60 results about "Spatial search" patented technology

The invention provides a track board drilling parameter intelligent optimization method and system based on deep learning, and relates to the technical field of deep learning, and the method comprises the steps: recognizing a feature region in three-dimensional scanning data through a deep learning semantic segmentation network, building a local coordinate system, calculating hole site deviation, constructing a pose correction field, and achieving the intelligent correction of drilling process parameters. And an optimal drilling track is generated by using a state space search algorithm. The method can adapt to actual geometric errors of the track plate, the drilling precision and efficiency are improved, the drilling failure rate is reduced, and the manufacturing quality of the track plate is improved.

According to the large power grid reactive power optimization method and device, the storage medium and the computer equipment provided by the invention, the advantages of the two algorithms are fully exerted through the hybrid chaos quantum particle swarm optimization algorithm and the dimension-by-dimension convex space search algorithm. According to the chaotic quantum particle swarm algorithm, the global search capability and the capability of jumping out of local optimum of a particle swarm are enhanced by utilizing the characteristics of quantum behaviors and chaotic mapping, and the problem of premature convergence of a traditional heuristic intelligent algorithm is avoided. And according to the dimension-by-dimension convex space search algorithm, fine search is carried out on each excellent particle in different dimensions, a local optimal solution is determined, and the search precision and efficiency are further improved. According to the design of the hybrid algorithm, special optimization is carried out aiming at the characteristics of a reactive power optimization problem model, such as variable property difference, constraint complexity and the like, and the technical defects of poor optimization effect and optimization efficiency of an optimization solution algorithm in the prior art are effectively overcome.

The invention discloses a diffusion model reasoning acceleration method based on optimal time step sequence search and knowledge distillation, which constructs a unified search space covering a time step sequence and corresponding model architecture configuration on the premise of not performing overall fine adjustment on a pre-trained diffusion model, and searches an optimal time step sequence. And designing a knowledge distillation training method based on the optimal diffusion time step sequence obtained by searching, constructing a main distillation loss function in a discrete time step subspace limited by the optimal diffusion time step sequence, and introducing adjacent time step consistency loss to relieve discrete errors introduced by a non-uniform time step span. In the reasoning stage, a teacher model is frozen, a distilled student model is combined with an efficient sampler ACDMS based on UniPC and fused with flow matching dynamic correction, finite step sampling is carried out on an optimal time step sequence, and reasoning acceleration under generation quality maintenance is achieved.

The application discloses a circuit input sensitivity analysis method based on Undef SAT, which comprises five steps of netlist analysis and input division, two groups of CNF clause construction, S set assignment and initial solving, first group of solution increment SAT processing and termination and result output. The method uses the incremental SAT processing mode to gradually determine the final assignment of the input variables of the L set, effectively avoids full space search, significantly reduces the search space, and improves the analysis and determination efficiency. The method constructs two groups of CNF clauses supporting irrelevant item propagation, directly processes the irrelevant input variables of the S set by using the Undef SAT solver, reduces the number of logic propagation, and thus reduces the operation time. The integrated process of the circuit input sensitivity analysis formed by the method is suitable for mainstream EDA tool chains, has good engineering applicability, and is suitable for different scenes such as logic synthesis stage, formal verification, functional equivalence checking and loop oscillation detection.

The invention relates to a scale-aware geographic flow clustering method and device, electronic equipment and a storage medium, and the method comprises the steps: introducing a pre-constructed scale factor to construct a relation mapping between a spatial search neighborhood of a flow and a flow length, and generating a to-be-processed flow connection cluster set based on the relation mapping and a plurality of flows; any one to-be-processed stream connection cluster is selected from the to-be-processed stream connection cluster set, and whether any one to-be-processed stream connection cluster is a strong connection stream cluster is judged; if any to-be-processed stream connection cluster is a strong connection stream cluster, adding any to-be-processed stream connection cluster into a stream clustering cluster set, otherwise, taking any to-be-processed stream connection cluster as a weak connection stream cluster, and processing the weak connection stream cluster to obtain a potential strong connection stream cluster meeting a preset condition; and obtaining all strong connection flow clusters and all potential strong connection flow clusters in the to-be-processed flow connection cluster set to generate an actual flow clustering result of the flow. Therefore, the technical problems that local spatial distribution characteristics of stream data are neglected when spatial heterogeneous stream data are clustered, accurate perception of stream analysis scales is lacked, stream clusters with different densities cannot be correctly divided and the like in related technologies are solved.

The invention relates to the technical field of data processing and engineering project process management, in particular to a full-process intelligent management method and system for bridge and tunnel maintenance engineering. The method comprises the following steps: extracting a disease geometric contour based on bridge and tunnel inner wall three-dimensional point cloud data, and calculating a centroid coordinate, a volume and a surface contour area; constructing a space retrieval sphere by using a target disease centroid, screening an adjacent disease set, and calculating a local aggregation density by combining the volume and the centroid distance; calculating the average depth of the target disease, and deducing the overlapping sharing rate of the working plane; constructing a group topology consumption equivalent through linear analysis; and inputting the extreme gradient lifting model to output an optimal repair material dosage table and a material distribution operation instruction book. According to the method, the disease space distribution and aggregation degree can be accurately evaluated, the construction loss surface overlapping probability and the material sharing potential are scientifically evaluated, the material preparation accuracy is improved, waste and cost are reduced, and the whole-process intelligent management of bridge and tunnel maintenance engineering is realized.

The invention discloses an electric vehicle charging load prediction method and system based on model-data cooperative driving, and belongs to the field of electric vehicle charging load prediction. According to the method, an improved Monte Carlo inversion mechanism and a double-flow GCN-Transform combined prediction framework are used, so that the problem that prediction mechanism consistency, data adaptability and space-time heterogeneity are difficult to consider at the same time in an existing single driving mode is solved. The method comprises the following steps: firstly, constructing a charging behavior-path cost double-layer coupling physical model, inverting user behavior features through parameter space search, and generating a multi-modal feature set; secondly, capturing a spatial topological relation by adopting a graph convolutional network, and deeply fusing spatial and temporal features by utilizing a shared embedded layer and a Transform network; the method is remarkably superior to the prior art in the aspects of prediction precision, robustness and cross-scene generalization ability, and particularly, stable and reliable prediction performance can still be kept in a data incomplete or load sudden change scene.

The present disclosure discloses an emergency rescue autonomous driving vehicle collision risk prediction method and a system based on HM-TRW and HAGENN structure search, abstracts the relationship between the emergency rescue vehicle and the surrounding moving objects into a dynamic heterogeneous diagram, captures the importance and dynamic of each surrounding moving object and various interactive relations by using HM-TRW, and merges with the original characteristics of each surrounding moving object. Then input HAGENN to predict the collision risk, and apply the dynamic heterogeneous graph embedding method to the hierarchical attention neural network to further learn the heterogeneous characteristics and dynamic changes of the surrounding moving objects. When calculating the attention force of different levels, the positioning space is used to determine the application position of attention, the parameterized space is used to search the attention function, and multi-stage differential search is introduced to accelerate the above search process. The present disclosure can more comprehensively and accurately predict the collision risk in the operation process of the emergency rescue autonomous driving vehicle.

This invention relates to the field of navigation assistance technology, and in particular to a dynamic obstacle prediction and obstacle avoidance path planning method based on multi-sensor fusion. This invention achieves comprehensive perception of dynamic obstacles through multiple sensors, providing a rich and reliable data foundation for subsequent processing. At the same time, it predicts the motion of dynamic obstacles through data processing fusion and model prediction, fully considering the motion inertia of obstacles to make their trajectory prediction closer to real physical laws. Furthermore, by "delineating the final obstacle avoidance target area", the global path search problem is transformed into a local but sufficiently large spatial search problem, significantly reducing the number of grids to be processed and the amount of search computation, meeting the stringent real-time requirements of mobile devices. Moreover, by constructing a cost function, it avoids areas that obstacles may occupy in the future in real time, thereby generating an obstacle avoidance planning path, which helps to improve path safety.

A physics-informed data-driven oil and gas pipeline network fault diagnosis method and system for type-imbalanced data scenarios, relating to the field of oil and gas pipeline network fault diagnosis, and aiming at solving the overfitting problem of current intelligent fault diagnosis models during processing of oil field type-imbalanced data sets, and reducing the risks of false alarms and missed alarms. Main steps are as follows: deeply understanding propagation and attenuation mechanisms of negative pressure waves when oil and gas move in a pipeline network, and establishing a negative pressure wave attenuation physical model reflecting the operating state of the pipeline network; on the basis of a long short-term memory network, constructing a deep generative adversarial model suitable for processing time series data; designing a reasonable series-parallel mechanism to fuse the physical model and a data-driven model, so as to construct a hybrid generative adversarial model; using the trained hybrid generative adversarial model to generate pipeline network fault data, and balancing an original training set; and training an intelligent fault diagnosis model to achieve pipeline fault type identification. The present invention considers both the prior knowledge from the physical model and the learning capability of the data-driven model and integrates same, and compared with simple data-driven models, uses the prior knowledge of the pipeline network contained in the physical model to reduce a parameter space search domain, thus reducing the number of estimated parameters, improving the interpretability and generalization performance of a deep generation model, and improving the physical rationality and feature distinguishability of generated fault data. Therefore, the present invention effectively overcomes the negative impact of type-imbalanced data sets on the performance of diagnosis models, further improving the accuracy of intelligent fault diagnosis of pipelines.

The invention relates to a registration method and system for mouse brain tissue slice images. On the basis of a two-dimensional mouse brain tissue slice image to be registered and a section parameter prediction model, spatial positioning parameters are determined, and tedious three-dimensional space search and iterative matching in the prior art are effectively replaced by one-time prediction based on the section parameter prediction model, so that the matching efficiency is improved, and the robust spatial positioning parameters are given; determining deformation mapping information based on the spatial positioning parameters, the two-dimensional mouse brain tissue slice image and the three-dimensional average mouse brain template map, determining a two-dimensional labeled slice based on the spatial positioning parameters and the three-dimensional standard map, and determining a registered mouse brain tissue slice image based on the two-dimensional labeled slice and the deformation mapping information, according to the method and the system, subsequent fine alignment processing is carried out on a two-dimensional level, so that the calculation complexity and the algorithm difficulty are greatly reduced, an automatic workflow of intelligent three-dimensional positioning and efficient two-dimensional registration can be formed, and powerful support is provided for high-throughput analysis and clinical application.

The prompt word determination method, device, electronic equipment and storage medium provided by the present application relate to the technical field of prompt word processing. The method obtains baseline prompt words and a test case set based on a preset service requirement; performs multi-round response testing on a target model by using the baseline prompt words and the test case set, obtains model behavior logs corresponding to each round of response testing, and obtains a prompt word position effect matrix and a vocabulary sensitivity knowledge base based on the model behavior logs corresponding to each round of response testing; and performs mixed variable space search processing by using the prompt word position effect matrix, the vocabulary sensitivity knowledge base and the model behavior logs corresponding to each round of response testing, to obtain effective prompt words, thereby improving the optimization efficiency of prompt words and enhancing the verifiability of results.

The application provides a multi-modal data security analysis technology based on a deep fuzzy wavelet learning model. The deep fuzzy wavelet learning model is widely used in the field of security analysis due to its self-adaptive ability in complex data environment. The method improves the deep fuzzy wavelet learning framework and applies it to multi-modal data security analysis. The model first uses the method combining wavelet transform and fuzzy clustering to perform feature decomposition and noise reduction processing on multi-modal data (text, speech, video, image), extracts key semantic, timing and spatial features, and provides high-dimensional information representation for subsequent security analysis. Secondly, a deep fuzzy wavelet neural network is constructed, the extracted features are input into the adaptive fuzzy decision layer, and the wavelet learning model is combined to capture the spatio-temporal correlation features, improve the perception ability of the model to complex attack patterns. Finally, the security analysis result is strengthened by using the space search optimization algorithm, so as to effectively detect malicious samples, identify backdoor attacks, and improve the defense ability against attacks, and finally realize accurate multi-modal data security evaluation.

The invention provides a path planning method and system for personnel evacuation and obstacle avoidance, and belongs to the technical field of personnel evacuation dynamic obstacle avoidance path planning. The invention provides a Bi-RRT *-based personnel evacuation path planning algorithm with self-adaptive bias and a new improved self-adaptive extended step length. On the basis of the standard Bi-RRT * algorithm, an adaptive bias strategy which is jointly influenced by search experience and obstacles is added, so that the convergence speed of the algorithm is effectively improved, the path search efficiency is improved, and the ability of passing through narrow terrains is improved; based on a self-adaptive step length search strategy combining the repulsion size and the direction included angle, the obstacle avoidance capability is improved; in combination with improved adaptive bias, adaptive step length and bidirectional search capability, the adaptive capability to a complex evacuation space search path and the search efficiency of a multi-obstacle region algorithm are effectively improved. The method has remarkable advantages in the aspects of path planning efficiency, path quality, convergence speed, complex environment adaptability and the like.

The invention relates to a multi-target cooperative power flow real-time distribution method for a hybrid tracked vehicle considering dynamic stability constraints, and belongs to the technical field of hybrid tracked vehicle control. The method comprises the following steps: estimating a road adhesion coefficient in real time based on UKF and generating a real-time dynamic stability boundary; identifying a driving intention based on multi-dimensional features and vehicle speed self-adaption; constructing an all-condition off-line mapping table based on reverse modeling; and performing multi-target collaborative MPC predictive control and real-time distribution based on MAP initial value hot start. According to the method, through the hybrid architecture of providing the initial value through offline all-condition traversal and processing stability constraint through online MPC, huge computing power consumption caused by online full-space search is effectively avoided, and meanwhile, the safety and economical efficiency of the vehicle under the complex road surface working condition are guaranteed.

The invention discloses an intelligent production scheduling method and device for a production workshop. The method comprises the following steps: establishing a mixed integer linear programming model for performing workpiece scheduling on the production workshop; performing initial solution space construction, and generating an initial scheduling solution set corresponding to the target function; performing neighborhood solution space search, solution space destruction and solution space reconstruction, and constructing a reconstruction solution corresponding to the initial scheduling solution set; replacing the initial scheduling solution set with the reconstruction solution, and repeating the steps of neighborhood solution space search, solution space destruction and solution space reconstruction until the total processing duration of all workpieces corresponding to the target function of the obtained reconstruction solution is lower than a preset value; and carrying out model decoding operation on the reconstruction solution of which the total processing duration of all workpieces corresponding to the target function is lower than a preset numerical value in combination with the coding structure to obtain a production scheduling scheme of the production workshop. The method is used for effectively optimizing the production schedule of the production workshop, reducing the maximum completion time of production and improving the production efficiency.

This invention discloses a pole angle consistency improvement method based on residue enhancement, relating to the fields of radar signal processing, target detection and recognition technology. This invention maps the complex values ​​of poles to two variables: real and imaginary parts. It uses an optimization algorithm to dynamically optimize the unnormalized real and imaginary parts of the poles. During the optimization process, the optimized poles and the frequency domain response echoes reconstructed by the original residues are brought closer to the frequency domain response echoes of the original poles theoretically obtained by increasing the residues in the vicinity of the frequency range corresponding to the optimized poles. This combines the spatial search of pole positions (adjusting frequency position) with the gain effect of the residue amplitude (increasing response intensity), effectively solving the problem of pole angle flickering caused by low echo energy contribution during pole extraction. It corrects pole positions at their root, identifies hidden poles, improves pole angle consistency, and generates a complete pole feature database.

The invention discloses an RCS reduction method of an electromagnetic digital coding metasurface based on a GAN-PSO joint algorithm, and belongs to the field of electromagnetic digital coding metasurfaces and electronic countermeasures. The method comprises the following steps: firstly, constructing a 1-bit metasurface unit with a phase difference of 180 degrees + / -10 degrees; thirdly, training a generative adversarial network (GAN) to enable a generator of the GAN to map a continuous vector into a discrete metasurface coding matrix; then, a fitness function of a particle swarm optimization (PSO) algorithm is set to be far field scattering function maximum value minimization; and finally, GAN-PSO joint optimization is executed: PSO searches in a continuous space, a generator maps particle positions into candidate coding matrixes and calculates fitness, and iterative updating is carried out until an optimal coding matrix is output as a final arrangement scheme. Through GAN-PSO joint algorithm optimization, the bottleneck that a traditional coding strategy is low in degree of freedom and uncontrollable in scattering is broken through, and high-performance broadband RCS reduction is achieved.

The application discloses a kind of based on layered joint graph index's house rental multi metric space search method, comprising: obtaining new house object, find the k nearest neighbor house object of the new house object in each metric space, under each metric space, add undirected edge in new house object and its k nearest neighbor house object, adopt splitting and hierarchical update strategy based on tree, dynamically adjust graph structure, improve node distribution, to build layered joint graph index, wherein the metric space includes price metric space, house type metric space, geographical position metric space and comment metric space;Based on the pruning technique of best priority search strategy, multi metric space approximate nearest neighbor search is carried out using the layered joint graph index, and the approximate-k nearest neighbor house object of given query house object is obtained.

The invention discloses a rural area truck and unmanned aerial vehicle cooperative distribution path planning and resource scheduling method, and belongs to the technical field of path planning. According to the method, a multi-target truck-unmanned aerial vehicle path problem with a flexible time window is modeled as a reward maximization problem in deep reinforcement learning; a time window is allowed to be deviated within tolerance time, and punishment is applied to serious deviation; a multi-agent model is adopted, a distribution unit composed of a truck and a carried unmanned aerial vehicle is regarded as an agent, space-time topological features of a complex logistics network are embedded and captured through a multi-order space-time diagram convolutional network, the attention mechanism of the multi-order space-time diagram convolutional network is utilized to perform weighted summation on adjacent node features to generate specific weights of client nodes, and the state sensing ability of the agent is enhanced; and based on a multi-agent near-end strategy optimization algorithm, the collaborative distribution path of the multiple truck-unmanned aerial vehicle units is optimized, and the parallel processing method is combined with ST-MGCN embedding, so that the action space search dimension is effectively reduced, and the network convergence speed and the solving efficiency are improved.

This invention belongs to the field of power system technology, specifically relating to a method and system for mining rules for electricity price and fee consistency verification. The method includes: acquiring a verification dataset and calculating the equivalence partitions of quantity, price, and fee data; using a depth-first spatial search strategy to mine data consistency relationships, obtaining a set of consistency relationships in the data; using the obtained set of consistency relationships to perform rule parsing on the quantity, price, and fee consistency relationships; and identifying rare data in the quantity, price, and fee data through rule parsing. This invention applies a novel consistency rule mining method for categorized data to the verification of electricity price and fee data. It can proactively mine potential rules in the data, effectively overcoming the shortcomings of previous manual rule definition methods, such as high workload, long time consumption, insufficient rule coverage, and the ability to only discover abnormal business data covered by defined rules. This assists business personnel in completing the verification of electricity marketing quantity, price, and fee data more intelligently and comprehensively.

The invention relates to the technical field of pipeline safety monitoring and integrity evaluation, and discloses an environmental corrosion evaluation and pressure pipeline failure analysis method based on an AI database, and the method comprises the steps: firstly constructing a high-dimensional phase space point cloud of monitoring data through a sliding time window, and generating a topological persistent landscape representing environmental steady state features through topological data analysis; constructing a Riemannian manifold dynamics database, and searching an optimal evolution path conforming to physical smoothness in a manifold space based on a Hessian energy minimization principle; and calculating a real-time topological entropy, dynamically driving the order of the variable-order fractional order differential equation by using the real-time topological entropy, and solving the equation in combination with an evolution path parameter to deduce the wall thickness attenuation process. Through fusion of algebraic topology and manifold geometry, deep coupling of data geometric features and a physical mechanism model is realized, the problems that a traditional model lacks interpretation and cannot adaptively describe a corrosion historical memory effect are solved, and the physical reliability of failure analysis is remarkably improved.

The invention discloses a power transmission line icing state intelligent monitoring method and system based on low-orbit satellite data, and relates to the technical field of satellite remote sensing monitoring, and the method comprises the steps: constructing a parameter equation of a lead axis, generating a buffer space set, carrying out the longitudinal strength search of each ground transverse coordinate, and generating a midspan reference sag. Calculating sag variation; calculating the vertical displacement of the sight line by combining the incident angle and the radar wavelength to obtain comprehensive sag change, calculating the total line load by combining the tension, and converting the unit line load into the total mass per unit length in the icing period; the total mass is decomposed into the dead weight of the wire and the icing mass, and the icing thickness is generated. According to the method, the recognition precision of tiny vertical displacement caused by icing is improved by combining low-orbit SAR interference imaging with construction of a wire geometric parameter equation, and the wire recognition stability under the conditions of complex terrains and low signal-to-noise ratios is improved by combining multi-source heterogeneous data fusion with a buffer space search algorithm.

The invention discloses a cycle slip real-time detection and restoration method suitable for low-height GPS non-difference data. The method comprises the following steps: step 1, constructing cycle slip detection fusing Kalman filtering and a hypothesis testing method; step 2, constructing quadratic difference non-geometric distance method cycle slip detection considering the elevation angle; step 3, cycle slip repair based on space search and an objective function minimum criterion; step 31, determining a cycle slip occurrence frequency and a cycle slip floating point value; step 32, rounding to obtain a cycle slip initial value; step 33, forming a cycle slip candidate combination; step 34, repairing the carrier phase observation value by using the cycle slip candidate combination, and carrying out cycle slip detection by using the methods in the step 1 and the step 2 to obtain a cycle slip combination meeting a detection condition; and step 35, carrying out carrier phase repair based on the cycle slip combinations, and substituting the carrier phase repair into the target function to enable the cycle slip combination with the minimum value to be a final cycle slip result. According to the method, the cycle slip in the low-elevation-angle GPS satellite phase data can be accurately detected and repaired, and the method is feasible and effective.

The application provides a track plate drilling parameter intelligent optimization method and system based on deep learning, relates to the technical field of deep learning, and comprises the following steps: identifying a feature area in three-dimensional scanning data through a deep learning semantic segmentation network, establishing a local coordinate system, calculating a hole position deviation, and constructing a pose correction field, so that intelligent correction of drilling process parameters is realized, and an optimal drilling track is generated by using a state space search algorithm. The application can adapt to actual track plate geometric errors, improve drilling precision and efficiency, reduce drilling failure rate, and improve track plate manufacturing quality.

The invention discloses a well track automatic obstacle avoiding method for guiding type fence searching. The method is characterized by comprising the following steps that S1, a searching fence environment is built; s2, initializing an algorithm; and S3, node searching: S4, smoothing a search path to obtain an optimal and smooth path. By building a multi-layer fence search environment, parallel operation of an algorithm (multiple layers search paths at the same time) is facilitated, so that the overall search process is accelerated; the provided guiding type heuristic function is beneficial to guiding search to be carried out to areas not occupied by obstacles, the search space is reduced, the decision-making time in the search process is shorter, and the algorithm robustness is higher.

The application discloses a semi-automatic trade-off space searching method and device based on preference adjustment and a medium, and comprises the following steps: S1, acquiring all design schemes of an online visual detection system architecture and forming an initial trade-off space; S2, coloring the initial trade-off space to form a colored trade-off space; S3, coloring the Pareto front optimal solution or the Pareto front of a specific level in the colored trade-off space to form a colored Pareto front trade-off space; and S4, navigating the colored Pareto front trade-off space by using an utility constraint region and a cost constraint region and using a pole distance method to obtain an optimal design scheme. The application proposes a process of giving a system architecture suggestion solution set by simulation process data driving on the basis of a colored segmented Pareto front preference set, in combination with constraint conditions and the pole distance method for scale reduction and directional screening of the Pareto front scheme, and aims to strengthen the decision quality in the concept design stage of a high-quality online visual system.

The invention discloses a circuit input sensitivity analysis method based on UnderfSAT. The circuit input sensitivity analysis method comprises five steps of netlist analysis and input division, construction of two groups of CNF clauses, S set assignment and initial solution, and first group solution increment SAT processing and termination and result output. According to the method, the final assignment of the input variables of the L set is determined step by step in an incremental SAT processing mode, so that full-space search is effectively avoided, the search space is remarkably reduced, and the analysis and judgment efficiency is improved. According to the method, two groups of CNF clauses supporting unrelated item propagation are constructed, unrelated item input variables of an S set are directly processed by using an UnderfSAT solver, and the number of logic propagation times is reduced, so that the operation time is shortened. The integrated process of circuit input sensitivity analysis formed by the method adapts to a mainstream EDA tool chain, has good engineering applicability, and is suitable for different scenes such as a logic comprehensive stage and form verification, function equivalence check and loop oscillation detection.

The application discloses a parameter setting method and system of PSS based on complex domain vector synthesis, and belongs to the technical field of power system automation and control. The method comprises the following steps: obtaining an open-loop frequency response, and extracting a phase lag characteristic curve of a channel; based on a complex domain vector synthesis model and the phase lag characteristic curve, a complex response calculation formula is constructed, a total open-loop phase is defined, a parameter vector is set and a parameter feasible region is determined, a target compensation phase is set, a weighted comprehensive phase error target function is defined, and a boundary value is set; the weighted comprehensive phase error target function is taken as a fitness function, parameter space searching is performed, and a parameter vector that minimizes the fitness function is solved; the parameter searching process of the global optimization algorithm is optimized; the optimal parameter set is verified in multiple dimensions and vector synthesis visual analysis is performed, and parameter setting of a power system stabilizer is completed. The application has correct principles, relatively high efficiency and accuracy in processing, and high engineering applicability.