868 results about "Loop network" patented technology

The invention discloses a ring main unit monitoring method and system based on the Internet of Things, and the method comprises the steps: building a multi-source data collection network of a ring main unit, extracting the multi-dimensional time-frequency domain features of a signal through an edge computing device, generating a multi-source feature data set, and constructing a dynamic digital twinborn model, real-time mapping of the running state of the ring main unit is achieved through a physical-virtual synchronization algorithm, and twin data volumes are output; according to the node influence coefficient of the twin data volume, analyzing a topological structure of a ring network system by adopting a graph neural network, identifying a key fault propagation path and generating a priority monitoring node list; based on the priority monitoring node list and the twin data volume, dynamically generating an adaptive monitoring strategy through a reinforcement learning algorithm, executing the adaptive monitoring strategy and collecting feedback data in real time, and completing closed-loop iteration of the monitoring strategy through an online learning algorithm; through closed-loop control of multi-source perception-digital twinning-intelligent decision, high efficiency, intelligence and reliability of ring main unit monitoring are realized.

The invention provides a time sequence prediction method and system for a KAN enabling graph attention parallel loop network, and relates to the technical field of time sequence prediction, and the method comprises the steps: obtaining time sequence input data; inputting the time sequence input data into a feature extraction layer based on fast Fourier transform and discrete wavelet transform, and preprocessing the time sequence input data by the feature extraction layer to obtain spliced feature extraction data; inputting the spliced feature extraction data into a feature aggregation layer of graph attention based on KAN, and preprocessing the spliced feature extraction data by the feature aggregation layer to obtain feature aggregation data; and inputting the feature aggregation data into a time sequence prediction model of the parallel gating circulation unit network based on KAN and attention, and outputting time sequence prediction data by the time sequence prediction model. According to the method, the calculation complexity of time sequence prediction is reduced, and the time sequence prediction efficiency and accuracy are improved.

The invention discloses a feature attention and bilinear gating fused speech emotion recognition method and device, and the method comprises the following steps: 1, collecting an audio file, obtaining corresponding label information, generating audio waveform and time frequency representation data through preprocessing, and constructing an audio waveform mask and a time frequency mask to mark an effective information region; 2, constructing a dual-path feature extraction module which comprises a time-frequency feature extraction module and a pre-training acoustic feature coding module; wherein the time-frequency feature extraction module models emotion correlation through local convolution and a multi-dimensional attention mechanism, and performs global time sequence modeling based on a bidirectional gated loop network; the pre-training acoustic feature coding module extracts high-level speech representation with high expression ability for emotion distinguishing by using a pre-training model; and step 3, constructing a feature fusion module and an emotion classification module, and combining with a dual-path feature extraction module to form a speech emotion recognition model.

The invention discloses a power transmission line fault diagnosis and operation and maintenance scheduling method and system based on networking learning, and relates to the technical field of power transmission line fault diagnosis operation and maintenance scheduling. Related data is extracted to construct a high-risk equipment area and a visual high-risk area thermodynamic diagram, a visual risk grading diagram is constructed in combination with electrical quantity data, and meanwhile, an intelligent recognition storage network and a fault type classification recognition model are constructed in combination with a convolutional neural network-long and short-term memory network hybrid model; the model is optimized through networking learning and an attention mechanism, maintenance teams and resources are autonomously allocated in combination with an operation and maintenance management system, then autonomous optimization and closed-loop operation are achieved, full-process coverage of fault sensing, intelligent decision making and efficient response is achieved, the response time after a line fault occurs is remarkably shortened, and the maintenance efficiency is improved. And the fault handling and operation maintenance capabilities of the power grid system are comprehensively enhanced.

The invention discloses a power anomaly detection method and system, and the method comprises the steps: obtaining multi-source power data, carrying out the preprocessing of the multi-source power data, and obtaining a data stream after the time-space alignment; establishing an improved isolated forest model, and obtaining a sample anomaly score with gradient information; in combination with the abnormal score of the sample, capturing time sequence characteristics of the sample by adopting a bidirectional gating loop network, obtaining forward data characteristics and reverse data characteristics, and performing space-time attention calculation to obtain a space-time attention weight vector; acquiring an anomaly score according to the data features, counting the input feature data by adopting a sliding window to serve as an anomaly judgment initial threshold value, and dynamically adjusting the anomaly judgment initial threshold value in combination with a neural network correction item to obtain a dynamic correction threshold value; the initial threshold value and the dynamic correction threshold value are synthesized, a time-varying anomaly judgment threshold value is obtained, the three-level anomaly level of the operation state of the electric power system is formulated in combination with anomaly evaluation, and the detection precision can be improved.

The invention relates to an RNA-protein binding site discrimination method based on a convolutional block attention mechanism dense network, and belongs to the field of bioinformatics. Firstly, a public RNA-protein binding site data set is acquired, then, a feature space is constructed by adopting four feature extraction methods, and finally, a dense connection convolutional network improved based on a convolutional block attention mechanism is used for learning a long-term dependency relationship in a combination capture sequence of advanced local features, a bidirectional gating circulation network and a self-attention mechanism. The RNA-protein binding site is discriminatively analyzed by creatively using the dense network improved based on the convolutional block attention mechanism in combination with the idea of the bidirectional gating loop network and the self-attention mechanism, richer feature information can be learned and extracted, and then the model discrimination accuracy is improved.

The invention relates to the technical field of hydropower engineering, and discloses a single-core self-healing type looped network networking method and system, and the method comprises the steps: dividing a whole network into a plurality of sub looped networks; each sub-ring network operates independently; the light switch in each sub-ring network adopts an A / B end connection mode; reversely connecting the B end of the last measuring point to the A end of the initial measuring station by using an optical fiber to form a closed physical structure; configuring a protocol on the switch, and controlling a data flow path through the protocol; different sub-ring networks are connected through RJ45 ports, and a CAT5E shielded twisted pair or an Ethernet interface is used; when a certain node or link fails, the protocol automatically triggers a switching mechanism; the master station optical transceiver monitors the states of all nodes in real time and displays fault points through a network management interface. The system comprises a sub-ring network module, a control data flow module and a fault point display module. The problems that a traditional networking method of hydropower engineering is poor in reliability, and self-healing cannot be achieved after an intermediate node is disconnected are successfully solved.

The invention relates to the technical field of coal mine fully mechanized mining, in particular to a coal mine communication network system based on a time-sensitive network, and the system comprises a wired communication system, the backbone network of the wired communication system is composed of a plurality of TSN network switches which are in ring network topology connection, and the wired communication system is used for accessing coal mine equipment as a terminal to a TSN network; the wireless communication system is connected with the wired communication system, consists of wireless communication equipment compatible with a TSN protocol, and is used for accessing the coal mine equipment into a TSN network through wireless connection; a communication system based on a time sensitive network (TSN) is established, a real-time, determined and reliable communication network is provided for a coal mine production system, wired and wireless communication systems are adopted, the whole coal mine production control system is covered together, and the requirement of automatic control for data communication is met.

The invention relates to the technical field of network security of a power monitoring system, and discloses an attack event prediction method and system based on low-intensity abnormal mode data, and the method comprises the steps: intercepting abnormal data fragments from historical multi-source heterogeneous data based on the low-intensity abnormal mode data selected from the historical multi-source heterogeneous data; analyzing to obtain a target abnormal data fragment; training the gating loop network model based on target key time sequence abnormal features obtained by performing feature extraction on the target abnormal data fragments to obtain a future time sequence feature prediction model; performing structured processing on the low-intensity abnormal event to obtain a spatial correlation feature topological structure; obtaining future time sequence features based on the future time sequence feature prediction model; and obtaining an attack event prediction result based on an abnormal event evolution graph constructed by fusing the future time sequence feature and the spatial correlation feature topological structure. According to the method provided by the invention, accurate identification can be carried out in the preparation stage of the attack event.

The invention provides a rail transit power supply digital twin system and a construction method, and relates to the technical field of data processing, and the method comprises the steps: constructing an urban rail transit power supply system digital twin model, achieving the digital virtualization associated with a physical entity, completing the model rendering, and displaying the state of the entity through a virtual model; according to the entity state, operating parameters of a substation and key equipment of a contact network are collected through a sensor and a hardware device; the operation parameters are cached by the station-level monitoring device body through the double-ring network architecture and then scheduled to a cloud platform communication ring network node, a data transmission path is optimized based on a load balancing strategy, and the data are uploaded to a cloud platform in real time. The management level can be improved, the operation and maintenance cost can be reduced, and a foundation is laid for intelligent operation and maintenance of a rail transit power supply system.

The invention relates to the technical field of coal mining equipment, and discloses a mining roadway stability real-time monitoring and early warning system which comprises the following steps: S1, multi-source sensor network deployment, S2, data transmission and preprocessing, S3, stability index dynamic calculation, S4, fusion early warning model construction and S5, graded early warning triggering. Roadway surrounding rock deformation, stress, vibration and environmental parameters are collected in real time through a multi-source sensing network, efficient data transmission and preprocessing are achieved in combination with an industrial looped network, surrounding rock strain energy density, displacement convergence rate, support failure coefficient and other key indexes are dynamically calculated, a fusion early warning model is constructed based on machine learning, and the early warning accuracy is improved. According to the method, accurate evaluation of the stability level is achieved, grading alarm and emergency control are automatically triggered when a threshold value is reached, a'perception-analysis-decision-response 'closed loop is formed through the design, the real-time performance of roadway stability monitoring and the early warning reliability are remarkably improved, and accidents such as roof fall and wall caving are effectively prevented.

The invention discloses a double-layer multi-target planning method and system for a bilateral looped network current collection system of an offshore wind plant, and the method comprises the steps: inputting the data of the offshore wind plant into a double-layer planning model of the bilateral looped network current collection system, and enabling an outer-layer model to be used for constructing a plurality of two-dimensional populations composed of weights and the number of clusters, and inputting the two-dimensional populations into an inner-layer model; the inner-layer model is used for grouping the wind turbine generators under different two-dimensional populations by adopting a clustering algorithm, taking minimum cable investment cost and minimum network loss as objective functions and adding constraint conditions to construct a path optimization model so as to carry out path planning on the wind turbine generator groups, solving total cost and outputting the total cost to the outer-layer model; and ending iteration of the outer layer model when the total cost is converged, if not, continuing to output an optimal solution result, and otherwise, continuing to update the two-dimensional population. The invention aims to spontaneously cluster wind turbine generators through a heuristic algorithm and find the optimal path of the bilateral looped network of the wind turbine generators so as to realize accurate planning of the bilateral looped network current collection system of the offshore wind power plant.

The invention discloses a sequence recommendation model based on a bidirectional simplified gated loop network and linear attention. The sequence recommendation model comprises an embedded layer, a bidirectional simplified gated loop network module, an addition attention module, a hybrid expert module and a prediction layer, dynamic preference characteristics of a user are captured by using a bidirectional simplified gated loop network, long-term preference of the user is captured by using an addition attention mechanism, and flexibility and expression ability of the model are enhanced by using a hybrid expert model.

The invention is suitable for the technical field of network congestion control, and provides a power system RDMA network dynamic congestion control method based on a DCQCN, and the method comprises the steps: collecting the multi-source state data of a power system in real time; inputting the multi-source state data into a preset electric power feature fusion model, and generating a dynamic ECN parameter group containing an ECN marking threshold value, a marking probability and a rate regulation factor; executing dynamic ECN marking on the data packet based on the dynamic ECN parameter group, and embedding a lightweight HMAC signature based on an electric power PKI system into the electric power service message with the highest priority; when a congestion event is detected, discarding a data packet which does not pass the HMAC verification, generating a security alarm log, and migrating congestion link flow to a standby loop according to a service priority weight based on a power double-loop network topology structure; and issuing the congestion decision result to a physical equipment control layer through the electric power SDN controller. According to the invention, while the service quality of the key business is guaranteed, collaborative optimization of the communication network and the power physical system is realized.

The invention discloses a two-phase cold plate liquid cooling data center and a control method, and the two-phase cold plate liquid cooling data center comprises a plurality of two-phase cold plate liquid cooling cabinets, a secondary side waterway looped network, and a monitoring adjustment module. A plurality of nodes to be cooled, two-phase cold plates in contact connection with the nodes to be cooled, and an electro-hydraulic integrated distribution module, a liquid cooling heat exchange module and a system detection module which are connected with the two-phase cold plates of the nodes are arranged in the liquid cooling cabinet; the secondary side waterway looped network comprises a water supply looped network and a water return looped network which are arranged at the bottom of the data center, low-temperature fluid of the water supply looped network is connected with the liquid cooling heat exchange module through a water supply branch and performs heat exchange to become high-temperature fluid, and the high-temperature fluid is output to the water return looped network through a water return branch; the monitoring and adjusting module obtains circulating system parameters, and / or power consumption information, and / or cooling capacity information of each liquid cooling cabinet, and controls the flow of the water supply branch and the water return branch corresponding to each liquid cooling cabinet.

The invention provides a modeling prediction method and device based on a dynamic knowledge graph, equipment and a medium, and the method comprises the steps: S1, taking the data of a fishing boat and a sea area as nodes, representing the migration and interaction between the fishing boat and the sea area through edges, and introducing a time attribute to represent a dynamic characteristic, thereby constructing the dynamic knowledge graph of the fishing boat and the sea area; and S2, constructing a prediction model in combination with the gated loop network GRU and the relational graph attention network rGAT, and processing updating of node representation and graph-level representation in the dynamic knowledge graph by using the constructed prediction model. The method has the advantages that the dynamic change of the fishing boat and the sea area can be accurately modeled in the time dimension, so that the migration mode and the group activity behavior of the fishing boat in the sea area can be accurately captured; meanwhile, on the basis of updated node representation and graph level representation, better fishing vessel migration can be analyzed and predicted, early warning can be performed on abnormal fishing behaviors, and the like, so that intelligent decision support can be well provided.

The sulfur hexafluoride insulation ring main unit comprises a ring main unit power distribution platform, a ring main unit information base, a ring main unit working condition unit, a power distribution interference unit, a ring network distribution unit, a power regulation and control unit and a power distribution management unit. According to the method, power distribution stability performance analysis is carried out from two points of working characteristics and working parameters of the target ring main unit, and analysis is carried out in combination with the sulfur hexafluoride gas interference degree of the target ring main unit, so that the working stability and continuity of the target ring main unit are improved; ring network distribution working performance risk analysis is carried out on the working condition information of the target ring main unit in an information feedback mode, so that the safety and reliability of the power distribution performance of the target ring main unit are improved, and electric power adjustment demand evaluation analysis is carried out on the electric energy management data. Therefore, the load distribution scheme of the target ring main unit is adjusted according to the information feedback condition, and the reasonable distribution of power resources and the operation efficiency of a power grid are improved.

This invention provides a method, apparatus, electronic device, and readable storage medium for redundancy control in ring network communication, relating to the field of vehicle-mounted communication. The method includes: controlling nodes in the vehicle-mounted ring network system to detect abnormal Ethernet link states and broadcast these states via CAN communication; a master control node can determine whether to switch redundant links based on the relevant link abnormal states of each node; if switching is required, a switching control message is broadcast via CAN communication; and each node can adjust its port settings based on the switching control message. By implementing Ethernet link detection and redundant link switching control based on Ethernet communication itself, and by integrating the functions of determining whether to switch links based on abnormal link states and broadcasting switching control messages into the master control node, the requirements for nodes other than the master control node are reduced. This lowers the hardware resource requirements of the vehicle-mounted ring network communication redundancy control scheme compared to existing solutions, making it simpler, easier to implement, and more usable.

The invention provides a long-term traffic flow prediction method based on a dynamic space-time diagram neural network, and the method comprises the following steps: inputting historical traffic data and a static adjacency matrix, and generating a dynamic diagram adjacency matrix through a diffusion probability model, which comprises the steps: carrying out the embedded representation learning of the static adjacency matrix and traffic features through a diagram convolution network, generating an initial dynamic adjacency matrix; iteratively optimizing the initial adjacency matrix through multi-step noise addition and conditional denoising, and generating a dynamic adjacency matrix reflecting the time-varying characteristics of the road network; based on the static adjacency matrix and the dynamic adjacency matrix, extracting spatial dependence characteristics of local and remote nodes through multi-hop diffusion convolution and bidirectional diffusion convolution; performing parallel processing on the time sequence through a simplified gating loop network SimGRU to eliminate serial dependence on a historical hidden state and retain long-term trend and periodic characteristics; and fusing the space and time features, and outputting a traffic flow prediction result of a future time period.

The invention discloses an electric power system operation state detection analysis method and system. The method comprises the following steps: collecting time sequence data of an electric power system in real time; performing feature extraction and mining processing on the time series data by using an LSTM loop network to obtain a corresponding first feature vector or advanced representation of a data feature; inputting the advanced representation of the first feature vector or the data feature into a fault diagnosis model, so that the fault diagnosis model performs multi-scale feature extraction and fusion and classification processing on the input data and outputs the classification probability of the fan fault type; and adopting a reinforcement learning algorithm to adaptively adjust the structure and parameters of the fault diagnosis model based on the classification probability of the fan fault type output by the fault diagnosis model and the real-time running state of the fan. By adopting the embodiment of the invention, the problems of gradient explosion and disappearance when the sequence data is processed by the traditional neural network can be avoided, and the efficient detection and analysis of the operation state of the power system can be realized.

The invention discloses a gas leakage monitoring system for an environment-friendly ring main unit, and belongs to the technical field of environment-friendly ring main unit monitoring. The system comprises a real-time acquisition looped network supervision platform, and a joint data real-time acquisition and storage module, an external environment gas dynamic analysis module, an internal environment gas risk analysis module and an emergency regulation and control decision processing module which are in communication connection with the real-time acquisition looped network supervision platform; multi-source data fusion monitoring of internal and external environments of the ring main unit is realized through the joint data real-time acquisition and storage module, the one-sidedness problem of single parameter monitoring is solved, and the leakage risk identification accuracy is improved; second-level data processing and grading risk early warning are realized by means of a real-time algorithm, and the fault response time is shortened; a grading emergency response mechanism is established based on the comprehensive risk index, different risks are precisely handled, and the operation and maintenance cost is reduced while the equipment safety is guaranteed.

The invention provides a method for tracing a multi-power-source power supply path in a power system, and the method comprises the steps: obtaining topological data of a power transmission line network diagram, a transformer substation primary wiring diagram, a distribution network looped network diagram and a user low-voltage side wiring diagram in a specified power network, and carrying out the multi-source heterogeneous fusion, so as to construct an integrated transmission, transformation and distribution network topology, corresponding switch state data and mobile power supply equipment operation data are configured in the transmission, transformation and distribution integrated network topology so as to obtain a digital power grid operation model; constructing a node admittance matrix by taking each piece of equipment as a node based on a topological relation between lines in the digital power grid operation model so as to determine lines belonging to a loop, a branch and a cut set in the digital power grid operation model; and determining a multi-power-supply power supply path according to the circuits of the loop, the branches and the cut set in combination with the switch state information and the mobile power supply equipment operation information. According to the invention, the transmission, transformation and distribution integrated network can be comprehensively covered, and the problems of low efficiency, high use cost and the like can be solved.

The invention discloses a power transmission line protection method suitable for a boundless flexible direct-current looped network. The power transmission line protection method specifically comprises the following steps: step 1, calculating a line mode voltage forward wave; step 2, performing minimum-maximum normalization on the line mode voltage forward wave in the step 1; step 3, setting a sliding time window T according to the normalized line mode voltage forward wave obtained in the step 2, and searching a voltage drop point meeting a condition for the normalized forward wave by using the sliding time window; and 4, comparing the voltage drop point with a preset intra-region priority action threshold value kset1 and an extra-region priority action threshold value kset2, and identifying and judging an intra-region fault and an extra-region fault according to a comparison result. By adopting the method provided by the invention, the protection of the borderless flexible direct-current looped network power transmission line can be realized.

The invention discloses a CAN network optical fiber communication method supporting ring network redundancy, a ring optical fiber communication network is constructed based on a plurality of CAN network optical fiber repeaters, the ring optical fiber communication network comprises two redundancy transmission lines, in a ring network mode, all CAN network optical fiber repeaters are divided into a control node and a plurality of common nodes, and the control node is connected with the common nodes. The control node works according to a set control node working rule, and the common node works according to a set common node working rule, so that when the current transmission line works normally, data transmission is carried out on the current transmission line in a one-way transmission mode, and when the current transmission line is abnormal, data transmission is carried out on the current transmission line in a one-way transmission mode. The CAN network communication reliability is improved by constructing the redundant optical fiber ring network, and meanwhile the problems that after a traditional CAN network-to-optical fiber repeater forms the redundant optical fiber ring network, CAN data can be circularly forwarded in the optical fiber ring network in an infinite mode, network storm is generated, and network paralysis is caused are solved.

The invention discloses a multi-sound event detection method based on a multilayer neural network, and relates to the field of sound event detection. The invention provides a weak label multi-sound event detection model based on a fusion empirical mode decomposition algorithm, a capsule network, an attention mechanism and a bidirectional gating loop network. In the model, firstly, an AMM-EMD algorithm is used for preprocessing, sound event feature information is enriched, and a parallel convolution structure is introduced to extract advanced feature information; further processing the advanced features by using the strong generalization ability of the capsule network and the ability of capturing global dependence by an attention mechanism; and meanwhile, the BiGRU module is used for acquiring context information. According to the model provided by the invention, contrast ablation experiments are carried out on different data sets, the sound event detection effect is evaluated by utilizing F1 and ER, the model provided by the invention is improved compared with other baseline models, and F1 and ER are improved.

A short-term scheduling rule extraction method for a water-optical complementary system comprises the following steps: correcting an existing business prediction result by adopting a probability prediction model based on historical observation data and business prediction data of runoff and optical power; generating multiple groups of future possible runoff and optical power scenes by adopting a data sampling method based on a commercial prediction result corrected by the probability prediction model in the step 1; constructing a water-light complementary peak regulation optimization scheduling model; 3, constructing a Bayesian gated loop network which can consider the commercial prediction result and the model parameter uncertainty, introducing a probability recalibration mechanism which can effectively relieve the uncertainty estimation deviation of the Bayesian gated loop network, taking the optimal scheduling solution set obtained in the step 3 as the basis, extracting a complementary system scheduling rule which considers the multiple uncertainties, and finally obtaining the optimal scheduling solution set. And the peak regulation scheduling of the water-light complementary system is guided by using a complementary system scheduling rule. The comprehensive benefits of the multi-energy complementary system can be improved.

Techniques are described for providing network provisioning by a network management system (NMS) based on fingerprint information determined by a network access control (NAC) system. An example method includes receiving, by the NAC system, a network access request for a client device to access an enterprise network; obtaining, by the NAC system, fingerprint information of the client device associated with the network access request, wherein the fingerprinting information comprises information specifying one or more attributes associated with the client device; authenticating, by the NAC system, the client device to access the enterprise network; sending, by the NAC system and to the NMS, the fingerprint information of the client device; and provisioning, by the NMS, one or more network resources associated with the client device based on the fingerprint information of the client device.

The invention relates to the field of power system carbon emission flow analysis, in particular to a closed-loop power grid-oriented electric energy tracking and power flow tracing coupled carbon emission flow analysis method, which is based on power flow distribution of an actual power grid and comprehensively utilizes an electric energy tracking method and a power flow tracing method. According to the invention, fine allocation and full-link physical traceability of nodes, branches and loads in a closed-loop power grid structure by carbon emission are realized. By establishing a power flow distribution matrix, a unit injection matrix, a load distribution matrix and a carbon emission intensity vector, the carbon potential of each node, the branch carbon flow rate and the load carbon flow rate are calculated in a recursive manner, an electric energy tracking method and power flow traceability are coupled, and node allocation is iteratively optimized, so that the actual flow path and spatial distribution characteristics of the carbon flow in a power grid are disclosed. The method can adapt to scenes such as complex multi-source access and a looped network structure, the scientificity and fairness of closed-loop power grid carbon emission management are improved, and theoretical and method support is provided for development of low-carbon scheduling, carbon accounting and low-carbon economy.

The invention relates to the technical field of ring network equipment management, and discloses a production, supply and marketing integrated automatic management system for ring network equipment and a data processing method.The production, supply and marketing integrated automatic management system for the ring network equipment comprises a data collection module used for collecting data related to production, purchase, inventory and sales of the ring network equipment in real time; the data preprocessing module is used for performing cleaning, standardization, missing value filling and abnormal value detection on the acquired data to ensure the data quality; the reinforcement learning module is used for dynamically optimizing a production plan, a purchase plan and inventory management by utilizing a reinforcement learning algorithm based on the collected real-time data and historical data; and a multi-objective optimization module. According to the system, strategy adjustment is carried out through reinforcement learning, target conflicts are solved through multi-target optimization, and a feedback adjustment mechanism is provided through a nonlinear dynamic model. And through real-time data updating and report generation, high-efficiency collaboration of links of production, purchasing and inventory is ensured, and the resource allocation and operation efficiency is improved.

The invention relates to a rail transit station digital monitoring operation system comprising a station layer comprising a station internet of things network, a station digital operation platform and station digital application software, the station internet of things network takes a multifunctional controller and an internet of things looped network node as a core to construct a station equipment access network, and the station equipment access network is connected with the station digital operation platform; the station digital operation platform realizes station field data acquisition and equipment control, the station digital operation platform takes a station server as a core and provides pooling ICT resource management and virtual host service, and the station digital application software is loaded on the station digital operation platform; the field layer comprises a monitoring object and an intelligent terminal; wherein the station layer constructs a station-level star local area network by taking a station service switch as a center, and the station service switch realizes communication connection with the line network production cloud platform through a backbone transmission network. The system can quickly respond to field changes, and the real-time performance and flexibility of data acquisition are improved.