211 results about "Algorithm complexity" patented technology

The application discloses a kind of based on 3D printing's three-dimensional model slice path processing method and device. Including: control 3D printer to starting point as starting point;Based on the region surrounded between starting point and reference point, determine the printing point to be printed that 3D printer needs to execute printing action during executing 3D printing job;Starting point and the printing point to be printed are sequentially connected by smooth straight line, generate printing path;Based on printing path control 3D printer executes 3D printing job and obtains target object, to effectively reduce the number of line segments of slice contour line of three-dimensional model forming target object, avoid 3D printer in printing job, in the pause frequency of line segment head, tail, simultaneously avoid the problem that 3D printing model surface protrusion is caused by long time scanning due to pause.In reducing pause frequency, printing is more smooth, improve printing efficiency, simultaneously also reduce algorithm complexity, help the simplification of algorithm.And effectively simplify the algorithm of 3D printer.

The invention discloses a GNSS (Global Navigation Satellite System) signal spoofing detection method, device and equipment, a storage medium and a program product. The method comprises the following steps: tracking and monitoring a plurality of GNSS satellite signals, calling a tracking loop correlator to carry out autocorrelation operation to obtain the autocorrelation power of each satellite signal, constructing the AP detection quantity of each satellite signal based on the autocorrelation power, verifying the Gaussian property of the AP detection quantity, and carrying out moving average filtering processing on the AP detection quantity conforming to Gaussian distribution to obtain the AP detection quantity of each satellite signal. The method comprises the following steps: acquiring Gaussian distribution characteristic information of a GNSS satellite signal, constructing an AP-MA detection quantity, determining a detection threshold according to the Gaussian distribution characteristic information, detecting the AP-MA detection quantity based on the detection threshold, and judging whether the GNSS satellite signal has deception jamming or not; the AP detection quantity is constructed based on the self-correlation power to carry out signal spoofing detection, so that the detection range and the detection precision are effectively improved, and the timeliness and the stability of spoofing detection are ensured under the condition of not increasing additional equipment and algorithm complexity.

The invention provides a TOF (Time of Flight) estimation method of a matrix bundle based on partitioning and random SVD (Singular Value Decomposition). The method comprises the following steps: firstly, acquiring channel state information (CSI) by using a commercial WiFi device, and constructing a CSI matrix; and secondly, performing block overlapping processing on the CSI matrix, constructing an augmented matrix for each CSI data block, and performing SVD decomposition on the augmented matrix, thereby reducing the calculation complexity of SVD decomposition. And then, the time of flight (TOF) of each CSI data block is estimated by using a matrix pencil algorithm, and the minimum TOF is direct path estimation. According to the method designed by the invention, the robustness of the system and the estimation precision of the TOF are improved under the condition that the algorithm complexity is greatly reduced.

The invention relates to an optimal consistency control method for a random multi-agent system with unknown control gain. The optimal consistency control method is suitable for leader following consistency control of the random multi-agent system with unknown control gain. According to the method, the system uncertainty is approximated by constructing a self-adaptive identifier; designing an observer based on a neural network to reconstruct an unknown control gain; a dynamic event triggering mechanism is introduced, an auxiliary dynamic variable and a dynamic threshold parameter are used for reducing the updating frequency of a controller, and communication and energy consumption are reduced; in combination with an act-critic reinforcement learning framework and based on a positive function related to a Bellman residual error, a neural network weight update rate is constructed, an optimal consistency controller is designed, and the algorithm complexity is simplified while the system stability is ensured. The method has high robustness and is suitable for efficient cooperative control of a multi-agent system in a complex environment.

The invention provides an inter-bank loan fund risk prompting method and device, equipment and a storage medium, and relates to the field of privacy computing, and the method comprises the steps: obtaining the first account information of a first account and the transfer information corresponding to the loan fund when it is detected that the loan fund in the first account is transferred to a second account of a second bank; performing encryption processing on the first account information to obtain a corresponding ciphertext; sending the ciphertext and the transfer information to a second bank, so that the second bank determines corresponding second account information based on the transfer information in a trusted execution environment, and performs loan fund risk query; and receiving a query result fed back by the second bank, and generating a loan fund risk prompt message under the condition that the query result indicates that the risk exists. According to the method, sensitive data are guaranteed not to be leaked by utilizing a hardware isolation mechanism of the trusted execution environment, and meanwhile, plaintext calculation is realized in the trusted execution environment, so that the algorithm complexity is remarkably reduced, and the timeliness of loan fund tracking is improved.

The invention discloses an aerospace network calculation unloading and trajectory collaborative optimization method based on reinforcement learning. In order to solve the problem of dynamic task response hysteresis caused by satellite high-altitude delay, limited unmanned aerial vehicle coverage and resource dispersion, an original problem is decoupled into two sub-problems of task unloading proportion distribution and unmanned aerial vehicle trajectory planning by constructing a mixed integer nonlinear programming model and taking minimization of system task average time delay as a target. According to the method, a K-means clustering algorithm is adjusted by utilizing attributes, dynamic task distribution characteristics are quantified, and track reference points are generated to simplify a state space; and meanwhile, a QPLEX-based multi-agent reinforcement learning framework is designed, and collaborative decision-making of the satellite and the unmanned aerial vehicle is realized. According to the method, a time delay perception optimization mechanism is established by fusing the trajectory reference points generated by clustering, closed-loop joint optimization of an unloading decision and a flight trajectory is formed, the algorithm complexity and the communication overhead are remarkably reduced, and the aerospace network resource scheduling efficiency is effectively improved.

The invention discloses a ship 3D target detection method and system based on image and laser radar fusion in the field of ship navigation intellectualization, and the method comprises the steps: obtaining a two-dimensional bounding box set of an object in a camera image through a Yolov11 detection frame, converting the eight vertex sets of a three-dimensional bounding box into a camera coordinate system, obtaining the minimum bounding rectangle coordinate of a plane coordinate set, and obtaining the minimum bounding rectangle coordinate of the plane coordinate set; calculating an IOU value for each minimum enclosing rectangle and a two-dimensional bounding box in the two-dimensional bounding box set, projecting a plane coordinate of the three-dimensional bounding box before the minimum enclosing rectangle is converted to a two-dimensional plane, and obtaining a three-dimensional detection frame and category information; information such as the position and the size of an object existing in the navigation direction is obtained in real time through a laser radar and fused with a camera picture; the improved DBSCAN clustering algorithm is designed, the core block is confirmed through the point cloud density in the local point cloud data block, the core point in the core block is searched, the clustering processing time is shortened, the algorithm complexity is reduced, and the operation efficiency of 3D target detection is improved.

The invention relates to the field of radio frequency power control, and discloses a radio frequency power control method and system for a 5G mainboard, and the method comprises the steps: 1, capturing the phase fluctuation of a millimeter wave frequency band through employing a superconductive quantum bit array, building a virtual channel response based on quantum entanglement state measurement, generating a channel topological graph, calculating and predicting a channel state based on the channel topological graph, and carrying out the calculation of a predicted channel state; calculating a prediction error according to the measured actual channel state, and adjusting the radio frequency power; 2, fusing the quantum measurement matrix and the deep residual network, introducing a residual attention mechanism to reconstruct an abnormal channel, carrying out local optimization, solving asymmetric Nash equilibrium, reducing algorithm complexity and calculation complexity, dynamically controlling radio frequency power, suppressing interference and improving power amplifier performance; and step 3, analyzing the influence of environmental factors on system errors, jointly calibrating and correcting the errors, constructing a learning model and expanding a dynamic range, determining the actual maximum transmitting power and distributing radio frequency power.

A method for estimating typhoon structural parameters by fusing spaceborne GNSS reflectometer data and radiometer data, which relates to the technical fields of data fusion and typhoon monitoring. The method comprises: acquiring GNSS-R reflectometer data and radiometer data; filtering typhoon data from the GNSS-R reflectometer data and the radiometer data to obtain filtered GNSS-R reflectometer data and radiometer data; performing data spatio-temporal matching on the filtered GNSS-R reflectometer data and radiometer data to obtain a spatio-temporally matched region; performing data fusion processing on the spatio-temporally matched region to obtain fused data; and performing typhoon structural parameter estimation on the obtained fused data to obtain structural parameters of a typhoon at each development stage. Fused data of spaceborne GNSS-R reflectometer data and radiometer data is used for typhoon structural parameter estimation, such that the temporal resolution and accuracy of parameter estimation can be improved, and the algorithm complexity is low.

The application discloses a radar plot and AIS track adaptive parallel matching method and system, and belongs to the technical field of radar and navigation information. In order to reduce the algorithm complexity and the dependence of AIS data, the application comprises the following steps: identifying the database arrival data, performing voting on the projected radar plot in a parameter space by a system error extraction module based on an error distribution function to obtain a plurality of counters and filtering the counters by a filter, finally accumulating the results of all the filters and performing peak value detection to extract the system error of the radar plot; performing speed correlation checking on the searched radar track point by a radar plot index module, and searching the radar track point with a speed parameter meeting the requirements; performing sum and optimization on an error function according to a PARZEN window based on the radar track point with the speed parameter meeting the requirements by an error function optimization module; and updating the membership ship number in the AIS table of the database by using the radar track point with the speed parameter meeting the requirements.

The invention discloses a feature recognition and path planning method for point cloud three-dimensional-two-dimensional-three-dimensional conversion of a curved-surface workpiece, and the method comprises the steps: obtaining three-dimensional point cloud data of the curved-surface workpiece, and carrying out the preprocessing of the three-dimensional point cloud data; projecting and converting the preprocessed three-dimensional point cloud data into two-dimensional point cloud data by adopting a slice projection method, and forming a fitting curve based on the two-dimensional point cloud data; identifying and obtaining a two-dimensional inflection point set meeting the requirement by a curve inflection point detection method based on an angle integral window; converting the two-dimensional inflection point set back to a three-dimensional coordinate space; adopting a two-stage fitting strategy to identify and obtain an optimal fitting plane from the three-dimensional inflection point set; and a plane cutting method is adopted to obtain a planned path for actual machining. According to the method, a complicated point cloud feature extraction problem is converted into a two-dimensional analysis problem with higher calculation efficiency, so that the algorithm complexity is reduced; the problem of low feature extraction accuracy in a noise environment is effectively solved; the robustness under the condition that the outliers exist is enhanced; an intermediate link of CAD modeling in a traditional method is avoided, and accumulative errors are reduced.

The invention belongs to the technical field of electronic countermeasure and target positioning, and relates to a TDOA radiation source tracking and trajectory prediction method based on EKF. The method comprises the following steps: S1, setting N sensor nodes to measure a TDOA observation value; s2, establishing a radiation source motion state space model; s3, calculating a Jacobian matrix to linearize the nonlinear observation equation; s4, executing an EKF prediction and updating step, and updating state estimation by using a TDOA observation value; s5, predicting a future trajectory based on the current state; and S6, performing loop execution to realize continuous tracking. According to the method, the nonlinear problem of TDOA positioning is solved through the EKF algorithm, high-precision tracking and trajectory prediction of the high-speed moving radiation source are achieved, good robustness and real-time performance are achieved, the algorithm complexity is moderate, and the method is suitable for accurate positioning of the moving radiation source in an electronic countermeasure system.

The invention provides a communication and computing resource joint allocation algorithm based on a diffusion model, and relates to the technical field of allocation algorithms. The communication and computing resource joint allocation algorithm based on the diffusion model comprises the following steps: S1, defining input and output; s2, constructing an environment information data set; s3, preprocessing the data set; s4, training an environment information classifier; s5, training a diffusion model; s6, generating a communication and computing resource joint allocation scheme by using a diffusion model; and S7, evaluating a resource allocation scheme. According to the method, real-time Internet of Vehicles environment information is input into a pre-trained environment information classifier to predict the category of the environment information, and the category of the environment information is used as a guide word to guide a diffusion model to generate a proper communication and computing resource joint allocation scheme in the environment. The problems that an existing algorithm is high in complexity, low in convergence speed and prone to falling into a local optimal solution are solved.

The invention relates to the technical field of cable manufacturing, in particular to a full-line automatic linkage cable production method and system, and the method comprises the steps: carrying out the data collection of the operation data of a plurality of process nodes on a cable automatic production line; performing extreme value feature extraction on the operation data to construct a minimum value feature array and a maximum value feature array; whether synchronization abnormity occurs or not is judged in real time according to the minimum value feature array and the maximum value feature array, and if the synchronization abnormity occurs, dispatching control is conducted on the cable automatic production line to eliminate the synchronization abnormity; according to the method, whether synchronization abnormity exists or not can be rapidly judged with the minimum computing power, linear speed deviation caused by process slowdown can be rapidly recognized, the algorithm complexity is low, the duration of the synchronization abnormity can be accurately recognized, the recognition precision of the synchronization abnormity under the condition that the duration of the ascending time period and the descending time period is short is improved, and the recognition efficiency of the synchronization abnormity is improved. And the problem of cable product quality caused by rapid accumulation of linear velocity deviation is avoided.

The invention discloses a novel non-uniform array-based signal source positioning method, which comprises the following steps of: determining an actual array element position through flexible array distribution according to environmental characteristics when a target signal reaches the ground; calculating a virtual domain differential common array, determining the position of a virtual array element, constructing a signal model, and calculating a covariance matrix; vectorizing the covariance matrix, and removing redundancy to obtain an equivalent receiving signal of the virtual domain; performing spatial smoothing processing on the equivalent covariance matrix of the virtual domain to obtain a smoothed data correlation matrix; after characteristic decomposition, constructing a signal subspace; and finally, dividing signal subspaces based on an ESPRIT algorithm idea, and calculating a rotation matrix to obtain an estimated angle. The designed non-uniform array can flexibly adjust the array element layout according to complex environment characteristics, the flexible array layout strategy improves the freedom degree of the array, the spatial sampling and signal analysis efficiency is optimized, meanwhile, the calculation path is simplified, and the algorithm complexity is reduced.

The invention discloses a high-resolution tomographic SAR four-dimensional imaging method for space-time baseline decoupling processing, and the method comprises the following steps: 1, reconstructing original satellite data into uniform baseline observation data, and solving the noise-free estimation of the uniform baseline observation data through the low-rank characteristic of a Hankel matrix; step 2, inverting elevation information based on noise-free estimation, taking the elevation information as priori, constructing a deformation spectrum estimation dictionary through space-time decoupling, and performing deformation information estimation; and step 3, matching the estimated elevation and deformation quantity by using a maximum likelihood method so as to realize four-dimensional imaging. According to the method, the algorithm complexity of four-dimensional imaging is reduced exponentially while high-precision imaging is realized, and differential tomography SAR four-dimensional imaging result verification is carried out based on domestic land exploration No.1 satellite data.

This invention discloses a chip layout image generation method based on a boundary tracking scan line algorithm. By designing a sub-image-polygon data structure and establishing a maximum matrix of adjacent polygons in the sub-image, this method improves the boundary scan line algorithm to quickly segment the complete chip layout into sub-images and generate corresponding binary images. The algorithm designs a sub-image bounding box-polygon bounding box data structure to quickly locate which polygons are most likely to intersect with the sub-image. Saving this data constructs the maximum bounding box of all polygons contained in the current sub-image, eliminating the need for the algorithm to calculate the intersection points of polygons with the sub-image, thus reducing the time consumption for intersection point calculation. Furthermore, a boundary tracking method is designed to improve the scan line algorithm. By saving each point in the boundary, when scanning a row, it is not necessary to calculate the intersection points of the scan line and the polygon boundary, further reducing the algorithm complexity.

The application discloses a kind of high-resolution tomography SAR four-dimensional imaging methods of space-time baseline decoupling processing, which comprises the following steps: step 1, reconstruct original satellite data into uniform baseline observation data, and utilize the low rank characteristic of Hankel matrix, solve its noiseless estimation;Step 2, elevation information is inverted based on noiseless estimation, and elevation information is used as priori, deformation spectrum estimation dictionary is constructed by space-time decoupling, and deformation information estimation is carried out;Step 3, the elevation and deformation variable estimated are matched using maximum likelihood method to realize four-dimensional imaging.The method realizes high-precision imaging while exponentially reducing the algorithm complexity of four-dimensional imaging, and the differential tomography SAR four-dimensional imaging result verification is carried out based on domestic land exploration No.1 satellite data.

The invention is suitable for the field of computer vision and artificial intelligence, and provides an automatic target detection and tracking method and system based on deep learning, and the method comprises the steps: obtaining a video stream and a scene structure diagram, extracting a target position bounding box, a category label and an appearance feature vector frame by frame, and calculating a behavior parameter and a priority score; generating a detection frame interval and a tracking algorithm complexity parameter according to the priority score, and initializing a special tracker to realize automatic tracking; when the target is lost, generating a space-time probability graph based on historical data and the scene structure graph; determining retrieval parameters according to the priority scores, and performing directional detection on the screened space-time regions; and after the target is retrieved, the identity is associated and parameters are redistributed to recover tracking. According to the method, through differentiated resource allocation, accurate loss prediction and directional retrieval, tracking precision, efficiency and robustness are considered, and the problems of target tracking interruption, resource waste and the like in a complex scene are effectively solved.

The invention discloses a channel estimation method of a time-varying channel in a time delay-Doppler domain pilot mode, which comprises the following steps of: superimposing a hybrid superimposed pilot and a data symbol in a time delay-Doppler domain, the pilot comprising a high-power center pilot and a low-power edge pilot; the signal is converted to a time domain through inverse symplectic Fourier transform and Hisenberg transform; a receiving end obtains a receiving signal through Wigner transform and Sextile Fourier transform; estimating integer time delay and Doppler taps by using a center pilot frequency, and constructing a sparse channel matrix; estimating fractional Doppler and channel gain through a sparse Bayesian learning algorithm; and detecting a data symbol based on a minimum root-mean-square error algorithm, and realizing accurate estimation of channel parameters through iterative interference elimination. According to the method, the peak-to-average power ratio and the algorithm complexity can be effectively reduced, and non-correlation channel components and cross interference are inhibited.

The application provides a traffic target detection and recognition method based on sound-vibration time-frequency characteristics and cross-attention fusion mechanism. First, sound-vibration signals of different traffic targets are collected by a sound-vibration sensor, and normalized least mean square (NLMS) noise filtering preprocessing is performed. Second, the preprocessed sound-vibration signals are subjected to variational mode decomposition (VMD), and scale spectrum segmentation method and summation fuzzy entropy minimum value method are adopted to decompose the sound-vibration signals into multiple intrinsic mode functions (IMFs). Third, Mel spectrograms are extracted from the sound signal IMFs, and wavelet transform time-frequency diagrams are extracted from the vibration signal IMFs, and the results are subjected to CNN convolution pooling, and transformer encoder is further used to extract sound-vibration signal characteristics of different traffic targets. Finally, cross-attention mechanism is used for coding, sound signal characteristics and vibration signal characteristics are fused into new characteristics, and Softmax function and Dropout function are used for normalization and overfitting prevention. The application has the advantages of low algorithm complexity, strong real-time performance and low cost, and solves the traffic target detection problem under extreme climate, weather, light and other scenes.

The invention discloses a self-adaptive task priority management method based on a dynamic entropy weight and a time decay factor, and relates to the technical field of computer task scheduling and resource allocation. The method comprises the following steps: acquiring a to-be-processed task set and constructing a multi-dimensional feature matrix, dividing features into benefit type indexes and cost type indexes, and respectively carrying out same-direction standardization processing on data by adopting forward and reverse normalization formulas; calculating entropy values and difference coefficients of the characteristic indexes in real time based on an information entropy theory, determining dynamic weights reflecting current task flow characteristic distribution, and calculating static basic scores of the tasks; introducing an exponential time decay model containing a natural constant and a time sensitive coefficient, performing nonlinear acceleration compensation on the static basic score according to the queue residence time of the task, and generating a final dynamic priority index; and screening the task corresponding to the maximum value according to the index to preferentially allocate computing resources. According to the method, the blindness of manual weighting can be eliminated, the hunger and deadlock problems of low-priority tasks are effectively solved through an exponential-level time compensation mechanism, and the method has the advantages of low algorithm complexity, high environment adaptability, high scheduling fairness and the like.

The demodulation method based on the ultra-weak fiber grating OFDR includes: performing fast Fourier transform on the time domain reflection signal collected by the OFDR system to obtain a frequency domain spectrum; setting an amplitude threshold, identifying the UW-FBG array reflection peak in the frequency domain spectrum, calibrating the spatial position of the sensing unit, and intercepting the window segment spectrum signal corresponding to each UW-FBG; performing inverse Fourier transform on the intercepted window segment spectrum signal to restore the spectrum signal of a single UW-FBG; for the reconstructed spectrum signal, selecting a fitting region containing the Bragg reflection peak, iteratively optimizing the Gaussian function fitting parameters by the least square method, and demodulating the peak Bragg wavelength; traversing the window segment spectrum signals of all UW-FBGs and repeating the above steps to obtain the Bragg wavelength shift distribution of the whole sensing region. The demodulation method based on the ultra-weak fiber grating OFDR improves the high-precision detection characteristics of the OFDR, realizes dynamic demodulation, significantly improves the sensing demodulation rate, and simplifies the algorithm complexity.

The invention provides an improved partial transmission sequence (PTS) technology capable of simultaneously improving the safety performance and the peak-to-average power ratio performance of a universal filtered multi-carrier (UFMC) system, and aims to enhance the signal safety and improve the transmission performance of the system while the algorithm complexity is not increased. According to the invention, a partial sequence transmission method is introduced, and a PTS method and a UFMC system both have the step of partitioning subcarriers, so that the PTS method and the UFMC system can be effectively combined, and the peak-to-average power ratio is reduced without increasing the system complexity. The chaotic system provides a chaotic sequence, the chaotic sequence is introduced into subband cyclic shift of the PTS, and signal encryption operation is realized while the peak-to-average power ratio of the system is reduced.

The invention relates to the technical field of wireless communication and signal processing, particularly discloses an efficient implementation method related to a frequency domain of a micro-frequency-hopping symbol, and aims to solve the problem of high demodulation calculation complexity related to the frequency domain when the length of the micro-frequency-hopping symbol is a prime number P. The method comprises the following steps of: decomposing a time domain point multiplication signal of which the length is prime number P into a zero point signal and a P-1 point out-of-order time domain point multiplication signal of which the length is non-prime number length, converting the P-1 point out-of-order time domain point multiplication signal into a frequency domain, performing rotation compensation in the frequency domain, performing secondary Fourier transform, and performing compensation by using the zero point signal and performing re-order; and finally, combining with the direct-current component to obtain a final frequency domain related signal. By using the method, the algorithm complexity is reduced from O (P * P) related to a direct frequency domain to 2 * O ((P-1) * nFactor) (wherein nFactor is the number of prime factors of P-1), and the calculated amount is reduced by dozens of times to hundreds of times.

A coding modulation method, demodulation and decoding method and device for a passive optical network are applied to the technical field of passive optical networks, and the coding modulation method comprises the following steps: carrying out system polarization coding on a received first data stream to obtain a second data stream with an information bit and a frozen bit; according to a preset many-to-one mapping rule, mapping the second data stream to a symbol set to obtain a mapped symbol stream; and adjusting the sending probability of each symbol in the symbol stream through truncation probability shaping in a preset format to obtain a modulated modulation signal. According to the invention, the coding modulation of the data is carried out by combining the system polarization coding technology, the many-to-one mapping technology and the truncation probability shaping technology, so that the data can be obtained through simple inverse operation, the introduction of a distribution matcher to realize probability shaping is avoided, the complexity source is reduced, and the data processing efficiency is improved. And meanwhile, the algorithm complexity is greatly reduced compared with the algorithm which needs to be subjected to iterative de-mapping and decoding.

The invention discloses an aircraft model identification method based on point cloud data. The method comprises the following steps: acquiring a three-dimensional point cloud and a two-dimensional Obtaining an aircraft source point cloud only containing an aircraft; dividing the aircraft source point cloud into an aircraft head point cloud and an aircraft body point cloud; obtaining a main plane and a vertical plane; engine spacing, rear wheel spacing, front and rear wheel spacing and nose front wheel spacing of the aircraft are calculated; and judging the aircraft type of the to-be-tested aircraft in combination with the standard aircraft type parameter library. By processing and analyzing the point cloud data of the airplane, the key characteristic parameters of the airplane can be accurately extracted, the problem of non-uniform distribution of the point cloud data acquired by a laser radar in the prior art is solved, the accuracy and stability of airplane type identification are effectively improved, and the method is suitable for popularization and application. Especially, aircraft targets with complex outlines can be stably captured. The method is simple in structure and low in algorithm complexity, does not need a complex deep learning model, reduces the requirement for the computing power of equipment, improves the detection speed, and improves the practicability of the system.

The invention discloses a teaching quality evaluation model construction method and a teaching quality evaluation system. The evaluation model construction method comprises the following steps: constructing an index system including four dimensions of teaching objectives, contents, methods and evaluation; evaluation of peers, students and supervisors is integrated, and course teaching direct quality is calculated through fuzzy comprehensive evaluation. Calculating the course teaching quality of the teaching team by taking the course score as a weight; the course teaching direct quality and the teaching team course teaching quality are combined, the course influence degree is calculated through the weighted Euclidean distance, and a course teaching indirect quality evaluation model is constructed; and when the evaluation results are the same, an elastic close degree mechanism is introduced to carry out fine sorting. According to the method, course teaching indirect quality indexes are introduced, and individual evaluation and team evaluation are effectively fused; an elastic close degree mechanism is introduced, so that the distinguishing capability under the same grade is improved; a multi-source evaluation integration model is constructed, and evaluation objectivity and accuracy are improved; the method is low in algorithm complexity and suitable for large-scale course evaluation scenes.

The invention discloses a rapid neighbor search method and device, equipment, a medium and a program product, and belongs to the technical field of data processing.The method comprises the steps that a geographic position set and an initial grid angle side length are obtained and rasterized, and a grid set is obtained; determining the number of geographic position points in the initial nine-rectangle grid of the target position, and calibrating the initial grid angle side length according to the numerical relationship between the number of the geographic position points and a preset threshold value to obtain an updated grid angle side length; rasterizing again to obtain updated grid coding information and an updated grid set; and obtaining the updated Sudoku and determining the distance between all the geographic position points and the target position to obtain a fast neighbor search result. According to the method and the device, the situation of missing detection of grid edge points is avoided by adopting Sudoku grid traversal, and the distance calculation amount and the algorithm complexity are greatly reduced, the operation efficiency is improved, and the use experience of a user is effectively improved by adaptively adjusting the grid side length and selecting a more appropriate grid size.

A three-dimensional millimeter wave time-varying channel estimation method and device belong to the field of wireless communication, comprising: establishing a millimeter wave communication system containing array motion; establishing a three-dimensional time-varying channel model considering Doppler effect, analyzing the synthesized signal in the three-dimensional time-varying channel caused by array motion, each column of the synthesized direction matrix being consistent with the array manifold of the planar array, indicating that the linear array in motion can form a virtual planar array; realizing sparse representation of the three-dimensional time-varying channel through linear array motion; recovering the three-dimensional time-varying channel based on the iterative meshless weighted algorithm of array motion; comprising AoAs estimation and path gain estimation. The application overcomes the negative influence of Doppler frequency shift in the time-varying channel on channel estimation, constructs virtual array elements, realizes channel estimation on the three-dimensional millimeter wave time-varying channel through linear array motion, improves channel estimation accuracy, and greatly reduces pilot overhead and algorithm complexity.