346 results about "Diagnostic analysis" patented technology

The invention relates to the technical field of medical data processing systems, in particular to an artificial intelligence-driven medical diagnosis and treatment data processing method and system. The method comprises the steps that a multi-modal medical data acquisition module acquires and processes multi-source heterogeneous medical data of a patient, and a standardized data set is generated; a medical feature depth extraction module performs multi-dimensional feature extraction on the data set, and constructs a dynamic evolution feature matrix; a multi-dimensional health state space construction module constructs a patient health state multi-dimensional space according to the matrix and determines a key medical early warning index set; the real-time medical data fusion module maps real-time data to the space to generate real-time health risk factors; and the medical risk prediction and decision-making module establishes a personalized model, outputs a disease occurrence probability and generates personalized treatment suggestions. The system solves the problems that medical data processing is difficult, diagnosis analysis is not comprehensive, and a treatment scheme lacks personality, and diagnosis accuracy and treatment pertinence are improved.

The invention discloses a numerical simulation prediction method and system for typhoon binocular wall structure reconstruction judgment and maintenance duration after terrain interference and a medium, and relates to the technical field of typhoon fine structure evolution description and numerical simulation. According to the method, the binocular wall typhoon is identified through multi-source data fusion, a high-resolution numerical simulation system is constructed, and a typhoon structure is divided into four quadrants for differential diagnosis according to the wind shear direction based on vertical wind shear phase limit analysis. And the distribution characteristics of the rapid vortex silking area are captured by calculating vortex silking time parameters. And establishing a ditch sinking airflow detection mechanism, and quantitatively evaluating the contribution of radial advection, tangential advection, vertical advection and friction force items to tangential wind evolution by using tangential wind income and expenditure diagnostic analysis. And calculating an energy growth rate, and predicting a double-eye wall maintaining time length. According to the method, the outer eye wall re-formation judgment is output, the outer eye wall radius zone, the maintenance duration interval and the uncertainty are predicted, and the method is suitable for real-time business and research.

The invention discloses a lingual surface diagnostic information acquisition, diagnosis and analysis method and system, and belongs to the technical field of medical auxiliary diagnos.The method comprises the steps that an acquired lingual surface image is matched with patient information, a symptom associated lingual surface area is determined, the associated lingual surface area is subjected to priority division, and whether the acquired image meets a clear standard or not is judged; the method comprises the steps of constructing an image index evaluation model based on tongue vibration and image texture, performing tongue vibration, texture stability and water vapor fuzzy interference evaluation on an image needing to be processed, constructing a stable frame evaluation model, and importing vibration intensity, texture stability and a water vapor proportion into the stable frame evaluation model to evaluate image area stability. Image registration is carried out on the stable frame set, multi-frame registration and fusion are carried out based on an image stable region, region stability and processing information are recorded, a high-quality image for tongue picture analysis is generated, the image definition of a key diagnosis region is improved, and the accuracy and stability of tongue picture analysis are improved.

The invention provides a circuit breaker action coil current characteristic diagnosis and analysis system and method. Belongs to the technical field of power system automation and substation state monitoring. The method comprises the following steps: carrying out synchronous wave recording on current of a plurality of circuit breaker actuating coils on a direct current feeder screen side to generate multi-channel synchronous wave recording data; meanwhile, SOE message information is collected, a circuit breaker with specific action is automatically matched and positioned by utilizing a timestamp and action identification information in an SOE message, and action circuit breaker positioning data is generated; the current waveform data of the circuit breaker actuating coil are collected in real time, and a high-precision algorithm is used for feature extraction and analysis, so that abnormal fluctuation in current change can be accurately captured, such as overlarge starting current and unstable holding current, the risk of misoperation or rejection of operation of the circuit breaker caused by abnormal current characteristics is reduced, and the service life of the circuit breaker is prolonged. Electric power system accidents caused by equipment faults are avoided, and the operation reliability of the circuit breaker is improved.

The invention relates to the technical field of power system power distribution network fault detection, in particular to an arc fault diagnosis and analysis method based on an artificial intelligence algorithm, and the method comprises the four steps: synchronous data collection and topological excitation, deployment of terminals at transformer area nodes, injection of characteristic current, and synchronous collection of response waveforms; signal preprocessing: separating a key frequency band through double-digital band-pass filtering, and calculating energy ratio and other enhanced fault features; performing multi-source feature fusion and intelligent diagnosis, constructing a vector containing statistical features, time domain distortion features and topology identification, and inputting a gradient boosting decision tree and deep neural network hybrid model to obtain fault confidence and type; and based on fault positioning and verification of topology, scheduling multi-node cooperative monitoring, and determining a fault point in combination with a topological relation. The method improves the data reliability and diagnosis precision, achieves the precise positioning of a fault, and guarantees the safe operation of a power distribution network.

The invention relates to the technical field of report optimization, in particular to a user medical history-based physical examination report conclusion priority adjustment method, which comprises the following steps of: acquiring multi-source data, and preprocessing the multi-source data to obtain a structured multi-source data set; based on the structured multi-source data set, mining an association relationship between health data and diseases through association analysis, constructing a health trend prediction model in combination with a deep Q network, and obtaining a multi-dimensional diagnosis analysis result; determining a diagnosis conclusion priority in combination with a multi-dimensional diagnosis analysis result and a priority calculation formula; calculating a comprehensive risk score through the data fusion score, and dividing risk levels according to the comprehensive risk score to match corresponding stage intervention strategies; and inputting a multi-dimensional diagnosis analysis result, intervention strategy execution feedback data and user health preference into a comprehensive diagnosis decision model, and obtaining a differentiated health association strategy in combination with a diagnosis conclusion priority. According to the scheme, the severity of the disease is accurately identified by dynamically adjusting the priority.

The invention relates to the technical field of medical assistant systems, and discloses a comprehensive intelligent medical assistant system, which comprises an information acquisition unit used for acquiring user state information; the user state information comprises user static data, dynamic data and feedback data; the model construction unit is used for performing feature extraction according to the user state information and establishing data association indexes of physiology, environment and behaviors; the information processing unit is used for automatically capturing symptom characteristic information and carrying out diagnostic analysis on the input information in combination with user state information; the pushing unit is used for performing local decision processing on the analysis result, forming a diagnosis path diagram and pushing the diagnosis path diagram to a user side; and the learning optimization unit is used for optimizing a recommendation strategy according to the feedback data and integrating long-term health features to carry out optimization suggestions. According to the invention, the service mode of medical health is remodeled, the unique health characteristics of each user can be more accurately understood, the service quality and credibility of a medical assistant system are improved, and the occupancy rate of medical resources is reduced.

The invention particularly relates to an engineering wire harness testing method and system, and relates to the technical field of engineering wire harness testing. A measurement and self-calibration module; a test channel health degree real-time monitoring module; and a data processing and diagnosis analysis module. According to the invention, the standard gold wire harness and an automatic self-calibration mechanism solve the problems of long period and low efficiency of traditional manual calibration; the system can automatically trigger self-calibration according to multiple conditions such as time, events and the like, the measurement deviation is captured through multi-dimensional reference measurement, real-time deviation compensation is achieved by means of software closed-loop calibration, meanwhile, fault components can be accurately positioned according to hardware deviation, a maintenance report can be generated, and reference drift caused by environment fluctuation and component aging in a calibration interval is avoided.

The invention discloses a production operation benchmarking system and method based on multi-source data analysis, and belongs to the technical field of production operation benchmarking, and the system comprises a multi-source confrontation fusion module, a production situation awareness map module, a self-adaptive diagnosis analysis module, a cross-domain collaborative deduction strategy module, a hidden variable benchmarking analysis module and a cross-scene migration module. According to the method, enterprise resources are quantized into a matrix, cross-domain resource allocation is optimized in combination with a collaborative gain function, collaborative gains are measured through a nuclear norm and a Frobenius norm, multi-target weighting is combined, priorities of different fields are balanced, the global optimality of a collaborative strategy is improved, the enterprise resource state is synchronized to a digital twinborn model, and the collaborative resource allocation efficiency is improved. A strategy execution process is simulated, burst interference is injected, strategy stability is verified, a cross-domain cooperation efficiency comprehensive value is calculated through a four-flow state, a single-domain independent optimization mean value is compared, and cooperation gain is quantified.

The invention discloses a river-groundwater response and connectivity quantification method based on an event scale. The method comprises the following steps: acquiring rainfall, river water level and groundwater level time sequence monitoring data of a research area; identifying an independent precipitation event; identifying an effective runoff response event, and calculating the features of the quantitative event scale river response; effective groundwater response events are identified and classified, and features of groundwater responses of quantitative event scales are calculated; and calculating the response peak time lag of the river and the underground water, and constructing a dynamic connectivity index. The method breaks through the limitation of a traditional steady-state analysis method, can realize dynamic response characteristic and connectivity evaluation with high time resolution, has high universality and expandability, can systematically reveal the event-driven coupling process of the river and the underground water, and improves the reliability of the river and the underground water. The method is suitable for water resource evaluation and connectivity diagnostic analysis in areas with complex climate-geological conditions.

The invention discloses a sluice intelligent control data real-time processing method based on edge calculation, and relates to the technical field of sluice control data analysis, and the method comprises the following steps: obtaining the basic feature data of a sluice, obtaining the sluice opening degree flow relation, and collecting the related data of a water body in real time; in the water gate opening or closing process, the gate passing flow under different opening degrees is collected, data preprocessing is carried out, and basic opening degree flow data are obtained; theoretical opening flow analysis is carried out to obtain theoretical opening flow data; gate blockage analysis is carried out based on the basic opening flow data and the theoretical opening flow data, and the gate blockage condition is obtained; the method is used for solving the problems that when an existing water gate control data analysis technology is used for diagnosing and analyzing the water gate blocking condition through the lockage flow, blocking abnormity and opening abnormity cannot be accurately distinguished according to the deviation between the theoretical lockage flow and the actual lockage flow in the gate opening change process, and meanwhile the blocking condition cannot be judged.

The invention provides a household small electric appliance diagnosis system based on AR imaging and AI interaction, which relates to the technical field of intelligent household appliance intelligent diagnosis and comprises a multi-mode intelligent sensing framework, a modular AR detection accessory, an AI diagnosis analysis center and a human-computer interaction interface. According to the modular AR detection accessory, the sensor module is rapidly replaced through the magnetic type universal interface base, the requirement for collecting temperature, circuit, sound and other multi-mode data is met, the hardware adaptability is remarkably improved, the modular AR detection accessory has the functions of low power consumption and efficient power supply, a battery is arranged in the base, reverse wireless charging is supported, and the reliability is high. The problem of endurance of a high-power-consumption module is solved, a modular AR detection accessory is combined with an AR terminal through a magnetic type sensor module, fault three-dimensional coordinates are superposed on real equipment in real time, a user can visually check internal problem points, a mixed reality operation table supports gesture, voice and eye movement control, the operation threshold is lowered, and the operation efficiency is improved. According to the invention, the user is directly guided to carry out precise maintenance through the AR marking system.

The invention belongs to the technical field of medical diagnosis, and discloses a quantum multicolor immune digital pathological diagnosis analysis system, which comprises the following steps: acquiring high-resolution digital images of an immunohistochemical or immunofluorescence slice and a slide, and analyzing multicolor signal distribution in the images to obtain a quantum chromatographic image set; pixel-level color separation and intensity quantization are carried out on the quantum chromatographic image set, and a quantum intensity index set of each pixel is generated; performing multi-target color overlay analysis and post-translational modification protein specificity evaluation on the quantum intensity index set to construct a multi-color modification index set; identifying a single cell boundary and a multi-cell community based on the multicolor modification index set, and extracting a cell morphological parameter and a spatial adjacency relation to obtain a cell quantum analysis set; performing consistency correction and traceable recording by applying an automatic quality control mechanism, and generating a digital diagnosis report; the accuracy of accurate qualitative and quantitative analysis of immunohistochemistry and immunofluorescence is greatly improved.

The invention provides a digital beauty teaching system based on artificial intelligence, and the system comprises a learning condition collection and analysis module which is used for collecting learning condition data, constructing a student file, and carrying out the calculation to obtain a learning condition comprehensive index; the course planning and recommendation module is used for performing course recommendation, learning planning, course dynamic adjustment and cross-domain course recommendation; the intelligent tutoring and question answering module is used for constructing an error type database and an operation diagnosis model and carrying out diagnosis analysis and intelligent question answering on real-time operation; the learning condition evaluation module is used for constructing an AI scoring model and carrying out evaluation on real-time homework; and the visualization and adjustment module visually displays the learning condition evaluation result and dynamically adjusts the homework improvement suggestions and the recommended courses. The system can quantify the overall learning participation degree and expression level of students, accurately analyze the learning condition, accurately recommend courses, intelligently diagnose common errors, generate improvement suggestions, and score works, so that the scoring result is more accurate, and efficient proceeding of beauty teaching is promoted.

The invention relates to the technical field of electric energy metering anomaly detection and diagnosis analysis, in particular to a dynamic return and compensation method and system based on an electric energy metering data restoration model, and the method designs a distortion traceability algorithm of multi-dimensional feature fusion and improved SVM (Support Vector Machine), and extracts the characteristics of load features, environment features, packet loss rate, sampling delay distortion and the like. And load fluctuation-dominated distortion, environment interference-dominated distortion and coupling-dominated distortion can be accurately positioned. For multi-source distortion restoration, a load self-adaption-environment compensation-coupling correction three-level restoration model is provided, and through differential compensation of different load types, dynamic calibration of environment parameters such as temperature and humidity, electromagnetic interference and the like and coupling coefficient matrix optimization, the measurement data restoration precision is stably maintained to be 98% or above. According to the invention, through the research on the metering data distortion traceability-coupling distortion data reduction-dynamic return and supplement calculation-return and supplement result closed-loop optimization method, the precise repair of metering errors and the scientific calculation of electric quantity return and supplement in a complex scene are realized.

The invention discloses a cement kiln thermotechnical diagnosis analysis method and system based on heat balance calculation, and relates to the field of cement kiln thermotechnical diagnos.The analysis method comprises the steps that a corresponding heat balance state is calculated by obtaining cement kiln thermotechnical parameters and material component data, the material balance relation between the material input quantity and the material output quantity of the cement kiln is analyzed; evaluating the energy utilization efficiency of the cement kiln firing system by taking the ratio of the cement kiln clinker formation heat to the input total heat as a thermal efficiency index, and generating a diagnostic index by combining a thermal balance state and a material balance relationship; and comparing the diagnosis index with a historical operation optimal value to determine a deviation condition, evaluating heat loss abnormity and an operation state of the cement kiln thermotechnical engineering according to the deviation condition, constructing a causal atlas to predict a development trend of the cement kiln thermotechnical engineering, and obtaining a cement kiln combustion control result. The method can help to understand how each factor affects the overall performance of the thermal system, and facilitates the optimization and adjustment of different parameters according to the causal relationship in the later stage.

The invention belongs to the technical field of heat supply equipment diagnosis, and discloses an equipment diagnosis analysis system based on a performance attenuation characteristic curve. The method comprises the following steps: a sensor network acquisition layer, a data governance layer, a curve construction module layer, a dynamic calibration layer and a prediction output layer are included; the sensor network acquisition layer is responsible for comprehensively acquiring operation state data of heat supply equipment and constructing an initial data set by using the data in a stable operation period of the first 7-10 days after the equipment is put into production; and the data governance layer is used for processing and standardizing the collected state data and the original data set to construct a high-dimensional original feature set. According to the method, the equipment performance attenuation characteristic curve is generated through the health score and the curve generation model, the time-varying trend of the performance, the attenuation rate and possibly existing stage characteristics are clearly displayed, the problems of threshold comparison and statistical analysis are solved, and meanwhile, the remaining service life of the equipment can be dynamically predicted.

The invention relates to the technical field of image data processing, and discloses an intelligent drawing auditing method and device based on a vector atlas and an active perception closed loop, and the method comprises the steps: obtaining a to-be-audited engineering drawing file and a compliance specification text; analyzing vector data of the engineering drawing file, and converting the vector data into an attribute graph used for representing a topological structure and semantics of the drawing; generating an audit instruction through a multi-modal large language model planner based on the attribute graph and the compliance specification text; executing the auditing instruction on the attribute graph through a graph signal processing verifier so as to carry out iterative compliance verification and diagnostic analysis on the attribute graph, and generating a corresponding compliance verification result and a diagnostic result; and generating a compliance audit report containing the traceable evidence based on the compliance verification result and the diagnosis result. According to the method, the accuracy and the reliability of the auditing result can be improved while automatic auditing of the engineering drawing is realized.

The invention discloses a fault analysis system and method for ship equipment, and the system comprises a multi-source sensing synchronization module which is configured to carry out the phase synchronization of the operation data of multiple equipment through a hardware phase locking mechanism; the multi-physical field modeling module is coupled to the multi-source sensing synchronization module, and is configured to construct a device virtual state field based on the multi-device operation data after phase synchronization, and generate a dynamic parameter representing a multi-device state time sequence imbalance degree; the adaptive diagnostic analysis module comprises a deep learning fault analysis model, and recessive feature extraction parameters and fault evolution time sequence modeling parameters of the deep learning fault analysis model are adaptively adjusted in real time according to dynamic parameters so as to output system-level fault characterization parameters; and the predictive decision-making module is configured to generate decision-making information containing fault development trend prediction and a preventive maintenance window based on the system-level fault characterization parameters. And high-precision correlation diagnosis and predictive maintenance decision-making of early faults of multiple devices of the ship can be realized.

The invention discloses a multifunctional cardiovascular monitoring system which comprises an electrocardiosignal acquisition module, a conduction block feature extraction module, an arrhythmia classification processing module, a cardiac function parameter calculation module, an intelligent diagnosis analysis module and a monitoring data output module. The electrocardiosignal acquisition module acquires and transmits original signals, the conduction block feature extraction module extracts feature parameters by using an exclusive algorithm, the arrhythmia classification processing module obtains classification results through a multi-dimensional classifier, and the cardiac function parameter calculation module generates calibration parameters by using a dynamic calibration model in combination with the classification results. The intelligent diagnosis analysis module generates diagnosis data through comprehensive analysis of the auxiliary platform, and the monitoring data output module processes the data and then outputs the data. The system realizes module cooperation and data interaction, improves the monitoring efficiency, guarantees data accuracy by using a professional algorithm and a model, and provides reliable support for clinical cardiovascular monitoring.

A system and method for distinguishing the type of seizures in a human patient, such as an epileptic seizure (ES), or a functional or dissociative seizure (FDS). The system and method use a diagnostic analytical platform that gets heart rate variability (HRV) analytical metrics from a ECG and uses an analytical diagnostic algorithm to determine if an ES or FDS has occurred in the patient. The diagnostic analytical platform can create a model for distinguishing that a predetermined type of seizure has occurred from the HRV analytical metrics.

The invention discloses a remote operation and maintenance diagnosis and predictive maintenance system for heterogeneous equipment, and relates to the technical field of equipment operation and maintenance. The system comprises a data acquisition and access module, a data processing module, a diagnostic analysis module, a prediction maintenance module, a man-machine interaction module and a storage module. The data acquisition and access module supports multiple communication protocols and acquires operation data of heterogeneous equipment; the data processing module preprocesses the collected data and completes multi-source data fusion; the diagnosis analysis module realizes equipment real-time fault detection, fault positioning and fault type association through a two-layer detection mechanism, and generates a fault diagnosis report; and the prediction maintenance module outputs the residual service life and the potential fault risk of the equipment based on the prediction model, and generates a targeted maintenance strategy and plan. According to the system, the problems of difficult access of heterogeneous equipment, poor fault detection real-time performance and insufficient maintenance planning are solved, remote intelligent operation and maintenance are realized, and the equipment shutdown risk and the maintenance cost are reduced.

A method for diagnosing and recommending treatment to a patient that may or may not present with occlusal disease includes acquiring 3D scans of the patient's maxillary and mandibular arches, and acquiring occlusal assessment data of the patient's teeth and dental arches. The method also includes automatically generating a treatment plan recommendation, and a recommended treatment plan projection and a non-treatment projection based, at least in part, on the acquired 3D scans of the patient's maxillary and mandibular arches, and the acquired occlusal assessment data of the patient's teeth and dental arches. The method also includes automatically displaying a rendering of the patient's simulated dental arch N years in the future based on the automatically generated recommended treatment plan projection, and a rendering of the patient's simulated dental arch N years in the future based on the automatically generated non-treatment projection, where N is greater than 1.

The invention relates to the technical field of electric power facility monitoring, in particular to an electric power inspection system and a satellite image processing method, and the system comprises a front-end sensing module which collects state data and establishes an inspection path after receiving an inspection instruction; the edge processing module is used for receiving the state data, performing real-time processing and feature extraction according to the inspection path and generating key parameter information; the central analysis platform receives the key parameter information, performs diagnosis analysis based on the key parameter information, and generates an inspection diagnosis report; the feedback optimization module is used for updating a feature extraction rule of the edge processing module and an intelligent diagnosis analysis model of the central analysis platform based on the inspection diagnosis report and the key parameter information; and the inspection result module sends the inspection diagnosis report to the operation and maintenance terminal. According to the invention, the problems of low inspection efficiency, high safety risk and response delay caused by incapability of realizing efficient, real-time and automatic fault detection and state monitoring in a remote or dangerous area in a traditional inspection mode are effectively solved.

The invention discloses an intelligent store Internet of Things method, and the method comprises the following steps: receiving a connection request of store hardware equipment, and synchronously carrying out the collection of anti-theft data and fitting data; performing anti-theft processing on the anti-theft data, and performing anti-theft efficiency self-diagnosis analysis through a self-adaptive algorithm; and analyzing the fitting data to obtain customer behavior analysis data. According to the intelligent store Internet of Things method, three-in-one store management improvement of equipment efficient access, anti-theft accurate diagnosis and sales strategy optimization is realized.

The invention discloses an ADHD screening method and system based on mobile terminal eye movement tracking and a multi-modal agent. The method comprises the following steps: (1) collecting and preprocessing mobile terminal eye movement data; (2) performing fixation point estimation based on a lightweight MGazeNet model; (3) eye movement feature extraction based on a multi-normal form interaction task; and (4) carrying out comprehensive diagnosis analysis based on machine learning and a large model agent. The invention further provides an ADHD screening system based on mobile terminal eye movement tracking and the multi-modal intelligent agent. The ADHD screening system sequentially comprises the following modules: (1) a front-end data acquisition and preprocessing module; (2) an end side eye movement tracking reasoning module; (3) an interactive task control and feature calculation module; and (4) a multi-mode intelligent diagnosis and analysis module. The ADHD screening method effectively solves the problems that traditional ADHD screening depends on expensive professional equipment and subjective scales, so that the popularity rate is low, and objectivity is poor, screening portability and diagnosis objective accuracy are improved, and the intelligent level of ADHD auxiliary diagnosis and user interaction experience are improved.

A method for cardiac diagnostic analysis is provided. The system includes a processor and memory coupled to the processing unit, receiving data from standard 12-lead ECGs from an external device; at least one computer-readable storage medium containing instructions, including program code that causes the execution of procedures according to the programs encoded on a disk or other media capable of using advanced algorithms such as deep learning networks, so that they can be processed during any given procedure without substantially altering the existing clinical workflow.

The application discloses a partial discharge signal denoising method, comprising the following steps: building a partial discharge signal detection platform and a partial signal collection platform; measuring and detecting characteristic signals in the partial discharge signals; screening and classifying the measured signals through a Gaussian test to obtain the characteristic signals; processing and denoising the signals through a fast S transform algorithm and a singular value decomposition SVD algorithm; testing and diagnosing the denoising effect by using two coefficients of noise suppression ratio and amplitude attenuation ratio; and displaying the diagnostic analysis result on a platform display interface. The application discloses a partial discharge signal diagnostic system based on a fast S transform and an adaptive singular value, and solves the problems that the existing technical methods are difficult to balance the denoising effect and extraction speed in the characteristic extraction of the partial discharge signals and are difficult to extract the characteristics.

The invention provides an intelligent grading identification method for a potential infiltration risk area of a drainage pipe network, and relates to the technical field of risk identification of the drainage pipe network. Establishing spatial relation mapping between triangular grid units of the surface water-underground water coupling model and pipe sections of the drainage pipe network model, calculating hourly liquid level difference time sequence data by utilizing underground water level time sequence data and corresponding pipe section liquid level time sequence data which are obtained through simulation calculation, and constructing risk indexes according to the hourly liquid level difference time sequence data; and then constructing an uncertainty scene sample set and respectively inputting the two models to obtain corresponding simulation calculation results, calculating to obtain a risk index corresponding to each pipe section, and judging a grading result of a potential infiltration risk area according to the risk indexes. Simulation calculation and intelligent diagnosis analysis are fused, and efficient identification of the potential infiltration risk area of the drainage pipe network is achieved on the premise that large-scale sensor deployment is not needed.

The invention relates to the technical field of intelligent diagnosis, and discloses a water meter measurement data quality intelligent diagnosis system which comprises a data preprocessing module, a service feature extraction module, a data anomaly detection module, an anomaly diagnosis analysis module, a self-adaptive updating module and an interaction report generation module. The data processing module is used for performing data denoising processing on an original signal of water meter measurement data, generating clean original data, extracting an enhanced feature set from the clean original data, performing abnormal mark analysis on the enhanced feature set, generating abnormal mark data, performing root cause analysis on the data in combination with external environment data to obtain a data quality diagnosis result, and performing configuration optimization analysis to obtain a data quality diagnosis result; optimal configuration is obtained, and finally an interactive visual report is generated. According to the invention, the diagnosis accuracy and efficiency of the intelligent diagnosis system for the water meter measurement data quality can be improved.