118 results about "Matrix optimization" patented technology

The invention relates to the field of road compactness monitoring, in particular to a road compactness real-time regulation and control method and system based on digital twinning, and the method comprises the steps: collecting vibration acceleration, temperature and position data, and generating a time-space aligned multi-source fusion feature data set after processing; inputting a pre-trained LSTM model to construct a dynamically updated digital twinborn body, and outputting a three-dimensional compaction energy spectrum; dispersing the atlas and calculating parameters, and generating a compaction degree deviation matrix; a regulation and control instruction is generated and issued based on matrix optimization; and according to the measured data and the predicted value residual error, triggering re-optimization and calibrating the sensor. According to the method, the problems of non-uniform compaction quality and over-high energy consumption caused by poor adaptability, decision lag and error coupling of a traditional static model are solved.

The invention provides a large-scale satellite three-dimensional image data processing method, medium and system, and belongs to the technical field of satellite three-dimensional image data processing.The method comprises the steps that Gaussian filtering and wavelet noise reduction preprocessing is conducted on an image, and then cooperative computing of a GPU and a CPU is achieved based on a computing task distribution function. Through multi-scale feature extraction and matrix optimization, a stable feature matrix and a variable feature matrix are obtained. A quadtree spatial index is adopted to carry out data partitioning, and SIFT feature extraction and K-means clustering are combined to optimize feature representation. And establishing a stereoscopic image registration model, determining an optimal splicing sequence through a minimum spanning tree algorithm, realizing image splicing by adopting a progressive texture fusion method, and finally generating a high-precision three-dimensional earth surface model. According to the invention, the unification of processing efficiency and precision is realized, and the technical problem that the processing efficiency of large-scale stereoscopic image data is difficult to improve on the premise of ensuring the processing precision in the prior art is solved.

The invention discloses an unmanned aerial vehicle cluster patrol path decision-making method under resource constraint, and relates to the technical field of unmanned aerial vehicle path planning, and the method comprises the steps: carrying out the discretization of an actual to-be-patrolled physical position, and constructing an undirected topological graph; generating steady-state distribution of each patrol node according to topological constraints and importance degrees of the nodes; generating a plurality of transfer matrixes with the same steady-state distribution and different transfer characteristics according to a multi-stage entropy driving random matrix optimization algorithm; initializing the position of a navigator according to the transfer matrix and determining a selected path; according to the reference path of the navigator, the self-adaptive active positioning decision is realized under the positioning constraint to ensure the path tracking effect; according to path selection and tracking of the navigator, the follower and the navigator form a humanoid marshalling cluster through a reward function; and according to the multi-state transfer matrix and the humanoid marshalling, automatically switching to the next transfer matrix when a transfer frequency threshold value is reached, and realizing unmanned aerial vehicle cluster intelligent patrol path decision under resource constraint.

The invention discloses an InSAR (Interferometric Synthetic Aperture Radar) and GNSS (Global Navigation Satellite System) robust adaptive fusion method for high-precision three-dimensional surface deformation monitoring, which belongs to the technical field of space geodetic survey and remote sensing, and comprises the following steps of: constructing a time sequence deformation field based on a small baseline set time sequence InSAR processing technology, and resampling GNSS observation data to a grid consistent with the InSAR by using Kriging spatial interpolation; a Helmert variance component estimation method is adopted to adaptively estimate variance components of InSAR and GNSS data, and reasonable weight fixing of a multi-source observation value is achieved; iterative reweighted least square estimation is introduced, outlier influences in various observation data are dynamically restrained through a Tukey double-weight function, robust optimization of a fusion model is achieved, and therefore a high-precision three-dimensional deformation field is reconstructed. The method comprises the steps of Helmert weight fixing, IRLS robust, variance component updating and a closed-loop feedback mechanism of weight matrix optimization.

The invention provides a method for generating a front-end prototype based on an artificial intelligence technology. The method comprises the steps of data acquisition, knowledge base construction, model training and optimization, demand analysis and prototype generation. The method comprises the following steps: constructing a knowledge base supporting multi-dimensional query by collecting and verifying historical data; in combination with the content of the knowledge base, a large language model is used for performing field specialized fine tuning, and low-rank matrix optimization and comparative learning are adopted to improve the task similarity detection precision; semantic analysis and structured conversion are carried out on the natural language requirements of the user; a front-end prototype is automatically generated based on structured data, and user interaction feedback and dynamic optimization are supported. According to the method, efficient data processing, intelligent model optimization and flexible prototype design are realized, the intelligent level and development efficiency of prototype generation are improved, and powerful support is provided for front-end design under rapid iteration and complex requirements.

The present invention belongs to the technical field of decoding algorithms, and specifically relates to normalized minimum sum decoding of BCH codes and post-processing decoding methods thereof, the steps of which include optimizing the standard form of the check matrix of the BCH code to obtain the best check matrix; inputting the received sequence into the improved normalized minimum sum decoding algorithm NMS based on the best check matrix for decoding; if the decoding is successful, outputting the best decoding estimate; if the decoding fails, inputting the sequence iteration trajectory after the NMS decoding failure into the DIA neural network model, analyzing and outputting the reliability metric of each bit, and then decoding again through the ordered statistical decoder OSD to obtain the best decoding estimate. The present invention successfully achieves the low density of the check matrix, the reduction of the number of short loops, and the uniform distribution of column weights through an innovative check matrix optimization algorithm, and provides a more efficient and better performance BCH code decoding solution.

The invention relates to a multi-modal emotion recognition method based on heart and brain coupling and a graph neural network, and belongs to the field of artificial intelligence. Comprising the steps of data preprocessing, graph representation construction, multi-view graph convolutional network construction, fusion graph network construction and cross-domain joint optimization and sentiment classification. The method has the advantages that an adaptive adjacency matrix optimization strategy based on a triple constraint mechanism is proposed to solve the modal alignment and deviation problems represented by a multi-modal diagram in a data-driven branch, redundant noise is eliminated by adopting global regularization constraint, and unique feature representation in a modal is enhanced through modal specificity; a deep association rule is mined in combination with a cross-modal interaction module, the modeling ability of a heart and brain emotional state is improved, a multi-view image convolutional network is further designed, global features and local features are extracted, features of a cognitive heuristic branch and a data driven branch are combined by adopting an attention mechanism-based image fusion network, a domain confrontation strategy is introduced, and a cognitive network is constructed. And the generalization of the method is enhanced.

The invention discloses a near-field flux-sensing integrated Bayesian Cramer-Rao bound beamforming optimization method, which aims at minimizing the Bayesian Cramer-Rao bound of an extended target response matrix, and constraint conditions comprise the maximum transmitting power of a base station, the minimum signal to interference plus noise ratio requirement of a communication user and the minimum energy efficiency of a system. Auxiliary variables are introduced, an original optimization problem is converted into an equivalent full-digital beam forming matrix optimization problem, and iteration is carried out by adopting a sequential convex approximation method until convergence is carried out to obtain a local optimal solution; aiming at the local optimal solution, constructing an optimization problem by using a method of approaching an equivalent full-digital beam forming matrix by using a hybrid beam forming matrix; and an analog beam forming matrix and a digital beam forming matrix are obtained through an alternate optimization method. According to the method, the Bayesian Cramer-Rao bound minimum transmitting beam design problem of the near-field communication and sensing integrated system is researched, and sensing of an extended target can be realized under the condition that the communication energy efficiency of the system is met.

The invention relates to an intelligent industrial control parameter self-tuning method and equipment. The method comprises the following steps: performing piecewise linearization processing on an actuator input and output characteristic curve in the industrial control system, constructing an upper-layer agent model and a lower-layer agent model, and combining to obtain a system dynamic characteristic prediction model; performing state observer design and gain matrix optimization on the industrial control system based on the system dynamic characteristic prediction model to obtain a system state observation vector and an interference compensation vector; inputting the system state observation vector and the interference compensation vector into a fixed time convergence controller for control parameter calculation to obtain a system control input matrix; and performing adaptive gradient descent operation and momentum item optimization on the system control input matrix, and outputting an optimal control parameter matrix in combination with the system dynamic characteristic prediction model. According to the invention, real-time automatic adjustment of the control parameters is realized, the steady-state error of the system is effectively reduced, and the control performance of the control system under different working conditions is ensured.

The invention relates to the technical field of power system stability control, in particular to a new energy station transient voltage and frequency coordination stability control method, which comprises the following steps of: analyzing a transient energy evolution rule of an equivalent synchronous motor and stored energy by constructing an alternating current and direct current hybrid dynamic model and fusing with multi-time scale characteristics; based on dynamic weight matrix optimization and a multi-target coordination control strategy, self-adaptive balance of power distribution is realized; a nonlinear feedback algorithm and a real-time compensation mechanism are designed in combination with the Lyapunov theory, and voltage frequency fluctuation in a disturbance scene is suppressed; establishing a multivariable cooperative control architecture, and dynamically adjusting a power adjustment priority and an inertia time constant through coupling iterative optimization of a source storage cooperative strategy to realize adaptive compensation of a transient energy propagation path; according to the method, the dynamic stability and the anti-interference capability of the new energy station under transient impact are remarkably improved, and safe operation of a high-proportion new energy power grid is guaranteed.

The invention provides beam generation of a BD-RIS-assisted dual-function radar communication system, and particularly relates to the technical field of wireless communication, and the scheme comprises the following steps: constructing the BD-RIS-assisted dual-function radar communication system, and constructing a user weighting and rate optimization problem under communication and perception constraints; decomposing a user weighting and rate optimization problem into a base station transmitting precoding matrix optimization problem and a BD-RIS matrix optimization problem; alternately and iteratively executing the step of solving the base station transmitting precoding matrix optimization problem and the BD-RIS matrix optimization problem to obtain an optimal precoding matrix and an optimal BD-RIS matrix; and based on the optimal precoding matrix and the optimal BD-RIS matrix, converting an initial beam pointing to the BD-RIS and the target area into a target beam. According to the scheme, the joint optimization problem is solved, the sensing performance is guaranteed, the communication rate of the communication user is remarkably improved, and the user communication and target detection requirements are met at the same time.

The invention discloses a fluid antenna position and radar communication precoding matrix optimization method in a URLLC scene. The method comprises an integrated architecture supporting communication and perception of multiple user terminals and a single perception target; constructing a communication signal model between the base station and the user terminal in the URLLC scene and a sensing signal model between the base station and the detection target; establishing a mathematical model of a radar communication signal optimization problem by taking maximization of a radar sensing signal-to-noise ratio as a target and taking base station transmitting power, URLLC time delay requirements and fluid antenna position limitation as constraints; the optimization problem is decomposed into three sub-problems, an alternating iteration optimization algorithm is adopted, the three sub-problems are alternately optimized, and a convergence solution is obtained through iteration. According to the invention, the URLLC technology and the multiple-input-multiple-output antenna array and fluid antenna technology are utilized, communication and radar sensing are executed on the same frequency spectrum at the same time, and the requirements of communication and radar sensing in a future network are met.

The invention relates to the technical field of machine vision and optical detection, in particular to a tea color automatic evaluation method and system based on machine vision. The method comprises the following steps: constructing a stable and repeatable optical measurement environment and calculating radiation baseline parameters; performing multi-band polarization sequence imaging on the tea sample to obtain original data with spectrum and structural characteristics; separating mirror surface and diffuse reflection components on the surfaces of the tea leaves, and performing spectrum consistency correction by combining a radiation baseline; performing multiband spectrum reconstruction and normalization processing on the basis of the corrected diffuse reflection image, mapping to a CIE Lab color space, extracting pixel-level brightness, chromaticity and color distribution statistical indexes, and generating a color feature vector in combination with spatial weighting matrix optimization; a final score is calculated by constructing a quantitative evaluation and grade judgment model and integrating perception color separation, drying calibration color separation and a penalty term, and a grade result is output. According to the method, objective, accurate and automatic evaluation of the color of the tea leaves is realized.

The invention discloses a multi-mode enterprise intelligent brain system based on artificial intelligence, and belongs to the technical field of big data processing. The method and the device are used for solving the technical problems of poor multi-modal data processing precision and poor system robustness in the existing scheme. Semantic alignment of multi-modal features can be realized through a closed-loop process of pre-extraction, comparative learning, dimension alignment, dynamic optimization and verification and fine adjustment, cross-modal semantic association is established through comparative learning, space mismatching is solved through dimension alignment, dynamic matrix optimization fits task requirements, and actual effects are ensured through verification and fine adjustment. Through a progressive process of confidence evaluation, task coding, weight calculation, feature fusion and end-to-end optimization, intelligent weighting of multi-modal features is realized, and through confidence evaluation, reliable data is screened, a decision target is determined through task coding, a gating network dynamically allocates weights, and end-to-end optimization ensures that a strategy meets service requirements.

The invention relates to a sanitation vehicle intelligent scheduling method and system and a storage medium, and the method comprises the steps: a data collection step: collecting environment data and vehicle information in real time, the environment data including garbage accumulation amount, traffic condition and weather forecast, and the vehicle information including vehicle position and state; a data processing step: cleaning and formatting the collected environment data and vehicle information, and constructing a feature vector and an input matrix; a scheduling optimization step: processing the data generated in the data processing step by using a quantum heuristic evolutionary algorithm, and finding out an optimal scheduling scheme; and an execution monitoring step: executing a cleaning task according to the optimal scheduling scheme generated in the optimization scheduling step, monitoring the cleaning progress in real time, collecting the cleaning progress and state information, and feeding back the information to the data acquisition step to form closed-loop control. According to the invention, the accuracy and reliability of scheduling decisions are improved.

The invention relates to the technical field of marketing operation based on big data analysis, and discloses a marketing operation method based on big data analysis. Preprocessing the user-commodity interaction data to construct an initial interaction matrix; constructing a co-occurrence counting matrix; automatically calculating a sparse penalty coefficient and a convergence threshold; setting an optimization objective function and a soft threshold function; initializing a projection matrix and iteratively solving a sparse projection matrix through a coordinate descent algorithm; constructing a commodity sparse co-occurrence adjacency set; and generating a personalized recommendation result in combination with user historical behaviors. The analysis quality is improved through data preprocessing and denoising, commodity potential correlation is mined through co-occurrence modeling, adaptability is enhanced through automatic parameter adjustment, a projection matrix optimization result is sparse and interpretable, the structure sensing capacity of recommendation is improved through an adjacent structure, and finally high-precision and high-correlation recommendation is achieved. The structure definition, the calculation efficiency and the model generalization ability are excellent, and an efficient and stable personalized marketing recommendation system is constructed.

The invention belongs to the technical field of tire engineering design, and discloses an engineering tire belted layer end structure finite element design analysis method and system. Comprising the steps of obtaining and preprocessing tire carcass contour data and initial belted layer parameters, conducting belted layer end parametric modeling on the basis of a standardized tire geometric model, constructing a durability finite element analysis model to obtain a stress-strain distribution field, conducting multi-parameter sensitivity analysis to determine a key influence factor weight matrix, and obtaining a stress-strain distribution field. And optimally designing a belted layer end structure based on the matrix, and finally performing durability verification and evaluation. According to the method, through accurate geometric expression, flexible parametric modeling, accurate performance analysis and multi-objective optimization design, belted layer end stress distribution uniformization and structural rigidity optimization are achieved.

The invention relates to a processing temperature optimization control method and system applied to a plastic pipe fitting, and the method comprises the steps: M1, collecting the change data information of historical temperatures of different sections of a charging barrel of an extruder in the processing process of the plastic pipe fitting in the extruder, the data information of the temperature of each section of a charging barrel of the extruder, the data information of the rotating speed of a screw rod and the data information of the pressure of a machine head are collected in real time; and M2, based on the change data information of the historical temperatures of the different sections of the extruder charging barrel, predicting the temperature of each section of the extruder charging barrel by adopting a BP neural network regression prediction algorithm based on random walk matrix optimization to obtain predicted data information of the temperature of each section of the extruder charging barrel. According to the invention, the temperature of each section of the extruder cylinder can be accurately controlled and adjusted, so that the energy consumption is reduced, and self-adaptive adjustment can be carried out according to different application scenes in the control process, so that the robustness of the system is improved.

The invention relates to the technical field of data processing, in particular to a digital twinning-based comprehensive energy efficiency analysis and judgment system for a high-speed rail station building, and the system comprises a multi-dimensional perception acquisition module which outputs an environment tensor, an equipment vector and a people flow matrix which are synchronous in time; the dynamic coupling modeling module generates an equipment coupling characteristic impedance coefficient matrix; the optimization algorithm knowledge base module dynamically activates a multi-target genetic algorithm, a particle swarm optimization algorithm and a model prediction control or reinforcement learning algorithm according to the passenger flow growth rate and temperature rise rate characteristic indexes, and outputs an optimization parameter set to the four-dimensional parallel simulation module; the simulation module generates an equipment state parameter combination and an energy efficiency index; the energy efficiency collaborative evaluation module generates a global energy efficiency evaluation report through Pareto frontier analysis and Markov decision; the energy efficiency report generation module outputs a visual chart and equipment maintenance suggestions, and the energy efficiency and state double-closed-loop module triggers model correction based on pipe network pressure difference, temperature uniformity and energy efficiency ratio deviation.

The invention discloses a large model training method based on second-order matrix optimization, and belongs to the field of deep learning model training technology optimization, and the large model training method based on second-order matrix optimization comprises the following steps: S1, decomposing a second-order matrix into row and column vectors, and carrying out sliding average and distributed block reduction storage; s2, generating a statistical row vector by combining row gradient aggregation with a historical attenuation factor; s3, carrying out block distributed statistics in the column direction and synchronously generating column vectors across equipment; s4, a low-rank matrix is constructed through outer products of row and column vectors, and estimation precision is improved through noise suppression; s5, performing dynamic sparse sampling, performing initial high-density focusing, and stabilizing the sampling rate of a key layer; s6, the sampling points execute time sequence attenuation updating, and asynchronous calculation is carried out to improve the resource utilization rate; s7, performing Gaussian kernel smoothing on a neighborhood value compensation coverage gap in an unsampled region; s8, fusing low-rank estimation and sparse data, and balancing global precision by self-adaptive weight; the method has the beneficial effects of reducing video memory occupation, and improving distributed computing efficiency and balance training precision and speed.

The invention discloses a coding method and system based on matrix optimization and a storage and calculation integrated accelerator and a medium, and relates to the technical field of communication, and the method comprises the steps: constructing the storage and calculation integrated accelerator which comprises an input buffer area, a dynamic reconstruction storage and calculation array and a result processing module; m * n sub-matrixes obtained by splitting the check matrix H are respectively stored in corresponding storage and calculation integrated cores of the dynamic reconstruction storage and calculation array; the total code c is divided into n segments through an input buffer area and then multiply-accumulate with the corresponding column of sub-matrixes, a calculation result of the dynamic reconstruction storage array is input into a result processing module for result processing, and a final coding result is obtained; according to the coding method, the coding system and the medium, the dynamic adaptability and the calculation efficiency are broken through, and a high-reliability and low-delay LDPC coding solution is provided for tape storage.

The invention relates to an unsupervised cell segmentation method based on Transform, and the method is characterized in that a multi-modal text image alignment module aims at effectively fusing text and image data, and achieves the high alignment of multi-modal information through a Transform architecture; the mutual relevance of the data is enhanced through a low-rank attention mechanism, so that the multi-modal features can be extracted and aligned more accurately in an unsupervised environment. The matching matrix feature optimization module further processes the aligned feature data. According to the method, a unique matching matrix optimization algorithm is utilized, the precision of feature matching is remarkably improved, parameters of segmented cells are extracted and adjusted through the optimized matching matrix, and a more accurate initial prompt is provided for the subsequent segmentation process. The optimized features are input to an SAM segmentation module. And the SAM realizes high-precision cell segmentation by utilizing the strong segmentation capability of the SAM. The module gives full play to the advantages of a low-rank attention mechanism and matching matrix optimization, and ensures the accuracy and robustness of a segmentation result.

The invention relates to the technical field of computers, and discloses an information source coding matrix data processing method based on a modified conjugate gradient algorithm. According to the method, sparsity constraint and a fixed-point quantization mechanism are fused in iterative optimization, and a sparse fixed-point coding matrix meeting hardware deployment requirements is directly generated through gradient shielding, correction conjugate parameter calculation and Armijo criterion line search and synchronous execution of hard threshold sparseness and fixed-point quantization after each update. According to the method, the sparsity constraint and the fixed-point quantization mechanism are embedded in the iteration process of the modified conjugate gradient algorithm, so that end-to-end alignment of coding matrix optimization and hardware deployment requirements is realized.

The invention provides a multi-domain power grid data collaborative modeling method and system based on a tensor game diagram, and the method comprises the following steps: firstly constructing a six-dimensional enhanced tensor model, constructing a six-order tensor based on the number of nodes, timestamps and other six dimensions, and obtaining a kernel tensor and a factor matrix through CPD-Tucker mixed decomposition; a joint optimization objective function containing reconstruction errors, game equilibrium and privacy risks is constructed, and an optimization kernel tensor and factor matrix is solved; and finally, inputting an optimization result into the MGGCN, processing double targets through a leader branch (a physical topology adjacency matrix guarantees reliability) and a follower branch (a market transaction incidence matrix optimizes economic cost), and generating a collaborative decision result through multi-head attention fusion. According to the method, the problems of low multi-source heterogeneous data fusion efficiency and difficulty in considering cross-domain collaborative privacy security and dynamic optimization are solved, the new energy output prediction error can be reduced, and the data utility loss caused by global encryption is reduced.

The invention discloses a matrix controllable switch fault self-inspection and life prediction system and a working method, and belongs to the field of matrix switch non-intrusive testing, and the matrix controllable switch fault self-inspection and life prediction system comprises a test module composed of a matrix switch unit, a to-be-tested circuit assembly and a function test unit, a non-intrusive matrix self-inspection module and a life prediction and data recording module. Wherein the matrix switch unit comprises # imgabs0 # matrix switches, # imgabs1 # is the total number of test points of a circuit assembly to be tested, # imgabs2 # is the total number of test points accessed by the function test unit, and # imgabs3 # is the number of common column lines; the matrix switch unit is connected with the non-intrusive matrix self-checking module; and the non-intrusive matrix self-checking module is connected with the life prediction and data recording module. By adopting the matrix controllable switch fault self-inspection and life prediction system and the working method, high efficiency, safety and intelligence of circuit assembly testing are realized through non-intrusive self-inspection, matrix optimization, an intelligent prediction model and accurate fault positioning.

The invention relates to the technical field of electric power engineering construction, and particularly provides a data processing method and system for scene decision optimization of electric power stringing construction, and the method comprises the steps: constructing a parameter matrix of multiple scenes, and generating a decision task of each scene; for the decision-making task, construction result data is calculated according to a set logic calculation chain through a data flow and logic association engine; the construction result data is displayed through a visual interface, and a working condition input window is provided; updating the parameter matrix, and calculating updated construction result data; and comparing the updated construction result data with a preset safety rule base, performing safety verification, and optimizing the construction scheme based on the parameter matrix under the condition that the verification is not passed. The technical problems of low calculation efficiency, poor accuracy and insufficient safety due to the fact that independent calculation needs to be manually performed according to different data sources in different sub-processes in electric power stringing construction in related technologies are solved.

The invention relates to an emergency field multi-dimensional inference knowledge graph construction and real-time response method, which comprises the following steps: defining entity attributes and semantic relationships of a disaster-bearing body layer, a disaster-inducing mechanism layer, an emergency resource layer and a disposal process layer, and constructing a multi-dimensional inference knowledge graph; performing adjacent matrix optimization on the multi-dimensional inference mapping knowledge domain by adopting a diffusion model; a disaster-bearing body entity and a disaster-inducing factor entity in the implementation intelligence are identified, spatial semantics are analyzed to GIS coordinates, and an event association strength index is generated in combination with historical cases; performing three-level propagation on the basis of the optimized knowledge graph by taking an entity of which the association strength index meets a threshold value as a seed node; and sorting and outputting according to the comprehensive risk values of the propagation paths, and generating a resource scheduling instruction. Structural defects of an existing system can be thoroughly overcome, and it is ensured that an emergency scheme is always matched with disaster evolution.

The invention discloses a complex soft foundation waterscape miniature steel pipe pile reinforcing method which comprises the following steps: accurately predicting a deformation value of a potential settlement area by fusing thickness distribution parameters, soil compression modulus and numerical simulation, and comparing the deformation value with an engineering safety threshold. When deformation exceeds the standard, an optimization algorithm is adopted to adjust pile foundation arrangement, optimized pile foundation position parameters are generated, then connection node coordinates are extracted from the bottom plate structure model, and stress transmission efficiency and load distribution uniformity are calculated. And if the uniformity is lower than a threshold value, a reinforcement configuration scheme is finally generated by iteratively updating a connection node stiffness matrix, optimizing connection mechanism parameters, extracting mechanical transmission path data, simulating long-term stability and evaluating the durability improvement degree. According to the method, multi-level parameter optimization and numerical simulation are combined, so that the foundation stability and the load distribution uniformity are ensured, and the engineering durability and safety are remarkably improved.

The present application discloses a method for making patrol path decisions of a swarm of unmanned aerial vehicles (UAVs) under resource constraints, which relates to the technical field of UAV path planning. The method comprises: discretizing the actual physical locations to be patrolled to construct an undirected topological graph; generating a steady-state distribution of each patrol node according to the topological constraints and the importance of the nodes; generating a plurality of transfer matrices with the same steady-state distribution but different transfer characteristics according to a multi-stage entropy-driven random matrix optimization algorithm; initializing the position of a navigator and determining the path selected by it according to the transfer matrix; implementing adaptive active positioning decisions under positioning constraints according to the navigator's reference path to ensure path tracking effects; according to the navigator's path selection and tracking, the followers form a humanoid grouping cluster with the navigator through a reward function; according to the multi-state transfer matrix and the humanoid grouping, automatically switching to the next transfer matrix when a transfer number threshold is reached, thereby realizing intelligent patrol path decisions of the UAV swarm under resource constraints.

The invention relates to the technical field of weighing, and discloses a truck scale weighing error compensation method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining training sample data, a first configuration parameter, a second configuration parameter and an initial model parameter; creating an initial error compensation model according to the initial model parameters; determining a target model parameter and a target parameter according to the first configuration parameter, a model parameter optimization algorithm and the training sample data; determining a target weight matrix according to the target parameter, the second configuration parameter, a weight matrix optimization algorithm and the training sample data; the target model parameters and the target weight matrix are set as model parameters and a weight matrix of the initial error compensation model respectively, a target error compensation model is obtained, and the target error compensation model is used for obtaining a target weighing result according to the input sensor signals. The problem that the accuracy of the weighing result of the motor truck scale is influenced by various factors, so that the weighing result has an error is solved.