192results about How to "Realize quantitative evaluation" patented technology

The invention relates to the technical field of block chains and security technologies, and particularly provides a security protection detection method and system for a smart contract based on a block chain. The security indexes at least comprise a password economics security index, a dynamic behavior characteristic index, a formalized verification index and a consensus layer security index; and performing quantitative evaluation on the collected multi-dimensional security index to obtain a comprehensive security score of the target block chain, scoring the cryptoeconomics security index through a preset cryptoeconomics security index scoring model to obtain a cryptoeconomics security score, and sending the cryptoeconomics security score to the target block chain. The current comprehensive security score of the target block chain is compared with the adaptive dynamic risk threshold, and if the current comprehensive security score is higher than the adaptive dynamic risk threshold, an early warning signal is sent out, and the effect of comprehensively evaluating the block chain smart contract is achieved.

The invention relates to the technical field of visual health management, in particular to a ciliary muscle adjustment training system and method based on eye movement tracking, and the method comprises the steps: collecting eye movement data of a fixation point track, an adjustment amplitude change rate and pupil response time in real time, and combining personalized parameters such as the age of a user, diopter and a training target; inputting a ciliary muscle training dynamic planning algorithm module, dynamically generating a stage scheme containing a training action type, a single-group duration, a stimulation interval and a focal length adjustment strategy, and adaptively adjusting the strategy according to real-time adjustment stability and a historical ability curve; through multi-dimensional matching analysis of a pre-stored standard eye movement template, a training specification score is calculated from the track goodness of fit, speed uniformity, amplitude standard-reaching rate and binocular coordination, visual fatigue parameters are obtained based on continuous training duration, pupil fluctuation and retina reflex change, a scoring result is dynamically corrected, and a visual fatigue evaluation result is obtained. Personalized, self-adaptive and quantitative safety control of the training process is realized.

The invention discloses an academic information conflict resolution method based on evidence intensity index grading. According to the method, all conflict viewpoints and traceability chains thereof are reserved by adopting a'full-amount retention, non-merging and non-deleting 'strategy; calculating a multi-dimensional weighted evidence intensity index of a comprehensive journal influence factor, an introduced frequency and an author academic influence for each literature; judging the conflict severity according to the difference value of the evidence intensity indexes of the two conflict parties, wherein the smaller the difference value is, the higher the severity is; modeling a conflict cluster into an auxiliary node layer in a graph database, and superposing the auxiliary node layer on a core graph without interfering with an ontology framework; conflict severity ranking results drive calibration reduction of downstream weight calculations. The method can be used for information conflict management in academic knowledge graph construction, and interpretable conflict severity grading reference is provided.

The invention relates to the technical field of safety operation and maintenance of power equipment, in particular to an active anti-condensation control method, system and equipment for a switch cabinet and a medium, which are applied to an active anti-condensation control system for the switch cabinet and used for preprocessing operation environment data of the switch cabinet acquired by a sensing layer based on a data acquisition unit; cabinet body surface temperature fusion and dew point temperature calculation are carried out based on the risk research and judgment unit, a dynamic condensation risk index and a global risk index are obtained, and a risk level is judged through the obtained dynamic condensation risk index and a preset threshold value; and according to the risk level and the global risk index, based on a cooperative control unit and according to a preset multi-mode control strategy, generating a control instruction to perform active condensation prevention of the switch cabinet. Therefore, the early condensation trend is pre-judged, and faults such as insulation reduction and metal corrosion caused by condensation formation are effectively avoided.

A space-based microwave and laser fused ocean subsurface layer temperature field reconstruction method solves the technical problems that current ocean subsurface layer temperature profile acquisition lacks direct participation of subsurface layer parameters and the reconstruction precision is low, and the ocean subsurface layer temperature field reconstruction method comprises the following steps: step 1, determining an error of laser radar inversion ocean subsurface layer temperature; 2, generating an input data set and an output data set; 3, constructing a sub-surface layer temperature profile reconstruction model, and performing training; and step 4, performing efficiency evaluation on the trained model. According to the method, the temperature field reconstruction precision is remarkably improved, and the traditional technical limitation depending on a pure sea surface parameter model is broken through.

The invention belongs to the technical field of geological disaster monitoring and early warning, and discloses a slope geological disaster multi-source remote sensing image intelligent early warning method and system, and the method comprises the steps: a multi-source remote sensing data adaptive collection step: constructing a multi-source remote sensing data cube; a multi-scale spatio-temporal feature coupling extraction step: generating a spatio-temporal coupling feature vector based on an improved Swin Transform architecture and an adaptive spatio-temporal attention mechanism; a disaster risk dynamic assessment and feedback step: calculating a disaster risk level based on a multi-modal deep learning network, and dynamically adjusting a feature fusion weight according to a risk assessment result to form closed-loop feedback; and an intelligent early warning decision and response step: constructing a multi-stage early warning threshold system to trigger early warning and generate an early warning report, the slope geological disaster early warning method realizes multi-scale spatial-temporal feature adaptive fusion and a closed-loop cooperation mechanism, and significantly improves the accuracy and intelligent level of slope geological disaster early warning.

The invention discloses a homeland space ecological protection and restoration effect evaluation method based on big data analysis, and relates to the technical field of ecological environment monitoring, multi-source big data and a remote sensing time sequence are utilized to divide a target restoration area into space units, and ecological carbon sink classification, vegetation index change, a biomass model and a soil carbon model are combined to evaluate the ecological protection and restoration effect of the homeland space. According to the method, quantification of carbon reserves of each space unit is achieved, the contrast unit which is not repaired but has similar conditions is matched, the carbon sink change prediction model is introduced to calculate the additional carbon sink increment of the repair unit relative to the contrast unit, contribution of natural succession and manual repair can be clearly distinguished, and quantification and traceability of the repair effect are achieved. According to the method, the extra carbon sink increment of each space unit is summarized to the region, and meanwhile, the space distribution pattern and the time change trend are analyzed, so that quantitative evaluation from a single point to the region and from the local to the whole is realized, and scientific reference is provided for ecological restoration planning and management.

The invention provides an obstacle association method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining measurement data from at least one sensor, and enabling the measurement data to comprise a plurality of obstacle measurements and detection information thereof; for each obstacle measurement to be associated, selecting a target reference point type from a plurality of predefined candidate reference point types according to detection information of the obstacle measurement to be associated; determining a measurement reference point from the detection information of the obstacle measurement based on a target reference point type selected for each obstacle measurement; determining a matching measure between a measurement reference point and a track reference point based on the measurement reference point measured by the obstacle and the track reference point determined for the fused track; according to the calculation result of the matching measurement between all the obstacle measurements and all the fused tracks, the association relationship between the obstacle measurements and the fused tracks is determined, and the accuracy and the reliability of obstacle association can be remarkably improved by adopting the method.

The invention provides an article processing method and system, electronic equipment and a storage medium, and the method comprises the steps: carrying out the image processing of a scene image of a target scene, and obtaining the target information of each to-be-processed article in the target scene; the target information comprises a mask pattern, a detection frame, a visibility score used for representing the shielding degree of the article, and a vertical height score used for representing the spatial height position of the article; according to the visibility score and the vertical height score, obtaining a target article from all the to-be-processed articles; performing attitude estimation according to the mask image of the target article, the detection frame of the target article and the scene image to obtain absorption pose prediction information of the target article and three-dimensional bounding box prediction information of the target article; and moving the target article according to the absorption pose prediction information and the three-dimensional bounding box prediction information. The carrying stability and efficiency of objects are improved.

The invention discloses a gene sequencing data management method for whole genome methylation sequencing, and relates to the technical field of gene sequencing data management.The method comprises the steps that original gene sequencing data are obtained and preprocessed, and a regional methylation matrix is generated; calculating a technical difference measure and a phenotype difference measure of each region unit based on the region methylation matrix and the sample metadata, identifying a technical methylation bias domain, and grading the technical methylation bias domain into a steady-state technical methylation bias domain and an event-driven technical methylation bias domain according to the occurrence frequency of the technical methylation bias domain in multiple batches; based on a linear adjacency relation and a co-bias relation, constructing a technical methylation bias super-domain and classifying the technical methylation bias super-domain; performing regional shielding on the methylation bias super-domain of the steady state technology, performing normalization correction on the methylation bias super-domain of the event-driven technology, and generating a corrected regional methylation matrix; calculating a sample quality index based on the corrected matrix and performing quality grading; according to the method, technical bias can be identified and corrected, and the methylation data quality and analysis reliability are improved.

The application discloses a cigarette case intaglio printing surface quality evaluation method and system based on image features, and relates to the fields of cigarette case intaglio printing quality detection and machine vision technology; the application collects cigarette case intaglio printing images through an image acquisition unit and pre-processes the images into gray-scale images, extracts multi-scale gradient features to obtain a comprehensive feature index for preliminary defect determination, analyzes edge morphology and texture periodicity in a transition area to obtain a comprehensive defect discrimination index, obtains a defect score based on the multi-scale comprehensive feature index and the comprehensive defect discrimination index, and completes defect classification and positioning through spatial clustering and secondary verification; four quality grades are divided based on defect proportions and defect numbers, a defect labeling map, a heat map and a standardized quality evaluation report are generated, and the problem that extracted image features cannot effectively distinguish normal dots from defective dots and result in a high false detection rate is effectively solved, and the precision and efficiency of cigarette case intaglio printing surface defect detection are significantly improved.

This invention discloses a path planning method for a snake robot based on fuzzy collision constraints and helical trajectory, comprising the following steps: S1, establishing an overall path model using the Archimedes' spiral equation; S2, obtaining the complete motion trajectory of the multi-node system through solving the state of the first node and recursion between nodes; S3, using a fuzzy collision risk function for flexible judgment to avoid path interference between the first segment and subsequent segments; S4, using the pitch as the optimization variable and employing a simulated annealing algorithm to find the optimal path parameters to ensure safe entry into the target area. The trajectory generated by this invention is smooth and continuous, can actively predict collisions, and can quantitatively optimize path parameters, making it particularly suitable for scenarios with high requirements for motion smoothness and safety, such as narrow pipe inspection, complex terrain exploration, space-constrained situations, or the need to maneuver into specific turning spaces.

The invention discloses a mining disturbance and rock mass weathering degree evaluation method based on rock core length intelligent identification, and belongs to the technical field of mine safety monitoring and rock mass engineering detection, and the method comprises the steps: constructing a multi-layer neural network model, identifying a single rock core section from a rock core image before mining, and determining the rock mass weathering degree of the single rock core section; calculating the average length of the rock core before mining in different depth intervals of each drill hole; geometric parameter information of a slope surface structural plane after mining is measured, a discrete fracture network model is constructed, and the average length of a virtual drilling rock core after mining is obtained through a virtual drilling method; and converting the average length of the rock core into fracture density, and constructing a fracture density change rate to quantitatively evaluate the influence of mining disturbance and weathering on the rock mass. According to the method, the influence of engineering disturbance and weathering is effectively separated from the total fracture development through the actual and virtual comparison thought, quantitative evaluation of the rock mass damage source is achieved, and the result is more accurate.

The invention relates to the field of hoisting, in particular to a multi-device collaborative hoisting safety scheduling system based on the Internet of Things. Surface point cloud data and wind direction data of a hoisted object are acquired through a data acquisition module; the cutting construction module constructs a three-dimensional model based on the point cloud data and constructs a fluid control group in combination with a hoisting movement velocity vector and a current wind direction, and the fluid analysis module constructs a plurality of morphological observation surfaces to cut local parts to form contours and extracts fluid interference characteristics to obtain interference characteristic values so as to analyze interference tendency; and the scheduling control module analyzes the hoisting path section in a strong interference tendency section by section to identify a risk local path section and controls the hoisting equipment to adopt a deceleration scheduling strategy. According to the method, quantitative evaluation and adaptive adjustment of the dynamic influence of the outdoor complex wind environment on the hoisted object in the hoisting operation are realized, and the safety and scheduling accuracy of multi-equipment cooperative hoisting are improved.

This invention discloses an intelligent detection and evaluation system for the entire life cycle of seismic isolation and damping structures, belonging to the field of intelligent monitoring technology for structural engineering. It includes: intelligent seismic isolation and damping bearings and multi-source sensing modules, a seismic isolation and damping layer edge gateway module, a full life cycle digital twin platform, and a regional building cluster collaborative monitoring and early warning module. It achieves system-level collaborative monitoring from individual bearings to regional building clusters; it integrates data across the entire chain from design to production, construction, operation and maintenance, and demolition, and uses blockchain technology to achieve tamper-proof traceability; it establishes a multi-physics field coupled damage evolution model, integrating environmental and mechanical parameters to achieve accurate quantitative damage assessment; and it constructs a remaining life prediction model based on historical damage accumulation, improving prediction accuracy. This invention solves the common problems of existing technologies, such as system fragmentation, lack of full life cycle coverage, insufficient multi-physics field coupling analysis, poor long-term sensor reliability, low remaining life prediction accuracy, and disconnect between monitoring and control.

The invention discloses a ship stern bearing interference press-fitting quality prediction method considering complex meteorological conditions. The method comprises the following steps: constructing a full-size multi-physical field simulation model; establishing a fluid calculation environment considering solar radiation; loading temperature field data for structural calculation; simulating a nonlinear contact interference press-fitting process; the press fitting curve, the press fitting contact area, the energy consumption in the press fitting process and the cylindricity error of the stern bearing after press fitting are used as evaluation indexes, and the press fitting failure risk is subjected to multi-dimensional quantitative evaluation. According to the method, the variable characteristics of the real environment temperature and the wind speed serve as the time-varying boundary conditions to be dynamically applied to the model, and quantitative evaluation of the dynamic change of the wind speed on the press-fitting quality is achieved; a solar radiation model and a DO radiation model are set in fluid calculation to realize accurate loading of non-uniform radiation heat flow, so that temperature field distribution of the stern shaft tube is accurately predicted; by constructing a multi-dimensional quantitative evaluation system, various failure risks in the press-fitting process are comprehensively identified.

The application provides a transformer fault monitoring method and system, which comprises the following steps: collecting multi-source monitoring data at a risk monitoring section in a tunnel; calculating a first state index reflecting structural stiffness degradation and a second state index reflecting a structure-seepage field coupling stability coefficient under the action of seepage water; calculating a comprehensive state index of the monitoring section at a target time through a preset dynamic evaluation model, and generating corresponding structure state grades and early warning information. Through the fusion of multi-source heterogeneous monitoring data and the construction of a dynamic evaluation model, more accurate and forward tunnel structure state perception and early warning can be realized, the limitations of traditional single threshold alarm are overcome, the lining mechanical property attenuation, groundwater seepage activity and water corrosion effect are coupled and quantitatively evaluated through the calculation of the structural stiffness degradation index and the seepage-structure coupling stability index, and the accuracy of state discrimination is significantly improved.

The present application belongs to the technical field of highway slope protection, and provides a highway slope flexible protection net safety state prediction method for highway construction, which comprises obtaining state characteristic data of the highway slope, highway compression characteristic data, state data of the flexible protection net and slope deep displacement data; a slope landslide probability analysis model is established according to the state characteristic data and the compression characteristic data, and a slope landslide comprehensive probability is generated; a state index is generated according to the protection net state data; a landslide impact estimation strength is generated according to the deep displacement data; then a damage analysis model is established in combination with the impact estimation strength, the landslide comprehensive probability and the state index, and a flexible protection net comprehensive damage probability is generated; the safety state of the protection net is predicted accordingly; the present application realizes quantitative evaluation of the damage probability of the flexible protection net by fusing the slope landslide risk, the protection net degradation and the landslide impact strength, and improves the accuracy and practicality of the safety state prediction.

This invention provides a machine learning-based method and apparatus for evaluating decision-making ability. The method includes: acquiring language data of target participants and converting the language data into text data; extracting candidate keywords and target sentences from the text data, extracting nodes from the target sentences to obtain target word pairs; constructing a participant influence graph using the candidate keywords as nodes and the target word pairs as directed edges; mapping the participant influence graph to a pre-constructed reference answer influence graph; and generating a decision-making ability evaluation result based on the hit ratio of nodes in the participant influence graph to their corresponding nodes in the reference answer influence graph, and the hit ratio of directed edges in the participant influence graph to their corresponding directed edges in the reference answer influence graph. This solves the problems of existing decision-making ability evaluations being highly subjective, lacking quantitative evaluation, and unable to be applied to multi-objective decision-making scenarios.

The present application relates to air conditioner detection technical field, propose a kind of air conditioner electrolytic capacitor health on-line detection method and system, the air conditioner electrolytic capacitor health on-line detection method includes collecting the voltage ripple signal of DC-DC step-down converter output in air conditioner, and detects the zero-crossing time of first time through direct current average voltage in a switching cycle, obtains first zero-crossing time;At the switching on starting moment or switching off moment of switching cycle, single sampling is carried out to output voltage ripple signal, and sampling voltage value is obtained;According to the running state of DC-DC step-down converter, it is judged that DC-DC step-down converter is currently in continuous conduction mode or discontinuous conduction mode;Based on the judgment result, corresponding capacitor parameter calculation model is selected, and the capacitance value and equivalent series resistance of output capacitor are calculated;The capacitance value and equivalent series resistance calculated are compared with preset threshold value, and if exceed threshold value, then trigger capacitor health state early warning.

The invention discloses a multi-layer fused island power transmission submarine cable channel dynamic risk management and control method, which comprises the following steps of: acquiring multi-source heterogeneous data of an island power transmission submarine cable channel, performing data preprocessing to obtain a multi-layer data set with consistent time and space, performing layer division on the multi-layer data set, and performing layer division on the multi-layer data set; three-dimensional modeling is carried out according to a layer division result through WebGL and Cesium engines, a multi-layer three-dimensional model of the island power transmission submarine cable channel is constructed, submarine cable risk factors of a multi-layer data set are extracted, a multi-dimensional risk evaluation index system of the power transmission submarine cable channel is constructed through a fuzzy AHP algorithm and a random forest algorithm, and an island power transmission submarine cable channel risk evaluation index system is constructed. And carrying out risk evaluation mechanism optimization on the multi-layer three-dimensional model, carrying out risk evaluation on the current state of the power transmission submarine cable channel according to a multi-dimensional risk evaluation index system, and matching an early warning mechanism of a corresponding risk level to carry out early warning response. The method has the advantages that the multi-source heterogeneous data are fused for visual modeling, and the response timeliness of risk assessment and early warning is improved.

This invention discloses a deep learning-based method for identifying enterprise contract compliance risks, comprising the following steps: S1, acquiring and preprocessing the enterprise contract text to generate a sequence of contract clauses; S2, semantically encoding the contract clause sequence to generate a clause semantic sequence and constructing a contract relationship graph; S3, performing embedding calculations and feature aggregation on the contract relationship graph to generate a risk representation sequence; S4, generating a rule embedding sequence based on a contract knowledge graph; S5, calculating the matching relationship between the risk representation sequence and the rule embedding sequence to generate a clause risk sequence; S6, combining manual review information to perform error calculations, and performing outlier detection and deviation correction to generate an error sequence; S7, updating the contract knowledge graph based on the error sequence. This invention, through deep learning semantic modeling, possesses advantages such as high accuracy in risk identification, high efficiency in contract review, and high stability of identification results.

The invention relates to the field of wafer code reading, and provides a wafer code reading method and system based on intelligent enhancement and dynamic optimization, and the method comprises the steps: firstly obtaining a wafer character image, and carrying out the image quality evaluation and image preprocessing; then, the preprocessed wafer character image is input into a deep transfer learning character large model for wafer character recognition, a wafer character recognition result and recognition confidence are obtained, and the model structure of the deep transfer learning character large model comprises a double-branch convolutional neural network and a Transform fusion framework; an incremental learning mechanism is introduced for abnormal sample learning; and finally, according to the wafer character recognition result and the recognition confidence coefficient, performing verification through a checksum algorithm, and judging whether a wafer code is directly obtained or a wafer character image corresponding to the wafer character recognition result is marked as an abnormal sample. According to the method, the accuracy, the adaptive capability and the engineering availability of a wafer code reading system in a semiconductor production environment can be remarkably improved.

The present application relates to the technical field of signal processing and analysis, and discloses a fetal heart rate and uterine contraction double monitoring system based on artificial intelligence, which comprises a signal decoupling module for constructing a signal decomposition network of multi-channel physiological signals; a signal feature extraction module for extracting a time domain feature set and a frequency domain feature set of a fetal heart rate signal sequence and extracting a time-varying nonlinear feature set of a uterine contraction signal sequence; a coupling relationship analysis module for constructing a heart rate-contraction joint space-time feature map of the time-varying nonlinear feature set, the time domain feature set and the frequency domain feature set, and determining a heart rate-contraction coupling relationship of a fetal heart rate state and a contraction intensity; an abnormal joint index analysis module for calculating a fetal hypoxia probability and a contraction frequency probability of a pregnant woman to determine an abnormal joint index of the pregnant woman; and a monitoring report generation module for generating a monitoring report of the pregnant woman. The present application can improve the accuracy and comprehensiveness of fetal heart rate and uterine contraction double monitoring.

This invention belongs to the field of process control and low-voltage electrical appliance testing technology, specifically relating to a circuit breaker short-circuit breaking capacity assessment system based on arc signal characteristics. The system includes: a signal acquisition device for real-time capture of transient voltage and current signals; a twin modeling architecture for constructing a lumped parameter model describing the evolution of the arc-extinguishing chamber; a state observer for optimal estimation of the nonlinear system state; a recursive identification module for analyzing the arc time constant, internal pressure, and arc root position; a feature mapping unit for identifying the degree of damage to the arc-extinguishing chamber caused by contact erosion or medium degradation; and an evaluation terminal for quantifying the score and outputting a report. This invention, through multi-field deep fusion and recursive estimation of latent variables, can accurately invert the internal erosion state and cooling capacity changes of the arc-extinguishing chamber, achieving quantitative assessment and trend warning of the circuit breaker's short-circuit breaking capacity.

The application provides a vehicle battery test method, device, equipment and storage medium, which can collect response data of the BMS under multi-dimensional operating conditions by automatically generating a BMS test script matched with the vehicle to be tested and performing driving condition test, fault simulation test and charging test in a test environment, and ensure that the test results fully cover the actual use scenarios of the vehicle. By inputting the driving message data and the charging message data into a health assessment model, the health status of the battery can be quantitatively evaluated; and by inputting the fault message data into a fault identification model, the abnormal type of the battery system can be accurately identified. The health assessment data and the fault classification data are integrated, the communication reliability index and the fault response index are calculated, thereby realizing quantitative evaluation of the BMS performance, generating a comprehensive score reflecting the response accuracy of the vehicle, and improving the objectivity and comparability of the evaluation results.

The invention discloses a high-speed rail station building railway passing area structure health monitoring system and method and a medium, and the system comprises a concrete structure self-sensing monitoring subsystem which is used for collecting sensing data; the image acquisition and recognition subsystem is used for acquiring surface images of the concrete structure and the steel structure, inputting a surface defect recognition model and outputting a structured surface defect recognition result; the data acquisition and preprocessing unit subsystem is used for receiving the sensing data and the structured surface defect recognition result, and respectively performing preprocessing and feature extraction to obtain a sensing data feature vector and an image feature matrix; and the data fusion and analysis subsystem is used for receiving the sensing data feature vector and the image feature matrix, inputting the sensing data feature vector and the image feature matrix into a structure defect recognition model and outputting a structure defect recognition result. According to the invention, all-around, accurate and real-time monitoring of the concrete structure and the steel structure of the railway passing area of the high-speed rail station building from inside to outside is realized.

The application discloses a control system design method for space gravitational wave detection. The method first performs modeling on a problem domain of the control system, constructs a first mapping matrix from a functional requirement to a behavior element, performs decoupling verification on the first mapping matrix until the first mapping matrix is in a lower triangular structure. Then, modeling is performed on a solution domain of the control system, a second mapping matrix from the behavior element to a design parameter is constructed, and independence verification is performed on the second mapping matrix until the second mapping matrix is in a lower triangular structure. Finally, a composite equivalent mapping matrix of the two-level matrix is calculated, and is used for change management of the control system. The application can realize formalized design and quantitative evaluation of a strong coupling control system architecture, identify coupling problems in an early design stage, improve design iteration efficiency and change management accuracy, and is mainly applied to control system design for space gravitational wave detection, and can also be applied to design scenes of various high-coupling high-precision space control systems.

The invention relates to the technical field of early screening of osteoporotic compression fracture, and provides an osteoporotic compression fracture screening method based on a multi-modal large language model. A multi-modal large language model is introduced to carry out quantitative evaluation on key functions such as posture alignment, motion coordination and pain-related reactions of a patient from images and videos, and the key functions are output in a standardized scoring form, so that structural features with clear clinical significance are automatically extracted. And constructing a machine learning model based on the above structured features, evaluating the OVCF occurrence risk, and determining key discriminant factors and the action direction thereof in combination with an SHAP feature contribution analysis and decision tree visualization method, so as to realize interpretable expression of the prediction process. The technical scheme which is safe, low in cost, explainable and easy to popularize is provided for early screening of the OVCF, and the method is suitable for various application scenes such as community and home screening.

This invention provides a dynamic response measurement device and evaluation method for electric spindles under multiple operating conditions, relating to the field of dynamic performance evaluation technology for electric spindles. The method includes: simulating multiple preset operating conditions using the multi-condition dynamic response measurement device to obtain electromagnetic force measured by a force sensor and displacement measured by a displacement sensor under these conditions; determining the dynamic stiffness of the electric spindle under these preset operating conditions based on the electromagnetic force and displacement; modeling the dynamic stiffness of the electric spindle under these preset operating conditions using the Kriging response surface method to obtain the dynamic stiffness of the electric spindle under all operating conditions; repeating the simulation and modeling N times to obtain the dynamic stiffness under all operating conditions after N modeling iterations; and determining the performance margin of the dynamic stiffness of the electric spindle under the tested operating condition based on the dynamic stiffness under all operating conditions after N modeling iterations and uncertainty theory. This invention achieves a quantitative evaluation of the dynamic response of an electric spindle under different operating conditions.