321 results about "Consistency analysis" patented technology

The invention discloses a high-precision static aeroelastic model optimization design method based on a model correction technology, and relates to the technical field of aircraft design, and the method comprises the following steps: S1, firstly constructing an initial model, and carrying out statics pre-analysis to verify integrity; s2, executing SOL 101 statics analysis based on the initial model and outputting a physical field result; s3, carrying out consistency analysis in combination with test data and generating a correction decision; s4, screening high-priority correction parameters through local or global sensitivity analysis; s5, correcting model parameters by adopting a mixed algorithm of a gradient method and an agent model, and verifying precision and generalization ability; s6, the corrected model is output as a Nastran file and a reduced-order model in a standardized mode, and a parameter change log is recorded; s7, executing static aeroelastic coupling and flutter analysis, and feeding back a result to drive optimization iteration; s8, constructing a multidisciplinary coupling optimization model in combination with aeroelastic and flutter results to realize collaborative optimization; and S9, finally performing engineering standardization packaging on the optimization model and outputting a verification report.

The invention discloses a software requirement modeling method and device based on a natural language, equipment and a medium, and relates to the field of software requirement modeling. According to the software demand modeling method and device based on the natural language, the equipment and the medium, through automatic demand analysis and model generation, the manual workload is greatly reduced, and meanwhile, errors caused by manual understanding deviation are reduced; close connection among the steps is ensured by sharing intermediate representation and a unified data model, output of the previous step provides structured information for input of the next step, and information loss or distortion is avoided; a demand provider is allowed to participate in a model confirmation process through an iterative demand acquisition and modeling framework of man-machine cooperation, and the quality of a demand model is continuously improved; by means of the model consistency analysis technology based on the data flow dependency relationship, the problem of inconsistency between the requirement specification and the software model can be automatically detected, and the software product quality is improved.

The invention discloses a pump set vibration monitoring method and system based on distributed sensing. The method comprises the steps that vibration collection signals and real-time working condition parameters of distributed sensing nodes are obtained, multi-node clock synchronization correction is executed, and synchronous vibration data frames are formed; time-frequency feature extraction is carried out on the multi-channel vibration data set, abnormal channels are identified through inter-channel consistency analysis, a local fault correlation frequency band is extracted, and a vibration feature vector is constructed; associating the vibration feature vector with a real-time working condition parameter to form a working condition-feature mapping table, identifying a feature mutation rotating speed point for the feature baseline, adaptively dividing a rotating speed interval to generate an adaptive working condition threshold value, and implementing out-of-limit detection to generate an abnormal trigger identifier; determining a traceability analysis window based on the abnormal trigger identifier, and executing multi-measuring-point coherence calculation and energy attenuation gradient analysis to construct an energy transfer link diagram; and finally, vibration source position positioning is performed to form fault source probability distribution, a fault mode label is matched, pump set vibration monitoring is completed, and quantitative positioning of a fault source is realized.

The invention discloses a multi-agent cooperative control system based on adaptive learning, and belongs to the technical field of multi-agent system control, and the system specifically comprises the steps: collecting and analyzing the wind field data of the position of each agent; distinguishing two typical working conditions of a global uniform wind field and a local disturbance wind field based on a consistency analysis result; for a global uniform wind field, a model prediction control method is adopted to generate a wind field compensation strategy for keeping formation stability; for a local disturbance wind field, an accurate airflow disturbance state is obtained through environment three-dimensional reconstruction and fluid simulation, then the expected track deviation of each agent is predicted, and a collaborative avoidance instruction set is generated in combination with a relative position relationship. According to the method, adaptive cooperative control of multiple agents under different wind field working conditions is realized, the hidden conflict problem caused by local wind field disturbance is effectively solved, and the flight safety and task reliability of an agent cluster in a complex environment are remarkably improved.

The invention discloses a supervision method and device based on construction project quality detection and a medium, and relates to the technical field of project quality supervision, and the method comprises the steps: collecting construction site data, carrying out the preprocessing, and generating a site data set; performing real-time comparison and verification on the building information model and the field data set to obtain verification data, inputting the verification data into the building information model, performing construction consistency analysis and deviation identification, and generating a quality evaluation report; performing risk assessment and priority classification on the quality analysis data to obtain risk assessment data, and performing problem priority analysis and rectification scheme formulation on the risk assessment data to generate rectification execution data; and performing rectification effect analysis and overall quality evaluation according to the rectification execution data, and generating a project quality summary report. The real-time performance and the accuracy of construction quality monitoring are improved, the construction deviation is reduced to the maximum extent, the quality management is optimized, and the smooth proceeding of a project is guaranteed.

A system and method for querying, processing, and ranking governmental data across heterogeneous databases may include integrating large language models (LLMs) and multi-agent orchestration to enhance accuracy and mitigate bias. The system receives a natural language query, processes the query using an agent orchestration LLM to generate search query instructions, and executes specialized data record processing agents to retrieve data from diverse sources, including relational, vector, and graph databases. A plurality of candidate natural language responses is generated and validated by data verification LLMs based on relevance and accuracy metrics to reduce hallucinations. The system further employs cross-source validation, statistical and linguistic consistency analyses, and weighted scoring to refine responses, which may include textual summaries, tables, or downloadable files presented via a graphical user interface. Accordingly, the approach ensures precise and contextually relevant results, particularly for applications involving sensitive or regulated data such as legislative and governmental records.

The invention discloses a data-enhanced fine-grained multi-mode false information detection method and system, and aims to improve the accuracy of image-text false news detection. The method comprises the following steps: acquiring a news text and associated pictures thereof, extracting a text core entity semantic sequence and a visual entity semantic sequence by respectively utilizing a pre-training language model and a visual entity recognition model, and performing knowledge enhancement on original word-level text representation and low-level visual features through an attention mechanism; calculating a correlation matrix between the enhanced text and the visual features, dividing consistent and inconsistent regions, and respectively extracting consistent and inconsistent features; and finally, fusing the two types of features and global text representation to generate classification features so as to judge the authenticity of the news. Through double-channel knowledge enhancement and fine-grained cross-modal consistency analysis, the detection performance is remarkably improved, and the method is suitable for scenes such as social media and news platforms.

The invention provides a medicine bottle array detection system based on vision and sensing fusion, and relates to the technical field of data processing, and the system comprises the steps: obtaining the spatial distribution data of a vision unit and the posture state data of a sensing unit, fusing the position parameters and state parameters of each bottle body, building a multi-dimensional data node under a unified time index, calculating the spatial aggregation degree of the medicine bottle array to obtain an array quantity, combining the aggregation change of each time point into an evolution chain table, carrying out joint operation on the difference result and the state parameter of adjacent nodes in the evolution chain table, calculating the dynamic coupling degree of the medicine bottle array to obtain a sequence potential energy value, and dividing the sequence potential energy values of the different numerical value intervals into a plurality of level nodes, and performing consistency analysis and anomaly detection on features of the different level nodes to obtain detection result data. By detecting the state of the medicine bottles and the arraying direction, bottle toppling early warning is achieved.

The invention relates to a multi-competency intelligent scoring method and system, and belongs to the technical field of intelligent evaluation and talent evaluation. The method comprises the following steps: acquiring multi-source response data of a target evaluation object, executing evidence sufficiency and consistency analysis for each competency dimension, generating a judgment state identifier, and constructing a structural description; performing responsibility distribution on the evaluation evidence, mapping the evaluation evidence into a corresponding evidence model and generating a priority constraint relationship; performing time sequence consistency and stability analysis on the evidence model, and constructing a time sequence constraint rule to prohibit unconstrained score backtracking; and carrying out resolution processing on the continuous undetermined dimension, generating a scoring result through a preset risk scoring rule and a conservative determination strategy, and synchronously generating a scoring basis path and confidence information. According to the method, standard management and control of multi-source evidences and accurate constraint of the whole scoring process are realized, the scoring accuracy and traceability are effectively improved, and reliable support is provided for multi-scene talent evaluation decision.

The invention discloses a brake pedal verification method, device and equipment based on a two-way pedal and a storage medium, relates to the technical field of vehicle brake control, and discloses the brake pedal verification method based on the two-way pedal. Comprising the steps that a brake lamp switch signal, an allowable deviation threshold value and a first sensor signal and a second sensor signal of a two-way pedal position sensor are obtained; performing processing according to the first sensor signal and the second sensor signal to obtain a target deviation value, and performing consistency analysis according to the target deviation value and the allowable deviation threshold to obtain a consistency analysis result; performing fault tracing on the consistency analysis result according to the brake lamp switch signal to obtain a fault positioning result; and fault isolation is carried out based on the fault positioning result, so that brake pedal verification based on the two-way pedal is completed. The technical effect of guaranteeing the signal reliability of the two-way pedal position sensor and the function continuity of the braking system is achieved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses an identification method, device and equipment based on semantic space consistency, and a medium, and the method comprises the steps: obtaining and preprocessing a target image, a positioning text region and an object region, and generating region positioning information; identifying and correcting the text region to generate a target text vector, extracting an object region image feature and normalizing to generate a target image vector, and mapping the two to the same semantic space for consistency analysis to generate a fusion confidence coefficient and a preliminary identification result; and retrieving, comparing and outputting final identification information and the fusion confidence coefficient in a preset knowledge base based on the fusion confidence coefficient and the preliminary identification result. According to the method, complementary fusion of the text and the image is realized through semantic space mapping, and standardized verification is completed by combining with knowledge base comparison, so that the problem that a single recognition mode is easy to make mistakes is effectively solved, and the accuracy and reliability of a result are improved.

The invention discloses a logic chip mask defect detection method, and relates to the technical field of integrated circuit manufacturing, and the method comprises the following steps: S1, constructing a scattering response model of sub-wavelength inorganic particles on the surface of a mask, and constructing a scattering response model of sub-wavelength inorganic particles on the surface of the mask under a set multi-angle polarized light incidence condition; non-linear disturbance influence of inorganic particles with different particle sizes on the reflected light phase in each incident polarization state is simulated, and a scattering response template containing standard phase disturbance characteristics is generated; and S2, based on a scattering response template, carrying out pixel-by-pixel matching analysis on the acquired multi-angle polarization phase diagram on the surface of the mask, identifying a local area matched with the template, and marking the local area as a suspected scattering interference area. According to the method, through construction of the scattering response template and multi-angle polarization consistency analysis, accurate distinguishing of artifacts and real defects is achieved, the recognition precision and stability under complex interference are improved in combination with structure retention type phase reconstruction and dynamic threshold recognition, and mask quality control and photoetching yield improvement are facilitated.

The invention relates to the technical field of image recognition, and discloses a glass fiber board production quality detection method and system, and the method comprises the steps: collecting a continuous infrared thermal image sequence through an infrared thermal imaging device; constructing a temperature difference image set; identifying a primitive region with an abnormal diffusion characteristic; further performing fitting enhancement and significance detection on the abnormal primitive region; and performing defect classification and quality judgment based on the regional feature vector. Compared with the prior art, the technical problems that in the prior art, high-precision parting judgment cannot be achieved under the conditions that the surface texture of a glass fiber board is complex, hot-pressing disturbance exists, particularly, hot diffusion behaviors of dry filaments, degumming and cavitation bubble defects are similar, and the false detection rate of a conventional algorithm is high are solved. Due to the fact that a multi-channel thermal diffusion consistency analysis mechanism and parallel discrimination logic based on feature vectors are introduced, infrared primitive anomaly enhancement detection and defect classification recognition under the complex background are achieved, and the accuracy of glass fiber board production quality detection is improved.

The invention relates to the technical field of nondestructive testing and intelligent manufacturing, and provides a weld defect intelligent identification system based on multi-source data fusion. The system synchronously obtains a visual image, a three-dimensional point cloud and ultrasonic array data of a welding seam through a multi-source data acquisition module; a hidden danger density field construction module generates surface and internal hidden danger density fields; the cross-space association and defect topology extraction module associates and binds high-density areas meeting spatial continuity and gradient consistency conditions in the two density fields into a cross-space association cluster through density gradient consistency analysis and a preset topology mode template, and generates a defect sign vector; and finally, the defect decision module outputs the defect type, position, size and confidence coefficient based on the sign vector. According to the method, multi-source heterogeneous data is innovatively unified to hidden danger density field characterization, collaborative, accurate and explainable recognition of internal and external defects of the weld joint is achieved through topology analysis of a cross-space correlation cluster, and the limitation of a traditional single detection means is overcome.

The invention discloses a titanium alloy skin processing compensation method based on a digital twinning residual stress prediction model, particularly relates to the field of aeronautical manufacturing, and aims to solve the problems that local resilience is difficult to prejudge and a compensation result is unstable due to inconsistent stress release time sequences and dispersed compensation basis in a subunit cutting process of a titanium alloy skin. A unit stress release state is constructed by collecting a unit contour offset and a unit cutting sequence, same-chain deduction is carried out by combining preorder unit residual stress mapping to generate a unit compensation candidate quantity, and execution judgment and a closed-loop updating parameter layer are formed based on playback and re-scanning consistency analysis. According to the method, compensation decision traceability, processing deviation convergence and continuous inheritance between working steps are achieved, the manual repeated trial cutting and parameter adjustment cost is reduced, and the transition smoothness of adjacent units and the forming consistency of the whole skin are improved.

The invention discloses a data transmission security protection system for an edge computing server, which relates to the technical field of edge computing and network security, is deployed in an edge computing environment comprising a plurality of edge computing nodes, and comprises five modules: a behavior fingerprint construction module, a data processing module and a security protection module, dynamically updated behavior fingerprints are generated through a lightweight convolution auto-encoder; the dynamic trust chain management module constructs a dynamic trust chain network and generates a comprehensive trust score based on node historical interaction data; the collusion attack detection module identifies a collusion attack in combination with trust chain topology and node behavior consistency analysis; the self-adaptive isolation and recovery module isolates malicious nodes and migrates tasks to guarantee service continuity; according to the edge computing security protection method, protection is converted into active immunity from passive feature matching, collusion attacks are effectively defended, data transmission security is guaranteed, and the edge computing security protection method is suitable for edge computing security protection.

The invention relates to the technical field of intelligent medical rehabilitation and computer vision, in particular to a muscle-bone rehabilitation posture motion analysis system based on visual recognition. Comprising a visual analysis management center, a motion feature extraction unit, a dynamics inversion unit, a compensation coupling analysis unit, a time sequence conduction unit and a rehabilitation evaluation decision unit. Geometric compliance is analyzed and discriminated through dominant kinematics characteristics; if so, performing visual moment inference consistency analysis by using a dynamic inversion unit; in response to moment discretization, acquiring and analyzing a compensation coupling coefficient and kinematic chain conduction delay respectively; and finally, calculating a compensation evaluation index through multi-dimensional compensation quantitative matching analysis, and generating a recessive compensation or effective driving signal. According to the method, a layered filtering mechanism is constructed, implicit problems are dominated, and the blank of monitoring of the internal force exerting mode in non-contact rehabilitation evaluation is filled up.

The invention belongs to the technical field of three-dimensional modeling, and discloses a city building three-dimensional model refined construction method based on oblique photography. Comprising the steps of collecting inclined image data of a to-be-modeled urban area and performing data cleaning to obtain a modeling image data set; carrying out projection consistency analysis, identifying a boundary dislocation region and removing the boundary dislocation region to obtain a dislocation removal data set; shielding area identification is carried out, shielding compensation is carried out based on an identification result, and an enhanced three-dimensional point data set is output; executing detail reservation to obtain a triangular facet data set; normal optimization is carried out, plane adjustment is carried out on the triangular surface data set based on an optimization result, and a three-dimensional grid data set is obtained; constructing an initial three-dimensional model, and performing map mapping on the initial three-dimensional model to obtain a detail map set; constructing a fine building three-dimensional model in combination with the detail map set and the initial three-dimensional model; and the boundary definition, the structural integrity and the texture fineness of the urban building three-dimensional model are improved.

The invention relates to the technical field of deformation monitoring, and discloses a large-range deformation measurement method and device based on multi-camera space-time consistency under the complex working condition of AI + vision + IMU fusion. The method comprises the steps of multi-camera data synchronization, IMU data fusion, AI algorithm optimization, space-time consistency calculation and deformation compensation. The method comprises the following steps: enabling multiple cameras to jointly work through a space-time synchronization technology to obtain a multi-view image, recording motion information by using an IMU (Inertial Measurement Unit) to compensate visual errors, fusing and optimizing data by using an AI deep learning model, constructing a measurement model through space-time consistency analysis, and carrying out deformation compensation by combining visual and IMU information. The method avoids the measurement limitation of a single camera, effectively eliminates the measurement error of a complex environment, can achieve high-precision real-time dynamic deformation monitoring in a large range, can be widely applied to the fields of civil engineering, aerospace and the like, and has remarkable innovativeness and application value.

The invention relates to the technical field of thermoelectric Internet of Things security, in particular to a thermoelectric Internet of Things terminal data credible situation awareness and protection platform, which comprises an integrated architecture for constructing a distributed probe acquisition layer, a behavior modeling and knowledge base, a dynamic trust evaluation engine, a situation awareness and decision center and a credible traceability module. Multi-dimensional trust evaluation is realized by fusing terminal behavior consistency analysis, service logic compliance verification and group behavior correlation detection, a self-adaptive weight adjustment mechanism is introduced to adapt to scene change, a hierarchical protection strategy is executed based on a dynamic credibility score, and non-interruption of network disconnection protection is guaranteed by relying on edge calculation. According to the method, the data credible sensing precision is improved, dynamic differential protection is realized, the operation and maintenance cost is reduced, multiple thermoelectric scenes are adapted, and a safety guarantee is provided for stable operation of a thermoelectric Internet of Things system.

The invention relates to the technical field of fire detection, in particular to a photovoltaic power station outdoor fireproof monitoring method and system, and the method comprises the following steps: obtaining a multi-source time record, screening a synchronous window, extracting gray scale distribution, forming a connected heat domain fragment, generating a survival span structure according to a gray scale sequence, and recognizing the position of a heat source based on coordinate aggregation. And generating a fire source correlation judgment structure in combination with thermal change and electrical change trends. According to the method, the cross-source synchronous window is established in the time dimension to realize matching of multi-source data in a continuous interval, so that thermal image change and electrical change are analyzed under the same time reference, and thermal domain connectivity, coverage form and gray evolutionary structure are refined in the space dimension, so that the thermal image change and the electrical change are analyzed. By introducing time consistency analysis of thermal characteristics and electrical increment trends into correlation judgment, a monitoring result is changed from static threshold judgment to dynamic recognition based on time-space consistency, and the accuracy and reliability of photovoltaic scene fire source recognition are remarkably improved.

The invention discloses a capacitor voltage transformer on-line verification and state early warning method and system, and the method comprises the steps: synchronously collecting the secondary side time sequence data of a target CVT and other CVTs of the same bus with the target CVT through a power monitoring system; based on secondary side time sequence data of a plurality of CVTs on the same bus, screening out a CVT equipment group with consistent state through group consistency analysis, and generating a virtual standard reference value for error calculation; calculating a relative deviation between the secondary voltage measurement value of the target CVT and the virtual standard reference value as a first evaluation result; performing feature extraction on the secondary side time sequence data to obtain a feature vector representing the operation state of the target CVT, inputting the feature vector into a pre-trained intelligent evaluation model to obtain an error prediction value or a health state score of the target CVT, and taking the error prediction value or the health state score as a second evaluation result; and fusing the first evaluation result and the second evaluation result to obtain a comprehensive evaluation conclusion of the running state of the target CVT, and if the state level exceeds a normal range, generating early warning information.

The invention relates to the field of medical monitoring, and discloses a multi-modal monitoring method, system and device based on hardware collaboration and a storage medium, and the method comprises the steps: quickly starting core monitoring after power-on, and supporting hot plug and plug-and-play access of medical equipment; multi-source access is carried out, a unified time axis is constructed through clock synchronization and resampling, and disconnection merging is supported; motion features are extracted to drive adaptive anti-interference, and abnormal authenticity is identified; based on redundancy observation and space-time alignment, weighting fusion parameters through consistency analysis, and intelligently judging and shielding low-quality channels; capturing an event trigger point, packaging a holographic structured event packet and establishing a bidirectional index; and constructing a full-time-sequence evolution view, executing association attribution and trend early warning, and feeding back an optimization algorithm through playback and redisk. According to the invention, the problems of low data quality, unexplainable alarm and traceability deficiency in an extreme dynamic environment are solved, and the requirements of pre-hospital first aid high-quality monitoring and rapid decision making are met.

The invention relates to an anomaly detection technology, and discloses an anomaly detection method and system for a flexible circuit board patch, and the method comprises the steps: obtaining a patch image in a preset flexible circuit board, carrying out the illumination normalization processing and background suppression processing of the patch image, and obtaining a background suppression patch image, and carrying out abnormal bending identification on the background suppression patch image based on multi-scale gradient fusion and local structure consistency analysis, judging whether the patch in the flexible circuit board has abnormal bending according to a bending identification result, if yes, judging that an abnormal detection result is abnormal bending, and if not, judging that the patch in the flexible circuit board has abnormal bending. And otherwise, calculating an offset coefficient of the normalized patch image based on double-flow alignment and differential significance enhancement, judging whether the offset coefficient is greater than a preset coefficient threshold, if so, judging that the abnormal detection result is abnormal offset, and if not, judging that the abnormal detection result is abnormal. According to the invention, the communication circuit board patch abnormity detection precision can be improved.

This invention discloses an artificial intelligence-based method and system for the inspection and control of power distribution networks using unmanned aerial vehicles (UAVs). Relating to the field of UAV power inspection technology, the method includes: adjusting the UAV's pose in real time based on target perception feedback during the inspection process to actively align with the target and acquire images of power components; uploading valid images from the selected images to a cloud-based management platform; identifying power component entities in the selected images and constructing a spatial semantic graph based on the spatial distribution relationships between components; performing topological logic reasoning and feature consistency analysis on the spatial semantic graph to identify abnormal component states and determine the power logic attributes of faulty components; generating maintenance instructions based on the identified abnormal states and power logic attributes and pushing them to the terminal. This invention addresses the problems of high redundancy in original images, insufficient physical positioning accuracy leading to ambiguity in component logical identity identification, and difficulty in identifying long-tail atypical hidden dangers during power distribution network inspection.

The invention discloses a forest fire smoke monitoring method and related device based on false detection feedback and model self-evolution, and the method comprises the steps: obtaining a video stream of a monitoring region, carrying out the preprocessing, inputting a target detection model and a scene understanding model, and judging whether a candidate alarm event is generated or not through consistency analysis; when a candidate alarm event is generated, a work order is generated for manual judgment, a real alarm executes a disposal process, and a false alarm is stored in a database; screening high-value false detection samples based on an information entropy priority strategy, storing the samples into a dynamic memory bank, selecting the samples to form an incremental training set, performing incremental training on a pre-training teacher model, and introducing low-rank constraint stability parameters; migrating the discrimination ability of the teacher model to the student model through knowledge distillation, wherein alignment of a channel dimension and a multi-scale space dimension is carried out; and redeploying the updated student model to execute target detection. The invention aims to solve the problems that the current monitoring system depends on manpower, the coverage capability is limited, the response is slow and the cost is high, and the traditional method is high in false alarm rate, easy to leak and misjudge, difficult in large-scale network edge deployment, limited in light-weight model discrimination capability, lack of linkage between alarm processing and model updating and the like, and more accurate and efficient forest fire smoke monitoring is realized.

The invention provides a network RTK base station coordinate precision detection method and system, and the method comprises the steps: carrying out the base station networking based on a Delaunay triangulation algorithm, and determining a baseline list; obtaining satellite precision orbit information, selecting a to-be-solved baseline vector from the baseline list, and resolving the to-be-solved baseline vector by adopting a fast static positioning algorithm to obtain a preset high-precision relative coordinate result; the preset high-precision relative coordinate result is converted into coordinate values of all reference stations, the coordinate values of all the reference stations are compared with preset known coordinate values in a system, coordinate difference values of all the reference stations on all the base lines are obtained through calculation, gross error elimination is conducted based on the coordinate difference values, and processed coordinate difference values are obtained; and integrating the processed coordinate difference values of each reference station on the plurality of base lines, performing consistency analysis and statistical judgment, determining the reference station with abnormal coordinates in combination with a preset threshold value, and outputting a diagnosis alarm result. According to the invention, the reliability and precision of the network RTK service are effectively guaranteed.

The invention relates to the technical field of automatic control, in particular to a pipeline blockage risk identification and early warning method for multiple water chilling units of a central air conditioner, which comprises the following steps: collecting a cold water pressure difference signal, processing temperature difference data, extracting offset characteristics, analyzing a temperature track, identifying sudden change and matching flow response, and judging response abnormity. According to the method, the blockage head is identified through the differential pressure change rate jump, the response sensitivity is enhanced, the temperature difference continuous offset and the differential pressure event are combined, the identification accuracy and time efficiency are improved, the temperature sudden change and the flow response rhythm are superposed, a physical chain verification mechanism is constructed, and the high-frequency abnormal pipe section is identified. The reliability of analysis logic is enhanced, response interval and adjustment period consistency analysis is introduced, the adaptive capacity under complex working conditions is improved, pipe sections are collected according to abnormal frequencies, a multi-feature structured result is output, the data support of maintenance decision is enhanced, and the perspectiveness of blockage recognition is improved through multi-source feature fusion and time sequence rule judgment.

The invention provides a claim settlement identification method and device, computer equipment and a readable storage medium, and relates to the field of data processing, financial science and technology and medical health. The method comprises the following steps: acquiring claim settlement data of a claim settlement case to be evaluated, and constructing a graph relation network; constructing a multi-modal vector according to the claim settlement data and the atlas relation network; performing similar case retrieval and prior probability estimation in sequence based on the multi-modal vector to obtain a prior compensation probability; obtaining a risk score based on the multi-modal vector and a pre-constructed risk assessment model; judging the multi-modal vector based on multiple claim settlement detection rules to obtain a rule score; performing consistency analysis on the multi-modal vector, the prior compensation probability, the risk score and the rule score to obtain a consistency score of the to-be-evaluated claim settlement case; calculating to obtain a comprehensive evaluation score of the to-be-evaluated claim settlement case; and determining a claim settlement result corresponding to the claim settlement case to be evaluated according to the comprehensive evaluation score.

The invention discloses a traditional Chinese medicine uric acid reducing preparation quality evaluation method and system based on big data, and relates to the technical field of traditional Chinese medicine preparation analysis. The quality evaluation method comprises the following steps: S1, acquiring chromatographic characteristic data, active ingredient content data and physical and chemical detection data, performing preprocessing, and constructing a preparation quality database; s2, carrying out consistency analysis through batch detection data of characteristic peaks, and dynamically optimizing a peak identification and correction strategy; s3, fusing the batch co-occurrence frequency, the distinction degree index and the inter-batch fluctuation which are subjected to correction processing, and performing characteristic peak screening through weighted operation; s4, performing multi-dimensional fusion quantitative evaluation by integrating a high-priority characteristic peak screening result, the effective component content data and the physical and chemical detection data; and S5, based on the multi-dimensional fusion evaluation result and historical sample data iterative training and abnormal mapping, parameter optimization and dynamic updating are realized. The problems of inter-batch feature drift and insufficient feature recognition precision of the traditional Chinese medicine uric acid reducing preparation in batch detection are solved.