263 results about "Status assessment" patented technology

The invention discloses a land degradation condition evaluation method and system, and relates to the technical field of image processing, and the method comprises the steps: collecting a land image through multi-source remote sensing, and classifying land organisms through multi-level extraction of remote sensing spatial features; constructing a space-time coupled dynamic particle evolution model based on the land organism distribution map, and performing dynamic particle evolution prediction on land organisms; establishing a non-biological value quantitative model, performing weight distribution according to historical data and a machine learning algorithm, and dynamically calculating a multi-dimensional value index system; and evaluating the land degradation condition according to the land abiotic value historical value and the dynamic particle evolution prediction of the land organisms. According to the method, adaptive feature extraction is carried out through the combination of deformable convolution and transfer learning, the classification precision under a complex terrain is improved, land degradation is evaluated by constructing a particle evolution model to quantify the space-time response of a biocenosis and constructing a multi-dimensional non-biological value index system at the same time, and the credibility of a block chain is enhanced.

The invention discloses a lithium ion battery health state evaluation method and device based on deconstruction physical information, a medium and a product, and relates to the technical field of lithium ion battery health state evaluation, and the method comprises the steps: extracting a multi-dimensional health factor, inputting the multi-dimensional health factor into a deep physical information neural network fusing a self-attention mechanism module and a Koopman neural operator module, and obtaining a deep physical information neural network; and outputting the SOH estimation value. By extracting the universal health factors suitable for multiple working conditions, the problem that the universality of the health factors is insufficient is solved; a self-attention mechanism is utilized to enhance features and reduce redundancy, and the perception ability of the model to key information is enhanced; physical information is deconstructed by means of a Koopman neural operator, a physical mechanism of battery degradation is fused into the model, and the physical interpretability of the model is enhanced; the deep physical information neural network is fused with multi-dimensional information for estimation, individual differences and complex working conditions of different batteries are effectively dealt with, and therefore high-precision and high-robustness lithium ion battery SOH estimation is achieved.

The invention provides a reservoir water ecological health diagnosis method and system, and relates to the technical field of reservoir ecological monitoring, and the technical scheme is characterized in that the method comprises the steps: obtaining water quality parameters, biological indexes and environmental data collected by distributed sensors in a reservoir; according to the water quality parameters, the biological indexes and the environmental data, generating reservoir ecological health assessment data through a data fusion algorithm; calculating a reservoir ecological health assessment index according to the reservoir ecological health assessment data; according to the reservoir ecological health assessment index, generating a reservoir ecological health state assessment result; judging whether the ecological state of the reservoir is abnormal or not according to the evaluation result of the ecological health state of the reservoir; and when the ecological state of the reservoir is abnormal, generating an early warning signal. The reservoir water ecological health diagnosis method and system provided by the invention have the advantages that multiple ecological pressure factors are comprehensively considered, and a complex water ecological system is rapidly and accurately evaluated.

The invention discloses a primary helium fan health state evaluation system and method based on state estimation, and relates to the technical field of nuclear power station equipment state monitoring and health management, and the system comprises a data acquisition unit, a feature extraction unit, a state estimation unit and a health evaluation unit. According to the system, strategies of multi-sensor data fusion, mechanism data hybrid modeling, active incremental learning and the like are adopted, and an end-to-end health state evaluation model is constructed; and the block chain technology is introduced to realize data security sharing and trusted computing. According to the system, multi-source heterogeneous data can be comprehensively utilized, the health state of the main helium fan can be automatically and accurately evaluated in real time, and a reliable basis is provided for intelligent operation and maintenance decision making. The safety and economical efficiency of the nuclear power station are improved, and intelligent development of nuclear power equipment is promoted.

The invention relates to the technical field of power grid equipment state monitoring, in particular to a transformer substation equipment health state assessment method, system and equipment based on few-sample multi-modal fusion and a storage medium. The method comprises the following steps: acquiring image, sound, text, structuralization and other multi-modal operation data of transformer substation power distribution equipment, preprocessing the data, and establishing a comprehensive equipment state data basis; cLIP, BERT and Wave2vec pre-training models are selected and finely adjusted, multi-modal features are deeply fused through a cross attention mechanism by using the transfer learning ability of large-scale pre-training knowledge, pairwise interaction and information complementation among different modals are realized, information redundancy is effectively eliminated, and a complex association relationship among the modals is mined; a hierarchical structured model is constructed, a structured data health state result is calculated in combination with a discrimination matrix, and traditional power system expert experience and quantitative analysis are organically combined; and carrying out weighted fusion on the multi-modal health state result and the structured data health state result.

The invention discloses a cable fault monitoring and health state assessment method based on multi-modal fusion, and the method comprises the following steps: 1), collecting cable multi-modal data, including electrical quantity data and cable image data; 2) preprocessing the electrical quantity data and the cable image data; 3) performing feature extraction and fusion on the preprocessed electrical quantity data and cable image data to obtain a multi-modal feature vector; 4) constructing a cable fault classification model; 5) inputting the multi-modal feature vector into a cable fault classification model to obtain a cable operation state type; and 6) calculating a cable health state index based on the cable operation state type and the multi-modal feature vector. According to the invention, through the large kernel convolution design of RepLKNet, the multi-modal feature fusion strategy, the feature learning optimization mechanism and the innovation of the data enhancement technology, the technical problems of insufficient robustness, low classification precision, poor real-time performance and the like of the traditional method in the cable monitoring process are successfully solved.

The invention provides a power station equipment state trend prediction method based on deep learning, and the method comprises the following steps: S1, obtaining the historical operation data of power station equipment, and carrying out the preprocessing; s2, constructing a trend early warning model, a temperature time sequence prediction sub-model and a vibration time sequence prediction sub-model based on the historical operation data of the power station equipment by using a deep neural network; s3, sequentially outputting a degradation degree prediction value, a temperature trend prediction value and a vibration trend prediction value through the model; s4, establishing a three-layer evaluation system and generating a state evaluation result; and S5, generating early warning information according to a state evaluation result. According to the method, a multi-level deep network is constructed, and multi-dimensional accurate prediction of equipment degradation, temperature and vibration trend is realized; technologies of seasonal coding, adaptive attention and the like are introduced, so that the perception and learning capabilities under complex working conditions are enhanced; a three-layer dynamic evaluation system is innovated, scientific health state evaluation from parts to the whole is provided, and a more comprehensive and intelligent solution is provided for early warning of a power station.

The invention discloses a cable state evaluation method based on Bayesian optimization and XGBoost, and the method employs a cross entropy loss function on the basis of an XGBoost algorithm, and can effectively reduce the loss. And hyper-parameters in the XGBoost algorithm are adjusted and optimized by using a Bayesian parameter optimization method, so that key features in the operation process of the cable are extracted more effectively, and finally, the state of the cable is evaluated. By using the cross entropy loss function, the complexity of the cable state can be better adapted, and the robustness of the model is improved. Meanwhile, the Bayesian hyper-parameter optimization is beneficial to finding the optimal model parameter configuration, so that the model can capture the cable state characteristics more accurately.

The invention provides a pet state analysis method and system based on remote monitoring, and relates to the technical field of pet state evaluation. The method comprises the following steps: collecting multi-source historical health monitoring data about a pet, and determining a plurality of multi-source combination states of the pet; performing physiological behavior joint analysis based on morphological change on the plurality of multi-source combination states of the pet, generating a plurality of physiological behavior joint modes of the pet, and determining a recessive compensation mode in each physiological behavior joint mode; constructing a state association map of the pet, and performing anomaly analysis on the multi-source real-time health monitoring data of the pet to determine a state anomaly event; and determining a target joint mode to which the pet belongs according to the multi-source real-time health monitoring data, performing compensation stability analysis on the pet according to a recessive compensation mode of the target joint mode, and generating a compensation imbalance evaluation result of the pet. According to the invention, accurate evaluation of the health state of the pet in the transportation process is realized.

The invention relates to a pilot air physiological state monitoring and flight state evaluation method, which belongs to the field of aeronautical medical diagnosis, and comprises the following steps: pre-determining a pilot # imgabs0 # reference value; the # imgabs 1 #, the # imgabs 2 # and the six-axis acceleration are monitored in the flight process; on the basis of the # imgabs 3 # reference value and the # imgabs 4 #, the offset proportion # imgabs 6 # of the # imgabs 5 # is calculated in real time; based on # imgabs7 and the six-axis acceleration, # imgabs8 and Coriolis illusion risk early warning is carried out on the pilot in real time; and a flight state evaluation result is obtained in real time through the real-time heart rates # imgabs9 # and # imgabs10 # and the six-axis acceleration.

The invention discloses a power equipment state evaluation method, system and device for intelligent monitoring and a storage medium, and belongs to the technical field of power equipment state evaluation, and the method comprises the steps: building a multi-physics field coupling calculation model based on the working characteristic parameters of power equipment, and simulating the behaviors of the power equipment in different working states; outputting an expected output value, comparing the expected output value with the physical field signal after time alignment, and calculating a deviation to obtain an initial prediction residual error; according to the initial prediction residual error, adjusting model parameters of the multi-physics coupling calculation model through an incremental correction algorithm, correcting the state estimation value of the power equipment, and updating the initial prediction residual error; based on the updated prediction residual error, analyzing an abnormal state of the power equipment through a residual error value, and extracting a fault feature; and identifying the fault type of the power equipment. According to the invention, through multi-physics field coupling calculation and an incremental correction algorithm, the accuracy and real-time performance of power equipment state evaluation are significantly improved.

The invention relates to the field of train informatization, in particular to a train running state evaluation system based on data analysis. The train braking stress diffusion abnormal tendency value is determined by setting the braking information responding to the train, calling the train body image of the corresponding braking time period, extracting the train shaking characteristics and combining the discrete difference of the loads of all the carriages in the braking time period, and then the data time period type is marked. And the data in the time period is classified, uploaded and stored according to different marks, so that the real-time bandwidth requirement is reduced. According to the invention, the inter-wheel temperature difference characteristic and the in-wheel temperature difference characteristic of each wheel are considered and extracted, the wheel end abnormal risk characterization value is calculated and compared with the early warning threshold value, and the wheel end data is analyzed in a targeted manner, so that the data processing efficiency of a large amount of train data is improved, the real-time communication bandwidth demand is reduced, and the identification accuracy is improved.

The invention provides a storage battery health state assessment method fusing ultrasonic data and hybrid machine learning, and belongs to the technical field of storage battery health state assessment. The method focuses on performance comparison of an ANN model and a GWO-ANN model in storage battery SOH regression prediction, and finds that the GWO-ANN model accurately adjusts and optimizes ANN parameters by means of a grey wolf optimization algorithm, so that the storage battery health state assessment accuracy is improved. Compared with the prior art, the method has the advantages that higher prediction precision and generalization ability are shown, the R2 value is close to perfect, the error is extremely low, and the ANN model is remarkably surpassed, the HHT-GWO-ANN model is innovatively introduced, the model integrates the advantages of HHT and GWO, the calculation process is accelerated, the robustness and prediction precision of the model are enhanced, efficient and stable model construction is achieved, and the method is suitable for large-scale popularization and application. And the test performance of the method is consistent with that of the training stage, so that the practical potential of the method in storage battery SOH nondestructive testing is verified. The ultrasonic nondestructive testing method proposed by the invention shows unique advantages in SOH recognition of the storage battery, and overcomes the limitation of a traditional method.

The invention provides a health state evaluation and product matching method and system based on a knowledge graph, and relates to the technical field of knowledge graphs, and the method comprises the steps: obtaining user health data, building a health knowledge graph, mapping the user data into a feature vector, and employing a multi-scale time convolution network to extract a time sequence change feature; and constructing a dynamic health feature association network, generating a health state evaluation result by using a graph convolutional neural network and a causal discovery algorithm, calculating a collaborative enhancement coefficient based on a product feature vector, and predicting an intervention effect to determine an optimal product combination. According to the method, personalized health assessment and precise product matching are realized, and the health management efficiency is improved.

The invention discloses a deicing system real-time state evaluation and fault prediction method and system, and the method comprises the steps: simulating the fault state of a deicing system through a ground state monitoring test of the deicing system, and obtaining sensor data in different fault states; constructing a data set by using sensor data in different fault states to train a state evaluation model and a fault prediction model; the method comprises the following steps: installing a sensor on an aircraft deployed with a deicing system to collect working data of the deicing system; and inputting data acquired by the sensor into the trained state evaluation model and the fault prediction model, evaluating the real-time state of the deicing system, and predicting possible faults. According to the invention, the current working state of the anti-icing and deicing system can be evaluated according to the current state monitoring data, and possible faults of the system can be predicted.

The invention relates to the technical field of petrochemical equipment tank health state evaluation, in particular to a petrochemical equipment tank health state evaluation method based on multi-dimensional data. The method comprises the following steps: acquiring original data of the petrochemical equipment tank, including information such as structure, material, use environment and the like of the equipment tank, and performing acquisition of the petrochemical equipment tank structure and construction of a simulation model based on the data to obtain simulation model data of the petrochemical equipment tank; identifying an abnormal area of the equipment tank through the simulation model, carrying out overlay analysis, evaluating the structural stability and fracture probability of the equipment tank, calculating the fracture probability of the equipment tank according to abnormal overlay data and structural stability attenuation data of the equipment tank, and carrying out health state abnormity evaluation on the equipment tank based on fracture probability data; according to the evaluation result, the petrochemical equipment tank is repaired; according to the method, the petrochemical equipment tank health state evaluation is optimized, so that the petrochemical equipment tank health state identification is more accurate.

The invention belongs to the technical field of distributed assessment, and particularly relates to an aluminum processing equipment health status assessment method and system.The method comprises the steps that cross-plant equipment attributes and operation information are combined with a Merkel tree and an assessment algorithm, and a distributed fault assessment forest is constructed; secondly, carrying out distributed edge interaction state evaluation by utilizing the tree and a preset cross-plant real-time monitoring network, and obtaining evaluation results of single equipment and process associated equipment; and 3, according to the results and the root cause traceability early warning rule and strategy generation library, carrying out fault traceability, generating and sharing a maintenance strategy, meanwhile, visualizing the whole traceability and sharing process by means of a traceability early warning visualization mapping space, and finally forming a visualization fault assessment early warning forest. Accurate evaluation of the state of the cross-factory aluminum processing equipment, fault traceability and effective generation and sharing of a maintenance strategy are realized.

The invention discloses a giant magnetostrictive bar health state evaluation method considering subharmonic content, which comprises the following steps of: firstly, obtaining output displacement data of a giant magnetostrictive bar through a built giant magnetostrictive bar test platform; in order to select the filter length and the mode decomposition number of the characteristic mode decomposition method, combining with the Shannon entropy coefficient, taking the square envelope spectrum kurtosis as a weight, constructing a weighted square envelope spectrum kurtosis Shannon entropy coefficient objective function, and performing parameter optimization by using a Harris eagle optimization algorithm; according to the method, the principle of maximum square envelope spectrum kurtosis is taken as a principle to select a main mode component, finally, envelope spectrum analysis is carried out, the health state of the giant magnetostrictive bar is evaluated by counting the sub-harmonic content in the envelope spectrum, and the method plays a guiding role in development and application of a giant magnetostrictive transducer. And the service life of the subsequent transducer can be detected and evaluated.

The invention discloses a user state evaluation method and system based on heart rate characteristics, and relates to the technical field of data analysis, and the method comprises the steps: collecting heart rate time sequence data; performing phase-space reconstruction on the heart rate time series data, and extracting chaotic attractor features representing heart rate complex dynamic characteristics; constructing a heart rate dynamic graph according to the chaotic attractor features, and performing feature aggregation on the dynamic graph by using a graph convolutional network to obtain global topological dynamic features; inputting the global topology dynamic feature, the attention mechanism and the user individual baseline feature into a pre-trained neural network, and outputting an intermediate state value reflecting the current state of the user; and performing adaptive state adjustment on the intermediate state value to generate a final state evaluation result. The method not only improves the precision and robustness of state evaluation, but also provides scientific and reliable technical support for intelligent health management and risk early warning.

The invention provides a health status assessment method and system and electronic equipment, and relates to the field of health status assessment, and the method can collect and process heterogeneous data from multiple sources, and carry out deep fusion analysis on the heterogeneous data to obtain user physique identification data. Therefore, the physical condition of the user is obtained, the health state of the user is analyzed and evaluated, and then an accurate health evaluation result is obtained, so that the processing flow from data acquisition to health state evaluation is completed.

The invention discloses an HER2 expression state evaluation method and device, equipment and a storage medium. The method comprises the following steps: dividing an HE dyeing image of a target object into a plurality of first sub-images, and determining an infiltration area of the HE dyeing image through the first sub-images; dividing the IHC dyeing image of the target object into a plurality of second sub-images, and determining tumor cells of various dyeing degrees in the IHC dyeing image through the second sub-images; determining a first expression state of the target object HER2 according to the infiltration area and the tumor cells; under the condition that the first expression state is IHC < 2 + >, determining a primary focus area in the image of the target object, and obtaining image features through the primary focus area; obtaining HE features and IHC features through the infiltration area and the tumor cells, and generating multi-modal features according to the image features, the HE features and the IHC features; and inputting the multi-modal features into the first classification network to obtain a second expression state of the target object HER2.

The invention discloses a comprehensive evaluation method for the health state of concrete filled steel tubes. According to the method, a multilevel evaluation index system for the health state of the concrete-filled steel tube structure is constructed, and factors influencing the health of the structure are divided into a plurality of key indexes including the ultimate bearing capacity, the overall stability performance, the anti-seismic performance and the like. The method comprises the following steps: firstly, quantifying each index by using a membership function in fuzzy mathematics, and constructing a membership matrix; secondly, determining the weight of each index according to the influence of each index on the whole bridge; then, the health state of the concrete filled steel tube is evaluated through the fuzzy comprehensive evaluation model, the structure health level is generated, an accurate health state evaluation result is provided, and the method is suitable for health monitoring and evaluation of complex concrete filled steel tube structures such as bridges, tunnels and high-rise buildings. The method has the advantages of being high in evaluation precision, wide in application range and easy and convenient to operate, and a scientific basis is provided for safety management and maintenance decision making of the concrete-filled steel tube structure.

The invention discloses a health condition evaluation method and system for new energy equipment, and the main points of the technical scheme are that the method comprises the following steps: multi-source data collection and preprocessing: deploying a sensor array to collect equipment operation data, fault feature data, environment data and operation and maintenance historical data, employing an EMD-SVD algorithm to carry out the denoising of a vibration signal, and carrying out the preprocessing of the data; processing the multi-dimensional data through Z-score standardization; constructing a dynamic weight calculation model; health state grading evaluation; and health trend prediction and active early warning: constructing an LSTM-Attention prediction model, inputting a historical CHI sequence, operation data and environmental parameters, and outputting health state prediction values in future 1-3 months. A complete closed loop is formed from data acquisition, evaluation and prediction to operation and maintenance decision, full-life-cycle health archive management of equipment is supported, and data support is provided for equipment type selection optimization and life cycle cost analysis.

The invention discloses a contact network state evaluation method and system based on adaptive empowerment optimization. The method comprises the following steps: collecting dynamic and static parameters of a contact network, calculating subjective and objective weights of each parameter by adopting an analytic hierarchy process and an entropy weight method, and calculating a static composite weight by adopting a combined weight method; historical degradation data of dynamic and static parameters of the overhead line system are collected, and the expected degradation rate of each parameter is obtained by adopting a time sequence neural network; calculating a parameter real-time degradation rate, and dynamically optimizing the static composite weight by adopting an adaptive method based on the real-time degradation rate and an expected degradation rate to obtain a dynamic composite weight optimization result; optimizing and updating the gain in the adaptive method; based on a dynamic composite weight optimization result, performing state evaluation on the contact network; the method and the system provided by the invention are not only suitable for contact network state evaluation, but also can be popularized and applied to other state evaluation fields, and have wide application prospects.

The invention relates to a geographic information collection method and system based on multi-source data fusion. The system comprises an automatic data capture module, a multi-source data integration module, an environment state evaluation monitoring module, a human activity influence analysis module and a management strategy generation module. According to the scheme, the real-time dynamic evaluation capability is provided, so that the evaluation of the environment state and the analysis of the influence of human activities can quickly respond to the environment change, and the timeliness of decision making is improved. The human activity data and the natural environment state evaluation result are combined for cross analysis, positive and negative influences can be recognized, it is ensured that multiple influence factors of human activities are fully considered in an environment management decision, and therefore the comprehensiveness and scientificity of the decision are improved. Based on positive and negative impact analysis results, the scheme can generate specific environment management suggestions and cooperative treatment measures for all provinces and cities.

The invention discloses a scheduling method and system, a terminal and a medium, and belongs to the technical field of power system automation. Analyzing and predicting a power demand based on a time sequence, and considering a local power grid state to generate a preliminary scheduling scheme based on a load prediction result; optimizing energy scheduling; adjusting an energy transfer strategy according to an analysis result of the edge computing node; periodically processing an analysis result of the edge computing node, and generating a new scheduling decision or adjustment instruction; according to the scheduling method and system, the terminal and the medium, the edge computing nodes are deployed in the transformer area, so that the real-time performance and the intelligence of energy distribution are realized, and the problems of low efficiency, slow response and poor flexibility in traditional power scheduling are solved.

The invention discloses a pilot landing quality evaluation method, which comprises the following steps of: respectively evaluating pilot manipulation behaviors of a plurality of flights according to a preset landing manipulation behavior evaluation monitoring index standard range to obtain pilot manipulation behavior evaluation results of the plurality of flights; according to the pilot manipulation behavior evaluation results of the multiple flights, obtaining an overall pilot manipulation behavior evaluation result; according to a preset monitoring index event warning standard for landing state evaluation, evaluating the landing state evaluation of the pilot of the plurality of flights to obtain landing state evaluation results of the pilot of the plurality of flights; obtaining an overall landing state evaluation result of the pilot according to the landing state evaluation results of the pilot of the multiple flights; and rating the landing quality of the pilot according to the pilot overall manipulation behavior capability evaluation result and the pilot overall landing state evaluation result to obtain a pilot landing quality rating result. According to the invention, comprehensive and scientific pilot landing quality evaluation is realized.

The invention relates to the technical field of network security assessment, in particular to an industrial internet network security big data state assessment method based on AI mode recognition, which comprises the following steps: S1, acquiring original network data from a plurality of network nodes, terminal equipment and a server in an industrial internet; s2, based on a deep learning feature extraction framework, respectively extracting time sequence features, spatial features and behavior features in the structured data set, and constructing a comprehensive feature vector; and S3, threat mode recognition model construction: constructing a network threat mode recognition model based on the training data by using a multi-classification deep learning network. According to the method, the comprehensive coverage of potential threats in a complex network environment is realized, and the accuracy and comprehensiveness of threat mode recognition are improved.

The invention relates to the technical field of power equipment state monitoring, in particular to a capacitive equipment state online evaluation method based on artificial intelligence and frequency domain interpolation, which comprises the following steps: acquiring a complex frequency spectrum of an equipment signal and extracting working condition characteristics; inputting the working condition characteristics into a working condition modulation model to generate dynamic parameters for adjusting an adversarial interpolation correction model in real time; the correction model converts frequency spectrum slices into optimized frequency spectrums, so that the dielectric loss factor is accurately calculated. Afterwards, the factor and other operation parameters are combined into a multi-dimensional time sequence, the multi-dimensional time sequence is input into a health state evaluation model, an abnormal score representing the real insulation degradation state of the equipment is output, and a diagnosis data object containing a measurement result consistency score is generated. According to the invention, the artificial intelligence model is introduced, the measurement error is dynamically compensated according to the real-time working condition, intelligent diagnosis is carried out, and the accuracy and reliability of online evaluation are remarkably improved.

The invention provides a method and a device for evaluating the state of industrial power supply maintenance equipment. The method provided by the invention comprises the following steps: collecting real-time state data of the industrial power maintenance equipment; performing first screening on the real-time state data according to a relationship between the real-time state data and a health threshold value to obtain first state data to be identified; clustering the first to-be-identified state data according to a system unit of the industrial power maintenance equipment; traversing each second to-be-identified state data class, and screening candidate system units; calculating the weight of the candidate system unit; mixing the second to-be-identified state data class corresponding to the candidate system unit to obtain to-be-identified state data; and identifying the fault state of the industrial power maintenance equipment based on the weight and the to-be-identified state data. The fusion degree of data use during state evaluation is improved, and the precision of state evaluation is improved.