133 results about "Industrial control system" patented technology

The application discloses a kind of power industrial control terminal network isolation method and system based on process security label binding;Belong to the technical field of power system network security, its operating steps include: decoupling physical network resources into independent partitions and mapping to independent user-mode network protocol stack instances;Through the bottom flow direction rule, the in-bound traffic is accurately delivered to the corresponding partition;Identify process security label at the application layer, establish the forced mapping of process and specific protocol stack instance and link;Through the controlled shared memory channel, realize the safe data interaction between partitions.The application realizes strong logical isolation on unified hardware, through the forced binding of process identity and network link, reduces the privilege promotion and horizontal penetration risk caused by traditional protocol stack sharing, while limiting the scope of failure impact, without relying on external physical isolation equipment, significantly improves the endogenous security protection capability of industrial control system network boundary.

This invention relates to the field of Automated Guided Vehicle (AGV) navigation technology, and proposes an AGV path correction control method based on an industrial control system. The method includes: generating multiple candidate correction control variables for adjusting the AGV's movement; analyzing the path deviation value of the AGV along the target path from its current position under each candidate correction control variable, to determine the number of correction events required for the AGV to move along the target path from its current position using the path deviation value; using the minimum number of correction events as the optimization objective, determining the constraint range using the current position, current lateral deviation, and current heading deviation, and selecting the target correction control variable from the candidate correction control variables; and sending the target correction control variable to the AGV's actuator to perform path correction control on the AGV. This invention enables effective control of the number of corrections.

The application discloses an industrial control system protocol fuzzing test method based on state conversion, and particularly, the communication flow of an upper computer and a PLC is analyzed, a protocol finite state machine of a communication interaction process is constructed, and the PLC is subjected to the fuzzing test by using test cases violating the finite state machine in a test process, so that the purpose of triggering unknown vulnerabilities of the PLC is finally achieved. The application uses the protocol finite state machine to guide the state conversion path of the fuzzing test process. The method for obtaining the test cases violating the original protocol finite state machine and then performing the fuzzing test on the equipment can efficiently find vulnerabilities. The confirmation method for the state conversion leading to the vulnerabilities can obtain the shortest state conversion path leading to the vulnerabilities.

The application discloses a ransomware identification method and system based on multi-source data fusion, and relates to the field of ransomware detection. The method obtains file system event data, system resource running indexes, process behavior data and industrial control system behavior data, identifies file features, system running behavior situation features, process features and industrial scene features from the data, judges whether each feature meets the essential behavior features of ransomware encrypted files, and divides the features into strong indication features, medium indication features and basic situation features. Different mapping methods are used to determine sub-risk scores for different features, and finally a weighted scoring mechanism is used to obtain an overall urgency score, based on which a ransomware attack is determined, and then suspicious process operations are automatically triggered and terminated in parallel, and network connections are blocked to prevent the spread of ransomware attacks. The application can accurately identify known ransomware and unknown variants, and has good interpretability.

The present application relates to the technical field of industrial control system, especially to a felt making machine control method and system for producing alkali-free glass fiber soft felt, comprising full-process multi-parameter real-time acquisition, reverse deduction of the processing effect of the previous link, two-way parameter closed-loop adjustment, backlight image acquisition, multi-camera cooperative calibration acquisition, hierarchical image analysis processing, time sequence anomaly verification and defect grading processing; the present application synchronously deploys laser thickness measurement and image detection devices in all key processing links of felt production, realizes real-time continuous acquisition of full-node quality parameters, simultaneously reversely deduces the actual processing effect of the previous link based on a process parameter mapping model, constructs a two-way closed-loop control mechanism of forward adjustment of subsequent link process parameters and reverse feedback optimization of processing parameters of the previous link, can timely discover and accurately correct in the early stage of quality deviation, and significantly improves the stability of product quality and the controllability of the production process.

The present application relates to the technical field of industrial control system security and deep learning, and particularly relates to a multi-scale feature fusion time series anomaly detection method based on a graph neural network. The method breaks through the limitation of traditional single perspective and can capture local and global time sequence features at different time granularities through a multi-scale fusion mechanism. Compared with existing single reconstruction or prediction models, the method introduces a variational autoencoder for adversarial training. This mechanism not only alleviates the influence of noise in the training data, but also enhances the model's expression ability for normal sample distribution. Moreover, the graph neural network explicitly models the spatiotemporal features and synergies between devices, ensuring that the model remains highly sensitive when facing multivariate coupled anomalies, thereby significantly improving the accuracy of anomaly detection and providing strong technical support for safety warning in industrial sites.

The application relates to the technical field of communication, in particular to a communication protection method and system, an electronic device and a computer readable storage medium, the method comprising: sequentially analyzing data packets, and constructing and maintaining the corresponding relationship between requests and responses according to the analysis results. The running state of the protected device is monitored through multi-dimensional indexes, and it is judged whether the protected device has a fault. When it is determined that the protected device has a fault, the requests in the waiting response state are acquired, an abnormal response conforming to a standard format is constructed for each waiting response request, and the abnormal response is sent to the corresponding upstream device. After the abnormal response is sent, a physical bypass switching is performed. The application can obtain an abnormal processing signal immediately after the fault occurs, avoids state abnormal problems such as protocol timeout and connection disconnection, and improves the real-time performance, stability and maintainability of the industrial control system.

This invention provides an industrial control system for electrically heated molten salt thermal energy storage that absorbs fluctuations in wind and solar power generation. Belonging to the fields of energy and electrical engineering, it includes a hardware architecture for the measurement and control of the electrically heated molten salt thermal energy storage system, a communication architecture for the system's equipment, and a software architecture for its measurement and control. This invention combines a stable hardware platform, an efficient communication architecture, and flexible industrial control algorithms to achieve real-time monitoring and industrial control of key parameters during the molten salt thermal energy storage process. While absorbing fluctuations in wind and solar power generation, it effectively improves the overall operating efficiency and reliability of the system, ensuring stable operation under different conditions and promoting the application and development of molten salt thermal energy storage technology in the field of large-scale energy storage.

This invention discloses a physical twin modeling method for cross-condition industrial control systems. It constructs a data-physical fusion physical twin model applied to an anomaly detection framework for industrial control systems. This framework uses the physical twin model as its core, expressing industrial physical processes through a system of differential equations and introducing physical constraints using PINN. Simultaneously, it continuously calibrates the model using real-time operational data to achieve dynamic updates. Furthermore, it achieves adaptive capabilities across conditions, stages, and scenarios through parameter sharing and transfer learning. The trained physical twin model is compared with real-time data, and the existence of anomalies is determined by analyzing the deviation between predicted and observed values. This framework organically combines the high fitting properties of data-driven approaches with the interpretability of physical-driven approaches, providing industrial control systems with high-precision, interpretable, and transferable anomaly detection capabilities.

This invention discloses a high-precision continuous interval logic control method supporting independent and controllable intelligent manufacturing, belonging to the fields of intelligent manufacturing industrial control, precision machining, domestically produced industrial chips, and AI intelligent control technology. Based on the mathematically fundamental continuous interval theory and STE industrial control-specific coding, this invention integrates domestically produced industrial control chips and precision control models to construct a full-process industrial control system, setting high-specification performance thresholds such as nanometer-level control precision, zero error accumulation, and low latency. By establishing a continuous interval mathematical model, calibrating fundamental parameters, replacing discrete control with continuous logic, issuing STE-coded instructions, and implementing real-time error correction, the method eliminates the accumulation of discrete control errors, achieving high-precision control in high-end precision manufacturing that is entirely domestically produced and free from reliance on foreign technologies. It is adaptable to various precision manufacturing scenarios, contributing to the independent upgrading of intelligent manufacturing and filling gaps in related domestic underlying control technologies.

The application discloses a control cabinet intelligent reconstruction method based on a standard communication protocol and relates to the technical field of industrial automation, and comprises the following steps: real-time scanning of communication port data of bottom-layer equipment in a control cabinet and loading of mutually independent drive containers based on equipment fingerprint features so as to establish physical connection; construction of a standardized semantic information model based on a mapping relationship between a physical address file and a semantic model; real-time monitoring of characteristic changes of data flow in a data acquisition process, dynamic segmentation of a data transmission channel and diversion of data to a real-time control channel or a telemetry archiving channel. The application can update state attributes based on a semantic model in real time, form an organic whole of perception, decision, execution and verification, effectively solve the problems of protocol closure, semantic loss and low integration efficiency in traditional control cabinet reconstruction and improve the interoperability and intelligent level of an industrial control system.

This invention relates to the field of industrial control system technology and discloses a fault diagnosis method for pipe production lines based on digital twins. The method includes: providing a discrete physical state library, wherein each address mapping node maps to a feature parameter vector and a control feedforward compensation matrix; generating multi-dimensional projection basis vectors based on random number seeds and fixing them into a mapping projection matrix; mapping the feature parameter vectors to projection sequences and generating mapping feature codes using a positive / negative sign quantization criterion; reconstructing the index topology of the discrete physical state library based on the mapping feature codes; acquiring real-time feedback noise sensing signals and generating real-time mapping feature codes; matching and addressing address mapping nodes in the addressing memory interval and retrieving the corresponding matrix; and implementing control command phase deviation correction. This invention improves the physical tolerance determinism of discrete digital model addressing and eliminates control command phase lag caused by analytical calculation delays.

This invention relates to the field of industrial control network detection technology, and more particularly to an industrial control network intrusion detection method based on a large language model. This invention constructs a joint analysis engine of process semantics and physical constraints based on a large language model, performing time-series prediction of the compliance of operational intentions and their physical consequences, thereby identifying the threat level of abnormal intentions. Combining operational instruction data and physical process state data, it identifies abnormal operational behaviors caused by identity impersonation, internal unauthorized access, or operating condition conflicts, and performs process context mismatch risk analysis on operational instructions. Based on the results of abnormal intention threat level identification and process context mismatch risk analysis, it predicts the intrusion threat level of the industrial control network, thereby executing an adaptive response control scheme. This method can detect complex attacks that are formally legitimate but semantically dangerous, effectively identifying abnormal operations caused by identity impersonation, internal unauthorized access, and operating condition conflicts, thus improving the overall security resilience of the industrial control system.

This invention discloses a secure cross-regional command interaction system and method for a visually isolated industrial control system, aiming to solve problems such as complex network gateway configuration, potential risks, incompatibility with outdated protocols, and low visualization in existing technologies. The system includes a command encoding module, a physically isolated display channel, a command capture and execution terminal, and a feedback channel. The method includes generating dynamic command images, transmitting them to the production control area via a split-screen device, capturing and verifying the images, and performing simulated human-machine interaction operations. By adopting the above technical solution, this invention can eliminate the network attack surface, effectively resist replay attacks and content tampering, achieve zero-intrusion integration with existing legacy systems, realize visualized confirmation and complete audit traceability of command execution status, and provide a secure control command exit and status feedback entry for the decision-making platform in the management information area, realizing an intelligent closed loop of "perception-decision-execution-verification".

This application discloses a new energy industrial control system platform with real-time early warning function applied in the sampling field. This system platform, through the setting of a zero-trust security model, is significantly different from traditional protection in that all entities in the network, regardless of whether they are on the external or internal network, are considered untrusted by default and require authentication and authorization. This allows for trust assessment and dynamic access control of all access from both inside and outside the enterprise, thereby achieving finer-grained access control, significantly reducing the network attack surface, and achieving the goal of protecting enterprise data resources. Furthermore, in conjunction with the setting of a network disconnection alarm strip, when access is made internally or externally or when a network attack occurs, the central controller controls the network disconnection alarm to inflate, disconnecting the network cable on the target access terminal, thus effectively preventing information leakage and improving confidentiality. Simultaneously, the audible and visual alarm is activated, providing real-time early warning of potential information leakage risks.

The present application belongs to the technical field of industrial automation and embedded system, in particular to a remote upgrading method of industrial control system, which can reliably ensure the stability and reliability of communication, and further ensure the implementation of remote upgrading. The communication security is high, which can effectively avoid the device out of control, data tampering distortion or data loss caused by attack behaviors such as data tampering or malicious injection.

The invention discloses an industrial control system anomaly detection method based on digital twinborn and reinforcement learning, and aims to solve the problems that in the prior art, abnormal samples are scarce, the false alarm rate is high under a dynamic working condition, and the generalization ability of a single detection model is insufficient. Generating synthetic data by simulating anomalies in the twinborn body, and mixing the synthetic data with normal data to form a training set after space-time and distribution alignment; based on the real-time data, generating a time sequence prediction residual error, a distribution reconstruction error and classification confidence multi-source evidence in parallel; constructing a reinforcement learning environment, constructing a state through multi-source evidence and the like, and guiding an agent to learn a fusion strategy through an asymmetric reward function; and training the intelligent agent to obtain a detection model and performing online deployment. According to the method, the multi-source evidence is adaptively fused through the reinforcement learning agent, so that the robustness, the accuracy and the adaptive capability of anomaly detection under the dynamic working condition are remarkably improved, and missing report and false report are effectively reduced.

The application belongs to the technical field of personalized federated learning, and discloses an industrial control system intrusion detection method based on personalized federated learning. The method solves the problem of insufficient model generalization ability under Non-IID data, realizes the balance between local adaptability and global threat perception through similar client cooperative learning, and proposes a similarity calculation mechanism based on JS divergence to realize collaborative modeling between similar clients, thereby ensuring personalization and enhancing global knowledge sharing ability. An industrial network feature extraction tool is designed to support Session, Flow and Stream three-layer modeling, extract high semantic features of industrial protocols, and improve the quality of model training data. The tool has industrial protocol perception ability, can extract various high semantic features from various key industrial control protocols, effectively support the standardized conversion of multi-entity data sources, and enhance the representativeness and difference of model training data.

The application discloses a kind of adaptive control parameter optimization methods for multi-field industrial control system, belong to industrial control and intelligent control technical field;By collecting the multi-source heterogeneous time series data in multi-field industrial control system, a unified system state representation is constructed;The non-invasive virtual state evolution of multiple candidate control parameters is deduced, and the corresponding system response trajectory is generated;Introduce the preferred parameter verification mechanism based on physical residual and parameter risk potential, and screen out the parameter combination that violates physical constraints or has high risk;Based on spectral entropy and spectral geometric center, the frequency domain topology gating analysis is carried out, the stability and safety of the control parameters are constrained in frequency domain, and the application of the control parameters is triggered only when the frequency domain stability condition is met. Realize the safe, robust adaptive optimization of multi-field industrial control system control parameters without interfering with the existing production process, improve the reliability and engineering practicability of complex industrial control system parameter setting.

This application relates to the field of industrial control system technology and discloses a modular connecting plate-based digital drilling control system for multiple holes. The system constructs a global spatial state matrix, calculates the nodal thermal residual vectors of unmachined holes based on ray crossing algorithms and time decay characteristics, and constructs a main objective function based on spatial distance to dynamically select target holes for machining. By quantifying the local stiffness loss caused by hole formation, a thermal-stiffness cross-coupling coefficient is generated. Based on this coefficient, the system proportionally scales and modulates the feed rate and spindle speed to maintain a constant single-edge cutting thickness. When the calculated actual cutting speed is lower than a safety threshold, the system automatically executes anomaly recovery logic, including hole rejection, suboptimal hole re-finding, or in-situ hovering with delay. This invention avoids local thermal deformation and tool slippage, achieves self-recovery from abnormal states without manual intervention, and ensures the continuity of multiple hole machining.

The application relates to a cross-architecture industrial control system binary code unified representation and efficient labeling method. The method comprises the following steps: firstly, acquiring binary codes of a cross-architecture industrial control system, and converting the binary codes into a unified intermediate representation containing industrial control specific semantic labels; then, constructing a semantic graph containing industrial control semantic features based on the unified intermediate representation; then, performing representation learning on the semantic graph through a graph Transformer to obtain a unified vector representation of the cross-architecture industrial control binary code; then, pre-labeling an unlabeled industrial control binary code segment based on the unified vector representation; finally, selecting samples from the pre-labeling result according to a preset strategy to perform artificial fine labeling, combining the artificial fine labeling result to construct a quality evaluation system, and forming a closed-loop improvement of labeling quality and model performance. The method solves the problems of inconsistent representation of multi-architecture industrial control binary codes, low efficiency and poor quality of large-scale labeling, realizes cross-architecture semantic unification and efficient and accurate labeling, can provide high-quality data set support for industrial control system vulnerability detection and malicious code tracing, and is suitable for key industry industrial control equipment scenes.

The application discloses an AI-based SCADA data request behavior anomaly detection method and system, which first performs multi-modal serialization and time sequence alignment on original request data flow and physical state data flow, and extracts deep context features of communication behavior and physical state in parallel by using a double-tower Transformer structure. Further, through a cross-modal gating interaction mechanism, consistency and inconsistency features between communication behavior and physical state are explicitly decoupled and enhanced, and joint context encoding containing physical-information causal logic is generated. Finally, based on joint reconstruction error analysis and dynamic threshold determination of communication and state sequences, accurate identification of abnormal behavior is realized. The method effectively improves the detection accuracy and robustness of the industrial control system when facing deep camouflage threats by constructing a logical constraint model of physical state on communication request.

The application relates to the technical field of industrial system protection, and discloses an industrial control system safety isolation protection device and method, which comprises an outer cabinet, the inner wall of the outer cabinet is rotationally connected with an inner cabinet driven by a rotation motor, an opening is arranged on the inner cabinet, the upper and lower ends of the inner cabinet are fixedly connected with rotating guide cylinders, the back surface of the outer cabinet is respectively provided with a direct-current ventilation component and a negative pressure isolation component communicated with the rotating guide cylinders, a mounting frame is arranged on the inner cabinet, an industrial computer is arranged on the mounting frame, and a single-computer detection and isolation component matched with the industrial computer is arranged in the inner cabinet. When the industrial control system in the industrial computer generates abnormal signal feedback, the isolation protection device can realize the whole-machine isolation of the device, and can also realize the single-computer isolation of the industrial computer when the single industrial computer generates abnormal signal feedback; and the device can realize power-off isolation when isolating the industrial control system.

This invention belongs to the field of network security technology and discloses a method for predicting industrial control system (ICS) time series based on multi-scale time-frequency joint sensing. It proposes an end-to-end learnable adaptive time-frequency feature extractor to replace the low-pass filtering method relying on fixed parameters in existing technologies. A data-driven dynamic multi-scale partitioning mechanism is proposed, which adaptively adjusts the scale partitioning method according to the actual distribution characteristics and fluctuation patterns of ICS time series data. A scale-adaptive residual projector is designed, and through multi-dimensional spatial projection and residual feature compensation, the problem of multi-scale feature dimension mismatch is solved, establishing a feature cascade enhancement relationship between coarse and fine scales, thus strengthening the effective fusion of multi-scale features and the ability to model long-term time series dependencies. A time-frequency cross-modal collaborative calibration mechanism is proposed, breaking through the limitation of traditional channel attention mechanisms that rely only on a single time dimension. It achieves joint learning of dual-domain information by fusing time-domain statistics and frequency-domain context.

The present application relates to the technical field of industrial control system, disclose a kind of basin ecological water resources multi-objective collaborative optimization control method, comprising: the state space model of controlled unit is built, disturbance parameter sequence is collected;According to model prediction solution obtains control feasible solution set under different working conditions, extract the public intersection satisfying threshold constraint to establish robust control envelope set;Real-time monitoring the state deviation rate of change of controlled main loop, according to this dynamic adjustment controlled slave loop control dead zone;According to the deviation attenuation characteristics of main loop, anti-saturation reset is implemented to the integral regulator of slave loop, and inertia filter is implemented to given value, the present application establishes online adjustment boundary using robust control envelope set, reduces the impact of random disturbance on control law stability, effectively suppresses the oscillation generated in the adjustment process, realizes the nonlinear decoupling of different time scale control loop, and guarantees the running stability of controlled object.

The present application belongs to the technical field of industrial control system, and particularly relates to a woven bag production process safety monitoring system, comprising: a data acquisition module, which acquires multi-dimensional data after pretreatment in the workshop; a long-scale prediction module, which predicts the data trend in a preset long-time cycle to obtain a predicted value; a microscopic baseline segmentation module, which obtains the microscopic baseline and the microscopic standard deviation of the data in a sliding observation window to calculate the absolute deviation degree, and updates the microscopic baseline when the adaptive boundary constructed based on the microscopic standard deviation is exceeded; a cross-scale clamping threshold acquisition module, which calculates the difference between the microscopic baseline and the predicted value to obtain the background drift rate, calculates the safety threshold based on the microscopic baseline when the background drift rate does not exceed the allowable limit, and calculates the threshold value based on the cross-scale clamping of the predicted value when the background drift rate exceeds the allowable limit; and a linkage control module, which issues an instruction when any dimensional data continuously crosses the safety threshold. The balance between monitoring sensitivity and anti-interference ability is achieved.

This invention discloses a method and apparatus for intelligent identification and classification of industrial assets based on a large language model. The method includes: actively probing the internet address space and collecting web page data; generating page clusters using a two-stage clustering approach that combines structure and semantics; performing semantic summarization and multi-round filtering using a large language model to refine data related to industrial control systems; constructing multi-dimensional feature summaries and generating atomic tags; and generating a hierarchical classification chain based on a dual-encoder retrieval enhancement generative model. This invention achieves automatic discovery and refined classification of industrial control system assets through multi-dimensional semantic fusion and retrieval enhancement reasoning, solving the problems of traditional methods such as reliance on strong rules, low recognition accuracy, and lack of classification structure.