293 results about "Potential evaluation" patented technology

The invention belongs to the technical field of oil and gas reservoir energy development, and discloses a three-dimensional injection and production well pattern construction method based on a multi-objective algorithm. The problem that the delimited potential area is difficult to effectively use due to the fact that existing potential evaluation is only based on a single index is effectively solved. Comprising the steps that a fault inner core, a fault outer core, a fracture development dense zone and a secondary fracture zone are proposed; carrying out inter-well connectivity analysis on the oil reservoir units by utilizing an oil reservoir engineering method and a numerical simulation streamline method; based on the embedded discrete fracture model, analyzing a remaining oil distribution mode under the control of the nuclear band structure; a multi-parameter comprehensive evaluation system is constructed by combining the remaining oil utilization potential, the displacement power, the flow capacity, the connectivity relation and the reserve abundance; a three-dimensional injection-production well pattern construction technology is formed for an oil reservoir with or without bottom water; based on a multi-objective optimization algorithm, three-dimensional collaborative optimization of the recovery ratio, the well control reserves and the net present value is taken as a core, and a high-efficiency well location deployment technology for different fault zones is constructed.

The invention relates to the technical field of multi-source information analysis, in particular to a niobium-tantalum metal ore metallogenic evaluation system based on multi-source information analysis, which comprises a geologic structure association module, a mineral distribution interpretation module, a metallogenic environment adaptation module, a spatial feature analysis module and a potential region judgment module. According to the method, by introducing a multi-source data analysis and fusion method, the evaluation precision and comprehensive capability of resource exploration are effectively improved, analysis after information fusion is combined with relevance and spatial modes of different data types, it is ensured that mineralization potential evaluation is more accurate, the limitation of a single data source and manual analysis is overcome, and the method is suitable for popularization and application. According to the method, the identification precision and reliability of the mineralization law are improved, the potential area can be identified more accurately, resource potential analysis is optimized in combination with the adaptability of the mineralization environment, the efficiency and accuracy of determining the mineralization area are remarkably improved, the problems of information isolation and analysis deviation in a traditional method are avoided, and the scientificity and the practical application value of the exploration process are enhanced.

The invention relates to the technical field of forest wetland carbon sink monitoring and evaluation, and particularly discloses a forest wetland carbon sink intelligent monitoring and evaluation system and method. According to the method, a vegetation carbon sequestration potential evaluation result is obtained by analyzing vegetation structure feature data of forest, water and soil composite ecology of the forest wetland, a water-soil-carbon cooperative evaluation result is obtained by analyzing specific hydrological-soil carbon cycle key elements of the forest wetland, and environmental disturbance response factors are obtained by combining various environmental disturbance factors. Through multi-source data fusion and intelligent modeling, a carbon sink quantitative evaluation value is output, and abnormal feedback is triggered; the problems that in the prior art, adaptability to a forest wetland compound ecological system is insufficient, evaluation refinement is insufficient, and dynamic response is lacked are solved. According to the method, accurate and dynamic monitoring and evaluation of forest wetland carbon sink are realized, carbon sink short plates can be accurately positioned, optimization support is provided, and technical support is provided for protection, restoration and optimization management of forest wetland carbon sink resources.

The invention discloses a method for constructing a large mineralization potential evaluation model based on LLM, and relates to the field of models.The method for constructing the large mineralization potential evaluation model based on LLM comprises the following steps that 1, a mineral geological map file is converted into a structured JSON format data set; step 2, constructing cue words, wherein the cue words at least comprise geological information extraction cue words, function task cue words and entity, relation and attribute extraction cue words; compared with the prior art, the method has the beneficial effects that the large mineralization potential evaluation model constructed by the method realizes text and map multi-mode mineral product geological map information identification and analysis, automatic mineralization geological feature analysis, mineralization feature information extraction and mineralization potential evaluation; the problems that in the prior art, man-made subjective influence exists, professional knowledge dependence is high, the geological information recognition accuracy rate in the recognition process is low, and information is lost during knowledge retrieval are solved.

The invention discloses a new energy power grid hybrid energy storage system multi-dimensional regulation and control capability quantitative evaluation method and system, and mainly relates to the technical field of new energy power grid hybrid energy storage system regulation and control. Comprising the following steps: respectively constructing layered evaluation index systems of the hybrid energy storage unit in frequency modulation and peak regulation based on short, medium and long time scales; determining the weight of each level index in the index system; based on the hierarchical evaluation index system and the weight of each hierarchical index, performing comprehensive potential evaluation on the hybrid energy storage unit; and according to an evaluation result, the regulation and control capability of the hybrid energy storage system under different time scales is comprehensively evaluated, and an optimization and scheduling strategy of the energy storage unit is formulated according to the evaluation result. The method has the beneficial effects that the frequency modulation, voltage regulation and peak regulation potentials of the hybrid energy storage system can be quantified under different time scales, and the optimal energy storage configuration and operation strategy can be formulated according to the frequency modulation, voltage regulation and peak regulation potentials.

The invention relates to the technical field of geothermal resource development, and discloses a geothermal resource prediction method and system based on multi-source data fusion, and the method comprises the steps: obtaining a multi-source geothermal basic data set, carrying out the weighted fusion analysis of the multi-source geothermal basic data set, and extracting a comprehensive geothermal feature vector meeting a resource enrichment condition; carrying out potential evaluation and weight fusion according to the feature vector, generating an earth heat flux density distribution potential map and determining a high potential area; on this basis, potential development point locations are identified, and a candidate well location preliminary set is formed; performing comprehensive evaluation and sorting on the candidate well positions to obtain an optimized well position set; performing combined influence evaluation and interference judgment by combining well location distance constraint conditions, and constructing a constraint relation matrix; and through multi-round combination screening and optimization, an optimal well position combination scheme is determined, and a complete implementation scheme is generated and used for small-scale geothermal development and implementation. According to the method, high-precision well position selection and development scheme optimization can be realized.

The invention discloses a geological carbon sequestration potential evaluation method, device and equipment, a medium and a program product. The method comprises the following steps: preprocessing an initial geological carbon sequestration training data set to obtain a target geological carbon sequestration training data set; constructing a migration optimization model according to the target geological carbon sequestration training data set; inputting the target geological carbon sequestration reasoning data set of the sea area depleted oil and gas field research area into the migration optimization model for multi-field coupling simulation to obtain target geological carbon sequestration evolution data; calculating an initial geological carbon sequestration total amount according to the target geological carbon sequestration evolution data; performing sea area correction on the initial geological carbon sequestration amount to obtain a target geological carbon sequestration amount; the target total geological carbon sequestration amount is used for representing the geological carbon sequestration potential of the sea area depleted oil and gas field research area. The method can improve the reuse rate of the oil and gas data, greatly reduces the training data to reduce the cost of evaluating the geological carbon sequestration potential of the exhausted oil and gas field, and can be widely applied to the technical field of intelligent geological carbon sequestration.

The invention provides a reserve land development potential grading evaluation method and system based on pattern spot data, and relates to the technical field of reserve land development management, and the method comprises the steps: carrying out the pattern spot data mining of reserve land, and obtaining an original pattern spot data set with pattern spot attribute information; performing multi-level classification and layering according to a spatial planning hierarchical relationship, and extracting a potential constructable base range; performing pattern spot spatial topology processing, extracting pattern spot geometric feature parameters, and performing pattern spot scale division based on a preset area threshold value; establishing a pattern spot potential evaluation index system, and performing pattern spot development potential category division; and executing the refined extraction of the available space to form a visual board for the reserve land. The technical problems that in the prior art, the development potential of the reserve land is evaluated by depending on manual judgment and a rule-based simplified evaluation model, the evaluation precision is insufficient, the development potential of the land cannot be comprehensively reflected, and the scientificity and rationality of a development decision are further influenced are solved.

The invention discloses a mineralization potential evaluation and target region optimization method and system based on machine learning, and relates to the technical field of mineral resource exploration. The method comprises the following steps: acquiring multi-dimensional data of a target area; preprocessing the multi-dimensional data to obtain preprocessed key data; a metallogenic system conceptual model is constructed, and a hydrothermal migration path and a metallogenic fluid pressure field of the key data are calculated through finite element simulation for visual display; generating an evidence layer based on the key data to obtain multi-dimensional evidence layer data; and constructing a mineralization potential evaluation model, and inputting the multi-dimensional evidence layer data into the mineralization potential evaluation model for prediction to obtain a mineralization probability prediction result. Through multi-source data fusion and machine learning algorithm innovation, intelligentization and precision of mineralization potential evaluation and target region optimization are realized, and an efficient technical means is provided for mineral resource exploration.

The invention discloses a paper mill load green power consumption potential evaluation method fusing ProbSparse and FEDformer, and belongs to the technical field of power system regulation and control, and the method comprises the steps: constructing an up-regulation capability evaluation model and a down-regulation capability evaluation model, and obtaining a maximum up-regulation capability and a maximum down-regulation capability; based on time-frequency domain decoupling modeling, predicting a future green power factor sequence; and based on the maximum up-regulation capacity, the maximum down-regulation capacity and the future green power factor sequence, a load green power consumption potential evaluation model is constructed, and according to the ProbSparse and FEDformer fused paper mill load green power consumption potential evaluation method, a ProbSparse-based paper mill load operation feature extraction method, the maximum up-regulation capacity, the maximum down-regulation capacity and the future green power factor sequence are extracted, and the maximum up-regulation capacity, the maximum down-regulation capacity and the future green power factor sequence are calculated. Improving the global perception capability of long-sequence load data by using a sparse attention mechanism, and accurately extracting the running rhythm, state change and key load mutation characteristics of each device; and on the basis of a green power factor prediction model of FEDformer, time sequence decomposition and frequency domain modeling capabilities are fused, and high-precision prediction of a future green power ratio factor is realized.

The invention discloses a forsythia suspensa growth vigor monitoring method based on unmanned aerial vehicle remote sensing, and particularly relates to the technical field of unmanned aerial vehicle remote sensing monitoring. S2, calculating a vegetation index; s3, obstacle recognition; s4, evaluating the growth vigor; and S5, grade drawing. According to the method, a structured data set is constructed through integration of multispectral data and space coordinates, a high-resolution vegetation index map is generated in a pixel-by-pixel calculation mode, non-vegetation obstacles are accurately recognized by combining band ratio analysis and threshold judgment, vegetation index change trends are analyzed through time series data, and phenological period standard interval deviation is calculated. Spatial growth trend grade distribution is generated through mapping evaluation coefficients, the processing logic realizes closed-loop linkage of data acquisition and analysis, eliminates interference of non-target ground features, improves the accuracy of monitoring results, establishes a dynamic evaluation mechanism, and provides reliable basis for precise agricultural management.

The invention discloses an urban garden carbon sink visual simulation and optimization system based on digital twinning, and belongs to the technical field of urban garden carbon sink management. The method solves the problems that in the prior art, data collection is not comprehensive, an optimization strategy lacks scientific basis and the like, feature extraction is conducted on multi-source data by introducing the random forest model, different weights are given to the complex relation between the features in combination with the weighted average method, and the multi-source data are obtained. Features having significant influence on carbon sink potential evaluation can be identified more accurately; by setting a score threshold value and comparison verification, when the feature importance score is lower than the score threshold value, removing or grouping the non-significant features to form new comprehensive features, and retraining the random forest model; therefore, the accuracy of urban garden carbon sink potential evaluation is improved, decision makers are helped to know the current carbon sink situation of urban gardens more visually, a more effective optimization strategy is formulated, and the ecological benefits and environmental quality of the urban gardens are improved.

The invention discloses a load response capability evaluation method and system, and the method comprises the following steps: 1, obtaining multi-dimensional power consumer information data, carrying out the statistics and summarization of the obtained multi-dimensional power consumer information data, and constructing a power consumer response potential evaluation index system; according to the method, multi-dimensional power consumer information is acquired, an evaluation index system is constructed, data are converted into fuzzy rough numbers, then a decision matrix is established, an entropy weight method and a step-by-step weighting evaluation analysis ratio method are fused to calculate a comprehensive weight, a weighting matrix is obtained through normalization, and then a comprehensive evaluation value is calculated through a fuzzy rough multi-attribute boundary approximation region comparison method. Screening users with high response potential; according to the matching system, through cooperation of seven modules, collection frequency self-adaption and power grid dispatching two-way communication are achieved, the problems that in the prior art, user screening is not accurate, evaluation is not comprehensive, and weight subjectivity is high are solved, and evaluation accuracy and reliability are improved.

The invention discloses an intelligent article selection scoring system based on fusion of a rule engine and machine learning. The system comprises a data preprocessing unit, a feature engineering unit, a hybrid calculation unit and a dynamic weighted fusion unit. The feature engineering unit is used for generating a feature data set, a statistical feature data set, a time sequence feature data set and a composite feature data set from the preprocessed intelligent product selection data; the hybrid calculation unit is used for synchronously executing preset business rule calculation and machine learning prediction; preset business rule calculation is completed through a business rule calculation function, and machine learning prediction is completed through a multi-dimensional linkage machine learning prediction model; and the dynamic weight fusion unit is used for fusing the preset business rule score and the machine learning prediction score according to the dynamic business rule weight factor, and finally obtaining an intelligent product selection score. According to the invention, scientific, automatic and high-precision commodity potential evaluation is realized, and a breakthrough technical framework is provided for the field of e-commerce commodity selection.

The invention discloses a front subframe structure MaOP method and device based on TPT, a medium and a program product, and the method comprises the steps: (1) constructing a MaOP design model capable of optimizing the weight, modal, strength and rigidity at the same time based on front subframe longitudinal beam and load analysis; (2) generating an elite population based on a diversity criterion and Latin hypercube, obtaining target values of the elite population, establishing a data set, and constructing a radial basis function model; (3) designing co-evolution operation driven by TPT to generate a candidate frame set; (4) an individual potential evaluation criterion is constructed based on the advantage and disadvantage target sets, and an optimal candidate frame is screened; and (5) obtaining each target value of the optimal candidate frame, updating the data set and the radial basis function model, returning to the step (3) until all optimization targets meet requirements, and outputting an optimal parameter value. According to the method, a TPT-driven coevolution mechanism is adopted, radial basis function model prediction is combined, and the multi-performance index optimization process for the front auxiliary frame can be effectively balanced.

The invention provides an energy monitoring method based on user working condition identification and energy-saving space prediction, and the method comprises the steps: carrying out the dynamic adjustment and collection of multi-source data based on a self-adaptive data collection strategy according to a user load rate, carrying out the data fusion of the collected multi-source data according to the spatial-temporal characteristics, and constructing high-dimensional working condition feature data; based on the high-dimensional working condition feature data, generating a working condition identification result by using a pre-trained light-weight deep learning working condition classification model in combination with an adaptive sliding window mechanism; and constructing a dynamic energy efficiency reference model, calculating optimal energy consumption under the current working condition based on matching of historical data and a real-time working condition identification result, and quantifying a single-point energy-saving space, predicting multi-point collaborative energy-saving potential and performing real-time energy efficiency monitoring intervention based on a preset layered energy-saving potential evaluation framework. According to the invention, dynamically changing user working conditions can be effectively identified, and energy space prediction can be accurately detected and quantified.

The invention discloses a power utilization potential prediction and response regulation and control system, and the system comprises a panoramic sensing module which is used for the synchronous collection of data; the dynamic modeling module is used for fusing multi-source data according to space, time and semantic association rules to generate a standardized electric power characteristic spectrum; the situation deduction module is used for identifying industrial adjustable capacity, commercial peak shifting potential and resident elastic interval and constructing a three-dimensional potential matrix; the collaborative optimization module is used for establishing an economy-environmental protection-user satisfaction multi-objective function by taking power grid security as a hard constraint, and driving a basic strategy library, a real-time optimization engine and emergency mode hierarchical regulation and control; and the efficiency verification module is used for tracking closed-loop optimization of indexes such as load peak-valley difference reduction rate and the like. According to the method, adjustment potential space-time quantification, multi-target collaborative optimization and strategy dynamic evolution are realized, and the adjustment potential evaluation time of the actual measurement region is shortened.

The invention discloses a corn planting suitability comprehensive evaluation method which comprises the following steps: step 1, data collection and index system construction: a system collects multi-source information of meteorological data, crop growth data and historical disaster data, and a comprehensive corn planting suitability evaluation index system is constructed, the method comprises the following steps of 1, selecting a corn growth period, 2, dividing the growth period: determining four key stages of the corn growth period in combination with related data and local production practice experience, 3, constructing a comprehensive evaluation model, 4, evaluating the planting potential and carrying out weighting processing, 5, carrying out comprehensive evaluation and zoning, and 6, carrying out comprehensive evaluation and zoning. According to the method, the blank of the existing research in comprehensive evaluation of the corn planting suitability is filled, a new thought and method are provided for coping with agricultural risks caused by climate changes, a scientific regionalization reference is provided for agricultural decision makers by constructing a perfect corn planting suitability evaluation system, and the corn planting suitability evaluation method has a wide application prospect. The guarantee of global grain safety and the sustainable development of an agricultural ecosystem are facilitated.

The invention relates to the technical field of low-carbon park carbon reduction potential estimation, and discloses a low-carbon park flexible resource aggregation carbon reduction potential evaluation method, system and device and a medium. Multi-stage preprocessing and analysis are performed on operation data and carbon emission data of flexible resources in the low-carbon park, and the polymerization carbon reduction potential of the low-carbon park can be accurately evaluated. In the data processing process, each preprocessing stage has unique mechanisms and logics, such as spatial-temporal feature extraction, resource clustering and aggregation parameter calculation in the second preprocessing, and carbon footprint dynamic prediction, time-varying capability evaluation and fuzzy weight self-adaption in the third preprocessing. And index calculation, uncertainty analysis, report generation and the like in the fourth preprocessing are carried out.

The invention relates to a transformer substation bus-level adjustable load potential evaluation and hierarchical regulation and control method and system for regional differences, and belongs to the technical field of intelligent power grid regulation and control. The method comprises the steps of obtaining transformer substation bus load data, and performing missing value processing on the transformer substation bus load data through a data missing processing model to obtain bus load reference data; performing curve clustering on the bus load reference data to obtain a load characteristic curve; performing potential evaluation based on the load characteristic curve and outputting a bus load potential evaluation result; and constructing a regional aggregation load characteristic model, and outputting an optimal load regulation and control strategy through hierarchical regulation and control according to the regional aggregation load characteristic model and a bus load potential evaluation result. And transformer substation bus-level adjustable load potential evaluation and hierarchical regulation and control oriented to regional differences are realized.

The invention relates to the technical field of geological information, and discloses a mineral resource potential rapid evaluation method and system based on big data, and the method comprises the steps: automatically collecting geological, remote sensing, geophysical and geochemical exploration and mining data through a multi-source interface, executing intelligent format recognition and structural analysis, and storing the data into a distributed data lake; scheduling parallel computing nodes according to task parameters, synchronously operating five sub-models including space superposition, geochemical anomaly identification, construction of mine control factor extraction, remote sensing alteration enhancement and mining intensity regression; and generating a comprehensive potential evaluation map through dynamic weighted fusion, and outputting a visual result and an uncertainty quantitative index. According to the technical scheme, the evaluation period is shortened from several weeks to several hours, the data processing accuracy is improved to 97% or above, the hit rate of the evaluation result in the known ore deposit area reaches 89%, and the mineral exploration efficiency and decision scientificity are improved.

The invention discloses a foreign language learning potential assessment method and system based on multi-modal data fusion, and the specific implementation scheme is as follows: creating a task event, collecting the multi-modal data of a subject in real time, and carrying out the time alignment of the multi-modal data and the task event through a timestamp or a trigger signal; performing data preprocessing on the multi-modal data and extracting behavior features and neurophysiological features; performing deep fusion analysis on the behavior features and the nerve physiological features in time dimension and space dimension to generate optimal comprehensive features; and performing quantitative evaluation on foreign language learning potential by using a deep fusion analysis result, and generating a feedback report. According to the method, foreign language learning potential is expanded into a behavior and cognitive nerve double-dimensional structure, and the problems of single evaluation dimension and insufficient mechanism interpretation caused by the fact that a traditional method only depends on behavior scores are solved; through synchronous acquisition and real-time analysis of the multi-mode reaction of the learner in the task, the change of the cognitive state along with time can be dynamically monitored.

The application discloses a multi-incentive mechanism-based multi-stage flat equalization cost modeling method and device, equipment and medium, comprising: constructing a flat equalization response cost model according to the implicit cost, direct cost and transferred power of a user, wherein the implicit cost comprises an air conditioner thermal comfort sacrifice cost and an SOC loss cost; determining the eigenvalue of the implicit cost of the user; constructing a flat equalization peak electricity price response cost model, a flat equalization sharp peak electricity price response cost model and a flat equalization demand response subsidy cost model based on the flat equalization response cost model, and obtaining a multi-stage flat equalization cost model; and constructing a consumer psychology model under a multi-incentive mechanism based on the multi-stage flat equalization cost model, wherein the consumer psychology model under the multi-incentive mechanism is used for determining the demand side resource response degree under different incentive levels. The application belongs to the field of energy evaluation. The application can realize the potential evaluation of demand side resources under different incentive levels.

The invention belongs to the field of power distribution area resource scheduling, and provides a power distribution area adjustable resource complementary potential evaluation method and system fusing subjective and objective, and the method comprises the steps: constructing a power distribution area complementary potential evaluation index system; based on the sorting result of each index, determining the subjective weight of each index by using an order relation analysis method; acquiring actual operation data corresponding to the indexes, and determining objective weights of the indexes based on the standard deviations of the indexes and conflicts among the indexes; performing fusion optimization based on the subjective weight of each index and the objective weight of each index to obtain a subjective and objective evaluation fusion weight; and obtaining actual operation data corresponding to each index, determining cloud model digital features of each index based on subjective and objective evaluation fusion weights, and performing weighted fusion by using a cloud model to obtain an evaluation result. According to the method, the operation characteristics of the multi-type adjustable resources in the power distribution area can be comprehensively depicted from the three aspects of source, load and source-load complementation.

The application discloses a kind of control fishing yield seal population recovery potential evaluation method and device, it is related to marine ecological protection and restoration field, mainly includes: four steps of modeling, predation rate calculation, fishing quantity reduction stage determination and seal population recovery potential evaluation.The application is based on Ecopath model, by introducing predation rate parameter, dynamically adjusts seal food habit matrix, reduces the data requirement of modeling, realizes the quantitative evaluation of fishing control measure protection benefit, provides practical method tool for seal population recovery potential evaluation.Therefore the seal population recovery potential evaluation method and device based on control fishing yield provided by the application have good seal population recovery potential evaluation ability, and can be widely applied to marine biological population resource recovery potential evaluation field.

The invention relates to the technical field of enterprise development evaluation, and particularly discloses an enterprise development potential evaluation method based on big data, and the method comprises the steps: collecting agricultural enterprise multi-source heterogeneous data through distributed nodes, importing a deep learning driven multi-modal fusion model, and generating a high-dimensional feature vector through cross-modal feature alignment and weighting; inputting the high-dimensional feature vector into a risk and pressure resistance evaluation model, constructing a dynamic risk conduction network mining risk association, generating a risk evaluation vector in combination with a time sequence feature, cascading the risk evaluation vector with the high-dimensional feature vector, and then generating a fusion feature vector through nonlinear transformation; and on the basis, a quantitative index is generated through multi-dimensional feature matching, and a comprehensive evaluation result is output through multi-scale decision analysis. According to the method, deep fusion of multi-source data is realized, risk changes are dynamically captured, evaluation comprehensiveness and accuracy are improved, and a reliable basis is provided for decision making of agricultural enterprises.

The invention discloses an electrified flexible source load adjustable potential evaluation method considering cold load characteristics, and particularly relates to the technical field of electric power system evaluation and control, and the method comprises the steps: S1, obtaining the environment and equipment parameters of a cold load monomer, S2, constructing an electric-thermal coupling refined model considering a comfort level elastic interval, and S3, constructing an electric-thermal coupling refined model considering a comfort level elastic interval; the method comprises the steps of S1, obtaining a plurality of monomers, S3, calculating the adjustable potential of the monomers and generating a constraint envelope line representing the dynamic attenuation characteristics of the monomers, S4, obtaining a regional cluster potential evaluation result through aggregation according to the spatial dimension and the time scale, and S5, uploading the result to a power grid dispatching system for security defense and intelligent dispatching of a power grid of 750 kilovolts and above. According to the method, accurate quantification and dynamic representation of the adjustable potential of the cooling load are realized, the blank of cognition of a large power grid on the distributed cooling load is filled, and the flexible resource utilization level and the operation safety of the power grid are remarkably improved.

The present application relates to the electric vehicle charging and discharging resource and air conditioner load mutual benefit potential evaluation technical field, relates to the demand side resource mutual benefit potential evaluation method and system considering the space-time coupling characteristics, including: obtaining the operation data of electric vehicle and air conditioner load;Build economic evaluation index model and feasibility evaluation index model, based on economic evaluation index model and feasibility evaluation index model, analyze the electric vehicle and air conditioner load operation data, and solve the subjective weight and objective weight of the analysis result;The combined weight matrix is obtained by combining weighting method, the combined weight matrix is multiplied by the normalized analysis result, and the demand side resource mutual benefit potential evaluation of electric vehicle and air conditioner load is completed.The present application can not only improve the utilization efficiency of electric vehicle as mobile energy storage, but also effectively alleviate the pressure of air conditioner load on power grid during peak period, so as to enhance the stability and flexibility of power grid.

The invention relates to the technical field of costume design of artificial intelligence, and particularly discloses an AI costume style customization design method based on multi-modal user portray.The method comprises the steps that firstly, multi-source heterogeneous data of network e-commerce, entity stores, free design communities and social media are collected; through feature extraction and alignment processing, the style semantic features of the text and the design element visual features of the image are mapped to a unified'style-element-market expression 'semantic space; analyzing a popularity change track of a design element based on the multi-modal feature vector set, and identifying a potential style vector in combination with a historical implosion feature mode; meanwhile, multi-modal user portrait vectors including consumption ability, social influence and aesthetic preferences are constructed; fusing the potential style vector and the user portrait vector, and inputting into a conditional generative adversarial network to generate a costume design atlas; and finally, optimizing and outputting a design scheme through explosion potential evaluation and multi-stage sorting.

The application provides a kind of industry typical user electric power demand response potential evaluation method, this method includes: S1, based on integrated empirical mode load decomposition and demand response willingness extraction industry user demand response characteristic;S2, based on industry user demand response characteristic and convolution self-attention mechanism realizes the evaluation of industry typical user demand response potential. The effectiveness of the method is verified by analyzing the demand response case of metal processing machinery manufacturing industry in a city of Zhejiang Province, China. The results show that the demand response potential evaluation results obtained based on the method are consistent with the actual response amount of the user. The application overcomes the modal aliasing problem, can capture the long-term and short-term dependence in time series, and considers the local context information of each period feature, which can ensure high accuracy and provide support for power supply companies or load aggregators to fully utilize user-side adjustable resources.