1820 results about "Protection mechanism" patented technology

The invention provides a charging pile line fire-fighting early warning method based on big data and a storage medium, and the method comprises the steps: collecting a line operation data set of a target charging pile cluster, the line operation data set comprises multi-source time sequence monitoring data, carrying out the cross-modal feature alignment of the multi-source time sequence monitoring data, and generating a time-space correlation feature matrix; inputting the space-time correlation feature matrix into a pre-trained fire risk prediction model, generating a line abnormal risk probability distribution diagram, generating a layered early warning signal set according to risk levels corresponding to space nodes in the line abnormal risk probability distribution diagram, and triggering a dynamic protection mechanism based on the layered early warning signal set. The dynamic protection mechanism includes performing a current cut-off operation on a high-risk line segment, and performing a power attenuation operation on an adjacent line segment. According to the invention, the reliability and the intelligent level of the charging pile line fire-fighting early warning system can be comprehensively improved.

The invention relates to the technical field of weak current intelligent terminal control, in particular to a hierarchical response management control system for a weak current intelligent terminal. The system comprises a state sensing module, a prediction optimization module, a task scheduling module, a fusing control module and an execution feedback module. The state sensing module collects a power supply state, task information and environmental parameters, and constructs a multi-dimensional operation scene model; the prediction optimization module adopts an exponential weighted moving average algorithm to predict a task peak value, and judges a key event probability through an index mutation rule; the task scheduling module generates a hierarchical response sequence and a power supply strategy based on a reinforcement learning algorithm; the fusing control module executes task delay, edge autonomy and key task guarantee mechanisms when resources are abnormal or communication is interrupted; and the execution feedback module collects an operation result and updates the model to realize scheduling closed-loop optimization. According to the invention, the response efficiency and operation toughness of the weak current system in a complex scene are improved.

The invention relates to a coal conveying control system and method for a thermal power plant. The method comprises the following steps that S1, operation data of a coal conveying system are collected in real time through a preset multi-source sensor network; s2, constructing a time sequence prediction model based on an attention mechanism, and dynamically predicting the operation state of the coal conveying system; s3, executing real-time optimization calculation at the edge calculation node, and generating a control instruction set including the belt speed, the coal feeder opening degree and the transfer station cooperation strategy; s4, the control instruction is issued to an execution mechanism through a 5G-MEC network, and meanwhile key data is uploaded to a cloud end for model iteration updating; and S5, dynamically adjusting a safety protection threshold value based on an equipment vibration spectrum analysis result, and triggering a hierarchical protection mechanism when a coal blockage risk is detected. The method has the effects of effectively improving the performance of the coal conveying system and being green and energy-saving.

The invention discloses a control method and device for heat dissipation equipment, and relates to the technical field of computers, and the method comprises the steps: employing a load dynamic perception and prediction model cooperative control technology, building a dynamic correlation model of a load and heat dissipation, and achieving the mode upgrade of a heat dissipation system from lagging regulation to advanced response. A control instruction is generated by predicting a load change trend in real time, and a dual-mode protection mechanism is combined, so that energy efficiency loss caused by traditional temperature control delay is avoided, system stability and energy efficiency performance are optimized, an efficient and energy-saving active heat dissipation solution is provided for high-load equipment, and the service life of the system is prolonged. The problems of low control efficiency of the heat dissipation equipment and energy efficiency loss caused by delayed adjustment in related technologies are solved, and the technical effects of improving the control efficiency of the heat dissipation equipment and reducing energy consumption are achieved.

The invention discloses a server BMC dynamic security authentication and firmware protection method and system based on a hardware root of trust, relates to the technical field of server remote management security, and discloses the server BMC dynamic security authentication and firmware protection method and system based on the hardware root of trust, which realize dynamic authentication by generating a device fingerprint through a security chip. According to the method, a multi-layer protection mechanism is constructed in combination with fragment encryption, differential hash check and memory integrity verification, and meanwhile, the data credibility is enhanced by using a block chain storage key hash value, so that the problems of static authentication risk, insufficient firmware tampering detection and missing of a trust chain in a traditional scheme are effectively solved, and the security protection capability of a BMC system can be improved.

The invention discloses a federated learning-driven cross-domain supply chain elastic inventory optimization system and a method thereof, and aims at realizing inventory data collaboration among same-level enterprises or regional nodes through transverse federated learning and ensuring data security by adopting a self-adaptive differential privacy protection mechanism. The method comprises the steps of constructing a transverse federated learning network, locally performing data preprocessing, adding differential privacy noise, iteratively training a global model based on federated deep reinforcement learning, generating a transverse inventory allocation and replenishment decision, and performing model adaptive adjustment in real time based on key performance indicators. According to the method, multi-target balance is considered, inventory configuration is dynamically optimized through a multi-target reward function, the inventory turnover rate is remarkably increased, the inventory holding cost is reduced, the service level is improved, and the method is suitable for various scenes such as retail chain, manufacturing industry distributed storage and cross-regional logistics distribution; and global optimal inventory configuration is realized on the premise of ensuring data privacy.

The invention relates to the technical field of cores, and discloses a method and a system for realizing USB flash disk equipment interaction based on a flash memory encryption technology, which comprises the following steps of: dividing an encryptable data block of a flash memory controller, and determining a dynamic entropy weight of the encryptable data block by utilizing an equipment interaction instruction and a random noise characteristic signal; generating a self-adaptive key generation sequence capable of encrypting data blocks through an address allocation mode and a dynamic entropy weight of a flash memory controller; calculating a side channel leakage risk index of the flash memory controller, and setting a security level label of an encryptable data block in combination with a self-adaptive key generation sequence; the method comprises the following steps: setting a differentiated security protection mechanism of an encipherable data block by constructing an access control matrix of the encipherable data block and an encryption risk area of a flash memory controller; and generating a secure interaction scheme of the USB flash disk equipment based on the self-adaptive key generation sequence in combination with the differential security protection mechanism and the access control matrix. According to the invention, the interaction safety and reliability of the USB flash disk equipment can be improved.

The invention discloses an autonomous protection control method for abnormal feedback of an LED switching power supply, and the method comprises the steps: obtaining parameter data in a switching period based on power supply operation data in a historical database; the method comprises the following steps: constructing a prediction model of the running state of the LED switching power supply, sampling based on a real-time feature window of the LED switching power supply, outputting a state prediction vector and abnormal probability distribution of a future switching period, constructing a real-time state feature matrix, dynamically matching the real-time state of the LED switching power supply with a prediction abnormal mode, and calculating a feature similarity index. And designing a hierarchical protection mechanism, uploading complete event data to a database, updating a power supply abnormity prediction model, and dynamically adjusting a protection trigger threshold value based on a deviation degree between a prediction result and an actual state. The method has the advantages that accurate pre-judgment and grading protection of the abnormal probability are achieved, model self-optimization and threshold value self-adaptive adjustment are driven through real-time data, and the system reliability and the protection response sensitivity are remarkably improved.

The invention discloses a water pump station operation adjusting device and method, and relates to the technical field of pump station operation adjusting, a first prediction result is output through operation data of a water pump station, and whether the first prediction result needs to be corrected or not is judged based on environment characteristics, equipment state data and water quality data to obtain inlet and outlet valve adjusting parameters; constructing a water pump station digital model, receiving a simulation regulation feedback result, and optimizing inlet and outlet valve regulation parameters by utilizing the simulation regulation feedback result; an inlet and outlet valve adjusting stability threshold value is obtained, relation feedback verification is conducted through the inlet and outlet valve adjusting stability threshold value, and whether a protection mechanism is triggered or not is judged through the simulation adjusting feedback result and the relation feedback verification result; and when the protection mechanism is triggered, the dynamic current stabilizer is started, and the current stabilization parameters of the dynamic current stabilizer are adjusted. Intelligent accurate control, active protection and energy efficiency optimization are realized, the system stability is improved, and the operation and maintenance cost is reduced.

The present application relates to the field of data processing technology, and discloses an intelligent fire and wind prevention linkage method and system based on meteorological perception of port quay cranes. The method includes: collecting equipment operation data through a multi-dimensional sensing unit, constructing a perception feature vector and calculating the working condition load index; according to the feature vector and the load index, the protection margin coefficient is calculated through a boundary mapping algorithm to generate an operation toughness value; based on the toughness value, a correlation analysis is performed to obtain the wind-fire coupling risk index and the disaster cascade impact; the prediction value is calculated according to the risk index and the impact, and the response matching degree is output; the strategy coefficient is adjusted according to the matching degree and fed back to the feature vector. The present application establishes a dynamic protection mechanism based on the actual bearing capacity of the equipment, so that it can maximize the operating time of the equipment while ensuring safety, while improving the system's ability to cope with complex disasters.

The invention relates to the technical field of machine learning, and discloses a decision tree data model establishment method, which comprises the following steps of: acquiring heterogeneous data sources such as a structured data table, a time sequence data stream and graph structure data through distributed nodes, sampling the time sequence data stream by using a dynamic sliding window, and vectorizing the graph structure data through a graph embedding algorithm; a multi-stage feature selection model is constructed to screen features, and a dynamic decision tree generation framework adopting an adaptive splitting criterion is established based on the features. A tree structure is adjusted by applying a multi-objective optimization algorithm, and the performance is improved by introducing an incremental pruning mechanism. And the online model updating module monitors data distribution change, reconstructs a local sub-tree in good time, and injects noise to protect data privacy in combination with a differential privacy protection mechanism. According to the method, heterogeneous data is effectively processed, the model classification precision is improved, the complexity is reduced, the generalization ability is enhanced, the model can be updated online, and the data privacy is protected. The electronic equipment calls related instructions to execute the method, and efficient data processing and analysis can be achieved.

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.

The invention provides an unattended video remote monitoring system. The unattended video remote monitoring system comprises a multi-mode monitoring module, a scene cognition module, a prediction and early warning module, an immune defense module, an autonomous control module and a knowledge evolution module. According to the system, intelligent perception of a scene is realized through collaborative acquisition of multi-source heterogeneous data and deep feature fusion, active early warning of risks is realized by using dynamic causal reasoning and a multi-agent game framework, and stable operation of the system is guaranteed based on an adaptive immune network and a three-level protection mechanism. And adopting a differential neural knowledge graph to support continuous optimization of the system. The technical bottlenecks of fixed threshold, passive response, scene understanding shallow layer and the like of a traditional monitoring system are broken through, autonomous perception, autonomous decision, autonomous operation and maintenance and autonomous optimization of the system are realized, and the system has important application value in the aspects of improving the monitoring quality, reducing the labor cost, enhancing the system reliability and the like.

The invention provides a text recognition method based on a language model, and the method comprises the steps: obtaining a to-be-detected text data flow in a target application program, including user input, interface display and network transmission text; preprocessing the text data stream to generate a multi-dimensional semantic vector sequence; performing feature modulation on the semantic vector sequence through a dynamic weight distribution module to generate a modulation feature matrix; inputting a logic judgment engine to execute multi-stage condition combination analysis, and generating risk probability distribution; demodulating the risk probability distribution to generate an interpretable risk feature vector; executing cross validation based on the risk feature vector and a preset threshold set, and generating a final risk judgment result; and when a high risk is determined, triggering a multi-layer protection mechanism, including content replacement, session interruption and safety alarm. The accuracy and interpretability of the system can be improved, and the safety and user experience of the target application program are guaranteed.

The invention relates to the technical field of electronic information communication, and provides a storable electronic information communication device which comprises a shell, a storage protection mechanism, an active cleaning mechanism and a fastening prompt mechanism are arranged in the shell, a sliding support is pulled out, and through the telescopic and rotating functions of a telescopic support, the electronic information communication device can be stored. The operation space is expanded, multi-angle adjustment is achieved, the use flexibility and comprehensiveness are improved, meanwhile, by means of combination of the transition groove and the protection plug, butt joint and stable connection of data interfaces are ensured, dust accumulation and damage of the interfaces are avoided, and the safety and durability of equipment are improved; according to the technical scheme, the dustproof effect is enhanced, plugging friction is reduced, the service life of equipment is prolonged, the protection and storage effects of the equipment are improved, and through the technical scheme, the problems that in the prior art, storage protectiveness is insufficient, and the fastening effect is poor are solved.

The invention provides a 5G message, short message and voice fusion information touch method and system, and relates to the technical field of information processing. The method comprises the following steps: acquiring to-be-sent information content, real-time geographic position information of a user and an initial multi-mode user portrait; optimizing the initial multi-modal user portrait by using the to-be-sent information content and the real-time geographical location information of the user to obtain a target multi-modal user portrait; analyzing the information content to obtain an analysis result, and determining a target communication strategy corresponding to the information content by using a reinforcement learning framework based on the target multi-mode user portrait and the analysis result; based on the target communication strategy, determining a target communication resource allocation scheme according to the real-time geographical location information of the user and the network condition by adopting a dynamic resource allocation algorithm; and based on the target communication resource allocation scheme, executing an information transmission process, and introducing a differential privacy protection mechanism to encrypt the information transmission process. According to the method and the device, the information content reaching accuracy is improved.

The embodiment of the invention provides a meteorological data processing method and device based on an edge protection gateway algorithm. The method is applied to the technical field of data processing, and comprises the following steps: acquiring meteorological data by utilizing an edge computing node, and transmitting, verifying and filtering through a mesh network to obtain an initial data set; establishing a protection mechanism for encrypted storage, and generating a hash value in combination with the block chain; establishing a standardized model for noise filtering and data restoration; constructing a fusion model to extract features; establishing a federated learning framework for prediction; and establishing a verification mechanism, and generating decision suggestions. According to the invention, by deploying a data processing and security protection mechanism at the edge node, efficient processing and security protection of meteorological data are realized, and the real-time performance and reliability of data processing are improved.

The invention, which relates to the technical field of network security protection, discloses a photovoltaic power station 5G network security protection device comprising the following components: a data acquisition module, an intelligent fusion analysis and verification system and a protection mechanism triggering module. According to the invention, by integrating an intelligent fusion analysis and verification system, multi-dimensional data such as photovoltaic power station operation data, 5G network characteristic parameters, external environment factors and personnel operation behaviors can be comprehensively collected and deeply analyzed, and a potential association relationship and a mutual influence mechanism among different factors can be accurately mined; meanwhile, a dynamic association rule self-adaptive updating mechanism can adjust the association rule in real time according to newly collected data and a mined new association relationship, the accuracy and timeliness of an analysis result are ensured, the credibility of the data is improved, and network security risks can be found and coped with in time.

The invention relates to the technical field of electric bicycles, and discloses a data-driven electric bicycle battery fire hazard detection and protection method, which comprises the following steps: S1, multi-modal data acquisition and fusion, S2, data preprocessing and feature extraction, S3, data-driven intelligent detection: classifying normal and abnormal states through a supervised learning model, S4, hierarchical protection strategy, and S5, data-driven intelligent detection. S5, cloud data optimization and model continuous learning; S6, model data updating; S7, intelligent linkage and long-term optimization; S8, cloud risk data acquisition; and low, middle and high three-level protection mechanisms are adopted. Through dynamic risk assessment, charging and discharging power is limited in a medium risk, a charger is automatically disconnected, a user is prompted to stop operation, active protection is triggered in a high risk, and sound-light alarm and remote notification are triggered at the same time, so that accidents are prevented from further spreading.

The invention discloses a digital twinning-based thermal heat supply system defense cooperative protection method and system, particularly relates to the technical field of industrial control system safety, and is used for solving the problems of hidden attack missing detection and response delay caused by splitting of energy flow and data flow in an existing protection mechanism. Physical-network behavior collaborative verification is realized by constructing a digital twinborn model, and precise protection is realized by quantifying coupling failure characteristics of energy flow and instruction flow. The method specifically comprises the following steps: collecting production data, industrial control logs and network traffic; constructing a digital twin model to simulate expected behaviors of equipment, communication and physical states; comparing and detecting abnormal data in real time; extracting a thermodynamic potential gradient characteristic quantity and a path integral characteristic quantity from the abnormal data; calculating a covariant difference value of the two characteristic quantities, and judging an external attack when the covariant difference value exceeds a threshold value; when an attack is triggered, a micro-isolation gateway is deployed, dynamic access control is implemented based on an equipment fingerprint, a user behavior sequence and an environment fingerprint, and an attack chain is blocked.

The invention relates to a large model data protection method and device based on a trusted environment, equipment and a medium. In the scheme, after a server side obtains original large model data, the original large model data needs to be encrypted and signed, and then the original large model data and an encrypted information table are issued to a target application side; when the target application end calls the large model data, performing exception check according to a credible strategy; if the check is passed, performing signature verification on each encrypted data slice; if the signature verification succeeds, decrypting the encrypted slice data by using the encrypted information table to obtain original large model data; wherein the server side and the target application side are both in a trusted environment. It can be seen that the trusted environment and the key system are combined, and a dual protection mechanism for large model data is achieved; moreover, before the large model data is called, the operation environment of the target application end needs to be subjected to exception check, so that an attacker is prevented from acquiring or tampering the sensitive data, and all-around data protection is provided for the large model data.

According to the intelligent rolling construction method and system for the concrete face rockfill dam and the computer readable storage medium, self-adaptive adjustment of the rolling speed and frequency is achieved through three-dimensional digital elevation model generation, optimal rolling path planning, rolling parameter dynamic optimization model and real-time compaction degree prediction. The system integrates satellite positioning, BIM model verification, cloud data management and an abnormal protection mechanism, can correct path deviation in real time, generates a pressure supplementing instruction and stores construction data, provides an intelligent solution for high-precision and high-reliability rock-fill dam construction, and is suitable for popularization and application. The technical problems that in traditional rolling construction, manual experience is relied on, parameters are static, monitoring lags behind, and the rework rate is high can be solved.

The invention discloses a data integrity protection method in an archive record tracing process based on a block chain, and the method comprises the steps: generating a hash value for each archive record as a tracing starting point, carrying out the traversal from the tracing starting point, generating an integrity check code when passing through a node of the block chain every time, comparing the current time with an archive secrecy period, and carrying out the data integrity protection. The method comprises the steps of obtaining an access control result, creating an encryption index for an archive record allowed to be accessed by a user in the access control result, selectively exposing data and obtaining identification data through the encryption index based on a traceability purpose, decomposing a traceability request into a plurality of sub-requests, and distributing the sub-requests to different block chain nodes to perform traceability processing based on the identification data. And aggregating different sub-query results to obtain a final traceability result. According to the method, a highly safe, reliable and transparent data integrity protection mechanism is provided for archive recording by utilizing the core characteristics of the block chain, the data integrity is enhanced, and meanwhile, the method has relatively good interpretability.

The invention provides an unstructured data processing method and system, and relates to the technical field of natural language, and the method comprises the steps: carrying out the tensor decomposition of unstructured data, obtaining a standardized feature vector, constructing a self-organizing critical state feature learning network in an isolation calculation space, constructing a feature interaction network based on quantum entanglement perception, and carrying out the recognition of the feature interaction network. And establishing a hierarchical causal graph for feature screening, realizing semantic feature vector generation by adopting multi-scale feature fusion, and establishing an intelligent protection mechanism. The feature extraction accuracy can be effectively improved, the data processing reliability is enhanced, and the system operation efficiency is improved.

The invention relates to the technical field of the Internet of Things, and discloses a wireless sensor network environment monitoring system and a safety protection method based on the Internet of Things, and the system method comprises the steps: collecting sensing node environment data to generate a multi-dimensional feature matrix, and constructing a safety protection strategy set; setting threat parameter types, and establishing a security threat association model by using historical data; extracting real-time attack features and flow parameters, and generating a dynamic protection instruction set and an optimization scheme in combination with the model; and reconstructing a network node control mechanism to generate a target protection scheme, and dynamically and synchronously calibrating an instruction time sequence. The system comprises a node data acquisition module, a security policy generation module, a threat parameter analysis module, a threat modeling module, a risk assessment module, a protection mechanism reconstruction module and a time sequence synchronization module. Through multi-dimensional feature analysis, dynamic threat modeling, adaptive risk assessment and time sequence encryption synchronization, the security protection capability of the wireless sensor network in a complex environment is improved, and the method is suitable for security protection requirements in the field of Internet of Things environment monitoring.

The invention relates to the technical field of data processing, and discloses a management method and system for software data assets and a computer readable medium. The method comprises the following steps: scanning transaction software to extract user behaviors and transaction information, and performing risk analysis to generate a data directory; calculating a risk coefficient through a compliance model to form a thermodynamic diagram; executing exception analysis and establishing a hierarchical protection mechanism; monitoring a data timeliness optimization storage strategy to form life cycle management; an anti-fraud algorithm is applied to generate risk control insights; and compliant cross-mechanism data exchange is realized based on insight data. According to the method, full-life-cycle intelligent management and cross-mechanism value collaborative mining of financial software data assets are realized on the premise of ensuring data security and privacy.

The invention belongs to the field of information technology and Internet of Things (IoT), and discloses a decentralized Internet of Things security framework system based on a block chain, and the framework specifically comprises a wallet component which is used for managing the identity of equipment, guaranteeing the uniqueness of each IoT equipment in a block chain network, and providing equipment private key storage and intelligent contract address association at the same time; the intelligent contract component is responsible for managing and distributing calculation tasks and realizing automatic processing of the tasks; the multi-layer security component is used for guaranteeing the security of identity authentication and data transmission of the IoT equipment; and the universal component is used for realizing the compatibility between an IoT equipment bottom layer module and a block chain application layer and providing a cross-platform universal interface. Through the embedded smart contract and a multi-layer security protection mechanism, data encryption transmission and multi-terminal task unloading are realized, the autonomy and security of IoT equipment are improved, and the security bottleneck of a centralized architecture is avoided.

The invention relates to the technical field of electric cooker intelligent control, in particular to an intelligent control system of an electric cooker. Comprising an electric cooker monitoring module, an intelligent temperature control module, a fault diagnosis and protection module, an energy-saving optimization module and a remote control module. The electric cooker monitoring module monitors state data such as voltage, current and temperature in real time, and the intelligent temperature control module controls the temperature through a fuzzy self-tuning PID algorithm. The fault diagnosis and protection module analyzes the state of equipment elements based on the graph neural network, diagnoses faults in real time and triggers a protection mechanism. And the energy-saving optimization module optimizes energy consumption scheduling by using a Monte Carlo tree search algorithm and a Q learning algorithm. And the remote control module realizes remote control through GSM and feeds back the state and progress of the electric cooker to a user. Through combination of multiple functions such as accurate monitoring, intelligent control and energy-saving optimization, the intelligent level, the operation convenience and the use safety of the electric cooker are improved.

The invention discloses a bus module self-recovery method and device, equipment and a storage medium. According to the invention, the communication state of the bus module is monitored in real time, and the abnormal detection signal is identified; when an abnormal detection signal is identified, the fault type is automatically diagnosed; dynamically selecting and combining an optimal recovery strategy according to the fault type; executing a self-recovery operation corresponding to the recovery strategy, and verifying a recovery effect; if the recovery fails, automatically adjusting and executing the recovery strategy of the next priority according to the fault type and the failure frequency; isolating communication between the fault bus module and other normal modules through a bus arbitration mechanism; recording fault features and a recovery process, and generating historical data for subsequent fault prediction; and if recovery fails for multiple times, triggering a system-level security protection mechanism, and giving an alarm. According to the method, the fault type can be quickly and accurately identified, the optimal recovery strategy is selected in a targeted manner, and the system maintainability and safety of the bus module system are effectively improved.

The invention relates to an access control method for a data request, and aims to construct a comprehensive security access control system. The method comprises the following steps: receiving a data request of an access client, wherein the data request comprises access identification information and a user identification; and evaluating a risk level based on the request information, and implementing a special control strategy on the high-risk request. The strategy comprises checking a preset access white list and a black list: the request in the white list is directly released, the request in the black list only initiates access authentication to an authorized user, the access is allowed if the authentication is passed, and otherwise, the access is rejected; in addition, access authentication is also carried out on requests which are not listed in a white list or a black list so as to enhance security. According to the method, a multi-level protection mechanism is formed by combining risk assessment, black and white list management and user identity authentication, malicious requests are effectively defended, different users and request characteristics are flexibly adapted, the user experience is guaranteed while the intranet data security is improved, and good expandability and adaptability are achieved.