228 results about "Security rule" patented technology

The present disclosure describes systems and methods for detecting and preventing data misconfigurations within a security-focused data fabric platform. The system integrates an advanced script migration engine designed to streamline the translation of security rules and scripts across different scripting languages while ensuring alignment with the fabric's unified schema. The method involves receiving inputs from data sources, mapping these inputs to entities of a target schema, monitoring real-time data changes, and simulating impacts on operational dependencies to detect misconfigurations proactively. Leveraging AI-driven mechanisms, including Large Language Models (LLMs), the system dynamically identifies breaking changes in third-party data streams, issues alerts, and provides suggested fixes. The script migration engine further enhances the platform's functionality by automating cross-platform script translations and enabling faster onboarding of security tools. Together, these innovations ensure scalable, accurate, and resilient integration and management of security data across heterogeneous sources, strengthening operational integrity and minimizing security risks.

A system detects changes in model context protocol (“MCP”) processes, and performs a remedial action. A server-sent events (“SSE”) bridge sends a request to an MCP server. A first resource profile is received from the MCP server. This is stored and compared against a second updated version of the resource profile. When a difference is detected, the SSE bridge determines whether to block a resource command from reaching the SSE bridge. The decision is based on comparing the difference to security rules, which can be defined as part of a management profile.

The invention discloses a blasting operation field behavior compliance identification method, system and device and a medium, and relates to the technical field of computers. The method comprises the following steps: acquiring a multi-source video stream and environment monitoring data of a blasting operation site; identifying a current operation stage according to the multi-source video stream; identifying a scene risk factor according to the multi-source video stream and the environment monitoring data; inputting the multi-source video stream into a pre-trained deep learning model, and analyzing identity information, a behavior sequence, an equipment wearing state and position information of each operator; calling a compliance rule set matched with the current operation stage and the scene risk factor in a pre-constructed security rule library; based on the compliance rule set, performing compliance verification on the identity information, the behavior sequence, the equipment wearing state and the position information; and if the violation behavior exists through verification, generating a violation judgment result containing the violation type, the risk level, the associated personnel and the spatial region identifier. According to the invention, intelligent monitoring and violation identification of the whole process of blasting operation can be realized.

The invention discloses a police affair studying and judging method based on behavior feature recognition, and relates to the technical field of intelligent police affair and behavior modeling, and the method comprises the steps: collecting dynamic behavior data of a target object, and constructing a dynamic behavior portrait map comprising a behavior event, a time node, a spatial position and an interaction object; time sequence modeling is carried out on the map, and individual behavior chain feature vectors are extracted; comparing the current behavior chain with the historical behavior chain and the group mean value model, and calculating the deviation degree; performing cross-regional universality evaluation on the deviation degree through a transfer learning model; if the deviation degree exceeds a preset threshold value, generating early warning information in combination with a regional safety rule and pushing the early warning information to a police research and judgment system; according to the method, accurate identification and hierarchical response of abnormal behaviors can be realized, and the method has high robustness, strong generalization ability and good actual combat adaptability.

The invention relates to the technical field of electric power, in particular to an unmanned aerial vehicle route automatic planning method and system for a complex space of a transformer substation. Analyzing at least one inspection task instruction input by a user to obtain a task type corresponding to each inspection task instruction and a data acquisition requirement corresponding to the task type; according to a preset three-dimensional semantic map of the transformer substation and the data acquisition requirement of each inspection task instruction, a flight route of at least one unmanned aerial vehicle is automatically generated by adopting a path planning algorithm, and the flight route comprises a waypoint sequence, a flight height, a shooting angle and a holder action instruction; the three-dimensional semantic map comprises no-fly zone information in the transformer substation, a plurality of devices, the position of each device, the device type, the electrification attribute and the safety rule label. And controlling the corresponding unmanned aerial vehicle to inspect target equipment in the transformer substation according to the flight route of each unmanned aerial vehicle. And large-scale and standardized inspection operation is realized.

The invention relates to a public multi-mode cloud network resource software security enhancement method based on neural symbol fusion reasoning, which comprises the following steps: an intermediate representation generation stage: generating and optimizing an intermediate representation of a program through a fine-tuned large language model, and establishing semantic mapping from a source code to a structured logic representation; in the symbol language conversion stage, the intermediate representation generated by the large language model is converted into a domain-specific language fact set which can be recognized in the symbol logic reasoning stage, and formal and logic expression of program semantics is achieved; and a symbol logic reasoning stage: matching the fact set with the rule base through a symbol logic reasoning engine, performing detection and verification according to the safety rule, generating a structured report, and feeding back a result for optimization. The method is suitable for security enhancement of various core software systems in a public cloud network multi-modal network environment, high-precision security analysis is carried out on cross-modal and cross-subsystem fragmented codes in a compiling-free environment, and verifiable technical support is provided for public cloud network security control.

The invention provides a rules engine that applies customizable security rules and artificial intelligence (AI) model routing to queries. The rules are evaluated with respect to the query, an anticipate result of the query, and the actual result. Remedial actions are taken when the rule evaluation exceeds a threshold. The remedial actions include transforming the query by replacing sensitive information with a reversible placeholder. The actual result can be modified by reversing the placeholder and redacting additional sensitive information. The system can notify the user, an administrator, and a supervisor regarding the security evaluation and remedial actions. The evaluations can be logged for auditing purposes.

A system detects changes in model context protocol (“MCP”) processes, and performs a remedial action. A server-sent events (“SSE”) bridge sends a request to an MCP server. A first list of resource profiles, including tools and instructions, is received from the MCP server. This is stored and compared against a later list of tools and instructions. When a difference is detected, a user interface can display the difference to an administrator. Security rules cause the SSE bridge to be blocked from sending commands to the impacts tool or MCP server until approved by the administrator.

A gateway securely executes model context protocol (“MCP”) processes for artificial intelligence (“AI”) agents by creating nano sandboxes in which the MCP processes execute. A server-sent events (“SSE”) bridge identifies a request to initialize an SSE channel with an MCP server, the request being sent from an MCP client that generates MCP-compliant commands in association with an AI agent. The SSE bridge instantiates a nano sandbox based on a definition extracted from the request, creating a custom and dynamic isolated execution environment. An MCP process is launched within the nano sandbox. The SSE bridge returns a session identifier, and receives a command and the session identifier. The SSE bridge authorizes the command and response by applying security rules, blocking either for non-compliance.

The invention relates to the technical field of railway engineering construction safety, in particular to a railway engineering construction potential safety hazard intelligent identification and classification system and method. The system comprises a multi-source heterogeneous data acquisition module, a 5M1E hidden danger feature deconstruction module, a security rule knowledge base module, a risk reasoning and early warning module, a security tag portrait module and a PDCA-SDCA dual-cycle management module. According to the method, a construction site can be comprehensively perceived through multi-modal data, hidden danger characteristics are deeply analyzed based on six dimensions, namely Man, Machine, Material, Method, Envirant and Measurement, of 5MIE, risk reasoning and early warning under a complex scene are realized by using a graph neural network, hidden danger rectification closed loop and knowledge base continuous optimization are realized through double-cycle management, and the method and the system have the advantages that the construction site can be comprehensively perceived, so that the construction efficiency is improved. And the intelligent level and long-term learning ability of railway engineering construction safety management are obviously improved.

The invention discloses a truss robot rehearsal control method based on digital twinning, and the method comprises the following steps: S1, constructing a truss robot digital twinning system, including constructing a truss robot virtual scene and constraint, performing data synchronization and bidirectional communication, and configuring a safety rule base and a data recording / playback interface at a twinning end; s2, rehearsing a task to be executed, generating a reference trajectory and a constraint set, and automatically checking the rehearsing process according to the safety rule base to obtain the reference trajectory and the constraint set which pass the rehearsing; and S3, performing parameterized predictive control and real-time tracking on the rehearsed reference trajectory and the constraint set, and controlling a trajectory tracking error to be within a tolerance range. According to the method, a digital twin system corresponding to the physical world is constructed, Laguerre function parameterized model predictive control is introduced to realize low-dimensional rapid solution, high-precision, reproducible and high-reliability rehearsal control of the truss robot is realized, and the field debugging and operation and maintenance cost is remarkably reduced.

The invention relates to the technical field of virtual reality simulation, in particular to a virtual reality immersive practical training content generation method, which comprises the following steps of: constructing a domain knowledge graph: extracting atomic knowledge elements representing operation actions, equipment entities, state parameters and safety rules from a standard document and a case library of a target training domain, establishing a multi-dimensional logic relationship including a causal relationship, a time sequence relationship, a hierarchical relationship and a probability relationship among the atomic knowledge elements, and forming and storing a domain knowledge graph; acquiring trainee data in real time; constructing and updating a student state model; a dynamic content decision step; and generating and injecting nonlinear practical training content. By collecting student data in real time and matching the student data with the knowledge graph, the system can provide accurate feedback for the student after each practical training, and help the student find and overcome own knowledge blind spots.

The invention includes a UI that allows addition of functional placeholders to an AI pipeline, and a rules engine that chooses pipeline objects during or before AI pipeline execution. The rules engine can select pipeline objects meeting the same function type and object rules of the functional placeholder. The rules engine also applies customizable security rules and artificial intelligence (AI) model routing to queries. Remedial actions are taken when the rule evaluation exceeds a threshold. The remedial actions include transforming the query by replacing sensitive information with a reversible placeholder. The actual result can be modified by reversing the placeholder and redacting additional sensitive information. The system can notify the user, an administrator, and a supervisor regarding the security evaluation and remedial actions. The evaluations can be logged for auditing purposes.

The invention provides a network security analysis method and system based on system log auditing analysis, and belongs to the technical field of network security. Behavior chain knitting processing is executed on a streaming log sequence by capturing the streaming log sequence generated by network nodes of a power monitoring system in real time, and the network security is obtained. Associating continuous operation record entries of the same operation main body to form a dynamic behavior chain, performing mode matching on the dynamic behavior chain based on a preset security rule base, identifying candidate behavior fragments, performing reverse traceability analysis on the candidate behavior fragments, constructing a risk diffusion diagram, and performing risk analysis on the risk diffusion diagram. And finally, generating a security analysis report containing a risk source node, a diffusion path and an affected resource list, and pushing the security analysis report to a security management and control center, so that the real-time performance, accuracy and comprehensiveness of network security analysis of the power monitoring system can be effectively improved, and safe and stable operation of the power system is ensured.

This invention discloses an automatic robot instruction inspection method and apparatus based on the PDPS platform. The method involves acquiring offline program data of the robot and the corresponding design rules, configuring a program inspection tool according to the design rules, inputting the program data into the inspection tool, and storing safety information from the first and second instructions into corresponding variables. The information in each variable is compared or judged to obtain a first judgment result. Chinese characters and punctuation marks in the program data are judged to obtain a second judgment result. Based on the motion parameters in the program data, the margin value of each joint when the robot performs each action is calculated, and collision risk is judged based on the margin value to obtain a third judgment result. Finally, the robot's compliance with safety rules is judged based on the motion radius to obtain a fourth judgment result. This method improves the accuracy of offline program inspection.

Systems and methods for advanced event ranking and correlation for threat detection. A method includes real-time processing of events with stateful threat detection, combining immediate detection capabilities with sophisticated threat analysis. A method further includes multi-stage processing in a detection engine, where generic events from endpoint detection and response (EDR) agents are scored and enriched to provide extended context based on machine learning models for event scoring, enriched events correlation, and applying security rules to detect threats.

The invention provides a BIM-based engineering safety management method and system and a medium, and relates to the technical field of engineering management, and the method comprises the steps: collecting a first construction event, transmitting the first construction event back to a BIM engineering model, determining a first feature set, carrying out risk control probability verification, and triggering a first safety management instruction; activating the intermediate plug-in, executing hidden risk mining and dynamic rule generation, and performing local lightweight reconstruction on the BIM engineering model; and executing the deduction and rule constraint decision under the multivariate combination, generating a first safety rule and a first risk control plan, and executing engineering safety management based on the first construction event. Through the method and the device, the technical problems of safety management lagging and low accuracy caused by difficulty in coping with dynamic changes and hidden risks of a construction environment in a traditional method in the prior art are solved, short-time simulation analysis of the BIM engineering model is performed by taking the construction event as a management mode, and the response speed and the safety management efficiency of a construction site are improved.

The invention discloses an intelligent contract vulnerability detection method and system based on semantic comprehension and program path analysis, and belongs to the technical field of network security. The method comprises the following steps: firstly, analyzing an intelligent contract code by using a large language model, and reasoning to generate a structured security rule for defining a taint source, a taint sink and a purifier; secondly, guiding a taint analysis engine by using a security rule, and tracking on a data flow and a control flow diagram of a program so as to efficiently screen out a high-risk taint path; then, carrying out reachability verification on the high-risk path by adopting a symbolic execution technology; and finally, performing final context review on the verified vulnerability path by using the large language model again to generate a vulnerability report. According to the method, the semantic comprehension ability of the large language model and the preciseness of traditional program analysis are subjected to multi-stage cooperation, so that the detection precision and efficiency of the intelligent contract logic vulnerability can be remarkably improved, the interpretability of a report result is greatly enhanced, and the method has important application value.

The invention discloses an infrastructure operation scene personnel safety early warning method and system based on multi-modal fusion, and relates to the field of safety early warning, and the method comprises the steps: analyzing video data to obtain behavior action sequence features, analyzing positioning data to obtain space activity track features, and analyzing sound data to obtain environment event features; matching the behavior action sequence features, the space activity track features and the environment event features with an operation scene feature library, and determining the operation type, the operation area attribute and the operation environment state of the current scene; calling a target security rule set from a security specification library; according to the target security rule set, performing logic correlation analysis on the behavior action sequence features, the spatial activity track features and the environmental event features to obtain an anomaly analysis result; and generating a corresponding early warning signal based on the abnormal analysis result. By implementing the method, the accuracy of safety early warning of the personnel in the capital construction operation scene can be improved.

The invention provides a docking station safety protection system and method based on AI malicious behavior recognition, and relates to the technical field of Internet of Things safety. The behavior parameter monitoring module is used for carrying out multi-mode behavior parameter monitoring, and when any parameter breaks through a real-time behavior baseline, a mirror image isolation mechanism is triggered; the behavior flow collection module is used for redirecting the equipment data flow to a sandbox environment and carrying out dynamic behavior flow collection; the hierarchical response identification module is used for executing malicious behavior hierarchical response identification; the global confidence output module is used for outputting global equipment confidence; and the safety protection action execution module is used for executing safety protection actions by adopting a hierarchical response strategy. The technical problem that in the prior art, security protection mainly depends on static security rules and databases and lacks real-time and dynamic recognition capacity for potential malicious behaviors during equipment access, so that the security protection capacity of a system is insufficient is solved.

The invention provides an autonomous driving control system based on LLM context reasoning, and the system comprises an LLM engine module which is used for receiving and analyzing a semantic natural language request, and outputting a semantic map fusing a context reasoning result; the navigation agent module is used for initiating a semantic analysis request to the LLM engine module and initiating a path planning request to the path planner module; the security agent module is used for querying security rules based on a semantic map and dynamically constructing a security feasible area; the path planner module is used for generating and optimizing the optimal driving path of the vehicle through a multi-objective optimization algorithm; the edge controller module is used for converting the optimal driving path into a bottom layer control instruction of an execution mechanism; the interpretation agent module is used for tracking the decision-making process of each module and generating natural language interpretation corresponding to the optimal driving path and the control instruction; the invention aims to adapt to the autonomous driving requirements of different types of mobile devices, and improve the safety, adaptability and user credibility of control.

Systems and methods for controlling visibility of user identifying information. In the event that documents or records are shared between different business partners or business organizations, usernames appearing on the documents, notification or records may be selectively masked to preserve the user identity security rules.

The invention belongs to the technical field of remote supervision, and discloses an electronic two-ticket remote supervision method and a related device thereof. The electronic two-ticket remote supervision method comprises the steps that a remote supervision platform receives a permission application of an electronic work ticket and / or an operation ticket initiated by an operation terminal, the remote supervision platform automatically verifies the permission application based on a pre-stored security rule base, and after the automatic verification is passed, the remote supervision platform initiates a remote supervision session, a multimedia communication link with at least one field terminal is established, a supervisor performs remote real-time supervision on an operation preparation state of a field operator based on the multimedia communication link through a remote supervision platform, and after the operation preparation state is confirmed to be qualified through remote real-time supervision, the remote supervision platform generates an electronic permission instruction and sends the electronic permission instruction to the field terminal. Authorizing to execute the corresponding electronic work ticket and / or operation ticket; according to the invention, real-time supervision and guidance can be realized, and the operation risk caused by information lag is avoided.

The invention relates to the technical field of electric power engineering construction, and particularly provides a data processing method and system for scene decision optimization of electric power stringing construction, and the method comprises the steps: constructing a parameter matrix of multiple scenes, and generating a decision task of each scene; for the decision-making task, construction result data is calculated according to a set logic calculation chain through a data flow and logic association engine; the construction result data is displayed through a visual interface, and a working condition input window is provided; updating the parameter matrix, and calculating updated construction result data; and comparing the updated construction result data with a preset safety rule base, performing safety verification, and optimizing the construction scheme based on the parameter matrix under the condition that the verification is not passed. The technical problems of low calculation efficiency, poor accuracy and insufficient safety due to the fact that independent calculation needs to be manually performed according to different data sources in different sub-processes in electric power stringing construction in related technologies are solved.

The invention relates to the technical field of construction management, in particular to a BIM (Building Information Modeling)-based engineering supervision management system and method.According to the BIM-based engineering supervision management system and method, spatial positions and states of construction machinery, vehicles and personnel are captured in real time through a sensor network deployed on site, and a core dynamic spatial conflict calculation module is used for calculating the safety of the construction machinery, the vehicles and the personnel according to a preset safety rule base; and millisecond-level conflict detection and prediction are carried out on the real-time dynamic safety envelope lines of the moving objects. And after potential risks are found, the system sends early warning of different levels from prompting to forced intervention to related personnel, so that a complete management closed loop from active perception and intelligent prediction to co-processing is constructed. Therefore, through pre-virtual simulation and in-process real-time intervention, the risk of major safety accidents such as collision and overturning caused by space conflicts is greatly reduced, and life safety is guaranteed. And by optimizing the construction space and logistics planning, shutdown and rework caused by conflicts are reduced, and the construction efficiency and the project management refinement level are improved.

The invention provides a safety management and control method based on multi-source fusion safety helmet positioning and a related device, and relates to the technical field of operation safety management and control. Indoor and outdoor seamless positioning is realized through the multi-source positioning module, a grading alarm strategy is generated by combining behavior portraits with safety rule matching, the problems of positioning fault, risk identification lag and alarm extensibility of a traditional scheme are solved, and the method has the advantages of improving positioning reliability, enhancing risk perception real-time performance and reducing false alarm rate.

The invention discloses an unsafe hoisting behavior identification method based on multi-sensor data fusion, which comprises the following steps of: 1, acquiring multi-dimensional monitoring data in real time through multi-sensor data acquisition; 2, data preprocessing and data fusion are carried out, data synchronization and alignment, data cleaning and abnormal value processing, filtering and denoising, feature extraction and data standardization normalization processing are carried out on the multi-dimensional monitoring data in sequence, and then a multi-source fusion feature matrix is constructed by adopting a weighted fusion algorithm; 3, screening core features associated with the unsafe hoisting behaviors from the multi-source fusion feature matrix; 4, establishing a quantitative safety rule base based on deterministic identification of the safety rule base, matching the core features with rule base judgment logic, and outputting a preliminary identification result; and 5, identification result verification and early warning: correcting the preliminary identification result through multi-source data consistency verification and time sequence continuity verification, and triggering graded early warning according to the correction result.

A system and method for exploring security rule chains in a security platform. The method includes providing a natural language description of a set of features of a security posture of an organization as a first input to a trained artificial intelligence (AI) model, providing telemetry data pertaining to a computing environment of the organization as a second input to the trained AI model, obtaining one or more outputs from the trained AI model, and extracting, from the one or more outputs, a set of generated features for the security posture of the organization.

The invention discloses a configuration rollback method, device and equipment for a switch and a storage medium, and relates to the technical field of network configuration management, and the method comprises the steps: collecting configuration change information and operation state data of the switch, the running state data comprises a CPU utilization rate, a memory utilization rate, network delay, an error packet rate and a packet loss rate; obtaining a historical configuration record with a timestamp according to the configuration change information; performing abnormal state analysis and rule judgment based on the running state data, a preset security rule and the historical configuration record to obtain a rollback strategy, wherein the rollback strategy is generated when a rollback triggering condition in the preset security rule is met; and executing a configuration rollback operation according to the rollback strategy and the historical configuration record. According to the invention, different configuration rollback strategies are adopted for different abnormal states when the configuration of the switch is changed, so that the recovery efficiency of the configuration management of the switch is improved.

The invention discloses an intelligent automobile sensor data correction method and system based on safety monitoring, and relates to the technical field of intelligent driving environment perception, and the method comprises the steps: carrying out the grading of safety feature codes based on a safety rule library, carrying out the mode matching through combining with a historical fault case library, and generating an abnormality diagnosis report of original monitoring data; a physical topology network of the intelligent automobile sensor is constructed, the node state of the physical topology network is dynamically adjusted according to the abnormity diagnosis report, and a physical topology network reconstruction instruction is obtained and executed; and correcting the original monitoring data twice based on the node state of the reconstructed physical topology network to generate vehicle safety monitoring data. According to the method, the weighted residual correction is combined with the iterative reweighted least square method, two-stage correction is performed on the monitoring data on the basis of the reconstructed node weight distribution table, progressive error compensation under the condition of dynamic performance degradation of the sensor network is realized, and the reliability and environmental adaptability of output data are improved.