1284 results about "Analytical hierarchy process method" patented technology

The invention relates to the technical field of computer networks, and discloses a computing power network real-time performance evaluation method based on an analytic hierarchy process, and the method comprises the steps: collecting a node operation state and task demand data through a sensor, and generating a local performance index in combination with an edge quantum algorithm; simulating a future network state by using digital twinning, and fusing to generate a multi-dimensional performance data set; the AHP weight is dynamically adjusted based on resource deviation and a geological classification model, high-frequency updating is started for high load / fault, and the weight range is expanded for low load; introducing a risk assessment algorithm to quantify a performance-cost-carbon effect conflict level, and triggering resource recovery, optimization prompt or single index suggestion; scheduling strategies are triggered in a grading mode according to evaluation results, and active intervention is started in combination with anomaly detection; the AHP weight is dynamically updated through reinforcement learning, and quantum-classical hybrid algorithm parameters and block chain verification weight are automatically optimized. The real-time response efficiency and the resource utilization rate of the computing power network can be improved.

The invention discloses a hybrid enhanced enterprise smart law platform system based on a generative language large model. Four modules including a hybrid enhanced legal knowledge engine, a multi-modal legal document analysis module, a risk quantitative evaluation module and a compliance verification workflow work cooperatively. The hybrid enhanced legal knowledge engine integrates multi-source data, realizes real-time updating and semantic reasoning, and comprises map construction, a rule base and an incremental learning mechanism; the multi-modal legal document analysis module performs structured analysis on the heterogeneous document to generate a feature vector; the risk quantitative evaluation module is combined with Monte Carlo simulation and an analytic hierarchy process, quantifies the risk according to a compliance reference and analysis characteristics, and outputs a thermodynamic diagram and a report; a compliance verification workflow is driven by a finite-state machine, a verification module and a conflict detection module are integrated, a generative language large model is called to generate an improved scheme, and audit records are solidified and fed back for optimization. And the system runs according to the processes of analysis, supply rule, bias calculation and verification correction, so that the intelligence and accuracy of legal affair processing are improved.

The invention provides a pressure-bearing equipment damage mode identification and risk assessment method and system, and relates to the technical field of safety engineering, and the method comprises the steps: collecting multi-source sensor data and image data, inputting the data into a deep neural network after preprocessing and feature extraction, extracting spatial features through a convolutional layer, and extracting time sequence features through a recurrent neural network. And using the attention mechanism to fuse the features to identify an injury pattern. And then, constructing a multi-level evaluation index system, performing combined weighting by adopting an analytic hierarchy process and an entropy weight method, inputting weights into an improved Bayesian network model based on a D-S evidence theory, dynamically updating a conditional probability table by the model by utilizing a deep neural network and a fuzzy inference rule, and finally obtaining a risk evaluation result. According to the invention, the damage mode of the pressure-bearing equipment can be effectively identified, risk assessment is carried out, and assessment precision and reliability are improved.

The invention provides an agricultural product processing quality traceability monitoring method and system based on edge nodes, and the method comprises the steps: constructing an analytic hierarchy process model through employing the multi-source data of time-space alignment, decomposing an agricultural product processing quality evaluation index into a plurality of sub-indexes, forming an index hierarchical structure, and employing a fuzzy comprehensive evaluation method to obtain a traceability result of the agricultural product processing quality. The importance weight and the satisfaction degree of each sub-index are comprehensively considered, the comprehensive score of each scheme is calculated, and the processing quality of the agricultural products is judged; after the quality problem is processed, key control points and influence factors in the agricultural product processing process are analyzed through data mining, a quality evaluation model and a rule base are dynamically updated by adopting an incremental learning algorithm, and continuous optimization of the model is realized by continuously receiving new data samples and updating model parameters; and the accuracy of agricultural product processing quality evaluation is continuously improved.

The invention discloses a geological disaster early warning method and system based on big data analysis, and particularly relates to the technical field of geological disaster early warning. Geological deformation time series data of a target area are obtained through satellite remote sensing monitoring, feature importance is evaluated through a random forest algorithm, and regional geological risk grades are divided; meanwhile, wavelet transformation and long-short-term memory network modeling are carried out on rainfall spatio-temporal data of the meteorological monitoring station, and the potential influence of rainfall is evaluated; high-risk areas are screened, an underground water level change rate and rock-soil body strength correlation model is constructed, and a support vector machine algorithm is adopted to evaluate the landslide probability; and finally, analyzing a geological risk grading result, the potential influence degree of rainfall on the target area and the landslide occurrence probability by adopting an analytic hierarchy process, calculating a comprehensive risk index, comparing the comprehensive risk index with a comprehensive risk threshold value, and judging whether to trigger grading early warning, thereby realizing data fusion and intelligent analysis, early warning in advance, and improving the early warning efficiency. And effective help is provided for disaster prevention and reduction.

The invention discloses an information security adaptive protection method and system based on artificial intelligence, and relates to the field of security protection, and the method comprises the steps: dynamically collecting multi-dimensional asset data through distributed nodes, carrying out the edge calculation preprocessing, and extracting features through a deep learning model; carrying out threat identification by fusing LSTM time sequence analysis, an isolated forest and a multi-modal AI detection engine of a knowledge graph; outputting a risk level based on an improved analytic hierarchy process and a fuzzy evaluation model; the AI strategy engine combines the risk level and the business scene to generate an optimal protection strategy, and continuous optimization is carried out through reinforcement learning; a standardized instruction is linked with safety equipment to execute protection, and interception effect closed-loop optimization is fed back in real time; a whole process log is stored through a block chain, and an attack evidence chain is generated through an AI traceability model. The method has the advantages that the information security protection capability is comprehensively improved through hierarchical data acquisition, multi-modal threat detection, scientific situation evaluation, dynamic generation of an optimization protection strategy and combination of block chain evidence storage and AI traceability.

The invention discloses a 5G message video monitoring method based on artificial intelligence. The method comprises the following steps: S1, constructing a multi-modal data set; s2, multi-modal fusion features are generated by using a joint convolutional neural network; s3, generating a target behavior trend curve and a target behavior prediction result by using the 3D convolutional neural network; s4, generating a target threshold curve by using an adaptive algorithm based on variable domain search, and obtaining behavior abnormality warning information; s5, generating comprehensive alarm information by using a method of combining fuzzy logic reasoning and a Bayesian network; s6, cross-camera data space-time correlation reasoning is carried out by using the graph neural network; s7, constructing an event severity curve by using a multivariable regression analysis and analytic hierarchy process, and generating a severity level and an event response measure of the event; and S8, pushing alarm information and event response measures to related personnel or equipment through the 5G message platform. The invention provides an efficient video monitoring method in combination with artificial intelligence and a 5G network.

The invention relates to the technical field of power systems, in particular to a multi-dimensional network performance index evaluation system and method based on measured data of a new energy station. Comprising a data acquisition preprocessing unit; a multi-dimensional index construction unit; the weight adjustment unit dynamically adjusts the index weight according to the operation condition based on a fusion algorithm of an improved analytic hierarchy process and an entropy weight method; the performance evaluation model unit adopts a comprehensive evaluation model fused with a random forest algorithm to generate multi-dimensional performance scores and risk early warning; and the visual interaction terminal unit is used for displaying the evaluation result in real time and generating a trend analysis report. According to the invention, the three-dimensional evaluation system covering the static electrical parameters, the dynamic stability indexes and the harmonic pollution indexes is constructed, so that the limitation of single-dimensional evaluation in the prior art is broken through, and the extension of the network-related performance of the new energy station from local detection to full-dimensional coverage is realized; and the multi-scene evaluation requirements of the power grid on the steady-state operation, the transient response and the electric energy quality of the station are accurately matched.

The invention discloses a territorial space planning index dynamic evaluation model, and particularly relates to the field of territorial planning, and the model comprises a multi-source heterogeneous data fusion module, an index system construction module, a dynamic adjustment model construction module, a scheme evaluation and optimization module, and a model application and dynamic updating module. According to the method, various spatial data are integrated through a multi-source heterogeneous data fusion technology, and the data quality is ensured by adopting a three-stage cleaning assembly line and an intelligent correction algorithm; the method comprises the following steps: constructing an index system based on six core contents of territorial space planning, and determining an index weight by applying a Delphi method and an analytic hierarchy process to realize adaptive adjustment of an index threshold; the model adopts time sequence analysis, regression analysis and a machine learning algorithm to predict a development trend, a dynamic feedback mechanism is established in combination with a system dynamics method, and an evaluation scheme is quantified through an analytic hierarchy process; the model performance is evaluated by adopting indexes such as accuracy rate and precision rate, and parameters are continuously optimized based on an evaluation result; finally, the model is integrated to a service system to realize real-time monitoring, and dynamic updating is realized through three mechanisms of data driving, performance triggering and regular maintenance.

The invention relates to the technical field of coal mine disaster monitoring, in particular to a gas coal mine goaf coupling disaster monitoring method, an electronic device and a storage medium, and the gas coal mine goaf coupling disaster monitoring method comprises the following steps: obtaining comprehensive data of a goaf through a sensor network, and carrying out data preprocessing to obtain standardized data; constructing a digital twinborn model through a 3D modeling and geographic information system technology; performing data synchronization and model correction processing on the digital twinborn model, and optimizing parameters and states of the digital twinborn model; constructing a fluid-solid-heat multi-field coupling model through a computational fluid mechanics and finite element analysis method; constructing a risk assessment model through an analytic hierarchy process and a fuzzy comprehensive evaluation method; training a disaster early warning model by using a machine learning algorithm based on the standardized data and the risk assessment model; a digital twinborn model, standardized data, a risk assessment model and a disaster early warning model are integrated, and a visual interaction platform is constructed. According to the monitoring method, multiple disaster elements can be coupled and dynamically evolved, and the disaster prediction accuracy and timeliness are greatly improved.

The invention discloses a market supervision data asset multi-dimensional evaluation method and system based on a hierarchy-network dynamic fusion model, and relates to the technical field of data asset evaluation. According to the method, a semantic association and index alignment relationship between data assets is comprehensively described by constructing a semantic map of the data assets; a hierarchy-network dynamic fusion model is formed by combining an analytic hierarchy process and a network analysis process, so that the determination of the index weight is more scientific and reasonable; a graph neural network is utilized to perform embedded representation learning on a data asset graph structure, comprehensive feature expression of each data asset is extracted, a multi-objective optimization method is combined, a Pareto optimal solution set is generated, scoring, sorting and grading are performed on the data assets, and reliability and interpretability of an evaluation result are ensured; the evaluation report is output in a structured data format and is provided for a market supervision system through an interface, thereby facilitating effective management and decision support for data assets by a supervision department, and having a wide application prospect.

The invention discloses an electric vehicle charging station layout optimization configuration method and system, and the method comprises the following steps: obtaining the traffic network information and power distribution network information of a planning target area, and determining the charging demand number of electric vehicles and the charging load demand distribution condition; according to the electric vehicle charging load demand distribution condition, determining the service range of each charging station, and constructing a proposed charging station capacity determination model; an upper-layer charging station site selection model is constructed, and the optimal site of each charging station is determined; establishing a lower-layer mixed integer programming model, and determining the configuration number of various charging piles of each charging station; solving the double-layer locating and sizing planning configuration model by using a hybrid genetic simulated annealing algorithm and an analytic hierarchy process to obtain an optimal locating and sizing scheme of the charging station; according to the method, the attraction of the charging station to the user and different types of charging pile configuration schemes are comprehensively considered, a bilevel planning configuration model is established, an optimal solution set is solved by using a hybrid genetic simulated annealing algorithm, and finally an optimal scheme is determined through an analytic hierarchy process.

The invention relates to the technical field of personalized pushing, in particular to a personalized music teaching content pushing system. The system comprises a user ability evaluation module, a teaching content matching module, a learning path planning module, a push strategy optimization module, a behavior analysis and prediction module, a teaching effect feedback module, a dynamic adjustment and optimization module and a personalized recommendation module. According to the invention, a clustering algorithm, a support vector machine, a random forest algorithm, accurate assessment of learner ability, K-means clustering and an analytic hierarchy process are adopted, teaching content and ability matching, a Dijkstra algorithm and dynamic planning are realized, a personalized learning path, a Q learning and strategy gradient method are designed, and a push strategy, a long and short term memory network and a seasonal decomposition time sequence are optimized. Learning requirements are predicted, the project reaction theory, the Markov decision process and the Bayesian network are utilized, feedback adjustment precision is improved, deep Q network and Monte Carlo tree search are carried out, personalized recommendation is optimized, and teaching efficiency and achievements are enhanced.

The invention provides a performance management assessment system based on big data and a mathematical model, and the system comprises a data collection module which is used for collecting the work performance data of employees from a plurality of data sources in real time; the data preprocessing module is used for carrying out cleaning, denoising, missing value complementing and normalization processing on the collected original data; the index weight calculation module is used for calculating weights of indexes of different dimensions based on an analytic hierarchy process; the performance scoring module is used for generating comprehensive performance scores of the employees through a fuzzy comprehensive evaluation model; the time sequence prediction module is used for predicting future performance of the employees in combination with historical performance data and a machine learning algorithm; and the dynamic feedback and adjustment module provides personalized feedback and improvement suggestions for the employees based on the real-time performance data. According to the invention, multi-dimensional quantitative evaluation can be carried out on the work performance of the employee, personalized improvement suggestions are provided, and future performance performance of the employee is analyzed through prediction.

The invention discloses a method for constructing a digital twinborn model of a full life cycle of an automobile part, which relates to the technical field of digital twinborn, and comprises the following steps: inputting a life cycle data set into an initial life digital twinborn model, carrying out time domain local feature fusion by a feature extraction layer, capturing a degradation trend by a time sequence analysis layer, and constructing a digital twinborn model. Extracting an actual detection value in the life cycle data set; calculating a deviation value between the life prediction value and the actual detection value of the automobile part; carrying out sequential relation mapping on the deviation value to generate a dynamic causal atlas; carrying out graph traversal on the dynamic causal atlas to obtain key degradation path nodes; the dynamic causal reasoning is used for carrying out correlation analysis on the key degradation path nodes, degradation characteristic parameters are generated, the depth-first search and analytic hierarchy process are adopted, dynamic causal correlation precise modeling of the life digital twin model is achieved, and the robustness of the life digital twin model under the actual complex working condition is remarkably enhanced.

The invention discloses a digital management system based on a VR technology, and relates to the technical field of digital management, and the system comprises a task execution module which executes a scenario-type decision simulation task according to a task demand in a virtual scene, records the decision behavior and interaction data of a user in real time in the execution process, and sends the decision behavior and interaction data to a server; generating a preliminary feedback report according to the decision behavior of the user and the interaction data; and the execution condition evaluation module is used for extracting task evaluation indexes by adopting data mining on the basis of the preliminary feedback report, evaluating the task execution condition of the user by adopting an analytic hierarchy process on the basis of the task evaluation indexes, and dynamically adjusting the situational decision simulation task in the virtual scene according to an evaluation result. According to the invention, the execution condition evaluation module is utilized to carry out multi-dimensional deep analysis on the decision behavior and interaction data of the user, accurate feedback and guidance are provided, the improvement of personal ability is promoted, and the design and adjustment of subsequent tasks are optimized.

The invention discloses a big data platform storage data isolation method in a SaaS mode, and relates to the technical field of big data, and the method comprises the steps: inputting a storage situation data set into a causal graph neural network, capturing a data time sequence mode and causal association features through a feature extraction layer, carrying out the multi-hop neighborhood feature aggregation through a feature fusion layer, and generating a storage anomaly detection vector; inputting the stored anomaly detection vector into a causal inference engine, executing risk quantification by using an improved causal inference tree algorithm, obtaining a causal effect score, carrying out risk division through a three-level threshold, generating an anomaly risk level, carrying out entropy calculation on the anomaly risk level by using a Shannon entropy formula, obtaining an anomaly entropy value, and obtaining an anomaly result. Carrying out interval classification on the abnormal entropy value to form a sensitivity level; according to the invention, through the constructed causal graph neural network, the improved causal inference tree algorithm and the analytic hierarchy process, dynamic identification of abnormal risks is realized, and redundancy isolation of low-risk data is also avoided.

The invention discloses an ecological restoration effect evaluation and simulation system after polluted soil restoration, and relates to the technical field of soil restoration, and the system comprises a soil feature generation module which generates an ecological feature data set; the ecological restoration evaluation module is used for establishing a multi-dimensional evaluation model by using an analytic hierarchy process and a fuzzy comprehensive evaluation method based on the ecological characteristic data set, and generating an ecological restoration effect index; the dynamic simulation prediction module is used for constructing an ecological restoration dynamic simulation model and simulating the evolution trend of a soil ecological system under different environment conditions; and the restoration strategy optimization module is used for generating a targeted ecological restoration optimization scheme by combining a preset restoration strategy library according to the ecological restoration effect index and the dynamic simulation prediction result. According to the method, a closed-loop system integrating multi-source data acquisition and processing, multi-dimensional evaluation, dynamic simulation prediction and stepped strategy optimization is constructed, and accurate evaluation of the ecological restoration effect after contaminated soil restoration and scientific optimization of the restoration strategy are achieved.

The invention belongs to the technical field of constructional engineering management, and particularly relates to a green construction evaluation method and system.The method comprises the steps that S101, energy consumption data, material use data and environmental parameters of a construction site are collected in real time through Internet of Things monitoring equipment; s102, constructing a multi-dimensional evaluation index system including resource utilization efficiency, carbon emission intensity and an environmental influence coefficient based on a BP neural network model; s103, calculating a dynamic weight coefficient by adopting an analytic hierarchy process in combination with a time factor; s104, generating a green construction comprehensive score by using a fuzzy comprehensive evaluation algorithm; and S105, carrying out iterative optimization on the evaluation model based on a reinforcement learning algorithm. Different from a traditional static evaluation mode, various data of a construction site are collected in real time by means of Internet of Things monitoring equipment, and dynamic monitoring of the construction process is achieved. From energy consumption data to environmental parameters, each link can be fed back in time, so that a manager can master the green construction condition of a project at any time, and is not limited to one-time evaluation at a completion acceptance stage any more.

The invention relates to the technical field of reservoir dam safety risk research, in particular to a small and medium-sized reservoir dam safety evaluation method, a computer readable storage medium and electronic equipment. The small and medium-sized reservoir dam safety evaluation method disclosed by the invention specifically comprises the steps of firstly obtaining dynamic indexes for dam safety evaluation based on an index classification function, then constructing an evaluation system based on the obtained dynamic indexes, then carrying out weight calculation on the dynamic indexes by adopting an improved analytic hierarchy process, and finally, carrying out dam safety evaluation. The dam safety is evaluated by adopting an improved fuzzy analysis method in combination with an evaluation system and the weight of a dynamic index, the dam safety real-time state can be accurately evaluated, the evaluated dam safety state is dynamically updated in real time, real-time information is provided for the safety management of the dam in the later extreme climate and environment, and the safety of the dam is improved. And a manager can make a timely and accurate decision when the dam encounters a risk.

The invention discloses a primary equipment state monitoring method and system in a new energy power system, and the method comprises the following steps: deploying a multi-mode sensor array on primary equipment, and synchronously collecting a voltage signal, a current signal, a temperature signal, a vibration signal and an environment parameter; inputting the collected data into an edge computing node for preprocessing to obtain a multi-modal signal sequence; variational mode decomposition is carried out to form a multi-dimensional feature vector; inputting a long-short-term memory neural network model containing an attention mechanism, performing training and reasoning by using an AdamW optimizer and a cosine annealing learning rate strategy, and outputting the health degree of equipment; determining the weight of each monitoring index based on an analytic hierarchy process, and dividing the equipment into a plurality of state grades; the fault probability is obtained through fuzzy Petri net reasoning, and a corresponding early warning mechanism is triggered according to a preset threshold value. According to the invention, through multi-dimensional data fusion and intelligent analysis, the accuracy and real-time performance of state monitoring are significantly improved.

The invention belongs to the technical field of display design evaluation, and particularly relates to an exhibition display layout design effect analysis and evaluation method, which comprises the following steps of: constructing a dynamic spatial topology network by collecting audience moving tracks, exhibit information and spatial structure data in real time; the problem of insufficient attention on space utilization influenced by dynamic factors such as human traffic fluctuation is solved. And dynamic changes in the space are converted into quantifiable network nodes and connection relationships, so that the space utilization state is changed from static design to dynamic monitoring, accurate capture of the real-time use condition of the exhibition space is realized, a comprehensive and real-time data basis is provided for subsequent analysis, and the real-time monitoring of the exhibition space is realized. Meanwhile, the dynamic space topology network is analyzed by means of spatial-temporal feature fusion to determine the edge weight and the exhibition area correlation degree, the spatial efficiency index and the dynamic line rationality index are generated in combination with an analytic hierarchy process and multi-dimensional analysis, and the defect that a space utilization rate core evaluation index is not established in the prior art is overcome.

The invention discloses a road line selection design scheme decision-making method based on multi-source fusion. The method comprises the following steps: collecting multi-source data of a road planning area, performing standardized preprocessing, identifying ground features through an improved target detection model, and extracting features; fusing multi-dimensional data to construct a constraint function aiming at cost, ecology and compliance, and determining a weight by using an analytic hierarchy process; and performing staged optimization on three-dimensional line selection based on deep reinforcement learning to generate candidate schemes, and determining an optimal scheme through comprehensive score and curvature comparison selection. The method solves the problems that traditional line selection depends on manpower, is low in efficiency and is one-sided in decision, improves the scientificity, economy and driving safety of a scheme, and is suitable for a road line selection design scene.

The invention discloses a deep learning algorithm comprehensive evaluation method and system based on game theory combination weighting-TOPSIS, and the method comprises the steps: constructing a deep learning algorithm evaluation index system, calculating different evaluation index values of each candidate algorithm, constructing an evaluation matrix, and carrying out the range standardization processing according to a positive or negative index type; obtaining importance weights of four groups of indexes based on an analytic hierarchy process, an optimal sequence diagram method, an entropy weight method and a CRITIC weight method; on the basis of the four groups of index importance weights, a final index importance weight is obtained based on a game theory combination weighting method; and according to the final index importance weight, in combination with a weighted TOPSIS method, obtaining a comprehensive evaluation and selection sorting result of all candidate algorithms. According to the method, the comprehensiveness and accuracy of evaluation are improved, and a decision maker can be better supported to make scientific judgment in the deep learning algorithm selection process.

The invention relates to a gulongshale oil reservoir dessert evaluation method based on an improved fusion algorithm, and the method comprises the steps: constructing a dessert parameter prediction and lithofacies recognition model based on time-frequency information and an improved fusion neural network BWO-CNN-LSTM, and achieving the parameter prediction and lithofacies classification of a gulongshale oil reservoir; main control parameter selection is realized based on gulonia shale oil reservoir dessert parameters; according to different lithofacies types, calculating based on an analytic hierarchy process and an entropy weight method to obtain a comprehensive master control parameter weight; weighting the screened master control dessert parameters and the corresponding weights to obtain a comprehensive dessert value, visualizing to form a spatial distribution diagram, introducing an adaptive adjustment mechanism, constructing a gulonium shale oil reservoir dessert evaluation model based on BWO-CNN-LSTM, and evaluating the gulonium shale oil reservoir dessert. According to the method, the time-frequency information and the improved fused neural network are combined, so that the accuracy of gulongshale oil reservoir sweet spot parameter prediction and lithofacies classification is improved.

The invention provides an artificial intelligence-based engineering risk prediction method. The artificial intelligence-based engineering risk prediction method comprises the steps of constructing an engineering risk knowledge graph, designing a quantitative risk index system, developing a risk prediction intelligent algorithm, realizing risk prediction visualization, dynamically optimizing and updating and the like. According to the method, automatic association fusion of concept nodes is realized by adopting a knowledge fusion algorithm based on semantic similarity in knowledge graph construction; determining an index weight by using an analytic hierarchy process and a fuzzy comprehensive evaluation method in risk index quantification; in prediction algorithm development, risk event time sequence features are extracted, and a plurality of prediction models are fused by adopting a Stacking and Blending ensemble learning strategy; and developing a risk knowledge graph visual modeling tool in visual presentation, and introducing quantitative evaluation indexes such as graph density and the like. According to the method, intelligence, digitization and visualization of engineering risk prediction are realized, the problems of high risk assessment subjectivity, low prediction precision and the like in a traditional method are solved, and the method has important application value in engineering project risk management.

The invention relates to a carbon emission comprehensive efficiency evaluation method, system and device and a storage medium. According to the method, after carbon emission data is obtained, based on the carbon emission data and a pre-constructed carbon emission comprehensive efficiency evaluation index model, at least one first-level evaluation index value and the index weight of each first-level evaluation index are obtained through a principal component analysis method and an analytic hierarchy process; the carbon emission comprehensive efficiency evaluation index model comprises a plurality of first-level evaluation indexes, and finally, based on first-level evaluation index values and corresponding index weights, obtaining a carbon emission comprehensive efficiency evaluation result. Compared with the prior art, the method has the advantages that the accuracy and objectivity of the evaluation result of the comprehensive efficiency of the carbon emission can be ensured under the condition that the original data is sufficient or insufficient, and the like.

The invention discloses an unmanned aerial vehicle cluster control method and system based on artificial intelligence, and relates to the technical field of unmanned aerial vehicle cluster control, and the method comprises the steps: obtaining global cooperative situation data through a sensor, and carrying out the preprocessing; calculating control output of each unmanned aerial vehicle by using a fuzzy C-means clustering algorithm and an analytic hierarchy process, generating a preliminary flight path, and dynamically adjusting the flight path according to real-time environment feedback through a reinforcement learning algorithm; the unmanned aerial vehicle cluster shares the adjusted flight path and global environment information through a 5G network, analyzes the speed, course angle, height, acceleration gradient and height change rate of each unmanned aerial vehicle by using a distributed control algorithm, and generates cooperative control parameters; according to the invention, by combining the fuzzy C-means clustering algorithm, the analytic hierarchy process, the reinforcement learning algorithm, the distributed control algorithm and the genetic algorithm, efficient cooperative control of the unmanned aerial vehicle cluster in a complex environment is realized.

The invention discloses a meteorological disaster risk assessment method and system based on disaster risk grading, and belongs to the technical field of meteorological disaster assessment, and the method specifically comprises the steps: collecting the meteorological and disaster-bearing body data and geographic information of a disaster area, and carrying out the preprocessing through a spatial interpolation method; setting meteorological disaster-inducing factors, disaster-pregnant environments and disaster-bearing body vulnerability indexes, and constructing a judgment matrix and distributing index weights by using an analytic hierarchy process; constructing a disaster risk grading model according to the risk indexes and the corresponding weights, inputting data of the to-be-evaluated region, calculating a risk value, judging a risk grade, and generating a report; establishing a real-time monitoring system, updating a risk assessment result, and issuing early warning information when a risk value reaches an early warning threshold value; according to the invention, through comprehensive analysis and real-time monitoring, the accuracy and timeliness of meteorological disaster risk assessment are improved.

The invention provides a simulation method and system for mechanical parameter prediction of a rock mass structural plane of a base-covering interface, and relates to the technical field of geological survey, and the simulation method specifically comprises the following steps: collecting environmental parameters and material parameters, and calculating a physical influence index, an environmental adaptability index and a fracture stability index; performing a dynamic load test on the rock mass to obtain dynamic response data of the rock mass, constructing a structural anti-seismic prediction model according to a physical influence index, an environmental adaptability index, a fracture stability index and a dynamic response index, and generating a weight in the model through an analytic hierarchy process, and utilizing the structure anti-seismic prediction model to generate a structure anti-seismic index of the base-covering interface rock mass structure. According to the scheme, the mechanical characteristics of the rock mass can be comprehensively reflected, and the influence of environmental change on the rock mass performance is effectively considered, so that the scientificity of anti-seismic performance prediction is improved.