78 results about "Score" patented technology

The invention provides an unsupervised industrial anomaly detection method based on an improved full-convolution cross-scale flow network. The method comprises the steps of performing data preprocessing on an industrial image data set; industrial image data is used as input, a pre-trained visual backbone network is used for extracting multi-scale features, a resolution-perceived channel attention module is used for enhancing expression of the multi-scale features, and an enhanced multi-scale feature map is obtained; performing multilayer reversible transformation by taking the enhanced multi-scale feature map as input and taking an ICSF-Net model based on hierarchical attention and expansion convolution as a cross-scale normalized flow network, and modeling multi-scale feature distribution; in the training stage, optimizing and updating ICSF-Net model parameters by maximizing the log likelihood of a normal sample under potential Gaussian distribution based on multi-scale feature distribution and combining an LSGR mechanism; in the reasoning stage, probability density estimation is carried out based on multi-scale feature distribution, an abnormal score graph is generated, and defect detection and positioning are achieved. According to the invention, the accuracy, robustness and pixel-level positioning precision of industrial product defect detection are improved.

The invention discloses an SVG valve hall cooling efficiency evaluation method and system based on a probabilistic graph model, and the method comprises the steps: obtaining original time sequence data which is obtained through the collection of a cooling system multi-parameter monitoring sensor group disposed in an SVG valve hall in continuous T sampling periods; preprocessing the original time series data to obtain a credible time series data set; constructing a Bayesian network topological structure comprising three-level nodes of an environment layer, a component layer and an efficiency layer and causal dependence edges, and optimizing parameters of the Bayesian network topological structure by adopting a maximum likelihood estimation method to form a dynamic Bayesian network model after parameter calibration; and the credible time sequence data set is used as an evidence variable to be input into the Bayesian network model after parameter calibration, calculation is carried out through a belief propagation reasoning algorithm, a final control instruction set is generated through probability weighted scoring processing, the final control instruction set is fed back to a valve group monitoring system, and early warning and automatic load reduction are achieved. The problems of large evaluation deviation and early warning lag in the prior art are solved.

The invention provides a gushing prediction method and system for grouting engineering, and belongs to the technical field of tunnel and underground engineering construction safety monitoring. The method comprises the following steps: collecting grouting pressure, flow, slurry viscosity and micro-seismic event data; calculating a slurry resistance index and a micro-seismic event aggregation density; inputting the initial gushing risk index into a pre-trained machine learning model to obtain an initial gushing risk index and a confidence score; according to whether the confidence score is lower than a preset threshold, selecting direct early warning or entering a parameter correction path; when the confidence coefficient is low, dynamic correction factors are generated based on scores and risk changes, the calculation weight of the slurry resistance index is adjusted, corrected parameters are obtained, and secondary prediction is carried out; and integrating the initial prediction result and the secondary prediction result to obtain a final risk index, and executing graded early warning. According to the invention, the accuracy and reliability of gushing risk prediction are improved through a confidence-driven double-path decision and parameter on-line adaptive correction, and a constructed early warning mechanism.

According to embodiments of the disclosure, methods, apparatuses, devices and medium for model performance evaluation are provided. A method includes: applying, at a client node, a plurality of data samples to a prediction model respectively to obtain a plurality of predicted scores output by the prediction model, the plurality of predicted scores indicating respectively predicted probabilities that the plurality of data samples belong to a first category or a second category; determining values of a plurality of metric parameters related to a predetermined performance indicator of the prediction model based on the plurality of predicted scores and a plurality of ground-truth labels of the plurality of data samples; performing perturbation on the values of the plurality of metric parameters to obtain perturbed values of the plurality of metric parameters; and sending the perturbed values of the plurality of metric parameters to a server node.

The invention discloses an enterprise ESG score prediction method based on machine learning, and the method comprises the steps: collecting multi-dimensional ESG, financial and risk data, carrying out the normalization and missing / abnormal value processing, and dividing a data set; a multi-model system is constructed, and the optimal LSTM is selected by using five-dimensional indexes to predict ESG rating; in combination with an LSTM result, by taking maximization of an ESG mean value-minimization of a fluctuation ratio as a target, GRU is used for early warning of a short-term risk, LSTM controls a long-term trend, after constraint is added, an optimal investment portfolio weight is output through PSO, and a model updating and portfolio rebalance mechanism is set. According to the method, the ESG prediction precision is improved, the ESG-investment optimization integration is realized, and the data timeliness demand is adapted.

The invention relates to the field of data processing, in particular to a carton production line monitoring method and system, and the method comprises the steps: collecting and preprocessing multi-dimensional time sequence data of a production process; constructing a standard hidden Markov model (HMM) based on historical normal data, and defining the hidden state of the HMM as a microscopic operation mode under a macroscopic process; extracting procedure context features, and constructing a procedure conformity evaluation model (PCAM) to calculate a conformity score; dynamically adjusting the emission probability of the standard HMM in combination with the score to form an improved IHMM; and calculating the log-likelihood probability of the real-time observation sequence by using the improved IHMM, and comparing the log-likelihood probability with a preset threshold to judge the production abnormality. According to the invention, the accuracy and reliability of abnormity monitoring can be effectively improved.

The invention relates to the technical field of data processing, in particular to a threat recognition system and method.The method comprises the steps that a self-adaptive collection module obtains network flow data and operation log output feature vectors; the multi-dimensional rule module generates rule confidence; the statistical modeling module outputs an abnormal probability score; the machine learning module outputs a classification probability in response to the abnormal probability score dynamic selection model combination; the simulation decision module is fused with the multi-source data to execute threat diffusion simulation and output hazard indexes; the grading execution module triggers grading response according to the hazard index; and the feedback optimization module adjusts model parameters in a cross-layer manner. According to the method, noise interference is eliminated through feature optimization, a multi-dimensional model is fused to calibrate an output result, a threat grading mechanism preferentially responds to key threats, and closed-loop feedback is continuously injected into a false alarm data training model, so that the problem of high false alarm rate caused by insufficient training data coverage of a machine learning model in a complex dynamic network environment is solved; security resource allocation is optimized and key threat response delay is shortened.

The invention belongs to the technical field of data processing, and particularly discloses a building outer wall hollowing evaluation method and system, and the method comprises the steps: obtaining a target building outer wall detection signal, carrying out the time window segmentation and normalization preprocessing, and inputting a self-encoder model pre-trained by a normal wall detection signal to obtain a reconstruction signal; calculating a residual error of the detection signal and the reconstruction signal, and extracting a multi-dimensional feature formed by residual error energy, a residual error maximum absolute value, peak density and a multi-scale feature score from the residual error; and substituting the multi-dimensional features into an abnormal scoring function to obtain an abnormal scoring value, comparing the abnormal scoring value with a threshold value based on normal sample statistical features, and judging whether hollowing or potential hollowing exists in the target outer wall area. According to the invention, tiny hollowing signals can be accurately captured, the detection objectivity and accuracy are improved, and the refined evaluation requirements of building outer wall hollowing are met.

The invention provides an adversarial attack sample generation method and related equipment, which are applied to the field of machine learning, and are used for determining an initial value of adversarial disturbance distribution of a diffusion model, determining a disturbance score of the adversarial disturbance distribution based on the initial value and an established classifier misleading evaluation function, and generating an adversarial attack sample. And updating the model parameters of the diffusion model based on the disturbance score until the disturbance score of the adversarial disturbance distribution corresponding to the diffusion model is not less than a preset score threshold, determining the current diffusion model, generating an adversarial disturbance sample according to the model parameters of the current diffusion model, and further generating an adversarial attack sample. According to the method for generating the adversarial attack sample, a plurality of data samples do not need to be acquired, the adversarial disturbance distribution of the diffusion model is adjusted, the disturbance sample is generated when the adversarial disturbance distribution meets the set condition, and the adversarial attack sample is generated based on the disturbance sample, so that the sample acquisition is fast, and the attack efficiency of implicit adversarial attack is improved.

The invention provides a watermark detection method and device based on Bayesian detection. The method comprises the steps of obtaining a target text to be subjected to large model watermark detection and a cue word corresponding to the target text; processing a token sequence spliced by the cue word and the target text through a language model to obtain probability distribution output by the language model for each token position; for the jth token in the T tokens of the target text, selecting the first k tokens before the jth token to be input into the hash function, and obtaining a random seed of the jth token; dividing a word list of the large model into a preferential selection set and a non-preferential selection set based on random seeds; on the basis of preset watermark bias, preferentially selecting the set and the probability distribution, and generating probability distribution after disturbance of the jth token; calculating a log-likelihood ratio by using the probability distribution before and after disturbance, and accumulating the log-likelihood ratio to a detection score of the current text; and if the detection score is higher than the threshold value, judging that the target text has the watermark of the large model.

Systems, methods, non-transitory computer-readable media to perform operations associated with the storage medium. One system includes a storage medium and an encoding / decoding (ED) system to perform operations associated with the storage medium, the ED system being configured to process a set of log-likelihood ratios (LLRs) and a syndrome vector to obtain a set of confidence values for each bit of a codeword, estimate an error vector based on selecting one or more bit locations with confidence values from the set of confidence values above threshold value and applying hard decision decoding to the selected one or more bit locations, calculate a sum LLR score for the estimated error vector, and output a decoded codeword based on the estimated error vector and the sum LLR score.

This application provides a model training method, training system, and related equipment. The method includes the following steps: acquiring positive sample data, wherein the positive sample data includes input samples and positive labels, the input samples include questions, and the positive labels include the answers corresponding to the questions; inputting the input samples into a first large model to be fine-tuned, obtaining the first negative label corresponding to the input sample; determining the first score of the first negative label based on the relevance of the first negative label to the question and the quality of the response to the first negative label based on a scoring model; inputting the input samples into the first large model; and fine-tuning the first large model based on a loss function to obtain a second large model. The loss function is used to guide the first large model to improve the prediction probability of positive labels and the prediction probability of negative labels with scores greater than a threshold, so that the model can not only learn standard answers that meet user expectations, but also generate more diverse answers, thereby improving the fine-tuning effect of the large model.

The invention discloses a load event detection method based on mixing of a rank likelihood ratio and physical characteristics, and relates to the technical field of intelligent power grids and non-intrusive load monitoring. The method comprises the following steps: acquiring an original aggregated power signal, and performing multi-scale preprocessing on an active power sequence to extract core power and trend components; a dual-path mixed discriminant variable is constructed, a statistical path calculates a rank likelihood ratio statistic by using non-parametric rank transformation, and a physical path calculates a physical difference score through logarithm space mapping; sensing an average power level in a current window in real time, dynamically adjusting a fusion weight factor of a statistical path and a physical path, and generating a mixed sensing score; performing state transition monitoring on the mixed perception score by using a finite-state machine with a time sequence memory feature, and locking preliminary candidate event points; and finally, gradient fine trimming positioning and physical median consistency verification are performed on the candidate points, pseudo events are eliminated, and a legal load event sequence is output. According to the method, the dependence on Gaussian distribution hypothesis is eliminated from the mathematics essence, the robustness to non-Gaussian interference is enhanced, the capture sensitivity for weak load and overlapping events is improved, the calculation overhead is low, and the real-time monitoring requirement of the edge side is met.

Systems and methods for optimizing multi-camera multi-entity artificial intelligence tracking systems. Visual and location information of entities from video feeds received from multiple cameras can be obtained by employing an entity detection model and re-identification model. Likelihood scores that entity detections belong to an entity track can be predicted from the visual and location information. The entity detections predicted into entity tracks can be processed by employing combinatorial optimization of the likelihood scores by identifying assumptions from the likelihood scores, entity detections, and the entity tracks, filtering the assumptions with unsatisfiable problems to obtain a filtered assumptions set, and optimizing an answer set by utilizing the filtered assumptions set and the likelihood scores to maximize an overall score and obtain optimized entity tracks. Multiple entities can be monitored by utilizing the optimized entity tracks.

The invention discloses a large language model multi-dimensional preference alignment method based on multi-attribute collaborative optimization, and the method comprises the steps: generating a multi-candidate response based on reference strategy sampling, constructing a preference data set containing main and auxiliary attributes through an attribute scoring mechanism, and precisely capturing human implicit multi-dimensional preferences; a learnable prompt pool and an expert group are constructed, after expert groups are divided through attribute combination, multi-dimensional features of samples are fused through a routing gating network, accurate matching of the samples and the experts is achieved, and different attribute combination modeling requirements are met; constructing a self-normalization importance sampling weight based on the log-likelihood difference of the current model and the reference model, and estimating and maximizing the joint expectation score of the primary and secondary attributes; and meanwhile, flexible control is carried out on the expected constraint violation condition of the secondary attribute, so that collaborative optimization of the primary and secondary attributes is realized. According to the method, the large language model better fits human multi-dimensional real preferences, performance does not need to be sacrificed in any attribute dimension, and the model output quality and scene adaptation capability are remarkably improved.

This invention relates to the field of fluid mechanics and transient process analysis of hydraulic systems, and discloses a method for determining the occurrence boundary and risk level of liquid column separation. The method utilizes a visual test bench to conduct multiple experiments under different combinations of test parameters, collecting data and simultaneously recording the liquid column separation state. Based on Buckingham's π theorem, dimensional analysis is used to construct multiple independent dimensionless parameter sets from the test parameter combinations. These are used as input features, with the separation state as the target variable. A machine learning algorithm is introduced for multiple batch training to calculate importance scores, selecting the dominant dimensionless parameter with the highest and most stable score. Subsequently, regression analysis is used to fit the data boundary, solving for the intersection point and outputting the critical equilibrium point as a quantitative benchmark for the occurrence boundary. After confirming separation, high-scoring dimensionless parameters are selected to construct composite judgment parameters, which are compared with preset thresholds to classify the risk level. This invention can accurately define the critical equilibrium point of cavitation occurrence and quantitatively assess its risk severity.

This invention relates to the field of financial risk assessment technology, specifically to a case risk assessment system and method based on machine learning algorithms. The system mainly includes a pre-training module and a prediction module. This invention is designed with the application of artificial intelligence algorithms in the risk assessment of suspicious cases as its core concept. Guided by theories such as supervised learning and unsupervised learning, it analyzes the performance of various features of cases within a fine-grained feature system through classification algorithms, anomaly detection algorithms, and statistical analysis methods. This process uncovers hidden and anomalous information, assesses the risk of cases, and assigns scores and rankings them. Through the application of AI technology, financial institutions can more quickly and accurately identify and handle high-risk cases, rationally allocate the time and effort of business personnel in handling cases, and promptly reduce risks and losses.

A method for model training, the method comprises: obtaining a data sample for a target machine learning model to be trained; and training the target machine learning model by performing an iterative process comprising: generating, using the target machine learning model, a first likelihood of a predicted response token based on a current token sequence, generating an advantage score for a predicted model response with respect to the current token sequence based on the sample model response, the predicted model response being updated iteratively to comprise the predicted response token, determining a first loss value of a first loss function for the target machine learning model based on the generated likelihood and the advantage score, and updating the target machine learning model based on the first loss value.

The invention discloses a recommendation method based on interpretable generalized logic transformation matrix decomposition. The recommendation method comprises the following steps: converting an original scoring matrix into normal distribution data through a generalized logic transformation function; constructing indexes based on similarity and indexes based on ranking; calculating probability distribution and expected scores of scores of the recommended items by the similar users, and generating interpretability indexes in combination with the similarity indexes; integrating the interpretability index into a matrix decomposition objective function for optimization; solving a user feature matrix and a project feature matrix through an alternating least square method or stochastic gradient descent; calculating a prediction score and mapping the prediction score back to an original score interval through generalized logic inverse transformation; generating a recommendation list; the method provided by the invention has wider applicability and higher performance in practical application.

The invention discloses a random sampling consistency GNSS (Global Navigation Satellite System) positioning method fusing random forest prior information, which comprises the following steps of: firstly, carrying out random forest classifier training according to multi-dimensional feature information and a label of each GNSS observation value; secondly, predicting each observation value of the current epoch by using the trained random forest classifier to obtain a probability score of normal observation; then, in a random sampling consistency iteration process, sorting all minimum subsets according to the sum of probability scores of observation values in each minimum subset, and preferentially selecting a subset with a high probability to carry out model parameter estimation; and then, based on a kernel density estimation method, probability density evaluation is carried out on all candidate positioning results, and a position with the maximum probability density is selected as a final positioning solution, so that the stability and credibility of the results are enhanced. According to the method, the positioning precision, the stability and the anti-interference capability of the GNSS system in the urban complex environment are effectively improved.

The invention provides a real-label-free multi-consultant sequential reasoning method and device based on uncertainty, and relates to the technical field of artificial intelligence machine learning and electronic data truth value inference. According to the method, the individual uncertainty of all consultants participating in decision making in the current question is calculated by constructing a question ordered set, and the average uncertainty is obtained; and then double-path probability calculation is executed, in a weighted voting path, the score probability of each candidate option is calculated, in a Bayesian aggregation path, the posterior probability of each candidate option is calculated, the average uncertainty is used as a scaling factor to carry out linear weighted fusion, and the candidate option with the highest fusion probability is selected as a final decision reasoning result. According to the method, synchronous evolution of a consultant evaluation system and decision accuracy can be driven through internal logic self-consistency in an environment which is completely lack of external truth value labeling, the problems of cold start robustness and malicious interference are effectively solved, and the calculation overhead is remarkably optimized.

The invention discloses a distributed thermal process fault diagnosis method based on dynamic information fusion, and the method comprises the steps: carrying out the dynamic hidden variable analysis of space-time separation of a temperature data set of a distributed thermal process, and solving a dynamic hidden variable, a dynamic space basis function and a dynamic residual error; calculating a dynamic hidden variable score and a corresponding residual error based on the dynamic hidden variable and the dynamic space basis function; decomposing the dynamic residual error through static PCA to obtain a static time coefficient matrix and a static space basis function matrix; calculating hidden variable scores and residual errors in the dynamic and static processes, defining dynamic subspace statistics, static subspace statistics and residual error subspace statistics, and performing multi-scale fusion on the three defined statistics to construct multi-scale statistics; when the multi-scale statistical magnitude is greater than a reference threshold value, judging that a fault occurs; and contribution degree analysis is carried out on the residual subspace statistics, and a fault position is positioned. According to the invention, rapid detection and accurate positioning of faults can be realized.

A method includes determining, by a trained machine learning model, a score based at least on one or more latent features. The method also includes monitoring the determining of the score by the trained machine learning model. The monitoring includes determining one or more production statistics associated with the one or more latent features, derived variables and input data elements, and accessing one or more reference assets persisted on a model governance blockchain. The one or more reference assets includes one or more reference statistics and a threshold indicating a deviation between the one or more production statistics and the one or more reference statistics. The method also includes generating an alert based on the one or more production statistics associated with the one or more latent features meeting the threshold. Related methods and articles of manufacture are also disclosed.

The invention discloses a high-voltage switch cabinet fault monitoring method and system based on an intelligent sensor, and the method comprises the following steps: collecting and processing temperature, electric parameters and environment data, and forming monitoring features, condition variables and sensor availability information; generating condition masks according to the condition variables, and performing grouping mapping on the monitoring features; performing conditional reversible transformation under grouping constraint to obtain a log-likelihood result and modal contribution; constructing static and sliding baselines and generating alarm scores in combination with log-likelihood and contribution information; and performing trigger judgment based on the alarm score, outputting a risk level and evidence, and updating a baseline online. According to the method, a conditional reversible likelihood modeling and double-baseline alarm fusion method is adopted, multi-source fault risk grading monitoring of the high-voltage switch cabinet is achieved, and the method has the advantages of being high in robustness, high in interpretability and excellent in online self-adaptive capacity.

The invention relates to a key index screening method supporting real-time updating. The method comprises the steps that a stream processing engine receives a multidimensional index observation value data stream containing a timestamp, a sliding time window is maintained according to a preset fixed length, the data stream is screened, and newest effective samples in the window are reserved to form a sample data set; updating a mean value, a covariance and a second-order central moment accumulated quantity of each index in the sample set online to obtain a real-time statistical magnitude; calculating the standard deviation of each index and the correlation coefficient between the indexes according to real-time statistics, establishing a dynamic correlation network in combination with a correlation threshold value, and taking the node connection degree as a correlation degree score; calculating a variable coefficient of each index by using the real-time mean value and the standard deviation to serve as an uncertainty score; normalizing the two types of scores, and fusing according to a preset weight to obtain a dynamic comprehensive score of each index; and screening the indexes with the comprehensive scores reaching the standard according to a preset rule to form a key index set. By adopting the method, second-level identification and updating of the key indexes can be realized.

The present application relates to the technical field of power system analysis, in particular to a power system source and load bilateral uncertainty characterization and tracing method and system, adopting the method provided by the present application, including dividing primary indicators, secondary indicators and tertiary indicators; obtaining data of the primary indicators at multiple time sections, setting a plurality of data judgment thresholds, classifying the current system operation state based on the judgment thresholds, performing step-by-step analysis and calculation through the feature importance score of each indicator about a certain indicator output by the machine learning model, through the above hierarchical and progressive analysis framework, combined with the machine learning method robust to collinearity, the source and load bilateral uncertainty in the high-proportion new energy power system can be quantified and traced systematically, providing a new, more accurate and scientific analysis tool for power grid risk warning, planning decision and operation optimization.

The invention relates to the field of computer science and technology and artificial intelligence, and particularly discloses a MaxSAT initial assignment method based on an improved Johnson algorithm. The maximum satisfiability problem (MaxSAT) is an optimized version of the Boolean satisfiability problem (SAT). The invention provides a new heuristic initial assignment generation method aiming at the problem of low search efficiency caused by the adoption of a complete random initialization strategy by an existing local search solver. The method is realized through the following technical scheme: firstly, inputting a conjunctive normal form of a MaxSAT problem, calculating positive and negative character scores of each variable based on a Johnson algorithm principle, and further obtaining probability estimation that each variable is assigned to be true; then, a threshold value is set to carry out deterministic assignment on the high-confidence-coefficient variable, and an assignment result is propagated in time to simplify a problem structure; iteratively executing calculation, assignment and propagation processes until a preset cycle cut-off condition is reached; and finally, performing probabilistic complementation on the residual variables to generate a complete initial assignment.

A computing system and method for training one or more machine-learning models to perform anomaly detection. A training dataset is accessed. An overall sensitivity score is determined that indicates an amount of sensitive data in the training dataset. Machine-learning models are trained based on the training dataset and the overall sensitivity score. The machine-learning models use the overall sensitivity score to determine a threshold. The threshold is relatively low for datasets having a large amount of sensitive data and is relatively high for dataset having a small among of sensitive data. When executed, the machine-learning models determine if a probability score of features extracted from a received dataset are above the determined threshold when a second overall sensitivity score of the received dataset is substantially similar to the overall sensitivity score. When the probability score is above the determined threshold, the machine-learning models cause an alert to be generated.

The invention discloses an oversampling and software defect prediction method based on space mapping and probability constraint. The method comprises the following steps: firstly, constructing a minority class probability score model by using dual-kernel density estimation, and realizing layered operation on a preprocessed defect sample; secondly, calculating the number of synthetic samples based on layered weight, performing three-dimensional space mapping and adaptive spherical domain nonlinear sampling on the seed samples, and reconstructing high-dimensional synthetic samples through inverse mapping; and finally, introducing manifold consistency and likelihood ratio dual probability constraints to perform quality verification on the samples, and triggering a radius feedback adjustment mechanism for unqualified samples until a balanced data set is constructed. And training the classifier by using the balanced data set to realize software defect prediction of an unknown sample. By selecting the high-quality seed samples and controlling the synthetic samples, the class imbalance problem is further processed from the data level, and the overall prediction performance of the model is remarkably improved.

The invention relates to the technical field of data statistics, in particular to a multi-dimensional index data statistics method and platform based on machine learning, and the method comprises the steps: extracting key information from an original delivery record, combining the key information to form a unified strategy unit set, and converting the strategy unit set into a corresponding vector set; constructing a path index set based on the delivery performance indexes, aggregating sample sets belonging to the same path structure in the strategy unit set, constructing a path index distribution difference score function to screen strategy paths, finally outputting output path sets meeting screening conditions, and combining the output path sets into a structure diagram; respectively outputting an index quantile structure and a fluctuation penalty coefficient corresponding to each path through a kernel weighted empirical quantile function and a distribution fluctuation metric function; and filtering the candidate recommendation path set through a comprehensive scoring function and a confidence constraint function, and outputting a final putting path suggestion.