1664 results about "Security controls" patented technology

The invention discloses a cloud desktop security access control method based on a zero-trust architecture, belongs to the technical field of network security, and is used for solving the problems of difficulty in hidden attack detection, cross-cloud attack chain breakage and conflict between security control and service continuity in a multi-cloud environment. Firstly, user identity attributes, session metadata and service call logs are aggregated, an identity-resource-behavior triple dynamic graph is constructed, cross-session association features are extracted, and a multi-dimensional behavior baseline is generated. And quantifying the access deviation degree based on the behavior baseline, triggering sensitive operation traceability analysis, constructing a time sequence risk propagation model, identifying latent attack features, predicting a penetration path and outputting a risk propagation coefficient. And finally, dynamically generating a process-level micro-isolation strategy according to a risk result, gradually adjusting the authority through a nonlinear authority attenuation function, inserting a secondary authentication node when unexpected resource jump is detected, reconstructing a communication white list, and realizing collaborative optimization of security protection and service continuity.

The invention relates to the technical field of management of the Internet of Things, and discloses a security control method and system for remote control of the Internet of Things, and the system comprises a security remote control platform, a zero-trust gateway, an authentication module, a security policy engine, edge proxy equipment and an encryption communication module. And the authentication module is used for performing identity authentication on the equipment and the user. The security policy engine dynamic access control table comprises access rule entries and associated signature information, and is issued to the edge proxy device through the zero-trust gateway. And the edge proxy device stores the dynamic access control table, verifies the signature information based on the platform public key, and performs matching and verification according to the access rule entry when receiving the control instruction. And the encryption communication module is used for performing encryption transmission on the access control table, the control instruction and the execution result. According to the invention, the whole process of remote control of the Internet of Things is dynamic, secure and credible, and the anti-attack capability and operation reliability of the system are effectively improved.

The invention relates to the technical field of data security management, and discloses a blockchain-based forestry data security management system and method, and the system comprises a data collection unit which is used for comprehensively obtaining a multi-dimensional information flow of forest ecology and resource states; the data processing unit is used for carrying out deep cleaning, standardized regulation and feature value extraction on the originally collected mass heterogeneous data; the block chain storage unit is used for constructing a permanent, tamper-proof and distributed secure storage infrastructure platform of the forestry core data; a verification unit; the safety control unit is used for constructing a covering data transmission, storage and access full-chain depth defense system structure model; a user interface unit; an auditing unit; and a network communication unit. The method is reasonable in design, and the intelligent analysis module is used for processing to form a forest interannual growth trend prediction function distribution map state visual output result set.

The invention discloses a cloud edge collaborative multi-source data fusion safety management and control system for intelligent power distribution equipment, relates to the technical field of intelligent power grids and power distribution automation, and is used for managing and controlling 10-35 kV power distribution equipment. The system comprises an edge data acquisition module, a preprocessing module, a cloud storage management module, a cloud edge collaborative scheduling module, a multi-source data fusion module, an intelligent risk assessment module, a safety control execution module, a safety protection module and a man-machine interaction module. The modules are interacted through a 5G / industrial Ethernet; the acquisition module obtains multiple parameters, the preprocessing module cleans standardized data, the cloud side performs hierarchical storage, the scheduling module allocates tasks, the fusion module integrates data, the evaluation module performs grading risk, the protection module guarantees safety, and the interaction module performs visual alarm. The method improves the power distribution data quality and risk assessment precision, optimizes the cloud edge cooperation efficiency, enhances the safety protection capability, achieves the preventive operation and maintenance of equipment, reduces the fault and power failure time, reduces the operation and maintenance cost, and provides support for the safe and efficient operation of a power distribution network.

The embodiment of the invention relates to the technical field of artificial intelligence, and provides an apartment security linkage method and system based on artificial intelligence, and the method comprises the steps: enabling initial security data obtained in real time to form a security space data set matched with an apartment space according to a space-time association relation; performing conjoint analysis on the security space data set by adopting a security intelligent decision engine to generate a corresponding linkage strategy; sending the corresponding control instruction and the security control parameter to the corresponding target security equipment based on the linkage strategy; the linkage priority of the security and protection equipment is set according to the security and protection event type and the mapping relation between the security and protection equipment and the security and protection event emergency degree, and a control array containing all levels of target security and protection equipment is constructed according to the linkage priority in combination with the current security and protection event type. The control array preferentially triggers the core target security and protection equipment to respond to the indication of the linkage strategy, so that remote control and linkage response of the security and protection equipment are realized, and the intelligent level and the emergency processing capability of apartment security and protection are improved.

The invention relates to a ship automatic berthing safety control system, and belongs to the technical field of port ship operation safety control. The system comprises four core modules which are sequentially connected to form closed-loop control: a situation synchronous sensing module generates total-factor dynamic attitude information through space-time synchronization and data fusion of a ship-shore inertial unit; the digital twin deduction module performs real-time simulation based on a ship six-degree-of-freedom motion model, and predicts a ship motion track and berth acting force in a prospective manner; the safety decision and early warning module compares the prediction track with the safe berthing corridor through a track-berthing force coupling algorithm, and outputs a cooperative control strategy; and the multi-terminal cooperative execution module converts the strategy into an instruction and synchronously distributes the instruction to a tug, a navigation bridge and a wharf system. Automatic closed-loop control of the whole process from sensing, prediction, decision making to execution is achieved, and the safety, accuracy and operation efficiency of ship berthing are effectively improved.

The embodiment of the invention discloses a double-truck-crane cooperative hoisting monitoring method and device, electronic equipment and a storage medium. The method comprises the steps that component parameters and operation environment parameters of hoisting operation are collected, a reference data model is constructed, and an action expected value and a safety control threshold value are set; state acquisition devices are arranged on the two truck cranes to acquire multi-source data in real time and transmit the multi-source data to a central control system so as to realize synchronous sensing of the two trucks; processing multi-source data based on a data fusion algorithm, and constructing a dynamic attitude model and a collaborative operation state model; a state prediction correction value in the model is compared with the reference data model in real time, deviation is judged, and safety early warning is triggered; and generating a feedback control instruction, dynamically adjusting the hoisting operation, correcting the coordination deviation, and realizing the coordination control of the hoisting posture. In this way, through multi-source data fusion and real-time feedback control, the safety and coordination of cooperative lifting operation of the double truck cranes are remarkably improved.

The invention relates to the field of artificial intelligence, in particular to an intelligent standard knowledge retrieval system based on an AI large model, which comprises a standard knowledge management database, a multi-source acquisition preprocessing module, a semantic understanding and indexing module, an intelligent retrieval engine module, a knowledge enhancement module, a user interaction feedback module, a security control module and a deployment expansion module. The standard knowledge management database is used for storing standard knowledge design data, real-time retrieval data and feedback data and constructing a dynamically updated standard knowledge resource pool; through the multi-source standard knowledge acquisition and preprocessing module, multi-channel objective standard data can be integrated and dynamically updated, and the problem of knowledge fragmentation is solved; and through the semantic understanding and indexing module, industry professional semantic accurate matching is realized, the limitation of traditional keyword retrieval is broken through, and the accuracy and efficiency of standard knowledge retrieval are remarkably improved.

The invention relates to the technical field of hoisting equipment monitoring, and discloses a hoisting equipment digital real-time monitoring system based on the Internet of Things. A dynamic load analysis module of the system collects multi-dimensional operation parameters in real time through distributed edge computing nodes; the risk situation assessment module executes tensor decomposition operation on the parameters, extracts feature vectors and generates a three-dimensional risk map; the self-adaptive safety control module dynamically adjusts the working state of the equipment according to the risk map; the digital twin mapping module is used for realizing time-space synchronous mapping of real-time parameters and a three-dimensional model and outputting holographic running state projection; and the cloud collaborative diagnosis module fuses the historical fault case library to generate a preventive maintenance strategy and returns the preventive maintenance strategy. The system can comprehensively monitor the equipment state, accurately assess the risk, realize dynamic safety control and preventive maintenance, and improve the safety and reliability of the operation of the hoisting equipment.

The invention discloses a bank system research and development project management system, and the system comprises an intelligent management platform which generates a compliance check list and a difference report through the real-time butt joint of a supervision database; the risk prediction module is used for predicting risk levels such as research and development cycle delay and resource hyperbranched, triggering early warning and suggesting a deployment scheme; the block chain evidence storage and tracing module is used for storing the hash value and the operation record of the key node document in a chaining manner, and solidifying a timestamp and a person in charge; the task collaboration module is used for analyzing a demand document by using NLP, automatically disassembling the demand document into an executable task tree, analyzing manpower and equipment conflicts through a resource occupation graph, and intelligently generating a deployment scheme; and the security control module is used for implementing hierarchical authority based on role and data sensitivity, and sensitive operation needs dual verification of biometric authentication and dynamic token to prevent unauthorized access. The system is high in risk resistance, high in overall research and development efficiency and fast in data management and auditing, and can meet the requirements in the research and development process of a bank system.

The invention discloses a self-adaptive safety control method of a nonlinear MIMO system under intermittent DoS attack, which comprises the following steps: establishing a nonlinear MIMO system model suffered from intermittent DoS attack, and the system model is in a strict feedback form and comprises an unknown nonlinear function and bounded external disturbance; the DoS attack causes periodic interruption of a communication link from a sensor to a controller, so that actual measurement data is unavailable in an attack activation period; designing a switching type adaptive state estimator; coordinate transformation is carried out by introducing a filter, and a system error is decomposed into a tracking error and a dynamic surface filtering error; a Lyapunov function is constructed; performing stability analysis in stages; designing an adaptive law to update unknown parameter estimation values of the FLS; and a final event triggering controller is designed, and the event triggering controller is combined with a virtual controller to track an error compensation term and an FLS approximation term. The problem that the state cannot be measured is solved.

The invention discloses a server temperature control method and system based on deep learning, and belongs to the technical field of server temperature control, and the method comprises the steps: firstly, synchronously collecting server hardware temperature, business load and environment parameters, capturing a heat conduction relation through combining a thermal imaging technology, and constructing a dynamic relation network through time-space fusion processing; a heat flow prediction model is constructed based on the network, model parameters are dynamically corrected in combination with thermal response characteristic attenuation coefficients generated by service load characteristic analysis, and temperature distribution changes are predicted; generating a candidate control strategy set, screening an optimal strategy through composite scene simulation, and generating a control instruction; and comparing the actual temperature after the control instruction is executed with the model prediction temperature in real time, switching to a safety control mode when the deviation exceeds a threshold value, and extracting an incremental sample to update model parameters after the deviation is recovered, thereby realizing accurate temperature control.

The invention discloses an encryption protection method and system for a deep learning large model, and belongs to the technical field of model security deployment. The method comprises the following steps: obtaining a target hardware resource state, dynamically adjusting the model coding precision according to a preset standard, generating coding strategy data, and further executing hierarchical recoding on the initial weight of a large model to obtain a mixed precision model parameter. Coding sub-networks are selected and fused in combination with task modal information, and a unified coding model is constructed. And carrying out encryption binding according to the equipment binding information to generate an encryption model of hardware binding. And when the task is executed, performing anomaly detection and protection operation according to the input and operation states, and outputting model safety output data. According to the scheme, efficient matching of the model and hardware, task adaptability enhancement and full-link safety control in the using process are achieved. The operation efficiency and accuracy of the model are improved, leakage and illegal calling of the model are effectively prevented, and stable and safe deployment in different environments is ensured.

A computerized system for dynamic cybersecurity policy using AI-based contextual adaptive learning includes an AI system that evaluates business contexts, risk tolerance, and productivity impact to generate threat intelligence assessments. The system includes a Contextual Adaptive Learning module that dynamically adjusts cybersecurity policies based on threat assessments to create security workflows. A Cybersecurity Mesh Development module that integrates policies across security frameworks. A Dynamic Scenario Catalog module that updates policy adjustments based on threat intelligence. An Automated Workflow Orchestration module that creates and refines security workflows for optimal efficiency. A Policy Recommendation and Automation module that generates prioritized security recommendations and automates policy changes based on organizational risk profiles and current security controls. This system harmonizes security policies while considering business context, risk, and productivity impacts.

The invention relates to the field of control systems, and discloses a vent valve control system with a pressure regulation function for a generator set, which is used for breaking through the limitation of static state, single target and passive response of a traditional vent valve control method. Comprising the following steps: acquiring pressure, flow velocity and valve opening data of a ventilation system in real time, generating a dimensionless parameter set for dynamic calibration, integrating parameter uncertainty disturbance terms to form a hybrid prediction model, solving an optimal valve opening instruction sequence by adopting a multi-target robust optimization algorithm, and designing a layered safety control mechanism. The system safety is guaranteed through dual-threshold monitoring and emergency intervention, redundant sensor data fusion and parameter reverse optimization are introduced, and fault tolerance and model self-adaption are achieved. According to the method, through collaborative innovation of dynamic modeling, robust optimization and intelligent fault tolerance, the control precision, the anti-interference capability, the safety and the reliability of the ventilation system of the generator set are remarkably improved, and the method is suitable for complex and changeable industrial operation scenes.

The invention provides an enterprise security management method and system based on AI, and relates to the technical field of security management, and the method comprises the steps: configuring a storage path and authority of a data storage unit, and initializing operation parameters of a calculation processing unit; acquiring images and / or video streams of the enterprise security control key area in real time; the real-time data is preprocessed; analyzing the preprocessed image and / or video stream by using the trained AI model, and identifying a preset target and action features thereof in enterprise production operation; comparing the recognized preset target and the action characteristics thereof with a preset early warning rule, and when the deviation exceeds a dynamic deviation threshold value, judging that the action is a wrong action, generating an early warning event and triggering an alarm mechanism; according to the method, data related to an early warning event is stored and examined, an optimization model and rules are fed back through an examination result, a security risk trend is analyzed, improvement suggestions are generated, and intelligentization, precision and high efficiency of enterprise security management are realized.

The embodiment of the invention provides a network security validity verification and quantitative evaluation method and system, and relates to the technical field of network security, and the method comprises the steps: obtaining global dynamic threat intelligence and a multi-dimensional global network security risk data source, and carrying out the preprocessing; constructing a global feature engineering system based on heterogeneous information network atlas and sequence analysis, forming a feature vector matrix, and mapping the feature vector matrix into an index state vector; inputting the feature vector matrix, the index state vector and the external environment information vector into an evaluation model, dynamically adjusting the weight of the feature vector matrix of each dimension, and outputting the validity score of each safety control point; based on the score, calculating a safety effectiveness index based on a time decay factor; identifying a weak link based on the index, and performing simulation verification to obtain a simulation attack actual measurement result; and an error vector is constructed based on the result and the validity score, and parameter adjustment and weight calibration are carried out. According to the scheme, the accuracy and the real-time performance of network security evaluation are improved.

The invention provides a PLC-based biomass gasification industrial steam full-automatic intelligent control system and method. The system comprises a data acquisition module, a modeling identification module, a multi-objective optimization module, a rolling prediction control module, a coordination execution module, a self-adaptive correction module and an abnormal fault tolerance and safety control module. The system collects operation data of a feeding system, a gasification furnace, a boiler, a steam-water system and an environmental protection system in real time through a PLC platform, a dynamic coupling model of a combustion side, a steam-water side and an environmental protection side is established, and a multi-target constraint optimization model with stable steam pressure, up-to-standard emission and minimum energy consumption as targets is constructed. According to the invention, unattended operation of the biomass gasification industrial steam system is realized, the system has the advantages of high control precision, low energy consumption, stable emission and strong fault-tolerant capability, and the intelligence and safety of the industrial steam system are obviously improved.

The invention discloses an internal and external network security service passing method and system based on edge computing, and belongs to the technical field of network security, and the method comprises the steps: obtaining multi-dimensional attribute information of an edge node, generating a security policy basic data set, generating a traffic feature fingerprint and a self-adaptive environment adaptive fingerprint, and calculating a fusion matching degree; edge security entity nodes are generated in combination with service scene adaptive threshold clustering, and then a security policy meta-model with a security policy blueprint as a core is constructed; analyzing the security policy meta-model through a multi-modal semantic analysis engine, generating a dynamic security enhancement model, and mapping the dynamic security enhancement model to a hierarchical security control model; based on the hierarchical model, outputting edge node internal and external network safety passage configuration and a corresponding safety passage ledger through a scenarized configuration generation algorithm; according to the method, adaptive generation, dynamic optimization and accurate execution of the security policy are realized, and the security, the automation level and the operation and maintenance efficiency of internal and external network passing in the edge computing environment are effectively improved.

The invention relates to a circuit board on-line defect detection method and system, and the method comprises the steps: carrying out the synchronous collection and structural integration of multi-source technological parameters such as production line environment temperature and humidity, equipment operation states, material batches and the like, and defect detection images, and achieving the construction of large-sample original data in a production process; through standardization and de-noising preprocessing, multi-modal features are fused, and a distribution mapping model of process and defect features is established by using algorithms such as mutual information analysis and principal component analysis. Based on a feature distribution model and real-time data, a dynamic anomaly detection threshold is adaptively generated, Bayesian inference and evidence reasoning are combined, multi-level confidence levels and risk response suggestions are output, and self-learning evolution of the model and the threshold is realized through a closed-loop feedback mechanism. According to the scheme, the accuracy of anomaly detection, the response timeliness and the risk disposal intelligent level are improved, the method adapts to complex working conditions and batch changes, and the closed-loop optimization and safety control capability of the production process is remarkably enhanced.

The invention discloses a hybrid attack defense control method and system for a heterogeneous cluster in an urban confrontation environment, terminal equipment and a medium, and relates to the technical field of unmanned platform cluster control, and the method comprises the steps: constructing an ideal system model of an unmanned platform cluster comprising a leader and a plurality of followers, constructing an information physical hybrid attack model based on the model; using the attack model to simulate an attack behavior of an attacker on an ideal system to obtain an attacked system model; based on an attacked system model, constructing a distributed elastic security estimator with a compensation mechanism, compensating attack influence for each follower and estimating an expected position; and based on the expected position of the follower, constructing a distributed elastic safety controller with a compensation mechanism, and controlling the follower. By designing the estimator and the controller, attack interference is counteracted, control input is generated, and safe collaboration of the heterogeneous nonlinear unmanned cluster under the cyber-physical hybrid attack is guaranteed.

The invention relates to the technical field of underground engineering and geotechnical mechanics, and provides an ABAQUS secondary development-based underground engineering blasting simulation method, which comprises the following steps of: 1, establishing a geometric model and dividing a calculation region into an Euler domain and a Lagrange domain; step 2, calling and realizing a JH-2 constitutive model through a UMAT user subprogram interface of the ABAQUS; step 3, modeling in an Euler domain by adopting a JWL state equation; step 4, applying a coupling Euler-Lagrange method; 5, setting boundary conditions and blasting loading parameters of the geometric model; and step 6, performing transient dynamics calculation, and outputting a result for analyzing a blasting effect. According to the scheme, a high-precision numerical test platform can be provided for underground engineering blasting design and safety control, engineering risks are reduced, blasting parameter configuration is optimized, and remarkable engineering application value and popularization significance are achieved.

A modular open system architecture for common intelligence picture generation is disclosed. The system receives intelligence requirements through a multimodal artificial intelligence system and calculates collection feasibility across multiple intelligence sources based on physical and temporal conditions. The system develops integrated collection plans through the containerized analytics workbench using containerized analytics modules and processes intelligence through GPU-accelerated deep learning models for automated target recognition. Satellite collection is orchestrated through satellite data acquisition optimization platform by evaluating weather conditions, orbital parameters, and sensor capabilities, while space domain awareness is maintained through space domain awareness system for real-time collection asset management. Multi-source intelligence data is fused through multi-source intelligence fusion system to populate a common intelligence picture. The system implements automated workflows for intelligence analysis and dissemination while maintaining security controls, with the containerized analytics workbench providing pattern of life analysis and dynamic exploitation through containerized microservices.

The invention relates to the technical field of battery management systems, and discloses an intelligent battery management and safety control system and an electric flame gas stove. The system comprises a sensing engine for collecting battery multi-physics field data through a heterogeneous sensor array; the calculation engine is used for performing spatial-temporal feature extraction on the data to generate dynamic security boundary parameters; the analysis engine is used for analyzing the external power request and fusing the security boundary to generate a power distribution constraint set; the strategy generation engine is used for synchronously solving an optimal power output track and a thermal management control instruction in the constraint set; and the execution engine is used for executing power driving and heat dissipation adjustment. According to the system, accurate sensing of the internal state of the battery and early warning of the safety risk are achieved through the dynamic safety boundary, efficient and stable power output of the battery within the safety boundary is ensured through cooperative control of power and heat management, and the safety and energy efficiency of the whole system are improved.

The invention discloses a beneficiation whole process optimization control system and method based on a large model, and relates to the technical field of beneficiation process optimizing.The beneficiation whole process optimization control method comprises the steps that a global prediction model is built based on unified feature embedding, and the global prediction model is distilled into an edge lightweight model with a conditional branch selection mechanism; carrying out hot start on the edge lightweight model in combination with historical experience obtained by knowledge graph retrieval, constructing a dual-objective optimization problem, and carrying out search by utilizing improved difference based on loss constraint to generate a candidate control strategy set; performing calibration and confidence screening on the candidate control strategy set under a multi-scene condition to generate a safety control instruction; in the execution process of the safety control instruction, data drift is monitored. According to the method, the prediction precision is ensured, and meanwhile, the real-time performance and the availability in a low-computing-power environment are considered.

The invention relates to the technical field of machine control, and discloses a chemical safety inspection robot control system and method based on sensor fusion, and the system comprises a data fusion module, a strategy construction module, a strategy analysis module, a real-time monitoring module, an optimization instruction module, and an event determination module. Original data of the chemical environment is fused into standardized data, and structural features are analyzed to identify risk factors; constructing a security situation map, mapping the security situation map to the robot, and determining a control strategy; when the robot begins to inspect, analyzing the control strategy to control the movement adjustment path action and the path safety inspection action of the robot; coding the movement adjustment path action and the path safety inspection action into a safety control instruction; optimizing the safety control instruction to obtain an optimized control instruction; when the robot completes inspection, an environment safety abstract and an abnormal event record are obtained; the control accuracy of the chemical safety inspection robot can be improved.

The invention provides an inert gas generation system, and relates to the technical field of industrial gas production. The inert gas generation system comprises the following modules: a gas pretreatment module, an intelligent separation module, a dynamic purification module, a pressure regulation and control module, a quality analysis module, a safety control module and an energy efficiency optimization module, and the gas pretreatment module is used for removing particulate matters through an electrostatic dust collection device by adopting a multi-stage gradient filtering algorithm based on ambient air input, adjusting humidity by combining a dew point control technology, forming a standardized gas source and generating a standard gas source. Through combination of a multi-stage gradient filtering algorithm and a dew point control technology, collaborative treatment of particulate matters and humidity is realized, stable gas source input is formed, the reliability of a subsequent processing link is improved, membrane separation parameters are dynamically predicted by applying a convolutional neural network model, and the permeation efficiency of a hollow fiber membrane stack is optimized.

The invention relates to the technical field of photovoltaic inverter regulation and control, in particular to a device capable of adjusting, controlling and monitoring a photovoltaic inverter. Comprising an intelligent adjusting unit which adopts a multi-parameter dynamic matching algorithm based on power grid situation awareness; an adaptive protocol unit; a state monitoring unit; and a safety control unit. According to the method, a multi-parameter dynamic matching algorithm based on power grid situation awareness is adopted, power, current and voltage parameters output by the photovoltaic inverter and voltage, frequency real-time parameters and fluctuation trend parameters of the power grid are collected, and the power grid situation dynamic matching logic is combined, so that accurate dynamic adaptation between the output of the inverter and the state of the power grid can be realized; the problem of insufficient adaptability caused by the fact that the power grid situation is not considered in traditional single-parameter adjustment is solved; and PWM adjusting signals are generated through the parameter acquisition module, the power grid situation analysis module and the adjustment decision module to drive an IGBT switch tube, so that the accuracy and stability of output adjustment are guaranteed.

The invention discloses an SM2 collaborative signature and encryption and decryption system and method fusing anti-quantum characteristics, and relates to the field of cryptography and information security. According to the method, an anti-quantum cryptographic algorithm and a national cryptographic SM2 cooperative computing framework are deeply integrated, and a key security system is constructed: SM2 sub-private keys, anti-quantum key pairs and public keys are acquired and generated through an anti-quantum algorithm software and hardware enhancement module, and the private keys are encrypted and stored and are regularly alternated; on the basis of anti-quantum collaborative signature, encryption and decryption modules, an anti-quantum verification mechanism is embedded, and data is transmitted in combination with a national secret TLCP protocol, so that signature, encryption and decryption operations are completed; the equipment integration and dynamic security control unit monitors a security state, calculates a threat index to generate a protection strategy, and dynamically switches a security mode, so that the problems of insufficient security, vulnerability to attacks and data tampering of a traditional SM2 algorithm under the threat of quantum computing are effectively solved; and the long-term anti-attack capability and the operation reliability of the equipment in a high-security demand scene are remarkably improved.

The invention relates to the technical field of data analysis and control, in particular to a hydraulic climbing frame safety control method based on digital monitoring, which comprises the following steps: acquiring monitoring data in real time through a sensor group arranged on a hydraulic climbing frame, preprocessing and analog-to-digital conversion are carried out on the monitoring data, then the data are formed and sent to a remote monitoring center through a wireless transmission technology, and the preprocessed monitoring data are input into a pre-stored structure safety evaluation model and a hydraulic system performance evaluation model so as to be analyzed in real time; analyzing the hydraulic climbing frame, judging a safety characterization state of the hydraulic climbing frame according to an analysis result, determining a safety risk type based on an abnormal state of the hydraulic climbing frame, sending early warning information through a user interaction platform when a risk is judged to exist, and executing a corresponding safety control instruction; automatic real-time monitoring, intelligent early warning and active control of the safety state of the hydraulic climbing frame are achieved, and construction safety and operation reliability are remarkably improved.