62 results about "Safety property" patented technology

The invention discloses an electro-hydrogen system safety domain dynamic regulation and control method based on digital twinning-reinforcement learning, and belongs to the field of power grid safety operation, and the method comprises the following steps: S1, constructing a multi-energy flow coupling model comprising new energy power generation, an electrolytic cell and a hydrogen storage tank; s2, decomposing a new energy original output sequence by adopting a method of combining continuous wavelet transform and an autoregressive moving average model to form time-frequency characteristics; s3, state space modeling considering space-time coupling; s4, generating a dynamic security domain based on digital twinning; s5, constructing a multi-target reward function, and solving by taking the dynamic security domain as a constraint; and S6, performing closed-loop verification and model evolution. By adopting the electric hydrogen system safety domain dynamic regulation and control method based on digital twinborn-reinforcement learning, safety domain dynamic adaptation and intelligent control of the electric hydrogen system under new energy fluctuation are realized, and the operation safety and economy of the system are remarkably improved.

The invention provides a pluggable multi-mode large model safety protection method, and belongs to the field of artificial intelligence safety protection, and the method comprises the steps: effectively covering a potential risk scene through a dynamic image-text variant adversarial generation technology, and remarkably enhancing the novel attack defense capability; a double-track collaborative filtering mechanism of a dominant rule filtering engine and a hidden semantic recognition engine is constructed, explicit and implicit violation contents are doubly protected, and malicious questions and normal interaction are accurately distinguished; then, by means of a cross-modal intention disassembling and self-adaptive risk matching technology, multi-modal content is deeply understood, and risk positioning is accurately achieved; finally, an ethical security defense line is constructed by using a value alignment bottom response, the input and output security, robustness and ethical compliance of the multi-modal large model are effectively guaranteed, and a modular pluggable architecture is adapted to various image-text multi-modal large models by constructing a closed-loop security protection link. And the method has model independence, strategy controllability and implementation convenience.

The invention relates to a power failure and transmission operation safety locking system and method, and the system and method achieve the precise locking management of an isolation switch device through the integration of SCADA real-time data, a dynamic locking counter mechanism and multiple safety verification. The system generates a unique job command number based on a Hash algorithm, dynamically renders a locking range in combination with a visual interface, and supports a multi-task superposition locking and priority strategy. Through triple protection of checking the state of the disconnecting switch equipment during plate hanging, checking the operation number during plate removing and forcibly removing the whole plate before power transmission, the problems of misoperation and electrified risks in traditional power failure and power transmission operation are solved, and the power supply dispatching safety is remarkably improved.

The invention provides an LLM command line generation tool based on OpenHarmony and a transplantation method, belongs to the technical field of industrial Internet of Things, and aims to solve the problems that a traditional TypeScript / Node. Js scheme has limitation, is insufficient in local reasoning performance, safety performance and reliability and is high in deployment cost. The tool comprises an ETS development environment module, a code migration module, a dependency library adaptation module, a security enhancement module certificate and a deployment and operation module, a zero-dependency transplantation scheme is designed for special environments of unpacked management tools such as OpenHarmony, the problem of strong coupling of a traditional Node.js application to the environment during operation is solved, and the safety of the Node. Js application is improved. And the deployment threshold of the heterogeneous system is further reduced.

The invention provides a zk-SNARK-based power grid data security cleaning and credibility verification method. The method comprises the following steps: firstly, sequentially performing static range verification, adjacent time difference rate judgment, missing value interpolation and sliding window outlier detection on original scheduling data by an acquisition end, generating cleaned data, and calculating a hash value to form sub-constraints; the method comprises the following steps: generating a proof key and a verification key based on security parameters in a trusted initialization stage, uniformly expressing all sub-constraints in an algebraic form, performing coding arrangement, and constructing a Rank-1 constraint system meeting zk-SNARK proof requirements as a basic structure of a generation circuit; the submitter constructs a private witness vector with the cleaned data by using the circuit to generate a proof parameter, and the verifier verifies the proof through a public parameter to confirm that the data meets a preset rule. According to the method, various logical judgments and Hash integrity in the cleaning process are unified into polynomial equality constraints, so that data privacy and consistency are efficiently guaranteed, trust cost and calculation complexity are remarkably reduced by means of a zero-knowledge verifiable mechanism, the security, flexibility and expandability of a system are enhanced, and the method is suitable for popularization and application. The method is suitable for complex scheduling scenes needing to process large-scale power grid data.

This application relates to a device capability description method, apparatus, computer device, and storage medium. The method includes: acquiring a capability description language document for a target device; the capability description language document includes device type information, operability status information, operation method information, physical constraint information, and feedback format information; the physical constraint information includes at least one or more of time constraints, energy constraints, safety constraints, lifetime constraints, and mutual exclusion constraints; upon receiving an intent instruction from an upper-layer application, parsing the intent instruction to extract one or more execution targets; and performing capability matching on the one or more execution targets according to the capability description language document to determine target operations from the target device that can achieve the execution targets. This method enables automatic capability adaptation between upper-layer applications and heterogeneous devices, reducing development costs and improving execution security.

The invention relates to the field of data security storage, in particular to a data security storage method of an artificial intelligence data platform. The method comprises the following steps: constructing a data security map based on multi-dimensional security information acquired by an artificial intelligence data platform, performing security risk grading on data according to the data security map, generating security grading labels, and constructing a grading encryption storage strategy; according to the hierarchical encryption storage strategy and the security hierarchical label, generating a polymorphic security snapshot and a temporary snapshot key, packaging the polymorphic security snapshot through the temporary snapshot key, and constructing a security storage index table and a polymorphic snapshot index; based on the security storage index table and the polymorphic security snapshot, performing binding with the task context information, and generating a security access log; and in combination with the data security map, the polymorphic snapshot index and the security access log, the access behavior of the polymorphic security snapshot is analyzed, and a hierarchical encryption strategy is adjusted. According to the invention, the data storage security can be improved.

The invention discloses a security test method and device for an industrial real-time database and a storage medium, and relates to the technical field of industrial internet. The method comprises the following steps: based on a three-security partition architecture, realizing security test on the industrial real-time database by verifying the transmission security of each interval. The logic isolation test between the first security zone and the second security zone emphatically checks access control strategies, industrial protocol transmission integrity and abnormal traffic filtering. The physical isolation test between the first security area or the second security area and the third security area comprises the steps of verifying one-way transmission and data integrity of the forward isolation device and verifying formats and security policies by the reverse isolation device. The cross-three-zone linkage transmission test covers end-to-end data transmission security verification, a simulation attack penetration test from a third security zone to a first security zone, and an emergency mechanism test under an isolation device fault. According to the method, the safety of the industrial real-time database can be improved through a three-partition testing method.

A method and system for adaptive adjustment of power grid operation modes involves acquiring multi-source heterogeneous power grid data and constructing a knowledge base based on this data. Based on the knowledge base, deterministic rule verification is performed on operation instructions to be verified, filtering out instructions that violate preset rules. A large-scale operation instruction model is constructed and trained using the knowledge base to obtain a trained model, outputting dispatch instructions that comply with safety constraints. For operation instructions that pass the deterministic rule verification, power flow calculation verification, N-1 security analysis verification, and a three-level verification for extreme power grid scenarios and major events are performed sequentially. If any level of verification fails, the dispatch instruction is reconstructed by the large-scale operation instruction model, and the three-level verification is re-executed until all verifications pass. This invention achieves automatic generation and closed-loop optimization of safe and compliant dispatch instructions, significantly improving the automation level and safety assurance capability of power grid operation mode adjustment.

Computing systems and methods are provided for defining, within a data platform, a segment having constraints at a level of the segment, implementing the constraints or the classification rules within the segment while insulating resources within the segment from inheriting the constraints, and controlling an ingestion of an external resource into the segment based on the constraints.

This invention discloses a safety interlock control method for thermal power units under deep peak shaving conditions, belonging to the field of thermal power generation automation control technology. The method includes: S10: acquiring relevant data of the target thermal power unit, including records of historical safety interlock control executions of the target thermal power unit, acquiring the load of the target thermal power unit corresponding to each record, and generating a database based on the acquired loads; S20: acquiring the group of loads with the most historical safety interlock control executions from the database, and acquiring correlation parameters related to the loads in the load group, including furnace negative pressure and burner air-coal ratio. This invention, by constructing a database containing historical safety interlock control records and load data, performs multi-parameter correlation analysis and load change trend monitoring, achieving accurate prediction of thermal power unit load changes and dynamic adjustment of interlock thresholds, thereby improving the operational safety and stability of thermal power units under deep peak shaving conditions.

The invention discloses an automatic disposal method for security risks of a supercomputing system, and particularly relates to the technical field of computer system security, which comprises the following steps: acquiring multi-source state data of a supercomputing node, performing anomaly detection on the acquired multi-source state data by using a mixed detection model, and determining the security risks of the supercomputing system. The method comprises the following steps: identifying potential safety risks, performing category division on the identified risks, determining a disposal priority of each category of risks, and obtaining a disposal action according to the identified risk categories and the disposal priorities. According to the invention, the pain point that the traditional safety system is visible but cannot move, can not be distinguished clearly, and is brighter when being used is solved, an intelligent, automatic, closed-loop and self-evolution novel safety protection system is constructed, and the system is especially suitable for complex environments with extremely high requirements on safety, real-time performance and reliability, such as a supercomputing center, a cloud computing platform, an industrial control system and the like.

Providing differing degrees of reliability or security for distinct devices within a reliable distributed system. Allowing virtual machines to operate in a reliable distributed system without either exposing the actual available devices to guest operating systems, or requiring the guest OS to maintain, or to maintain information about, reliability or security. Methods are responsive to a hierarchy of possible reliability or security failures, to guard more carefully against simultaneous failures of reliability or breaches of security, without additional work or delay. Functions invoked by the user that relate to reliability and security are hidden, so the user can specify a degree of reliability or security without having to implement those requirements themselves. Providing a uniform set of resources available to all users, separating out those individual resources that are allocated to particular users, and emulating particular devices at the request of those particular users. Users do not have access to “real” devices the distributed system can use, only “virtual” devices presented by the distributed system, where those virtual devices have properties similar, but not necessarily equal, to real devices.

The invention provides a safety function block active defense method and system for a safety instrument system. The method comprises the following steps: before executing a core algorithm, performing pointer security check and numerical field check on input data of a security function block; if the check is passed, entering a pre-operation stage, and monitoring an operation risk in real time through a preset check point by utilizing a reverse analysis pre-operation algorithm or a soft floating point pre-operation algorithm on the premise of not executing an actual instruction which possibly causes hardware exception; and executing a deterministic output strategy according to a pre-operation result, and outputting a unified hierarchical state code (ERR) through a special state pin. The method has the advantages of endogenous safety, fault behavior determination, high diagnosis precision, high configuration efficiency and the like, controller shutdown caused by data exception can be effectively avoided, and the robustness of an industrial control system is improved.

The invention discloses an agricultural digital human learning security enhancement system and a security enhancement method, and the system comprises a multi-modal data collection module, a cross-modal data fusion encryption module, an agricultural digital human behavior generation module and a digital human data security management module, and the multi-modal data collection module is used for collecting multi-modal data of digital human learning; the cross-modal data fusion encryption module is used for carrying out cross-modal fusion on features of different modals in the multi-modal data to form fused feature data and carrying out encryption processing on the fused feature data, and the agricultural digital human behavior generation module is used for updating agricultural digital human behavior features in real time according to the fused feature data. The digital human data security management module is responsible for performing security management on data in the digital human learning and application process, the security problem existing in the learning process of the existing agricultural digital human is solved, the security and reliability of the agricultural digital human are improved, and the development of agricultural digitization is promoted.

The application provides a business process intelligent optimization method and system based on multi-source data fusion, relates to the technical field of data processing, and comprises the following steps: obtaining a to-be-done business process request of an enterprise network; performing multi-node safety accident prediction on to-be-done business data; performing safety configuration adjustment on the to-be-done business process request according to a business process accident graph; performing multi-level evaluation and optimization on a business process safety adjustment group through a multi-level safety risk evaluation model; performing multi-party evolution and global optimization on the business process safety adjustment group according to a multi-level safety adjustment benchmark; and obtaining a safety adjustment strategy to perform business process safety optimization management. The application solves the technical problem that, in the prior art, due to a static and passive protection mechanism in a traditional business process, it is difficult to avoid risks before execution, thereby affecting the safety of the business process, and improves the safety of the business process of the enterprise through active risk prevention and control and optimized safety configuration.

The application provides a kind of embedded software requirement security verification method by means of failure data, which includes: establishing software requirement model, formalizing model description of software requirement, carrying out the patternization processing of failure data, explicitly failure data mode information, establishing the mapping relationship between software requirement model and failure data, carrying out the security verification of software requirement model, obtaining software requirement security verification result, the elements of the software requirement model include function input interface, function output interface and function processing process.The application is driven by failure data and security verification rules, and focuses on the key information that can easily cause engine control software to run failure, which can significantly improve work efficiency and quality, and ensure that potential failure modes and system hazards in software requirements are fully identified.

The invention relates to the technical field of power systems, and particularly discloses a hydrogen energy micro-grid energy management method based on Lyapunov safety reinforcement learning, and the method comprises the steps: constructing a system operation cost model; defining an energy scheduling strategy as a reinforcement learning action, and performing real-time security assessment and correction by using a mixed integer programming model; introducing a Lyapunov function to construct a security induction strategy set; the economy of the reinforcement learning strategy and the operation safety of the system are balanced in combination with a safety evaluation mechanism; and constructing a Markov decision process of safety evaluation, and adopting an improved Actor-Critic algorithm to realize strategy optimization to obtain a management scheme. The method has the advantages that the problems that an existing convex optimization algorithm is difficult to process non-convex and non-linear characteristics of the system, traditional reinforcement learning depends on soft constraint, potential safety hazards exist and the like are solved, and finally the operation cost is reduced while the operation safety of the system is guaranteed.

The invention relates to the technical field of safety protection of power equipment, and discloses a safety protection method for a data acquisition terminal of a metering box, which comprises the following steps of: firstly, acquiring operation parameters of the terminal, constructing a safety state model, setting a safety strategy of a protection module, testing the protection characteristic of the protection module, and adding the strategy into the safety state model. And then safety demand parameters, theoretical protection loads and protection control quantities are calculated, protection instructions of the protection units are generated, protection control is carried out, and finally the protection effect is evaluated. According to the method, through space division of an operation scene, state feature analysis, interference factor extraction, construction of a state transition equation and comprehensive consideration of different stages of a protection process, fine control and dynamic adjustment of a protection unit are realized, a scientific protection effect evaluation system is established, the safety and stability of terminal operation are improved, and the method is suitable for popularization and application. And reliable proceeding of a data acquisition task is ensured.

The invention relates to the technical field of data processing, in particular to a mechanical part machining data dynamic safety protection method which comprises the steps that a machining current data sequence of a mechanical part is collected; obtaining an initial standard current factor according to the processing current data sequence; obtaining an abnormal current data segment according to the initial standard current factor; obtaining a current trend difference factor according to the abnormal current data segment; obtaining abnormal current data according to the current trend difference factor; obtaining data similarity according to fluctuation trend differences between different abnormal current data in the abnormal current data segment; obtaining an associated abnormal data set according to the data similarity; obtaining a vibration abnormal factor according to the associated abnormal data set; and compressing the processing current data sequence according to the vibration abnormal factor. According to the method, the segmentation of the current data is more reasonable, the compression efficiency of a revolving door compression algorithm is improved, and the dynamic safety of processing data is improved.

The invention belongs to the field of artificial intelligence safety, and discloses a large model safety protection method and system and electronic equipment. In order to solve the problem that safety and efficiency cannot be considered at the same time through existing one-step filtering or static rules, sensitive word hard filtering, injection detection and content safety double-model preliminary screening are provided, the risk difference of model output results and the comprehensive risk score of uncertainty indexes are calculated, and a preset dynamic determination threshold value is combined; and distributing the user request to low-risk, medium-risk and high-risk three-path processing. Wherein the low risk uses a lightweight model for rapid verification, the high risk is directly rejected, the medium risk executes a pre-formulated adaptive streaming generation control strategy, and an adaptive sliding window is adopted for monitoring generation and triggering early stop and self-repair. According to the scheme, the average delay and calculation cost can be remarkably reduced while the cue word injection and harmful content recognition accuracy is met, and therefore the safety and practicability of a large model are improved.

This invention discloses an adaptive control method for bearing grinding processes based on reinforcement learning, comprising: S1, real-time acquisition of multi-source state data; S2, construction of a high-dimensional state vector; S3, construction of a historical grinding dataset; S4, construction of an improved IQL model incorporating an uncertainty perception mechanism, offline training using the historical dataset, outputting the Q-value distribution, and learning a safety policy at the safety policy layer; S5, online deployment of the model, generation of candidate commands, and evaluation of uncertainty; S6, judgment by the safety constraint layer: issuing candidate commands when uncertainty is low, and invoking the safety policy to generate safe commands when uncertainty is high; S7, execution of commands and storage of new data in the dataset, periodically optimizing the model. This invention improves the safety and reliability of online decision-making through uncertainty quantification and a safety policy safety net, achieving adaptive and precise control of complex grinding conditions.

The invention relates to the technical field of artificial intelligence, and discloses a safety control method, device and equipment, a storage medium and a computer program product.The method comprises the steps that in response to an input prompt, a safety control module of a large language model is called, the safety control module is trained in advance based on a pre-constructed thinking chain path, and the thinking chain path is input to a user; the thinking chain path is used for representing hierarchical decision logic of hierarchical security policies, the hierarchical security policies comprise a global security policy and a user security policy, performing hierarchical security policy inference on the input prompt based on the security control module, and determining a response action mode of the large language model according to an inference result, and the output response corresponding to the input prompt is generated through the large language model based on the response action mode, so that excessive rejection or insufficient constraint can be avoided, and the balance of safety and help can be realized.

The present invention provides a robot control method and system based on a large language model. This system employs a dual large language model evaluation mechanism. The large language model of the code generation and modification module uses Claude 3.5sonnet to generate control code, which is then evaluated and optimized using OpenAI's GPT-4O evaluation model. This reduces the risk of difficulty identifying problems during evaluation due to the homogeneity of the same module, thereby improving the quality, safety, and security of the generated code. In addition to the dual large language model evaluation mechanism, the system incorporates safety prompts into the prompts of the generated model and provides generation template examples. This restricts operation within a specific framework, reduces the likelihood of errors, and further ensures the standardization and security of the generated code. Furthermore, a control verification module constructs a simple simulation environment after the control code is generated, enabling early screening of the generated code for syntax errors and logical vulnerabilities, further reducing potential safety hazards.

To provide a safety evaluation device that evaluates safety of code encryption more strictly.SOLUTION: A safety evaluation device 1 comprises: an input part 11 for receiving an input of a parameter set of an SD problem, and an upper limit of a space calculation amount; an initial processing part 12 for creating an aggregate of valid parameter sets, with respect to a Revisited MMT method; a space calculation amount estimation part 13 for calculating a space calculation amount required for storing input data, and a list for constituting solution candidates; a time calculation amount estimation part 14 for summing up a calculation time required for executing Gaussian elimination, and a calculation time required for constituting and merging a list, and multiplying the sum by an expectation loop frequency based on a correct answer probability, to calculate a time calculation amount; a condition determination part 15 for determining whether or not a space calculation amount exceeds the upper limit; and an output part 16 for outputting the minimum time calculation amount together with a parameter set that minimizes the time calculation amount, out of the aggregate, by excluding an element of which the space calculation amount has been determined to exceed the upper limit.SELECTED DRAWING: Figure 1

A qualitative and quantitative (Q2) method for verifying safety of movement of complex learning-enabled cyber-physical systems (Le-CPS) using a computer is provided. The method includes determining an initial set of states of the Le-CPS. The computer is used for determining a reachable set of the Le-CPS using a probstar reachability algorithm saved in memory of the computer. The computer includes a Q2 algorithm saved in the memory that is configured is used to simultaneously check (i) if the reachable set satisfies a predetermined safety constraint (qualitative verification) and (ii) determine a probability of satisfaction that the reachable set will satisfy the predetermined safety constraint (quantitative verification). Movement of the Le-CPS is planned based on the step of using the computer including the Q2 algorithm.

The invention discloses an incremental SVM security updating method and system for power grid transient stability discrimination, and the method comprises the steps: obtaining typical and additional scene data based on electromechanical transient simulation, and respectively constructing a pre-training set and an incremental updating set; training the initial SVM model; and performing incremental learning and updating on the model by adopting a safety updating mechanism fusing incremental sample screening, negative sample hard constraint and double-threshold dynamic adjustment, and finally realizing online transient stability judgment. By introducing a safety updating mechanism fusing incremental sample screening, negative sample hard constraint and double-threshold dynamic adjustment, the problems that a traditional discrimination model is difficult to adapt to a complex and changeable power grid scene, an unstable sample is easy to misjudge, and conventional incremental learning efficiency and safety are difficult to consider at the same time are solved.

The invention discloses a safety protection method, a control device, a storage medium and a program product, and relates to the technical field of industrial production, the safety protection method is applied to a collaborative operation system, the system comprises a first device and a second device, and the method comprises the following steps: obtaining real-time position data of the first device; generating a first security permission signal under the condition of determining that the first equipment arrives at and stays at a preset security position according to the real-time position data; acquiring motion state data of the first equipment, and generating a second security permission signal under the condition of determining that the first equipment leaves a dangerous area where the second equipment executes the target action based on the motion state data; and when the first security permission signal and the second security permission signal are detected, outputting a first security enable signal to the second device to permit the second device to execute the target action. According to the invention, the overall safety and reliability of the collaborative operation system are improved.

A method for analyzing system safety requirements. The method includes parsing a plurality system and / or software specific requirements, and filtering the plurality system and / or software specific requirements using a generic system safety requirements (GSSR) model to generate a matched generic safety requirement in pre-set tables.

This invention discloses a method for runtime verification and safety recovery of control logic in embedded real-time systems, relating to the fields of embedded real-time control and functional safety technology. This invention constructs an independent safety monitoring layer outside the operating system kernel, defines runtime contracts containing safety invariance and timing logic constraints for critical control tasks, and performs real-time contract verification through periodic data collection. When a contract violation is detected, a layered recovery mechanism is initiated according to fault levels, including output clamping, task rollback and restart, and algorithm degradation switching. A memory protection unit is used to achieve spatiotemporal isolation protection for tasks. This invention achieves non-intrusive real-time monitoring and hierarchical safety recovery of critical control tasks, improving the functional safety and operational stability of embedded real-time systems. The monitoring overhead is controllable and does not affect real-time system scheduling, making it widely applicable to safety-critical scenarios such as vehicle control and industrial robots.