667 results about "Loop network" patented technology

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 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 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 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 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 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.

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.

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 discloses a multi-modal data source probe state monitoring and failover method and system, and relates to the field of Internet, the method comprises the following steps: respectively deploying data acquisition probes at a wireless access network node and a core network UPF node to obtain preprocessed network data of the wireless access network node and preprocessed network data of the core network UPF; obtaining abnormal risk prediction information through an AI monitoring model; adjusting parameter configuration of a wireless side and a core network UPF side according to the abnormal risk prediction information; and based on the adjusted parameter configuration of the wireless side and the core network UPF side, feeding back to the service awareness module so as to realize failover. According to the method, a cross-domain and cross-layer unified probe monitoring system is realized, a dynamic, explainable and closed-loop network fault analysis and regulation and control mechanism is constructed through AI technologies such as a graph neural network and reinforcement learning, and the network state sensing precision, the response timeliness and the optimization intelligent level can be remarkably improved.

A communication self-inspection and emergency shutdown method for an offshore wind turbine generator comprises the following steps: acquiring heartbeat signal data between a fan main control unit and a looped network, analyzing frequency and intensity, and judging whether a signal is normal or not; if the signal intensity is low, the interference detection module is started to identify external interference or equipment faults, and a communication interruption timer is started accordingly. And once the communication interruption exceeds the preset duration, a fault is judged and a confirmation mechanism is triggered, and after the fault is confirmed, the main control unit sends an emergency shutdown instruction. And after shutdown, acquiring running state data of the fan to judge whether the fan is completely stopped, recording shutdown time and state change, generating a fault report covering fault types, interruption, shutdown time and fan state change, and uploading the fault report to a looped network monitoring system. According to the method, accurate self-inspection of the communication state of the offshore wind turbine generator is achieved, faults can be found in time, emergency shutdown measures can be taken, detailed data are provided for operation and maintenance personnel, and safe and stable operation of the wind turbine generator is guaranteed.

The invention discloses a broadband digital pre-distortion method and system based on an external attention mechanism, and the method comprises the steps: constructing an external attention intensive gated loop network model which comprises a feature extraction layer, an input layer, a GRU layer, a hidden full-connection layer, an external attention layer and an output full-connection layer; based on the baseband IQ signal after feature extraction and preprocessing and an IQ signal output by a power amplifier, constructing input data of an external attention intensive gated loop network model; training an external attention intensive gating loop network model; and inputting data to the trained external attention intensive gated loop network model to obtain a predicted value output by the network model to the power amplifier. The invention provides a novel broadband digital pre-distortion architecture of an external attention intensive gated loop network. An external attention mechanism is combined with an intensive connection layer, so that the network better compensates the nonlinear characteristic of a power amplifier.

The invention discloses a planning method and system for a high-reliability current collection system of an offshore floating type wind power plant, and the method comprises the steps: applying a looped network topology in the current collection system of the offshore floating type wind power plant to improve the operation reliability, and enabling the looped network topology to be an electrical connection mode that a plurality of fans are connected into a closed loop through cables; wherein the energy can flow bidirectionally, and performing two-stage dynamic planning for the current collection system: firstly executing dynamic site selection and topology planning of an offshore substation, then executing optimization and topology planning of substations by fans, and outputting the optimal substation position, fan distribution set and wind power plant planning total cost. The method aims at rapidly achieving dynamic multi-target planning of the offshore floating type wind power plant so as to ensure the operation reliability of the offshore floating type wind power plant and the economical efficiency of the whole life cycle cost, precise optimization of substation site selection is achieved, and the optimal configuration of the membership relation of fans and substations is ensured.

The invention provides a cooling system, a control method of the cooling system and a data center, and relates to the technical field of server heat dissipation. The cooling system comprises at least one liquid cooling subsystem and at least one air cooling subsystem, the liquid cooling subsystem is connected with a first interface end of the cold source system looped network, the air cooling subsystem is connected with a second interface end of the cold source system looped network, a third interface end of the cold source system looped network is connected with the cold source system, the first interface end is provided with a first electric valve, and the second interface end is provided with a second electric valve. The first interface end is provided with a first electrically operated valve, the second interface end is provided with a second electrically operated valve, the first electrically operated valve is configured to obtain current flow data of the liquid cooling subsystem and adjust the opening degree of the first interface end according to the current flow data of the liquid cooling subsystem, and the second electrically operated valve is configured to obtain current flow data of the air cooling subsystem and adjust the opening degree of the first interface end according to the current flow data of the air cooling subsystem. And the opening degree of the second interface end is adjusted according to the current flow data of the air cooling subsystem. The problems that an existing cooling system is complex in structure, high in water resource consumption and poor in control precision are solved.

The utility model discloses a coal mine fire safety intelligent early warning control module, which belongs to the technical field of coal mine fire safety early warning and comprises a temperature monitoring unit, a gas monitoring unit, a dust monitoring unit, an underground looped network switch, a ground control device and an alarm device. The temperature-sensing optical fibers comprise a distributed temperature-sensing optical fiber host and distributed temperature-sensing optical fibers, and the distributed temperature-sensing optical fibers are laid in the transmission belt and the goaf and can monitor the temperatures of different areas in the mine in real time; the gas monitoring unit comprises a toxic gas monitoring subunit and an inflammable gas monitoring subunit, and can monitor the content of various gases in the mine; according to the coal mine dust monitoring system, the safety of coal mine operation is improved, the safety of workers and equipment is guaranteed, and smooth proceeding of coal mine production and operation is effectively promoted.

Provided are a method and apparatus for transmitting data, relating to the technical field of communications. The method comprises: a first node sends a fault detection sequence to a second node, the sequence comprising a plurality of spaced apart pieces of fault detection information. The first node receives fault notification information sent by the second node, the fault notification information being determined by the second node on the basis of the number of pieces of fault detection information received within a preset window. The first node sends a first buffered packet and a second user packet on the basis of a switching path, the first buffered packet being obtained by buffering a first user packet sent to the second node within the preset window, and the packet sequence number of the second user packet being greater than the packet sequence number of the first user packet. In this way, since the first node sends the buffered packet within the preset window to the second node on the basis of a switching path, the first user packet sent by the first node to the second node within a preset window in which a link fault has occurred can reach the second node, thereby resolving the problem of packet loss in ring network switching technology.

The invention relates to the technical field of coal mine dynamic mine pressure monitoring and intelligent early warning and prediction, and particularly discloses a coal mine dynamic mine pressure monitoring and intelligent early warning and prediction system, which comprises a monitoring terminal layer, which comprises a plurality of mine pressure monitoring sensors, and the mine pressure monitoring sensors are respectively arranged in different monitoring areas of an underground coal mine. The mine pressure parameter acquisition module is used for acquiring mine pressure parameters of a corresponding area in real time, and the mine pressure parameters comprise roof subsidence, support working resistance, coal seam stress and surrounding rock deformation; and the data transmission layer is connected with the monitoring terminal layer and comprises an underground ring network switch and a ground industrial Ethernet. According to the dynamic mine pressure monitoring and intelligent early warning prediction system for the coal mine, the data reliability is ensured in combination with sensor state monitoring, the mine pressure trend in the next 24 hours is accurately predicted by adopting an improved LSTM neural network and an AWA-GC optimization algorithm, and the system adapts to nonlinear changes under complex geological conditions.

The invention relates to the technical field of artificial intelligence and social computing, and discloses a user-based digital duplicate interaction system and privacy protection method.The user-based digital duplicate interaction system comprises a dynamic personality modeling engine used for extracting behavior features, emotion features and personality features of a user through a multi-source heterogeneous data fusion framework and generating a five-dimensional personality feature matrix; the emotion migration training module adopts a double-loop adversarial training framework, comprises a main loop network and an adversarial network, and is used for generating an emotion response mode; and the multi-modal interaction control module can realize a three-layer decision architecture, including semantic understanding, emotion calculation and decision generation, and is used for processing interaction between the user and the digital clones. Through adoption of a differential privacy technology, privacy protection is carried out on interaction data between a user and a digital avatar. The differential privacy ensures that the influence of single user data on a statistical result is limited within a certain range by adding a proper amount of noise in the data, so that the privacy of the user is protected.

The invention belongs to the field of underground equipment monitoring, particularly relates to a coal mine underground equipment real-time monitoring method based on a red mine system, and aims to solve the technical problems of low information transmission speed and low accuracy of manual monitoring equipment. The method comprises the following steps: acquiring underground equipment information in real time through a sensor and an adaptive module of a red mine system; the Modbus TCP protocol data stream is uploaded and converted into a Modbus TCP protocol data stream; the data stream is transmitted to a centralized management and control platform through an underground looped network; on the gateway side, edge side preprocessing is carried out on the collected data stream; and the centralized management and control platform receives the processed data flow, carries out platform-level data cleaning and fusion processing, and carries out real-time state monitoring, health degree evaluation and fault early warning on the equipment. According to the invention, the centralized management and control platform is established by adopting the Hong mine system to monitor and manage the underground equipment in a unified manner, so that the intelligent level, the information transmission speed and the accuracy of a coal mine are improved, and real-time monitoring, fault early warning and maintenance management can be better carried out on the underground equipment.

The invention relates to the technical field of the intelligent power grid industry, in particular to a power distribution and utilization ring network fault intelligent troubleshooting method and system. According to the method, a looped network situation primitive is generated through multi-source heterogeneous data fusion. The primitives are input into synchronous compression transformation to extract transient fault initial features, and the transient fault initial features are mapped into fault propagation path features through a graph convolutional network considering topological structure constraints; then, inputting the features into a space-time correlation prediction model, integrating defect type judgment and trend pre-judgment features, and generating a fault diagnosis evidence set; and starting an investigation mechanism based on the evidence set to determine an interval to be investigated, and performing closed-loop verification by constructing a fusion judgment matrix based on multi-modal physical sensing information to realize physical space locking of a fault defect point. According to the method, deep learning diagnosis and multi-mode positioning are integrated, and the detection precision and the positioning efficiency of looped network defects and the identification capability of transient hidden faults are effectively improved.

The invention relates to a looped network power supply intelligent monitoring method and system based on multi-source signal comparison, in particular to the field of looped network power supply intelligent monitoring, high-precision synchronous acquisition of electromagnetic-temperature-vibration three-source signals is achieved through quantum tunneling coupling, the contribution degree of weak correlation signals is remarkably improved in combination with a dynamic graph neural network fusion technology, and the real-time performance of the looped network power supply intelligent monitoring system is improved. A strong-correlation electromechanical thermal coupling representation is constructed; a quantum annealing processor is used for executing Hamiltonian optimization to complete fault tracing and harmonic risk quantification, a communication channel is dynamically reconstructed based on a metasurface transmission equation, spectrum resources are adaptively spread when harmonic threats rise, undistorted transmission of higher harmonic monitoring instructions is ensured, and finally closed-loop feedback control driven by risk indexes is formed. The step breakthrough of synchronization precision, positioning capability and response speed is realized, and the resonance risk of the power system is effectively inhibited.

The invention discloses a power transmission line unmanned aerial vehicle communication networking system based on an optical fiber ring network, and relates to the field of power transmission line communication networks. The plurality of integrated base stations form an optical fiber ring network through the OPGW optical cable and the LTE MESH ad hoc network; the unmanned aerial vehicle executing the power transmission line inspection task and the corresponding fixed airport are accessed to an information intranet through an optical fiber ring network; wherein the integrated base station comprises an OPGW integrated base station and a MESH integrated base station; the OPGW integrated base station is deployed at a node with an optical fiber splice closure on a power transmission line tower, and is accessed to the information intranet in a wired connection mode; and the MESH integrated base station is deployed at a node, without an optical fiber splice tray, on a power transmission line tower, and is accessed to the OPGW integrated base station in a wireless connection mode. The power transmission line unmanned aerial vehicle communication networking system based on the optical fiber ring network can guarantee timely, safe and efficient large-scale data transmission.

The invention relates to a mining remote leakage protection calibration system and method. The system comprises a software platform, a looped network switch, a wireless substation, a multi-stage protector and an adjustable linear leakage test resistance box. And the software platform is connected with the wireless substation through the ring network switch, broadcasts a synchronous timestamp and controls the resistance adjustment of the resistance box so as to measure the action current and the tripping response time of the protector. And when the measurement result exceeds a preset boundary value, the software platform starts a self-calibration program and dynamically corrects the set value of the protector. Through a linear resistance adjusting mechanism of the adjustable linear leakage test resistance box, the action current value of the protector is accurately obtained, automatic calibration during hardware aging is realized, manual power-off maintenance is not needed, and the reliability of a power supply system is improved. Meanwhile, a high-precision clock synchronization technology is adopted, tripping response time is accurately obtained and automatically calibrated to a safety response range, and safety risks are avoided.