241 results about "Qualitative property" patented technology

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 artificial intelligence, in particular to an intelligent qualitative data analysis method based on an AIGC large model, and the method comprises the following steps: S1, constructing a multi-dimensional scoring model, and automatically screening participants according with a qualitative research project; s2, intelligently generating an interview outline matched with the current qualitative research project by utilizing historical data of the qualitative research project and combining a model fine tuning technology and a retrieval enhancement algorithm; s3, monitoring the interview progress in real time through an AIGC large model, recording interview record data, transversely comparing the interview record data with the interview outline generated in S2, generating a guide problem and a guide prompt of the current interview progress, and guiding a host to interview; s4, deeply mining the interview record data to obtain a data analysis result; s5, the AIGC large model automatically generates a personalized research report according to a data analysis result and report requirements; and S6, multi-layer encryption and access control technologies are adopted to ensure the privacy and security of the data in the acquisition, processing, storage and transmission processes.

A method for facilitating qualitative assessment of natural language data via artificial intelligence is disclosed. The method includes receiving, via an application programming interface, an input from a source, the input including the natural language data; determining, by using a model, a confidence score for the input, the confidence score relating to a clarity level of the natural language data; determining, by using the model, whether the confidence score exceeds a predetermined threshold; generating, by using the model, a request for additional information when the confidence score is below the predetermined threshold, the request including a prompt in a natural language format; and transmitting, via the application programming interface, the request back to the source.

The invention provides a multi-granularity open vocabulary query method based on an object-level lossless Gaussian field, and the method comprises the steps: introducing an object-level Gaussian field with a global consistency codebook, rendering a learnable semantic tag vector in the Gaussian field back to a corresponding object tag; the direct mapping between the label and the corresponding uncompressed high-dimensional feature is established through the code book, so that the semantic feature of any dimension is supported, additional compression is not needed, and the understanding capability on an object is remarkably improved; according to the method, wide quantitative and qualitative evaluation is carried out in a plurality of scenes, excellent performance in the aspects of object level zero sample segmentation and open vocabulary understanding is shown, the highest precision is particularly achieved in object-component hierarchical retrieval, and meanwhile multi-granularity scene editing is supported.

Techniques for evaluating the output of a large language model are disclosed. A training data set that includes deterministic computational metrics that measure features of large language model output and qualitative metrics that provide a non-deterministic measure of large language model output quality may be used to train a ML model. The ML model may then be used to estimate the qualitative metrics of large language model output by using deterministic computational metrics as input.

The invention relates to the technical field of geotechnical engineering investigation and artificial intelligence crossing, in particular to a marine fine-grained soil content prediction method based on multi-source CPTU data and machine learning, comprising: acquiring CPTU data and corresponding geotechnical test component content data; constructing a CPTU prototype vector of the geotechnical test point, and calculating a cosine similarity weight of the data point and the prototype vector and a Gaussian distance weight of the data point and a window center; generating a plurality of sliding windows by taking each test point as a center, and extracting a high-dimensional feature vector by using double weights; training a random forest regression model by using the feature vectors; moving a window of a to-be-predicted drill hole according to the step length, extracting features, inputting the features into the trained model, and generating a continuous depth profile of the fine-grained soil component content. According to the method, an indirect qualitative judgment mode of a traditional experience method is broken through, direct, quantitative and continuous prediction from CPTU data to the soil body component content is achieved, and the technical problems that a traditional method is high in cost, long in period, discrete in data and incapable of achieving quantification are solved.

The invention discloses a control system digital model verification method, and relates to the technical field of digital control systems, and the method comprises the steps: S1, analyzing and obtaining a simulation model; s2, obtaining a hierarchical relationship of each index, distinguishing a qualitative index and a quantitative index, and determining weight information of each index; s3, obtaining verification data of the bottom layer verification index through a test; s4, performing data preprocessing on the verification data to obtain reference data for model verification; s5, acquiring a bottom-layer index in the indexes, and acquiring a verification value of the bottom-layer index; and S6, according to the weight information and the verification value of the bottom layer index, obtaining a model comprehensive verification evaluation result based on a weighted average comprehensive calculation formula. The invention also provides a control system digital model verification system. The method has the advantages of accuracy, stability, reliability and wide verification range.

The invention discloses a dominant flow evaluation method based on tracer migration. According to the method, in the design process, the influence of soil layer permeability difference on the tracer breakthrough curve is fully considered, and a systematic tracer breakthrough curve calculation method and a qualitative judgment process are provided. And predicting the change trend of the breakthrough curve through a numerical simulation test. According to the method, the accuracy of breakthrough curve judgment can be effectively improved, and the method is suitable for tracer migration analysis under different types of soil layers and complex geological conditions. By means of the method, the diffusion and dominant flow migration process of the tracer agent can be better evaluated, and reliable data support is provided for underground fluid migration and pollution monitoring.

The invention relates to the technical field of energy management, in particular to a fuzzy logic-based multi-objective decision analysis method, which comprises the following steps of: firstly, dividing an evaluation criterion into quantitative and qualitative index sets; constructing an interval type-2 fuzzy set capable of describing data uncertainty according to quantitative indexes such as a full-life investment payback period; for qualitative indexes such as technology maturity, a spherical fuzzy set is introduced, and an intelligent operator for perceiving hesitance is used for aggregation; calculating a group utility value, an individual regret value and a comprehensive sorting index of each energy configuration scheme by applying a hybrid fuzzy VIKOR algorithm, outputting an optimal compromise sorting result, and identifying a reference optimal strategy and a risk avoidance strategy; according to the method, the explainable enhanced fuzzy rule is extracted, the robustness of the decision method is improved by analyzing different types of fuzzy logic, and the transparency, the credibility and the compliance of the energy configuration decision process are remarkably enhanced by the finally generated explainable decision basis, so that a feasible configuration decision reasoning model is provided for energy planning.

The invention relates to the technical field of fault diagnosis, and provides a self-adaptive diagnosis system and evaluation method for faults of aluminum processing equipment, which accurately senses a logic chain of multi-level feature intelligent reconstruction and multi-dimensional health dynamic evaluation through a multi-modal signal. A complete technical system for self-adaptive diagnosis of aluminum processing equipment faults is constructed, the system breaks through the one-sidedness of traditional single-sensor monitoring, through deep fusion and dynamic modeling of multi-dimensional information, the equipment health state is judged from fuzzy qualitative judgment to accurate quantitative evaluation, through benchmark dynamic matching to difference intelligent judgment, the equipment health state is accurately and quantitatively evaluated, and the fault diagnosis accuracy is improved. According to the method, a signal reliability guarantee mechanism is established through a logic chain of multiple calibration iterations, common features are mapped to health levels through fuzzy clustering, a high-dimensional feature space is converted into interpretable health state probability distribution, and the assessment complexity is reduced.

The invention relates to the technical field of computer systems, and particularly discloses a data resource migration risk prediction method and system, terminal equipment and a storage medium, and the method comprises the steps: respectively constructing explicit dependency features and implicit dependency features of various resources; comprehensive dependency characteristics of various types of resources are obtained, and then the association strength between the resources is calculated; constructing a dynamic semantic association graph based on the association strength among the resources, and updating the affected sub-graphs in response to resource change and operation log update; on the basis of the topological features of the dynamic semantic association graph and the risk features of the resources, in combination with the association strength between the resources, obtaining neighborhood aggregation features of the resources; and calculating a risk score of data resource migration by using the neighborhood aggregation feature after feature enhancement. According to the method, map topological features and resource features are deeply fused, so that risk assessment is changed from qualitative judgment to quantitative prediction, and quantitative assessment of dependency intensity and risks is realized.

The invention provides a cogeneration unit peak regulation capability evaluation method, which comprises the following steps: firstly, constructing a thermodynamic system mathematical model, solving system coupling parameters under a deep peak regulation working condition through a multi-layer iterative loop architecture, and generating a multi-working-condition data set which covers a full working condition and meets physical consistency; secondly, deducing a linear qualitative relation equation of the power, the main steam flow and the heat supply steam extraction flow, preprocessing a data set, training a pure data driving model as a performance reference, introducing the linear qualitative relation equation as a physical constraint item into a loss function, and constructing and training a PINN; and finally, the rated boundary condition of the peak regulation capacity is determined, a thermal characteristic curve is obtained through PINN simulation, the peak regulation capacity range and the real-time peak regulation margin under different heat supply requirements are calculated, and the result is transmitted to a power system dispatching platform. According to the method, the all-working-condition peak regulation evaluation precision and reliability can be improved, and the flexibility and universality of the model are enhanced.

The invention relates to the technical field of computer-aided planning, and discloses a territorial space planning decision-making method and system based on multi-dimensional influence assessment, and the method sequentially comprises the steps: multi-source data fusion and problem diagnosis, multi-dimensional influence quantitative assessment, scheme collaborative optimization, and scheme comprehensive screening and decision-making. The system corresponds to the method. According to the method, through multi-source data fusion processing, the data reliability is improved, and a solid foundation is provided for subsequent problem identification; according to a quantitative index and a GIS spatial analysis technology, key problem region identification is more targeted, and the defect of generalization adjustment of a traditional qualitative method is avoided; through traffic, environment and municipal dimension parallel evaluation, the efficiency bottleneck of serial evaluation is avoided, and the evaluation period is shortened; a Pareto candidate scheme set generated through a multi-objective evolutionary algorithm is combined with a comprehensive screening mechanism to ensure that a final scheme is balanced between performance requirements and constraint conditions, and the feasibility of planning landing is enhanced.

The invention provides a comprehensive evaluation method and system for evaluating bridge capability improvement, and is applied to the technical field of data processing. The method comprises the following steps: preprocessing quantitative and qualitative indexes in basic data to generate bridge dynamic feature information; processing weight calculation related influence factor data and evaluation abnormal state information based on weight adjustment logic of an analytic hierarchy process, and generating abnormal state identification information in an evaluation process by combining dynamic factor correlation analysis of fuzzy comprehensive evaluation; processing the bridge dynamic characteristic information to generate an evaluation grade of a bridge capability improvement effect; an evaluation result, an index type and a data source in the weight calculation related influence factor data are processed in combination with a multi-stage evaluation model, and key factors influencing the bridge capability improvement effect are generated; and processing the evaluation result, the key factors influencing the bridge capability improvement effect and the abnormal state identification information, and generating dynamic adjustment information for bridge reinforcement.

The invention discloses an MBD-based three-dimensional part dimension mark automatic generation method, which comprises the following steps of: firstly, carrying out structural analysis on a topological object of a geometry, extracting all edge domain elements on the geometry, traversing each edge domain element in a set, analyzing a topological structure and a geometric curved surface related to the edge domain element, and finishing screening, filtering and classifying; obtaining a labeling element set of each type; secondly, for different types of labeling elements, qualitative positioning correlation between the labeling elements and the sizes is analyzed, and a corresponding size labeling algorithm is designed; the method comprises the following steps of: firstly, establishing an annotation plane according to a size generation requirement, and repeatedly judging the annotation plane and an existing annotation view set, and finally, quickly generating size information, and carrying out redundancy check and redundant size removal on the generated annotation information to realize the construction of a three-dimensional inspection model. According to the method, part manufacturing and detection information can be directly expressed on the three-dimensional model, the product development efficiency is improved, and the production period is shortened.

The invention relates to a tunnel earthquake vulnerability analysis method based on a fuzzy comprehensive evaluation method, and the method comprises the steps: selecting an optimal earthquake intensity index through the fuzzy comprehensive evaluation method, and building a tunnel structure earthquake vulnerability curve through the selected earthquake intensity index. According to the method, the influence of different tunnel burial depths can be considered in the selection process of the seismic oscillation intensity index IM; according to the method, index evaluation optimization can be performed in consideration of four aspects of correlation, effectiveness, practicability and benefit; according to the method, a fuzzy comprehensive evaluation method is adopted, and on the basis of a fuzzy relation comprehensive theory, qualitative factors are converted into quantitative factors for comprehensive evaluation; by using the method, the seismic oscillation intensity index IM can be effectively selected, the calculation result is relatively accurate, and the method has positive significance for accurately analyzing the seismic vulnerability of the underground structure.

The invention discloses a high-density electric quantitative interpretation method based on a soil resistivity calculation model, and the method specifically comprises the steps: 1, measuring the physical characteristics of a sampling soil body in a detection region, carrying out the orthogonal test of main influence factors of the resistivity of the soil body, and analyzing the primary and secondary factors which affect the change of the resistivity; 2, selecting three factors with large influence degrees to carry out a quantitative test, and determining a quantitative relationship between the resistivity of the soil sample and the influence factors; step 3, based on the quantitative relation between the resistivity and each influence factor, fitting to obtain a soil resistivity calculation model of the detection area; 4, carrying out a nondestructive testing field test on the detection area to obtain an apparent resistivity image of the area; and 5, performing qualitative and quantitative double interpretation on a field detection result by combining an indoor test and a field test. The problem that in the prior art, high-density television resistivity result analysis depends on manual subjective experience judgment, and large uncertainty exists is solved.

The invention provides a power distribution network project consistency comprehensive evaluation method based on quantitative evaluation. The method comprises the following steps: step 1, obtaining basic data of a power distribution network project; based on the basic data, constructing a power distribution network project consistency evaluation index system; 2, performing quantitative processing on each subdivision evaluation index; 3, according to a weighted calculation result, calculating a consistency comprehensive evaluation score of each power distribution network project; and verifying the sorting result and the actual operation condition of the power distribution network project, and if the sorting result is inconsistent with the actual operation condition, adjusting a weighted calculation mode or a fuzzy evaluation method in the evaluation index system so as to enable the evaluation result to better fit the actual operation condition. According to the technical scheme, the quantitative index and the qualitative index are quantified by adopting the normalization processing and the fuzzy evaluation method respectively, so that the influence of subjective factors is reduced, and the objectivity and credibility of the evaluation result are improved.

The invention discloses a water temperature ecological scheduling effect qualitative and quantitative evaluation method based on deep learning, and the method comprises the steps: calculating an equivalent water taking elevation through employing a segmented cubic Hermite interpolation method based on vertical temperature distribution data, and representing the water taking elevation positions in different scheduling modes; based on a random forest model, a mapping relation between the hydrodynamic parameters and the water taking elevation is trained, and the model is optimized based on MSE, NSE and MAE indexes; a Shapley value method is adopted to analyze key variables influencing the water taking elevation, and the contribution degree of each hydrodynamic force factor to ecological scheduling response is quantified; when other conditions in the ecological scheduling period are not changed, the stoplog gate state is set to be 0, and the variable quantity of the water taking elevation without ecological scheduling is calculated; according to a water taking elevation result without ecological scheduling, a predicted value of the tail water temperature under the anti-fact framework is obtained, and the improvement effect of ecological scheduling is quantitatively evaluated in combination with an uncertainty analysis method.

The invention discloses a quantitative evaluation method and system for fault disturbance of a current crustal stress field of a tight reservoir, and belongs to the technical field of petroleum and natural gas development. The method comprises the following steps: S1, obtaining an actually measured crustal stress value I of a target interval of a target fault and an actually measured crustal stress value II under a non-disturbance condition; s2, obtaining a theoretical ground stress value of a target interval of the target fault under a non-disturbance condition; s3, establishing an average principal stress and shear stress intensity calculation model, and calculating to obtain the average principal stress and shear stress intensity under actual measurement and non-disturbance conditions; s4, a crustal stress fault comprehensive disturbance index calculation model is established, a crustal stress fault comprehensive disturbance index is obtained through calculation, and the larger the index value is, the stronger the fault disturbance is. According to the method, the quantitative characterization of the force-fracture coupling relationship can be realized, and the problems of poor repeatability and lack of unified quantitative standards caused by a qualitative method as a main method are effectively solved, and the technical problem that the geomechanical environment around the well near the fault is not matched with the engineering response is effectively solved.

The embodiment of the invention is suitable for the technical field of computers, and provides a risk qualitative method and device, electronic equipment and a storage medium. The method comprises the steps that API abnormal operation information of a user is received; obtaining abnormal context information, related to the API abnormal operation information, of the user; inputting the abnormal context information into a generative network model to obtain a qualitative result of the API abnormal operation information output by the generative network model, the qualitative result representing whether the API abnormal operation of the user is a malicious behavior; the generative network model is obtained by training a preset basic model based on abnormal context information related to preset API abnormal operation information and a corresponding qualitative result.

The invention discloses a mass spectrum data high-throughput alignment and parallel qualitative method based on deep characterization learning, and relates to the field of chromatography-mass spectrometry data processing and calculation mass spectrography. The method comprises the following steps: performing streaming, micro-batch and single traversal analysis on original mass spectrum data files of a plurality of samples to be detected; extracting metadata and main data by taking the fragmented spectrogram as a unit, inputting the metadata and the main data into a pre-trained depth representation learning model to generate a high-dimensional embedded vector, and incrementally writing the metadata and the main data into a column type storage database; constructing a sparse approximate nearest neighbor graph; executing a clustering algorithm on the graph to generate a consensus spectrogram set, and generating a consensus feature vector for each clustering cluster and a single example cluster; determining the nature of the consensus feature vector; and constructing a sample * feature matrix based on the cluster affiliation relationship and the qualitative result. According to the method, the technical problems of I / O bottleneck, memory overflow, combinatorial explosion, computing power waste and the like in large-scale mass spectrum data analysis are effectively solved.

The invention discloses a multi-dimensional enterprise department and wound ability evaluation method and a computer program product, and relates to the technical field of big data processing and application. The multi-dimensional enterprise department and wound ability evaluation method comprises the following steps: pre-constructing a quantitative index and a qualitative index; obtaining enterprise related data; and counting the enterprise related data, and dividing the enterprise related data into numeric data and text data. Calculating the numeric data, assigning values to the corresponding quantitative indexes according to calculation results of the numeric data, and recording the values as quantitative index values; analyzing the text type data, assigning values to corresponding qualitative indexes according to analysis results of the text type data, and recording the values as qualitative index values; and normalizing the quantitative index value and the qualitative index value, and generating an enterprise department and wound ability score. By adopting the technology provided by the invention, the enterprise can be scored in multiple dimensions, and the accuracy of the enterprise department and wound scoring mechanism is improved.

The invention relates to the technical field of engineering suitability evaluation, in particular to a qualitative and quantitative multi-type index full-life-cycle comprehensive fuzzy evaluation method and a product, and the method comprises the steps: constructing a mutual influence degree matrix in each evaluation time period, constructing a comprehensive evaluation coefficient matrix, and employing a qualitative and quantitative multi-type index comprehensive fuzzy static evaluation method. The method comprises the following steps: generating a fuzzy comprehensive evaluation value, then calculating a time period membership degree vector and an influence degree membership degree matrix, finally obtaining a comprehensive fuzzy evaluation result in a whole life cycle by weighting and summarizing the fuzzy comprehensive evaluation value of each evaluation time period, and sorting a plurality of schemes.

The invention provides a short-period multi-round situational awareness reliability evaluation method considering anchoring effect and confirmation deviation, which comprises the following steps of: 1, acquiring relevant knowledge of abnormal states, analyzing possible reasons of the current abnormal states, and determining a corresponding solution of each possible reason; 2, qualitative modeling: considering the dynamic nature of the short-period multi-turn SA, the anchoring effect and turn correlation caused by confirmation deviation, and modeling a short-period multi-turn SA establishment process by using a dynamic Bayesian network; step 3, quantitative modeling: on the basis of constructing the dynamic Bayesian network structure, determining node parameters in the dynamic Bayesian network; and step 4, short-period multi-turn SA reliability evaluation: evaluating the reliability of the short-period multi-turn SA according to the evidence and the determination of the conditional probability table of each node. According to the method, the problem that the round correlation caused by the anchoring effect and the confirmation deviation is not considered in the conventional short-period multi-round SA reliability evaluation is solved.

The invention discloses a qualitative detection method and device for tiny linear defects on the surface of a magnetic shoe and a medium, and relates to the technical field of magnetic material nondestructive testing. Scattering characteristics of defects in different directions are highlighted through dynamic adjustment of a light source azimuth angle, and a multidirectional illumination imaging system comprising a low-angle linear light source and a high-resolution polarization camera is constructed; except for the light source, the camera moves along the axial direction through the stepping motor to dynamically collect images; designing a preprocessing algorithm based on histogram of oriented gradient enhancement, and performing multi-scale feature fusion analysis on the defect region in combination with a deep convolutional neural network; and a lightweight classification model based on transfer learning is developed, and accurate distinguishing of microcracks and pseudo defects is achieved. The corollary equipment integrates a light source control module, an image acquisition unit and an embedded processing platform, and supports real-time dynamic detection. According to the invention, the detection limit of traditional machine vision on submillimeter linear defects is broken through, and the omission ratio is reduced to 0.5% or below.

The representation learning apparatus acquires M second-type mixed feature representations extracted from M first-type mixed feature representations. The apparatus acquires M mixed labels obtained by mixing N labels. The apparatus calculates the L similarities of L label combinations selected from the M mixed labels. Each of the L label combinations has K mixed labels selected from the M mixed labels, each of the L similarities represents the degree of similarity of the K mixed labels, and the L similarities are formulated based on a qualitative and quantitative constraint condition. The apparatus performs a computation of a loss function based on the M second-type mixed feature representations and the L similarities.

The invention discloses a qualitative and quantitative analysis method and system for explosives based on terahertz time-domain spectroscopy, sample preparation is carried out by using unified standards, and the method comprises the steps of grinding, weighing and pressing; the method comprises the following steps: measuring a terahertz time-domain spectrogram of an explosive sample by using a terahertz time-domain spectroscopy system, and obtaining a terahertz frequency-domain spectrogram through Fourier transform; data preprocessing algorithms such as data expansion, SG smoothing, multivariate scatter correction (MSC) and competitive adaptive reweighting (CARS) are used for preprocessing the data; and performing classification / regression analysis by using machine learning algorithms such as a support vector machine classification (SVC) model, a support vector machine regression (SVR) model and a one-dimensional convolutional neural network model (1D-CNN). According to the method, the problems of difficulty in constructing a spectrum database, insufficient algorithm robustness, limited practicability and the like in the prior art are solved, and a more efficient and reliable solution is provided for safety detection and prevention and control of explosives.

The invention provides a multi-factor coupling gravity retaining wall reliability evaluation and optimization method, and belongs to the technical field of geotechnical engineering. The method comprises the following steps: constructing a physical model containing parameters such as self weight, friction coefficient, height, filling weight and compaction degree; establishing a multi-factor dynamic coupling model which comprises a water level time-varying model, an earthquake coefficient probability model and a compactness degradation model; constructing a limit state function integrating the soil pressure, the water pressure and the seismic inertia force; calculating a time-varying failure probability through random numerical simulation; the importance degree of each factor is quantitatively evaluated by adopting standardized indexes; optimization measures such as compaction degree improvement, water level fluctuation reduction and dead weight increase are put forward, and the effect is verified. According to the method, the problems of single-factor analysis, static modeling, importance evaluation and the like of a traditional method are solved, dynamic coupling analysis of three factors of earthquake, water level fluctuation and compactness degradation is achieved, and a scientific basis is provided for design and maintenance of the gravity retaining wall.