362 results about "Potential evaluation" patented technology

The invention relates to the technical field of anti-cracking performance testing, in particular to an ultra-high performance concrete anti-cracking performance testing system and a dynamic monitoring method thereof.The system comprises a dynamic stress field simulation feedback module used for sensing three-dimensional distribution of a micro stress field in concrete in real time by constructing a flexible loading array to obtain dynamic stress field data; the multi-scale damage evolution tracking module is used for deploying a high-resolution acoustic emission network and a distributed optical fiber sensing layer based on dynamic stress field data of the flexible loading array to form a full-scale evolution graph from microdefects to macroscopic cracks; and the intelligent healing efficiency evaluation module obtains stress redistribution data and a full-scale evolution graph, and a repairing medium containing a tracer agent is injected into a preset crack path. According to the method, the whole process of crack resistance of the material from defect initiation to repair and regeneration can be quantitatively evaluated; and finally, outputting a dynamic evolution rule of the crack resistance and enhancing potential evaluation.

The invention discloses an interdisciplinary scientific research potential assessment method based on a dynamic multi-modal knowledge graph, relates to the technical field of scientific research management and intelligence analysis, and aims at the dynamic requirements of college scientific research ecology by relying on an incremental assessment framework. Comprising the steps of multi-source data acquisition and incremental knowledge graph construction, time sequence trend identification, online and transfer learning optimization, scene simulation and multi-scene strategy verification and multi-modal data fusion. The map structure is updated in real time through concept drift detection and ontology extension, and emerging hotspots are identified by combining time sequence difference indexes such as citation acceleration and a cooperative network; and the accuracy and continuous iteration capability of the evaluation model are kept in multiple environments by means of online learning and a cross-domain migration strategy. Finally, potential evolution is quantified through scene simulation, strategy suggestions are output, the sensitivity to the cross domain is further enhanced with the assistance of multi-mode embedding, a closed-loop evaluation system from data to decision is formed, and college subject cross layout and resource allocation are assisted.

The invention relates to the technical field of database construction, in particular to a construction method and system for a reproductive medicine database. The method comprises the following steps: acquiring a biological tissue sample of a patient; acquiring reproductive endocrine hormone data, embryo morphological parameter data, immune cell function data and genome variation data from the biological tissue sample of the patient; performing time sequence analysis processing on the hormone fluctuation mode according to the reproductive endocrine hormone data to obtain endocrine dynamic characteristic data; performing image recognition processing based on coherent superposition characteristics of light on the embryo morphological parameter data, and performing embryo development potential evaluation to obtain embryo development potential data; and performing hierarchical clustering processing on the individual heterogeneity of the patient according to the immune cell function data and the genome variation data to obtain immune-genetic typing data of the patient. By means of the quantum dot sensing technology and the multi-channel micro-fluidic chip, standardization of data acquisition is achieved, and the heterogeneity problem of a data source is solved.

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 an electric vehicle V2G regulation potential evaluation method based on electricity price regulation, particularly relates to the technical field of electric energy storage cooperative control, and is used for solving the problems of power grid resonance risk and battery life loss caused by power synchronism in existing V2G centralized scheduling. Through real-time fusion of a dynamic electricity price time period division signal and a power grid frequency deviation value, carrying out dual constraint on a charging and discharging power range in combination with a battery health state attenuation coefficient, and generating an initial instruction set; a power grid resonant frequency distribution interval is further extracted based on a frequency sweep test, an asymmetric phase disturbance rule is generated for a dominant resonant frequency point, and a harmonic superposition effect of power output is destroyed through differential time sequence offset; in the instruction execution stage, the phase disturbance amplitude is dynamically adjusted to adapt to the power grid frequency fluctuation and the battery aging degree, a closed-loop control mechanism is formed, collaborative optimization of the power grid stability and the equipment service life is achieved, and the safety of the electric vehicle cluster participating in power grid adjustment is improved.

The invention provides a recruitment interview evaluation system based on artificial intelligence. The recruitment interview evaluation system comprises an information acquisition module which acquires enterprise recruitment information and corresponding applicant resume information and applicant interview information; the skill evaluation module extracts position skill features and applicant skill features, performs skill learning path analysis based on the established industry skill knowledge graph, and further obtains skill potential evaluation results of the applicants; the culture adaptation evaluation module extracts enterprise culture characteristics and applicant culture characteristics, and performs culture adaptation analysis based on the enterprise culture characteristics and the applicant culture characteristics to obtain a culture adaptation evaluation result of an applicant; the interview evaluation module analyzes the interview performance of the applicant according to the interview video record data of the applicant to obtain an interview evaluation result; and the report module is used for obtaining a comprehensive evaluation result of the applicant for the enterprise position. According to the invention, an enterprise can be helped to improve the intelligent level of evaluation of an applicant for a specific position.

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 relates to the technical field of power system demand response, and discloses an interpretable demand response potential evaluation framework and system. Comprising the steps of establishing a data carding and feature extraction model to collect power utilization feature data, constructing a demand response potential prediction model based on the feature data, sequentially inputting the feature data to complete model training, establishing a feature importance visualization model, quantitatively analyzing and displaying a prediction result, updating and iterating the model, and optimizing power utilization scheduling of a power grid. According to the method, the TensorFlow technology is used for carrying out standardization processing on the extracted feature data, the effect of improving the efficiency and accuracy of data processing of a prediction model is achieved, meanwhile, the feature contribution degree is calculated through an interpretability analysis SHAP value, and the causal effect of key variables on potential evaluation is analyzed through a causal inference framework causal forest; and a theoretical data support is provided for power consumption regulation and control of the power grid.

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 relates to the technical field of artificial intelligence and city updating, in particular to an artificial intelligence assisted city updating decision management system, which comprises an information acquisition module, a city physical examination module and a scheme generation module, the information acquisition module acquires data input by a multi-role user, processes and verifies the data and transmits the data to the city physical examination module; the city physical examination module performs multi-dimensional potential evaluation on the received data based on a machine learning model, generates a visual evaluation result and transmits the visual evaluation result to the scheme generation module; and the scheme generation module generates a structured update strategy and an effect picture according to the evaluation result in combination with the knowledge base, and feeds back the effect picture to the information acquisition module to form a data closed loop. According to the system, a multi-module technology is fused, information collection ensures efficient collaboration of multi-role data, urban physical examination accurately evaluates the land potential, scheme generation gives consideration to compliance and innovation, the system is ensured to be safe, stable and elastically expanded, urban update decision intelligence is realized, and decision scientificity is improved.

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 a peak load shifting potential evaluation system based on two-stage clustering, and the system is characterized in that a load data collection module is used for collecting historical power load data of a specified user, thereby obtaining a power load curve of the specified user during the implementation period of a peak load shifting project; the clustering analysis module is used for performing clustering analysis on the power load curve of the specified user during the implementation period of the peak load shifting project to obtain a user typical load curve set and a user power utilization regularity parameter; the power consumption mode determination mode is used for performing DTW and kmeans clustering analysis on the user typical load curve set to obtain a user power consumption mode; and the peak valley filling potential evaluation module is used for calculating a peak load shifting response factor according to the user power consumption mode parameter and the user power consumption regularity parameter, and calculating the peak load shifting potential of the user according to the peak load shifting response factor. According to the method, the peak load shifting potential evaluation of the user can be completed only according to the historical power consumption data.

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 carbon sink statistical method and system based on nori cultivation carbon sink potential monitoring and evaluation. The method comprises the steps that environmental parameter data are collected in real time through a multi-parameter sensor array deployed in a cultivation area; constructing a carbon flux evaluation model by combining nori culture biomass; simulating a migration and transformation process of carbon elements in'frond-water-sediment 'through a three-dimensional carbon transfer path tracking algorithm to obtain a three-dimensional carbon flux dynamic matrix; constructing a carbon sink space-time distribution model, and correcting the carbon sink space-time distribution in combination with the environmental data of the laver culture area; carbon element accumulation curves of different time dimensions are extracted, and a carbon sink potential evaluation function is constructed in combination with the growth cycle of the laver; and establishing a grading judgment system, and constructing a three-level carbon sink capacity grade and a corresponding environmental parameter threshold combination thereof. The method has the advantages that the carbon adsorption potential of the laver culture area is accurately evaluated through real-time environment data acquisition, carbon flux evaluation, three-dimensional path simulation and spatial-temporal distribution correction.

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 discloses a big data matching recommendation system and method based on a technology transfer platform, and relates to the technical field of artificial intelligence, and the system comprises a data processing module, a knowledge graph construction module, an intention analysis module, a similarity calculation module and a recommendation sorting module. Wherein the data processing module adopts a field adaptive encoder to realize heterogeneous data standardization; the knowledge graph construction module extracts technical entities and relationships based on a pre-training language model; the intention analysis module extracts core features of a supply party and a demand party through a double-decoder mechanism; the similarity calculation module fuses graph structure analysis and differentiation weight distribution and realizes dynamic compensation; and the recommendation sorting module generates a personalized recommendation result in combination with the market potential evaluation. According to the invention, the intelligent matching precision of the technology transfer platform is obviously improved, the precise docking of cross-field technology supply and demand is realized, and the method has important practical value.

The invention belongs to the field of power systems, and provides a multi-user-side resource cooperative adjustment potential evaluation method, system and product, and the technical scheme is that the upward and downward adjustment potential of a single user-side resource is evaluated based on short-term prediction data and historical data of distributed photovoltaic and user-level flexible load user-side resources; then, considering the sensitivity of different user side resources to the electricity price signal, and correcting the adjustment potential; and finally, considering the cooperative effect of the aggregator internal user side resources, and evaluating and calculating the cooperative regulation potential of the aggregator internal user side resources. According to the method, the problems that a traditional multi-user-side resource adjustment capability evaluation method depends on equipment modeling and is large in workload are solved, and a new thought is provided for evaluation of the multi-user-side resource adjustment capability.

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.