163 results about "Condition evaluation" patented technology

The invention discloses a servo motor dynamic overload detection and protection method based on a heat accumulation model, which relates to the technical field of servo system fault protection application, and comprises the steps of constructing a heat accumulation equivalent model, dynamically detecting an overload state, executing a heat dissipation recovery strategy, implementing a multi-stage protection mechanism, self-adaptive environment compensation and a historical data learning function. Through discretization processing of overload curve data and a nonlinear acceleration calculation strategy, the problems of protection lag and false triggering caused by traditional single-parameter detection are avoided, it is ensured that the motor can respond in time in light load, heavy load and load fluctuation scenes, the burn-out risk of the motor is reduced, and through integration of an environment temperature sensor and a heat dissipation condition evaluation unit, the reliability of the motor is improved. The heat accumulation coefficient and the heat dissipation attenuation parameter are dynamically adjusted, the problem of model deviation caused by environment interference is solved, when heat dissipation deterioration and temperature feedback abnormity are detected, the heat dissipation recovery period is automatically prolonged, calculation logic is corrected, and the continuous operation reliability of the system is improved.

The invention provides an electric motor coach whole vehicle OTA upgrading control method based on a BMS and a VCU, a temperature control dynamic threshold value and a multi-sensor weighted scoring algorithm are adopted, the BMS adaptively adjusts an SOC threshold value according to the battery temperature, the VCU synthesizes the vehicle speed, a hand brake and other states to generate upgrading condition evaluation, a user portrait database and a logistic regression model are combined, and the whole vehicle OTA upgrading control method based on the BMS and the VCU is obtained. Differentially pushing popup windows and predicting user cancel behaviors, implementing a block verification and low-temperature delay write-in strategy, dynamically optimizing remaining time display and low-bandwidth mode switching by a VCU, constructing a multi-stage rollback strategy library, automatically selecting a rollback path according to failure reasons, and cooperating with a safe restart mechanism of key state linkage; and in combination with NLP analysis user feedback driving system iteration, the low-temperature environment compatibility, the user interaction experience and the upgrading success rate are effectively improved, and a closed-loop optimized OTA upgrading ecological system is formed.

According to the clinical nutrition intelligent management system and method disclosed by the invention, nutrition status evaluation is carried out by applying a nutrition comprehensive evaluation formula, the standardization and objectivity of evaluation are greatly improved, the formula quantifies multiple key factors and gives weights, different evaluators carry out calculation based on the same information and formula, and the consistency of evaluation results is ensured; besides, the comprehensive nutrition evaluation formula comprehensively considers the body mass index, the disease-related nutrition influence coefficient, the nutrition-related index comprehensive score, the diet health degree score and other aspects adjusted by age and gender, accurately reflects the actual nutrition condition of the patient, and solves the problem of insufficient accuracy of traditional evaluation. And the nutrition plan generation module can customize nutrition plans for patients according to the nutrition comprehensive evaluation indexes in combination with a preset rule base, so that individual requirements of different patients are fully met, and a scientific, accurate and personalized solution is provided for clinical nutrition management.

The invention relates to a safety protection method based on dynamic monitoring of circulating flow of heat source equipment. The method comprises the following steps: collecting pipeline pressure data, circulating water flow data and circulating water temperature data in a circulating pipeline of heat source equipment through a multi-source sensor; inputting the circulation pipeline data into a preset heat source equipment state model to perform working condition evaluation so as to generate an equipment working condition report; performing fault feature extraction on the equipment working condition report, and identifying an abnormal operation mode according to the extracted fault features; in response to the identified abnormal operation mode, determining a fault type matched with the abnormal operation mode according to a preset fault type library; and based on the determined fault type, generating a corresponding control instruction by using a preset risk-instruction mapping rule. According to the invention, real-time monitoring of the operation state of the heat source equipment, automatic identification of abnormal modes and active safety protection control are realized.

The invention provides an operation condition evaluation method and system for a water supply system. Belongs to the technical field of water supply system intelligent management. The method comprises the following steps: acquiring real-time data of a water supply system through various types of sensors and monitoring equipment arranged in each link of the water supply system; meanwhile, collecting non-real-time data of the water supply system to form a multi-dimensional heterogeneous water supply system data set; aiming at data of different types and sources, adopting a self-adaptive preprocessing strategy to carry out data preprocessing; and generating a standard multi-dimensional heterogeneous water supply system data set. The spatio-temporal data analysis technology is adopted, spatio-temporal characteristics in water supply system data are deeply excavated, and the operation condition and dynamic change of the water supply system can be reflected more accurately.

The invention provides a children practice situation evaluation method, medium and system, and belongs to the technical field of deep learning models.The children practice situation evaluation method includes the steps that children and standard playing audio signals are collected and preprocessed into time-frequency representation, a time window sequence is constructed to calculate window similarity, beat mark points are extracted to form a rhythm sequence, and the rhythm sequence is obtained; a rhythm perception analysis model is input to generate a rhythm feature representation vector, a multi-dimensional deviation vector is formed by applying multi-level time model analysis, a rhythm deviation tolerance range matrix is constructed, deviation similarity is calculated, and finally a rhythm performance score is output through a rhythm evaluation neural network model. According to the method, a convolutional neural network, a long-short term memory network and an attention mechanism are fused, and comprehensive quantitative evaluation of the playing rhythm performance of the children is realized through large-scale data set training and expert scoring verification.

The invention discloses a method for screening marine container ship port berthing assistance based on a group relative strategy, and belongs to the technical field of artificial intelligence and ship automatic berthing assistance. The method comprises the following steps: constructing a docking environment situation cache data set, constructing a marine container ship port docking assistance model, performing preliminary environment understanding on docking environment situation cache data, writing the data into a shared context, and adaptively calling a tool library module by using an intelligent agent to obtain an updated shared context; a scheme generation and evaluation tool module is used for obtaining candidate berthing schemes and safety and feasibility scores of the candidate berthing schemes, convergence judgment is carried out on the candidate berthing schemes and the safety and feasibility scores of the candidate berthing schemes, and a final berthing assistance scheme is obtained through a termination condition evaluation module. Through the optimized large language model and the self-adaptive tool arrangement mechanism, the tool calling accuracy and the scheme convergence efficiency are improved, and safe and efficient marine container ship docking operation is achieved.

The invention discloses a fetal heart rate signal data enhancement method based on a conditional diffusion model, and the method comprises the steps: firstly obtaining and preprocessing a fetal heart rate signal, and obtaining a fetal heart rate signal containing noise; then, a noise prediction model based on conditional diffusion is constructed, the noise prediction model based on conditional diffusion comprises a conditional auxiliary information coding module and a noise prediction network module, and the fetal heart rate signal data containing noise are used for training the noise prediction model based on conditional diffusion; and finally, denoising the fetal heart rate signal by using the trained noise prediction model based on conditional diffusion. According to the method, the defects of a traditional generation model in the aspects of training stability, mode diversity and signal quality are effectively overcome, high-quality and high-fidelity data support is provided for fetus health condition evaluation, and a referable mode is provided for a data generation task of a time sequence signal.

The invention provides an online state evaluation method and system for a high-speed motor, and relates to the technical field of motor state monitoring and fault diagnosis, and the method comprises the steps: carrying out the feature fusion of time domain and frequency domain feature vectors, and forming a fusion feature vector; on the basis of the fusion feature vector, identifying abnormal features in the operation parameters and analyzing a time sequence evolution rule of the abnormal features to obtain an abnormal feature mode; generating a motor state comprehensive evaluation index based on the abnormal feature mode, and matching a pre-stored fault feature library to obtain a motor fault type and a state level; according to the fault type and the state level of the motor, a fault-tolerant control instruction is obtained, and the fault-tolerant control instruction is used for controlling the motor to maintain safe operation. According to the method, closed-loop management from data processing to motor safety control is realized through a complete technical chain of multi-modal data acquisition and fusion, adaptive feature extraction, abnormal mode recognition and fault-tolerant control instruction generation.

The invention relates to the technical field of vehicle engineering, and provides a vehicle NVH simulation report generation method, a storage medium and electronic equipment. The method comprises the steps of judging report types as processing dynamic stiffness, a vibration transfer function and a noise transfer function, post-processing simulation data according to the report types to obtain a text result and a curve graph, obtaining a target file, comparing simulation result data with a target value to generate a data table, and generating a simulation report in combination with a report template. And standard reaching condition evaluation, risk evaluation, standard reaching rate statistics and the like are realized. Wherein the vibration transfer function report and the noise transfer function report support a user to select or reject response points. Through automatic post-processing, target value comparison and template report generation, the problems that traditional manual processing is large in data size, disordered in format, difficult in chart generation and the like are solved, efficiency and accuracy are improved, a report containing a complete analysis conclusion is generated, and practicability is high.

The invention discloses an intelligent grease injection quantity accurate control method based on equipment working conditions. The method comprises the steps that S1, system initialization and parameter configuration are conducted; s2, collecting data of a multi-source sensor in real time; s3, after acquiring the acquired equipment operation state data, preprocessing the data, and extracting feature data; carrying out feature data fusion to generate a comprehensive feature vector; s4, performing equipment working condition state evaluation based on the comprehensive feature vector; s5, when the equipment working condition evaluation result shows that grease injection maintenance is needed, intelligent decision making is conducted on the grease injection requirement; s6, after the final grease injection strategy is determined, the grease injection process is precisely controlled; and S7, after grease injection is completed, the grease injection effect is evaluated, and feedback optimization is conducted. Grease injection parameters can be dynamically adjusted according to the actual working condition of equipment, the optimal lubricating state of the equipment is guaranteed, waste of lubricating grease is avoided, the reliability of the equipment is remarkably improved, the service life of the equipment is remarkably prolonged, and meanwhile the maintenance cost is reduced.

The invention provides a lifting machine working condition evaluation method and system combining a digital model and physical simulation, and relates to the technical field of industrial equipment safety evaluation.The method comprises the steps that firstly, a working condition evolution two-way conduction mechanism of a digital twin evolution model and a physical simulation deduction system is established, and two-way data transmission is achieved; executing multiple rounds of interaction evolution cycles based on the working condition evolution bidirectional conduction mechanism to generate a full-quantity evolution data set; then analyzing the full evolution data set to obtain an operation parameter trajectory, and associating a safety threshold to mine safety risk triggering trajectory features; and finally, generating a working condition safety evaluation result according to the safety risk triggering track characteristics. The working condition safety of the hydraulic system of the lifting machine can be comprehensively and accurately evaluated, and potential risks are found in advance.

The invention discloses an electric energy metering device working condition evaluation system and method based on cooperation of big data and cloud computing, and the system comprises a data obtaining module which is used for obtaining multi-dimensional operation data of an electric energy metering device, and the multi-dimensional operation data comprises power consumption data, equipment operation state data and environment temperature and humidity data; the evaluation module is used for performing working condition evaluation on the multi-dimensional operation data based on the big data and cloud computing collaboration platform; and the output module is used for outputting the working condition evaluation result of the electric energy metering device. Management personnel are helped to find potential problems in time before equipment breaks down, and equipment outage and high maintenance cost are avoided.

The invention relates to the technical field of asphalt pavement performance evaluation, in particular to an asphalt pavement technical condition evaluation method based on relative entropy information fusion, and the method comprises the steps: S1, building two parallel probability distribution paths; s2, inputting the average maintenance interval time theta into a Bayesian dynamic linear model, and adjusting a PQI distribution mean value mu in real time through a prediction step and an updating step; s3, non-linear correlation between subitem indexes and the PQI is established, a logarithm-logarithm regression model is adopted for a truncated normal path, and a logarithm regression model of a scale parameter lambda is adopted for a Weibull path; s4, respectively calculating the KL divergence of the distribution functions under the two paths; and S5, solving the minimum value of the target function h by adopting an SLSQP algorithm, and iteratively optimizing a regression coefficient beta-beta through gradient descent by taking a least square analytical solution as an initial value. According to the method, the relative entropy information fusion technology is introduced, and a double-probability model of truncated normal distribution and Weibull distribution is combined, so that a dynamic and accurate evaluation system is constructed, and the reliability and the practical value of an evaluation result are improved.

The invention belongs to the technical field of psychological health condition evaluation, and particularly relates to a psychological evaluation system and method based on multi-modal data fusion. The psychological assessment system based on multi-modal data fusion comprises a multi-modal data acquisition module, a data preprocessing module, a multi-modal feature fusion module, a psychological state analysis module, an AI interaction suggestion module and a system management security module. The psychological state is comprehensively evaluated in combination with multi-dimensional information of eye movement, text and voice; the intelligent dialogue analysis function is achieved through the AI interaction and suggestion module, evaluation is upgraded from static result output to dynamic interaction guidance, the psychological state of a user can be more accurately understood, and personalized support is provided.

The invention relates to the technical field of tooth image analysis equipment, and particularly discloses a tooth image analysis method for oral surgery. Through the steps of periodontal condition assessment and intelligent adjustment of image shooting positions, image data acquisition and preprocessing, intelligent segmentation of teeth and surrounding tissue structures, three-dimensional reconstruction and feature quantitative extraction, intelligent risk assessment and surgical scheme recommendation, analysis report generation and interactive optimization and the like, accurate analysis and surgical assistance of tooth images are realized; various advanced algorithms and models are adopted, the accuracy and efficiency of image analysis are improved, and a scientific and reliable reference basis is provided for oral surgery.

The invention discloses a concrete crack intelligent detection method based on four-cardinal-number coordinate attention enhancement, and belongs to the technical field of computer vision and civil engineering detection. According to the invention, a QuadCoord-YOLO network is constructed by improving a YOLO11 network architecture, and a CoT-CSP module and a QCE module are innovatively introduced: the CoT-CSP module deeply fuses a context converter mechanism and a CSP structure, so that the long-distance dependency relationship modeling capability is enhanced; the QCE module proposes a four-cardinal parallel channel attention mechanism for the first time, realizes three-path parallel multi-dimensional feature enhancement in combination with coordinate attention and deep convolution, and breaks through the information bottleneck of single-path compression of a traditional SE module. Experimental results show that according to the method, the bounding box detection mAP50 is improved from 72.6% to 78.1%, the segmentation mAP50 is improved from 58.9% to 66.6%, and only 3.2% of parameter quantity (82, 456 parameters) is increased. The method can be applied to an intelligent structure health monitoring system, and provides accurate technical support for concrete structure condition evaluation and maintenance decision.

The invention belongs to the technical field of life science, and particularly relates to a biological age construction method and system based on clinical physiological and biochemical indexes and an accelerated aging condition evaluation method. Initial clinical indexes and actual ages of the participants are collected, and outcome variables of the participants are recorded in a follow-up visit mode and comprise death or survival conditions, cardiovascular disease morbidity conditions and corresponding event occurrence time; screening key clinical indexes according to absolute Spearman correlation coefficients of the initial clinical indexes and actual ages; the key clinical indexes and the actual age are used as independent variables, death is used as a dependent variable, and a relational expression between the biological age and the key clinical indexes and the actual age is obtained through a Gompertz proportional risk model. According to the method, the biological age is calculated based on conventional clinical physiological and biochemical indexes, and compared with the biological age calculated based on omics and image data, the method can be more conveniently and efficiently applied to the majority of people, and aging evaluation is incorporated into conventional medical care items.

This invention discloses a dynamic overload detection and protection method for servo motors based on a thermal accumulation model, relating to the field of servo system fault protection application technology. The method includes constructing an equivalent thermal accumulation model, dynamically detecting overload states, executing heat dissipation recovery strategies, implementing multi-level protection mechanisms, adaptive environmental compensation, and historical data learning functions. By discretizing overload curve data and employing nonlinear accelerated calculation strategies, it avoids the protection lag and false triggering problems caused by traditional single-parameter detection, ensuring timely response of the motor under light load, heavy load, and load fluctuation scenarios, reducing the risk of motor burnout. By integrating an ambient temperature sensor and a heat dissipation condition evaluation unit, it dynamically adjusts the thermal accumulation coefficient and heat dissipation attenuation parameters to solve the model deviation problem caused by environmental interference. When heat dissipation deterioration and abnormal temperature feedback are detected, it automatically extends the heat dissipation recovery cycle and corrects the calculation logic, improving the continuous operational reliability of the system.

The invention relates to the technical field of tractor working conditions, in particular to a tractor speed-slope driving working condition construction method, system and device, and the method comprises the steps: collecting the real driving data of a tractor in a target scene through a vehicle-mounted terminal device, and obtaining the collected original data in batches through a cloud platform; preprocessing the original data; based on the Markov property of the operation condition, discretizing the processed operation data per second of the vehicle into state variables including the vehicle speed, the acceleration and the road gradient, counting each state transition probability matrix, and generating a plurality of condition sequences by using a Markov chain Monte Carlo method; establishing working condition evaluation indexes containing working condition characteristic parameters, and constructing a working condition characteristic objective function; and performing iterative optimization on the generated working condition sequence by adopting an elitist adaptive genetic algorithm based on the working condition characteristic objective function until the comprehensive deviation value reaches a preset target value, and obtaining an optimal working condition sequence. And the accuracy of the speed-slope working condition can be effectively evaluated.

The invention discloses a complex sea condition take-off and landing interaction platform based on a virtual reality technology and a design method. The complex sea condition take-off and landing interaction platform comprises a virtual take-off and landing simulation platform software module and a virtual take-off and landing hardware system. The virtual take-off and landing simulation platform software module is used for providing environment configuration, task scenario and simulation test functions; and the virtual take-off and landing hardware system is used for receiving driving operation input of a user and feeding back a virtual reality visualization effect and six-degree-of-freedom motion of the cabin. According to the invention, on the basis of complex sea condition take-off and landing motion safety evaluation requirements of the amphibious aircraft, simulation verification of a whole process of task scenario setting-human-in-the-loop simulation verification-execution condition evaluation can be realized through software module and hardware system design, and a coupling task efficiency result of unit operation and a design scheme is evaluated; and the complex sea condition take-off and landing adaptability design scheme iteration of the amphibious aircraft is supported.

The invention discloses a machine vision judgment method applied to clearance radar blade tip recognition, and belongs to the technical field of safety monitoring of wind driven generators. Comprising the following steps: acquiring radar ranging data synchronized with an image sequence in real time; performing online quantitative evaluation on the data quality of the radar ranging data to generate a working condition evaluation result; and based on the evaluation result, adaptively switching between a standard working mode and an auxiliary working mode. In the auxiliary working mode, the image sequence is processed to extract real-time motion visual features, and the real-time motion visual features are compared with a pre-constructed reference visual model, so that point-by-point reliability verification is carried out on radar ranging data, and finally screened high-reliability clearance measurement values are generated. According to the method, real blade signals and interference pseudo signals can be effectively distinguished, the false alarm rate is remarkably reduced, and the reliability of a clearance monitoring system in a complex environment is improved.

The application discloses a kind of safety warning method and device, comprising: image is collected and image is preprocessed;According to the image after pre-processing, work content identification, safety condition evaluation and rule violation determination are carried out, when it is determined that there is rule violation, warning information will be generated;Warning information is sent to the rule violation personnel, and its rule violation and correct operation method are informed.In addition, the application also discloses a kind of safety warning method and device, which is used to monitor and guide the construction process in real time and accurately, effectively improve the accuracy and timeliness of operation safety rule violation determination, reduce the risk of safety accident.

The application provides a hemodialysis water treatment PLC system networking monitoring platform and a building method, relates to the technical field of medical data processing, and comprises a system management layer, a network communication layer and a field measurement and control layer. The system management layer further comprises three functional modules, namely, a pure water demand amount prediction module, a use condition evaluation module and a load balancing scheduling module. The pure water demand amount in a future preset time period can be accurately predicted according to the historical pure water demand amount and the use conditions of each pure water unit, and input into the load balancing scheduling module, so as to schedule the use strategy of each pure water unit. The technical scheme can accurately predict the pure water demand amount in the future preset time period from multiple dimensions, and generate an individual scheduling strategy according to the actual use conditions of each pure water unit, so that the production efficiency of pure water can meet the requirements, and the production energy consumption can be effectively reduced through load balancing scheduling.

The application discloses a long-term service hydroelectric generating set operation state evaluation and early warning method and system, and belongs to the technical field of hydroelectric generating set state monitoring and fault early warning. The application converts a traditional point-to-point regression problem into a signal recovery problem in graph signal processing based on a graph smoothing regression method, integrates and constructs a high-dimensional working condition space adaptive neighbor graph by integrating a training set and a to-be-measured set, calculates an edge weight through an adaptive kernel bandwidth and constructs a graph Laplace matrix, introduces a variational objective function with global potential energy anchoring to complete full graph signal analytical solution, obtains a health benchmark value corresponding to a working condition, and realizes unit state evaluation and adaptive early warning through residual comparison of a measured value and the health benchmark value. The application has strong robustness, high full-working-condition evaluation accuracy, and can stably anchor the health benchmark without fault samples, accurately identifies early performance degradation of the unit, and is suitable for state maintenance application of long-term service pumped storage units.

The invention relates to the technical field of teaching evaluation, and discloses an AI learning condition evaluation and teaching effect quantification system based on a local model, and the system comprises a bottom layer which comprises a heterogeneous computing resource scheduling module which directly calls a reasoning layer of a Kylin AI subsystem, achieves the computing power scheduling of a domestic CPU and a GPU / NPU, and achieves the energy efficiency balance; and the middle layer comprises a localized AI model library and a data processing engine. According to the system, the whole process of collection, cleaning, analysis and storage of all learning condition data of the system is completed in a local terminal in a closed loop mode, uploading to a cloud end is not needed, risks such as privacy disclosure and unauthorized access caused by data transmission and cloud end storage are eradicated fundamentally, and a national secret algorithm encryption storage module and an authorized access mechanism are matched; the data privacy protection is further enhanced, the use requirements of the fields such as education and government affairs with strict data security requirements are completely met, and data autonomy and controllability are achieved.

The present application relates to the field of robot structure simulation optimization, and relates to a wind power tower cylinder cleaning robot structure simulation optimization method and system, which comprises a parameter acquisition module, a stress decomposition module, a working condition evaluation module, a risk discrimination module and a deformation screening module, can effectively reduce the risk of safety accidents caused by structural instability on the one hand; on the other hand, the scientificity and accuracy of the analysis results are ensured, important theoretical basis and data support are provided for subsequent structure improvement, material optimization and control strategy design, so as to realize efficient, safe and reliable operation of the cleaning robot.

The invention discloses a main network equipment power failure intelligent arrangement method and system based on equipment topology and workload saturation, and belongs to the technical field of power systems, and the method comprises the steps: determining a topological connection relation between to-be-maintained equipment based on topological structure data of main network equipment, and generating equipment topological association data; according to a preset equipment power failure strategy rule, on the basis of the topological association data, carrying out strategic grouping on equipment to be overhauled; for each to-be-overhauled equipment set, executing multi-dimensional limiting condition evaluation based on the corresponding power failure demand data; and generating an optimized main network equipment maintenance plan meeting equipment topology constraint and resource constraint conditions through a plan arrangement model based on a result of the multi-dimensional limitation condition evaluation. According to the invention, automatic processing and intelligent decision making of the power failure maintenance task of the main network equipment are realized, the workload of manual overall planning and repeated adjustment is effectively reduced, and the arrangement efficiency of the power failure plan is greatly improved.

The invention provides a method for quantitatively evaluating the residual life echelon condition of an aircraft group based on an optimal echelon difference rate. The method comprises the following steps of: collecting and sorting data of residual life and specified life of a stage of each aircraft; calculating the actual residual life ratio K of the group; according to the value of K, determining the corresponding optimal echelon under the condition that the remaining life of the cluster is not changed in two situations; calculating the difference value between the actual residual life of each airplane and the residual life of the corresponding airplane in the optimal echelon; calculating the optimal echelon difference rate CP of the aircraft; and carrying out quantitative evaluation on the residual life echelon condition of the cluster based on the CP value. The method changes the defects of time and labor waste, lack of uniformity and the like based on an aircraft residual life echelon graph, overcomes the defects of complex calculation process, weak interpretability and the like in an evaluation method based on residual life echelon value variance and residual life echelon distribution uniformity, has the advantages of simplicity and convenience in calculation, easiness in understanding, wide application scene and the like, and is suitable for popularization and application. And a foundation is laid for carrying out airplane group residual life echelon condition evaluation, airplane group use control and the like.

The application discloses a kind of low temperature extremely cold environment under optical fiber coating detection equipment and method, belong to coating detection technical field, comprising: obtaining temperature cycle times, coating thickness, heating gradient and other detection condition data, and coating adhesion, shrinkage, cracking length and other performance data;Construct detection condition evaluation model output detection condition coefficient;Based on detection condition, coating performance analysis model is established to output performance evaluation coefficient;Combined with the content of silicon dioxide nanoparticles and polyether polyurethane data, material analysis model is output material analysis coefficient;If the coefficient exceeds the preset threshold, then determine that the coating is unqualified and generate target formulation parameters.The application solves the technical problems of coating performance quantification evaluation and formulation precise optimization under extreme environment, significantly improves detection efficiency and reliability.