112 results about "Benchmark database" patented technology

The invention discloses an integrated collaborative management method for construction site risk operation data, and belongs to the technical field of data management. The method comprises the following steps: constructing and calibrating a space-time risk reference database; deducing a potential risk conduction link set, and generating an associated intervention knowledge base; during operation, through multi-sensor cooperative verification, an abnormal signal is confirmed as a risk event; matching the risk event with a conduction link to calculate a risk upgrade level and dynamically adjust an early warning threshold; and when the early warning is triggered, generating and issuing a dynamic collaborative response instruction, and feeding back a processing result to correct the reference database to form a management closed loop. According to the method, the technical means of constructing the space-time risk reference, deducing the conduction link, cooperatively verifying the risk and dynamically regulating and controlling the threshold are adopted, so that the predictability of project risk management and control, the efficiency of resource cooperative scheduling and the scientificity of overall management decision are improved.

The invention discloses a reservoir dam safety monitoring method and system based on edge calculation, and relates to the technical field of hydraulic engineering safety monitoring, and the method comprises the steps: obtaining monitoring data by each edge calculation node, carrying out the preprocessing, and generating a standardized data matrix; establishing a reference database, executing anomaly detection, and generating a labeling time sequence data matrix; performing multi-scale decomposition on the labeled time sequence data matrix, and constructing a node response feature matrix; the central processing unit receives data of each edge computing node, analyzes a multi-parameter spatial propagation mode by constructing a parameter-spatial correlation matrix, and obtains a spatial correlation feature matrix; establishing a Bayesian risk assessment model, predicting a dam risk level and outputting a risk evolution trend; and issuing a differentiated early warning instruction according to the risk assessment result. Through a distributed architecture combining edge calculation and central processing, real-time monitoring, intelligent analysis and accurate early warning of the dam safety state are realized, and the monitoring efficiency and the risk identification accuracy are improved.

The invention relates to the technical field of bridge health monitoring, and discloses a method for judging rigidity change of a bridge structure based on bridge health monitoring deformation data. The method comprises the following steps: establishing an initial data set of bridge deformation monitoring data and performing multi-scale decomposition processing to generate deformation component data of different time scales; inputting the deformation component data of different time scales into a pattern recognition engine, and recognizing a characteristic pattern data stream associated with the structural rigidity; constructing a rigidity influence factor sequence based on the characteristic mode data flow, and calculating a statistical characteristic quantity of the rigidity influence factor sequence through a sliding time window; performing multi-dimensional matching analysis on the statistical characteristic quantity and a historical reference database, and outputting a stiffness anomaly probability index; and activating a hierarchical verification mechanism according to the stiffness anomaly probability index, and confirming a stiffness change trend through a cross validation algorithm. Reliable data support is provided for bridge structure health condition evaluation.

The invention discloses a welding robot self-adaptive wire feeding control method, a platform and a medium, relates to the technical field of welding automation process control, and is suitable for welding process management under complex working conditions. According to the method, an arc voltage sequence and a molten pool characteristic parameter sequence in the welding process are collected, a welding state characteristic value is obtained through calculation, a reference database containing welding process parameters, welding working condition parameters and the welding state characteristic value is constructed based on historical experimental data, and corresponding preset reference indexes are generated through a mapping relation function; and in real-time welding, the welding state characteristic value of the current wire feeding control period is obtained and compared with a preset reference index, the deviation value is calculated, the welding state is judged according to the grading threshold value interval, the wire feeding speed is dynamically adjusted, and self-adaptive matching of the wire feeding speed and the welding state is achieved. According to the method, instability caused by traditional adjustment depending on experience can be overcome, dynamic control over the welding process is achieved, and the stability of a welding pool and the welding seam forming quality are improved.

The invention provides a method for evaluating the crack resistance of tunnel body concrete, and belongs to the technical field of wind tunnels, and the method comprises the steps: uniformly arranging multiple types of sensors on the surface of a tunnel body concrete structure to form a high-density monitoring network, employing a concrete circular ring constraint test piece to establish an anti-crack reference database, and constructing a multi-working-condition temperature stress database; acquiring multi-dimensional data such as strain, temperature and acoustic emission in real time, inputting the monitoring data into a liquid adaptive neural network model to predict crack initiation probability and expansion risk, establishing a crack risk assessment function, formulating a corresponding processing strategy according to a risk coefficient range, and starting an intelligent spray maintenance system; a closed-loop control system from monitoring perception to risk early warning to active protection is formed, and the technical problem that crack initiation and expansion risks of a hole body concrete structure cannot be accurately predicted under complex working conditions is solved.

The invention relates to the field of pollutant monitoring, in particular to an indoor decoration harmful substance monitoring and early warning method and system based on multi-sensor analysis. The method comprises the following steps that decoration whole-process parameter collection is carried out based on multiple sensor nodes, regional parameter distribution fitting is carried out, and an all-weather monitoring reference database is constructed; performing air pollutant identification marking on the all-weather monitoring reference database, and performing multi-time-point concentration change evolution analysis to obtain a multi-time-point concentration distribution evolution model; carrying out dynamic pollutant diffusion mining on the multi-time-point concentration distribution evolution model, carrying out combined pollution evaluation calculation, and constructing a multi-pollutant combined diffusion risk chain; dynamic interpolation modeling is carried out on the multi-time-point concentration distribution evolution model, micro-scale space-time disturbance reconstruction is carried out, and a pedestrian disturbance space-time diffusion model is constructed. According to the invention, through accurate and efficient risk assessment of indoor decoration harmful substances, dynamic early warning is carried out, and the safety of indoor decoration is improved.

The invention discloses a geotechnical engineering monitoring and early warning method and system, and relates to the field of geotechnical engineering, and the method comprises a construction module which is used for constructing an initial reference model for geotechnical engineering deformation instability monitoring according to geotechnical engineering design parameters and an initial structure state, extracting reference parameters, and storing the reference parameters to create a reference database; the sensing module is used for deploying a distributed sensing unit in a geotechnical engineering monitoring area, collecting real-time state data of a geotechnical structure, comparing the real-time state data with the reference model parameters in real time and outputting structure state deviation data; according to the method, real-time data is collected through a distributed hybrid sensing architecture, structural state deviation is accurately recognized by combining space-time coupling deviation calculation, multi-domain deviation characteristics are extracted, structural weakness is quantified, weak part orientation is defined, environment dynamic parameters and operation loads are fused to construct a risk coupling model, and the risk coupling accuracy is improved. And quantifying an instability risk coupling degree by using a dynamic index.

The invention provides an auxiliary rock core geological logging method based on drilling parameters, and relates to the technical field of geological investigation, the method is applied to a logging control terminal, and the method mainly comprises the following steps: obtaining regional typical rock core samples and five types of drilling parameter signals, classifying and extracting static characteristics of the rock core samples, and carrying out parallel noise reduction analysis on the parameter signals; obtaining a matching relationship between the rock core and the parameter signal, and constructing a lithologic parameter bidirectional association reference library; on the basis of the matching relation of the lithologic parameter bidirectional association reference library, the model is embedded into an incremental learning module to update the weight, and a subsection lithologic pre-judgment result is output; and calling a preset catalog template to load a pre-judgment result, generating a catalog report after man-machine interaction recheck and correction, collecting correction data, reversely transmitting the correction data back to the reference library to update a matching relationship, and triggering incremental learning of the model to complete a closed loop. The technical problems that in traditional rock core logging, regional lithology adaptation is poor, parameter interference is large, a model is not dynamically optimized, the deep operation risk is high, and efficiency is low are effectively solved.

The invention relates to the technical field of building construction, and discloses an attached vibrator vibration parameter matching decision-making system, which comprises a perception execution dual-mode vibrator network, a vibration acoustic fingerprint generation and analysis module, a fingerprint evolution time sequence inference module and a multi-target dynamic decision-making engine, generating a vibration acoustic fingerprint formed by multi-dimensional physical characteristics according to the response signal; the method comprises the following steps: constructing a fingerprint evolution sequential sequence, comparing the fingerprint evolution sequential sequence with a reference database, deducing the performance of a concrete material on line, and further calling a mapping function matched with the performance to evaluate a real-time compactness state; and finally, based on the evaluated compactness, the multi-target dynamic decision engine adopts a model prediction control strategy to generate an optimal dynamic operation instruction for each vibrator node. Closed-loop self-adaptive control of detection, evaluation, decision making and execution is constructed, the optimal vibration parameters can be matched in real time, the vibration quality and uniformity of concrete are remarkably improved, energy consumption is reduced, and dependence on artificial experience is reduced.

The invention provides a credible manuscript review decision-making method, system and equipment and a readable storage medium. The method comprises the following steps: receiving a manuscript to be reviewed, analyzing a document structure of the manuscript to be reviewed, and identifying a paper type; based on the identified paper type, calling a data processing agent, extracting a performance index corresponding to the paper type from at least one of a text, a table and a chart of the to-be-reviewed document, and converting the performance index into standardized structure data; traversing a preset database, comparing the data in the reference database with the structured performance indexes, and determining the similarity and the relative sequence of the performance indexes of the to-be-reviewed manuscript and the data in the database; and based on the similarity and the ranking percentage, outputting a quantitative score of the to-be-reviewed manuscript and the confidence of the quantitative score. Compared with the prior art, the method has the advantages that the professionality, objectivity and credibility of review are remarkably improved; and interference caused by training data prejudice and text subjective description is effectively reduced.

The invention discloses a digital transformation maturity evaluation system and method based on a multi-dimensional quantitative index in the technical field of building industry digitization. The method comprises the following steps: acquiring various system data of an enterprise; the method comprises the following steps of: normalizing original data of sub-indexes of each dimension in data of each system of an enterprise through an evaluation engine deployed in a cloud server, weighting and aggregating, and calculating a comprehensive maturity index; calculating a dynamic evolution rate by using an evaluation engine in combination with historical comprehensive maturity index data, an enterprise resource investment ledger and an industry reference library; calculating bottleneck factors by using an evaluation engine in combination with enterprise self-evaluation data and an industry benchmark database; the enterprise bottleneck dimension is evaluated based on the bottleneck factor and then an improved scheme is fed back, the enterprise efficiency level is evaluated based on the dynamic evolution rate and then resource investment is adjusted, and the enterprise maturity is evaluated based on the comprehensive maturity index and then a stage route map is generated. According to the invention, the digital level of the building enterprise can be scientifically diagnosed, and transformation path planning is guided.

The invention discloses a sky-base collaborative tower camera image high-precision positioning method, which comprises the following steps of: carrying out virtual squint projection on a satellite image based on a high-resolution satellite orthoimage of an area around a tower camera, and mapping the satellite image to a tower camera squint coordinate system; performing multi-scale phase fusion terrain enhancement feature extraction and cross-view matching on the satellite reference image and the tower camera image to obtain high-precision homonymy points and meter-scale control points; block adjustment and absolute positioning are carried out on a reference database image collected by a tower camera; and based on the positioned reference database, carrying out high-precision geographic positioning on the natural resource illegal behaviors. According to the method, high-precision positioning of the tower camera can be efficiently and automatically realized, the positioning efficiency of the tower camera image is remarkably improved, and protection of natural resources is facilitated.

The invention relates to the technical field of transformer winding fault detection, in particular to a preventive maintenance method and device for transformer winding faults. The method comprises the following steps: acquiring current signals of a three-phase winding of a transformer and vibration acceleration signals in different directions; obtaining an excitation-response spectrum matrix based on the current signal and the vibration acceleration signal, and constructing a health state vibration mode base; projecting the excitation-response spectrum matrix to a health state vibration mode base to obtain a vibration mode contribution coefficient vector; constructing a frequency-mode association graph structure according to similar conditions of different frequencies in a health state vibration mode base at the initial stage of equipment commissioning; endowing nodes of the graph structure with features to obtain winding structure state feature vectors, and establishing a reference database of each temperature interval; and matching the winding structure state feature vector and the vibration mode contribution coefficient vector in the current time period with a reference database, and determining a fault reason. According to the invention, the timeliness of transformer winding fault early warning is improved.

The invention provides a road bridge displacement monitoring method and system based on the Internet of Things, and relates to the technical field of the Internet of Things, and the method comprises the steps: obtaining an electrical vibration signal generated by a road bridge, forming an original vibration data set, packaging the original vibration data set, obtaining a packaged data package, carrying out the integrity verification, and obtaining a verified data package; according to a pre-stored bridge vibration measurement benchmark database, noise reduction and time sequence alignment processing are carried out on the verified electrical vibration signals in the verified data packet, a vibration sequence is generated, then the displacement trend is analyzed, a displacement trend data set is obtained, feature parameters are extracted, candidate displacement modes are extracted from historical displacement records according to the feature parameters, and the candidate displacement modes are stored in the bridge vibration measurement benchmark database. Generating a pattern recognition result; and comparing the displacement change parameter of the result with the vibration baseline range parameter list, and generating a structure safety early warning instruction containing an abnormal position and an abnormal degree, thereby realizing real-time and accurate monitoring and abnormal early warning of the road and bridge displacement.

The invention discloses a honey walnut kernel raw material two-dimensional quality screening system, and relates to the technical field of food quality detection, and the system comprises a raw material reference database module; a raw material two-dimensional detection module; a dynamic parameter calibration module; a multi-dimensional quality determination module; a reference data system of multiple production areas and multiple seasons is established through the raw material reference database module, basic indexes and interface interaction data are synchronously obtained in cooperation with the raw material two-dimensional detection module, and the problem that traditional detection only pays attention to the quality of a single raw material and ignores the combination quality of honey and walnut kernels is solved; meanwhile, the dynamic parameter calibration module calculates the comprehensive quality deviation and the equipment adjustment amount, self-adaptive optimization of detection parameters is achieved, insufficient precision caused by fixed parameters is avoided, and raw material quality abnormity can be recognized from multiple aspects of raw material basic attributes and interface interaction states by constructing a two-dimensional screening system of raw material basic quality and interface combination degree.

The application discloses a kind of project-oriented full life cycle intellectual property risk collaborative management method and system, it is related to project full life cycle management data processing technical field, the present application obtains the multi-source relevant data of each stage of target project full life cycle, constructs project intellectual property risk management and control benchmark database, completes data standardization processing and risk characteristic mapping, establishes the intellectual property risk grading management and control system covering project full stage, completes project full cycle risk identification, grade division and early warning by intellectual property risk intelligent identification model, issues risk disposal task to corresponding responsibility subject to realize multi-subject collaborative management, iteratively optimizes risk management and control strategy and outputs adapted management and control optimization scheme.The present application realizes project full life cycle intellectual property risk full coverage management and control, improves risk identification accuracy and multi-subject collaborative disposal efficiency, provides stable support for project intellectual property risk management and control.

The invention discloses a particle size channel correction method based on a capture efficiency evaluation system, and relates to the field of aerosol measurement, and the method comprises the steps: constructing a static box and a capture efficiency evaluation unit of an aerodynamic particle size spectrometer, and in a calibration state of the capture efficiency evaluation unit, carrying out the calibration of the capture efficiency evaluation unit; establishing a particle size channel reference database by measuring the monodisperse standard substance; the method comprises the following steps: preparing mixed aerosol of polydisperse test dust and monodisperse standard microspheres with known particle sizes, and respectively measuring particle size distribution original data of the mixed aerosol passing through the upstream and downstream of a cutter to be evaluated by utilizing a trapping efficiency evaluation unit; and inputting standard microsphere channel information in the particle size distribution original data of the particulate matters into a transfer learning model pre-trained based on a historical data set in the particle size channel reference database. According to the method, by analyzing the comprehensive correction quality factor calculated in the transfer learning model, quantitative evaluation of the reliability of the correction process is realized, and black box operation is converted into transparent and measurable quality control.

This invention discloses a method and system for real-time prediction of laser strike effects for rapid deployment. The system includes: defining standard reference environmental parameters, calibrating standard attenuation coefficients and standard power-to-target (PTO) values; constructing a benchmark database; collecting target environmental parameters and calculating the power attenuation ratio between the target environmental parameters and the standard reference environmental parameters; calculating the PTO power under the target environmental parameters based on the power attenuation ratio, and calculating the PTO power density based on the PTO power; finding the benchmark minimum effective power density of the target target material based on the benchmark database; and, if the PTO power is greater than or equal to the benchmark minimum effective power density, estimating the estimated penetration time of the target target material based on the PTO power, the power density at which the target material penetrates the target material under the standard reference environmental parameters, and the penetration time. This invention features extremely low computational complexity, fast response speed, and guarantees laser strike effects superior to empirical estimates.

This application discloses a visual laser composite monitoring method and system for disconnector switch contacts, relating to the field of power system monitoring technology. The method includes: establishing a corresponding reference database based on the target disconnector switch; acquiring real-time image information, real-time laser ranging information, and real-time control information of the target disconnector switch after completing the closing action, and analyzing these to determine if the switch is in place, obtaining closing determination information; and issuing a closing warning result based on the closing determination information. This application addresses the problem that existing disconnector switch position monitoring relies on external indicators and cannot intuitively and accurately determine the insertion depth of internal contacts. By combining visualization and laser ranging principles, it solves the difficulties of existing methods in determining whether the switch is in place and quantifying the insertion depth, aiming to fundamentally avoid power safety accidents caused by incomplete closing.

This application relates to the field of semantic analysis technology, specifically to a method and system for extracting audit anomaly semantics from unstructured data. The method includes: acquiring a benchmark graph and a benchmark database and their entity mentions; obtaining the strings of entity mentions based on their positions; acquiring an audit candidate set of strings from the benchmark database; determining a matching score based on the consistency of audit roles, and then filtering the candidate entity set; encoding the candidate entities, their audit role sets, entity mentions, and strings to obtain the probability distribution of entity mentions and candidate entities, and determining the audit role consistency score based on the probability distribution; classifying the association between entity mentions and candidate entities based on the overall magnitude of the audit role consistency score, and applying different audit rules to each category; and extracting audit anomaly semantics using the audit rules. This application improves the accuracy and efficiency of semantic extraction.

The invention relates to the technical field of block chain power supply management, and discloses a distributed power supply transaction management method of block chain nodes. According to the method, historical normal power supply transaction data is collected to establish a reference database, and the transaction detection period duration is determined according to the data size of a real-time power supply transaction data set. And monitoring node transaction records in a detection period, calculating a transaction frequency average value as a detection threshold value, and identifying suspected abnormal transactions in combination with the reference database. After the suspected abnormal transaction is confirmed, extracting the total transaction amount and the time distribution characteristic to judge whether the transaction is the abnormal transaction, and counting the occurrence frequency to calculate an initial risk coefficient; and the category of the abnormal transaction, the participation node identifier and the behavior pattern are extracted and converted into feature vectors, and malicious transaction node groups are grouped, analyzed and detected through a neural network classification algorithm. And if the malicious group exists, calculating an adjustment coefficient to correct the initial risk coefficient, and dynamically adjusting parameters of the next detection period.

The invention belongs to the field of prediction, and particularly relates to a drainage tube blockage risk prediction method and system, specifically, historical flow data and patient physiological parameters in a preset time window are obtained; extracting a flow macroscopic fluctuation degree, screening a matched patient subgroup from the reference database according to the similarity between the patient information and the flow fluctuation degree, constructing a multidimensional normal distribution model in a normal state, and calculating a mahalanobis distance between a current patient feature and the center of the model to obtain a baseline deviation degree; performing time-frequency analysis on the weighted flow sequence to obtain an energy spectrum, calculating physiological association weights of different frequency bands by combining with a blood coagulation function index, and further extracting energy distribution of a key frequency band in the weighted energy spectrum as a flow fluctuation feature; and inputting the baseline deviation degree and the flow fluctuation characteristics into a pre-training model, and outputting a drainage tube blockage risk value.

The invention discloses a cloud-edge integrated natural resource illegal clue real-time identification and positioning method, and the method comprises the steps: collecting natural resource data through an edge computing device, recognizing a natural resource illegal behavior through a target detection and segmentation network, and storing the target data of the illegal behavior; and matching the illegal behavior target data with the cloud geometric positioning reference database, and determining the specific coordinate position of the illegal behavior. According to the method, the target detection and segmentation network is constructed based on the visible light and near-infrared double-branch lightweight backbone network to identify the natural resource data, lightweight feature extraction is realized, the calculation amount of a network model is reduced, and the detection and identification efficiency is improved; meanwhile, a visible light branch adopts MobileNetV3-Lite, a near-infrared branch adopts a 5 * 5 depth separable convolution group for feature extraction, and the recognition effect on natural resource data is improved; and a modal alignment feature fusion module is embedded, so that the dislocation problem is avoided, the feature extraction capability of the network is enhanced, and the recognition precision is ensured.

The present application relates to the field of expressway green channel violation monitoring, and particularly relates to a kind of fresh agricultural products transport truck intelligent rapid detection method based on multi-source data fusion. Including the following steps: step one, build goods packaging loading proportion benchmark database and empty car mass benchmark database;Step two, collect total weight of loaded vehicle, actual vehicle information and transport goods information, obtain goods packaging loading proportion;Step three, obtain difference absolute value according to actual goods loading quality and theoretical maximum goods loading quality;Step four, determine trust level according to the ratio of difference absolute value and theoretical load;The trust level includes mild suspicion and severe suspicion;For the vehicle determined as severe suspicion, it is suggested to carry out artificial strict inspection, and the vehicle determined as mild suspicion is normally inspected. The present application can automatically identify insufficient loading, mixed loading goods and other various violations, significantly improve the accuracy and efficiency of green channel vehicle detection.

The present application relates to the technical field of product traceability, and in particular to a full life cycle traceability method and system for smart supply chain, which solves the technical problem of poor model generalization ability in the traceability process caused by data missing and the dependence of artificial intelligence model on high-quality labeled data and high-computing power cost in the prior art. The method comprises: constructing a standardized traceability benchmark database with a globally unique identifier as the primary key; analyzing the business correlation strength between each link and the target batch and the business causal correlation between different links, determining the completion value of the missing data and the credibility of the completion value; according to the credibility and the original labeled data, automatically labeling the completion value and determining the false noise index; filtering the completion value according to the false noise index, fusing the filtered completion value, the labeled data and the original data, and constructing a full life cycle closed loop traceability link with the globally unique identifier as the main line.

The application discloses a method for instant sensing and early warning of inorganic salt impurity content in urea, comprising: calibration reference database, including: pure urea solution density-temperature-concentration reference curve and data table calibration, typical inorganic salt impurity conductivity characteristic calibration and mixed urea solution conductivity-impurity content mapping relationship calibration; real-time acquisition of field data; calculation of inorganic salt impurity static content, including: calculation of total inorganic salt impurity concentration in the current urea solution; combined with urea dissolving tank geometric parameters and real-time liquid level data of the urea dissolving tank, the total mass of the inorganic salt impurity in the dissolving tank is calculated; dynamic calculation of impurity increment per unit time, based on multiple groups of static impurity mass data collected in continuous time sequence, the increase of the inorganic salt impurity in the urea dissolving system per unit time is calculated; dynamic calibration based on historical data; hierarchical early warning and interlocking control. Corresponding systems, electronic devices and computer readable storage media are also disclosed.

The application relates to the technical field of electrical testing, and discloses a cable connection reliability testing method for a locomotive braking system, which comprises the following steps: S1, reference modeling: performing diagnostic grouping on a connection point, collecting an initial electrical fingerprint of the connection point, and establishing a health reference database; S2, excitation and monitoring: applying dynamic excitation to a cable to be tested, synchronously collecting a transient resistance signal in real time, and calculating a resistance fluctuation spectrum; S3, fault diagnosis: performing correlation analysis on the resistance fluctuation spectrum and dynamic excitation characteristics, combining the reference database, and diagnosing potential connection defects; and S4, evaluation and reporting: performing quantitative evaluation on the health state of the connection point according to the diagnosis result, and finally generating a comprehensive diagnosis report. The application adopts electrical response monitoring and health index evaluation technology under dynamic excitation, accurately identifies intermittent defects, and realizes quantitative evaluation and prediction of the cable connection reliability.

The invention relates to the technical field of neural amplitude application, and discloses a system for identifying mental health based on neural amplitude, and the system comprises a signal collection module which is used for collecting electroencephalogram signal data and facial information of a user; the signal processing module is used for preprocessing the electroencephalogram signal data; the feature extraction module is used for carrying out time-frequency domain analysis on the pure neural amplitude signal and extracting an amplitude mean value, a peak value, a standard deviation and a frequency band energy ratio feature; the psychological health assessment module is used for comparing and analyzing the amplitude mean value, the peak value, the standard deviation and the frequency band energy ratio characteristics with a preset health reference database and calculating a psychological health deviation index; correcting the mental health deviation index according to the facial information; and the state identification module is used for identifying the psychological health state level of the user based on the psychological health deviation index. According to the invention, objectivity and accuracy of mental health assessment are realized.

The invention provides a circuit breaker fault diagnosis method and system based on a simulation model, and relates to the technical field of fault diagnosis, and the method comprises the steps: pre-constructing a three-dimensional electromagnetic field simulation model and a temperature field simulation model of a circuit breaker; coupling simulation is carried out, and a reference database is constructed; acquiring real-time operation current, acquiring real-time temperature distribution, and retrieving target associated working condition mapping data; calling a target simulation temperature, and carrying out pixel-level deviation comparison on the target simulation temperature and the real-time temperature distribution; performing backtracking parameter adjustment according to the temperature deviation threshold exceeding area; and performing fault judgment according to the fault root cause and the temperature deviation threshold exceeding region, and outputting a real-time fault type and an alternative maintenance strategy. Through the circuit breaker fault diagnosis method and device, the technical problem that in the prior art, due to the fact that a traditional method is not sensitive to early-stage local faults and slow-change deterioration of a circuit breaker, the circuit breaker fault diagnosis precision is low is solved, and through coupling simulation of the three-dimensional electromagnetic field simulation model and the temperature field simulation model, the circuit breaker fault diagnosis precision is improved.

The invention relates to the technical field of power equipment monitoring, in particular to a steam turbine generator unit dynamic vibration grading and scoring method and device, and the method comprises the steps: collecting vibration signals and operation parameters of a unit; identifying the current start-stop or load working condition of the unit according to the operating parameters; calling a preset dynamic reference database, and matching a vibration reference threshold value corresponding to the current working condition; calculating the deviation degree between the actually measured vibration and the reference threshold value; and calculating a health score by using a score deduction model and outputting a vibration grade. According to the method, a traditional fixed alarm threshold value is abandoned, a dynamic evaluation standard changing along with the working conditions is established, the real mechanical state of the unit under the variable working conditions can be reflected more accurately, complex vibration data are quantized into visual scores, operation and maintenance are effectively guided, and the false alarm rate is reduced.