744 results about "Analytic hierarchy process" patented technology

The invention discloses a load flow calculation and simulation control method and system for a digital twin power grid, and relates to the technical field of digital twin simulation control, and the method comprises the following steps: constructing a digital twin power grid model, and carrying out the dynamic topology optimization processing of the digital twin power grid model based on remote signaling credibility weighting; according to the optimized digital twin power grid model, identifying the power grid operation risk based on an integrated learning model; according to the risk identification result, generating a transfer path control strategy based on an analytic hierarchy process and a fuzzy comprehensive evaluation method; mapping the transfer path control strategy into a control action instruction set, and simulating execution and establishing a feedback correction mechanism on the digital twin power grid model; by generating the optimal path control strategy and performing control strategy analog simulation and self-adaptive feedback correction based on the digital twin power grid model, the problem of lack of intelligent path control strategy selection and simulation verification based on state dynamic identification in the prior art is solved.

The invention relates to the technical field of process optimization control, and discloses a silk-covered enameled winding process optimization control method and system based on intelligent analysis. The method comprises the steps that multi-area production parameters are collected in real time and preprocessed; constructing parameter and product quality mapping through principal component dimensionality reduction, grey correlation and support vector regression; training an Actor-Critic structure by using a depth deterministic strategy gradient network to obtain an adjustment strategy; a non-dominated sorting genetic algorithm is combined with analytic hierarchy process to carry out multi-objective optimization, and a control scheme for balancing mass, efficiency and energy consumption is realized. According to the application, on the basis of considering the complex coupling relationship among the process parameters, multi-objective dynamic balance optimization of product quality, production efficiency and energy consumption is realized, the process parameters can be adaptively adjusted according to the production state and the task demand, and the stability and the optimization degree of the silk-covered enameled winding production process are improved.

The invention discloses a territorial space planning toughness identification and evaluation method, and belongs to the technical field of territorial space planning. The method comprises the following steps: step 1, acquiring spatial information, social economic data and ecological environment elements of an evaluation area; 2, dividing functional units as network nodes, and constructing a weighted adjacent matrix to form a complete spatial relation network structure; 3, determining an index weight in combination with an analytic hierarchy process and an entropy evaluation method; 4, calculating the toughness index of each node, and comprehensively evaluating the overall toughness level of the network in combination with network topology analysis; 5, identifying key weak nodes and links based on a network topology structure and node importance analysis; step 6, adopting a multi-objective optimization algorithm to generate candidate schemes of specific improvement measures, and utilizing a Monte Carlo simulation method to evaluate the feasibility and effect of each candidate scheme to determine an optimal implementation path; and step 7, forming an evaluation-improvement-re-evaluation loop optimization mechanism.

A fuzzy cloud-based risk assessment method for reservoir operation schemes, including: collecting basic data of target reservoirs; constructing optimal operation model of conventional reservoirs to obtain model calculation results; acquiring change process of operation characteristics during whole operating period according to model calculation results, and constructing multi-level reservoir multi-dimensional risk assessment index system; dividing different risk level intervals according to multi-level reservoir multi-dimensional risk assessment index system; obtaining large number of sample data xij of reservoir risk index under corresponding working conditions by multiple parallel calculations to conduct risk assessment; calculating cloud digital features of different indexes by inverse cloud generator; and weighting indexes in index layer and criterion layer by Analytic Hierarchy Process to obtain multi-dimensional risk assessment results. In order to promote sustainable development and utilization of water resources and enhance level of reservoir risk management, scientific evaluation of reservoir risk is conducted by comprehensively considering various uncertainties.

The invention relates to the technical field of risk analysis, in particular to a safety engineering comprehensive management system which comprises a geological parameter dynamic monitoring module, a risk threshold early warning module, a stability weight evaluation module and a risk comprehensive management and control module. According to the method, lithology data are extracted through a drilling profile to calculate the layer thickness sudden change rate and the strength attenuation rate, a lithology abnormal area is positioned through a variable coefficient method, lithology sudden change marks are analyzed through spatial clustering, a dynamic trend is captured through sliding window mean value comparison, and gradient amplitude and slope direction dispersion are calculated through a digital elevation model. Fusing the abnormal point density to generate a terrain instability index; establishing a multi-dimensional quantitative evaluation framework; carrying out fuzzy comprehensive evaluation and cross validation on the instability index and an engineering parameter trend; generating a dynamic priority sequence, carrying out stake number interval matching to construct a risk measure table, carrying out analytic hierarchy process dynamic resource allocation support drainage scheme, and carrying out real-time monitoring and prediction closed-loop iteration. And adaptively adjusting the control strategy.

The invention relates to the technical field of data processing, and discloses a mangrove forest ecosystem population distribution analysis method and system. The method comprises the following steps: acquiring a high-resolution remote sensing image and field data, and establishing a multi-dimensional database through an image enhancement algorithm; a random forest algorithm is used to identify community types, and plant position information is extracted through deep learning; calculating indexes such as relative multi-degree to obtain characteristic parameters; key patches are obtained through landscape index analysis; building a prediction model by combining the environmental parameters; and constructing an evaluation model based on analytic hierarchy process, and generating a management decision suggestion. According to the application, an analysis method for mangrove forest population distribution prediction and intelligent management decision is realized, so that the scientificity of mangrove forest ecosystem research and the effectiveness of management are improved.

The invention belongs to the field of submarine routing planning, particularly relates to a pipeline routing line selection method and system based on a path planning algorithm and analytic hierarchy process, and aims to improve the routing line selection efficiency and precision and reduce the routing risk in the pipeline routing line selection process. The method comprises the following steps: establishing an evaluation sample matrix X based on measured value data of evaluation indexes, and converting pixel points of a target sea area topographic map into nodes of a two-dimensional matrix to obtain a grid obstacle map; establishing a submarine pipeline routing risk quantitative evaluation model; converting the sea area topographic map into a risk level grey-scale map through the risk quantitative evaluation model of the submarine pipeline; and carrying out route planning through a route planning algorithm based on the grid obstacle map, and finding a route with the minimum accumulated gray value in the target sea area risk level gray-scale map through a greedy algorithm to serve as a final route. According to the method, risk parameters are taken into consideration for the generated path, and finally, the obtained route is a routing path with small length and low overall risk.

The invention discloses a social risk early warning method and system based on multi-source data, and the method comprises the steps: collecting multi-source heterogeneous data, and constructing a risk index library; identifying key risk indexes from the evaluation values by using a clustering algorithm; finding an alternative index for an unquantized key risk index, and establishing a quantitative model; updating the risk index library, performing hierarchical division, and calculating the weight of each risk index; constructing an index comprehensive weight matrix; quantized values of the key risk indexes are obtained through a quantitative model, and a comprehensive score is obtained in combination with the weight matrix and used for social risk early warning. According to the method, for social risk indexes which are difficult to quantify, index substitutes and a measurement and calculation model are constructed through expert experience and data analysis, and index quantification is achieved. Meanwhile, an analytic hierarchy process is adopted to construct a risk index system and give a weight, and a risk comprehensive scoring mechanism is formed. According to the method, the index system is dynamically adjusted by continuously collecting risk data and applying a time sequence analysis method and the like, so that the effectiveness of early warning is ensured.

The invention relates to the technical field of intelligent teaching, in particular to an intelligent teaching system for traditional Chinese medicine clinical skill cultivation. The system comprises a virtual reality simulation module, an operation interaction module, an operation data analysis module, an adaptive learning feedback module and a teaching evaluation module. The virtual reality simulation module constructs a vivid traditional Chinese medicine skill training scene through three-dimensional modeling, ray tracing and physical simulation technologies, and the vivid traditional Chinese medicine skill training scene covers pulse feeling, acupuncture and moxibustion, massage and other operations. The operation interaction module collects user operation data and extracts features in combination with a variational auto-encoder. The operation data analysis module analyzes user operation deviation by using a graph neural network and a generative adversarial network, the adaptive learning feedback module generates a personalized learning strategy, and the teaching evaluation module evaluates a learning effect through fuzzy evaluation and analytic hierarchy process. According to the invention, intelligence, individuation and high efficiency of traditional Chinese medicine clinical skill cultivation are realized, the fidelity of teaching and the accuracy of feedback are improved, and the skill level and the training effect of learners are also improved.

The invention discloses a project performance evaluation and risk early warning method for multi-source heterogeneous scientific research data, and relates to the technical field of scientific research project management, and the method comprises the steps: collecting the multi-source heterogeneous scientific research data, cleaning, converting, filling missing values, and storing in a unified format; extracting performance and risk features, and fusing to obtain a fused feature set; combining an analytic hierarchy process, an entropy weight method and the like to construct a dynamic weight performance evaluation model; constructing a multi-modal risk early warning model by adopting multiple algorithms and optimizing a threshold value; collecting data in real time to update a feature set, and dynamically adjusting the model; and visually outputting a result, generating an improvement suggestion, and forming closed-loop feedback. According to the method, the multi-source data processing efficiency and evaluation accuracy are improved, the risk early warning timeliness and adaptability are enhanced, a management closed loop is formed, and the problems of difficult data integration, evaluation lagging, insufficient early warning and the like of a traditional method are solved.

The invention discloses a battery energy storage power station risk evaluation method based on fuzzy comprehensive evaluation, which belongs to the field of battery energy storage power station safety risk evaluation, and comprises the following steps: comprehensively considering risk factors influencing the safety of a battery energy storage power station, carrying out induction and classification on the risk factors, and selecting representative index parameters; and establishing a battery energy storage power station risk evaluation index system. And determining subjective weights and objective weights of the evaluation indexes through an analytic hierarchy process and an entropy weight method according to the established evaluation indexes of the battery energy storage power station. And calculating the comprehensive weight of each evaluation index of the battery energy storage power station by combining the subjective weight and the objective weight of the evaluation index of the battery energy storage power station with a game theory. According to a fuzzy comprehensive evaluation method, a fuzzy relation matrix is established by using a trapezoidal membership function, and the safety risk evaluation level of the energy storage power station is determined according to a maximum membership principle. Guidance is provided for operation and maintenance of the battery energy storage power station, and the safety of system operation is improved.

The invention belongs to the technical field of power grid big data, and particularly relates to a power grid regulation and control data asset value evaluation method and device, equipment and a medium. According to the method, key factors (data integrity, data diversity, data timeliness, data scale and data relevance) influencing the value of the power grid regulation and control data assets are determined, the weight coefficient of each factor is calculated by using an analytic hierarchy process, and then the value of the power grid regulation and control data assets is calculated. According to the scheme, a unified data value evaluation system is established, and a unified value measurement standard is provided for power grid regulation and control data assets by determining the key influence factors and the weight coefficients. Through data value evaluation, the value and distribution condition of each data asset can be clearly known, and efficient management and reasonable utilization of the data assets can be realized. Meanwhile, through data value evaluation, the potential value of the data assets can be found, and possibility is provided for value increment and achievement of the data.

The invention discloses a multi-dimensional collaborative AI training processor benchmark evaluation device and method. According to end-to-end performance evaluation, TTA and throughput indexes are obtained through a standardized training process; the operator support degree evaluation adopts a dynamic parameter generation technology to evaluate the speed-up ratio and the floating point performance utilization rate of calculation-intensive and memory-access-intensive operators; the energy consumption evaluation calculates a power consumption delay product based on steady-state power consumption monitoring. And finally, integrating multi-dimensional indexes through an analytic hierarchy process model and a fuzzy theory, realizing level mapping by adopting a four-section membership function, and finishing multi-level aggregation evaluation in combination with matrix operation. According to the method, three dimensions of performance, operator support degree and energy efficiency are creatively integrated, the problems of single evaluation index and high environment dependence of the heterogeneous AI processor are solved through automatic container deployment, dynamic parameter generation and steady-state power consumption monitoring technologies, and a comprehensive and quantifiable evaluation basis is provided for processor model selection.

The invention discloses an automobile cabin user experience evaluation method and system based on digital interconnection, and relates to the field of information processing, and the method comprises the steps: constructing an automobile cabin comfort evaluation index system; designing an automobile cabin user comfort single-factor test under typical working conditions of static, constant speed and acceleration of an automobile, wherein noise, illumination, temperature and intelligent degree are typical; carrying out dimensionless operation; performing calculation through an analytic hierarchy process to obtain subjective weights of the evaluation indexes; using an entropy weight method to calculate an objective weight of the automobile cabin comfort; synthesizing subjective and objective weights by using a game theory method; and obtaining an automobile cabin comfort comprehensive evaluation model by using a multiplication synthesis method. By arranging an evaluation system construction module, a user experience evaluation module, a secondary index calculation module, a primary index calculation module and an evaluation model construction module, the comfort of the intelligent cabin is researched from the aspects of a cabin sound environment, a light environment, a thermal environment and a man-machine interaction environment.

The invention relates to a smart city planning monitoring system, which comprises a multi-source data acquisition module, a database construction and management module, a planning analysis and monitoring module, a communication cooperation module and an interactive decision support module, the database construction and management module is used for constructing and managing a database of urban planning and monitoring data, the planning analysis and monitoring module is used for planning scheme evaluation and progress tracking, the communication cooperation module is used for data communication and module cooperation, and the interactive decision support module is used for data interaction, monitoring visualization and decision support. According to the smart city planning monitoring system, a city planning multi-source data fusion algorithm based on kernel density analysis is put forward to fuse multi-source data, and a smart city planning monitoring algorithm based on analytic hierarchy process and fuzzy comprehensive evaluation is put forward to monitor and evaluate smart city planning.

The invention relates to a safety production equipment risk monitoring method and system, and belongs to the technical field of risk monitoring, and the method comprises the steps: obtaining historical operation data and real-time operation data of to-be-monitored safety production equipment; setting a dynamic threshold according to the historical operation data, predicting the range of the dynamic threshold according to preset target process operation data and the historical operation data through a bidirectional long-short-term memory network, and updating the range of the dynamic threshold in combination with the real-time operation data to obtain an updated dynamic threshold; the comprehensive risk index is calculated according to the updated dynamic threshold value and the real-time operation data through the analytic hierarchy process, the risk of the safety production equipment to be monitored is evaluated according to the comprehensive risk index, an evaluation result is obtained, dynamic monitoring and evaluation of the risk of the safety production equipment are achieved, and the safety production equipment risk evaluation efficiency is improved. The actual operation state of the equipment can be reflected more accurately, potential risks can be found in time, corresponding measures can be taken, and the operation safety and reliability of the equipment are improved.

The invention discloses a fabricated beam bridge finished product quality evaluation method fusing fuzzy reasoning and a neural network, and belongs to the field of comprehensive evaluation, and the method comprises the steps: constructing a fabricated beam bridge finished product quality intelligent evaluation system which comprises a first-level index and a second-level index; adopting a fuzzy number to describe quantitative and qualitative indexes, and constructing a fuzzy judgment matrix; calculating an initial weight vector by using an analytic hierarchy process and a defuzzification technology; calculating an improved regularization item balancing data driving and expert priori; and performing weight optimization and weighted summation on the parameters of each component by using a weighted neural network, and outputting a finished product quality score of the fabricated beam bridge. The invention aims to construct an intelligent evaluation system for the quality of the finished product of the fabricated beam bridge, realizes collaborative evaluation of expert knowledge and a data driving method by fusing a deep learning technology, improves the accuracy and robustness of the quality evaluation of the finished product of the fabricated beam bridge, and provides an efficient and reliable technical means for the intelligent evaluation of the quality of the fabricated beam bridge.

The invention relates to the technical field of aircraft optimization, and discloses an expressway low-altitude aircraft optimization arrangement method, which comprises the following steps: constructing an evaluation index system, determining the comprehensive weight of each evaluation index by using an analytic hierarchy process and an entropy weight method, and identifying the importance of the evaluation indexes; constructing a low-altitude aircraft optimization arrangement model, and solving by utilizing a simulated annealing algorithm to obtain static optimal quantity configuration of the low-altitude aircrafts of the service site; dynamically adjusting the static optimal number configuration of the low-altitude aircrafts of the service site by using a social emotion resonance avoidance algorithm; and according to the final number configuration of the low-altitude aircrafts of the service site, combining the social emotion data to optimize the flight path of the low-altitude aircrafts. According to the method, various complex factors are comprehensively considered, efficient arrangement and path optimization of the low-altitude aircraft are achieved, investment of the low-altitude aircraft is more global and economical, and important technical support is provided for application of the low-altitude aircraft industry.

The invention discloses an automatic driving test scene multi-dimensional evaluation method, and the method comprises the steps: carrying out the decomposition of each automatic driving test scene based on an evaluation category, and obtaining a dynamic element and a static element corresponding to the evaluation category; the evaluation category comprises risk, complexity and rare degree; determining a subjective weight of each automatic driving test scene by using an analytic hierarchy process, determining an objective weight of each automatic driving test scene by using an entropy weight method, and fusing the subjective weight and the objective weight based on a game theory to obtain a comprehensive weight; inputting an evaluation model based on the comprehensive weight to obtain an evaluation grade corresponding to the evaluation category; the evaluation grades corresponding to the automatic driving scenes are classified based on the clustering algorithm to obtain the corresponding evaluation grades, so that the risk, the complexity and the rare degree of the scenes are comprehensively considered, and the performance of the automatic driving vehicle is comprehensively evaluated.

The invention provides a rock damage evaluation method and device, and the method comprises the following steps: carrying out a compression test on a rock sample until the rock sample is damaged; treating the damaged rock sample to obtain a multi-scale damage fractal dimension of the damaged rock sample; based on the multi-scale damage fractal dimension, calculating a weight coefficient of each scale fractal dimension to the damage of the rock sample through an analytic hierarchy process; constructing a multi-scale rock damage evolution fractal model based on the weight coefficient; and evaluating the damage degree of the rock to be measured based on the rock damage evolution fractal model. The defect that the rock damage degree is determined from a single scale is overcome, rock damage can be more comprehensively and accurately quantified, long-term rock damage and stability evolution are effectively predicted, and reference is provided for comprehensively and accurately representing the rock damage degree and long-term evolution.

The invention provides a computer big data storage control system, and relates to the technical field of computers, and the system comprises a data evaluation module which is used for comprehensively calculating the real-time popularity of data, and the real-time popularity is obtained through the comprehensive calculation of the data access frequency and the access duration; performing quantitative evaluation on the real-time popularity of the data in combination with a multi-dimensional factor to obtain a value evaluation value of the data; analyzing the system resource information of the storage device on the computer to obtain a resource analysis result; and the intelligent contract execution module is used for carrying out comprehensive analysis and judgment on the real-time popularity, the value evaluation value and the system resource analysis result of the data so as to trigger adjustment of a storage strategy. According to the method, the real-time popularity model is constructed, the analytic hierarchy process is combined to achieve multi-dimensional value evaluation, the intelligent contract dynamically adjusts the storage strategy according to the evaluation result and the resource state, the data value and the storage medium are accurately matched, the problems that a traditional system is extensive in evaluation and rigid in strategy are solved, and efficient and accurate scheduling of storage resources is achieved.

The invention discloses an alpine grassland no-tillage restoration suitability evaluation method based on a 3S technology, and particularly relates to the technical field of data processing. Through the combination of multi-temporal satellite images and global positioning system data, the spatial dynamic change of the frozen soil layer of the alpine grassland and the long-term influence of the spatial dynamic change on vegetation growth are accurately obtained, and a frozen soil layer depth and distribution dynamic monitoring model is constructed based on multi-source remote sensing data and historical weather and soil temperature and humidity data. Analyzing the influence of freezing and thawing alternation on plant root growth; establishing a vegetation recovery potential index and a soil moisture supply index by combining the vegetation coverage and the dynamic change of the frozen soil layer, and generating a suitability partition map through a GIS spatial analysis and analytic hierarchy process; through field sampling and long-term vegetation growth monitoring data verification and model optimization, the accuracy of no-tillage restoration suitability evaluation is remarkably improved, and scientific and accurate decision support is provided for ecological restoration of alpine grassland.

The invention belongs to the field of engineering risk prediction, and provides a mine rockburst engineering risk prediction method based on a random forest and an analytic hierarchy process, and the method comprises the steps: carrying out the data collection of a to-be-predicted sample, and carrying out the calculation of a comprehensive attribution degree formula; the construction process of the comprehensive attribution degree formula comprises the steps of rock burst evaluation index determination, rock burst engineering case database construction, index calculation set acquisition, attribution degree data set acquisition, data preprocessing, random forest model prediction, weight calculation and parameter fusion. According to the method, through the random forest model and the analytic hierarchy process, the prediction weight of each index is obtained, the accuracy of the comprehensive attribution degree formula is improved, and a more accurate method is provided for evaluation and judgment of the engineering rockburst risk grade.

The invention provides an equipment running state optimization control method and device, electronic equipment and a storage medium. The method comprises the steps that historical data and real-time data of equipment operation are acquired, and key features related to the equipment operation state are screened out; preprocessing the historical data, dividing the preprocessed historical data into a plurality of categories, and generating a reference sample set of each category; performing weight calculation on the key features by using an analytic hierarchy process, and constructing a feature weight matrix; performing state estimation by using an improved multivariate state estimation algorithm; predicting the running state of the equipment according to the state estimation result, and judging whether the running state of the equipment is normal or not; and if the operation state of the equipment is normal, writing back an optimal control parameter generated based on the predicted operation state to the distributed control system. The parameter adjustment precision can be improved, the prediction precision can be improved, and the equipment operation efficiency can be improved.

The invention provides a production line product quality tendency defect determination method, medium and system, and belongs to the technical field of production digital data processing.The production line product quality tendency defect determination method comprises the steps that firstly, quality detection data is collected to construct a feature matrix, and a time sequence feature vector is obtained through singular value decomposition; and calculating a defect tendency index, and establishing association mapping in combination with a defect category vector. Then calculating the parameter utility by adopting a multi-level analysis method, and weighting the defect tendency index; a bidirectional long-short-term memory neural network is constructed as a discrimination model, a special defect screening layer is included, and dynamic identification of defect types is realized. According to the method, a defect feature database is established to store historical data, real-time monitoring and early warning of production line products are realized through model training and association rule mining, and finally early warning information is output, so that the technical problem that the quality defect tendency of the production line products cannot be accurately identified and predicted in the prior art is solved.

The invention discloses a data and knowledge dual-drive wheel set multi-parameter comprehensive state evaluation method and system, and belongs to the technical field of railway vehicle maintenance. The method comprises the following steps: firstly, constructing a mapping relation between geometric parameters and dynamic performance indexes through sampling and dynamic simulation, and carrying out global sensitivity analysis to screen key dynamic performance indexes; secondly, determining subjective and objective weights of the indexes in combination with an analytic hierarchy process and an entropy weight method, and introducing a dynamic optimization model based on a Bellman equation to generate a final combined weight; then, constructing a multi-dimensional state space division model by applying adaptive kernel density estimation and fuzzy C-means clustering, and determining the probability density and membership function of each index under different health levels; and finally, performing simulation prediction on the target wheel set, inputting a predicted value into the state space model, fusing a dynamic combination weight and a D-S evidence theory, calculating a comprehensive health index, and outputting a grading result, so that the health state of the wheel set can be accurately and efficiently evaluated.

The invention provides a highway bridge intelligent maintenance decision-making system and decision-making method. The system comprises a data acquisition module, a processing and storage module, a bridge condition evaluation module, a maintenance decision making module, an implementation and management module, and a monitoring and feedback module. The data acquisition module collects various types of data of bridges, environments, traffic and the like through multiple means such as fiber bragg gratings and vibration sensors. And the processing and storage module cleans the data by using a DBSCAN algorithm, processes the classified data by one-hot coding, and constructs distributed database storage based on a block chain. And the evaluation module adopts an Isomap algorithm to evaluate technical conditions and predict diseases. And the decision module generates a scheme by using a particle swarm optimization-simulated annealing algorithm, and analyzes and optimizes by using an analytic hierarchy process-grey clustering. And maintenance implementation is managed by means of a BIM model. The monitoring module collects data in real time and feeds back an adjustment strategy. The system can acquire data in real time through multiple sensors, accurately process data, accurately evaluate diseases, optimize maintenance decisions, dynamically adjust strategies, guarantee bridge safety and prolong the service life.

The invention relates to the technical field of fire control management, and discloses an intelligent fire control door management method and system based on the Internet of Things, and the method comprises the following steps: S1, collecting multi-environment data of a fire control door; s2, performing standardization processing to obtain multi-environment data in a unified format, and establishing a feature matrix; s3, determining the importance degree of each item of data in the feature matrix to obtain a comprehensive weight; s4, constructing a membership function, and outputting a fire risk level; s5, determining the fire stage of the current environment; s6, selecting a final risk response strategy through a fuzzy game method; and S7, controlling the action of a fire exit door according to the selected final risk response strategy. According to the method, the information entropy and the analytic hierarchy process algorithm are combined to determine the importance degree of the multi-environment data and calculate the comprehensive weight of the fire risk, so that the assessment is more objective and comprehensive, overlarge deviation of a single model is avoided, and the problems of bias and poor applicability of assessment are solved.

The invention belongs to the technical field of virtual power plants, and particularly relates to a virtual power plant resource aggregation method for a dynamic peak regulation demand of a power grid, which comprises the following steps of: acquiring multi-source data, preprocessing the multi-source data, and then verifying the data quality; aiming at different resource types including temperature control load, energy storage and charging piles, respectively constructing refined models, setting constraint conditions of the refined models, and solving a resource operation feasible region by applying multi-dimensional space mapping and linear programming; establishing a target function and a constraint condition by taking the lowest cost and the minimum energy abandoning as targets; solving a target function by using a dung beetle optimization algorithm, and screening an optimal resource aggregation scheme by using an entropy weight method; and based on the optimal resource aggregation scheme, dividing peak, valley and normal periods, constructing a four-dimensional peak regulation index, determining a weight by using an analytic hierarchy process, and screening an optimal resource combination in each period to execute scheduling. The method can guarantee the accuracy and high efficiency of the peak regulation demand response of the power grid, and assists in improving the stability of the power system and the renewable energy consumption level.

The invention provides a multi-source information fusion mining overlying strata high separation layer water disaster prediction evaluation method, which comprises the following steps of: constructing a coal mining, overlying strata structure index, strata space occurrence and cretaceous system aquifer prediction index system according to related data of hydrogeology, engineering geology and coal mining in a research area; through the separation water disaster multi-source information fusion prediction evaluation model of the subjective-objective weight combination of the analytic hierarchy process, the entropy weight method and the CRTIC method, accurate prediction evaluation of the separation water disaster is realized, the error of the traditional separation water disaster prediction result is reduced, the water disaster threat of the stope face is greatly reduced, a guarantee is provided for safe mining of a coal mine, and the economic benefit of the coal mine is improved. And coal mine water disaster prevention and control measures and safety production are effectively guided.