26 results about "False positives and false negatives" patented technology

The present application belongs to the technical field of industrial process monitoring and fault diagnosis, and particularly relates to a kind of multidimensional data monitoring and linkage early warning method for VIGA powder production process, comprising: obtaining multidimensional time series data and dividing it into multiple data sample matrices;Sparse autoencoder model is constructed, data sample matrix is input into the trained sparse autoencoder model, historical reconstruction error sequence is obtained, its covariance inverse matrix is calculated, and abnormality judgment threshold is determined;Real-time reconstruction error of real-time data sample is calculated, real-time Mahalanobis distance reconstruction error is calculated, and when it is greater than the abnormality judgment threshold, it is determined that the VIGA powder production process is abnormal;Partial derivative is calculated, and the absolute value of partial derivative is used as abnormal contribution degree;The variable set whose cumulative contribution degree meets the preset condition is identified, and early warning information is sent out.The present application solves the problems of insufficient early abnormality capture, difficult balance of false positives and false negatives, and low abnormality disposal efficiency of existing methods.

The application provides a robot collision detection method based on a friction model and a momentum observer, comprising: establishing a friction model capable of accurately estimating joint friction torque; secondly, constructing a momentum observer that can be adaptively adjusted according to the motion state, for calculating the external torque representing the collision state; further, setting a dynamic threshold range based on the normal motion data without collision, and finally realizing collision determination by comparing the real-time external torque with the threshold. Through high-precision modeling and adaptive observation mechanism, the technical scheme significantly reduces the false positives and false negatives caused by model errors and noise interference, realizes fast and accurate collision detection in the full speed range, and effectively improves the system safety and robustness in human-machine collaboration scenarios.

The application relates to the technical field of natural language processing, in particular to an AI generated text detection method and system based on a deep learning model. The method comprises the following steps: performing character normalization and sentence segmentation on a to-be-detected text, segmenting the text by a sliding window to generate a segmented sequence; constructing deep semantic discrimination features, logarithmic probability difference trace features, style structure features and entity consistency features; inputting a gate fusion discrimination network, outputting a generated probability and confidence degree after probability calibration, and outputting a segmented contribution degree; performing threshold adaptive determination according to a risk level, a text length and the confidence degree, outputting a review state and generating a traceable record when the confidence degree is insufficient; and triggering incremental updating based on drift monitoring and a feedback sample pool. The application improves cross-domain robustness and interpretability, reduces false positives and false negatives, and supports long-term stable operation.

The embodiment of the application provides a kind of information system code vulnerability detection method and system based on artificial intelligence big model, belong to information system security field.The method comprises: obtaining the source code of information system to be detected;The source code is preprocessed;The code after preprocessing is input into pre-trained artificial intelligence big model and is analyzed multidimensionally;Potential vulnerabilities in the code are identified based on the analysis results, and the vulnerability detection results and repair suggestions are output.It reduces false positives and false negatives, improves the accuracy and coverage of vulnerability detection, can detect complex vulnerabilities that are difficult to detect by traditional methods, and is efficient and widely applicable.

The present application belongs to the technical field of data processing, and particularly relates to an engineering cost file intelligent quality control method and system based on multidimensional data. The method comprises the following steps: analyzing a cost file to obtain project characteristic text, comprehensive unit price and engineering quantity of a target cost item, and searching for a standard reference item containing standard reference text, standard unit price distribution data and scale reference engineering quantity; determining text variability based on the number of missing core process characteristic words and string edit distance; calculating the degree of deviation of comprehensive unit price from standard unit price distribution data and adjusting using engineering quantity ratio to obtain price and quantity disturbance energy; adjusting the price and quantity disturbance energy using the difference between the text tolerance boundary parameter and the text variability to obtain an abnormality index, and outputting an intelligent quality control diagnosis report when the index exceeds a threshold. The present application can break through the limitation of single-dimensional comparison, accurately identify multi-dimensional coupled hidden bid defects, and effectively reduce false positives and false negatives.

The application provides a component monitoring method and system based on observability, and a medium. The method comprises the following steps: obtaining a component type, configuring metadata based on the component type, associating a monitoring template, enabling the component type, and applying isolation configuration; collecting performance index data of the component based on collection parameters; querying corresponding performance index data based on a query request to obtain a query result; configuring a data source, a query expression, and a visualization type of a monitoring board to perform real-time display of the performance index data of the component; calculating a health score and abnormal situation information based on the query result, analyzing a health state level, and generating a diagnosis suggestion; obtaining multi-dimensional load indexes, calculating load values and a final load degree of each dimension, analyzing a load level, and generating load warning information; and performing multi-dimensional health evaluation through weighted scoring and abnormality detection, which can more accurately reflect the real health state of the component, reduce false positives and false negatives, and improve the accuracy of fault early warning.

This invention discloses an intelligent early warning and closed-loop management system for financial business risks, including a risk data center, a risk scanning module, a risk prediction module, a risk rating module, a work order management module, a handling tracking module, a feedback statistics module, and a model optimization module. This invention uses the feedback statistics module to quantitatively evaluate the accuracy of early warnings and the timeliness of handling, and automatically collects false positive and false negative samples. Utilizing this real business feedback data, the parameters of the decision tree model are dynamically adjusted, and the logistic regression model is incrementally trained, enabling the early warning model to continuously iterate and evolve with business development and data accumulation, adaptively reducing false positives and false negatives, forming a complete management closed loop of early warning-handling-feedback-optimization, ensuring the long-term effectiveness and vitality of the system.

The application discloses an airborne equipment reliability prediction method based on field data, and relates to the technical field of machine learning, comprising: inputting a potential failure label into an equipment prediction model, performing deep feature extraction through a feature enhancement layer, performing spatio-temporal attention fusion through a prediction decision layer, and generating a reliability prediction report; extracting pre-position features of a historical failure case library, associating and analyzing the pre-position features in combination with spatio-temporal context features, and obtaining a failure trigger rule set; performing simulation and modeling according to the failure trigger rule set, generating simulation data, and performing deviation quantitative analysis on the reliability prediction report and the simulation data to generate a verification report. The application realizes the early identification of pre-position signs, associated causes and trigger relationships of failures by constructing a failure trigger rule set and performing deviation quantitative verification on the reliability prediction result, improves the reliability of the prediction conclusion, reduces the risk of false positives and false negatives, and enhances the reliability of engineering application.

This invention discloses a method and system for detecting fraudulent order behavior based on multimodal data fusion. The system includes a data parsing module, a feature fusion calculation module, an automatic training module, a real-time recognition module, and an evidence chain output module. The data parsing module can receive multi-source heterogeneous raw data from the business platform system in real time through a predefined data channel, and perform standardized parsing and cleaning on the received data to generate a data stream. The feature fusion calculation module calculates numerical features in five dimensions—order density, capacity load, spatiotemporal trajectory, behavioral time series, and cost mileage—in parallel based on a calculation model and the data stream. This invention significantly reduces the false positive and false negative rates by automatically optimizing the judgment thresholds for features such as order density and capacity load, achieving accurate and adaptive recognition of fraudulent order patterns. Simultaneously, it can record the item-by-item verification logic and results from order density to cost mileage, forming an evidence chain.

The application relates to the technical field of electric appliance control, in particular to an intelligent control method and system for electric appliance equipment monitoring, which comprises the following steps: obtaining a time sequence of operating parameters and environmental data and calculating a change rate, generating a comprehensive change rate index based on equipment type weight and environmental adaptation weight, determining a dynamic threshold interval in combination with historical operating data, further determining a probability distribution interval and performing operating condition calibration and consistency verification, obtaining a state judgment result, and outputting an early warning signal or performing control according to the state judgment result. The application can improve the state judgment accuracy, reduce false positives and false negatives, and enhance the timeliness of control response.

This application provides an intelligent access control management system for network boundary protection, relating to the field of access control management technology. By connecting a threat intelligence and behavior analysis module, this application no longer relies on static rules but dynamically adjusts access control policies based on real-time threat intelligence and traffic anomaly detection results. This enhances the protection capabilities against unknown threats, zero-day vulnerabilities, and internal abnormal behavior, and shortens threat response time. Furthermore, by introducing a business context-aware module, this application can deeply understand the business attributes of network traffic, allowing policy formulation and execution to extend beyond the network layer to the business layer, reducing false positives and false negatives, and ensuring that core business operations are not affected while maintaining security.

This application discloses a data processing and insight method and system based on a multimodal intelligent assistant, belonging to the field of computer technology. By integrating device physical model deduction and domain knowledge graph retrieval, a dual-track coupled reasoning framework is constructed, effectively solving the problems of decoupling between evidence and the spatiotemporal evolution of the device, and the logical defocusing of knowledge retrieval in traditional diagnosis. Through heterogeneous intelligent agent collaborative data collection and real-time conflict detection, the reliability and consistency of evidence are improved. Dynamic graph fusion and traceable reasoning mechanisms provide a transparent causal attribution chain and comprehensive credibility assessment, overcoming the limitations of black-box diagnosis. The introduction of metacognitive reflection and closed-loop optimization instructions can proactively address uncertainty, achieve continuous knowledge evolution and self-improvement of diagnostic capabilities, thereby reducing the false positive and false negative rates and supporting accurate operation and maintenance decisions.

This application provides an anomaly identification method, apparatus, electronic device, and storage medium. It determines the anomaly type by matching image features with descriptive text vectors in a pre-established mapping relationship. Even when faced with anomaly types not present in the training set, accurate identification is possible as long as their descriptive text vectors exist in the mapping relationship. This effectively overcomes the difficulty in identifying new anomaly types due to scarce labeled data, significantly improving the comprehensiveness of anomaly identification. Because the fused image integrates multimodal information, it enhances the cross-modal capability and generalization ability of the anomaly identification method, enabling more accurate differentiation between normal changes and genuine anomalies, reducing the probability of false positives and false negatives, and improving the reliability and accuracy of the anomaly identification method under different environmental and temporal conditions.

This invention provides a network attack detection method and apparatus based on a binary classification model, comprising the following steps: Step 1: Calculating network parameters and preprocessing them; Step 2: Genetically optimizing the weights of the binary classifier, wherein the weight vector of the binary classifier is evolved through crossover, mutation, or permutation operators, and the optimal weight configuration is selected; Step 3: Using the genetically optimized binary classifier to detect network attacks, wherein the preprocessed network parameters are used as input feature vectors and input into the trained binary classifier, and the network traffic is judged to be normal or abnormal based on the comparison result between the output of the binary classifier and a preset activation threshold. This technical solution can quickly and accurately detect abnormal traffic in the network, reducing false positives and false negatives.

The invention discloses a psychological risk assessment method and system based on artificial intelligence. The psychological risk assessment method comprises the steps of data acquisition, representation learning, risk assessment and assessment result output. The invention relates to the technical field of psychological risk intelligent assessment, in particular to a psychological risk assessment method and system based on artificial intelligence. Dynamic modal gating is introduced to weight modals, unreliable signals are eliminated, and prototype memory is matched to quickly adapt to typical high and low risk modes; a parallel self-attention and microstable state space mixed time sequence model is adopted, local and global time sequence dependence is captured, and the robustness, the few-sample adaptability and the interpretability of feature representation are improved; meanwhile, classification scores and instantaneous danger rates are output, confidence intervals are constructed through uncertainty decomposition, criteria are fused, parameters are fitted, posterior risk probabilities are provided in psychological risk scenes, early warning accuracy is improved, false and missing reports are reduced, and clinical practicability is enhanced.

The application relates to the technical field of data analysis, and particularly provides a keyword-based alarm analysis method, device, medium and equipment. The method can comprise the following steps: acquiring the occurrence frequency of a keyword in a preset time period in a data stream to be processed; acquiring an abnormal parameter of the keyword based on the average frequency of the keyword in a corresponding historical preset time period and a target model constructed in advance; the target model is a distribution model based on probability statistics; and whether to generate alarm information is determined based on the abnormal parameter and the occurrence frequency. Some embodiments of the application can realize accurate and automatic monitoring of keyword abnormalities, effectively reduce false positives and false negatives of alarms, and improve alarm accuracy.

This invention discloses a medical hazard analysis system for multi-source data, belonging to the field of medical data analysis technology. It includes a storage module, an extraction module, and an analysis module. The system segments and stores multi-source data, and based on a Master-Slaves cluster architecture, it calls the segmented multi-source data for feature extraction to obtain feature data. Based on a preset medical safety rule base, it matches the feature data with the judgment conditions in the rule base, performing medical hazard feature matching and anomaly judgment, and finally outputting the hazard analysis results. By designing differentiated data types and collection frequencies, it optimizes storage and efficiency. Relying on the Master-Slaves cluster and Spark, it extracts features, mines the value of multi-source data, and shortens the processing time to adapt to medical needs. Multi-dimensional verification avoids false positives and false negatives, accurately outputting anomalies, reducing clinical false alarms, and minimizing redundant checks by medical staff, providing reliable support for medical safety identification and treatment decisions.

The application relates to a power equipment fault diagnosis technology, in particular to an early fault identification method of an electric energy metering box, and aims to solve the problem that in the prior art, the reliability of a single detection technology is significantly affected by the fact that the temperature and humidity of an outdoor environment fluctuate sharply and electromagnetic noise interference in a power distribution room. The application effectively distinguishes real faults from environmental noise by fusing dielectric relaxation spectrum data, photoacoustic spectrum data and environmental parameter data, and implementing mutual calibration and misjudgment exclusion mechanism, has the advantages of being capable of effectively distinguishing real faults from environmental noise, reducing false positives and false negatives, and improving early fault identification accuracy.

This invention relates to the field of information security technology, specifically to a method for detecting network security event correlations based on big data analysis. It includes the following steps: data acquisition; feature extraction and fusion; correlation detection: using an improved Apriori-Bayesian fusion algorithm to discretize the event feature vectors; mining frequent itemsets using the improved Apriori algorithm; risk assessment and result output. This invention employs an improved Apriori-Bayesian fusion algorithm to discretize event feature vectors by type, while introducing a security event weight factor to calculate the weighted support of itemsets and dynamically adjusting the minimum support threshold to mine frequent itemsets. It combines this with a Bayesian network to calculate correlation confidence and corrects the confidence through spatiotemporal correlation coefficients, enabling scientific determination of correlation relationships between network security events. This solves the problems of limited accuracy and lack of quantitative correction in traditional correlation determination techniques, reducing the probability of false positives and false negatives.

The application provides a bearing fault automatic detection method and system based on iterative likelihood ratio test, relates to the technical field of bearing fault periodic signal modeling and period automatic detection, and comprises the following steps: S1, collecting a noisy periodic signal from a fault bearing by using a sensor; S2, constructing a constrained linear model, performing parameter estimation and calculating a likelihood function after segmenting the signal by the constrained linear model; S3, obtaining a likelihood function waveform graph by scanning a period parameter; S4, obtaining an accurate period from the likelihood function waveform graph by iterative likelihood ratio test; and S5, performing fault diagnosis on the bearing by using the period estimation result. The application can make the regression model more stable, the period estimation effect more accurate, effectively eliminate false positives and false negatives of the fault period when the signal is long enough through iterative hypothesis testing.

The present application relates to the technical field of fault prediction, in particular to a switch device fault early warning method based on multi-source data fusion, comprising the following steps: acquiring channel transition time sorting to generate time interval, marking behavior label to generate time sequence, constructing sliding window to form combined alarm, analyzing cross matrix to extract abnormal period, and identifying dominant channel to issue early warning. In the present application, the time intervals of multi-channel state transition response are aligned for processing, realizing dynamic linkage identification among multiple signals, strengthening the time sequence correlation between abnormal behaviors by means of behavior label combination and sliding statistical strategy, enhancing the sensitivity and accuracy of abnormal identification through the aggregated expression of composite alarm events, combining cross-channel behavior label matrix analysis to determine abnormal label persistence and identify key channels, improving the precision and timeliness of switch device early warning, reducing the risk of false positives and false negatives, and adapting to the actual needs of multi-source perception data fusion analysis of switch devices.

The application belongs to the technical field of city operation management, and discloses a method for intelligent event reporting and case closing based on a CV model. The application realizes real-time intelligent reporting by using a fine-tuned Qwen-VL large model to analyze image data in real time, realizes automatic identification and timely reporting of city events, significantly improves event response speed, and reduces manual patrol cost. Efficient intelligent case closing: by using a deep learning algorithm to compare photos before and after disposal, objectively and accurately judging the event disposal result, greatly reducing the workload of manual auditing, improving the efficiency of case closing, and ensuring the fairness and consistency of the city event management process. Cross-modal understanding enhances recognition accuracy: the cross-modal understanding ability of the Qwen-VL large model helps to accurately identify various events in complex urban environments, reduces the risk of false positives and false negatives, and improves the overall efficiency of city operation management.

The application discloses an intelligent sensing control system for riding protective equipment, relates to the technical field of riding safety protection, and is used for solving the problem of inaccurate early intervention of inertial side slip; the application is composed of riding data collection, behavior event identification, behavior matching, risk evolution analysis and pre-response control; the system constructs a behavior graph with a plurality of source sensors, carries out feature difference value and structural consistency checking on an inertial side slip template, combines a trend change mode and a minimum continuous threshold value, triggers a pre-response (pre-inflation, prompt rhythm and optical signal adjustment) in an early risk stage, and carries out closed loop comparison on an execution state and a risk state; compared with a single threshold value triggering scheme, the application can reduce false positives and false negatives, improve response advance and intervention accuracy, reduce unnecessary triggering, and enhance adaptive reliability under complex roads.

A computer-implemented method for computational textual search to find and display search identified information in documents. Search queries are processed over one or more documents either at a server or user device using match schemes that produce both binary (match or no match) and non-binary (i.e. multiple matching values) results and have relative fuzziness relationships. A fuzzier match scheme implies more results and fewer false negatives and a less fuzzy match scheme implies fewer results and fewer false positives. This fuzziness relationship allows, without changing a search string, for users to quickly change from a match scheme to a fuzzier or less fuzzy match scheme depending on user evaluation of results as containing too many false negatives or too many false positives—without becoming a programmer of complex search string metadata or an expert user of advanced search capabilities.

This invention proposes an algorithm for identifying and detecting insulator defects during transmission line inspections. It includes the following steps: S1: Video stream access and preprocessing; S2: Insulator target localization; S3: Insulator defect classification and identification; S4: Defect annotation and image saving; S5: Real-time display and exit control. An improved YOLOv8 nano model is used to achieve accurate insulator target localization. Combined with a ResNet18 defect classification model incorporating an SE attention mechanism, it can effectively extract insulator defect features, especially significantly improving the identification ability of subtle defects such as small cracks and slight contamination. Simultaneously, confidence threshold filtering reduces false positives and false negatives. It is suitable for real-time inspection scenarios carried out by UAVs, accurately identifying various insulator defects and completing defect localization, type judgment, and related image saving, solving the problems of low efficiency, high false positive and false negative rates, and reliance on human experience in traditional inspections.

The present application belongs to the technical field of abnormal alarm, and particularly relates to a feed production control abnormal alarm method and system, comprising the following steps: obtaining feed production historical parameter data, filtering parameter points located in the forbidden hyperspace based on a production process constraint model, and the remaining parameter points constituting a candidate set; for each candidate point of the candidate set, extracting parameter time series within a preset time window, calculating the Frobenius norm of the time gradient matrix as a state fluctuation measure; calculating the number of points within a specified neighborhood radius of each candidate point as a local density, and calculating the Euclidean distance from the candidate point to the nearest point with a local density higher than itself as a dispersion degree. The present application can trigger an alarm for malignant boundary crossing phenomena that violate the rules, and can also identify hidden deviations according to the boundaries set by each normal sub-working condition, reducing false positives and false negatives, and providing a guarantee for the safe and reliable operation of the feed production process.