367 results about "Environment surveillance" patented technology

The invention discloses a charging pile operation safety management and control system based on artificial intelligence, and belongs to the technical field of charging pile operation safety management and control. A charging pile operation safety management and control system based on artificial intelligence comprises a data acquisition module, a physical environment monitoring module, a central processing unit, a safety evaluation model, a fault detection module, a risk warning and execution module, a multi-charging pile collaborative management module and a user interface. The problems that existing charging pile data collection and environment monitoring are not comprehensive, an intelligent safety evaluation mechanism is lacked, the fault detection and response efficiency is low, and resource allocation is unreasonable are solved. Precise monitoring and safety trend prediction of the operation state of the charging pile are achieved through comprehensive data collection and the safety evaluation model, an intelligent fault detection and grading early warning mechanism can rapidly recognize and respond to potential and existing faults, resource allocation is optimized through the multi-charging-pile collaborative management module, the overall performance is improved, and the system is suitable for large-scale popularization and application. The operation safety is ensured.

The invention relates to the technical field of cloud computing, and discloses a resource scheduling method, device and equipment, and the method comprises the steps: generating a multi-dimensional data topological graph of cloud environment monitoring data, decomposing a to-be-scheduled job set into quantum computing unit sets which can be processed in parallel, and adding priority labels for the quantum computing unit sets through a quantum heuristic algorithm, according to priority labels and a multi-dimensional data topological graph, physical resources of a cloud environment are divided into non-uniform resource fragments matched with task requirements, based on the non-uniform resource fragments, resource scheduling is carried out through an improved hybrid particle swarm optimization algorithm, and weighting and fitness evaluation and resource constraint correction are introduced. According to the resource scheduling method and device, the multi-dimensional data topological graph is generated to comprehensively present the resource state and association, then the to-be-scheduled job set is decomposed into the quantum computing unit sets, the priority labels are added, and then the non-uniform resource fragments are divided for resource scheduling, so that the resource adaptation accuracy is improved, and the scheduling efficiency and the resource utilization rate are improved.

The invention discloses an intelligent monitoring method and a monitoring system for an IDC (Internet Data Center) machine room environment, particularly relates to the field of IDC machine room environment monitoring, is used for solving the problems of multi-parameter relevance and real-time early warning, and aims at solving the problems of multi-parameter relevance and real-time early warning by constructing a high-dimensional topological structure, a Bayesian network model and a dynamic threshold adjustment mechanism of the IDC machine room environment. The relevance and the causal relationship among the multi-dimensional parameters are effectively captured, and the analysis and prediction capability on the complex environment data is remarkably improved. And by utilizing a persistent coherence algorithm and random forest regression, nonlinear correlation of parameters is accurately identified, and the limitation of a traditional method in multi-parameter analysis is overcome. Through Granger causal relationship test, a parameter causal sequence is determined, and a basis is provided for accurate prediction of environmental changes. A dynamic threshold adjustment mechanism flexibly adapts to environment changes, false alarm and missing alarm are reduced, it is ensured that the system rapidly identifies abnormities and triggers alarms in a complex environment, and the intelligence and stability of the system are remarkably enhanced.

The invention provides an intelligent agricultural environment monitoring system based on the Internet of Things, and relates to the technical field of data processing. The feature extraction and analysis module is used for performing segmented statistical analysis on the soil humidity original data according to the soil humidity original data in combination with historical soil humidity data, historical environment temperature data and preset near-period soil humidity feedback data, and automatically extracting a humidity change rate and an abnormal trend index data set under multiple time windows; the self-learning judgment module is used for dynamically adjusting the current judgment threshold value based on a self-learning algorithm, judging the soil humidity original data in combination with the humidity change rate and the abnormal trend index, and generating a judgment signal for judging whether local irrigation response is triggered or not; the response control module is used for automatically generating an irrigation equipment starting instruction; the irrigation execution module is used for executing irrigation operation according to the irrigation equipment starting instruction; according to the invention, the accuracy of agricultural environment monitoring is improved.

The invention relates to the technical field of environment monitoring, in particular to an intelligent environment monitoring system based on the Internet of Things, which comprises a data quality checking module, a data exception correction module, a data association analysis module, an exception monitoring marking module and an environment early warning transmitting module. According to the method, the checking of data equipment numbers and time labels and the filtering of abnormal or discontinuous data fragments are emphasized, so that the integration of the data is improved, the calling and mode analysis of historical data are carried out, and the abnormity is identified and corrected, and the data better meet the requirements of a time sequence mode; according to the method, key monitoring item combinations can be accurately identified and recorded in data correlation analysis, so that the real-time analysis and response capability of monitoring data is remarkably improved, the adaptive capacity to environmental changes and the early warning accuracy are greatly enhanced through timely marking of abnormal states and rapid release of early warning information, and the early warning efficiency is improved. And the efficiency of environment monitoring and decision support is optimized.

The invention discloses a multi-source environment monitoring data fusion method based on adaptive Bayesian reasoning. The method comprises the following steps: S1, collecting an environment monitoring data set; s2, obtaining a preprocessed environment monitoring data set; s3, carrying out uncertainty evaluation on the preprocessed environment monitoring data set; s4, forming an initial prior probability; s5, calculating the posterior probability distribution of each environment monitoring data source through the Bayesian theorem, and dynamically updating the weight of each data source in the environment monitoring data fusion process according to the posterior probability distribution; s6, performing weighted fusion on each environment monitoring data source according to the dynamically updated weight to form fused environment monitoring data; and S7, carrying out post-processing on the fused environment monitoring data. According to the invention, the data fusion process can adapt to environmental changes in real time, so that the accuracy of data fusion is improved.

The invention discloses a data security policy automatic adjustment system and method. The system comprises a data acquisition module (DCM), a data analysis module (DAE), a security policy adjustment module (SPAM), a monitoring feedback module (MFM) and a training data module (TDR). The DCM collects key data such as user access records, network traffic and system logs. The DAE analyzes these data and generates security event alerts and risk assessments in combination with a risk knowledge base. And the SPAM formulates or updates security parameters according to the evaluation result, and generates and pushes a new security policy to the cloud environment. And the MFM monitors network flow and user behaviors, compares the network flow and the user behaviors with a preset baseline, and feeds back when abnormity is found. The TDR uses historical data to carry out scene simulation training, a data analysis model is optimized, and the safety performance of the system is improved.

The invention relates to the technical field of environment monitoring, in particular to a machine room dynamic environment monitoring and management method. Comprises: collecting machine room environment parameter data; adding a data label to each piece of parameter data and recording a timestamp; creating an environment monitoring data frame, and filling basic information; adding an abnormal event indication field in the data frame, and comparing with a preset threshold to mark an abnormal condition; identifying a sudden change trend in the time sequence data and recording a result; calculating relevance indexes, and analyzing data relevance among different sensors; the signaling modules are mapped to different data processing units, and data processing and circulation services of corresponding levels are provided; the data frames are interpreted step by step, and corresponding monitoring and management strategies are executed; dynamic weight distribution is implemented, and the data frame processing priority is adjusted according to historical weight changes and the current state; data frame specific processing fields are registered and tracked. According to the method, real-time monitoring, comprehensive analysis and accurate management of the dynamic environment of the machine room are realized.

The invention provides a liquid cooling equipment control method and system, electronic equipment and a storage medium, and relates to the technical field of cooling control, and the method comprises the steps: collecting the state data of a temperature and humidity sensor; predicting the temperature and humidity data according to the state data of the temperature and humidity sensor; based on the prediction result of the temperature and humidity data, predicting the state of the liquid cooling equipment and a plurality of corresponding control actions to obtain a predicted state of the liquid cooling equipment and a plurality of corresponding predicted control actions; based on the prediction state of the liquid cooling equipment, determining a target control action which most accords with a preset energy consumption condition and a safety condition from the plurality of prediction control actions; and executing the target control action on the liquid cooling equipment. The intelligent level of environment monitoring is improved, the energy efficiency, the stability, the safety and the like are remarkably improved, and the system has wide application prospects and market value.

The invention discloses a communication machine room environment monitoring system and method based on edge computing, and the system comprises a data collection device, an edge gateway end, and a machine room monitoring terminal device, and the edge gateway end comprises a communication machine room map construction module, a risk assessment module, and a grading processing module. The computer room monitoring terminal is used for receiving the environment data and the equipment operation data information transmitted by the data acquisition equipment through the data communication link, comprehensively processing and analyzing the environment data and the equipment operation data to generate a data analysis result, and displaying the environment state and the equipment operation state of the computer room through the computer room monitoring terminal. According to the invention, by combining the construction of the communication machine room map and the risk assessment technology, the safety and efficiency of communication machine room monitoring are remarkably improved, abnormal conditions can be found in time and response can be made, stable operation of the communication machine room is ensured, and the reliability and safety of a communication network are enhanced.

The invention relates to the field of power systems, in particular to a power plant inspection robot and method based on a 5G network. The robot is provided with the movable base to achieve movement in a power plant, and the camera shooting assembly and the sensing device are integrated on the equipment base and used for monitoring the environment and automatically detecting the position of workers, the equipment temperature and obstacles correspondingly. And the 5G communication module is connected with a base station to realize real-time transmission of data. The control module is internally provided with a path planning algorithm and a deep learning model so as to intelligently plan an inspection path and identify personnel in the inspection path. The method comprises the steps of robot driving, environment monitoring, data sensing, data transmission, path planning, personnel identification and path updating based on an identification result. Through combination of a path planning algorithm and a deep learning model, the autonomous navigation and intelligent identification capabilities of the robot in a complex environment are enhanced, and the inspection efficiency and safety are improved.

The invention discloses a computer remote login identification system based on artificial intelligence, and the system comprises a remote login request receiving and processing module which is responsible for receiving a remote login request of a user, and carrying out the preliminary processing and verification; the multi-mode identity authentication module adopts a three-factor verification matrix of biological characteristics, behavior characteristics and equipment fingerprints, and adjusts weights of different authentication dimensions according to security requirements and user requirements; the lock screen state sensing module is responsible for sensing a lock screen state; the dynamic behavior analysis engine is used for monitoring behavior characteristics of the user in real time and comparing the behavior characteristics with a preset abnormal behavior mode so as to find abnormal login behaviors in time; the intelligent unlocking decision tree is used for making an intelligent unlocking decision according to the authentication score, the equipment environment and the behavior abnormality; and the security execution module is responsible for automatically filling a password and executing an unlocking operation after the user identity authentication is passed, and monitoring an abnormal condition in the unlocking process in real time in combination with equipment environment monitoring and an abnormal detection model.

The invention, which relates to the technical field of coal mine transportation scheduling, discloses a coal mine intelligent transportation scheduling optimization system comprising a map construction module, a data acquisition module, a communication module, a monitoring module, an intelligent scheduling module, an emergency processing module and a man-machine interaction module. The system has the advantages that the operation states of coal mine collection equipment and transportation equipment are monitored in real time through the equipment monitoring unit and the environment monitoring unit, the environment state of a mining area is comprehensively monitored, equipment abnormity and environment risks can be found and early warned in time, safety accidents are prevented, and the system is safe and reliable. The service life prediction unit can predict the residual service life of the equipment by monitoring and analyzing the operation time and the accumulated workload of the equipment, and gives out early warning when the equipment is close to the end point of the service life, so that potential safety hazards caused by equipment aging are avoided, and the safety of coal mine transportation is improved.

The invention discloses an intelligent warehousing dynamic optimization system based on a digital twinborn technology. The system comprises the following steps: S1, creating a digital twinborn model: acquiring basic data of an entity warehouse and constructing an accurate digital model; s2, data collection and integration: deploying Internet of Things equipment and realizing real-time transmission and processing of data; s3, constructing an analysis algorithm: developing an advanced analysis algorithm and implementing a machine learning model; s4, dynamic optimization and adjustment: automatically adjusting a storage layout and optimizing a warehouse-in and warehouse-out process; and S5, prediction and early warning: analyzing and predicting potential problems and setting an alarm mechanism based on historical data. According to the invention, the real-time performance and accuracy of data are obviously improved, the environment monitoring capability is enhanced, the cargo safety is guaranteed, and the space utilization rate is greatly improved.

The invention discloses a server fault monitoring system, which relates to the technical field of servers, and is characterized in that a data acquisition terminal deployed in a machine room acquires environment monitoring data and software monitoring data of a server, and the environment monitoring data and the software monitoring data are used as server monitoring data to be sent to a corresponding edge server; and when the edge server detects that the software / hardware monitoring data is abnormal, extracting related abnormal data from the corresponding server monitoring data, and generating an abnormal report log. And the central server determines that the server has a fault according to the exception report log and the related exception data, and generates fault data information. And the monitoring terminal locates a graphic object corresponding to the fault server in the three-dimensional machine room virtual model according to the equipment identifier, updates the display state of the graphic object of the server into a fault state, and generates a fault data interaction label according to the fault data information. According to the invention, the problem of relatively large fault response delay in related technologies can be solved, and the server fault monitoring efficiency can be effectively improved.

The invention relates to the technical field of dangerous chemical storage and transportation, and discloses an intelligent dangerous chemical storage and transportation system, which comprises a sensing layer provided with an RFID automatic identification module, an environment monitoring module and a video monitoring and behavior analysis module; the data processing layer is configured with a data acquisition gateway, a data storage platform and a data processing and analyzing module; the application layer is configured with a management platform, an intelligent early warning and alarm system, an intelligent decision support system and a report generation and data archiving system; all the layers are connected through a communication network and are used for realizing real-time monitoring and management of the whole storage and transportation process of the hazardous chemical substances. By integrating technologies of RFID automatic identification, environment monitoring, video monitoring, behavior analysis and the like, real-time monitoring and automatic early warning of the whole storage and transportation process of the hazardous chemical substances are realized. Once environmental parameters exceed standards or abnormal behaviors occur, the system gives an alarm immediately and starts emergency response, the safety of the storage and transportation process is remarkably improved, and accidents are prevented and reduced.

The invention relates to a server test environment automatic monitoring method and system. The method comprises the following steps: acquiring various data in a test environment of a plurality of servers in a specified period, and dividing the data into a training set, a test set and a verification set; the method comprises the following steps of: acquiring various monitored server test environments, inputting the monitored server test environments into a server test environment monitoring model for training, testing and verifying, acquiring various data in the monitored server test environments in real time, inputting the data into the server test environment monitoring model, and outputting monitoring results of the monitored server test environments in real time through the server test environment monitoring model. And the server obtains abnormal information according to the monitoring result, obtains an abnormal position and an abnormal severity degree of the monitoring test environment based on the abnormal information according to whether the abnormal information needs to be processed or not, analyzes an abnormal solution according to the abnormal position and abnormal data, and processes the abnormality based on the obtained abnormal solution. The server test environment monitoring efficiency is effectively improved, and the missed test rate is reduced.

The invention relates to the technical field of environment monitoring, in particular to an electric power carbon emission real-time monitoring system, which comprises a data acquisition module, a data processing module, a data processing module, a data processing module, a data processing module, a data processing module, a data processing module and a data processing module, wherein the data processing module receives sensor data of a plurality of monitoring points of a power plant and comprises a carbon emission value, a timestamp and an environment parameter of each sensor; and sorting according to the time sequence characteristics of the data, and establishing a serialized data set. According to the method, data cleaning and time serialization processing are applied, data accuracy and availability are improved, data management is more adaptive and prospective by dynamically adjusting calculation of strategies such as kurtosis and skewness, geometric and topological analysis methods are introduced to improve understanding of data structure features, complex modes in data are effectively recognized, and the method is suitable for being popularized and applied. According to the method and the system, the dependency relationship of a hole, a cluster and an emission source and the application of an anomaly detection technology are applied, so that timely identification of abnormal behaviors is enhanced, potential equipment faults or operation errors are allowed to be found in an early stage, timely intervention is realized, and unnecessary environmental influence is reduced.

Automated, environmentally monitored and controlled storage units and systems for storing, monitoring, and maintaining a supply of products, particularly temperature sensitive pharmaceutical and / or other high-value products, be they temperature sensitive or not. Such automated storage units contain an array of independently addressable holding locations for containers with product in one or more environmentally monitored (e.g., for temperature, humidity, etc.) and controlled zones fitted with appropriate environmental sensors. In preferred embodiments, the automated storage units also include a reader to track product information and status. Product loading, retrieval, and movement within such automated storage units is performed by a computer-controlled robot. A user interface device, preferably in communication with an application service provider to provide remotely managed inventory management and other services, provides users with inventory- and product-specific information.

The invention discloses a base liquor full-life-cycle traceability management method based on a sovereign block chain. The base liquor full-process management is realized by constructing systems such as double-chain cooperation, full-process digital monitoring and credible evidence storage. In the raw material link, a block chain is used for establishing a quality file, and in the production link, the industrial Internet of Things is used as a basis, and a layered chaining and double-code anti-counterfeiting technology is adopted; a dynamic environment monitoring chain is built in a warehouse logistics link, and a multi-source sensing and condition triggering evidence storage strategy is applied; constructing a two-way verification system in a sales link to combine with risk assessment; and a carbon footprint tracking mechanism is introduced in the recovery link. The method can reduce the risk of raw material adulteration, improve the quality inspection efficiency, guarantee the production quality and brand safety, improve the safety and efficiency of warehouse logistics, achieve the guarantee of full-link certified products, improve the full-life-cycle management, improve the reliability of a block chain network, employ the verification mechanism of an oracle machine to verify and guarantee the real chaining of the data of each link, and improve the efficiency of the whole-link certified products. And consistency of online and offline data is ensured.

The invention relates to the technical field of building construction site environment monitoring, in particular to an AI-based construction site Internet of Things monitoring equipment group management tool, which comprises an application layer, a platform layer, a network layer and an equipment layer, and is characterized in that the platform layer comprises a unified management platform, an AI analysis module and a data storage management module; the equipment layer comprises a movable camera, an unmanned aerial vehicle, a sensor module and personnel identification equipment; by integrating various AI technologies and IoT technologies, the functions of real-time monitoring, state detection, fault diagnosis, maintenance recording and the like of various monitoring devices are realized.

The invention discloses a multi-source environment monitoring and intelligent alarm system and method based on a Palladium platform. The method comprises the following steps: S1, obtaining preprocessed environment monitoring data; s2, forming transmission environment monitoring data; s3, carrying out distributed aggregation and fusion on the transmission environment monitoring data on the central Palladium platform; s4, identifying abnormal fluctuation in the data; s5, generating adjusted alarm threshold data by using a self-learning mechanism; s6, according to the adjusted alarm threshold data, carrying out grading evaluation on the detected environment abnormal events, forming multi-grade risk determination, and generating corresponding intelligent alarm signals; and S7, transmitting the intelligent alarm signal to each edge computing node and a related monitoring terminal, issuing alarm information in a short message, mail, voice broadcast or system interface display mode, and starting a corresponding emergency processing plan. According to the invention, the transmission efficiency of the alarm information can be effectively improved, and the emergency response system can make proper response according to different risk levels.

The invention discloses an intelligent research and judgment and power recovery method and system based on a low-voltage power distribution network, and relates to the technical field of intelligent power grid and power system automation, and the method comprises the steps: collecting monitoring data, carrying out information fusion check based on geography and power management, dynamically generating a topological file, and constructing a hierarchical visual power grid model. Analyzing the states of a circuit breaker and protection equipment, calculating main and backup protection deviation, analyzing electrical quantity change in combination with wavelet transform and singular value decomposition, and identifying a fault section; a whole-network sensing system is constructed, three-phase imbalance and zero-sequence current are monitored in real time, and rapid power restoration and active research and judgment are carried out by combining a dichotomy and an intelligent switch optimization power restoration strategy. According to the intelligent research and judgment and power recovery method based on the low-voltage power distribution network, the reliability and safety of the power distribution network are improved by constructing an environment monitoring system, the accuracy of fault analysis of a low-voltage transformer area is improved through topology accurate checking and intelligent fusion, and the fault analysis accuracy of the low-voltage transformer area is improved through a dichotomy fault isolation and intelligent power recovery strategy. The fault recovery efficiency is improved, and better effects are achieved in the aspects of safety, accuracy and efficiency.

The invention relates to the technical field of automatic testing and automatic operation of mobile applications, and discloses a method for automatic user interface operation of an app. According to the method, all clickable controls on a current page and related information of the clickable controls are obtained through a UI control obtaining module, meanwhile, the real-time change state of the page is monitored, a UI tree is constructed and updated by utilizing a UI tree construction and management module according to the obtained information, different page states and conversion paths of the different page states are recorded, and through an action selection module, the user interface of the current page is selected. According to an operation mode selected by a user, executing random action selection or shortest path selection, determining an optimal operation path, executing selected action operation through an action execution and information recording module, and recording page state change and path conversion information before and after the action; the traffic capturing module is responsible for capturing network traffic of the application program in the whole operation process and storing traffic data as a PCAP file, and the environment monitoring module is responsible for monitoring normal operation of the program, so that the quality of the application program is effectively improved.

The present disclosure describes storing and transporting technologies for temperature sensitive materials. More specifically, storage and transport of apheresis material. The described technologies include environmental monitoring systems, location tracking systems, an environmental control system, and customized systems and interfaces allowing for non-expert use. The storing and transporting technologies described herein may be ideally suited for the unique requirements of apheresis material.

The invention relates to the technical field of engineering monitoring, measurement and control, in particular to an intelligent acquisition instrument based on AI analysis and self-adaptive sampling and a self-adaptive interrogation method. According to the technical scheme, the device comprises a multi-source sensing interface module, an edge intelligent processing module, a self-adaptive sampling module, a communication module, a storage module, a key interrogation module, a power supply protection module and a structure packaging module, multi-dimensional feature fusion and edge decision making are achieved, the self-adaptive sampling module automatically adjusts sampling frequency according to an AI analysis result and an environment threshold value and has a dynamic threshold value adjusting function, the communication module supports various wireless protocols, and the key interrogation module can achieve automatic sensor recognition and instant data acquisition and uploading. The system can be widely applied to the fields of industrial monitoring, environment monitoring and equipment health management, and efficient and intelligent data acquisition and management are realized.

The invention discloses a power environment monitoring system applied to machine room monitoring, and relates to the technical field of power environment monitoring, the power environment monitoring system comprises a UPS power supply monitoring module, a power monitoring module, an environment monitoring module and a control terminal, by analyzing whether each UPS power supply in a machine room operates normally or not, monitoring in various aspects is executed in the machine room, and the monitoring efficiency is improved. The operation performance state corresponding to each power device in the machine room and the environment state corresponding to each UPS in the machine room are analyzed, centralized power environment management is executed, efficient management of the machine room is achieved, safe and stable operation of the machine room is guaranteed, and meanwhile based on monitoring of all aspects in the machine room, the device information is mastered in real time, and the real-time monitoring of the equipment information is achieved. Therefore, timely power environment abnormity management can be carried out, operation and maintenance personnel can be helped to rapidly carry out abnormity maintenance on equipment in a machine room, equipment operation stability is improved, burden of the maintenance personnel is reduced, and safety and energy efficiency of the machine room are improved.

The invention relates to the technical field of machine room monitoring, in particular to an Internet of Things monitoring method for unattended machine room monitoring, comprising an environment monitoring module, a comprehensive gateway, a radar module, a sound collection module and a machine room camera group, the environment monitoring module is used for monitoring machine room environment data; the sound collection module is used for collecting sound data in the machine room in different time periods and transmitting the sound data to the sound analysis module; the machine room camera group is used for acquiring video data in a machine room and monitoring intruders; the radar module is used for multi-target tracking and positioning; the comprehensive gateway comprises a video analysis module and a sound analysis module, and is used for generating an alarm signal according to the machine room environment data, the sound data and a video analysis result. According to the method, video monitoring, millimeter wave radar detection and sound recognition technologies are comprehensively applied, comprehensive monitoring of the internal environment of the machine room is achieved, the detection precision of abnormal events and the accuracy of fault diagnosis are improved, and meanwhile the visual effect of environment monitoring is enhanced.

The invention discloses a geographic information synchronization system and method under coordinate systems in digital twinning, and the method comprises the steps: collecting geographic information data in a real world through a data collection module, recording a corresponding coordinate system, and converting the geographic information data collected by different data sources into a unified target geographic coordinate system through a coordinate conversion module; the information synchronization module integrates the geographic information data after coordinate conversion into a digital twin model, the error correction module monitors and corrects errors generated in the information synchronization process, and the data updating module sets the updating period and updating strategy of the geographic information data. The geographic information data in the digital twinborn model can reflect the change of the real world in time, the geographic information data from different sources are standardized, the accuracy and the real-time performance of the digital twinborn model are ensured, the method can be widely applied to the fields of smart cities, intelligent transportation, environment monitoring and the like, and the decision support capability can be effectively improved.

The invention discloses a coal storage and transportation intelligent management method and system, relates to the technical field of intelligent management systems, and aims to solve the problems of lack of environment monitoring, scheduling decision lag, extensive safety response and serious data island in the prior art. The method comprises the following steps: collecting the physical state, environmental parameters and operation behavior data of a coal storage yard in real time through a distributed sensor network; constructing a three-dimensional dynamic digital twinborn model and inputting the model into a time sequence neural network to predict the risk of spontaneous combustion, collapse or dust explosion in the future; and generating a dynamic scheduling instruction in combination with the task priority and the equipment state, and triggering a multi-level security response mechanism. The system comprises a sensor network module, a data fusion modeling module, a risk prediction module, a scheduling optimization module, and a closed-loop control and safety response module. According to the scheme, safety early warning preposition, scheduling decision intelligent optimization and resource energy efficiency collaborative improvement in the storage and transportation process are achieved, and the response capability and operation reliability of the coal supply chain are remarkably enhanced.