887results about How to "Good precision" patented technology

The invention discloses a hydropower station gate detection method and system, and relates to the technical field of hydropower station gate detection, and the method comprises the steps: collecting environment parameters, building a dynamic environment model, and determining a real-time impact factor on a gate state; synchronously collecting gate vibration and sound wave data to obtain a composite signal; constructing a short-term and long-term trend model, and identifying a potential abnormal trend; automatically adjusting the sampling frequency of the sensor according to an abnormal detection result; performing fault classification on the multi-parameter signals by using an event pattern recognition algorithm; and generating an automatic response strategy according to a fault classification result. According to the hydropower station gate detection method provided by the invention, the accuracy and efficiency of hydropower station gate detection are improved through multi-dimensional parameter fusion and adaptive sampling adjustment. Through monitoring of various parameters, the system can comprehensively analyze the state of the gate, the misjudgment risk caused by a single signal is reduced, and comprehensive recognition of faults is enhanced.

The invention discloses a condensation monitoring method for a digital substation. The method comprises the following steps: acquiring data information of a target digital substation; a high-risk area where condensation occurs is determined, and monitoring equipment is arranged to obtain monitoring data; selecting a monitoring frequency band, acquiring and analyzing data of the monitoring frequency band, and constructing a feature set related to the condensation degree; acquiring monitoring data of the target digital substation in real time and selecting an electromagnetic wave monitoring frequency band; feature extraction and analysis are carried out to construct a condensation monitoring model, and optimization is carried out; generating a condensation risk prediction result of the target digital substation based on the optimized condensation monitoring model; and giving an optimal anti-condensation measure for the target digital substation according to a prediction result, and completing condensation monitoring of the target digital substation. According to the method, the environmental parameters of the digital substation are acquired and processed, and the condensation prediction model is established, so that the condensation monitoring of the digital substation is realized, the reliability is higher, and the accuracy is better.

The invention provides an RSVP type unbalanced electroencephalogram signal classification method based on a self-adaptive channel mixed attention mechanism and decoupling learning, and aims to improve the feature extraction and classification performance of RSVP electroencephalogram signals. Firstly, collected RSVP electroencephalogram signals are preprocessed, including common average reference, band-pass filtering and standardization processing, and a training set and a test set are divided; then, multi-scale time and space features are extracted through a parallel convolution layer and a space convolution layer, and the features of different levels are fused through an interpolation method; the training process is decoupled into representation learning and classifier learning, the representation learning uses a triple loss function based on difficult sample sampling, and the classifier learning uses the optimized Focal Loss to perform feature learning and classifier optimization. A self-adaptive channel mixed attention mechanism is introduced, and the flexibility and robustness of the model are enhanced through dynamic weighting and multi-level feature fusion. According to the method, the problem of class imbalance in the RSVP task is solved.

The invention provides a material simulation test method and platform for mold flow analysis, and relates to the technical field of material simulation testing, and the method comprises the steps: carrying out the product demand analysis of a target test material, and obtaining the product demand information; performing injection molding analysis to obtain an injection molding control parameter threshold; obtaining an injection molding simulation test model; carrying out an injection molding simulation test, and carrying out image pre-acquisition to obtain a first pre-acquired image; defect feature analysis is carried out, attention area distribution is carried out, and a target attention area is obtained; an injection molding defect area image is obtained, and defect feature information is obtained; and feedback testing and parameter optimization are carried out, optimized injection molding control parameters are obtained, and injection molding control is carried out. The technical problems that in the prior art, injection molding analysis generally depends on a fixed mathematical model and limited technological parameters, and problems in the molding process cannot be recognized and adjusted in real time, so that the production efficiency and the product quality are low are solved.

The invention belongs to the technical field of three-dimensional geologic modeling, and particularly relates to a three-dimensional geologic model construction method based on multi-source data fusion, and the method comprises the steps: 1, extracting drilling data, and constructing a drilling database; 2, constructing a topographic and geomorphic surface model, and completing the fusion of topographic and geomorphic data and drilling data; 3, coupling information of strata, rock mass, fracture, ore body and the like, and constructing a geologic body comprehensive model; 4, extracting geophysical and geochemical data, and constructing a geophysical model and a geochemical model; and 5, coupling the borehole database, the landform surface model, the geologic body comprehensive model and the geophysical model, and constructing a multi-source data fused three-dimensional geologic model. The method improves the accuracy and precision of the three-dimensional geologic model, improves the data processing and analysis speed, improves the data analysis capability and depth, provides technical support for uranium resource potential analysis and prediction exploration, and guides uranium exploration.

The invention discloses a terminal data automatic backup and recovery method and system, and relates to the technical field of data backup and recovery, and the method comprises the steps: dynamically adjusting the priority, and generating a personalized backup strategy; capturing data change in real time and executing incremental backup; and encrypting the backup data for version recovery. According to the terminal data automatic backup and recovery method provided by the invention, accurate analysis and priority division are performed on the terminal data through the classification model, so that the backup plan can be dynamically adjusted according to the importance of the data in the backup process. Data change is captured in real time, incremental backup is executed, and the real-time performance and efficiency of backup are effectively improved. An incremental backup algorithm is adopted, only a file change part is recorded, and a change area of a data block is quickly positioned by utilizing hash value comparison, so that the requirement of a storage space is reduced, and meanwhile, the transmission overhead of backup data is reduced. According to the method, better effects are achieved in the aspects of backup real-time performance, backup accuracy, backup pertinence and backup efficiency.

The invention discloses a surface water river-entering sewage draining exit pollution abnormity tracing method and device based on intelligent Internet of Things. The method comprises the following steps: constructing a database according to surface water pollution basic data; all pollution sources in the database are divided into different levels of pipeline logic relations according to the upstream and downstream communication relation of the land parcel area, the pipe network and the water system, and a pollution source-land parcel-pipe network-monitoring station intelligent Internet of Things grid is constructed; carrying out preprocessing and feature analysis on all data in the intelligent Internet of Things grid to construct a model database; a traceability model and a pollution contribution model are built based on the model database, the traceability model is used for calculating the pollution contribution rate of each pollution source, and a target discharge port or a target pollution discharge node is determined according to the pollution contribution rate; the pollution contribution model is used for generating and displaying a pollution contribution value of the target discharge port or the target pollution discharge point in response to an input instruction. The problem that a pollution source cannot be quickly and accurately determined in surface water environment management is solved.

The invention discloses a mixed collision detection method in a virtual operation simulation system. The method comprises the following steps: 1) constructing a surgical instrument and soft tissue model; 2) dividing a virtual space into grids with the same size, indexing an object model, accelerating the construction and updating of a spatial information hash table by utilizing GPU multithreading, mapping the grids occupied by all primitive bounding boxes into the hash table, and determining objects in the same space according to the spatial hash table; 3) before object intersection solving, space reduction is carried out, and hash table data is optimized; 4) constructing a hierarchical bounding box for an object which may collide, and performing intersection detection; if it is detected that a certain node or child nodes of the node intersect, the next step is executed, and otherwise, detection is pushed out; and 5) accurately judging whether the basic triangular units contained in the nodes in the step 4) are intersected or not, and completing collision detection. According to the method, the problem of missed detection and wrong detection in the existing discrete collision detection algorithm is effectively solved, and the detection algorithm has excellent accuracy and calculation efficiency.

The invention discloses a method for analyzing mineral trace elements by utilizing a micro-Raman spectrum, which comprises the following steps of: fixing a mineral sample on a rotary sample table of a micro-Raman spectrometer, selecting an area to be detected by combining a micro-imaging technology, collecting a Raman background signal under the pre-illumination of low-power laser, and analyzing the trace elements in the area to be detected according to the Raman background signal. The laser focal length, the spot size and the incident angle are adjusted accordingly, the laser is focused on a target area, under the calibrated laser condition, through a multi-angle excitation micro-area, after an algorithm is applied to optimize signals, reduce impurity interference and integrate multi-angle Raman signals, the characteristic spectrum information of the microelements can be extracted, and the microelements can be accurately detected. High-resolution analysis of sample components is realized, and a three-dimensional distribution model of microelements in a microcell is constructed in combination with microscopic imaging data. The method has the characteristics of non-destructiveness, high resolution and rapid detection, and is suitable for mineral component analysis in the fields of geology, material science and the like.

The invention relates to the technical field of safety evaluation, in particular to an ultra-deep soft soil foundation pit safety evaluation method and system, and the method comprises the following steps: obtaining the geological data of an ultra-deep soft soil foundation pit, recording the soil type, soil density, underground water level and temperature data, carrying out the data arrangement and formatting, and generating a geological data record sheet. According to the method, the detailed geological data of the ultra-deep soft soil foundation pit are acquired and arranged, the accuracy and the applicability of the basic data are enhanced, so that subsequent analysis is carried out according to clear actual conditions, and the timeliness and the accuracy of risk prediction are ensured by directly associating prediction of mechanical behaviors with risk assessment; the risk assessment model can be dynamically adjusted, the effectiveness and accuracy of early warning are improved, the risk dependency relationship model and the monitoring scheme are integrated, theoretical and actual operations are effectively linked, the systematicness and adaptability of construction safety management are effectively improved, and the accident prevention capability is remarkably improved.

The invention discloses a carbon emission prediction method. The method comprises the following steps: acquiring carbon emission data information of a target area; performing carbon emission data accounting of the target area based on the carbon emission offset data; constructing a carbon emission prediction model of the target area; and performing carbon emission prediction of the target area based on the set scene information by adopting the carbon emission prediction model. The invention also discloses a system for realizing the carbon emission prediction method. According to the carbon emission prediction method and system provided by the invention, through the acquisition of the historical data, the accounting of the carbon emission data and the construction and evaluation of the prediction model, the prediction of the carbon emission of the target object in the whole life cycle is realized, and the reliability is higher and the accuracy is better.

The invention relates to the technical field of medium and small span bridge construction and control, in particular to a precast beam size matching method and system based on a three-dimensional laser scanner, and the method comprises the steps: obtaining precast beam point cloud data, carrying out the preprocessing of the precast beam point cloud data, carrying out the registration of the preprocessed precast beam point cloud data with a BIM model, and obtaining the size of a precast beam. The automatic detection of the structure size is realized; then, by obtaining interface angular points of the precast beams, conducting virtual splicing by aligning the cross section angular points of the adjacent precast beams, simulating the field splicing process and obtaining key construction details, accurate and reliable data support is provided for follow-up construction, it is ensured that each beam section can conform to design specifications during splicing, and therefore potential safety hazards are reduced; the controllability and scientificity of the construction process are improved, the construction efficiency is improved, the construction period is shortened, the service life of the bridge structure is prolonged, and the structural reliability is improved. The problems of low precast beam parameter detection efficiency, high operation difficulty and low accuracy in the prior art are solved.

The invention discloses a data prediction method based on a kernel regression primary function Bayesian dynamic linear model, and belongs to the technical field of bridge structure health prediction. A to-be-analyzed bridge is detected by using a structural health monitoring system (SHM), and a time series dynamic model KR-BDLM of a Bayesian dynamic linear model is established to describe a bridge structure strain response changing along with time, so that bridge structure health monitoring data prediction and missing value filling are realized; performing parameter optimization reasoning on parameters of the KR-BDLM model by adopting a maximum likelihood algorithm; providing a time-varying state parameter and an observation prediction value of a bridge structure through the optimized KR-BDLM model, and further realizing SHM monitoring data single-step prediction; and multi-step prediction is carried out on the basis of single-step prediction, so that longer-time prediction of bridge data is realized. The prediction result is high in precision, the engineering structure safety can be effectively guaranteed, and the leading-edge development of the structure health monitoring field is promoted.

The invention discloses a rock mass instability grading early warning method based on micro-seismic feature perception and a classification enhancement algorithm. The method comprises the following steps: acquiring rock mass instability case data information of different geotechnical engineering projects; selecting a plurality of microseismic parameters as rock mass instability risk candidate evaluation indexes, and performing correlation analysis to obtain rock mass instability grading early warning evaluation indexes; constructing a training data set; constructing a rock mass instability grading early warning primary model based on a classification enhancement algorithm and a genetic algorithm, and training to obtain a rock mass instability grading early warning model; and the obtained rock mass instability grading early warning model is adopted to complete rock mass instability grading early warning based on micro-seismic feature perception and a classification enhancement algorithm. The invention also discloses a system for realizing the rock mass instability grading early warning method based on the micro-seismic feature perception and classification enhancement algorithm. According to the method, rock mass instability grading early warning based on micro-seismic feature perception and a classification enhancement algorithm can be realized, the reliability is higher, and the accuracy is better.

The invention provides a solid-state laser radar and IMU (Inertial Measurement Unit) robust tight coupling system under the assistance of point cloud intensity, which comprises the following steps of: firstly, pre-processing a solid-state laser radar point cloud by the system, eliminating point cloud defect points, correcting a point cloud intensity value and performing down-sampling; and then geometric feature extraction and intensity feature extraction are carried out on the preprocessed point cloud, and intensity calibration of the laser radar point cloud is carried out. And then compensating the motion of the point cloud by using the high-frequency prior information predicted by the IMU. For geometric and intensity feature point clouds, a classical registration method and a fast plane fitting method are respectively adopted to calculate residual errors, and an intensity feature assisted point cloud residual error measurement model is constructed. And a robust strategy based on an IGGIII robust weight function is designed by using a residual error estimation value in a filtering algorithm. According to the invention, on the premise of keeping relatively low calculation cost, the positioning and mapping capabilities and robustness of the small-view-angle laser radar tight coupling system in a complex environment are improved.

The invention relates to the technical field of obstacle detection, in particular to a laser point cloud-based obstacle framing method in a converter station scene, which comprises the following steps of: based on a converter station environment, performing three-dimensional scanning through a laser radar, collecting point cloud data, performing noise filtering on the data, performing spatial alignment of the data, and obtaining processed point cloud data. According to the invention, three-dimensional scanning is carried out through the laser radar, the scanning angle and the resolution are adjusted, the coverage rate and the precision of the point cloud data are optimized, the point cloud data are mapped to the adjacent grid center, the data structure is simplified, the point cloud data are horizontally cut, the linear features of the electric wire are identified, and the obstacle identification process is optimized. Through combination of spatial statistical analysis and clustering analysis, the accuracy and the meticulous degree of obstacle recognition are improved, and the obstacles are framed by constructing a bounding box of each obstacle, so that three-dimensional space positioning and form recognition of the obstacles are realized.

The invention discloses an electric power system load prediction method based on a meteorological evolution process. The method comprises the following steps: constructing a target electric power system load prediction index set comprising meteorological factors; obtaining corresponding historical data and future prediction data to construct a historical data set and a future data set; performing threshold judgment on the temperature change of the day to be predicted; constructing a training data set and training the load prediction primary model to obtain a load prediction model; and adopting the load prediction model to carry out actual load prediction based on the meteorological evolution process on the target power system. The invention also discloses a system for realizing the power system load prediction method based on the meteorological evolution process. According to the invention, through processing and matching of historical meteorological data and meteorological data on a prediction day, a data set conforming to a prediction condition is constructed, and a load prediction model is adopted to learn the data set; therefore, the method not only can realize power system load prediction based on the meteorological evolution process, but also is higher in reliability and better in accuracy.

The invention belongs to the technical field of point cloud semantic segmentation, and discloses a power corridor laser point cloud semantic segmentation method and system collected by an unmanned aerial vehicle, and the method comprises the steps: collecting laser point cloud and image data; reconstructing point cloud data based on the image data to obtain an image point cloud, performing spatial registration on the laser point cloud and the image point cloud, and projecting the registered laser point cloud onto the image; operating the image data to obtain a depth image and a normal image, and performing fusion operation on the depth image, the normal image, the image and the projected laser point cloud to obtain multimode fusion data; and carrying out two-dimensional semantic segmentation on the multi-mode fusion data, and carrying out back projection operation on a segmentation result to obtain a three-dimensional point cloud semantic segmentation result. According to the method, the visible light image of the power corridor and the laser point cloud data are fused, high-precision semantic segmentation of the laser point cloud is realized by using the appearance information contained in the visible light image, and the accuracy, efficiency and fineness of semantic segmentation of the laser point cloud of the power corridor are improved.

The invention discloses an element plugging test system based on visual perception and a control method and device.The system comprises a visual perception module, a grabbing device, a conveying device and an upper computer, and the visual perception module is used for conducting visual recognition on jacks of elements to be tested and comprises a 2D camera and a 3D camera; the 2D camera has a first field of view, the 3D camera has a second field of view, the first field of view comprises the second field of view, and the first field of view range is larger than the second field of view range; the grabbing device comprises a clamping end, and the clamping end is used for clamping the plug to plug and unplug the jack; the visual perception module is mounted at the clamping end so as to move along with the clamping end; the conveying device is used for conveying to-be-tested elements; the upper computer is in communication connection with the visual perception module, the grabbing device and the conveying device. The visual perception module can be compatible with large-deviation incoming materials, so that multi-degree-of-freedom position and posture adjustment is realized; therefore, the application range of the system is greatly expanded, and the system has excellent flexibility, compatibility and accuracy.

The invention provides a point cloud semantic segmentation method for improving a RandLA-Net network. The method comprises the following steps: step 1, carrying out data preprocessing grid downsampling; step 2, calculating a point cloud normal vector by a local curved surface fitting method; thirdly, local multi-dimensional feature fusion is carried out, and local geometric information is captured; fourthly, important features are automatically learned and aggregated through a self-attention module and attention pooling; 5, cascading an up-sampling result with the intermediate feature of the coding layer through jump connection; and 6, predicting the semantic tag by using a full connection layer. According to the invention, high-precision and high-efficiency automatic point cloud semantic segmentation in a large-scale outdoor scene is realized.

The invention discloses a rapid assessment method and system for shock resistance toughness of main and aftershocks based on a cycle-Transform network, and the method comprises the steps: building a finite element model of a building structure, inputting a plurality of groups of earthquake accelerations of main and aftershocks into the model, and obtaining a database comprising a plurality of groups of accelerations and corresponding structure displacement responses; building a loop-Transform neural network, and carrying out the training and optimization of the loop-Transform neural network; inputting the main shock and aftershock sequence into the optimized cycle-Transform neural network, predicting the displacement response of the structure under the double amplitude modulation main shock and aftershock sequence, then obtaining the building damage probability corresponding to different earthquake intensities under the main shock and aftershock situation by using maximum likelihood estimation, evaluating the corresponding damage index, calculating the recovery time, and finally obtaining the building damage probability corresponding to the different earthquake intensities under the main shock and aftershock situation. A function recovery curve of the structure is predicted, and shock resistance toughness evaluation is completed. According to the method, the precision of structure accumulated damage prediction under the aftershock effect is improved, and the calculation complexity and time consumption of toughness evaluation under the main and aftershock earthquake scene are remarkably reduced.

The invention belongs to the field of optical fiber sensing technology and artificial intelligence, and particularly relates to a mining belt conveyor intelligent monitoring method and system based on the optical fiber sensing technology. The monitoring method comprises the following steps: acquiring sound wave optical fiber sensing data and carrying out LogFBank preprocessing; constructing a sound wave optical fiber sensing state monitoring model based on the PANNs-CNN; performing feature extraction on the preprocessed sensing data by using a monitoring model; carrying out feature fusion on the extracted features by utilizing a deep learning convolutional network and a full-connection classification aggregation network structure; and outputting the data after feature fusion by using an output layer to obtain a state classification result of the belt conveyor. The belt conveyor intelligent monitoring system is constructed by using the monitoring method, and belt conveyor transportation is scientifically managed by using the monitoring system. According to the mining belt conveyor intelligent monitoring method and system based on the optical fiber sensing technology and the sound wave classification neural network, the reliability and stability of continuous monitoring of the state of the mining belt conveyor are improved, and the remarkable effect is achieved.

According to the double-end traveling wave distance measurement wave head calibration method and device suitable for the power distribution network and the medium, a proper wavelet high-frequency component is selected through a characteristic frequency band, and a proper mode extreme point is calibrated to serve as initial estimation of a wave head starting point; constructing a smooth threshold value on the low-frequency component corresponding to the characteristic frequency band based on the preliminary estimation and the hard trigger point; a more accurate wave head starting point is obtained through the self-adaptive smooth threshold value search; and finally, calculating the position of a fault point according to a double-end traveling wave fault location formula. The invention provides a novel principle and method for completing coordinated point marking by taking waveform break point calibration of a wave head as wave head starting point calibration and fully utilizing appropriate-scale high and low frequency components of a wavelet domain, and practices prove that the novel calibration principle and method are not easily influenced by traveling wave attenuation, distortion and noise interference caused by traveling wave dispersion and wave impedance; and meanwhile, the problem of low calibration precision caused by inconsistent wave head characteristic frequency spectrums of the traveling waves at the two ends can be solved.

The invention discloses a universal movement mechanism and a surgical instrument, and relates to the field of medical instruments. The universal movement mechanism comprises a spherical pair assembly, a support and a locking assembly. The inner ball sleeve can rotate relative to the support through the first pivot, the outer ball sleeve can rotate relative to the support through the second pivot, and universal rotation between the inner ball sleeve and the outer ball sleeve is converted into rotation movement around the first pivot and the second pivot. When the motion posture of the spherical pair assembly needs to be fixed, a user can lock the rotation angle of the first pivot by operating the second locking piece to limit and lock the first locking piece, and can lock the rotation angle of the second pivot by operating the third locking piece to limit and lock the fourth locking piece; the ball pair assembly can be adjusted and locked by controlling the first pivot and the second pivot. The universal movement mechanism provided by the invention is good in structural locking strength and high in structural installation, adjustment and locking precision, and the surgical instrument adopting the universal movement mechanism has good locking precision and adjustment effect.

The invention discloses an image super-resolution reconstruction method based on compressed sensing, and belongs to the field of image reconstruction. The problems that the updating speed of existing hardware cannot meet the requirement for signal precision, and on the premise that the hardware cost is not increased, an image with higher definition cannot be reconstructed are solved. The invention discloses an image super-resolution reconstruction method based on compressed sensing, and the method comprises the steps: designing a dictionary learning process, and obtaining high-frequency component parameters needed in image restoration; comprising the steps of constructing a training data set and performing dictionary training; wherein a corresponding low-resolution image is obtained by carrying out quality reduction and scale reduction calculation on an image so as to carry out a process of training a data set; constructing an over-complete dictionary pair by using a K-SVD algorithm, and training the dictionary; firstly, a reduced low-frequency component is obtained, and then the resolution is reconstructed. According to the method, the GPSR algorithm is utilized, the limitation of fixed sparseness of a traditional OMP algorithm is overcome, a more accurate sparse coefficient is obtained, and the precision of the reconstructed image is further improved by adopting a bilateral filtering mode.

The invention provides a photovoltaic short-term power prediction method and device, a server and a storage medium, and the method comprises the steps: generating first solar radiation data based on a meteorological large model and WRF-Solar; performing solar radiation data training reasoning on the meteorological large model according to the historical meteorological reanalysis data set; performing fine adjustment on the meteorological large model after solar radiation data training reasoning according to the actually measured solar radiation data of the cloud picture; generating second solar radiation data based on the fine-tuned meteorological large model; performing fusion processing on the first solar radiation data and the second solar radiation data to generate fusion data; and inputting the fused data into a photovoltaic short-term power prediction model to generate a photovoltaic short-term power prediction result. According to the photovoltaic short-term power prediction method and device, the server and the storage medium provided by the invention, the combination of the meteorological large model, the satellite cloud picture and photovoltaic short-term power prediction can be realized, and the accuracy and effectiveness of photovoltaic short-term power prediction are optimized.

The invention discloses a power load prediction and optimization method based on artificial intelligence, particularly relates to the field of power load prediction and optimization, is used for solving the problem of system stability caused by a nonlinear resonance effect, and comprises the following steps: preliminarily extracting key features of a power load signal through a multi-scale decomposition and mode recognition technology; and identifying a load mode which may cause system resonance. Two key indexes of frequency resonance intensity and time phase difference are emphatically utilized, the resonance risk of a load mode is comprehensively analyzed through a Bayesian network, the weight configuration of a high-risk mode is dynamically optimized in combination with a genetic algorithm, and the sensitivity and adaptability of the model to different resonance effects are improved. And then, the classified load modes are input into a deep neural network for multi-mode prediction fusion, and the model weight is adjusted in real time through a particle swarm optimization algorithm, so that the prediction precision and the operation stability of the system under a complex load condition are ensured, and a powerful guarantee is provided for the reliability of the power system.

The invention provides an asymmetric short-circuit current calculation method and system for a large-scale new energy station. The method comprises the following steps: judging a fault type and a fault phase; the negative sequence impedance of each new energy station is calculated, and the positive sequence initial voltage of each new energy station is calculated; calculating the output short-circuit current of each new energy station; according to the fault composite sequence network, a simplified equivalent circuit is obtained through circuit simplification; calculating the positive sequence voltage of each new energy station; comparing the positive sequence voltage and the positive sequence initial voltage of each new energy station, judging whether a voltage difference value meets convergence precision or not, and solving negative sequence current and zero sequence current output by each new energy station according to a circuit equation; and outputting the output phase current of each new energy station. According to the method, the influence of converter control on a phase selection element is overcome, the number of iterations can be effectively shortened, the process of solving matrix inverse operation by a node voltage method is avoided, and the method has relatively good engineering practicability and precision and can be suitable for a large-scale new energy field station.

The battery top cover for the secondary battery comprises a positive / negative terminal, a top cover substrate and a lower plastic part, the positive / negative terminal comprises a plane part and a protruding part formed by stamping, and the back surface of the protruding part is provided with a concave part; the protruding part of the terminal penetrates through the top cover substrate and the lower plastic part, the lower end of the terminal can be connected with the switching piece when the battery is assembled, and the plane part of the terminal is matched with the switching piece to clamp the top cover substrate and the lower plastic part; a positive / negative insulating ring, a fixing ring and a sealing ring are sleeved on the protruding part of the terminal; a plurality of upper bulges matched with the terminals and lower bulges matched with the top cover substrate are respectively arranged on the upper surface and the lower surface of the positive / negative electrode insulating ring; the array radiuses of the upper bulges and the lower bulges are different; a plurality of lower grooves matched with the upper bulges are formed in the plane part of the terminal; a plurality of upper grooves matched with the lower bulges are formed in the top cover substrate; an annular bulge is arranged on the upper surface of the fixing ring; sawtooth structures are arranged on the inner circumferential wall and the outer circumferential wall of the annular protrusion. According to the utility model, the simplification and integration of the structure are realized, and the performance and the manufacturing efficiency of the battery cell top cover are improved.

The invention discloses a manufacturing method of a UNS N06690 alloy seamless heat exchange tube, which belongs to the technical field of material processing, and comprises the following steps of: (1) performing peeling, PT inspection, shearing, centering, grinding, heating, perforating, water cooling and inspection on a round steel material; (2) carrying out acid pickling on the hot-punched blank, and then carrying out incoming material inspection and inner and outer surface grinding; (3) two-pass intermediate product cold rolling is carried out, and after cold rolling, intermediate product deoiling, intermediate product heat treatment, intermediate product straightening, intermediate product pipe cutting, acid pickling, intermediate product grinding and repairing and intermediate product inspection are carried out; (4) carrying out finished product cold rolling; (5) deoiling a finished product; (6) performing heat treatment on a finished product; (7) straightening a finished product; (8) carrying out finished product ultrasonic eddy current, water pressure and pipe cutting, sampling during pipe cutting, and carrying out physical and chemical tests; and (9) the obtained steel pipe is subjected to the procedures of finished product surface inspection, size inspection, cleaning, mark spraying and finishing and finally packaged and warehoused, and the product produced through the method is high in one-time inspection qualification rate and high in yield.