494 results about "Transformation algorithm" patented technology

The invention belongs to the technical field of humanoid robot control, and particularly relates to a humanoid robot two-arm self-adaptive force control operation method and system, and the method comprises the steps: building a coordinate conversion algorithm between a world coordinate system and a left arm base coordinate system, a right arm base coordinate system, a left arm tail end coordinate system, a right arm tail end coordinate system and an operation target coordinate system; six-dimensional force data and torque data collected by six-dimensional force sensors at the tail ends of the left mechanical arm and the right mechanical arm are obtained, and gravity compensation and conversion are conducted; defining a grasping matrix to decompose the six-dimensional force data and the torque data into internal force and external force; dividing the cooperative task into a free motion stage, a constraint establishment stage and a constraint motion stage, and generating a corresponding control instruction in each stage; and the generated control instruction is input into a double-arm robot system, and coordinated motion control of the double-arm robot in different stages is achieved. According to the invention, coordination work can be completed and anti-interference capability is possessed.

The invention belongs to the technical field of aviation energy design, and particularly provides an aviation energy power device fault intelligent inquiry system and method based on a knowledge graph. The method comprises the following steps: firstly, showing a complex data structure and a knowledge relationship to a user in a visual and dynamic manner through knowledge extraction, knowledge graph database construction, knowledge graph visualization and other methods, and constructing a knowledge graph basic framework; according to the method, the depth information of the target object can be extracted from the images from different angles, the fault characteristics of the equipment can be intuitively displayed, and the fault state of the equipment can be visually represented by extracting the depth information of the target object from the images from different angles. The evolution of the equipment state can be tracked through the change of the three-dimensional space; a conversion algorithm of the knowledge graph and the Bayesian network is realized through the Bayesian network, and rapid reasoning of fault causes is realized in combination with a Bayesian network reasoning algorithm; and finally, based on an intelligent inquiry system of a large model, a deep learning technology and a large-scale training model are utilized, and previous fault data and professional knowledge of the aviation energy power device are combined to provide intelligent auxiliary diagnosis support.

The invention provides a water quality heavy metal pollution detection method and system based on Raman spectrum. The method comprises the following steps: collecting a water sample through a water area to be detected, and scanning the water sample by adopting a Raman spectrometer to generate an original Raman spectrogram; based on the original Raman spectrogram, using a wavelet transform algorithm to perform de-noising processing, reducing background interference, using a principal component analysis technology to extract characteristic peaks, and generating a heavy metal preliminary prediction result; based on the preliminary heavy metal prediction result, constructing a heavy metal concentration prediction model by applying a partial least squares regression algorithm, performing quantitative analysis, evaluating prediction accuracy by adopting a cross validation technology, and generating a heavy metal actual concentration result; and based on the actual heavy metal concentration result, comparing with a water quality safety standard, evaluating a pollution condition, and generating a water quality heavy metal pollution detection report. The technical scheme provided by the invention has high sensitivity, can efficiently denoise and accurately predict, and provides a scientific means for monitoring and treating heavy metal pollution of water.

The invention discloses a machine vision-based intelligent detection method for steel galvanized surface defects, which comprises the following steps: S1, acquiring and preprocessing a steel galvanized surface image to obtain a standardized image; s2, constructing a specular reflection probability graph according to the brightness distribution and the gradient magnitude, and calculating a reflection intensity value; s3, calculating a structure tensor matrix, determining a main direction angle and an anisotropic consistency coefficient, and generating a direction feature matrix; s4, establishing a multi-scale direction adaptive phase kernel function, and performing phase modulation in a frequency domain by adopting an improved phase stretching transformation algorithm; s5, inverse Fourier transform is executed, and a phase response matrix is extracted; s6, performing weighted fusion to obtain a comprehensive phase response diagram; and S7, setting a threshold value according to the noise variance and the statistical characteristics, executing binarization and morphological processing, and outputting a defect region and boundary coordinates. According to the invention, high-precision identification and boundary positioning of steel galvanized surface defects are realized.

A broadband measurement method and apparatus for a power system, and a computer device, which relate to the technical field of broadband measurement. The method comprises: acquiring a target electrical signal; on the basis of a local mean decomposition algorithm, decomposing in a time domain the target electrical signal into a plurality of signal components having a single frequency; determining the instantaneous frequency of each signal component; on the basis of the instantaneous frequencies, determining a corresponding time-frequency transformation algorithm and a corresponding time-frequency transformation parameter; and on the basis of the corresponding time-frequency transformation parameter, using the corresponding time-frequency transformation algorithm to perform time-frequency transformation on each signal component, so as to obtain a broadband spectrum that comprises frequency-domain information corresponding to each signal component. The method is applicable to spectrum analysis performed on a voltage / current signal that includes a plurality of signal components, the calculation is quickly and precisely performed, the broadband measurement requirements of a new power system can be met, and a strong support is provided for constructing a wide-frequency-oscillation wide-area monitoring system for the power system.

The present disclosure relates to systems, methods, and non-transitory computer-readable media that recolors a digital design according to colors of a digital image and further generates an enhanced recolored digital design. In particular, in some embodiments, the disclosed systems identify a digital image for recoloring a digital design and recolors the digital design utilizing a color affine transformation algorithm to generate a recolored digital design. Further, in some embodiments, the disclosed systems generate the enhanced recolored digital design by transforming one or more colors of the recolored digital design to be within a range of the colors of the digital image utilizing a convex hull projection method. Moreover, in some embodiments, the disclosed systems further enhance the recolored digital design utilizing a contrast enhancement algorithm to modify luminescence values.

The invention relates to the technical field of regional security and protection monitoring, in particular to a regional intrusion identification method and system under the view angle of an unmanned aerial vehicle, which adopts a multi-source information fusion framework, combines the real-time position, attitude and flight state information of the unmanned aerial vehicle through an accurate space coordinate conversion algorithm, and realizes the regional intrusion identification under the view angle of the unmanned aerial vehicle. GIS geographic information, unmanned aerial vehicle monitoring and a deep learning detection result are deeply fused, high-precision positioning of an intrusion area in an image shot by an unmanned aerial vehicle is realized, and robustness in a complex environment is improved. Targets such as personnel and vehicles in the image are efficiently recognized by applying a YOLO series algorithm, and violation intrusion behaviors are accurately judged by calculating the intersection-to-union ratio IoU of the targets and the intrusion area. The unmanned aerial vehicle is utilized to quickly cover a large-area monitoring area, and the problem that the monitoring range of a traditional fixed camera is limited is solved. In addition, by means of a space coordinate conversion algorithm, the influence caused by height and angle changes in the flight of the unmanned aerial vehicle is effectively overcome, and accurate positioning of the intrusion area in the image is achieved.

The invention discloses a polynomial operation acceleration method, device and equipment and a storage medium, and belongs to the technical field of cryptographs.The method comprises the steps that a first numerical value and a second numerical value of a first sub-algorithm are obtained, the first numerical value is the number of first polynomials which are generated in the operation process of the first sub-algorithm and need to be operated, and the second numerical value is the number of second polynomials which need to be operated; the second numerical value is the number of second polynomials needing to be operated with the first polynomial in the NTT domain; comparing the first numerical value with the second numerical value; under the condition that the first numerical value is greater than or equal to the second numerical value, performing number-theory transformation on the first polynomial by adopting a bit inverse number-theory transformation algorithm; performing polynomial multiplication on the first polynomial and the second polynomial after the number-theory transformation; or under the condition that the first numerical value is smaller than the second numerical value, the shuffling ordinal number theory transformation algorithm is adopted to carry out number theory transformation on the second polynomial; and performing polynomial multiplication on the first polynomial and the second polynomial after the number theory transformation. The method can realize accelerated polynomial operation.

The invention provides a crack three-way measurement research method and system based on multi-coordinate system fusion, and relates to the technical field of earth and rockfill dam monitoring. The method comprises the following steps: shooting concentric circle target images at multiple angles through a monocular camera, extracting feature points through preprocessing, and correcting projection distortion of non-parallel shooting; establishing a mapping relation between pixels and a three-dimensional coordinate system, and optimizing camera pose parameters; reconstructing a three-dimensional coordinate point cloud based on the multi-view data, screening outliers and fitting plane optimization data; and registering the three-dimensional point cloud to a world coordinate system through a coordinate system conversion algorithm, and comparing two measurement results to calculate the three-way displacement of the crack. According to the method, concentric circle target constraint feature precision is utilized, projection correction and plane fitting are combined to compensate monocular vision depth deficiency, high-precision displacement detection is achieved, and engineering monitoring requirements are met.

The invention discloses a storage and charging system battery capacity calculation method, and relates to the technical field of storage and charging system optimization control, and the method comprises the steps: collecting multi-modal data of a battery pack, and generating time-space aligned multi-modal data through a timestamp alignment and three-dimensional coordinate conversion algorithm; extracting local temperature gradient characteristics of an infrared thermogram and dynamic time characteristics of a charging and discharging curve through DSC and Bi LSTM based on the multi-modal data of space-time alignment, segmenting spatial distribution characteristics of electrolyte flow abnormity of an ultrasonic image by using U-Net, dynamically distributing weights of the multi-modal characteristics through a space-time attention mechanism, and generating a fusion characteristic vector; and inputting the fusion feature vector into a GRU-Transform hybrid model to generate a dynamic capacity prediction value, and identifying an aging type by dynamically analyzing historical data. According to the invention, multi-modal features are extracted through DSC, Bi LSTM, U-Net and other technologies, and comprehensive monitoring and dynamic adjustment of the battery health state are realized.

The invention discloses an LED spherical display screen-oriented geographic data dynamic adaptive display method, which comprises the following steps of: constructing a non-uniform grid mapping model based on LED spherical screen physical parameters (diameter, support structure cavity size and pixel row / column spacing); the method comprises the following steps: accurately mapping geographic data (converted into longitude and latitude coordinates in advance) from a longitude and latitude coordinate system to a spherical screen pixel space, and carrying out resampling adaptation on the data based on the resolution of a display; meanwhile, a quaternion rotation transformation algorithm and a dynamic mask technology are adopted, the shielding area of the supporting structure of the LED spherical screen is calculated in real time, invisible data are automatically cut in the three-dimensional rotation process, and the problems of coordinate mapping distortion, rotation tearing and shielding area adaptation delay existing in a traditional LED spherical display system are solved. According to the method, the sub-pixel-level mapping precision of the geographic data on the LED spherical screen and the self-adaptive shielding compensation response of real-time rotation are realized.

The invention relates to a polylactic acid test piece internal defect detection method, and belongs to the technical field of target detection, the method comprises the following steps: S1, image processing to obtain an input image; s2, attention enhancement feature extraction: preliminarily extracting spatial features, capturing global information and local information, focusing a defect area, and generating an enhancement feature map; s3, carrying out feature fusion, and outputting a fused feature map; s4, performing self-attention re-parameterization, and outputting a local feature map; s5, HSV spot detection including a color conversion algorithm and a defect enhancement algorithm; and S6, outputting a result, establishing a bounding box regression task, predicting a frame coordinate, outputting a defect position, identifying a category label, outputting a defect type, and evaluating reliability and outputting confidence. According to the method, feature extraction is carried out by using depth separable convolution, the visibility and detectability of low-contrast defects are enhanced, and the attention enhancement network is established to adaptively pay attention to key areas related to the defects, so that defect detection is more accurate and efficient.

The invention discloses an automatic welding method of a reinforcement cage welding robot, and relates to the technical field of welding robots. Based on an improved ResNet-50 multi-mode visual system, the welding point identification error is reduced to 0.1 mm level, and by matching with an SE (3) space coordinate conversion algorithm, the positioning precision reaches + / -0.03 mm (3 sigma); the visual algorithm fusing the deformable convolution and the coordinate attention mechanism still keeps the recognition rate improvement of 23.6% under the illumination fluctuation of 200-1500 lux; the improved RRT algorithm is combined with the self-adaptive B-spline interpolation, so that the motion smoothness of the six-degree-of-freedom mechanical arm is improved by 400%, and the welding defect caused by path sudden change is effectively avoided; motion control of the mechanical arm is optimized through the friction compensation model, and the welding stability is remarkably improved.

The invention relates to the technical field of clean laboratory environments, and discloses a predictive analysis method and system of a laboratory heating and ventilation system based on digital twinning. The method comprises the following steps: acquiring a plurality of first target data and a first reference value in a first time period, and respectively calculating first trend data and first spectrum data through a first trend algorithm and a first conversion algorithm; acquiring a plurality of second target data and a second reference value in a second time period, and calculating second trend data and second spectrum data; then, the difference between the first trend data and the second trend data is calculated as first state information based on the digital twin platform, and the difference frequency band between the first spectrum data and the second spectrum data is calculated as second state information. And then, matching corresponding equipment state data according to the state information, calculating a similarity value of the two, and if the similarity value is smaller than a preset equipment reference similarity value, sending out an equipment early warning prompt. According to the invention, hardware linkage can be effectively promoted, accurate prediction of the equipment state is realized, and the equipment fault risk is reduced.

The invention discloses an unmanned ship dynamic obstacle avoidance control system and method based on stochastic nonlinear model predictive control, and adopts a control structure of a planning layer and a control layer to solve the problem of dynamic obstacle avoidance of an unmanned ship in a complex obstacle environment. The planning layer is based on a random nonlinear model predictive control technology, and firstly, random modeling is carried out on the unmanned ship and dynamic obstacles encountered by the unmanned ship in the sailing process; designing obstacle avoidance switching conditions according to the whole actual obstacle avoidance process; in an obstacle avoidance random nonlinear model prediction planning control algorithm, uncertainty propagation of a dynamic obstacle and an unmanned ship in a whole prediction time domain is evaluated through unscented transformation, then obstacle avoidance constraint is described in a chance constraint mode, a chance constraint expectation operator transformation algorithm and a unit jump function approximation algorithm are designed, and a chance constraint expectation operator transformation algorithm and a unit jump function approximation algorithm are designed. Opportunity constraints are converted into deterministic constraints, and then a nonlinear optimization problem is constructed for planning. And the control layer adopts a PI D algorithm to track a planning value so as to realize dynamic obstacle avoidance of the unmanned ship.

The invention provides a colorization point cloud multi-view key point positioning-based cattle body size measurement method, which comprises the following steps of: acquiring cattle 3D point cloud to construct an initial point cloud data set, acquiring a three-view picture set by utilizing a spatial point colorization model, and dividing the three-view picture set into a training set, a verification set and a test set; constructing a key point identification model, performing training by using the training set, performing verification by using the verification set, performing key point identification on the test set after obtaining the key point identification model with the optimal performance, and obtaining two-dimensional plane coordinates of a plurality of key points; converting the two-dimensional plane coordinates into three-dimensional space coordinates by using a different-dimensional key point coordinate conversion algorithm, and calculating the body size parameters of the cattle by using the three-dimensional space coordinates of all key points; according to the invention, accurate positioning of the measurement key points of the cattle 3D point cloud body scale is realized, and the measurement precision is improved; meanwhile, based on a plurality of key points which are accurately positioned, various cattle body size parameters can be accurately calculated, and complete parameter calculation is realized.

The invention provides an automobile part visual inspection correction method and equipment. The method comprises the following steps: extracting a part contour through edge detection, calculating an inclination angle by adopting Hough transform, executing affine transform on an original image, enabling a rotation angle to be negative to the inclination angle, and ensuring that the parts are horizontally aligned; in the corrected horizontal image, calculating a transverse offset through a feature matching algorithm or template matching, and translating an image area to enable the center of the detection window to be aligned with the actual position of the part; if inclination and offset are detected at the same time, inclination correction is executed preferentially, the offset is calculated based on the image after inclination correction, synchronous correction is achieved through a composite transformation algorithm, and accumulative errors are reduced through iterative optimization; and extracting a size parameter from the corrected image, comparing the size parameter with a predetermined tolerance range, and if the measured size is outside the predetermined tolerance range, marking the image as a defective product, and removing the defective product. The method is used for upgrading the visual inspection system for automobile manufacturing.

The invention is suitable for the technical field of cross fusion of three-dimensional scene reconstruction and wireless channel modeling, and provides an indoor scene wireless channel modeling method based on laser radar perception reconstruction, which comprises the following steps: firstly, collecting indoor scene point cloud data by using a laser radar, then carrying out noise reduction sampling processing on the point cloud data, and obtaining a noise reduction sampling result; then, structural elements including walls, ceilings and floors are extracted based on random sampling consistency and a Hough transform algorithm, a wall opening structure is detected by adopting an image semantic segmentation model aiming at internal elements, furniture reconstruction is achieved based on template deformation, three-dimensional surface reconstruction is achieved by utilizing a surface element subdivision algorithm, and the wall opening structure is obtained. And finally, matching electromagnetic parameters on the surfaces of different materials, and completing wireless channel modeling through ray tracing simulation. Therefore, a complex scene is divided into structural elements and internal elements, and corresponding algorithms are designed for different elements to extract key geometric features, so that the precision and robustness of reconstruction are improved.

The invention relates to the technical field of tower crane control, and provides an aerial high-risk area identification and hoisting path reconstruction method and system. The method comprises the following steps of: respectively mounting two rotary radars on a rotatable bracket of the tower crane to form a radar sensing module comprising a double-radar module; dynamically scanning the potential risk area in the construction scene through a radar sensing module to obtain real-time position information of the potential risk area; receiving the real-time position information through a processing module, and converting the real-time position information into real-time three-dimensional coordinate data in a motion coordinate system of the hoisting equipment by adopting a three-dimensional coordinate conversion algorithm; constructing a three-dimensional model of the potential risk area based on the real-time three-dimensional coordinate data; and identifying the three-dimensional model to determine a high-risk area, adopting an improved fast exploration random tree algorithm as a path obstacle avoidance algorithm, combining the three-dimensional model to dynamically reconstruct an initial hoisting path set by a user, and generating an optimal risk-avoiding hoisting track avoiding the high-risk area.

The invention discloses an intelligent fructus aurantii sorting machine based on a five-connecting-rod mechanical arm and a sorting method. The sorting machine comprises a collecting mechanism, a conveying mechanism, a supporting mechanism, a sorting mechanism, a camera support, a feeding device, a lower computer, an upper computer and a vacuum adsorption control system. The sorting mechanism comprises a vacuum adsorption sorting device, a five-connecting-rod mechanical arm and a sorting mechanism supporting base, double motors are adopted to cooperatively drive and control space positioning of an end effector of the mechanical arm, and high-precision target grabbing is achieved through angle difference compensation. The upper computer converts the target center position recognized by the visual recognition device into space coordinates of the tail end of the mechanical arm through a coordinate conversion algorithm, and the five-connecting-rod mechanical arm is driven to complete precise sorting operation. Stable adsorption of the fructus aurantii is completed by means of vacuum negative pressure, the fructus aurantii naturally falls off by directly cutting off a negative pressure air source, and intelligent grading and sorting operation of the quality of the fructus aurantii is achieved. The device is suitable for automatic sorting of the traditional Chinese medicinal material fructus aurantii and is high in sorting efficiency, accurate in positioning and high in adaptability.

The embodiment of the invention discloses a power quality disturbance identification method and device, electronic equipment and a storage medium, the method is applied to a power quality disturbance identification system, a time-frequency information extraction module of the system adopts an adaptive incomplete S transformation algorithm, and a shallow double-path neural network module of the system adopts a double-path structure; the method comprises the following steps: acquiring an electric energy quality disturbance signal in a preset time period; determining time-frequency information of the signal through an adaptive incomplete S transformation algorithm; the time-frequency information is processed in parallel through a double-path structure, and target disturbance characteristics of the signal are obtained; determining a disturbance category of the signal based on the target disturbance characteristic to obtain a plurality of disturbance categories, each disturbance category corresponding to a disturbance probability; determining a loss value of each disturbance category based on the plurality of disturbance categories and the disturbance probability corresponding to each disturbance category to obtain a plurality of loss values; and taking the disturbance category corresponding to the minimum value in the plurality of loss values as a target disturbance category of the power quality disturbance signal.

The invention relates to the technical field of image enhancement, in particular to an intelligent abdominal B-ultrasonic data analysis system for gynaecology and obstetrics, which comprises an image acquisition module, an image analysis module, an algorithm optimization module and an image enhancement module, the image analysis module is used for acquiring gradient factors of pixel points according to gray difference among the pixel points in the ultrasonic image, performing region segmentation on the ultrasonic image, acquiring a boundary line in the ultrasonic image, and determining the gradient factors of the pixel points according to gray change conditions of all the pixel points from a starting point to an ending point in the boundary line in the vertical direction; obtaining a boundary area pixel point set; the algorithm optimization module is used for optimizing a gamma transformation algorithm by combining the position information of the pixel points in the pixel point set of the boundary area; the image enhancement module is used for image enhancement. According to the method, the enhancement effect of the gamma transformation algorithm on the abdominal ultrasound image is improved.

The invention discloses a crystal oscillator circuit fault classification technology based on a multi-source information fusion network, and belongs to the technical field of analog circuit fault diagnosis. Firstly, a multi-source data set of different measurement points of the crystal oscillator circuit is acquired; carrying out conversion from a time domain to a frequency domain on the data set by utilizing fast Fourier transform; extracting data fault features by using a convolutional neural network, and performing a trust distribution function under each piece of source data by using a softmax classifier; fusing different trust distribution functions under the multi-source data by adopting a D-S evidence theory to obtain a final diagnosis result and diagnosis probability output, and calculating cross entropy loss; and finally, training model parameters through back propagation to obtain a final diagnosis model. According to the method, the time-frequency transformation algorithm, the deep neural network algorithm and the information fusion algorithm are combined, a multi-source information fusion network is constructed, the defects of an existing diagnosis model in crystal oscillator circuit fault classification are overcome, and the accuracy and stability of crystal oscillator circuit fault diagnosis are remarkably improved.

The invention discloses an image fusion method and device based on discrete wavelet transform and a convolutional neural network, a terminal and a storage medium. The method comprises the steps of preprocessing a received original image group to obtain an input image group; performing multi-scale decomposition on the input image group through DWT to obtain a decomposition result, and performing feature extraction on the decomposition result according to CNN to obtain an infrared low-frequency feature, an infrared high-frequency feature, a visible low-frequency feature and a visible high-frequency feature respectively; performing fusion through a preset feature fusion strategy to obtain a low-frequency fusion feature and a high-frequency fusion feature; and performing feature reconstruction on the low-frequency fusion features and the high-frequency fusion features through a preset inverse discrete wavelet transform algorithm to obtain and output a fusion image of the original image group. According to the method provided by the invention, the multi-scale characteristic of the DWT is combined with the depth feature extraction capability of the CNN, so that the comprehensive and accurate fusion of the image information is realized, and the quality and efficiency of image fusion are greatly improved.

The invention discloses a metal roof deformation analysis method based on linear interpolation, and the method comprises the steps: taking a node coordinate of a roof steel structure, node deformation and a node coordinate of a metal roof member as main input parameters, and carrying out the conversion of the main input parameters through a corresponding conversion algorithm; possible deformation and component stress conditions of the metal roof system in the construction process are obtained through rapid calculation, repeated manual modeling and analysis work are greatly reduced, and guidance can be provided for metal roof system design, scheme compilation and on-site construction processes.

The invention provides a cross-modal feature fusion method based on joint attention, which combines multi-modal data from different sources to predict the relative position of a target to be tracked, and comprises the following steps of: firstly, designing a cross-modal data representation network model for the data of different modals; generating a time sequence high-dimensional implicit code retaining key dependency information from the original data through a code conversion algorithm; secondly, a heterogeneous attention network unit is designed, and features of time sequence high-dimensional implicit codes corresponding to different modal data are extracted through two different forms of attention; and finally, through a cooperative training mechanism, information complementation among different modal data is utilized to generate the most effective feature representation for a time sequence prediction model, and accurate prediction of the moving position of the tracked target is realized.

The invention is suitable for the field of intelligent jet cleaning, and provides a dirt recognition cleaning method, device and equipment, and the dirt recognition cleaning method comprises the steps: obtaining an original image corresponding to target dirt and a sound reflection signal corresponding to the target dirt; converting the sound reflection signal corresponding to the target dirt into an image representation; obtaining a fused image corresponding to the target dirt according to the original image corresponding to the target dirt, the image representation and a preset multi-channel image fusion algorithm; inputting a fusion image corresponding to the target dirt into the improved deep neural network to obtain segmentation mask data and size data of the target dirt; adjusting the pose of the dirt cleaning equipment according to the segmentation mask data and a preset coordinate conversion algorithm; according to the size data of the target dirt, dirt cleaning parameters are adjusted, and dirt cleaning equipment is controlled to execute cleaning operation. According to the method, the dirt recognition and cleaning precision and efficiency are remarkably improved, and various cleaning requirements and operation environments can be better met.

The invention provides a battery broadband electrochemical impedance spectroscopy time domain rapid detection method and system, and the method comprises the steps: S1, dividing a target signal frequency band into a low frequency band, a middle frequency band and a high frequency band from low to high, and setting the parameters of an excitation signal DIBS of each frequency band; s2, enabling the discrete binary sequence to have spectrum distribution corresponding to the target signal through an optimization algorithm, and obtaining a DIBS signal containing all set frequency points; s3, performing current excitation on the battery by adopting DIBS excitation signals of different frequency bands to obtain excitation current and response voltage; and S4, respectively calculating the frequency spectrums of the excitation current and the response voltage under the three frequency bands, and obtaining the final impedance. A multi-band excitation mode is adopted, the sequence length of a single-segment signal is shortened, the clock period of the single-segment signal is prolonged, and the storage burden of excitation equipment and the data processing complexity of a time-frequency conversion algorithm are effectively prevented from being aggravated due to the long sequence period.

The invention discloses an underground multi-source fusion positioning method and system. The method comprises the following steps that initialization is carried out based on a known initial pose of underground equipment during starting; acquiring environment point cloud data scanned by a vehicle-mounted laser radar of underground equipment and motion information acquired by an inertial measurement unit; registration is carried out based on the environment point cloud data and a pre-constructed high-precision three-dimensional map, and first pose estimation is obtained by using an OpenMP-based parallel normal distribution transformation algorithm; using the motion information to obtain second pose estimation through integral operation; inputting the first pose estimation and the second pose estimation into a Kalman filter for loose coupling fusion, and outputting the fused equipment pose; and outputting the final pose as a positioning result of underground equipment for vehicle navigation and pose control. The method effectively improves the positioning precision of underground equipment.

The invention discloses a quantum circuit conversion method and system, electronic equipment and a storage medium, and relates to the technical field of quantum circuit conversion, and the method comprises the steps: extracting a feature vector of a logic quantum circuit based on initial quantum bit mapping; the feature vector is input into a pre-trained decision tree, recommendation parameters of a line conversion algorithm are obtained, the decision tree is constructed by a training set, and the feature vector of each sample corresponds to the optimal parameter of the decision tree, namely the parameter with the minimum number of physical line gates; and configuring a line conversion algorithm by using recommended parameters, converting a logic line into a physical line, and ensuring that all two quantum bit gates meet quantum computer connected graph constraints and are equivalent in function. According to the method, adaptive parameter configuration can be achieved through the feature vectors and the decision tree, the line conversion quality is effectively improved, the number of physical line gates is remarkably reduced, and the quantum program execution efficiency is improved on the premise that function equivalence and connectivity are guaranteed.