266 results about "Failure data" patented technology

The invention discloses an automatic driving test scene set optimization method, apparatus and device, and a storage medium. The method comprises the steps of generating a simulation scene file in an OpenSCENARIO format through preprocessing data; analyzing the simulation scene file through a teacher model, outputting a risk description text, receiving a scene feature vector and the risk description text through a student model, and outputting a failure probability; determining a comprehensive value index according to the failure probability, dynamically updating a scene library of automatic driving test scenes, collecting failure data in an AUT test, performing incremental fine tuning on the student model, and obtaining an optimized target test scene set; the problem of gradient estimation variance explosion caused by sparseness disasters can be effectively solved, the stability of model training is improved, the recognition capability of a rare long-tail failure scene is enhanced, the accuracy of failure probability prediction is improved, the judgment accuracy of the model to a boundary scene is improved, and the accuracy and comprehensiveness of scene value quantification are improved.

The invention is suitable for the field of equipment fault prediction, and provides an underground equipment multi-source fault prediction method and system based on a large model, and the method comprises the following steps: synchronously collecting an acoustic signal, a visual image, a vibration signal, environment data and working condition parameters of underground equipment in real time, and integrating the acoustic signal, the visual image, the vibration signal, the environment data and the working condition parameters into a multi-modal time sequence data stream; performing time alignment and cleaning on the multi-modal time sequence data stream, extracting deep features of each modal, and generating a multi-source fusion feature vector; inputting the multi-source fusion feature vector into a preset prediction model to obtain prediction information of the underground equipment, and generating fault traceability information; continuously optimizing parameters of the prediction model based on fault data collected in real time; and dynamically displaying prediction and traceability results, and automatically generating a maintenance strategy and an equipment scheduling scheme based on the results. According to the invention, panoramic perception of the operation state of the equipment and accurate capture of early weak fault features are realized, and the comprehensiveness and accuracy of fault detection are greatly improved.

The invention discloses a dynamic correction method and system for equipment fault mode occurrence degree, and belongs to the technical field of power plant electrical overhaul and maintenance, and the method comprises the steps: collecting operation parameters, maintenance records and historical fault data of target electrical equipment, and recognizing a potential fault mode set of the target electrical equipment; failure mode and influence analysis is carried out on each failure mode, and the initial occurrence degree, severity and detectability of each failure mode are determined; calculating an initial risk priority number of each fault mode, and determining an initial risk according to a preset risk grade division rule; dynamically adjusting the initial occurrence degree, recalculating the updated risk priority number, and generating a corrected risk level; and constructing a dynamic maintenance decision model, and outputting a maintenance priority and a maintenance time window for each fault mode. According to the method, the defects of real-time performance, comprehensiveness and flexibility of electrical operation and maintenance of the power plant are overcome, and efficient and accurate data support is provided for operation and maintenance of electrical equipment.

The invention discloses a data transmission method and computer equipment, and relates to the technical field of data transmission. The method comprises the following steps: acquiring a sending work request containing a local sending queue pair number and a work queue element; querying target sending information by taking a local sending queue number as an index; storing the working queue elements and the target sending information as operation contexts into an internal memory of a network card of a request end, and recording corresponding indexes; assembling a target data packet based on the target sending information and the memory index; receiving a feedback data packet of the target data packet and extracting a memory index and a serial number carried by the feedback data packet; if the feedback data packet is a correct confirmation data packet, judging that the sending of the work request is completed and releasing the context; and if the feedback data packet is a confirmation failure data packet, calculating a retransmission data initial address and organizing retransmission. The technical problem that the effective connection number of network cards is limited by hardware resources in the prior art is solved, and the technical effect of supporting large-scale network parallel communication is achieved.

The invention discloses a high-speed rail vehicle body aluminum alloy material corrosion fatigue performance simulation method and system, and belongs to the technical field of material corrosion fatigue testing. The method comprises the following steps: acquiring multi-source data of a high-speed rail body aluminum alloy material on an actual service route; establishing a material corrosion conversion coefficient database; obtaining an equivalent mapping relation based on the service environment and the load input set; generating a laboratory equivalent accelerated environment spectrum; and executing the laboratory equivalent accelerated environment spectrum, obtaining material failure data, and if the comparison difference is not less than a preset threshold value, correcting the calculation parameters of the equivalent mapping relation and regenerating the spectrum system. Through synchronous acquisition and fusion analysis of the multi-source environmental data and the mechanical load and in combination with the corrosion conversion coefficient database construction and closed-loop verification optimization method, the problem of test result distortion caused by separation of a laboratory simulation environment and an actual service condition in the prior art is solved.

The invention relates to the technical field of building construction, in particular to a prefabricated building construction progress automatic monitoring method. The method comprises the following steps: obtaining a building construction design drawing; based on the building construction design, prefabricated building component design data is extracted; building a building construction three-dimensional model based on the prefabricated building component design data; building component transportation simulation is conducted according to the building construction three-dimensional model, and building component transportation data are obtained; identifying a component cracking degree based on the building component transportation data; performing connection failure analysis based on the component cracking degree to obtain component connection failure data; predicting a reinforcement corrosion risk based on the component connection failure data; evaluating assembly durability based on the reinforcement corrosion risk; and extracting hoisting design data of the building construction design drawing. Based on the building construction technology, the efficiency and accuracy of prefabricated building construction progress monitoring are improved, and the accuracy of construction plan execution and the resource scheduling efficiency are improved.

The invention discloses an intelligent switching method and system based on multi-platform overseas payment, and the method comprises the following steps: accessing a plurality of overseas third-party payment platforms, and building a unified order processing flow through a transaction service; a monitoring point is embedded in the payment interaction process of the application program and the transaction service, payment success data and payment failure data are collected, and corresponding counters are maintained respectively; regularly pulling the counter in a parallel task mode, and counting the failure rate of each payment mode in the first time window T1; when it is detected that the failure rates of a certain payment mode in continuous N first time windows T1 all exceed a threshold theta, triggering exception handling, including marking the payment mode as abnormal, and automatically switching to a standby payment mode; and when it is detected that the failure rate of the off-shelf payment mode in the second time window T2 is recovered to be below the threshold value, automatically executing recovery operation, including re-shelving the payment mode.

The invention discloses a safety valve service life evaluation method based on pressure history statistics and dynamic damage accumulation. According to the method, only safety valve inlet pressure time sequence data is needed, a load cycle is extracted through an improved rain flow counting method, and the nonlinear damage increment is calculated according to an S-N curve model dynamically degraded along with the accumulated damage degree. According to the method, a self-adaptive failure probability threshold is established based on historical failure data, and probabilistic prediction of the residual life is realized. The model is clear in physical significance, only needs a single data source, significantly reduces the implementation complexity and cost, greatly improves the precision of life prediction by considering the degradation of material performance, and is especially suitable for predicting the accelerated failure risk of the safety valve in the later period of long-term service.

The invention relates to the technical field of data mining, in particular to a mining analysis method and system for vehicle wire harness fault data, and the method comprises the following steps: extracting a maintenance timestamp and a path segment index to construct a two-dimensional coding matrix, carrying out the segment division of a vehicle operation mileage, generating a total number of segment faults and a probability sequence, and carrying out the mining analysis. And executing logic comparison according to the serial numbers of the node major loop and the branch line, configuring a path weight parameter, executing tensor operation on the probability sequence and the weight parameter, obtaining an aggregation degree parameter, and constructing a mining analysis result. According to the method, a data multi-dimensional space topological structure is remodeled, dynamic recession mapping is calculated to capture a deep derivation relation, a probability sequence and a weight parameter are input into a sensing network to execute feature extraction, an aggregation degree index is obtained, and according to feature collection operation, space associated conduction paths of wire harness state degradation at different positions are deeply mined. And a fault data mining analysis system with a network deep topology feature analysis capability is formed.

The invention discloses a ship mechanical and electrical product accelerated life test stress profile generation method and device, a medium and a product, and relates to the field of ship reliability engineering and test.The method comprises the steps that working condition profile data and marine environment stress data of a target ship mechanical and electrical product under a typical navigation task are obtained, and a working condition-environment mapping model is established; according to the designed life target of the target ship mechanical and electrical product, the whole life cycle total damage to the target ship mechanical and electrical product is determined by using the working condition-environment mapping model and the failure physical damage model, an accelerated stress profile is generated by strengthening the stress level with damage equivalence as the principle, an accelerated factor is calculated, and an accelerated life test is performed; and failure data of the failure event is recorded, parameters of the failure physical damage model are calibrated, and the accelerated stress profile is iteratively optimized by using the calibrated failure physical damage model. Accurate and dynamic generation of the accelerated life test stress profile of the ship mechanical and electrical product is realized.

The invention relates to the technical field of data synchronization, in particular to a financial data synchronization method and system, and the method comprises the steps: checking factor packaging: extracting original financial data, adding checking factors to generate a checking packaging unit, and enabling the checking factors to comprise data structure fingerprints, articulation relation codes and environment dependence parameters; dynamic integrity verification: inputting the verification packaging unit into a dynamic verification engine, loading a corresponding dynamic verification matrix to carry out data integrity verification, and outputting a verification passing unit which passes the verification; adaptive feedback regulation and control: performing attribution analysis and strategy feedback on an abnormal mode in a verification failure unit to realize system internal control closed-loop optimization; and exception handling writing: executing an exception handling tree-based handling operation on verification failure data, writing a verification passing unit into a target financial system, and generating an audit tracking chain. According to the method, each piece of write-in data is ensured to have traceability and compliance, and the intelligence, self-healing property and audit credibility of the financial data synchronization process are enhanced.

The invention discloses a film capacitor life intelligent prediction method fusing multi-dimensional data, and relates to the technical field of capacitor reliability. According to the method, a multi-dimensional data fusion model is constructed, and three types of core data are creatively integrated: 1, process parameters of the whole manufacturing process of the thin-film capacitor; 2, operation condition data is monitored in real time; and 3, a large-batch historical failure database based on acceleration factors. And dynamically associating the multi-source heterogeneous data by adopting a machine learning algorithm, establishing a nonlinear life attenuation model, and realizing early life early warning by quantifying the contribution degree of process defects to electrical performance degradation. Compared with a traditional method, the method has the advantages that the prediction precision is improved by more than 40%, progressive failure caused by metal layer micro-scratch, self-healing effect accumulation and metal spraying contact degradation hidden factors can be identified, and an accurate basis is provided for preventive maintenance of power electronic equipment.

The invention discloses a metro vehicle supervision manufacturing multi-station data traceability control system, and relates to the technical field of industrial data traceability, and the system comprises a data collection module which obtains the original manufacturing data of each station of metro vehicle manufacturing; the feature extraction module is used for processing the original manufacturing data and generating behavior fingerprint vectors and station features; the identity analysis module is used for carrying out feature matching calculation and outputting a candidate component identity set and matching similarity; the logic verification module is used for carrying out logic consistency verification and determining the identity of the target component; and the traceability control module establishes a mapping relation with the original manufacturing data based on the identity of the target component, generates a unique traceability node, and writes the traceability node into a multi-station traceability data chain. According to the method, the problems of identification failure, data link breakage and affiliation errors caused by station switching or equipment sharing are solved, supervision data continuity and identity recognition accuracy are improved, and reliable support is provided for vehicle full-life-cycle data tracing.

The invention discloses a reliability index distribution method and system suitable for a ship power system. The objective of the invention is to overcome the defects of a traditional ship power system reliability evaluation method in the aspects of dynamic performance, real-time performance and adaptability. According to the method, real-time operation data, including temperature, pressure, vibration frequency, abrasion loss and other key parameters, of all subsystems in a ship power system are collected, and dynamic reliability influence factors are constructed based on the data. By introducing a deep reinforcement learning model, the system can adjust the weight coefficient of each subsystem according to the current dynamic reliability influence factor. In addition, in combination with historical fault data, a fault propagation path is modeled through a graph neural network (GNN), and a fault rate correction factor is calculated, so that the accuracy of reliability distribution is improved. And finally, verifying the rationality of a reliability distribution result through a Monte Carlo simulation method, and dynamically adjusting system parameters according to a simulation result to ensure stable operation of the ship power system.

The invention relates to a method for establishing an SIL dynamic evaluation model based on Weibull distribution, and belongs to the technical field of safety protection systems. The method comprises the following steps: (1) establishing a dynamic cumulative failure probability function model based on Weibull distribution according to obtained failure data through a reliability test; (2) calculating Weibull distribution parameters by using a median rank method, a linear regression fitting method and a least square method; (3) drawing a function model image by using MATLAB software; and (4) in combination with a fault tree method, establishing an SIL dynamic evaluation model based on Weibull distribution. According to the SIL dynamic evaluation model for the final executive component of the safety instrument system based on Weibull distribution, the dynamic change trend of the final executive component PFDavg along with time obtained by the model basically accords with the reality, the accuracy of SIL evaluation is improved, and scientific method support is provided for design personnel of the safety instrument system.

The invention discloses an unmanned aerial vehicle inspection method and system for a wind turbine generator set, and belongs to the technical field of intelligent operation and maintenance of the wind turbine generator set. The method comprises the following steps: establishing a digital inspection task model based on the layout and historical operation fault data of the wind generating set, and generating an initial inspection path scheme; controlling the unmanned aerial vehicle to fly along the path, and collecting multi-source inspection data by using an airborne sensor; carrying out processing and fusion analysis on the data, realizing abnormity identification and fault diagnosis, and outputting a diagnosis result containing a fault type and a severity level; and generating a structured inspection report according to a diagnosis result, and dynamically adjusting a flight path, a hovering position and sensor parameters. According to the invention, intelligent optimization of the inspection path and adaptive control of data acquisition are realized, the inspection efficiency and the data quality are improved, real-time diagnosis and predictive maintenance of faults are supported, the labor cost and the safety risk are effectively reduced, and the method is suitable for intelligent operation and maintenance of a large-scale wind power plant.

The invention discloses a rehabilitation equipment fault data monitoring method and system, and particularly relates to the technical field of electric digital data processing, comprising the following steps: S1, synchronously acquiring equipment operation time sequence data and patient interaction time sequence data, S2, performing feature extraction and adaptive fusion on the two types of data, and S3, acquiring the data of the equipment operation time sequence and the patient interaction time sequence; s3, equipment health prediction and rehabilitation curative effect deviation prediction are executed in parallel based on the fusion features, and the relevance of the equipment health prediction and the rehabilitation curative effect deviation prediction is analyzed through a cross attention mechanism, and S4, a personalized dynamic baseline model is combined, and an equipment fault risk value, a curative effect deviation degree and a relevance confidence degree are integrated to generate hierarchical monitoring feedback. According to the invention, by constructing an equipment-patient-curative effect ternary closed-loop analysis framework, the spanning from pure fault early warning to treatment effectiveness guarantee is realized, the problems of single monitoring dimension and unhooking with clinical curative effect in the prior art are solved, and the method has the outstanding effects of strong early warning perspectiveness, low false alarm rate and support of root cause analysis.

The invention discloses a service life prediction and reliability evaluation method for a DC-DC converter. The method comprises the following steps: carrying out consistency screening on test samples; determining the type and level of acceleration stress; screening pre-test and stress sensitive parameters; the implementation of the life test is accelerated; and constructing a life model and evaluating reliability. According to the method provided by the invention, by establishing the voltage-temperature dual-stress acceleration model, the degradation process of the device under the actual working condition is reflected more accurately; an analysis framework suitable for scenes containing failure data and non-failure data at the same time is provided, and point estimation and confidence intervals of parameters for reliability evaluation under the non-failure data condition can be provided; the method realizes accurate evaluation of the service life and reliability of the DC-DC converter before installation, has the characteristics of high prediction precision, high engineering practicability, wide application range and the like, can be widely applied to the field of reliability evaluation of various DC-DC converters, and provides data support for installation type selection, maintenance strategy formulation and system reliability design of devices.

The invention discloses a data reconstruction method and electronic equipment, and relates to the technical field of computers, the data reconstruction method comprises the following steps: determining running state information of a current disk and respective access heat information of a plurality of data blocks in the current disk, the running state information comprising a state quantity used for indicating at least one current physical state of the current disk; determining a risk assessment value of the current disk according to the running state information and the access popularity information; under the condition that the risk assessment value of the current disk is greater than a fault early warning threshold value, writing a target data set in at least one target data block in the current disk into a persistent memory; when it is detected that the current disk breaks down, data reconstruction is conducted on the fault data in the current disk according to the target data set obtained from the persistent memory, and the technical problem that in the prior art, the data reconstruction efficiency is low is solved.

The invention provides a reliability evaluation method and device based on highly accelerated life test fault data in order to solve the problems that the reliability of existing highly accelerated life test fault data lacks an effective quantitative evaluation means and the evaluation precision is insufficient due to limited fault data. The method comprises the following steps: converting fault data in a step stress test into equivalent fault data under a normal stress condition according to an acceleration factor and time reduction principle; based on a Bayesian theory and a self-service sampling method, constructing a single-parameter reliability evaluation model; and constructing a multi-parameter reliability evaluation model by adopting an entropy-distance fusion algorithm, and carrying out comprehensive evaluation on the reliability of the test complete machine. According to the method, the time reduction principle and the Bayesian theory are combined, reliability quantitative evaluation under the stepping stress condition is achieved, the evaluation precision is improved, compared with performance degradation data, the method has wider applicability, and the actual evaluation requirement can be better met.

The invention relates to the technical field of data processing, in particular to a polycaprolactone polyol synthesis path optimization method based on a genetic algorithm, which comprises the following steps: constructing an antagonism framework taking the genetic algorithm as a generator and a process stability discriminator; directionally generating virtual failure data by using a failure path deduction model so as to solve the problem of data sparsity and train a discriminator; quantifying the robustness risk of the candidate process parameters through a virtual disturbance unit, and taking the risk as a key part of a fitness function of the genetic algorithm; meanwhile, the interpretability analysis result of the discriminator is used for guiding the variation direction of the genetic algorithm. According to the method, the process path with high performance and high robustness can be found, and the industrial practicability and decision-making efficiency of an optimization result are remarkably improved.

The application discloses an industry data management method based on big data, and belongs to the technical field of industry data management, and specifically comprises the following steps: constructing an after-sales data warehouse based on historical after-sales maintenance records; extracting the correlation relationship of spare parts and restoring the level of finished parts from the after-sales data warehouse to construct a coupling correlation topology; screening abnormal samples deviating from the expected failure mode, extracting corresponding supplier production process parameters and batch information, and forming a process parameter set; deconstructing the coupling correlation topology and combining the process parameter set to establish an attribution mapping parameter set; constructing a causal inference model between the attribution mapping parameter set and the failure mode to obtain the attribution weight of each supplier batch to the failure mode; and marking the supplier batch with an attribution weight exceeding a preset threshold as a target traceability batch and outputting the same. The application realizes accurate attribution from after-sales failure data to a supplier batch, and provides a quantitative basis for supply chain quality control.

This invention relates to the field of nuclear safety equipment testing technology. It provides a method for optimizing parameters in accelerated reliability testing of nuclear safety-grade valves. The method involves the following steps: establishing a correlation model between accelerated stress and life parameters; calculating the acceleration factor and formulating an initial test plan; deriving the theoretical sample size based on the success-failure test method combined with confidence requirements; constructing an extended coefficient table using the range-extended test method to optimize test parameters; generating multiple accelerated test plans through chi-square distribution verification; eliminating plans that do not meet the life characteristic threshold; determining the optimal sample size, number of failures, and test cycle through comprehensive test cost and risk analysis; and finally outputting a complete test plan including the action interval time. This method solves the problems of limited test samples, insufficient failure data, low confidence levels, and difficulty in adapting accelerated models in existing high-reliability valve technologies.

The invention relates to the technical field of solid-state storage, in particular to a solid-state disk data consistency control method, and aims to solve the problems that a solid-state disk is poor in data consistency, high in power failure loss risk and difficult to balance writing efficiency and consistency under the scenes of power failure, parallel writing and garbage collection. Full-process consistency control is realized by constructing a'cache-persistent cache 'layered architecture and combining a multi-channel write synchronization, power failure emergency migration and periodic check repair mechanism: receiving data and storing the data in a cache firstly, distributing a multi-channel write task according to weighted polling and synchronizing the state; the standby capacitor is triggered to supply power during power failure, and the cache data is migrated to the persistent cache according to the FIFO; and according to a configurable period of 1-24 hours, flash memory block data are verified through SHA-256 hash, and when the flash memory block data are inconsistent, the flash memory block data are repaired from a persistent cache / backup block. According to the method, the data consistency compliance rate of the solid-state disk is larger than or equal to 99.9%, the power-down data loss rate is smaller than or equal to 0.01%, the write-in delay is reduced by 15%-20%, and the method is adaptive to consumption-level, enterprise-level and industrial-level solid-state disks.

The invention discloses a natural disaster emergency response method and system based on power failure monitoring, and the method comprises the steps: obtaining a set of power failure data, which comprises a power failure influence unit, a power failure public and special variable, a power failure cell and an initial power failure influence user number; and a data processing and analysis algorithm is introduced at the rear end to carry out collaborative analysis on a set of the power failure data, so that the total number of the power failure users is accurately predicted, and corresponding natural disaster emergency response is carried out. Therefore, the intelligence of emergency management and response of natural disasters can be realized, so that the disaster response efficiency is improved, and the loss is reduced.

A semiconductor test apparatus comprises a failure memory (FM) block configured to store failure data generated from a result of testing a semiconductor device, a buffer memory (BM) block to / in which the failure data stored in the FM block is configured to be copied / stored, and a field programmable gate array (FPGA) configured to perform a first control operation to control the FM block and a second control operation to control the BM block.

The invention provides a corrosion life prediction method, system and equipment for a high-cleanliness chemical supply system pipeline and a storage medium. The corrosion life prediction method comprises the following steps: obtaining target parameters related to corrosion in a high-cleanliness chemical supply system pipeline, wherein the target parameters comprise a pipeline corrosion state parameter, a medium characteristic parameter, an operation environment parameter and historical failure data; inputting the target parameter into a pre-constructed corrosion life prediction model to enable the corrosion life prediction model to extract a potential corrosion characteristic sequence based on the target parameter, and performing time sequence processing on the potential corrosion characteristic sequence to generate a corrosion life prediction value; and grading the corrosion risk of the high-cleanliness chemical supply system pipeline according to the corrosion life prediction value. According to the technical scheme, quantitative evaluation and real-time prediction of the corrosion state of the pipeline of the high-cleanliness chemical supply system can be achieved.

The invention discloses an electronic hand accelerator rotating speed storage and control system, method and device, and relates to the technical field of commercial vehicle electronic control, the storage and control system is arranged in a storage control unit, and comprises a signal acquisition module, a logic processing module, a storage management module, an operation control module and an interactive output module. The system verifies a preset action sequence to activate a configuration mode when an engine is not started by collecting an external switch signal, the target rotating speed is calculated through an add-subtract switch, boundary amplitude limiting is conducted, and setting can be completed without a special calibration tool. And the operation control module executes and starts anti-galloping inspection, and activates constant rotating speed control only when detecting that the edge of the opening switch jumps and the power takeoff is physically combined. In addition, the system has the functions of abnormal power failure data protection and braking forced quit in operation. According to the invention, potential safety hazards caused by dependence on tools on rotation speed setting and forgetting to turn off the switch are effectively solved, and the robustness and operation safety of the system are improved.

The application provides a kind of urban rail burst event influence on passenger flow quantitative evaluation method.The method comprises: based on the multi-source data of urban rail transit, a multi-layer dependent network model of urban rail transit is constructed, a node and edge traffic propagation process is considered to construct an urban rail transit point-edge joint cascading failure model, the parameter calibration of the model is carried out using the historical failure data of urban rail, and a calibrated urban rail transit point-edge joint cascading failure model is obtained;the parameter information of urban rail burst event is input into the calibrated urban rail transit point-edge joint cascading failure model, and the calibrated urban rail transit point-edge joint cascading failure model outputs the influence of the burst event on urban rail transit passengers.The application is based on the physical law and real-time multi-source data of urban rail transit system, designs dynamic cascading failure rules, and accurately simulates the dynamic processes such as passenger flow transfer, station congestion propagation and multi-transportation mode connection obstruction under burst event.

The invention discloses an inverter post-evaluation method and device, equipment and a medium, and the method comprises the steps: determining the complete machine fault frequency, the complete machine fault type, the construction unit fault frequency and the construction unit fault type of a to-be-evaluated inverter through complete machine fault data and construction unit historical fault data; determining a first post-assessment score of the to-be-assessed inverter based on the complete machine fault frequency and the complete machine fault type of the to-be-assessed inverter, and determining a second post-assessment score of the to-be-assessed inverter based on the construction unit fault frequency and the construction unit fault type; based on the after-sales service data, determining a service type of the to-be-evaluated inverter, and based on the service type, determining a third post-evaluation score corresponding to the after-sales service data; and based on the first post-evaluation score, the second post-evaluation score and the third post-evaluation score, determining a comprehensive post-evaluation score of the to-be-evaluated inverter, thereby realizing comprehensive quantitative evaluation of the comprehensive quality and service level of the inverter, and effectively improving scientificity of equipment type selection and operation and maintenance decision.