2539 results about "Security controls" patented technology

The invention discloses a geographic information big data-oriented multi-source data processing system, and relates to the technical field of data acquisition and sensors, and the system comprises a data acquisition module which accesses a remote sensing satellite, an unmanned aerial vehicle, an Internet of Things sensor and social media in real time through a multi-source heterogeneous interface, and carries out the adaptive analysis of a data format and metadata marking; the distributed storage module is used for performing partition storage on the geographic information data based on a space-time database and an object storage architecture, and establishing dynamic space-time index and version control; the data fusion module is used for realizing coordinate system conversion, time sequence calibration and semantic knowledge graph matching based on a multi-source data alignment method of dynamic weight distribution; and an intelligent analysis module and a security control module. According to the method, core pain points such as data splitting, low storage efficiency, extensive analysis and compliance risks in the geographic information field are systematically solved, and a full-stack type technical base is provided for scenes such as smart cities, emergency disaster relief and environment monitoring.

A federated distributed computational system enables secure, privacy-preserving biological data analysis and engineering through interconnected nodes coordinated in a distributed graph architecture. A federation manager allocates resources, manages data flow and lineage, establishes privacy boundaries, and maintains cross-institutional knowledge relationships. Each node contains a processing unit for biological data analysis, privacy preservation protocols for secure multi-party computation, a knowledge graph structure with supporting data stores, and encrypted network connections. The federation manager enforces all computation and data exchange through secure channels while maintaining privacy, security, and contractual boundaries. This architecture enables research institutions to collaborate on complex biological analyses without compromising sensitive data, facilitating breakthrough discoveries through shared computational resources while maintaining strict data privacy and security controls.

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 data management, in particular to an industrial control system remote monitoring data synchronization method, which comprises the following steps of: 1, generating a dynamic synchronization time window based on an equipment state sudden change frequency and a network fluctuation degree; 2, dividing transmission priorities according to the influence degree of data on equipment security control; 3, quality characteristics of available transmission paths are evaluated, data of different priorities are transmitted in a fragmented mode according to a differentiation strategy, and data streams are recombined at a receiving end based on time sequence identification; 4, after network disconnection is recovered, synchronous consistency guarantee is achieved through data segment cascade hash verification and control instruction logic conflict detection; and 5, optimizing a time window generation algorithm and a path evaluation rule according to the historical synchronization effect data. Through accurate control, priority division, network adaptability enhancement and consistency guarantee of data transmission, an efficient and safe industrial control system remote monitoring data synchronization scheme with adaptive capability is provided.

The invention discloses a cloud-edge collaborative intelligent storage node dynamic deployment method and system. The method comprises the following steps: monitoring performance indexes such as edge node data traffic and storage resource state in real time; a deep learning algorithm combining time sequence analysis and an LSTM neural network is adopted to analyze traffic information, and a data access demand is predicted; edge nodes and cloud storage resource configuration are dynamically adjusted based on a prediction result, and an intelligent scheduling algorithm, a data cold and hot separation strategy and a self-adaptive fragmentation technology are introduced to allocate resources; the storage node layout is optimized in real time, and an optimal data distribution path is selected through a reinforcement learning strategy; data security and access control are realized by adopting end-to-end encryption, multi-level access control and block chain technologies. The system comprises a monitoring acquisition module, a prediction analysis module, a resource scheduling module, a path optimization module and a security control module. According to the method, the utilization efficiency of storage resources is improved, data delay is reduced, system stability and data security are enhanced, and the method is suitable for a cloud edge collaborative storage scene.

A federated distributed computational system enables secure, multi-institutional biological data analysis and genomic medicine through interconnected, decentralized nodes in a federated distributed graph architecture. A federation manager coordinates computational resource allocation, control and data flows, establishes privacy and security boundaries, implements multi-scale spatiotemporal analysis and simulation modeling, models cross-species or intrapopulation elements, and maintains cross-institutional knowledge relationships. Each node includes a local processing unit for biological data analysis, including multiomics and gene editing, privacy-preserving protocols for secure multi-party computation, a hierarchical knowledge graph for managing multi-domain biological relationships across spatial and temporal scales, and encrypted network connections. The system implements cross-species genetic analysis via phylogenetic integration, environmental response modeling through spatiotemporal tracking, and multi-scale tensor-based data integration with adaptive dimensionality control. This architecture enables research institutions to collaborate on complex biological analyses and genomic medicine applications while maintaining strict data privacy and security controls.

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 provides a network security protection method and system based on information fusion, and the method comprises the steps: firstly obtaining a multi-source heterogeneous data set, which comprises a traffic interaction data unit, an equipment log data unit and a protocol analysis data unit, of a target network, then carrying out the time sequence correlation analysis of the traffic interaction data unit, and generating a traffic behavior feature set; and executing state mode analysis on the equipment log data unit to generate an equipment operation feature set, and executing semantic recognition on the protocol analysis data unit to generate a protocol analysis feature set. Then, on the basis of a multi-dimensional feature fusion rule, cross-dimensional feature fusion processing is carried out on the feature set, and a network situation feature set is generated; and calling a threat identification model to carry out threat identification on the set, generating a threat identification result set containing threat type identifiers and influence range parameters, and finally generating a security response strategy set according to the threat identification result and issuing the security response strategy set to a security control node to execute protection operation, thereby effectively improving the network security protection capability.

The invention provides a software supply chain risk detection protection method and system, and the method comprises the steps: obtaining a third-party component from a software supply chain, and analyzing the multi-level dynamic behaviors of the third-party component; predicting a risk behavior pattern by using a Bayesian network algorithm, and constructing a behavior pattern library; monitoring a behavior of the third-party component during operation, and recording a behavior data stream; identifying an abnormal behavior deviating from a normal behavior mode by applying a time sequence anomaly detection algorithm of deep learning; evaluating risk levels of the abnormal behaviors by using a graph neural network, and generating an abnormal behavior list in combination with relevance and propagation paths among the behaviors; according to the list, a response strategy in the safety control measure library is dynamically adjusted through an adaptive strategy selection algorithm, and safety control measures are formulated for the third-party component. According to the invention, the identification and prediction capabilities of potential risk behaviors are 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 relates to the technical field of data processing, and discloses a multi-data fusion method and system in airport station safety supervision and a medium. The method comprises the following steps: based on a target feature matrix, judging potential conflicts through a set safety distance threshold and a speed threshold, and constructing a risk assessment index in combination with historical risk event data to obtain a safety risk score; according to the security risk score, establishing a spatial association relationship and a risk propagation relationship between the apron targets, calculating regional risk density through a risk accumulation effect, and generating real-time security situation data; and for the real-time security situation data, calculating a target function value of security control, and setting a security constraint condition for optimization solution to form a control strategy parameter. According to the invention, effective fusion of multi-source heterogeneous data is realized, accurate awareness of the security situation of the station level is constructed, and an optimized management and control strategy is generated based on situation information, so that the accuracy and real-time performance of security supervision of the station level are improved.

The invention discloses a gait rehabilitation body-equipped robot time series data modeling optimization method based on frequency domain learning, and the method comprises the steps: obtaining multi-modal sensor data, carrying out the preprocessing, calculating gait parameters, and generating a patient state vector; enhanced features are obtained by applying multi-scale convolution and frequency domain processing; performing block processing and position coding to obtain a coding enhancement feature, and performing noise suppression processing to obtain a residual fusion feature; a final fusion feature is obtained by applying a self-attention mechanism and frequency domain alignment fusion processing; performing frequency domain decomposition and multi-scale memory filtering to obtain space-time fusion features; evaluating the rehabilitation progress, generating a gait correction vector and a correction time sequence curve, and outputting a smooth safety control sequence. According to the method, the influence of high-frequency noise is reduced, so that the robustness and accuracy of the model are improved; stable characterization of key gait features is realized, and the precision and consistency of the model in processing complex time sequence data are enhanced; and the control performance and the simulation precision of the gait rehabilitation robot are improved.

The invention discloses a hydraulic motor fault diagnosis method and system based on heterogeneous asynchronous data fusion, and the method comprises the steps: collecting heterogeneous asynchronous data of a sensor network, carrying out the preprocessing of the data, inputting the data into a parallel dynamic pruning residual network, extracting features, and carrying out the preliminary fusion, thereby obtaining an initial feature plane; and inputting the initial feature plane into a multi-connection neural network, fusing data, extracting features, obtaining a final feature plane, using the fused feature data as a training set, and building a feature classification model to test and evaluate the performance of the feature classification model. According to the method, the limitation of a traditional fusion model in asynchronous processing of high-frequency vibration signals and low-frequency thermodynamic data of a hydraulic system is effectively overcome, the robustness of key fault features in a strong noise environment is remarkably improved, and rapid virtual-real mapping of bench test simulation data and online monitoring data is realized; typical faults such as plunger pair abrasion and valve plate cavitation of the hydraulic motor can be accurately supported, and safety guarantee is provided for equipment life prediction and safety control.

The invention discloses an intelligent operation and maintenance method and system based on an MCP protocol, and belongs to the crossing field of artificial intelligence and operation and maintenance automation. The system comprises an AI Agent module, a central intelligent analysis engine module, a man-machine interaction module and a safety control module. The method comprises the following steps: collecting system operation data in real time through an AI Agent module, and packaging the system operation data into an MCP protocol format; the central intelligent analysis engine module predicts the load based on an LSTM model, and generates operation and maintenance decision suggestions in combination with log anomaly recognition and reinforcement learning; the man-machine interaction module realizes understanding of user input through a natural language large model, automatically converts the user input into an MCP instruction, and supports multiple rounds of interaction confirmation and instruction verification; the safety control module provides an MCP operation sandbox and semantic instruction verification, and the operation safety is guaranteed. The system can dynamically schedule resources and automatically dispose faults, realizes that non-technical personnel can execute complex operation and maintenance operations, and is suitable for application scenes with high reliability requirements, such as industrial data acquisition and chemical engineering.

The invention discloses a safety control method based on a knowledge graph, and belongs to the field of safety control, and the method comprises the following steps: crawling various types of data according to keywords through a crawler program to construct a fireproof database; carrying out semantic analysis on data in the fireproof database by using a natural language processing technology, and carrying out data processing on the fireproof database after semantic analysis through word vector modeling and entity relationship extraction; constructing an internal large-scale language model by using the processed fireproof database, and performing hierarchical clustering on the internal large-scale language model by adopting a Leiden technology; establishing a multi-dimensional mapping model based on historic building spatial features, cultural relic features and disaster-inducing factors in a hierarchical clustering result, and constructing a multi-modal dynamic association fireproof knowledge graph by using a knowledge graph technology based on the multi-dimensional mapping model; and dynamically selecting a search mode based on a user query keyword, and generating a security control result according to the query keyword based on the search mode.

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 invention discloses a safety risk assessment system based on slope construction, relates to the technical field of slope construction, constructs a complete system composed of a data acquisition module, a dynamic disturbance identification module, a risk assessment module and an intelligent early warning and decision-making module, and acquires multi-source data such as displacement, cracks, pore water pressure, seismic disturbance and blasting vibration at high frequency. The time sequence analysis and disturbance feature extraction algorithm is combined to realize the rapid identification of the sudden disturbance event; risk score prediction is further carried out based on a polynomial regression model, and multi-level early warning is generated through comparison with a set threshold value; and finally, by combining the construction state, the support parameters and the disturbance intensity, a safety control suggestion is dynamically generated, so that the response speed and prediction precision of the system to the sudden slope instability risk are effectively improved, and real-time perception and intelligent management and control of the risk in the slope construction process are realized.

The invention provides a full-process monitoring and fault diagnosis method and system for a pneumatic conveying system. The method comprises the following steps: collecting multi-source monitoring data of the pneumatic conveying system; processing the multi-source monitoring data to obtain standardized monitoring data; constructing a training set based on the historical operation data, and training a pre-constructed deep learning model to obtain a system state evaluation model; based on the system state evaluation model and the multi-source monitoring data, adopting a system state vector algorithm to generate a differential flatness index; according to the differential flatness index, designing a k induction control algorithm, and generating control parameters for the rotating speed of an induced draft fan and the opening degree of a material distributing valve of the pneumatic conveying system; and constructing a disturbance observer with a self-adaptive safety boundary, and controlling the pneumatic conveying system based on the control parameters. According to the method, intelligent monitoring and safety control of the pneumatic conveying system are achieved in a mode of combining deep learning and intelligent control, and the operation efficiency and reliability of the system are improved.

The invention provides an AI-based security and protection monitoring video analysis method and system, and the method comprises the steps: firstly obtaining an initial video data set which corresponds to a to-be-analyzed monitoring video stream and comprises a plurality of video segment units which are continuously collected and are provided with timestamp marks, and then carrying out the video feature extraction processing of the initial video data set; the time sequence behavior characteristics of the target object and the spatial structure characteristics of the scene where the target object is located are obtained, then a pre-constructed abnormal behavior detection model is called to carry out joint abnormal behavior detection on the two characteristics, and an abnormal behavior detection result is generated; and determining an abnormal event type and spatial and temporal distribution feature information in a video picture according to the abnormal behavior detection result, and finally generating a security early warning instruction containing event positioning coordinates and sending the security early warning instruction to the target security control terminal to trigger linkage response operation, thereby improving the accuracy of abnormal behavior detection. Accurate positioning and rapid early warning of the abnormal event are realized.

A system and method for mitigating cyber-threats. A method includes analyzing data in-memory for at least one software component during execution of each of the at least one software component; generating a base risk score based on the analysis of the data in-memory; analyzing a configuration of each of at least one security control with respect to the at least one software component; generating an applicable score based on the base risk score and the analysis of the configuration of each of the at least one security control; and mitigating at least one cyber-threat with respect to the at least one software component based on the applicable score.

The invention discloses an intelligent water affair monitoring management method and system based on the Internet of Things, particularly relates to the technical field of water affair management, and is used for solving the problems of control instruction mismatching, redundant execution and equipment overload caused by the fact that a static topology model cannot sense the dynamic change of a pipe network in real time in the prior art. Through real-time collection of pipe network operation data and analysis of time-space correlation characteristics of water flow propagation delay parameters and pressure mutation, a pipe network topology change event is dynamically perceived; the flow direction sudden change reasonability is verified in combination with fluid mechanics conservation constraint, and a corrected topological mapping table is generated through reverse calculation; candidate paths are screened based on water flow inertial parameters and pressure gradient threshold values, a safety control instruction set is generated through a multi-stage verification rule, dynamic matching of a pipe network regulation and control instruction and a real topological structure is achieved, the matching degree of the control instruction and the physical state of a pipe network is effectively improved, the leakage risk and energy waste are reduced, the manual maintenance requirement is reduced, and the safety of the pipe network is improved. The service life of equipment is prolonged.

The invention discloses a mine tunnel operation period deformation monitoring method and system, and relates to the technical field of mine tunnel monitoring, and the method comprises the following steps: carrying out the real-time monitoring of the deformation condition in a mine tunnel through a sensor device, and transmitting the collected monitoring data to a data processing center through a wireless transmission module. Real-time monitoring of mine tunnel deformation is achieved through the multiple sensors and the wireless transmission technology, the problem of data lag is solved, and monitoring accuracy and timeliness are improved. And in combination with a multi-objective optimization algorithm, the system automatically adjusts key parameters such as a supporting structure and a mining sequence according to real-time data, so that an optimal safety control scheme is ensured. According to the system, the operation efficiency and safety are improved, manual misjudgment is reduced, and the control strategy can be continuously optimized according to the operation stage.

The invention relates to the technical field of vehicle network control, in particular to an efficient vehicle network control system and method based on CAN-FD, and the system comprises a data transmission optimization module, a bandwidth management module, a signal scheduling control module, an encryption security control module and an anomaly detection module. In the invention, the bit rate of a data frame, the length of a data packet and the proportion of filling bytes are optimized, the transmission efficiency is improved, the bandwidth occupied by invalid data is reduced, a priority screening mechanism is called, the stable transmission of high-priority signals is ensured, the bandwidth allocation is adjusted based on the flow state and node frequency monitoring, the burst flow congestion is relieved, and the resource allocation is balanced; in combination with arbitration priority and bus conflict state, non-emergency signal priority is optimized, key data blocking risk is reduced, encryption and integrity verification are adopted, data security is ensured, tampering and leakage are prevented, data abnormity score and detection are introduced, abnormal data are screened, identification capability is improved, and network potential safety hazard is reduced.

The invention relates to the technical field of energy efficiency management, in particular to an information access security control method and system. The method comprises the following steps: performing secure physical space authorization on a target access environment, and deploying a secure environment acquisition network; performing access environment benchmark monitoring by using a secure environment acquisition network to obtain environment benchmark feature data; a user carries out real-time access environment monitoring through user terminal equipment to generate real-time access environment monitoring data; performing multi-dimensional access risk assessment on the real-time access environment monitoring data through the environment reference feature data, and performing dynamic information security access execution to obtain access control execution data; and monitoring an operation behavior of the user in real time based on the access control execution data so as to realize information access security control. According to the invention, information access in an unauthorized physical environment can be effectively prevented, and the capability of resisting position cheating, illegal invasion and potential internal threats is remarkably improved.

The invention discloses a real-time pressure compensation method for a multi-axis linkage precision press, which relates to the technical field of control of a press, and comprises the following steps of: when a coefficient is detected to exceed a warning threshold value, executing abnormal identification and generating abnormal axis information; a short-stroke reciprocating test is carried out on an abnormal shaft under a safe working condition, force-displacement and force-speed curve characteristics are recorded through high-frequency sampling, and friction model parameters are updated by utilizing a self-learning fitting algorithm. And calculating a life degradation index by comparing a historical parameter sequence with the updated friction model parameters, and formulating a pre-maintenance strategy including a replacement period, spare part requirements and priorities. If the pre-maintenance strategy is not executed in time and it is monitored that the friction increasing coefficient continues to rise or the compensation amount is close to the upper limit, real-time compensation is applied, and the speed-limiting and load-limiting or emergency shutdown process is triggered. Through a multi-level safety control strategy, sudden friction abnormity can be accurately captured, and reliability and maintainability are remarkably improved.

The invention relates to the technical field of medical equipment, and discloses a multi-head transfusion intelligent switching control system, which comprises the following modules interconnected with a control bus through a data bus: a multi-source information binding module; a multi-modal sensing data fusion module; an intelligent safety control module; an edge-cloud collaborative decision module; a human factor engineering interaction module; a system full life cycle management module; and an infusion pipeline switching execution mechanism. Through the multi-mode sensing fusion and real-time data processing technology, the whole-course automatic monitoring of the infusion progress, the flow speed and the remaining medicine amount is achieved. The nurse station central billboard and the mobile terminal synchronously display infusion states of all beds, sound-light alarm is triggered in time when abnormal conditions occur, manual patrol video frequency is remarkably reduced, an edge computing node is applied to carry out localized data analysis, and it is ensured that alarm response delay is lower than a clinical safety threshold value. And the problem of low efficiency caused by manual regular patrol traditionally is effectively solved.

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 belongs to the technical field of intelligent power grids, and particularly relates to a circuit breaker control method, system and device of the intelligent Internet of Things, and the method comprises the steps: obtaining the operation data of a sensor; data extraction is carried out on the operation parameters of the sensor, the operation parameters are transmitted to a cloud server through the wide area Internet of Things for analysis, and whether the circuit breaker has an abnormal condition or not is judged according to a preset rule and a historical data training model; if an abnormal condition exists, based on a risk assessment matrix and real-time data monitoring, generating a safety control strategy and establishing a strategy library, performing different-level response according to an abnormal type, and generating a control instruction; and after an instruction is received, dynamically adjusting parameters according to the state of the circuit breaker, interactively verifying the instruction through the block chain node, operating the circuit breaker according to a safety control strategy after the instruction passes, and meanwhile, feeding back and adjusting the strategy. Therefore, the problems that in the prior art, the data processing dimension is single, strategy response lags behind, and the abnormal type recognition capacity is insufficient are solved.

A process includes determining, by a recommendation engine, a security risk profile for a container environment. The container environment includes a plurality of pods that are to be deployed on an infrastructure that includes a plurality of nodes. Determining the security risk profile includes determining an infrastructure context characterizing the infrastructure and determining a workload context characterizing a workload associated with the container environment. The process includes determining, by the recommendation engine, a recommendation of a security policy for the container environment based on the security risk profile. The security policy includes a security control. The process includes deploying an agent to the infrastructure to manage compliance of the container environment with the security control.

The invention relates to the technical field of computers, and discloses a server cluster security control method combined with multi-level encryption strategy joint scheduling, which comprises the following steps: S1, carrying out sensitivity evaluation on calculation tasks and data in a cluster, and classifying the data into low-sensitivity data, medium-sensitivity data and high-sensitivity data according to a preset rule; s2, encrypting the low-sensitivity data by adopting a first encryption algorithm, encrypting the medium-sensitivity data by adopting a second encryption algorithm, and encrypting the high-sensitivity data by adopting a homomorphic encryption algorithm; and S3, based on a joint scheduling mechanism of reinforcement learning, calculating task requirements and data sensitivity information by acquiring the load of the cluster in real time. Through a multi-level encryption strategy, dynamic resource scheduling based on deep Q learning and a security protection mechanism combining a zero-trust architecture and deep learning, the problems that cluster resource utilization efficiency is low, data security is insufficient and security threats cannot be responded in real time are solved.