171 results about "Qualitative property" patented technology

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 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 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 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 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 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.

The invention discloses a multi-modal AI material quantitative and qualitative rapid detection method, which comprises the following steps: acquiring multi-modal data through a double-pulse laser excitation system, carrying out time-space alignment, and cooperatively exciting light and heavy element characteristic plasmas by using femtosecond laser and nanosecond laser; spectral decoupling and dynamic compensation are carried out based on physical rule embedding, spectral signals are separated, and interference is suppressed; a light-heavy element dual-channel deep learning model is constructed, and multi-modal data is fused, so that feature extraction and prediction capabilities are improved; and a double-source mutual verification optimization model is adopted, so that the result reliability is ensured. According to the method, the limitation of single-mode detection is overcome, multi-component high-precision real-time synchronous detection of coal is realized, the adaptability of the model to complex coal quality and environmental change is improved, and an effective and accurate solution is provided for online monitoring of coal quality of a coal-fired power plant.

The application provides an intelligent processing method and device for auditing problem qualitative analysis and rectification tracking based on a large model and a medium. It belongs to the technical field of auditing. The system comprises a model construction module: an intelligent auditing knowledge cloud brain is constructed, and knowledge base data is generated; based on the knowledge base data, key auditing element extraction is performed on the auditing business scene, and auditing element extraction result data is generated; auditing problem matching model training is performed according to the auditing element extraction result data, and an auditing problem accurate matching model is constructed; relying on the powerful knowledge base and intelligent matching capability of the large language model, knowledge content closely related to the problem is rapidly and automatically filled in, covering key information such as problem name, manifestation form, problem qualitative analysis, qualitative analysis basis and treatment and punishment basis, which greatly saves the time of auditing personnel for manually searching and sorting data, more ensures the accuracy and integrity of the problem description, and lays a solid foundation for subsequent auditing analysis.

The invention relates to the technical field of information security, in particular to a penetration test effect evaluation method and system based on an attack mode framework, and the method comprises the steps: firstly, constructing an evaluation model comprising a target layer, a criterion layer and an index layer; the evaluation indexes under each criterion are weighted through a large language model, and the indexes are quantitatively scored by experts. And for each criterion, on the basis of a grey theory, calculating a comprehensive evaluation vector of the criterion by using index scores, weights, a preset comment set and a whitening weight function so as to obtain a quantitative score and a qualitative evaluation result. And finally, calculating a final comprehensive evaluation result of the target layer by integrating the quantitative score vectors and the criterion weights of all the criteria. During decision making, the final result needs to be combined with the qualitative evaluation conclusion of each criterion so as to formulate a comprehensive and targeted security reinforcement strategy. According to the invention, the accuracy of penetration test effect evaluation is improved.

The invention relates to a method for providing a coating composition-related prediction program, the method comprising:providing a database (204, 904) comprising associations of qualitative and / or quantitative characterizations of coating surfaces and one or more parameters;training a machine learning model for providing a predictive model (M2, M3) having learned to correlate qualitative and / or quantitative characterizations of one or more coating surfaces with one or more of the parameters; andproviding a composition-quality-prediction program configured for using the predictive model (M2) for predicting the properties of a coating surface to be produced from one or more input parameters; and / orproviding a composition-specification-prediction program configured for using the predictive model (M3) for predicting, based on an input specifying at least a desired coating surface characterization, one or more output parameters related to a coating composition predicted to generate a coating surface having the input surface characterizations.

The invention discloses a debris flow silt arrester disaster prevention benefit evaluation method, and the method comprises the steps: constructing a hierarchical evaluation system which faces a future operation state and can integrate landform shape constraints, reservoir capacity operation conditions and a safety risk state on the premise of not remarkably increasing the data acquisition cost; according to the invention, an integrated technology of AHP weighting, two-level fuzzy synthesis and maximum membership level judgment is provided; wherein a calculation framework based on threshold determination and piecewise linear membership functions, qualitative index feature vector assignment, penalty mapping of interval optimal indexes and two-stage fuzzy comprehensive evaluation jointly form core features of the method; the weight is determined based on the analytic hierarchy process, the consistency check is passed, the two-stage fuzzy comprehensive evaluation and the maximum membership principle are further adopted to output the levels of'excellent, good, medium and poor ', and a quantitative basis is provided for inspection and maintenance of the silt arrester, desilting modification and risk grading management and control.

The invention relates to a quantitative collaborative evaluation method and system for power measurement differences and a storage medium. The method comprises the following steps: acquiring quantitative index data and qualitative index data of power measurement; performing normalization processing on the quantitative index data; respectively calculating subjective weights and objective weights of all indexes based on an analytic hierarchy process and an entropy weight method, and carrying out weighted fusion to obtain subjective and objective combined weights; processing the qualitative index data based on fuzzy comprehensive evaluation, and combining subjective and objective combination weights to obtain quantitative scores of qualitative indexes; calculating a correlation coefficient of the normalized quantitative index data and a corresponding reference sequence based on grey correlation analysis, and calculating a comprehensive correlation degree of the quantitative index; and integrating the comprehensive correlation degree of the quantitative indexes and the quantitative scores of the qualitative indexes, and calculating to obtain a comprehensive difference degree. Compared with the prior art, through subjective and objective combination and qualitative and quantitative collaborative evaluation process, accurate and consistent quantitative output of the power measurement difference is provided.

The present application relates to the field of oil and gas exploration, in particular to a kind of dual induction eight lateral logging curve semi-quantitative acceptance method, equipment and medium. Including according to the curve of adjacent well and acceptance area dual induction eight lateral logging experience, based on adjacent well comparison template obtains preliminary qualitative acceptance conclusion;Dual induction eight lateral original data is loaded by resistivity comparison template, whether the relative error of curve repeated measurement value meets acceptance standard is judged, and semi-quantitative acceptance conclusion is obtained;Comprehensive preliminary qualitative acceptance conclusion and semi-quantitative acceptance conclusion, obtain final conclusion.The present application combines the characteristics of dual induction eight lateral logging instrument and the curve law of the region, forms two templates, can quickly according to adjacent well, regional experience and resistivity curve carries out repeatability two-step inspection, obtains comprehensive analysis result, realizes the rapid and accurate acceptance of dual induction eight lateral logging curve.

The embodiment of the invention provides a project deviation degree evaluation method and device, computer equipment, a medium and a product, and the method comprises the steps: determining a current progress stage of a target project according to progress feature data of the target project; based on the current progress stage, determining an association degree between the target project and other projects and a qualitative evaluation level of the target project; according to the correlation degree and the qualitative evaluation level, adjusting an evaluation index coefficient of the target project; and determining the deviation degree of the target project according to the operation data of the target project and the adjusted evaluation index coefficient. In the project proceeding process, the progress of the project is recognized in real time, the corresponding evaluation index weight is matched, it can be ensured that the evaluation result is matched with the actual state of the project, and evaluation deviation caused by stage mismatching is avoided. In addition, by adding a collaborative index, collaborative recommendation among multiple projects is concerned, and the comprehensiveness of project evaluation is improved.

This disclosure relates to method and system for determination of personality traits of agents in a contact center. The method includes retrieving textual data corresponding to a conversation between a first agent and a first customer. The method further includes generating a natural language justification corresponding to a set of personality traits of the first agent based on the textual data through a first Machine Learning (ML) model. The natural language justification may include one or more sentences. The one or more sentences may include a mapping of the textual data with the set of personality traits and a qualitative label associated with each of the set of personality traits. The method further includes determining a value corresponding to each of the set of personality traits of the first agent through the first ML model based on the natural language justification and the associated qualitative label.

This invention specifically relates to a performance testing method for laser cladding repair, encompassing the fields of laser additive manufacturing and non-destructive testing. The method includes: screening tracer particles based on three criteria: high physical field contrast, high-temperature thermal stability, and rheological compatibility; determining the tracer particle ratio; preparing uniform composite powder; verifying mixing uniformity; and standardizing powder pretreatment and storage. In this invention, tracer particles with high physical field contrast are introduced, and a direct mapping model between signal intensity and dilution rate is constructed using the law of conservation of solute mass. Normalization calculations eliminate interference from instrument response coefficient drift, enabling the method to penetrate the cladding layer surface and directly quantify the component mixing ratio within the molten pool. This not only achieves a leap from qualitative judgment to quantitative measurement but also replaces fuzzy empirical judgments with explicit physical laws, providing a non-destructive, high-confidence, and fully physically interpretable in-situ characterization method for laser repair quality.

The application provides a component adaptive processing method, device and equipment for enterprise service. The method comprises the following steps: acquiring a plurality of heterogeneous data sources of an enterprise; processing the plurality of heterogeneous data sources to obtain multi-dimensional enterprise portrait multi-dimensional data; performing multi-dimensional attribute correlation processing on the multi-dimensional enterprise data portrait to obtain a correlation matching result between multi-dimensional attributes of a service subject of the enterprise; performing co-occurrence or co-cited analysis on the multi-dimensional enterprise data portrait according to the correlation matching result to obtain a coupling degree of the enterprise and the service subject; constructing a qualitative index analysis model in an enterprise value chain according to the coupling degree; performing collaborative enterprise evaluation according to the qualitative index analysis model to obtain an evaluation result and outputting the evaluation result. The scheme of the application can help an enterprise user to select an optimal collaborative enterprise, improve the collaborative design, manufacturing and supply capacity between enterprises, and enhance the market competitiveness of the enterprise.

The invention relates to the technical field of building heritage protection, and provides a railway building heritage protection method based on multi-source data fusion in order to solve the problems of data splitting, significant subjective deviation and decision staticization in the building heritage protection process. The method comprises the following steps: constructing a multi-dimensional digital file by comprehensively collecting a BIM model, an Internet of Things sensor network, a historical literature database and a geographic information system; establishing a hierarchical evaluation index system, setting an objective reference value, and determining the weight of a corresponding index by adopting a collaborative process integrating an analytic hierarchy process and a Delphi method; carrying out quantitative scoring through an expert team, and carrying out weighted calculation on a total score of a comprehensive value to determine a protection grade; and performing section division according to the spatial position, identifying regional dominant value features through clustering analysis, and generating a hierarchical protection strategy in combination with a protection rule base. According to the method, normal form conversion from subjective qualitative to objective quantitative in the evaluation process is realized, subjective deviation is remarkably reduced, and the accuracy and operability of protection decision making are improved.

The invention discloses a function handling method, device and equipment based on a life cycle and a medium, and relates to the technical field of artificial intelligence. The method comprises the steps of obtaining a confidence factor set corresponding to a to-be-selected evaluation function in a current function evaluation service; performing quantitative judgment on the confidence factor set based on the target low-efficiency affirmation model to obtain a quantitative judgment result, and determining a corresponding intermediate evaluation function based on a preset function category field and the quantitative judgment result; generating a qualitative affirmation starting instruction based on the intermediate evaluation function, and determining a qualitative affirmation result corresponding to the current function evaluation service based on the qualitative affirmation starting instruction and a preset qualitative affirmation rule; and performing function disposal on the intermediate evaluation function based on the qualitative affirmation result, and generating a function disposal result corresponding to the to-be-selected evaluation function. According to the technical scheme of the invention, the method can achieve the accurate recognition and disposal of the low-efficiency function, improves the recognition efficiency and accuracy of the low-efficiency function, and reduces the maintenance and operation cost of a system.

The invention relates to the technical field of data processing, in particular to a method for evaluating the uniformity of qualitative characteristic values of a standard sample based on a quantitative result. The invention provides an evaluation and expression method for defining the qualitative characteristic uniformity of the standard sample by using the definite probability, so that reasonable quantitative expression of the qualitative characteristic uniformity of the standard sample is realized, and the problem of reasonable evaluation and explanation of the qualitative characteristic uniformity of the standard sample is solved.

This invention relates to the field of power plant data processing, and discloses a natural language processing-based operation and maintenance data processing system and method to address the problems of existing technologies relying on big data, lacking interpretability, and being unable to quantitatively integrate textual experience with physical laws. The system includes parsing operation and maintenance text into structured semantic units; quantifying these units into continuous influencing factors driving the physical model; using a non-machine learning theoretical physical model for state inference and prediction; comparing the prediction results with historical real states to generate difference data; and finally, optimizing the semantic quantification rules based on the difference data. This system achieves interpretable prediction and continuous self-evolution under small sample conditions, effectively transforming qualitative operation and maintenance experience into quantitative decision-making basis, and improving the reliability of operation and maintenance decisions and the ability to accumulate knowledge.