5011 results about "Transmission line" patented technology

The invention discloses a power transmission line connection fitting fault detection method based on acoustics and electromagnetic induction, and belongs to the technical field of intelligent inspection and nondestructive detection of power transmission lines. A multi-mode sensor is carried by an unmanned aerial vehicle, ultrasonic wave, vibration, images and power frequency electromagnetic field data are synchronously collected, and a multi-dimensional original data set is constructed. Signal quality is improved by adopting wavelet noise reduction, beam forming and sound image fusion technologies, and flight vibration and electromagnetic interference are effectively suppressed by combining an adaptive filtering algorithm and a physical shielding structure. And acoustic, image and electromagnetic characteristics are extracted and normalized and fused, a dynamic threshold reference library is established, and intelligent grading discrimination of abnormity, defects and faults is realized through multi-stage early warning logic. A detection result automatically generates a report and is mapped to a three-dimensional line model, and operation and maintenance system linkage is supported. According to the method, synchronous identification of surface and internal defects is realized, the anti-interference capability is high, the detection accuracy is high, the inspection efficiency and safety are remarkably improved, and the method is suitable for intelligent operation and maintenance of the high-voltage transmission line.

The invention belongs to the technical field of power system safety, and discloses a high-voltage transmission line safety assessment method based on an intelligent algorithm. A database is constructed through multi-source data acquisition and fusion processing, a deep learning model is utilized to analyze relevance between meteorological conditions and an icing formation mechanism, an evolution law of the meteorological conditions and the icing formation mechanism is mined, multi-scene simulation and sensitivity analysis are performed in combination with physical characteristics and landform information of a power transmission line, and a line icing risk partition assessment system is established. According to the method, a critical value of line mechanical strength and icing thickness is calculated based on a mechanical model, a grading early warning threshold standard is formulated, a time sequence prediction algorithm and a risk propagation model are constructed to realize intelligent early warning, and a prevention and control strategy is formed through optimal configuration of anti-icing resources and self-adaptive generation of a deicing scheme. According to the invention, the capability of resisting icing disasters of the high-voltage transmission line is effectively improved, and the safe and stable operation level of a power grid is enhanced.

The invention discloses a power transmission line inspection unmanned aerial vehicle obstacle intelligent detection and obstacle avoidance method. The method comprises the steps that multi-source sensing data is acquired, and alignment is completed through calibration and timestamp matching; heterogeneous data preprocessing and feature enhancement; constructing a high-precision environment fusing a geometric structure and a semantic tag, mapping a two-dimensional target detection result output by the recognition network to a three-dimensional coordinate system through spatial transformation, and fusing the two-dimensional target detection result with a point cloud structure to construct a semantic occupation grid map; performing preliminary route planning according to a preset power grid topological structure and task coverage requirements, and generating a barrier-free flight path covering the whole inspection area; reinforcing learning of a dynamic obstacle avoidance strategy; track dynamic reconstruction and energy consumption optimization scheduling are carried out; the technical problems that an existing technical system has defects in the aspects of obstacle recognition accuracy, complex environment adaptability, data fusion capacity and obstacle avoidance strategy intelligence, and the requirements for high-reliability, low-energy-consumption and high-efficiency unmanned aerial vehicle power transmission line inspection are difficult to meet are solved.

The invention provides an unmanned aerial vehicle high-precision autonomous navigation method and system based on GNSS and IMU deep fusion, and aims to solve the problems of insufficient navigation precision and poor robustness in a complex electromagnetic environment. Through a tight coupling architecture, GNSS original observed quantity and IMU pre-integration results are jointly modeled in an observation layer, and multi-source constraints are introduced in combination with factor graph optimization, so that the positioning precision and consistency in weak signal and shielding scenes are remarkably improved. For abnormal observation, a robust kernel function is adopted to dynamically adjust the weight, and the influence of electromagnetic interference and a multipath effect is effectively inhibited. Meanwhile, navigation calculation and model prediction control MPC are combined, and sub-meter hovering and high-precision trajectory tracking are achieved. According to the method, in high-voltage transmission line inspection, dependence on a high-cost sensor is reduced, the engineering application value is high, the method can be widely applied to the fields of electric power inspection, disaster emergency, infrastructure monitoring and the like, and reliable technical support is provided for high-precision autonomous navigation of the unmanned aerial vehicle in a complex environment.

The invention discloses an efficient denoising power transmission line point cloud processing method and system, and relates to the technical field of three-dimensional point cloud data processing and intelligent analysis, and the method comprises the steps: carrying out the segmentation processing of original power transmission line point cloud data, dividing the point cloud into power equipment parts, carrying out the local density analysis of the segmented point cloud, and generating a density distribution diagram; based on the density distribution diagram and the local geometric features of the point clouds, multi-stage filtering is carried out, denoising model parameters are optimized, a statistical filtering method is combined to remove noise points, curvature features and normal vector consistency features of the point clouds are extracted, classification feature vectors are generated, and based on the classification feature vectors, fine classification is carried out on the point clouds. Through data preprocessing, multi-stage filtering, self-supervised learning, feature extraction and refined classification, the technical defects of insufficient multi-modal feature fusion efficiency and poor equipment feature adaptability in power transmission line point cloud denoising are effectively solved.

The invention belongs to the technical field of fault detection, and relates to a high-voltage transmission line fault detection and identification method. The method comprises the following steps of: dividing candidate fault sections according to a topological structure by synchronously acquiring steady-state and transient-state traveling wave signals of key nodes of a power grid; identifying a fault by using a multi-feature fusion criterion and extracting traveling wave features; executing mode recognition-based steady-state section judgment and multi-terminal ranging and polarity verification-based transient positioning in parallel to generate two types of positioning result sets; and finally, carrying out intelligent fusion judgment on the two types of results under topological constraints. According to the method, the problems that a traditional single-point signal analysis method cannot determine a fault section, lacks space positioning capability and is insufficient in adaptability are solved, section-level accurate positioning of the fault is realized, and the positioning accuracy, robustness and fault processing efficiency are remarkably improved.

The invention discloses a ground wire multi-machine synchronous traction pay-off scheduling method and system, relates to the technical field of high-voltage transmission line construction, and is used for solving the problem of insufficient multi-machine real-time tension synchronization precision in the existing method. A tension sensor and a speed sensor which are arranged on each traction device collect a tension value, a traction speed value and position coordinate data of a ground wire in real time, and transmit the data to a central scheduling controller; establishing a multi-machine unified time synchronization reference and tension reference model, calibrating a timestamp through a GPS module, and initializing a threshold value based on historical parameters; comparing tension values one by one to calculate a deviation vector sequence; activating a compensation algorithm to execute a speed fine adjustment and tension compensation instruction; iteratively calculating the feedback adjustment value until the deviation is stable; and integrating the synchronous data to the overall paying-off scheduling model to dynamically update the path planning and speed curve. According to the method and the system, the problem of insufficient multi-machine real-time tension synchronization precision in the prior art is effectively solved.

The invention discloses a linear polarization in-band scattering reconfigurable radiation time-sharing regulation array antenna, which belongs to the technical field of antenna engineering, and comprises an antenna unit and a one-to-sixteen power divider, the antenna unit is connected with the one-to-sixteen power divider, and the antenna unit is connected with the one-to-sixteen power divider. The antenna unit comprises a first radiation patch, a first dielectric substrate, a second radiation patch, a second dielectric substrate, a feed coupling branch knot, a feed metal column, a third dielectric substrate, a perforated metal floor and a fourth dielectric substrate, and a first PIN tube, a second PIN tube, a feed transmission line, a direct current bias device, a reflective phase shifter and a low-pass filter are arranged at the bottom of the fourth dielectric substrate; the direct-current bias device is connected with the reflection type phase shifter and the feed transmission line through the low-pass filter, and the feed metal column sequentially penetrates through the third dielectric substrate, the perforated metal floor and the fourth dielectric substrate and is connected with the feed transmission line. According to the invention, the normal radiation efficiency of the antenna can be ensured, and the reconfigurability of antenna scattering can be effectively improved.

The invention relates to the technical field of electromagnetic environment influence prediction, and particularly discloses an alternating current transmission line electromagnetic environment influence prediction and evaluation method. The method comprises the following steps: constructing a spatial region model; modeling and predicting the electric field intensity based on the spatial region model; based on the spatial region model, modeling and predicting magnetic induction intensity; determining a plurality of basic typical working conditions, and performing electromagnetic influence classification prediction; performing limit value comparison and space risk evaluation on the electric field distribution prediction data, the magnetic induction distribution prediction data and the classification prediction data; and carrying out prediction centralized induction and report output. According to the method, the phase balance charge distribution network is established, the mirror image charge is dynamically calibrated by combining actual measurement data of the ground sensor, and the adaptive charge safety envelope line is generated based on the road section type, so that the problem of electric field prediction deviation caused by neglecting space phase difference in the prior art is solved.

The invention discloses a power transmission line fault positioning method and system based on ground potential end transient information perception, and relates to the technical field of line fault positioning, and the method comprises the steps: obtaining a voltage traveling wave signal and a current abrupt change rate signal detected by a trunk line and branch node sensor array in a power transmission line; processing the voltage traveling wave signal and the current abrupt change rate signal to obtain traveling wave components and polarity-amplitude calibration results of different modes; according to traveling wave components and polarity-amplitude calibration results of different modes, in combination with topological parameters of the power transmission line and spatial distribution of a ground potential end sensor, adjusting edge weights or connection relationships in the graph structure to obtain a composite feature matrix and an edge weight matrix of each node; and inputting the composite feature matrix and the edge weight matrix of each node into a graph neural network positioning model, and outputting a power transmission line fault positioning result. According to the invention, through ground potential end information fusion and the dynamic graph neural network, accurate fault positioning under a complex power transmission topology is realized.

The invention relates to the technical field of large-scale power grid safety guarantee, in particular to an optimal decentralized coordination control method for a micro-grid in a new energy power system, which comprises the following steps of: performing virtual disconnection verification on key power transmission lines one by one based on online acquired power grid topology and real-time line power flow data, and establishing a whole-grid cascade risk quantitative index. According to the method, power grid topology and line power flow data are acquired in real time, virtual disconnection verification of key power transmission lines is implemented one by one, simulation and analysis of a line fault scene are realized, whole-network cascade risk quantitative indexes are established on the basis, and an initial fault line with the maximum threat to system safety is identified by comparing the indexes with a safety threshold value in real time, so that the safety of the system is improved. A fragile line risk contribution list is generated, and high-risk lines are positioned and prevented in advance; and further directly calculating the power adjustment amount required by the micro-grid group according to the risk contribution list, and forming a forward-looking tide dredging instruction set.

The invention relates to the technical field of vibration monitoring, in particular to a self-powered transmission line fitting aeolian vibration damage diagnosis system and method, and the system comprises a sensing collection module, a signal decoupling module, a damage identification module, a damage association module and a risk assessment module. According to the method, stress wave velocity and acceleration data are synchronously collected, time alignment is implemented, feature coupling precision is enhanced, wave crest offset and energy density are respectively extracted by using moving average filtering and wavelet transform, effective data segments are dynamically screened, and environmental noise interference is suppressed. A stress wave propagation change rate is quantified based on a path attenuation model, a continuous energy abnormal node is matched to realize damage positioning, a breeze response abnormal region is identified by combining vibration direction change and a signal envelope offset degree, multi-dimensional features are coded and subjected to risk judgment through a neural network, a damage positioning and risk assessment closed-loop framework is formed, and the risk assessment accuracy is improved. And the spatial resolution and evaluation precision of aeolian vibration damage identification under complex working conditions are significantly improved.

The invention provides a deicing control method and system for a power transmission line in a cold region, and relates to the technical field of power system power transmission line operation and maintaining.The method comprises the steps that icing data of the power transmission line are collected, the icing data are multi-source data and comprise the ice layer thickness, the ice type category and the distribution characteristic, and the ice layer thickness, the ice type category and the distribution characteristic are obtained by combining environment temperature, humidity and geographical location information; dynamically selecting a plurality of spatial sampling positions on the target line to construct a spatial correlation area for feature analysis; and obtaining correction parameters based on morphological characteristics of the spatial correlation region, performing spatial superposition and feature fusion analysis on the multi-source data according to the correction parameters, and extracting an icing feature set which is dynamically corrected and reflects the overall icing state of the line. According to the invention, differentiated ice removal in different climate areas can be realized, and intelligent deicing control of accurate identification, mode adaptation and closed-loop adjustment is realized in combination with multi-source monitoring data.

The invention discloses an insulation state fusion detection method, system, medium and equipment under transient impact in a cable operation process, and the method comprises the steps: synchronously collecting a transient impact signal and a partial discharge signal of a cable under the transient impact effect; performing time-frequency domain separation on the acquired transient impact signal and the partial discharge signal to obtain a transient impact component and a partial discharge component after separation; when it is detected that the amplitude of the transient impact signal exceeds a preset threshold value, a partial discharge detection window is started, and whether a partial discharge signal exists or not is detected in the window period; if a partial discharge signal is detected, multi-dimensional characteristic parameters of the partial discharge signal are extracted, a state vector is constructed in combination with environmental parameters, the state vector is input into a pre-trained machine learning model, and a cable insulation state grade is output; if the partial discharge signal is not detected, performing inversion calculation on the transient impact signal based on a transmission line model, and reconstructing an overvoltage waveform; and extracting propagation characteristic parameters according to the reconstructed overvoltage waveform, and judging the insulation defect type according to the propagation characteristic parameters.

The invention relates to an unmanned aerial vehicle inspection method and system based on an overhead transmission line, and the method comprises the steps: S1, generating an unmanned aerial vehicle three-dimensional space coordinate signal, and outputting a position correction signal based on a tower topology database; s2, in response to the position correction signal, controlling a multispectral sensing module to synchronously acquire a visible light image signal, an infrared temperature signal and a laser radar point cloud signal of the line; s3, inputting the visible light image signal into a defect identification model to generate a first defect feature signal; s4, fusing the first defect characteristic signal and the second defect characteristic signal to form a cross-modal diagnosis signal, and triggering a defect classification decision signal; and S5, classifying decision signals and cross-modal diagnosis signals according to defects. According to the air quality intelligent monitoring method and system based on sensing data feedback, the problems of inaccuracy in routing inspection positioning, defect missing detection and insufficient endurance in a complex environment can be solved.

The invention provides a water surface target automatic grabbing device and a control method thereof, and relates to the technical field of marine operation equipment. The device comprises a computing platform, a mechanical arm, a hook, a telescopic rod, a binocular camera, a depth camera, an inertial sensor and a self-stabilizing holder, the computing platform is fixedly mounted on the unmanned ship; the self-stabilizing holder is fixed on a deck of the unmanned ship; the mechanical arm is mounted above the self-stabilizing holder; the hook is mounted at a specified position on the unmanned ship; the telescopic rod is mounted at the tail end of the mechanical arm; the binocular camera and the depth camera are installed at preset positions of the tail end of the mechanical arm and connected with the computing platform through high-speed data transmission lines. The inertial sensor is installed in the mechanical arm and connected with the computing platform. According to the automatic grabbing device, the grabbing precision, stability and adaptability of the system in the dynamic marine environment are improved.

The invention relates to the field of power transmission line channel risk hidden danger monitoring, and provides a data fusion power transmission line channel risk hidden danger monitoring method and system, and the method comprises the steps: collecting the multi-modal sensing data of a power transmission line channel, and generating a multi-modal data flow of a unified time-space coordinate; constructing a three-dimensional space point cloud through a phase unwrapping and stereo matching fusion algorithm, and fusing multi-modal data to generate a space probability tensor; extracting risk semantic latent variables, constructing a Bayesian network and identifying potential risks; performing tensor product on the potential risk and the environmental data to generate a dynamic risk enhancement feature matrix, and constructing a nonlinear dynamic threshold curved surface through quantum annealing and Gaussian process regression; a mechanical equation is constructed, Gaussian kernel density estimation and numerical simulation are combined, the evolution trajectory of the risk in the space-time dimension is predicted, and a risk thermodynamic diagram and early warning information are generated; and generating a structured risk early warning report by adopting a natural language processing method. And the accuracy of power transmission line channel risk hidden danger monitoring is improved.

According to the power cable multi-defect nondestructive testing method and system based on the broadband impedance spectrum, impedance characteristics of a cable under different frequencies are measured, frequency domain data are converted into time domain data through inverse Fourier transform, and accurate positioning and evaluation of internal defects of the cable are achieved; the invention discloses a power cable multi-defect nondestructive testing method based on a broadband impedance spectrum. The method comprises the following steps: step 1, signal output and acquisition; the method comprises the steps that a broadband impedance spectrum tester is used for outputting a broadband sine wave voltage signal, frequency domain measurement data are collected, and the frequency domain measurement data are impedance data of a cable under different frequencies; 2, carrying out time-frequency conversion; converting the frequency domain measurement data into time domain data through inverse Fourier transform, generating a time-amplitude curve, and analyzing the electrical characteristics of the cable; 3, establishing a defect identification model; analyzing the electrical characteristics of the cable at different frequencies by using the transmission line model, and evaluating the state of the cable; step 4, defect positioning and evaluation; and 5, displaying and analyzing a result.

The invention discloses a power transmission line inspection abnormity early warning method based on multi-mode perception and digital twinning, and belongs to the technical field of power transmission line monitoring. The problems that in the prior art, inspection efficiency is low, multi-source data utilization is insufficient, early warning lags behind and closed-loop control is lacked are solved, and efficient on-site processing and feature fusion of data are achieved by deploying a multi-modal sensing terminal cluster and a layered edge computing node; a digital twinborn model fusing multi-physical field simulation and data-driven AI is constructed at a cloud end, so that virtual-real interaction and deep analysis of a physical mechanism and real data are realized; the health state of the power transmission line is evaluated based on the deep analysis result, multi-stage early warning can be generated accordingly, a reverse control instruction is triggered, closed-loop control from state perception and intelligent diagnosis to active intervention is finally formed, and the intelligent level and operation and maintenance safety efficiency of power transmission line inspection are remarkably improved.

The invention relates to the technical field of power transmission line detection, and provides a high-voltage overhead power transmission line insulator string detection device and a control method thereof. Comprising an unmanned aerial vehicle body, and an airborne data transmission module, an airborne insulator detection module and an image acquisition and analysis module which are carried on the unmanned aerial vehicle body, the image acquisition and analysis module is used for acquiring an image signal of the insulator string and analyzing the image signal so as to output a position control signal of the unmanned aerial vehicle and an angle control signal of the airborne insulator detection module; the unmanned aerial vehicle body flies to a specified position based on the position control signal; the airborne insulator detection module is used for rotating to a specified angle based on the angle control signal and detecting an insulator string; and the airborne data transmission module is used for receiving the detection data sent by the airborne insulator detection module and determining the health state of the insulator chain based on the detection data. Zero-value insulator live-line detection of a full-voltage-class full-string type insulator string of a power transmission line can be realized.

The invention discloses a power transmission line insulator defect identification method based on deep learning, relates to the technical field of insulator defect identification, and aims to solve the technical problem that an existing identification method is low in identification precision under a photovoltaic fluctuation working condition. Comprising the following steps: S1, multi-modal data acquisition: synchronously acquiring image data, line harmonic time sequence data, insulator dielectric loss value data and photovoltaic power fluctuation data of a power transmission line insulator; s2, data preprocessing: performing scene adaptive enhancement on the image data, and performing fluctuation correlation interference filtering on harmonic time series data to obtain preprocessed data; and S3, constructing and training a deep learning model: constructing a dynamic weight fusion deep learning model containing a power fluctuation-feature sensitivity mapping mechanism, and training the model by adopting cross-scene and cross-component type labeling samples. The method has the advantage of improving the defect identification accuracy of the insulator in the photovoltaic station.

The invention relates to the technical field of wireless power supply, and provides a non-contact power supply transmission line, which comprises a transmission module and a mobile power taking module, and is characterized in that the transmission module comprises an excitation cable and a linear transmission unit, and the linear transmission unit comprises a plurality of linear stators along the transmission direction; the movable electricity taking module comprises a rotor and a first electricity taking piece, and the rotor moves under current excitation; the first power taking piece can act with a magnetic field generated by electricity around the excitation cable and generate current so as to supply the current to an electric element installed on the mobile power taking module. According to the non-contact power supply transmission line, the mover is driven to move in a magnetic drive mode, and in the moving process of the mover, the first power taking part can be always coupled with the excitation cable, so that electric energy is uninterruptedly obtained to be supplied to an electric element, and the first power taking part does not need to be in contact with the excitation cable when obtaining the electric energy, so that the power utilization efficiency is improved. The problems that power cannot be continuously supplied to the execution part, and the conveying continuity of the rotor cannot be guaranteed are effectively solved.

The invention discloses a telegraph pole tower part defect identification method based on an improved YOLOv8 model, and the method comprises the steps: constructing a model based on YOLOv8, introducing a mixed attention mechanism, and obtaining an improved YOLOv8 model; training the improved YOLOv8 model on the basis of the target training set, and adjusting parameters by adopting a UIoU-Focal-inv loss function to obtain an optimal improved YOLOv8 model; and based on the test set, inputting the test set into the optimal improved YOLOv8 model, and outputting to obtain the defect type of the telegraph pole tower component and the frequency of the corresponding defect type, thereby realizing defect identification of the telegraph pole tower component in the power transmission line. According to the invention, matching improvement is carried out based on the YOLOv8 model, the mixed attention mechanism of the HAT module can enhance the feature representation of the model, and the method is suitable for complex recognition scenes with shielding, low contrast ratio, unobvious target fine granularity and the like in telegraph pole tower components.

The invention relates to the technical field of power system optimization scheduling, and discloses a distribution robust opportunity constraint low-carbon economic scheduling method considering an N-1 safety criterion, and the method comprises the steps: building a wind-solar-energy-storage combined power generation system carbon transaction model which considers the bidirectional flowing of a carbon emission right, and taking the minimization of the total operation cost of the wind-solar-energy-storage combined power generation system as a target; establishing a low-carbon economic dispatching model considering the safe operation constraint of the power grid; aggregating wind and light output to establish a fuzzy set based on uncertainty moment information, establishing a distribution robust opportunity constraint economic dispatching model considering wind power uncertainty, and adopting a conditional value-at-risk theory to measure a system operation risk; on the basis, a power transmission line N-1 safety constraint rapid dynamic checking and adding method is provided based on branch circuit breaking distribution factors, and an economic dispatching model considering an N-1 safety criterion is established and solved. The power supply reliability of the system can be ensured under the condition of key line faults, and the solving efficiency and the practicability of the scheduling scheme are improved.

A power amplifier includes a 3-dB coupler which splits a first signal into a second signal and a third signal lagging behind the second signal by 90°, a carrier amplifier, a peak amplifier, and a hybrid coupler. The carrier amplifier amplifies the second signal and outputs a fourth signal when the first-signal power level is a first level or higher. The peak amplifier amplifies the third signal and outputs a fifth signal when the first-signal power level is a second level or higher, which is higher than the first level. The hybrid coupler includes a first transmission line receiving the fourth signal at its first terminal, and a second transmission line receiving the fifth signal at its first terminal and being open at its second terminal. The first transmission line outputs, from its second terminal, an amplified first signal obtained by combining the fourth and fifth signals.

The invention relates to the technical field of wireless communication, and provides a coupler which comprises a substrate layer, a buried oxide layer, a first metal layer, a second metal layer and a third metal layer which are sequentially stacked and fixed. A main transmission line is formed on the third metal layer, the main transmission line is provided with a signal input end and a signal output end, the signal input end is used for accessing a radio frequency signal, and the signal output end is used for accessing an antenna; an auxiliary transmission line is formed on the first metal layer, the auxiliary transmission line is provided with a coupling end and an isolation end, and the main transmission line and the auxiliary transmission line form partial coupling and are coupled and output through the coupling end; the coupler further comprises a substrate, a monitoring circuit, a control circuit and an impedance matching circuit. The monitoring circuit is used for monitoring radio frequency signals output by the power amplifier, and the radio frequency signals are fed back to the power amplifier through the monitoring circuit after being subjected to impedance matching through the impedance matching circuit. The coupler disclosed by the invention is high in power amplifier monitoring precision and good in reliability.

The invention discloses a hydropower line economic dispatching and load collaborative optimization system and method, and relates to the technical field of hydropower dispatching, and the system comprises a data collection module which is used for collecting hydropower station unit water consumption characteristic curve data, power grid real-time load demand data, reservoir water level dynamic data and unit vibration area constraint data; the model construction module is used for preprocessing the data acquired by the data acquisition module and constructing a multi-target optimization model with the minimum total water consumption and the minimum line transmission loss as targets in combination with constraint conditions; the improved optimization module is used for receiving the target function and the constraint condition output by the multi-target optimization model module and dynamically calculating the optimal load distribution scheme of each unit; and the result output module is used for receiving the optimal load distribution scheme output by the improvement and optimization module and realizing visual output and decision assistance of the scheme. The problem that a traditional scheduling scheme is low in traditional scheduling decision-making efficiency is solved, and the scheduling decision-making efficiency and engineering practicability are improved.

The invention discloses an emergency power supply-based anti-loosening cable plugging head, and relates to the technical field of cable plugging heads, the emergency power supply-based anti-loosening cable plugging head comprises a front plug body and a rear plug body, the front plug body and the rear plug body are mutually clamped, the emergency power supply-based anti-loosening cable plugging head further comprises a vertical limiting mechanism and a connecting port sealing mechanism, and the vertical limiting mechanism comprises a fixing plate and a fixing ring; the fixing plate is fixedly connected to the front plug body, and the fixing ring is fixedly connected to the rear plug body, so that the supporting plate can support the side plate, the front plug body and the rear plug body are kept vertical and parallel, and the rear plug body is prevented from being influenced by self gravity or pulling. When the rear plug body is connected with the front plug body, the phenomenon of offset in the process of connecting the rear plug body with the front plug body can be avoided, accurate butt joint of a signal transmission line can be ensured, signal interference, attenuation or loss caused by connection offset is reduced, and the situation that the normal operation of a whole system is influenced by deviation of data transmitted through an interface due to offset of the rear plug body is prevented.

A vertically-polarized omnidirectional antenna, including: a body including: a host printed circuit board (PCB) including: first and second slots, and a metal flooded ground plane, first and second antenna PCBs forming four dipole pairs, the first and second antenna PCBs mounted to the host PCB, respective first to third ground connection fillet tabs, the first and second tabs being on an opposite side of the host PCB from the third tab, the first to third tabs being on a same side of its antenna PCB, an amplitude taper on each antenna PCB at transitions between the host and antenna PCBs, including: a first transmission line splitting off into a second transmission line and a third transmission line, the second transmission line feeding two antennas to form one dipole pair, the third transmission line stepping down using a quarter-wave transformer, and a radio frequency (RF) connector to receive a power supply.

The invention discloses an identification and positioning method for power cable defects and short-circuit faults. The defect identification and positioning method comprises the following steps: establishing a power cable transmission line simulation model and a partial discharge simulation model by using electromagnetic transient simulation software; obtaining a partial discharge signal according to the power cable transmission line simulation model and the partial discharge simulation model, and identifying the partial discharge signal based on an SSA-BP algorithm; performing preliminary positioning on the power cable by adopting an ultrasonic, high-frequency and ultrahigh-frequency detection method; accurate positioning of power cable defects is realized through an infrared heat and sound wave positioning principle. According to the method, a simulation analysis method is adopted to obtain a sample, and then accurate identification of a cable fault is realized through a BP neural network (SSA-BP) optimized based on a sparrow search algorithm. And meanwhile, accurate cable fault positioning is realized aiming at different fault positions.