1062 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 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 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 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 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 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 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 belongs to the technical field of pollution traceability, and provides a pollution traceability analysis method and system based on water quality sampling data. The method comprises the following steps: receiving multiple pieces of returned water quality monitoring data corresponding to a target sampling area, and obtaining geographic information, hydrological information and known pollution source information of the target sampling area; performing evaluation by adopting an analytic hierarchy process to obtain hydrological complexity of the target sampling area, and formulating a simulation strategy of the pollution diffusion simulation model; controlling the pollution diffusion simulation model to perform analogue simulation according to the simulation strategy to obtain a plurality of pollutant traceability path simulation results; and screening the plurality of pollutant traceability path simulation results by using a generative adversarial network to obtain an optimal pollutant traceability path simulation result. According to the invention, accurate and efficient pollution tracing can be realized, a reliable basis is provided for treatment, and the pertinence and effectiveness of pollution treatment can be remarkably improved.

The invention provides an emergency resource intelligent scheduling optimization method and system based on large model analysis, and relates to the technical field of emergency resource scheduling. The method comprises the steps of collecting emergency event data and environment data; extracting emergency event features and environment features, constructing a prediction model based on a long short-term memory network, and obtaining a required emergency resource list; constructing an emergency resource evaluation model based on an analytic hierarchy process, and obtaining priority scores of the required emergency resources; constructing a multi-objective optimization model, and solving the multi-objective optimization model by using a particle swarm algorithm to generate a scheduling scheme; and constructing a decision function, and solving by using a genetic algorithm to obtain a scheduling decision. According to the method, through accurate prediction, scientific evaluation, multi-objective optimization and decision in combination with priority, the accuracy and efficiency of emergency resource scheduling and the resource utilization efficiency can be remarkably improved, powerful support is provided for emergency management work, and the method has important practical significance and application value.

The invention discloses a method and a system for evaluating the reliability of a contact network system under an icing galloping condition. The method comprises the following steps: collecting icing galloping data of the overhead line system in real time, and carrying out load conversion and statistics on parts of the overhead line system in icing galloping by adopting a rain flow counting method; the reliability of parts under the icing galloping condition is calculated through the Miner cumulative damage theory, and weight distribution of the overhead line system is conducted through an analytic hierarchy process; based on the reliability and the weight distribution result of each part under the icing galloping condition, evaluating the reliability of the overhead line system of the data acquisition anchor section until the evaluation task is finished; the method and the system provided by the invention are suitable for evaluating the operation reliability of the contact network system in the icing galloping section, can provide support for operation and maintenance of the contact network system after icing galloping occurs, and have wide application prospects.

The invention discloses a large transport vehicle bridge safety assessment method based on fuzzy comprehensive evaluation, which comprises the following steps: constructing a bridge bearing capacity reduction coefficient model, integrating four factors of material performance degradation, structural damage, environmental corrosion and load combination, and determining index weights by adopting an analytic hierarchy process; and a multi-stage evaluation system is established in combination with a fuzzy mathematics theory. A prestressed concrete continuous beam bridge in Jiangsu section of Jinghai highway is taken as an object, finite element simulation is used for analyzing the load effect of large vehicles under independent and mixed passing conditions, and verification shows that when the large vehicles pass at the mixing rate of 20%, the most unfavorable section safety coefficient of the bridge reaches 1.35, and the standard requirements are met. According to the method, fuzzy mathematics and structural mechanics analysis are creatively fused, a standardized process of parameter collection, model calculation and safety judgment is established, the efficiency is improved by 40% or above compared with a traditional static load test, the method is applied to transportation examination and approval of five bridges in Jiangsu province, a scientific basis is provided for selection of large transportation channels, and the risk of bridge damage is effectively prevented.

The invention specifically relates to an intelligent water affair dynamic supervision method and platform based on digital twinning, and relates to the technical field of intelligent water affair supervision. Risk early warning and situation deduction; emergency decision making and dispatching command; and post-treatment evaluation and knowledge precipitation. According to the method, a comprehensive and scientific risk quantification system is constructed through a combined weighting mechanism of multi-source data fusion, an analytic hierarchy process and an entropy weight method, and dynamic and accurate description of risks of each section of pipeline and each node of a pipe network is achieved in combination with a real-time working condition correction coefficient and a second-level updating mechanism; and grading accurate early warning triggered by a dynamic threshold value is matched with risk situation deduction of a simulation model, so that the influence range and the development trend of risks such as pipe explosion and water quality pollution can be pre-judged in advance.

The invention provides a multi-direct-current feed-in power grid transformation method and system based on risk assessment. The method comprises the steps that risk assessment comprehensive indexes of flexible transformation schemes of all receiving ends are obtained through an electromechanical electromagnetic hybrid transient simulation model; obtaining a risk evaluation result of each receiving end flexibility transformation scheme according to the risk evaluation comprehensive index, and screening a preset number of receiving end flexibility transformation candidate scheme sets; performing multi-target evaluation by using an evaluation model based on an analytic hierarchy process, and determining a final receiving end flexible transformation candidate scheme; and transforming the multi-direct-current feed-in power grid according to the determined final receiving end flexible transformation candidate scheme. According to the method, comprehensive, accurate and efficient evaluation of the receiving end flexible transformation candidate scheme is realized aiming at the direct current planning requirement of a load concentration large area, and the technical effect of improving the overall stability and the fault handling capacity of a power grid is further achieved.

The invention discloses a self-adaptive evaluation method and system for the running state of a contact network. The method comprises the following steps: constructing a contact network operation state evaluation index system, and obtaining time sequence detection data of each evaluation index; dynamically calculating the length of an adaptive window at the current evaluation moment, and extracting a nearest data subset matched with the length of the adaptive window from the time sequence detection data; using an improved CRITIC method to calculate and obtain a first objective weight vector; updating the gain coefficient vector, and calculating a second objective weight vector; combining the second objective weight vector with a subjective weight vector determined by an analytic hierarchy process by adopting a combined weight method to generate a final combined weight vector, and evaluating the running state of the overhead line system at the current evaluation moment until the evaluation task is finished; according to the method and the system, the accuracy and the timeliness of state evaluation of the overhead line system are effectively improved.

The invention relates to a multi-master election method and device based on an OpenHarmony soft bus, and the method comprises the steps: constructing six efficiency indexes according to the networking characteristics of the soft bus; operating system kernel state extraction: increasing a subsystem in an operating system layer, expanding system capability, obtaining index data of each device in a network in real time, storing the index data in a distributed database, and extracting required data from the distributed database by a multi-main election component to calculate and lift the device; performing analytic hierarchy process index weight construction and election formula derivation: establishing an efficiency index score system through an analytic hierarchy process, and calculating the weight of each index at the level to which the index belongs; and after the weights are determined, performing corresponding fuzzy judgment on each index by using a fuzzy comprehensive judgment method from the bottom-layer index, and finally obtaining a judgment score. According to the invention, the problem that the traditional open-source soft bus solves the problem that the single-center networking survivability of the traditional soft bus is insufficient is solved.

The invention discloses a low-carbon transformation method, system and device for a building air conditioning system. The objective of the invention is to solve the problems of static model, single target, one-sided evaluation and non-standardized process in the existing reconstruction technology. According to the method, building multi-dimensional data are collected; constructing a building energy consumption prediction physical simulation model coupled with a user behavior prediction model based on machine learning to dynamically reflect a real operation condition; a multi-dimensional objective function is defined, and a Pareto optimal transformation scheme set is generated; constructing an evaluation model based on an analytic hierarchy process and a fuzzy comprehensive evaluation method, and calculating a comprehensive evaluation value of each scheme; and selecting an optimal scheme according to the comprehensive evaluation value, and generating a detailed implementation report. The method can scientifically and efficiently determine the global optimal transformation strategy considering energy conservation, carbon reduction, comfort and economy, and significantly improves the scientificity and comprehensive benefits of building transformation decision.

The invention provides a comprehensive energy efficiency evaluation method and system of a 5G base station and a storage medium. The comprehensive energy efficiency evaluation method comprises the following steps: establishing a comprehensive energy efficiency evaluation index system of the 5G base station based on carbon emission-energy efficiency coupling; calculating the comprehensive weight of each index based on an entropy weight method and an analytic hierarchy process; calculating an evaluation result based on a fuzzy comprehensive evaluation method of hierarchical recursion; a 5G base station comprehensive energy efficiency reference line considering multiple typical operation scenes is constructed, and an efficient reference and a tolerance threshold are set; and comparing the comprehensive evaluation score of each typical operation scene of the 5G base station with a set efficient reference and a tolerance threshold, and performing comprehensive energy efficiency evaluation on the 5G base station. According to the method, a referenceable standardized reference line is established for comprehensive energy efficiency evaluation of the 5G base station, clear guidance is provided for energy efficiency benchmarking of the 5G base stations of different types and different regions, and a subsequent energy efficiency improvement scheme can be systematically developed.

The invention relates to a real-time health monitoring method for pigs in a complex environment. The method comprises the following steps: constructing a pig behavior recognition model based on YOLOv8, and obtaining pig action behavior characteristic data; establishing a pig sound state recognition and classification model, and obtaining pig sound feature data; and establishing a pig health evaluation system based on an analytic hierarchy process, and evaluating the pig health state by combining the pig health evaluation system based on the pig action behavior characteristic data and the pig sound characteristic data. A behavior classification method based on a multi-level deep learning model, a joint recognition model based on a deep convolutional neural network and a noise suppression technology and a health assessment model based on multi-dimensional index fusion and hierarchical sorting are provided, so that a high-accuracy micro behavior recognition effect is obtained, and the cough sound detection capability is effectively improved; the method can still accurately identify the health state of the pig in an environment with complex pig house noise, and obtains an accurate pig health state evaluation result.