198 results about "Weight analysis" patented technology

The embodiment of the invention provides a method and system for generating medical suggestions based on multi-modal data fusion, and the method comprises the steps: integrating a medical image, a physical examination report and dynamic physiological parameters of a patient through a multi-source data fusion module, generating a multi-modal data set, and synchronously inputting the multi-modal data set into a hybrid reasoning module and a dynamic knowledge graph engine. And the dynamic knowledge graph engine accurately recall a target diagnosis and treatment guide associated with the current multi-modal data set. The rule reasoning sub-module generates a first diagnosis suggestion containing a diagnosis conclusion, a treatment scheme and an evidence level based on a guide structured rule, and meanwhile, the neural network reasoning sub-module analyzes a multi-modal data set by relying on a triple topological structure and an edge weight; and generating a second diagnosis suggestion comprising the disease risk probability, the differentiated treatment suggestion and the evidence source. And finally, the interactive output module fuses the two suggestions to generate a medical suggestion report covering the diagnosis basis, the evidence level and the treatment scheme, so that the diagnosis and treatment precision of chronic disease management and health risk assessment is remarkably improved.

The invention belongs to the technical field of data processing, and particularly relates to a hidden ore body evaluation and positioning method based on multi-source data processing. The method mainly aims at the problems of incompleteness and isomerism of multi-source geological data in acquisition, fusion and modeling. Comprising the following steps: acquiring hyperspectral, geochemical and magnetic anomaly multi-source data of an evaluation area; intelligently complementing missing modal data by using a generative adversarial network based on geological constraints and modal outburst to form a complete multi-source data set; an unsupervised clustering algorithm combining geological correlation and entropy weight analysis is adopted to construct high-confidence-coefficient pseudo-label data, and knowledge mining of unlabeled samples is achieved; feature purification and dimension reduction are carried out through multi-modal feature fusion and hierarchical principal component analysis, and key feature vectors representing the existence of the ore body are extracted; and finally realizing space prediction of the concealed ore body by utilizing the classification model. According to the method, a high-quality data basis and a unified processing framework are provided for intelligent recognition of the hidden ore body, and efficient and accurate positioning of the hidden ore body is achieved.

The invention relates to the field of computer industry, and discloses an integrated computer software and hardware combined sales optimization system and method, and the method comprises the steps: extracting software and hardware sales records, customer transaction behaviors and version updating tracks, and constructing a fusion data set of heterogeneous sales data; performing association weight analysis on the fused data set, and extracting strategy sensitive points in software and hardware linkage transaction in combination with a customer behavior intention model and product life cycle characteristics; constructing a product combination feasible region in a specific sales window by adopting a combination optimization graph search algorithm based on the tag nested structure; mapping the candidate combined product list to a historical sales behavior map, and extracting dynamic feedback modes of a combined strategy under different sales paths by introducing a price elastic response model and market disturbance fluctuation characteristics; and carrying out iterative adjustment and node intervention on the strategy path based on the high-sensitivity area. The method has the advantage of improving the scientificity of sales strategy formulation.

The invention discloses a building pollution reduction and carbon reduction collaborative evaluation method based on LCA and dynamic weight analysis, and relates to the field of building pollution reduction and carbon reduction collaborative evaluation. According to the method, the influence parameters of the design, construction and operation stages of the whole life cycle of the building are systematically identified and quantified, and the cooperative and unified evaluation of pollution reduction and carbon reduction is realized through dynamic weight reconstruction analysis and a function coupling LCA model, so that the environmental performance of the building in the whole life cycle can be integrally and accurately reflected, and meanwhile, the environmental performance of the building in the whole life cycle can be accurately evaluated. According to the method, the interaction between indexes and the time sensitivity of inter-stage energy consumption and pollutant transfer and emission can be considered at the same time, the complex multi-index and multi-stage environmental influence is converted into a comprehensive synergistic effect value, and an operable environmental performance grade is formed through grading judgment.

The invention provides a wind and light equipment predictive maintenance system and method based on big data and hierarchical weight. The system comprises a data acquisition and edge processing layer, a data management and storage layer, a hierarchical weight analysis and model construction layer and a fault prediction and intelligent decision-making layer. Multi-source data are collected by deploying various sensors, and the multi-source data are transmitted to a cloud for deep cleaning and storage after being preprocessed by edge computing. The index weight of each level is determined by using an improved algorithm, and a fault prediction model is constructed by fusing a plurality of intelligent algorithms. The model combines real-time data and hierarchical weights, predicts the fault probability, type and remaining service life of equipment components, and gives an alarm when a threshold value is exceeded. Meanwhile, a multi-objective optimization model is constructed according to a fault prediction result to formulate a maintenance strategy, and the maintenance strategy is executed after digital twinborn rehearsal, so that the operation and maintenance efficiency is improved. According to the method, multi-source data fusion, accurate fault prediction and intelligent maintenance decision making are realized, the operation and maintenance cost can be effectively reduced, and the equipment reliability and the power generation efficiency are improved.

The invention discloses an intelligent auxiliary method, a medium and equipment for constructional engineering title review. According to the method, key feature information of unstructured text data in a declaration material is extracted by using a natural language processing technology, image data features are analyzed by using a ResNet-50 deep learning model, and a comprehensive quantitative scoring mechanism of a construction engineering performance material is established. And integrating education background, working experience, scientific research achievements and multi-modal performance data, constructing a constructional engineering title review rule base, setting a standard score threshold based on historical declaration data, and generating a title simulation review score and a review prediction result. And finally, aiming at the model, designing a feature weight analysis layer, and automatically outputting improvement suggestions of the declaration material defects according to the simulation review score after being associated with the associated review rule base. According to the method, automatic feature extraction and quantitative evaluation of the unstructured material are realized, the problems of low manual auditing efficiency and fuzzy standard are solved, and the objectivity of title evaluation is improved through the rule base and the evaluation prediction model.

The invention relates to a large silo coal blending analysis system, and belongs to the technical field of coking. The system comprises a silo coal resource access statistics module, a switching analysis module, a coal blending ratio difference analysis module, a coal blending and coke quality prediction module and an information display module. Firstly, the silo coal seam distribution condition is established, then the coal seam resource switching condition is analyzed and judged according to the discharging weight, the accumulated coal blending execution deviation rate is calculated, and the coal blending amount and the switching condition of the next period are estimated; the blended coal and coke quality prediction module predicts the quality and analyzes the change condition according to the actual coal blending ratio and the like; and the information display module stores and visually displays the result. The system has strong analysis and prediction capabilities, and can comprehensively and deeply analyze yesterday coal blending conditions, scientifically predict coke quality, intelligently plan today coal blending by means of accumulated coal blending execution deviation rates, simply, conveniently and quickly grasp dynamics and trends, guide coal blending and quality control, improve product quality, reduce cost and enhance competitiveness.

The invention relates to the technical field of drilling parameter optimization control, and provides a petroleum drilling parameter optimization method and system and a storage medium. Comprising the steps that torque fluctuation amplitude and bit pressure response delay data are collected in real time, an original data sequence is constructed, and parameter trend data are obtained; analyzing the parameter trend data to identify a stratum change mode, extracting trend feature information, and when the trend feature information meets a preset condition, determining a corresponding abnormal mode identifier; generating a parameter adjustment direction of drilling control according to the abnormal mode identifier, and optimizing the parameter adjustment direction in combination with parameter weight analysis and a real-time feedback mechanism to obtain final optimization guidance data for guiding drilling operation; and mapping the final optimization guidance data to a control interface, and generating a dynamic adjustment instruction for drilling operation. The problems that in the existing petroleum drilling process, parameter adjustment lags behind, and stratum changes are difficult to adapt are solved, and intelligent optimization and dynamic control over drilling parameters are achieved.

The invention discloses a drainage pipe network data cleaning and intelligent repairing method fusing multiple models. The method comprises the following steps that S1, original monitoring data flow is collected; s2, carrying out the preliminary detection and elimination of the abnormity based on IQR; s3, performing upstream and downstream multi-source feature extraction and XGBoost weight analysis; step S4, multivariable depth autoregression prediction based on PatchTST is carried out; s5, generating and publishing continuous high-quality data; according to the method, the key technical problems of various abnormal types, incomplete abnormal detection, low cleaning and repairing precision, poor data continuity and trend consistency, high system integration and real-time processing difficulty and the like commonly existing in the actual collection and transmission process of the online monitoring data of the drainage pipe network are solved.

The invention relates to the technical field of anti-seismic performance evaluation of ECC jacketed combination columns, and particularly provides a quantitative evaluation method for anti-seismic performance indexes of an ECC jacketed combination column, which comprises the following steps of: calculating key anti-seismic performance indexes such as bearing capacity, accumulated energy dissipation, ductility coefficient and limit value of repairable interval, and combining weight analysis and importance coefficient adjustment to evaluate the anti-seismic performance indexes of the ECC jacketed combination column. A scientific and comprehensive anti-seismic performance evaluation system is established; a method for determining the damage point of the ECC jacket combination column is provided, and the damage state is accurately determined; reparability interval evaluation is particularly introduced, repair grades are divided based on residual deformation and Park-Ang damage indexes, and a basis is provided for post-earthquake maintenance decision making; the provided anti-seismic performance index evaluation index comprehensively considers different loading working conditions, and a clear engineering applicable threshold value is set, so that engineering personnel can quickly evaluate and optimally design; the method is of great significance to promotion of engineering application of the ECC jacket combination column.

The invention discloses a data real-time analysis and fault early warning method and system based on a digital electric port box transformer substation, and belongs to the technical field of power equipment monitoring and control systems. The method comprises the following steps: acquiring environmental parameters of each component of the box transformer substation in real time through a monitoring network, and performing data smooth denoising to obtain a parameter sequence; constructing a dynamic storage framework based on the sequence in combination with space-time attributes; extracting historical records of the target component to perform multi-dimensional sorting and change rate calculation, and identifying potential anomalies; when the change rate exceeds the limit and the distribution boundary breaks through, generating an alarm sequence and determining a preliminary abnormal mark; comparing the mark with the historical record, fusing the influence weight analysis verification accuracy, and obtaining a final early warning grading result; and parameter summary information is pushed to a control system according to a grading result, response feedback is obtained, and closed-loop control of the operation state is formed. According to the invention, the problem of control lag caused by data noise and delay is solved, and the timeliness, reliability and automation level of box transformer substation control are improved.

The invention provides a data grading method and device, electronic equipment, a medium and a program product, and can be applied to the technical field of big data, the technical field of artificial intelligence and the field of financial science and technology. The method comprises the following steps: acquiring to-be-graded data, and processing the to-be-graded data to generate a standard data stream; extracting multi-modal features based on the standard data stream, and fusing the multi-modal features to obtain semantic representation information; acquiring real-time risk information, and performing dynamic risk weight analysis on the real-time risk information to generate risk weight information; and performing classification and grading decision based on the semantic representation information and the risk weight information to obtain a data grading result, and performing data grading on the to-be-graded data based on the data grading result.

The invention belongs to the technical field of building design, and provides an optimization method and system based on the mix proportion of a heavy slag water permeable brick, and the method comprises the following steps: determining the mix proportion parameter of the heavy slag water permeable brick; establishing a performance data set of the heavy slag water permeable brick, and constructing a nonlinear mapping relation function between the mix proportion parameters of the heavy slag water permeable brick and the performance data set of the heavy slag water permeable brick; constructing a multi-objective function based on the nonlinear mapping relation function, establishing a multi-objective constraint condition, and constructing a multi-objective optimization data set according to the objective function and the multi-objective constraint condition; and performing weight analysis on each performance by adopting entropy weight TOPSIS, and substituting a weight value into the multi-objective optimization data set to determine an optimal mix proportion. According to the method, the RSM is combined with the NSGA-II and the entropy weight TOPSIS, so that the mix proportion is more accurate, and the adaptability is higher.

The invention relates to the technical field of data processing, and discloses a fault analysis method and system based on a label propagation algorithm. The method comprises the following steps: acquiring operation state data through a monitoring equipment network, extracting the data to obtain a system topology data set, performing label propagation fault analysis on the system topology data set to obtain node fault information, and performing fault influence weight analysis on each node based on the node fault information to obtain a maintenance priority, and inputting the influence weight data into the graph neural network for coping strategy analysis to obtain a target processing strategy. According to the invention, the efficiency of fault propagation analysis and the accuracy of decision making are improved.

The invention discloses a real-time virtual environment view layout algorithm based on spherical space guidance. The algorithm comprises the following steps that an original view is acquired and preprocessed; abstracting into a circular structure and completely mapping to a constructed spherical view projection structure; weight analysis is carried out, a preset threshold value is compared, a high-weight standard view set and a low-weight standard view set are divided, and dynamic batch layout optimization processing is carried out on the high-weight standard view set; constructing a space pressure function field for the low-weight standard view set, and determining a target position by using a space pressure field guiding method; smooth movement from the current position to the target position is achieved through a Bezier curve interpolation method; and when the viewpoint or view angle change exceeds the threshold value, re-calculating the layout until the change amplitude is smaller than the threshold value, and generating a final layout. According to the method, the spherical space projection and the adaptive mechanical model are combined, the large-scale view layout is optimized, the method has the advantages of being efficient, stable and real-time, and the data visualization effect in the virtual environment is improved.

The invention discloses a safety toughness evaluation method for complex constructional engineering. The method comprises the steps of 1, constructing a safety accident case database; 2, selecting basic indexes of an evaluation model for different accident types; step 3, according to the safety accident case database, identifying an interaction relationship among different evaluation indexes, and constructing an interaction matrix; 4, a complex network is constructed, indexes are points in the network, the interaction relation is a connecting line between the points, and the middle centrality, the proximity centrality and the degree difference of the points of all the indexes in the complex network are calculated; 5, performing entropy weight analysis to obtain objective weights of the evaluation indexes in the toughness evaluation model; and step 6, modeling based on an extension cloud model through the indexes and the weights so as to determine the safety toughness level of the complex project. Therefore, the safety environment of building engineering construction of the tower crane is improved, the construction management level is improved, and occupational injuries of employees are reduced.

The invention relates to the technical field of data verification processing, in particular to a consistency verification method and system applied to multi-source communication data. According to the technical scheme, historical data of various communication parameters of a plurality of data sources and target data obtained by performing consistency verification on target parameters through the historical data are obtained; on the basis of fluctuation correlation feature analysis of historical data and target data, obtaining an influence intensity index of each communication parameter on target parameter deviation; performing influence weight analysis according to the difference between the influence intensity indexes and the operation requirement value of each communication parameter to obtain a weighted difference score of inconsistent performance between the data sources; correcting partial judgment caused by historical stability difference in combination with the weighted difference score and the unreliability of each data source in the historical time period; and when the target parameter executes the verification task, marking the real-time data according to the inconsistency score corresponding to each data source, thereby realizing accurate identification of abnormal data.

The invention belongs to the field of clean coal separation, relates to a data analysis technology, and aims to solve the problem that raw coal in dense medium coal separation cannot be automatically matched with suspension density in the prior art, in particular to a suspension density intelligent matching and decision control system based on raw coal ash content. The decision control platform is in communication connection with a density test module, a density matching module, a weight analysis module and a storage module; before clean coal separation, the ash content of raw coal is obtained and marked as an original value, the initial density of suspension liquid of the raw coal subjected to dense medium coal separation is marked as a density value, the clean coal ash content and the coal separation duration in the dense medium coal separation process are marked as a fine separation value and a coal separation value correspondingly, and a test period is generated; and marking all dense medium coal preparation processes in the test period as test processes. According to the method, the suspension liquid density can be tested and analyzed in the clean coal separation process, and the corresponding initial suspension liquid density can be directly matched according to the category and ash content of raw coal.

The invention provides a flood inundation situation intelligent perception and lightweight analysis model construction method, and relates to the technical field of hydrological monitoring and flood control and disaster mitigation. Unmanned aerial vehicle LiDAR point cloud data and satellite multispectral image data are acquired, a differential manifold topographic representation model is constructed, the earth surface is regarded as a Riemannian manifold, and complex topographic features are accurately described; a lightweight submerging calculation model is constructed based on a manifold diffusion theory, and a Laplace-Beltrami operator and a multi-scale solving strategy are adopted to realize rapid and accurate prediction of a flood submerging range and water depth; through a multi-terminal early warning information pushing mechanism, full-process intelligent support is provided, the simulation time is shortened to be within 2 hours from traditional 24 hours, the submerging range prediction goodness of fit reaches 89.7%, the water depth prediction root-mean-square error is controlled to be 0.35 m, and the flow velocity prediction error in a gradient sudden change area is reduced by 12%.

The invention provides an abnormal user identification method and device, electronic equipment, a medium and a program product, and can be applied to the technical field of big data and the technical field of artificial intelligence. The method comprises the following steps: acquiring multi-dimensional discrete feature data of a to-be-detected user set, and converting the multi-dimensional discrete feature data into corresponding number information; generating a continuous embedding vector set based on the number information, and outputting a target distance function; taking the continuous embedded vector set as input, and executing K-means clustering based on a bisection strategy based on a target distance function to obtain a cluster set and a corresponding centroid set; for at least one cluster in the cluster set, constructing a transaction network graph, and obtaining a community structure based on the transaction network graph; executing edge weight analysis based on the community structure and the edge weight information in the transaction network graph; target nodes in the transaction network diagram are removed based on an edge weight analysis result, and a user subset is obtained; and outputting an abnormal user identification result based on the user subset.

The invention provides a macro-cell layout evaluation optimization method based on a large language model, and belongs to the field of electronic design automation. According to the method, a large language model is introduced to assist layout generation, a current initial layout is improved in combination with connection weight analysis, on the other hand, an improved result is judged and continuously iterated according to natural language feedback of an experience manual or an expert, and closed-loop optimization is formed; carrying out weight quantization on direct and indirect signal paths among macro units, constructing a logic association strength matrix, and realizing accurate identification and priority ranking of key time sequence paths; the availability score of each candidate layout scheme is calculated through a layout availability scoring module, dynamic measurement is carried out on surrounding standard unit resources, the resource distribution state is evaluated in real time, the position of a macro unit is actively adjusted, and resource congestion or waste is avoided; the method is deeply integrated with an open source EDA tool, the dependence on a commercial tool is reduced, the tool chain deployment and iteration period is shortened, and the transportability and usability of the method are guaranteed.

The invention belongs to the technical field of production line monitoring optimization, and particularly relates to an automatic optimization method and system for can-making stamping parameters, and the system comprises a multi-source data collection module which comprises an industrial camera, a material component sensor and an equipment state sensor; the defect intelligent analysis module is used for identifying stamping part defects based on a deep learning model and associating causes; the parameter dynamic optimization module is used for generating an adjusting instruction according to the defect cause; multi-source data collection is matched with defect intelligent analysis, the defect type and the defect position of the can making stamping part are determined, then dynamic weight analysis is carried out, defect causes are matched according to the defect type, then the matched adjusting quantity is obtained, then the adjusting quantity is optimized and verified according to a compensation execution structure and a virtual verification module, and therefore the accuracy of the can making stamping part is improved. The finally obtained adjusting quantity is used for adjusting production line parameters, in addition, closed-loop optimization is carried out through an incremental learning controller, and the system can systematically optimize the problems of slow defect response, high misjudgment rate and poor adjusting precision in the can making stamping field.

The invention provides a hydraulic engineering prevention and control information digital management method and system, and the method comprises the steps: firstly obtaining a real-time data stream, extracting overflow risk features through employing a data fusion technology, and then screening out a core feature index from the overflow risk features through constructing a feature weight analysis logic; then, performing anomaly detection on the core characteristic indexes, and when an abnormal index is detected, generating a dynamic risk distribution diagram corresponding to the abnormal index and determining an influence range so as to predict a change trend of the abnormal index in a future target time period in a risk distribution area and lock a risk evolution time interval; and finally, carrying out comprehensive risk analysis based on the abnormal indexes and the initial characteristic indexes associated with the abnormal indexes, and generating standardized early warning information for outputting. According to the method, real-time monitoring, multi-dimensional analysis and accurate early warning of the overflow risk of the target reservoir are achieved, the response efficiency of water conservancy risk early warning is improved, and then the safety management level and the disaster prevention and reduction capacity of the water conservancy project are effectively improved.

The invention discloses a multi-source heterogeneous credit data dynamic cleaning and fusion analysis system, and belongs to the technical field of data cleaning and fusion, and the system comprises a multi-source data collection module which collects credit data and generates an original multi-source data flow; the data cleaning module is used for cleaning the original multi-source data stream to generate a clean data sequence; the entity analysis and sharing management module is used for extracting entity feature data in the clean data sequence and generating global entity state data and an incidence relation graph; the weight analysis engine is used for formulating a similarity calculation strategy, calculating entity similarity and generating weighted entity feature data based on an association path in the association relationship graph; and the incremental version management module is used for recording the state change history of the weighted entity feature data and generating an incremental data snapshot. According to the invention, a unified entity portrait library and a shared memory pool are constructed through the entity analysis and sharing management module, and dynamic credit data changes are effectively coped with.

The invention discloses a real-time optimization method for a reservoir water level prediction model, and particularly relates to the technical field of water level prediction. The method comprises the steps of processing a multi-source data sequence through a long-short-term memory network model to generate water level prediction intermediate data; performing attention mechanism analysis on the prediction intermediate data, identifying key factors influencing water level changes, and outputting feature weight analysis data; optimizing parameters of the long short-term memory network model to obtain optimized parameter data; integrating the feature weight analysis data and the optimization parameter data to obtain water level prediction fusion data; collecting and processing current water level data of the reservoir in real time to form a real-time reservoir water level data sequence; and inputting the water level prediction fusion data and the real-time reservoir water level data sequence into a long short-term memory network prediction model, judging whether the water level exceeds a preset warning value or not, generating real-time water level early warning information, and realizing accurate real-time monitoring and early warning of the reservoir water level.

The invention provides a personalized diet recommendation method for pregnancy diabetes management, and the method comprises the steps: processing diet option data inputted by a patient through an optimized blood glucose response model, and generating an alternative diet recommendation list if the carbohydrate content in the diet option data causes a predicted blood glucose fluctuation trend to exceed a safety range, determining a personalized diet scheme; analyzing the matching degree of the historical blood glucose fluctuation sequence and the personalized dietary scheme according to an iterative blood glucose reaction model, and if the matching degree is judged to be lower than a preset threshold value, re-classifying interaction modes among dynamic factors to obtain refined blood glucose influence weight analysis; and integrating multi-dimensional data from the obtained refined blood sugar influence weight analysis to generate final real-time personalized diet recommendation, and if it is judged that the final implementation of the personalized diet recommendation can stabilize the abnormal blood sugar fluctuation during pregnancy, outputting a complete diabetes management guidance sequence.

The invention discloses an electromagnetic environment-friendly radiation protection assessment method and system, and the method comprises the steps: obtaining electromagnetic field data containing power frequency and high-frequency components through a data collection device, generating a multi-dimensional index data set and a radiation characteristic distribution diagram through the combination of exposure time and a crowd sensitivity index through a computational simulation method, and accurate quantitative analysis of the complex electromagnetic environment is realized. On the basis, a weight analysis method is adopted to fuse effect parameters to calculate a risk quantitative score, when the score exceeds a safety threshold value, radiation limit value data is automatically extracted to generate a visual risk map, and a processing module is linked to output a grading protection instruction. By activating protection configuration in the sensor network, decision unit parameters are dynamically adjusted according to real-time electromagnetic field data, and an optimized protection scheme including a dynamic strategy is formed. According to the invention, the conversion from passive protection to active early warning is realized, and the intelligent level and protection effect of electromagnetic radiation protection are significantly improved.

The invention provides a traffic flow simulation scene extraction method and device, electronic equipment and a storage medium. The method comprises the following steps: extracting static scene elements and dynamic scene elements from a data source after data preprocessing; performing data fusion on the static scene elements and the dynamic scene elements to generate a plurality of structured reference simulation scenes; performing weight analysis processing and state probability distribution processing on each scene element in each reference simulation scene by adopting an analytic hierarchy process and an information entropy theory, and determining scene complexity of the reference simulation scene; and screening out a high-complexity simulation scene based on the scene complexity, and outputting a target simulation test scene set for testing the automatic driving system. Information of various data sources can be fully utilized, and the authenticity and complexity of a simulation scene are improved. Meanwhile, through the analytic hierarchy process and the information entropy theory, the characteristics of the actual traffic environment can be reflected more accurately, and more effective support is provided for testing and verification of the automatic driving automobile.

The invention provides an unmanned distribution vehicle path planning method and system, and relates to the technical field of intelligent logistics, and the method comprises the steps: obtaining distribution task point information and environment map data; by improving a sparrow search algorithm, joint optimization is carried out on a distribution task point access sequence and path nodes, and an initial global path is generated; segmenting the initial global path to obtain a plurality of path segments; local dynamic obstacle avoidance is carried out through a dynamic artificial potential field method of real-time fusion sensor data, and a local correction path is generated; carrying out weight analysis on the plurality of optimization targets through an analytic hierarchy process, and constructing a multi-target optimization model; embedding the multi-objective optimization model into a hybrid optimization framework formed by an improved sparrow search algorithm and a dynamic artificial potential field method, and performing global and local collaborative optimization on the local correction path to obtain a global optimization path; and in a simulation environment, verifying the global optimization path through a vehicle dynamics model, and outputting a final planning path.