve the anomaly recognition capability of radioactive source equipment.

[0126] Quantum Bayesian Network (QBN) optimizes causal reasoning: Combining QBN with risk analysis improves the computational efficiency of causal reasoning, enabling the system to analyze the causes of equipment failures more quickly.

[0127] Examples: Optimization of nuclear energy systems for deep space probes: Using quantum computing to accelerate thermodynamic data analysis and improve the efficiency of fault diagnosis of nuclear energy systems for deep space probes.

[0128] Nuclear power plant accident emergency response: Use quantum Bayesian networks to perform multivariate causal reasoning, complete complex accident analysis in a short period of time, and improve emergency response speed.

[0129] 3. Adopt decentralized data storage architecture to ensure the security and traceability of radioactive source equipment data; Traditional centralized data storage methods (SQL databases, cloud storage) are vulnerable to security issues such as hacker attacks, data tampering, single point failures, etc. This invention adopts a decentralized data storage architecture and improves the security, integrity and traceability of radiation source equipment data through blockchain and distributed storage technology.

[0130] Specifically include: Blockchain stores equipment operation data and maintenance records: The operation status, maintenance logs, and permission change records of radioactive source equipment are encrypted and stored on the blockchain to ensure that the data cannot be tampered with.

[0131] Distributed storage based on IPFS (InterPlanetary File System): Large-scale sensor data of radioactive source equipment is stored using IPFS or distributed hash table (DHT) to improve the redundancy and security of data storage.

[0132] 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.

[0133] Examples: 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.

[0134] Remote monitoring of deep-sea nuclear energy systems: IPFS is used to store data of deep-sea nuclear energy equipment, ensuring that equipment data can be fully recorded and remotely accessed even in harsh environments such as high pressure and high humidity.

[0135] Example 4, a radiation source equipment safety assessment system based on multi-factor analysis, see Figure 1 As shown, it includes the following modules: 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: Reduce the dimensionality of multi-dimensional data, optimize data processing with quantum computing, and use multi-scale analysis models to predict trends in time series data; Cognitive intelligence assessment module: constructs a Bayesian causal network, analyzes 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 decentralized autonomous organization based on blockchain that manages the status, data sharing and permissions of radioactive source equipment, and executes maintenance strategies in conjunction with smart contracts; Environmental adaptability simulation module: simulates the operating status of radioactive source equipment in special environments, optimizes risk assessment strategies with generative adversarial networks, uses digital twin technology for simulation, and combines computational fluid dynamics to analyze the thermal conductivity characteristics and operating safety of equipment in high-pressure environments; Decision optimization module: Receives evaluation results and combines reinforcement learning to dynamically optimize equipment maintenance strategies to achieve predictive maintenance.

[0136] 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: Obtain multi-dimensional data of radioactive source equipment, use distributed sensor networks to monitor the operating status of equipment in real time, and share data from different radioactive source equipment through federated learning; Data dimensionality reduction and analysis: Reduce the dimensionality of multi-dimensional data, extract characteristic variables, optimize data processing efficiency with quantum computing, 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; Self-organization management: A decentralized autonomous organization based on blockchain can realize automatic tracking of the status of radioactive source equipment, data sharing and authority management, and implement security policies in combination with smart contracts; Extreme environment adaptability modeling: simulate the operating status of radioactive source equipment in special environments, and optimize risk assessment strategies in combination with generative adversarial networks; Evaluation result output and decision optimization: Input the risk assessment results into the adaptive decision-making system, adjust the equipment maintenance strategy in combination with reinforcement learning, and form a five-level risk warning mechanism.

2. A method for safety assessment of radioactive source equipment based on multi-factor analysis according to claim 1, characterized in that: The data collection adopts Bayesian causal reasoning combined with distributed sensor networks, performs real-time causal analysis in the data collection stage, automatically screens key influencing factors, and combines federated learning to achieve data collaborative training between different devices; The causal reasoning results of data collection are directly used as input for neural symbolic artificial intelligence analysis, and 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.

3. The method for safety assessment of radioactive source equipment based on multi-factor analysis according to claim 1, characterized in that: The risk assessment combines Bayesian causal reasoning with neural symbolic artificial intelligence, adopts causal graph modeling method to analyze the key failure factors of radioactive source equipment, and makes inferences through symbolic logic reasoning based on expert knowledge base. The result of causal reasoning is used 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 reinforcement learning mechanism to automatically adjust the weight of the causal reasoning model during the assessment process.

4. The method for safety assessment of radiation source equipment based on multi-factor analysis according to claim 1, characterized in that: 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: When the assessment result is an abnormal risk, the smart contract automatically triggers the equipment self-check procedure and adjusts the equipment'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.

5. The method for safety assessment of radioactive source equipment based on multi-factor analysis according to claim 1, characterized in that: 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 the radiation source equipment enters a high-risk state, the smart contract automatically switches the equipment to a safe operation 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 is used to review whether to execute major maintenance or shutdown strategies.

6. A 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 conducts state analysis through computational fluid dynamics, specifically including: During the extreme environment simulation process, the Bayesian causal network is 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.

7. A 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, and 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; all decision adjustments are managed in real time by smart contracts on the blockchain.

8. A radiation source equipment safety assessment system based on multi-factor analysis, characterized in that: The system applies any one of the radiation source equipment safety assessment methods based on multi-factor analysis as described in claims 1 to 7, 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: Reduce the dimensionality of multi-dimensional data, optimize data processing with quantum computing, and use multi-scale analysis models to predict trends in time series data; Cognitive intelligence assessment module: constructs a Bayesian causal network, analyzes 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 decentralized autonomous organization based on blockchain that manages the status, data sharing and permissions of radioactive source equipment, and executes maintenance strategies in conjunction with smart contracts; Environmental adaptability simulation module: simulates the operating status of radioactive source equipment in special environments, optimizes risk assessment strategies by combining generative adversarial networks, uses digital twin technology for simulation, and combines computational fluid dynamics analysis equipment; Decision optimization module: receives the evaluation results and dynamically optimizes the equipment maintenance strategy in combination with reinforcement learning.

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