430results about How to "Realize evaluation" patented technology

The invention provides a coal mill fault diagnosis and prediction method and system based on big data analysis, and relates to the technical field of big data analysis, and the method comprises the steps: collecting a vibration signal of a coal mill bearing through a vibration sensor, carrying out the multi-scale analysis through wavelet decomposition, extracting features, constructing a deep convolutional neural network model, and carrying out the fault diagnosis and prediction of a coal mill bearing. A maximum mean value difference adaptive layer is introduced to carry out feature alignment on data of a source domain and a target domain, identification of fault types and degrees is realized, a health state evaluation index including a fault severity coefficient and a development trend coefficient is established, and the remaining service life is calculated and early warning is carried out. According to the invention, the accuracy and generalization ability of fault diagnosis are improved, accurate prediction and timely early warning of the bearing fault of the coal mill are realized, and the service life of equipment is prolonged.

The invention discloses a bridge structure detection method based on an unmanned aerial vehicle, a product, a medium and equipment, and relates to the technical field of bridge structure detection, and the method comprises the steps: obtaining the basic data of a bridge structure, and constructing a three-dimensional model according to the basic data; layering the three-dimensional model of the bridge structure according to a preset interval, and calculating an initial path of the unmanned aerial vehicle; optimizing the initial path by adopting a gradient descent method to obtain an optimal path of the unmanned aerial vehicle; acquiring to-be-detected data of the bridge structure based on the optimal path of the unmanned aerial vehicle; preprocessing the to-be-detected data to obtain preprocessed to-be-detected data; and performing crack detection and deformation detection based on the preprocessed to-be-detected data to realize bridge structure detection. According to the bridge structure detection method based on the unmanned aerial vehicle, the product, the medium and the equipment, efficient and accurate detection of the bridge structure can be realized.

The invention provides an agricultural product processing quality traceability monitoring method and system based on edge nodes, and the method comprises the steps: constructing an analytic hierarchy process model through employing the multi-source data of time-space alignment, decomposing an agricultural product processing quality evaluation index into a plurality of sub-indexes, forming an index hierarchical structure, and employing a fuzzy comprehensive evaluation method to obtain a traceability result of the agricultural product processing quality. The importance weight and the satisfaction degree of each sub-index are comprehensively considered, the comprehensive score of each scheme is calculated, and the processing quality of the agricultural products is judged; after the quality problem is processed, key control points and influence factors in the agricultural product processing process are analyzed through data mining, a quality evaluation model and a rule base are dynamically updated by adopting an incremental learning algorithm, and continuous optimization of the model is realized by continuously receiving new data samples and updating model parameters; and the accuracy of agricultural product processing quality evaluation is continuously improved.

The invention relates to the technical field of process data optimization, and discloses a traditional Chinese medicine extraction process optimization method and system based on dynamic countercurrent extraction and a medium. The method comprises the following steps: carrying out online monitoring data processing on a traditional Chinese medicinal material digital feature data set and a dynamic countercurrent extraction parameter initial data set to obtain a countercurrent extraction process real-time monitoring data stream and parameter associated data, and carrying out multi-dimensional parameter optimization processing; a dynamic countercurrent extraction process optimization parameter group is obtained and is analyzed and processed in real time, and an automatic parameter regulation and control instruction sequence and process abnormity early warning data are obtained; and performing quality characteristic data processing on the extract after execution of the automatic parameter regulation and control instruction sequence to obtain a process quality evaluation data set and a process tracing data set, and performing association analysis processing to obtain a dynamic countercurrent extraction process parameter optimization rule base. According to the invention, intelligent monitoring and automatic parameter optimization of the extraction process are realized, so that the accuracy and controllability of the traditional Chinese medicine extraction process are improved.

The invention relates to a corn disease intelligent grading evaluation and renovation recommendation method fusing multi-modal information, and relates to a corn diversity disease identification and multi-stage evaluation model based on position coding. The method comprises the following steps: introducing a position attention mechanism, and carrying out multi-layer stacking by taking an inverse movement residual module integrated with position coding as an element movement block; the capabilities of complex image feature extraction and spatial information analysis are enhanced, and a scab region is segmented, so that disease type distinguishing and severity evaluation are realized; constructing a model based on a corn disease entity relationship triple library: performing deep context coding on a corn disease text, realizing classification and prediction of complex relationships among disease entities through a multi-layer structure, and analyzing complex links and influence factors among diseases; and a corn disease treatment method recommendation model combined with an implicit relationship: according to a disease identification and grading result and a disease triple, mining an optimal implicit relationship among different disease entities through complex number embedding and a graph neural network, and giving an accurate disease treatment method recommendation.

The invention discloses a method for rapidly and nondestructively detecting seed quality based on a near infrared spectrum technology, and relates to the technical field of modern agriculture, and the method comprises the following steps: using a deep learning model to carry out image recognition on a seed to be detected, determining a seed type and automatically adjusting spectrometer parameters; collecting spectral data by using the adjusted near-infrared spectrometer, and preprocessing the spectral data; obtaining the internal structure and density information of the seeds, and combining the internal structure and density information with the spectral data to form a multi-modal data set; constructing a seed vigor prediction model, and predicting the germination rate of the seeds; judging the growth trend of the seeds according to the prediction result; grading according to the growth trend of the seeds; according to the method, the seed vigor prediction model is constructed, so that high-precision prediction of the seed germination rate is realized; through classification and grading of growth trends, a scientific basis is provided for seed screening and planting management.

The invention discloses a new energy project intelligent planning method and system based on a large language model, and relates to the technical field of new energy planning, and the method comprises the steps: constructing a new energy project planning knowledge base; the new energy project planning knowledge base comprises basic data required by project planning and is used for providing data support for the optimization model; based on the training data in the new energy project planning knowledge base, performing field adaptability training on the large language model to obtain a special large language model in the new energy project planning field; and based on a project demand input by a user, analyzing project parameters through the special large language model, inputting an analysis result into the optimization model, and generating a planning scheme of a new energy project. According to the method, automatic conversion from a natural language demand to a professional planning scheme is realized, the accuracy of energy balance constraint is ensured through the topological structure diagram of the energy system, a flexible switching optimization target of economic benefits and environmental benefits is supported, and finally intelligent generation and evaluation of the planning scheme are realized.

The invention discloses a building foundation settlement and stability long-term monitoring method and system. According to the method, building settlement related data are collected through a distributed optical fiber sensing network, an inclination sensor array and a soil pressure sensor; establishing a soil stress memory effect calculation model, and calculating effective stress considering the memory effect; constructing a building foundation settlement total model containing main consolidation settlement, creep settlement and additional settlement caused by environmental factors; according to the method, risk grade division is carried out based on multi-level threshold values, early warning advance is calculated, an emergency response strategy is generated, intelligent monitoring and early warning of building foundation settlement are achieved, and the method has important engineering application value.

The invention relates to the technical field of behavior recognition, in particular to a machine learning-based surgical personnel video behavior recognition method and system. The method comprises the following steps: acquiring a video frame sequence and a point cloud frame sequence from a hand washing area of an operator through an RGB camera and laser ranging equipment to obtain the video frame sequence and the point cloud frame sequence; performing hand point cloud segmentation on the point cloud frame sequence to obtain hand point cloud data; performing action original data generation according to the video frame sequence and the hand point cloud data to obtain action original data; and extracting position and attitude information of hand key points from the original motion data to obtain video key point feature data. Visual information is captured through the RGB camera, three-dimensional depth information is obtained through the laser ranging device, fine analysis and evaluation are carried out on actions in combination with the deep learning network, and finally higher recognition precision, robustness and automation level are achieved.

The invention provides a civil construction monitoring intelligent sensor information detection method and system under signal mismatch, and the method comprises the steps: carrying out the time-frequency analysis of temperature and strain fundamental frequency components, selecting a wavelet basis function adaptive to the concrete setting and strength increasing process, carrying out the self-adaptive threshold processing of a wavelet coefficient, removing high-frequency noise, and carrying out the detection of the temperature and strain fundamental frequency components. Low-frequency signal characteristics reflecting concrete strength increase are reserved; performing feature extraction on the denoised temperature and strain signals by adopting a singular value decomposition algorithm, detecting internal defects of the concrete through abrupt change of a singular value spectrum, and if crack and hole defects exist, causing strain signal abrupt change by stress concentration; if it is detected that defects exist in the concrete, that is, spatial heterogeneity of temperature and stress distribution exists, temperature and strain monitoring data of multi-point arrangement and a defect detection result serve as boundary conditions and constraints, and a temperature field and a stress field in the whole concrete structure are obtained through numerical simulation.

The invention relates to the technical field of synchronous positioning and map construction, and discloses a multi-sensor fusion SLAM method and system based on data quality comprehensive evaluation, and the method comprises the steps: carrying out the multi-dimensional quality evaluation of laser radar point cloud data and camera image data, and enabling the evaluation indexes to comprise the scene degradation degree, the view field integrity and the intensity information entropy of a point cloud, according to the point cloud data and the definition, self similarity and natural quality of the image, calculating the comprehensive weight of each index by adopting a subjective and objective weight fusion method, and dynamically selecting a laser radar pose, a visual sensor pose or an IMU integral pose increment as an initial value according to the comprehensive quality score of the point cloud data and the image data; and matching optimization of the point cloud and the map and global pose map optimization are carried out. According to the method, the sensor data quality is evaluated in real time, and the fusion strategy is dynamically adjusted, so that the positioning precision and robustness of the SLAM system in a complex environment are remarkably improved, and meanwhile, the calculation complexity is reduced.

The invention discloses a wireless spectrum intelligent allocation and edge computing cooperation method, which relates to the technical field of mobile edge computing and wireless communication, and comprises the following steps: acquiring spectrum occupation data and service load data; constructing a spectrum resource allocation matrix according to the spectrum occupation data, and constructing an edge computing resource allocation matrix based on the service load data; and inputting the spectrum resource allocation matrix and the edge computing resource allocation matrix into a trained deep reinforcement learning model, outputting a joint optimization strategy, and performing collaborative scheduling of spectrum resources and computing tasks through a bidirectional matching algorithm. According to the invention, a multi-dimensional resource data acquisition mechanism is established, so that real-time monitoring and evaluation of a network resource state are realized; quantitative expression and association mapping of heterogeneous resource characteristics are realized by constructing a mathematical model of a spectrum resource allocation matrix and an edge computing resource allocation matrix; a deep reinforcement learning model is adopted for strategy optimization, and the limitation of a traditional fixed rule algorithm is overcome.

The invention provides a close-range radio frequency electromagnetic field immunity test method and system, and relates to the technical field of communication detection, and the method comprises the steps: collecting near-field electromagnetic field distribution data and near-field frequency spectrum power distribution data formed by a radio frequency interference source on the surface of a tested device; constructing a near-field electromagnetic field distribution model; according to the frequency spectrum overlapping degree between the near-field frequency spectrum power distribution data and the working frequency band of the tested equipment, calculating the interference intensity of a radio frequency interference source on the tested equipment, and calculating the power of a target interference source according to the interference intensity through a dynamic frequency spectrum adaptation algorithm; regulating and controlling a radio frequency interference source to generate a radio frequency interference source signal with target interference source power; and monitoring an electromagnetic coupling coefficient of the tested equipment for the radio frequency interference source signal based on the electromagnetic field intensity distribution model so as to generate an immunity test result. Therefore, an actual interference scene of modern wireless equipment in a near-field environment can be simulated more accurately, and comprehensive evaluation of the equipment immunity under high-frequency and near-field conditions is realized.

The invention discloses an advertisement compliance knowledge question-answering method, device and equipment for foreign enterprises, and the method comprises the steps: obtaining advertisement compliance data of a global data source, carrying out the document structuring and knowledge extraction of the advertisement compliance data, and constructing an advertisement compliance knowledge graph; converting text data in the advertisement compliance knowledge graph into vectors to obtain vector data; semantic analysis is conducted on a query question input by a user, construction is conducted in combination with the vector data, prompt words are obtained, the prompt words are input into a preset large language model, and target answers are obtained; and outputting the target answer based on the advertisement compliance knowledge graph, and visually displaying the target answer in a corresponding answer query graph. And comprehensive and efficient compliance information query and answer services can be provided, so that compliance review requirements of foreign enterprises on advertisements in a multi-language and cross-regional environment can be met.

The invention relates to the technical field of color correction, and discloses a publication printing color correction method and system based on deep learning, and the method achieves the precise correction and compensation of the color of a publication printed matter through a series of steps of cosine distance calculation, color-environment matching model, color deviation calculation, operation parameter adjustment and the like. The core of the method lies in that the accuracy of the printing color is effectively improved and the color quality of the printed matter is ensured to meet the expected standard through careful analysis and processing of the printing color by using the deep learning technology. The correction method provided by the invention has obvious advantages, not only can realize automatic color correction, reduce human intervention and improve the stability and consistency of correction, but also can optimize the operation parameters of the printing equipment, so that the printing equipment can keep a good working state in different printing environments, and the adaptability of the printing process is enhanced.

The invention discloses an urban safety risk assessment method and system based on a digital standard, and the method comprises the steps: obtaining urban safety risk monitoring data and historical data, constructing a risk index event library according to the historical data, carrying out the abnormal screening and classification of the monitoring data, and obtaining risk indexes, matching the risk indicator with the risk indicator event library to determine a first risk event, predicting an uncertain risk event according to the first risk event, and constructing a city safety risk assessment model according to the first risk event, the uncertain risk event and the risk event weight, and inputting to-be-assessed city safety risk monitoring data and historical data into the city safety risk assessment model to obtain a city safety risk assessment result. The method not only can improve the efficiency and accuracy of urban safety risk assessment, but also has good interpretability, and can be directly applied to an urban safety risk assessment system.

The invention discloses a micro-seismic monitoring-based deep hole blasting crack evolution evaluation method and system. The method comprises the following steps of: obtaining a spatio-temporal evolution process of crack extension and a final crack distribution characteristic; microseismic signals in the blasting process are collected in real time, the spatial-temporal evolution process of crack expansion and the final crack distribution characteristics are combined, the microseismic signals are preprocessed, and preprocessed microseismic waveform data are obtained; calculating a spatial position coordinate of the micro-seismic event, and generating a complete three-dimensional micro-seismic positioning image; drawing a fracture density cloud picture according to the generated complete three-dimensional micro-seismic positioning image in combination with the acquired spatial distribution density and energy release rate of the micro-seismic event; a three-dimensional visualization model of fracture evolution is established, and evaluation indexes are obtained; and establishing a comprehensive index system for crack evolution evaluation, carrying out weight distribution and quantitative scoring on evaluation indexes, generating a quantitative evaluation report of crack evolution, and completing deep hole blasting crack evolution evaluation. The accuracy and reliability of evaluation are improved.

The invention discloses a water quality self-adaptive regulation and control system and method based on multi-point detection, and relates to the technical field of water quality self-adaptive regulation and control. A sensor network collects water quality data in each sub-region of a target water area in real time, and abnormal regions are screened out after water quality abnormal states in the sub-regions are determined according to water quality abnormal scores; performing inspection on each abnormal area by using an inspection unmanned aerial vehicle, and summarizing to generate a water area inspection data set after obtaining corresponding inspection data; outputting an intervention strategy for the target water area by the water quality intervention knowledge graph, executing the intervention strategy, automatically controlling the operation of the water treatment facility by using the automatic control model, collecting corresponding intervention feedback data, and adjusting and optimizing the intervention strategy according to the intervention feedback data; and a water quality monitoring report is sent to a data receiving end at the time node meeting the constraint condition. When the water quality in the target water area is abnormal, automatic and adaptive adjustment control is realized, and the water quality in the target water area is prevented from deteriorating.

The invention discloses an automatic control method for tipping paper printing based on image detection, and relates to the technical field of tipping paper printing, and the method specifically comprises the following steps: obtaining surface feature information of each sub-region in a wrinkling region in a tipping paper printing process in real time, and analyzing the surface feature information after obtaining, respectively generating a light intensity anomaly index and a tension instability index of each sub-region; and constructing a wrinkling influence evaluation model for the light intensity anomaly index and the tension instability index of each generated sub-region, respectively generating a comprehensive evaluation coefficient of each sub-region, performing analysis after generation, and evaluating the influence degree of each sub-region in the wrinkling region on the generated printing alignment parameter. And dividing each sub-region in the wrinkling region into a non-influence region, a low-influence region and a high-influence region according to an evaluation result. According to the invention, the problem of insufficient characteristic identification and evaluation of the wrinkling area is solved, and intelligent improvement of printing alignment precision and finished product quality is realized.

The invention relates to the technical field of Internet of Things gateways, in particular to an Internet of Things gateway and an operation method thereof. The method comprises the following steps: acquiring power system data, and constructing a power grid node topological structure diagram, so as to form a power system Internet of Things gateway; based on an Internet of Things gateway, power grid transmission demand prediction is carried out, an abnormal load state is analyzed, abnormal fluctuation of voltage and frequency is detected, and the stability of a power system is ensured; according to the frequency abnormity and voltage fluctuation data, the aging trend of power grid equipment is estimated, power resource loss estimation is carried out, abnormity early warning is carried out on power resource loss and equipment damage prediction data by utilizing an Internet of Things gateway, safe operation of a power grid is ensured, and early warning information is fed back to a terminal in time. According to the method, the intelligent level of the power system is effectively improved, and efficient resource configuration and fault management can be realized. According to the method, the Internet of Things gateway early warning is optimized, so that the Internet of Things gateway prediction is more efficient and accurate.

The invention relates to the technical field of medical monitoring and early warning, in particular to an acute kidney injury risk assessment and early warning system which comprises a urine bioelectrical impedance analysis module, a data acquisition module, a data preprocessing module, a model construction module, a risk assessment module and an early warning module. Wherein the urine bioelectrical impedance analysis module is used for acquiring urine electrical impedance spectroscopy data; the data acquisition module is used for acquiring physiological parameters of a patient and laboratory inspection indexes; the data preprocessing module is used for extracting urine electrical impedance characteristic parameters, physiological characteristic parameters and laboratory characteristic parameters; the model construction module is used for constructing an acute kidney injury risk assessment model; the risk assessment module is used for calculating the risk score and the risk grade of the acute kidney injury of the patient. According to the system and the method, early-stage accurate evaluation and graded early warning of the acute kidney injury are realized through multi-source data fusion and intelligent analysis technologies, timely intervention of medical staff is facilitated, and the accuracy and response efficiency of risk detection are remarkably improved.

The invention discloses a cultivated land quality grade evaluation method and system, and is applied to the technical field of intelligent evaluation, and the method comprises the steps: obtaining a cultivated land distribution state and a plurality of sampling points of a target region; acquiring basic information and soil information of the sampling points; the missing soil information of the missing sampling points is fitted through the soil information of the matched sampling points; and evaluating the cultivated land quality grade in the cultivated land improvement process through the basic information and the soil information of all the sampling points. According to the invention, the supplement of soil information loss caused by the fault or power failure of the soil sensor is effectively realized, so that the monitoring of the cultivated land quality grade change at different time points is more accurate, and the evaluation of the cultivated land quality grade change in the cultivated land improvement process is facilitated; a high-standard farmland cultivated land quality grade evaluation program and a technical method suitable for regional reality are formed, and a foundation is laid for accumulating experience for comprehensively developing high-standard farmland cultivated land quality grade evaluation.

The invention discloses a reservoir particle migration damage and crack filling layer composite damage productivity evaluation method, which comprises the following steps of: predicting the damage degree of reservoir particle migration to reservoir permeability and productivity through a particle migration permeability damage evaluation method of a loose sandstone reservoir under different production conditions; evaluating the fracture flow conductivity damage and productivity damage degree through a flow conductivity damage evaluation method under the compaction-embedding-blocking composite action of the fracturing filling well; reservoir particle migration damage and fracture conductivity damage are comprehensively considered, and dynamic productivity prediction and evaluation are achieved. According to the method, reservoir particle migration damage and fracture conductivity damage are comprehensively considered, the production time effect is considered, all-factor productivity dynamic prediction and evaluation are achieved, and optimization and implementation of particle migration and sand production control countermeasures are supported.

The invention relates to the technical field of water conservancy projects, in particular to a water conservancy project digital management method based on a BIM model. The method comprises the following steps: acquiring real-time monitoring data; carrying out high-precision topographic data acquisition on a water conservancy project area, and carrying out data preprocessing to obtain preprocessed data; according to the preprocessed data, hydrological information integration modeling is carried out by utilizing a BIM platform, and a hydrological information integration model is obtained; performing multi-dimensional terrain dynamic updating on the hydrological information integration model by using the real-time monitoring data to obtain digital terrain data; performing flood simulation calculation based on the digital topographic data to obtain a flood simulation calculation result; and performing flood scene simulation according to the flood simulation calculation result to obtain flood simulation data. Through real-time monitoring, accurate prediction and timely early warning of the risk of the water conservancy project, a more effective technical means is provided for flood control and disaster reduction and safety management of the water conservancy project.

The invention relates to the technical field of social media platform analysis, and discloses an abnormal psychology detection method based on multi-dimensional psychological feature modeling, which comprises the following steps: setting an emotion large category label and a behavior sub category label according to a contradictory dispute text on a social media; collecting a data set of contradictory dispute texts on the social media; cue words are designed, and an emotion classification task and a behavior recognition classification task of the contradictory dispute text are combined into one task; selecting a large language model and a parameter scale of the large language model, and performing training fine tuning on the large language model by using LLaMA-Factory based on cue words and training samples in a training set; and inputting the contradictory dispute text and the prompt word in the test set into the trained large language model to obtain an abnormal psychological detection result. According to the method, the emotion classification spanning single dimension can be realized, and the comprehensive assessment of the individual psychological state is realized.

The invention provides a layered optimization scheduling method and system for a wind power cluster and a medium, and relates to the technical field of power system optimization scheduling, and the method comprises the steps: determining evaluation indexes, obtaining the variable weight of each evaluation index, employing a membership function and a fuzzy comprehensive evaluation method, and obtaining the variable weight of each evaluation index; obtaining a membership score of each wind power plant of the wind power cluster under each evaluation index, and obtaining a comprehensive degradation score of each wind power plant; taking the ultra-short-term power predicted value of the wind power plant as the basis for judging the power change trend of the wind power plant, determining the climbing classification of the wind power plant, and determining the scheduling sequence of the wind power plant; on the premise that the power balance constraint between the wind power plants is met, the total output power of all the wind power plants is adjusted and controlled in real time according to the dispatching requirement for power increasing or power reducing of the wind power cluster and based on the dispatching sequence of the wind power plants of all the wind power plant groups. According to the method, the time-varying property and uncertainty of the wind power plant are fully considered, and the fatigue and fluctuation conditions of the whole wind power cluster are reduced.

The invention discloses a power load prediction and optimization method based on artificial intelligence, particularly relates to the field of power load prediction and optimization, is used for solving the problem of system stability caused by a nonlinear resonance effect, and comprises the following steps: preliminarily extracting key features of a power load signal through a multi-scale decomposition and mode recognition technology; and identifying a load mode which may cause system resonance. Two key indexes of frequency resonance intensity and time phase difference are emphatically utilized, the resonance risk of a load mode is comprehensively analyzed through a Bayesian network, the weight configuration of a high-risk mode is dynamically optimized in combination with a genetic algorithm, and the sensitivity and adaptability of the model to different resonance effects are improved. And then, the classified load modes are input into a deep neural network for multi-mode prediction fusion, and the model weight is adjusted in real time through a particle swarm optimization algorithm, so that the prediction precision and the operation stability of the system under a complex load condition are ensured, and a powerful guarantee is provided for the reliability of the power system.

The invention discloses a leakage current test analysis method and platform for a low-power-consumption chip, and belongs to the field of semiconductor testing, and the method comprises the steps: collecting the chip feature data of a target low-power-consumption chip, and determining a plurality of environment parameter test ranges and working voltage test ranges of the target low-power-consumption chip; generating an environment test sequence and a voltage test sequence, combining the environment test sequence and the voltage test sequence, and generating an adaptive test scheme; constructing a leakage current data matrix based on the adaptive test scheme; obtaining a first analysis result; obtaining a second analysis result; and obtaining a leakage current characteristic evaluation result of the target low-power-consumption chip based on the first analysis result and the second analysis result. The technical problems that in the prior art, the leakage current testing condition is single, the characteristics of the low-power-consumption chip under the multi-environment parameter and voltage condition are difficult to comprehensively evaluate, and consequently the accuracy and applicability of the testing result are insufficient are solved.

The invention relates to a rotary inertial navigation digital twinning system and a construction method thereof. The rotary inertial navigation digital twinning system comprises a rotary inertial navigation digital twinning physical model layer, a rotary inertial navigation digital twinning geometric model layer, a multi-source information data acquisition layer, a decision control layer and a diagnosis and prediction analysis layer. Data information collection and transmission of a rotary inertial navigation system entity can be completed, association and interaction between a twin virtual body and the entity are achieved, iterative optimization of model parameters can be achieved through the decision control module, and finally prediction and evaluation of the overall operation state of the system and the performance of the digital twin inertial navigation system are achieved in the diagnosis and prediction analysis module.

The invention relates to the technical field of charging piles, in particular to a new energy automobile charging platform management system, a charging pile and a storage medium. According to the application, the charging matching degree is evaluated through the charging access identification module, then the operation data is collected by the charging process monitoring module, then the current state of the equipment is calculated by using the performance evaluation module, and finally the monitoring threshold value is dynamically updated by the early warning threshold value adjustment module according to the performance attenuation degree and the environmental condition. The characteristic that the equipment performance changes along with time is considered, and the dynamic influence of environmental factors is also considered, so that the accuracy and reliability of monitoring of the charging equipment are remarkably improved, and false alarms and missing alarms are reduced.