1464 results about "Security management" patented technology

The invention provides a mine abnormal event real-time identification method and system based on time sequence characteristics, and relates to the technical field of mode identification, and the method comprises the steps: carrying out the time-space alignment and semantic annotation of multi-modal monitoring data, and constructing a time sequence knowledge graph; calculating a dynamic association weight between entities, and analyzing a risk propagation path; predicting a risk situation based on a historical evolution rule; and dynamically generating a differential early warning strategy and establishing a closed-loop tracking system. According to the invention, early identification, accurate prediction and efficient disposal of mine safety risks can be realized, and the mine safety management level is improved.

The invention relates to a control method for a multi-protocol switching anti-quantum security gateway, and the method comprises the steps: carrying out the safety self-inspection of a hardware environment after the security gateway is powered on, generating a unique root key of equipment based on a physical unclonable function, continuously collecting and analyzing the network traffic characteristics, and carrying out the self-inspection of the hardware environment. Based on a machine learning model, a quantum attack mode is identified and an encryption algorithm mode is dynamically decided, protocol analysis is carried out on a communication data packet, dynamic conversion and adaptation are carried out between a traditional protocol and an anti-quantum cryptography protocol according to an instruction and a preset rule, and an anti-quantum cryptography engine is called to carry out encryption or signature operation. And dynamically deriving and managing a session key in a complete period based on a root key, switching an algorithm mode, a communication protocol and a key in a linkage manner according to a quantum attack detection result, and starting anti-side channel attack protection and hardware security response. According to the method, active protection of the security gateway on quantum attacks, multi-protocol seamless compatibility and full-life-cycle security management of keys are realized.

The invention relates to the technical field of data encryption and transmission, in particular to a power grid cross-domain operation and maintenance data secure transmission method and system based on encryption, and the method comprises the steps: pre-judging a current risk through a historical network security event, dividing a network environment into different risk intervals based on a risk level evaluation value, encryption strategies with different intensities are correspondingly matched, so that refined security management and control with higher risk and stronger protection are realized; a triple progressive identity authentication system of an equipment layer, a dynamic layer and a biological layer is constructed, a dynamic permission management and control mechanism driven by identity attributes and risk levels is constructed, a basic permission boundary is determined based on inherent identity attributes of an operation and maintenance terminal, and permission strength and validity period are dynamically adjusted in combination with a risk level evaluation value. And finally, generating an access control strategy only adapted to the current terminal, the current risk and the current access request, so as to perform data encryption and transmission through the target encryption strategy and the access control strategy, thereby ensuring the security of data transmission.

The invention discloses a dam safety monitoring agent system based on a multi-dimensional large model and digital twinning, and belongs to the technical field of safety monitoring of water conservancy and hydropower engineering. According to the method, a cognitive decision kernel is used as a core innovation module, multi-source data is collected by relying on a sky-ground hydraulic engineering integrated sensor network, cross-modal data fusion and anomaly recognition are achieved through a multi-dimensional large model cluster, and multi-physics coupling simulation is completed in combination with a digital twinborn body; the cognitive decision kernel generates an optimal combination scheme through case retrieval and measure unit adaptation degree calculation, a control instruction is output after digital twin rehearsal verification, and finally closed-loop control of dam safety real-time early warning, autonomous decision making and efficient disposal is achieved through an autonomous response mechanism. The problems of data island, passive response, expert dependence and the like of a traditional monitoring system are solved, the dam safety management intelligent level and the emergency response capability are remarkably improved, and the method has wide engineering application value.

The invention relates to the technical field of pipeline monitoring and artificial intelligence, in particular to a multi-source data fusion pipeline monitoring method and system. The method comprises the steps of performing field sorting, structure unification and risk segmentation processing by obtaining pipeline line data, historical operation archives and strategy update configuration records, and generating a session primary key configuration table; a multi-source acquisition time window is configured, an acquisition task is issued, time anchor point registration and field aperture unification are completed, and a multi-source session data packet set is generated; performing session and risk unit association, performing multi-modal feature extraction and cleaning aggregation based on artificial intelligence, and constructing a pipe network risk map structure by using a map structure data model; and calling a multi-task reasoning model and a rule component based on the atlas, and performing risk type reasoning and grade judgment to obtain a risk assessment result and a strategy updating record. According to the invention, intelligent fusion of multi-source data and closed-loop optimization based on machine learning can be realized, and intelligence and reliability of pipeline safety management are effectively enhanced.

The invention relates to the technical field of power equipment monitoring, and discloses a hydropower station high-altitude equipment fault intelligent identification system based on multi-sensor fusion, which collects multi-modal data in real time and evaluates data quality by deploying multi-source sensors at key parts of high-altitude equipment. Extracting multi-scale features of each modal, performing normalization processing, calculating a fusion weight based on feature saliency and data credibility, and performing weighted fusion and dimension reduction on the multi-modal features; based on the fusion feature vector, intelligent matching analysis of fault features and intelligent identification of fault types are carried out; a fault identification result is obtained; in addition, the system also comprises safety monitoring of overhead working personnel, and realizes closed-loop management from fault identification to safety maintenance. According to the invention, early weak faults can be accurately identified, and the safe operation level of equipment and the intelligent degree of operation safety management are improved.

The present application relates to the technical field of safety management, in particular to a mine air compressor safe operation management method and system based on deep learning, comprising the following steps: collecting output pressure and input current, calculating slope change rate and direction switching frequency, clustering fluctuation amplitude and frequency by time period, generating working condition fluctuation label, identifying state mutation point and duration, calculating risk level label, analyzing load fluctuation coefficient, judging periodic fatigue early warning, combining synchronous rate to identify abnormal section, judging the need to switch state and generating instruction set. In the present application, by constructing the load risk level sequence on the basis of state duration and mutation point identification, the quantitative identification of multi-period load deviation is realized, the early warning period is marked by combining risk level and fatigue deviation ratio, and the abnormal load trend is judged according to the change direction of pressure and return gas flow, which strengthens the capture ability of abnormal operation section and improves the accuracy of operation safety risk identification and the adaptability of response strategy.

The invention relates to an intrusion detection method based on cross-domain security management and a shared behavior model. The method comprises the following steps: acquiring multi-dimensional original data according to a preset cross-domain data acquisition rule and multi-domain node deployment; performing compliance, integrity and format matching degree verification on the original data, shielding sensitive information by using a dynamic desensitization technology based on a verification result, converting a heterogeneous data format, filtering missing field abnormal data, and obtaining compliance data; the method comprises the following steps: extracting a multi-dimensional feature set of user cross-domain access, constructing a shared behavior feature vector through weighted calculation, constructing a reference behavior model library in combination with a cross-domain security policy, and screening out intrusion behaviors and feature deviation data through feature comparison and behavior deviation calculation; according to the method, cross-domain security audit logs are fused for correlation analysis, intrusion behavior types and risk levels are judged by means of a Bayesian network model, differential security response strategies are generated and executed, and cross-domain intrusion detection and protection are achieved.

The invention discloses a multi-model game method for multi-scene intelligent safety management. The method comprises the following steps: acquiring multi-domain original data and generating information structure features; the heterogeneous indexes are uniformly mapped into information loss components based on the features, and stable routing of the game template is carried out in combination with sequential statistics and hysteretic decision; performing game payment-oriented decision sensitive subspace compression on the unified semantic state output after routing; a dynamic risk-to-go (dynamic risk surplus) time difference method is adopted to generate step risk signals meeting total amount conservation, and the step risk signals are injected into a compression space; and through online estimation of the spectral radius of the Jacobian matrix of the optimal response operator, an equilibrium vulnerability index is generated, and stability management and closed-loop adjustment of an equilibrium point are realized. According to the invention, the decision adaptability, stability and robustness of the intelligent system in a dynamic confrontation environment can be improved.

The invention relates to the technical field of fire safety management, and discloses a fire safety management supervision platform system. The system comprises a building information acquisition module for acquiring building structure data and environment monitoring data of a target building in real time; the risk area division module is used for dividing a plurality of independent fire risk areas according to the building structure data and distributing area identifiers; the historical data indexing module is used for indexing a historical fire accident data set and a fire-fighting facility maintenance record set of each region according to the region identifier; the dynamic risk analysis module fuses the environment monitoring data and the historical fire accident data set to generate a real-time fire risk coefficient sequence; the fire-fighting resource scheduling module is used for generating a fire-fighting resource allocation scheme based on the real-time fire risk coefficient sequence and the facility maintenance record set; and the emergency response decision module generates a multi-stage fire emergency response strategy according to the resource allocation scheme. The system improves the timeliness and pertinence of fire safety management.

The invention provides a multi-modal data desensitization method, system and device and a medium, and belongs to the technical field of information security. The method comprises the following steps: firstly, acquiring multi-modal original data based on preset access information and acquisition frequency, and after standardized preprocessing, positioning sensitive information by using a classification recognition algorithm and generating an analysis report. Then, according to a report, matching a strategy from a desensitization strategy library bound with the sensitivity level, adjusting a dynamic confusion algorithm parameter to desensitize, integrating and generating desensitized multi-modal data, and recording and storing a mapping relationship between the original data and the desensitized data; and if a restoration request is received, after authorization verification is passed, reverse desensitization restoration data is carried out by using the mapping relation. Besides, full-process feedback information is collected, the desensitization effect is quantitatively evaluated according to a preset index, the desensitization strategy and algorithm are iteratively optimized accordingly, and effective desensitization and safety management of multi-modal data are achieved.

The invention provides a digital twin smart park security and protection management system based on artificial intelligence, and relates to the field of security and protection management, and the system comprises a multi-mode sensing network which is used for collecting park environment data through a multi-mode sensor network, and carrying out the preprocessing of the data, and obtaining an environment data set; the digital twin modeling module is used for constructing a park three-dimensional virtual model based on the environment data set; the semantic alignment module is used for defining a dynamic security semantic rule based on the three-dimensional virtual model and carrying out semantic alignment of multi-modal features on the environment data set; the threat assessment module is used for performing threat assessment on the aligned semantics based on a knowledge graph and a Bayesian network; and the resource scheduling execution end is used for generating a disposal plan according to the evaluation result and realizing security response through an execution terminal. The method is used for solving the problems of information isolated island, response lag, high false alarm rate, fuzzy situation awareness and the like of a park security system in the prior art.

The invention belongs to the technical field of Internet of Things security, and particularly relates to a multi-level Internet of Things equipment security management and access control method, which comprises the following steps of: performing bidirectional verification on a digital certificate and a hardware fingerprint through an edge node when equipment is accessed; calculating credibility in real time and dynamically dividing security levels by using a hybrid evaluation algorithm based on equipment operation context data; a fine-grained access strategy is generated in combination with equipment attributes and environmental risks, and cross-level collaboration is realized through a lightweight protocol; abnormal behaviors are detected in real time in the access process, and high-risk operation is blocked within milliseconds; and strategy conflict resolution and adaptive optimization are realized through formalized verification and deep reinforcement learning. According to the technical scheme, the identity authentication reliability, the authority dynamic adaptability, the strategy consistency and the threat response speed of the Internet of Things equipment are remarkably improved, and a systematic security guarantee is provided for large-scale Internet of Things applications with high security requirements.

The invention provides a new energy construction small target full-link safety management and control method based on cross-view feature collaboration and a dynamic anti-interference closed loop, and relates to the field of new energy construction safety management, and the method comprises the steps: S1, deploying a plurality of heterogeneous sensing devices, and collecting the multi-source data of a small target in a construction region; s2, unifying small target features of different visual angles to a global coordinate system of a construction area through coordinate mapping and feature decomposition fusion according to the multi-source data collected in the step S1, and outputting an aligned small target feature map; and S3, adaptively performing multi-scale feature enhancement and lightweight processing according to the aligned small target feature map output in the step S2 and the scale and texture characteristics of the small target, and outputting an initial recognition result of the small target. Small construction targets such as bolts and buckles can be accurately identified, the cross-view space-time dislocation problem of multi-source equipment is solved, dynamic illumination and equipment vibration interference is inhibited, and full-link management and control from data acquisition, feature enhancement and precision calibration to rectification closed loop is realized.

The invention relates to a security management and control method and system for a mobile terminal of a visitor in a secret-related area and a medium, and the security management and control method comprises the steps: obtaining visitor identity information inputted by the mobile terminal, and generating a session ID and a first dynamic two-dimensional code; in response to the reading of the code scanning verification device on the first dynamic two-dimensional code, the server verifies the legality of the session ID, and issues a preset management and control strategy after the verification is passed; when the mobile terminal is in the controlled state, monitoring violation operation violating the control strategy and generating a violation log containing violation operation information; generating a second dynamic two-dimensional code in response to a leaving instruction of the mobile terminal; in response to the reading of the code scanning verification device on the second dynamic two-dimensional code, triggering the mobile terminal to upload the violation log to the server; and after confirming that the summary information is consistent with the data of the violation log, the server sends a control releasing instruction to the mobile terminal, so that the mobile terminal is recovered to a normal state. According to the invention, the information protection capability of the secret-related place is improved.

The invention discloses a personnel information security screening method and system based on big data, and relates to the technical field of image processing, and the method comprises the steps: obtaining to-be-processed face data in a region, screening out unregistered personnel, carrying out the matching and correlation of the images of the same unregistered personnel at different image collection points through calculating the feature similarity, and carrying out the recognition of the unregistered personnel. And connecting time points and spatial positions corresponding to the images of the same unregistered person at different image acquisition points into a spatial-temporal trajectory through a trajectory reconstruction algorithm, and pushing multi-modal feature information of the person to an adjacent preset area for the identified abnormal behavior. According to the invention, through a density clustering algorithm based on time and space constraints, feature association is carried out on personnel images collected by different cameras, a cross-region behavior track of the unregistered personnel is constructed, high-risk behaviors such as wandering are detected by using track analysis and an abnormal behavior identification technology, and early warning information is generated and sent to other regions. And the initiative and the coverage range of safety management are improved.

The invention discloses a regional security management system based on multi-source data fusion, and relates to the technical field of security management. The system comprises a security intrusion detection model, a perimeter security linkage module and a security disposal effect evaluation module. According to the method, after key safety intrusion area marking is carried out on the designated area, when the perimeter safety condition of the designated area is monitored in real time, multi-source data is input into the designated safety intrusion detection model for analysis, the intrusion detection result containing the existence of the intrusion object and the perimeter safety is output, and if the intrusion object exists, the intrusion detection result is output. If yes, a perimeter security alarm mechanism is triggered immediately to take security linkage measures, and after security disposal is finished, security disposal effect evaluation is carried out to determine whether intrusion detection qualification verification is carried out or not, so that the security management efficiency of the perimeter of the specified area is improved, and the security of the specified area is ensured. The problem that in the prior art, when safety intrusion detection is carried out on the area perimeter, linkage synchronism of alarm and emergency response is poor is solved.

The invention discloses a dynamic access control method and device of a baseboard management controller, equipment and a storage medium, and relates to the technical field of server hardware security management.The method comprises the steps that when an access request is detected in a trusted operation environment, multi-dimensional context attributes associated with the access request are collected; performing dynamic trust evaluation based on the multi-dimensional context attribute, and generating a dynamic trust score corresponding to the access request; and executing an access control decision corresponding to the access request according to the dynamic trust score. Compared with a traditional static trust model, the dynamic trust evaluation is carried out based on the multi-dimensional context attribute under the trusted operation environment of starting verification of the baseboard management controller, and the differentiated access control decision is executed according to the dynamic trust score, so that the access risk is accurately identified, and the reliability of the system is improved. The security vulnerability of'permanent trust in one-time authentication 'in the prior art is avoided, and the access security of the substrate management controller is improved.

The invention discloses an intelligent security and protection management system and method based on abnormal target tracking, and relates to the field of security and protection management, which analyzes the space-time accessibility of a target in an environment topology after the target is lost, models the dynamic evolution process of target characteristics, and compares the judgment reference of identity re-identification from the static global appearance, so that the identification accuracy of the target is improved. And transferring to a dynamic probabilistic spatio-temporal search model with perspectiveness. And furthermore, based on the model, actively predicting a time-space window where a target may appear and an attenuated appearance portrait, performing targeted search and matching in a specified real-time video stream, and realizing reliable re-association of the identity according to the confidence of a matching result. Therefore, two key variables, namely environment topology and time decay, are introduced into a tracking mechanism, so that active prediction of the space-time position and the appearance state of the missing target can be realized, and the continuity and the accuracy of identity tracking can be guaranteed under the complex condition that the target is separated from the visual field for a long time.

The invention discloses a heating furnace group control system and method based on the Internet of Things, and relates to the technical field of industrial Internet of Things heating furnace control. The adaptive edge calculation module fuses algorithm processing data and reduces response delay; the dynamic thermal efficiency optimization module predicts and improves thermal efficiency; the digital twinborn decision support module realizes fault prediction; the collaborative energy scheduling module optimizes multi-furnace operation; the block chain security management module guarantees data security; and the cognitive interaction interface module realizes multi-modal interaction, and the system also comprises extension modules such as a multi-scale combustion optimization module and the like, so that accurate control and intelligent management are realized. According to the invention, accurate acquisition and real-time processing of multi-dimensional data of the heating furnace group are realized, the heat efficiency is improved, and energy consumption and emission are reduced; the system has the capabilities of fault prediction, cooperative scheduling and data security guarantee; and the man-machine interaction experience is improved, the production efficiency is improved, and intelligent upgrading of industrial heating is promoted.

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

The invention provides an intelligent grading and excess subscription management system and method for a GPU video memory, and relates to the technical field of GPU video memory management, and the method comprises the steps: firstly building a grading storage system comprising a first performance region and a second performance region; then receiving a video memory allocation request containing task priority and service quality requirements, and monitoring a data access mode and access frequency; the initial placement position of the data block is dynamically determined through the intelligent engine according to the task priority, the data access mode and the frequency; predicting target access probability distribution of each data block by adopting an LSTM network; then migrating data between the first performance area and the second performance area through a swap-in and swap-out mechanism based on the distribution and periodicity characteristics, and ensuring the service quality of the first performance area; finally, the global oversale proportion is dynamically adjusted through an overpurchase safety management module, an independent virtual address space is distributed for each task, and safe overpurchase can be achieved through the process.

The invention discloses a construction potential safety hazard identification method and system based on multi-agent cooperation. The method comprises the steps that an intelligent agent network is constructed, the intelligent agent network comprises a plurality of intelligent agents responsible for different tasks, and the intelligent agents cooperatively work in a serial mode and a parallel mode; obtaining construction related data; performing preliminary identification of potential safety hazards to obtain a preliminary analysis result; verifying and optimizing the potential safety hazard based on the preliminary analysis result in combination with an intelligent agent network to obtain a verification result; correcting the preliminary analysis result based on the verification result to obtain a conclusion of hidden danger identification and risk assessment; and generating a standardized security management document based on the conclusion according to the selected document type. By implementing the method provided by the invention, more accurate hidden danger identification, risk assessment and regulation matching can be realized, standardized and high-quality safety management documents can be generated, the working efficiency can be improved, and the professionality and accuracy of the documents can be ensured, so that the construction safety management practice is effectively supported.

The invention discloses a bridge structure crack real-time monitoring and progress analysis system based on deep learning, and belongs to the technical field of bridge structure health monitoring. The system comprises a time sequence image acquisition and multi-modal data fusion module, a self-adaptive crack feature extraction and identification module, a dynamic evolution tracking and trend prediction module and a closed-loop feedback optimization and early warning decision module, and innovatively introduces feature fusion of time sequence consistency constraint and mutual information maximization based on Riemannian manifold. Deep fusion of multi-modal data is realized; a multi-scale convolutional neural network and an attention mechanism are adopted to accurately extract crack features; predicting a crack development trend by using a long short-term memory neural network and Bayesian inference; a closed-loop feedback mechanism is designed to dynamically optimize system parameters, the four modules are deeply coupled to form a complete closed loop of data forward transmission, performance evaluation and parameter reverse feedback, real-time monitoring, dynamic tracking and intelligent early warning of cracks are realized, and a scientific decision basis is provided for bridge safety management.

Provided is a smart gas Internet of Things (IoT) system for gas safety management. The system includes a smart gas user platform, a smart gas service platform, a smart gas safety management platform, a smart gas indoor device sensing network platform, and a smart gas indoor device object platform. The smart gas user platform is configured as a terminal device; the smart gas indoor device sensing network platform is configured as a plurality of groups of gateway servers or a plurality of groups of intelligent routers; the smart gas indoor device object platform includes a safety valve control device object sub-platform, the safety valve control device object sub-platform is configured as indoor gas devices and gas safety detection devices; and the smart gas safety management platform includes a smart gas indoor safety management sub-platform and a smart gas data center.

The invention relates to the technical field of video monitoring, in particular to a livestock farm safety management method and system based on video monitoring, and the method comprises the steps: collecting a monitoring video of a livestock farm, and equally dividing each frame of video image into each CTU block; marking pixel points corresponding to the target object in each frame of video image as salient pixel points, and determining the static saliency of each CTU block; obtaining the motion pixel aggregation degree of each coordinate point in each frame of video image; determining the center-of-mass coordinate and the motion displacement of the target object in each frame of video image, and obtaining the dynamic saliency of each CTU block; and combining the static saliency and the dynamic saliency, determining a saliency weight of each CTU block, distributing a code rate for each CTU block, and carrying out coding compression on the video image for livestock farm safety management. Therefore, the monitoring video quality of the livestock farm is improved, and the safety management effect of the livestock farm is enhanced.

The invention discloses an industrial AI model security management and control system, and the system comprises a permission granularity control module which is used for declaring a required minimum permission set when an AI model is registered, and generating a dynamic access token containing a permission list for the model; wherein the identifier is a resource operation authority identifier; the permission mapping table module is used for maintaining a mapping relation from the resource operation permission identifier to the actual resource address; the access verification module is used for verifying the validity and the permission range of the dynamic access token when the model requests the system resources, and refusing the non-permission access; and the dynamic permission recovery module is used for removing the target permission identifier in the authorized permission set of the model in real time to update the permission set, marking that the old token is invalid, and generating a new token containing a timestamp based on the new permission set. According to the method, the industrial AI model can be effectively prevented from abusing the authority, and the system security is improved.

The invention discloses a steel structure construction safety risk real-time identification and early warning method, which comprises the following steps of: constructing a space-time group behavior model by collecting multi-source context data such as a construction stage, an environmental parameter, a task type and personnel allocation and combining space trajectories and behavior characteristics of multi-role personnel through standardization and feature coding; context-enhanced behavior anomaly detection is realized through deep learning and a graph neural network, and abnormal behaviors are discriminated in real time and early-warned and pushed based on an early-warning information generation mechanism adaptive to a dynamic threshold value and a context; on-site feedback is utilized to realize online incremental learning and adaptive optimization of the model, the sensitivity of behavior recognition to on-site state and task change is enhanced, the accuracy and response timeliness of anomaly detection are improved, and intelligent upgrading of construction safety management is assisted.

The invention relates to the technical field of thermal power plant safety production, and discloses a thermal power plant inspection and maintenance work site risk assessment method and system based on an AI visual technology, and the method comprises the steps: obtaining the AI visual data and environment perception data of an inspection and maintenance site through a data collection module; the risk factor extraction module identifies three types of risk factors including personnel violation, equipment defects and environment abnormity from the visual data; the risk assessment module calculates the weight of each risk factor through a subjective-objective fusion weight method, obtains a dimension risk value in combination with a correction coefficient, and further solves a comprehensive risk value; and the risk early warning and disposal module divides early warning levels according to the comprehensive risk values and generates targeted disposal measures. According to the invention, automatic and precise assessment of the multi-dimensional risk of the thermal power plant inspection and maintenance site is realized, the problems of missed judgment, misjudgment and early warning lag of traditional manual inspection are solved, the safety management cost is reduced, and the safety guarantee capability of inspection and maintenance operation is improved.

The invention provides a harbor district oil depot control system, belongs to the technical field of harbor district oil depot management and control, and particularly relates to a harbor district oil depot control system which comprises a multi-source data acquisition module, a data processing module, an index generation module, a production scheduling module, a safety management module, an equipment protection module and a control module. Universe data collection of the storage tank area and the loading and unloading area is achieved through the multi-source monitoring equipment, a scheduling, safety and equipment dynamic index system is formed in combination with the AI algorithm, and the data value density is improved; loading and unloading task allocation, four-level safety early warning and equipment health intervention measures are dynamically generated based on a threshold value, and automatic closed-loop management of'monitoring-analysis-execution 'is achieved; through a multi-stage early warning mechanism, full-process plan triggering from daily maintenance to emergency response is supported, and the accident prevention efficiency is improved; a three-dimensional visual billboard and a data cockpit are provided through the integrated management and control platform, cross-department collaborative decision making is achieved, and the emergency response speed is increased.