56results about How to "Implement early warning" patented technology

The invention discloses a multi-mode flexible intra-abdominal pressure measuring device and method based on AI, and belongs to the technical field of medical monitoring. The device comprises a flexible wearable carrier, a multi-mode sensor module, a signal conditioning and intelligent wireless transmission module and a data processing module, the flexible wearable carrier is provided with an adjustable fixing assembly and a positioning identifier; the multi-modal sensor module integrates various sensors of pressure, stress strain, sound, acceleration and oxyhemoglobin saturation infrared light, and synchronously collects multi-dimensional physiological signals; the data processing module is provided with an AI algorithm, dynamic scene recognition, self-adaptive weight fusion and individual difference compensation can be achieved, and an intra-abdominal pressure measurement value is obtained through calculation. Non-invasive, real-time and high-precision continuous monitoring of the intra-abdominal pressure is achieved, the clinical measurement error is small, the system is suitable for ICU, postoperative rehabilitation, home management and other scenes, infants and old people are covered, and the comfort, accuracy and clinical efficiency of intra-abdominal pressure monitoring are greatly improved.

This application provides a method for predicting the wear state of steel-based ceramic wear-resistant components, relating to the field of material life assessment technology. The method includes the following steps: emitting excitation eddy currents containing a first frequency and a second frequency into a steel substrate, and receiving the response signal modulated by the state of the steel substrate and the ceramic-steel interface; processing the response signals corresponding to the first and second frequencies respectively, extracting the first phase change of the first reference signal corresponding to the initial health state of the component relative to the first frequency, and the second phase change of the second reference signal corresponding to the second frequency; calculating the difference between the first and second phase changes to characterize the change in thermal conductivity state of the ceramic-steel interface; obtaining the real-time operating temperature of the wear-resistant component, and based on the real-time operating temperature and the difference, assessing the current state level of the ceramic-steel interface, and predicting the remaining safe service time of the ceramic wear-resistant layer by combining the trend of the difference over time.

The present application relates to the technical field of animal health monitoring, and relates to a wild animal non-invasive genome monitoring and population health evaluation system. A time indication marker is generated based on the ratio of a predetermined indicator bacteria genus, and microenvironment data is input into a pre-trained microbial succession model to intelligently infer the actual exposure time of the sample in the wild. Then, the initial intestinal flora map of the sample at the instant of discharge is reconstructed. The high-fidelity flora map after reconstruction is further deeply coupled with health evaluation to realize scientific diagnosis and early warning of the physiological state of wild animal individuals. The accuracy of health evaluation, nutritional status analysis and pathogen detection is greatly improved, and major misjudgments such as misjudging environmental bacteria as pathogenic bacteria or misdiagnosing flora natural decay as ecological imbalance are avoided. A stable and reliable technical tool is provided for wild animal protection management.

The application provides a loess hilly vegetation disease and pest remote sensing monitoring and evaluation method and device, relates to the technical field of remote sensing monitoring and evaluation methods, and comprises the following steps: calculating the coverage of vegetation by using a double threshold plane method, taking the coverage of vegetation of each grid area as a correction term of ground surface temperature; constructing a temperature difference anomaly index of each grid area; based on the temperature difference anomaly index and transpiration efficiency index of each grid area, calculating the transpiration efficiency index of each grid area by using a vegetation temperature difference method, constructing a vegetation classification model by using a logistic regression method, and classifying each grid area according to a preset threshold value to distinguish a risk grid area and a normal grid area. A prediction model is constructed through the statistical relationship between the vegetation index and the ground surface temperature, the temperature difference anomaly index is quantified, early stress signals of vegetation that are difficult to find can be recognized, the transpiration efficiency is introduced to comprehensively evaluate the water utilization of vegetation, and through a dynamic threshold value and a statistical model, potential risk grids can be recognized before the outbreak of diseases and pests.

This invention provides an artificial intelligence-based internet property rights transaction risk assessment and decision-making system, relating to the field of data processing technology. The system includes: a data acquisition and preprocessing module for real-time acquisition of raw transaction data, followed by cleaning and standardization; a multi-dimensional feature construction module for modeling by time windows and constructing a multi-level interaction relationship graph to obtain time-series feature data and graph-related feature data; and for normalizing and weighting the features; a risk identification module for extracting suspicious transfer behavior chains and tracing the flow of benefits, performing composite risk discrimination; and an intelligent decision-making module for performing hierarchical adaptive discrimination and case migration comparison, summarizing and generating risk assessment results and pushing early warnings; and receiving feedback from the management terminal, which is then used to optimize identification and decision-making parameters. This invention improves the autonomy and accuracy of property rights transaction risk assessment and decision-making.

This invention relates to the field of fault early warning technology, and provides a method and system for early warning of faults in large oil-filled equipment, comprising: Step 1, collecting acoustic fingerprint data throughout the entire lifecycle of the large oil-filled equipment and constructing an acoustic fingerprint sample set; Step 2, preprocessing the acoustic fingerprint sample set, extracting acoustic fingerprint feature parameters, and establishing a standardized acoustic fingerprint database; Step 3, constructing a multi-channel deep learning acoustic fingerprint recognition model and a discharge severity assessment model based on deep learning; Step 4, based on the output of the trained acoustic fingerprint recognition model and combined with a preset multi-level early warning threshold system, performing real-time assessment of the operating status of the large oil-filled equipment, and generating corresponding early warning information when the assessment result meets the early warning triggering conditions; Step 5, pushing the early warning information to the operation and maintenance terminal, and automatically generating operation and maintenance suggestions based on the defect type and severity. This invention can effectively provide early warning of faults in large oil-filled equipment.

The invention discloses an intelligent acoustic positioning system for sick pigs in a hog house, and relates to the field of sound recognition and positioning. The problems that an existing sick pig acoustic positioning method is poor in voice recognition accuracy and large in positioning error in a complex acoustic environment are solved. According to the invention, a time difference and phase difference complementary positioning principle is adopted, and an adaptive beam forming technology is combined, so that the problem of inaccurate sound source positioning in a pig house environment with strong reflection and high noise is solved, multi-feature extraction and intelligent diagnosis are carried out on enhanced sound data through the sick pig sound recognition unit, and the recognition accuracy is improved. The method is mainly used for positioning sick pigs in a complex breeding environment.

The application relates to the technical field of computer vision and image processing, and particularly discloses a paper surface mold spot growth dynamic detection method and device; the detection method comprises the following steps: S1, acquiring a surface visible light image of a paper sample to be detected; S2, pre-processing the surface visible light image; S3, inputting the pre-processed image into a mold spot segmentation model pre-trained to obtain a pixel-level segmentation result image, wherein the segmentation result image at least contains a mold spot area category and a paper healthy area category; and S4, based on the segmentation result image, calculating one or more quantitative indexes for representing mold spot growth states; the detection device comprises an image acquisition module, a preprocessing module, a segmentation module and an analysis module; and the application is beneficial to realizing high-precision segmentation of a paper surface mold spot area and dynamic quantitative analysis of a mold growth process.

This application relates to the field of medical data processing technology and discloses a method and system for screening infertile patients with polycystic ovary syndrome (PCOS). The method includes: acquiring data on the number of antral follicles, anti-Müllerian hormone (AMH) concentration, and ovarian volume from multiple patient monitoring cycles; performing feature extraction and normalization to obtain a set of normalized feature vectors; determining the synchronous relationship between the number of antral follicles and ovarian volume based on the vector set, and identifying signs of asynchronous changes; combining hormone concentrations for joint grouping to obtain the distribution of abnormal indicator clusters and identifying ovarian function stage markers; performing sequence matching based on the stage markers of continuous cycles and historical medical records to generate an ovarian function progression trajectory curve; extracting inflection point features from the curve and performing causal association rule mining to construct a logical chain between abnormal indicators and clinical outcomes; and determining conditions layer by layer according to the logical chain to output the final screening results. This application improves the accuracy of screening infertile patients with PCOS.

The application discloses a kind of sepsis multiple complications prediction method, system, storage medium and equipment, belong to intelligent medical technical field, method includes the following steps: S1. acquisition sepsis patient multidimensional time series physiological data;S2. medical perception feature vector is constructed to the time series physiological data;S3. sepsis multiple complications prediction model is constructed and introduced physiological logic space-time composite mask matrix;S4. the output multiple complications prediction probability using hierarchical expert hybrid network H-MoE;S5. the model is trained using dynamic weighted loss function based on task uncertainty;S6. risk output and feature contribution degree explanation;The model of the application is introduced by physiological logic mask and H-MoE architecture, realizes the accurate, synchronous prediction of sepsis complex complications, can effectively filter redundant features, and reveal the complex correlation therebetween, provide quantitative decision support for clinician, to realize early intervention and individualized treatment.

The application provides a kind of alien fish invasion risk dynamic identification method and system, it is related to data processing technical field, method includes: collecting the multi-source ecological monitoring data of each monitoring point in a specific time period;The multi-source ecological monitoring data is preprocessed, and the standardized ecological data set is obtained;Based on the standardized ecological data set, the discrete state space model is constructed for different fish populations respectively;By extending the least square method, the parameters of discrete state space model are dynamically identified;According to the dynamic identification of discrete state space model, the dynamic change trend of local fish state variable under given invasion pressure is predicted;Combined with multi-source ecological monitoring data and dynamic change trend, fuzzy reasoning model is constructed;Multi-source ecological monitoring data is input into fuzzy reasoning model, and the invasion risk comprehensive evaluation value is output;Alien fish invasion risk grade is dynamically divided;According to the invasion risk grade of alien fish, intervention strategy instruction is automatically generated.

This invention relates to the field of intelligent control technology and discloses an automated intelligent control system for industrial production lines, comprising: a surface data acquisition module for real-time acquisition of surface roughness data and surface image data of workpieces of the same type; a data processing and analysis module for preprocessing and feature extraction of the acquired data, calculating comprehensive surface roughness indices and dynamic defect indices, and simultaneously acquiring process health scores fed back by quality inspection of workpieces of the same type; and an evaluation model construction module for constructing an evaluation model based on comprehensive surface roughness indices of workpieces of the same type in a historical database. This invention, by establishing a complete "perception-analysis-decision-control" closed loop, achieves real-time monitoring and automatic adjustment of the production line's process status, significantly improving the automation level and response efficiency of process control, reducing reliance on human experience, and effectively reducing the defect rate and production interruption risk caused by process fluctuations.

This invention relates to the field of water injection well detection technology, and particularly to a method and system for detecting anomalies in water injection wells. The method includes: acquiring multivariate historical time-series data of the water injection well under normal operating conditions, constructing a graph structure, and using the graph structure and the multivariate historical time-series data to construct a time-series graph data sample set; constructing a spatiotemporal graph attention network (STGAT) model; reconstructing the multivariate historical time-series data using the STGAT model, obtaining a reconstruction error sequence, and statistically distributing it to determine an anomaly judgment threshold; acquiring real-time operating data of the water injection well and constructing real-time time-series graph data, inputting it into the STGAT model to obtain reconstructed values, and obtaining real-time observed values; when the reconstruction error between the reconstructed value and the real-time observed value exceeds the anomaly judgment threshold, determining that the water injection well is in an abnormal state and issuing an early warning signal. This invention achieves early warning of latent early anomalies by integrating the physical coupling relationship between multiple parameters of the water injection well for spatiotemporal modeling and dynamic threshold determination.

The invention discloses a diesel particulate filter blockage early warning method and electronic equipment. According to the method, first pressure data of differential pressure sensors arranged at the front end and the rear end of a diesel particulate filter between a first moment and a second moment, first operation data of a vehicle and first environment data are obtained and preprocessed to obtain second pressure data, second operation data and second environment data; the second pressure data, the second operation data and the second environment data are input into a first blockage prediction model and a second blockage prediction model respectively to obtain a first blockage probability and a second blockage probability of the diesel particulate filter, and then a weighted voting mode is adopted for processing to obtain a target blockage probability of the diesel particulate filter. Therefore, the problems of sensor data distortion, data asynchronization and early warning lag can be effectively solved, the accuracy and the real-time performance of DPF blockage early warning are improved, and the false alarm rate is reduced.

The invention discloses a dynamic environment adaptability evaluation system for the power generation efficiency of a photoelectric curtain wall. The dynamic environment adaptability evaluation system adopts a three-layer framework of'multi-source perception-dynamic modeling-self-adaptive evaluation '; the system comprises a sensing layer which is a multi-dimensional sensing network, comprises optical, thermal, electrical and environment sensing modules, and is used for collecting real-time data of a photoelectric curtain wall and a surrounding microenvironment; the analysis layer is a self-adaptive model engine, comprises a photoelectric curtain wall-microenvironment coupling digital twin model and a hybrid machine learning self-adaptive evaluation module, and is used for calculating dynamic power generation efficiency, performing short-term prediction and anomaly detection and generating a dynamic datum line; the application layer comprises an evaluation and decision module and is used for realizing dynamic efficiency evaluation, fault diagnosis and output optimization suggestions; according to the method, through multi-source data fusion and dynamic modeling, accurate, real-time and adaptive evaluation of the power generation efficiency of the photoelectric curtain wall is realized, and the operation and maintenance efficiency and the power generation performance of the photoelectric curtain wall are effectively improved.

The invention discloses a hydrogen leakage risk prediction system and method for a generator set. The method comprises the steps that S1, a hydrogen leakage detection device is additionally arranged on the generator set; s2, collecting operation parameter data of the generator, and performing data processing and data correlation analysis; s3, acquiring historical change data of the generator operation parameter data, and extracting historical curve change trend characteristics; s4, constructing a hydrogen leakage risk assessment model based on the generator operation parameter data after correlation analysis and historical curve change trend characteristics; s5, acquiring information of all sealing elements for preventing hydrogen leakage of the generator; and S6, monitoring the hydrogen leakage condition of the generator in real time through the hydrogen leakage risk assessment model and the sealing element information, judging and tracking the main cause of hydrogen leakage of the generator, giving an alarm, and generating a control processing scheme. According to the method, the generator hydrogen leakage risk assessment system is constructed, and deep mining and dynamic analysis are carried out on data, so that advanced early warning and tracking of the hydrogen leakage main cause of the generator are realized.

The application relates to the technical field of plastic processing, and discloses a plastic melting uniformity analysis method and system. The method comprises the following steps: collecting fluctuation of a supply amount and material distribution data, obtaining a temperature deviation fluctuation interval and a material abnormal distribution graph; combining the temperature deviation and a dynamic disturbance frequency to judge a fluctuation type and generate temperature distribution abnormality prediction data; extracting key features from the temperature distribution abnormality prediction data to determine a plastic flow characteristic change trend; integrating dynamic characteristic data to evaluate the influence degree of a melting process on uniformity; quantifying a shear rate mutation response based on the influence degree to adjust a melting time fluctuation interval parameter; generating a uniformity distribution graph through multi-scenario simulation to determine a product quality quantization index; generating an adjustment strategy according to the index and iteratively optimizing the adjustment strategy to finally determine melting process control parameters. The application solves the problems of poor melting uniformity and control lag caused by disturbance coupling, and improves the stability of a plastic melting process and the consistency of product quality.

The invention discloses an operator DICT project management and control system and method based on machine learning, belongs to the technical field of communication network operation and maintenance, and aims to solve the technical problem of how to realize automatic monitoring and fault prediction of each stage process of a DICT project and improve the operation and maintenance efficiency and accuracy. Comprising a data acquisition layer which acquires multi-source data through an edge operation and maintenance box and uploads the preprocessed multi-source data to a data fusion layer; the data fusion layer is used for carrying out standardization processing and feature extraction on the preprocessed multi-source data; the intelligent analysis layer is used for performing fault prediction analysis, root cause positioning and optimization suggestion generation on the time series data based on the model in the model library; and the management and control display layer issues the early warning notice to the executor and is used for displaying an execution result log recorded by the executor and returning the execution result log to the data acquisition layer to form closed-loop management and control.

This application proposes a performance evaluation method and system based on battery tab production data analysis, relating to the field of battery tab production data analysis technology. The method includes acquiring production line data and environmental parameters during the battery tab production process. The production line data includes process parameters and equipment status information. It analyzes the characteristics in the production line data and, in conjunction with the impact of environmental parameters on microscopic defects, obtains a potential risk index for microscopic defects in the battery tabs. The characteristics include at least the characteristics of the operating process parameters. Based on the potential risk index for microscopic defects, and combined with environmental parameters and equipment status information, it generates process parameter adjustment suggestions for defect prevention. It simulates the process parameter adjustment suggestions and evaluates their effectiveness to obtain evaluation results. Based on the evaluation results, it flexibly adjusts the process parameters of the production equipment. This application can improve the overall performance and long-term reliability of batteries.

The present application relates to a kind of forest climate monitoring system and method based on high-resolution remote sensing satellite, it is related to environmental monitoring technical field, including the high-resolution remote sensing satellite for capturing forest area high-resolution image and ground receiving station, also include multiple fixed-point monitoring devices buried in each region of forest, for collecting the environmental climate data of forest fixed-point area, high-resolution remote sensing satellite and fixed-point monitoring device are wirelessly connected with ground receiving station, ground receiving station is used to process and analyze remote sensing data and acquisition data;Fixed-point monitoring device includes annular water storage cylinder, the underground monitoring module for collecting forest underground environmental data on the surface of annular water storage cylinder, the column in the center of annular water storage cylinder.This system cooperates by high-resolution remote sensing satellite and multiple distributed fixed-point monitoring devices buried in different positions of forest area, quickly identifies risk area, improves the monitoring accuracy of system, realizes early warning and emergency response of forest fire.

This invention discloses a multi-source data large model fusion system for collaborative supervision and anomaly identification of utility tunnels, specifically relating to the field of intelligent operation and maintenance technology for utility tunnels. It includes a multimodal spatiotemporal unified coding module, a dynamic baseline self-learning module, a multidimensional anomaly joint judgment module, and a control coordination module. The multimodal spatiotemporal unified coding module constructs an environmental fingerprint feature matrix using a trend vectorization method; the dynamic baseline self-learning module constructs a differentiated environmental fingerprint baseline with embedded ventilation performance evaluation coefficients; the multidimensional anomaly joint judgment module performs triple verification of temporal drift detection, spatial correlation analysis, and visual-data cross-validation, outputting anomaly judgment results with confidence; the control coordination module integrates a knowledge graph with a vertical large model of the utility tunnel, automatically generating semantic reports on anomaly causes and cross-professional equipment linkage control commands. This invention can solve the problems of missed reporting, high false alarm rate, and low coordination efficiency of slowly changing anomalies in existing technologies.

The invention discloses a physical and mental index dynamic characteristic multi-modal identification analysis system based on artificial intelligence, and relates to the technical field of biomedical signal processing, and the system comprises a multi-source data acquisition module, a dynamic risk assessment module, a blood flow abnormity analysis module, a multi-modal risk layering module and an intelligent early warning module; the multi-source data collection module collects respiration, heart rate and blood oxygen data, the dynamic risk assessment module predicts risks through a long-short-term memory network, the blood flow abnormity analysis module identifies blood flow abnormity through a convolutional neural network, the multi-mode risk layering module assesses the risk level, and the intelligent early warning module generates a report and takes early warning measures. Through multi-modal identification analysis, comprehensive, real-time and accurate monitoring of physical and mental indexes is realized, the risk assessment accuracy is improved, potential health risks are early warned in time, powerful support is provided for clinical decision making, the public health management level is improved, the medical cost is reduced, and the life quality is improved.

The invention belongs to the technical field of steam turbine state monitoring and fault diagnosis, and particularly relates to a steam turbine valve jamming early warning system and method based on force-position cooperative monitoring, and the system comprises a sensor array, a data acquisition and conditioning unit, an edge calculation unit, a cloud early warning platform and a client interaction unit. By synchronously collecting force parameter signals such as valve displacement, oil pressure and steam pressure, characteristic quantity is extracted through collaborative analysis, and jamming fault identification is achieved in combination with a reference model and a dynamic threshold value. According to the method, a health reference curve library is established, force-position characteristics are analyzed in real time on the edge side, motion resistance is estimated, local early warning is carried out, historical data are deeply mined at the cloud end, and fault types and trend prediction are intelligently identified. According to the method, the jamming nature is directly struck, early-stage accurate early warning is achieved, the false and missing report rate is reduced, the real-time performance and diagnosis depth are guaranteed through edge cloud cooperation, and technical support is provided for predictive maintenance of the valve.

The application relates to the cross field of artificial intelligence and environmental health, and discloses a membrane nephropathy prognosis risk stratification method based on exposure spectrum and clinical indicators. First, demographic information, clinical detection indicators and various environmental pollutant exposure data of patients are collected to construct a multi-level exposure spectrum feature, including single pollutant concentration, same type comprehensive exposure factor and pollutant synergistic interaction term. Then, the exposure spectrum and the clinical indicators are fused, various machine learning algorithms are used to construct candidate prediction models, the optimal prognosis risk stratification model is screened through cross validation and multiple performance indicators, and the SHAP value is used to quantify the contribution degree of each feature. Further, a double machine learning method is used to control confounding variables and estimate the causal effect of key pollutants on prognosis. On this basis, individual risk stratification and main risk driving factor identification of membrane nephropathy patients are realized, and scientific basis and guidance are provided for clinical decision making.

The invention relates to the field of slope abnormity early warning, in particular to a slope abnormity early warning method and device based on vehicle behavior data and a medium. The method comprises the following steps: acquiring preset type behavior data of historical vehicles in a normal state in a target slope area; the normal state is a state in which no slope abnormity occurs; establishing a standard model of a target slope in a normal state according to the preset type of behavior data of the historical vehicle; acquiring preset type behavior data of a preset target type of vehicle passing through the target slope area in the current time period; judging whether the target slope area is abnormal in the current target time period or not according to preset type behavior data of a preset target type of vehicle passing through the target slope area in the current time period and the standard model; and if the target slope area is abnormal in the current target time period, generating a slope abnormity early warning signal. The invention provides a novel slope abnormity early warning method.

The invention relates to the technical field of data processing, and discloses a breast cancer risk assessment and intervention method based on a multivariate coupling model, which comprises the following steps: quantifying the interaction between environmental parameters such as temperature, humidity and air pressure and human energy metabolism, and quantifying the interaction between food cold and heat attributes, kinetic energy consumption and midnight-noon ebb-flow rhythm; real-time prediction and hierarchical management and intervention of deterioration risks are realized by quantifying a dynamic balance relationship between blood pressure energy and breathing energy, adopting a synergistic effect of a classical prescription, an acupuncture therapy, guide movement and the like according to a principle and adopting a machine learning algorithm. According to the invention, a multi-dimensional correlation and regulation coupling model of environment, psychology and physiology is established, and breast cancer disease prediction and tumor prognosis evaluation are realized.

This invention relates to the field of aviation safety monitoring technology, and discloses a fiber optic dual-line icing detection system and method. It employs Raman scattering distributed optical fibers laid in high-icing areas on the upper surface of the wing to monitor temperature data in the corresponding areas; FBG fiber gratings with a metal coating are bonded along the spanwise direction of the wing skin to stress concentration areas to monitor strain data in the corresponding areas; the temperature and strain monitoring data are demodulated separately; the demodulated temperature and strain data are received, and icing is determined and thickness assessed using a preset icing identification algorithm; during flight, the icing situation is sensed by changes in ambient temperature and ice pressure caused by icing at the installation location, and the signal is transmitted to the anti-icing and de-icing system, improving reliability and safety during flight.

The application discloses a kind of based on time-frequency phase feature rolling bearing fault diagnosis and physical positioning method, this method includes: obtaining the vibration time series signal at two different circumferential positions of the monitored bearing seat, and carry out time-domain impact feature enhancement processing;Two enhanced impact signals are analyzed by cross spectrum, and extract coherence function spectrum and cross-phase spectrum;According to the energy peak value of impact signal, coherence function spectrum and cross-phase spectrum set fault diagnosis condition, find out the real fault characteristic frequency that meets the condition, compared with various fault characteristic frequency theoretical value, determine the fault type of rolling bearing;According to the geometric relationship of fault point and two vibration collection points on bearing seat, establish circumferential angle solution model, the stable phase difference and collection point position are substituted into model, and the circumferential position of fault point on bearing seat is calculated.The method can distinguish real bearing fault from external random disturbance from physical principle, greatly improve the reliability and accuracy of diagnosis.

The present application relates to the technical field of predictive maintenance and fault diagnosis of industrial equipment, and particularly relates to a multi-motor coupled vibration intelligent diagnosis method and system. The technical scheme comprises the following steps: based on the collected vibration data of the multi-motor under the normal operation state and the physical parameters of the motor equipment, a parameterized fault simulation module is used to generate a coupled vibration simulation data set with accurate fault labels. The present application realizes the cold start of the diagnosis system under the condition of no historical fault samples; the deep reinforcement learning agent is used to process the space-time coupled signal, and the layered reward mechanism is combined to significantly improve the decoupling diagnosis accuracy and early warning capability; through the online adaptive learning module, the system can be fine-tuned in real time according to the field feedback, and quickly adapt to the individual equipment state; the simulation-model evolution module uses the accumulated real data to continuously optimize the simulator and the diagnosis model, and forms a self-iterating closed loop.

The invention relates to a fault diagnosis method for a circulating washing water pump based on wireless vibration waveform frequency spectrum monitoring, which solves the problems of difficulty in wiring and easiness in corrosion in a chemical field through LoRa wireless transmission and rigid installation of a sensor, and ensures the stability of signal acquisition. In addition, the method utilizes a frequency domain integral algorithm and fast Fourier transform to realize multi-dimensional spectrum analysis of vibration acceleration, speed and displacement, and overcomes the limitation of single index monitoring; meanwhile, in combination with characteristic frequency automatic labeling and fault judgment logic based on equipment parameters, accurate positioning and early warning of faults such as bearing abrasion and impeller abnormity are achieved, the operation and maintenance mode is converted into predictive maintenance from passive first-aid repair, and the risk of unplanned shutdown is effectively reduced.