54 results about "Time series processing" patented technology

The invention provides a structure health early warning method and system based on space-time correlation characteristics and digital twinning, and relates to the technical field of data processing. The method comprises the following steps: acquiring multi-source heterogeneous data of a target structure; performing dynamic sampling alignment and wavelet packet decomposition on the multi-source heterogeneous data to extract energy features to obtain synchronous data, and performing abnormal data filtering on the synchronous data to obtain cleaned fusion data; performing wavelet decomposition on a high-frequency vibration signal in the fused data to obtain a damage impact feature, performing time sequence processing on low-frequency temperature data in the fused data to obtain a temperature time feature, and performing dynamic graph convolutional network processing on strain data in the fused data to obtain a spatial correlation feature; constructing an input vector; calculating a damage degree index; and according to the damage degree indexes, early warning grades are divided, and corresponding control instructions are triggered for different early warning grades. By implementing the technical scheme provided by the invention, the accuracy of structural health early warning can be improved.

The invention discloses a method and system for constructing a city information model, and belongs to the field of city intelligent management, and the method comprises the steps: carrying out the spatial registration and semantic alignment of future multi-source heterogeneous data through a unified coordinate reference, and constructing a multi-dimensional incidence relation between a spatial entity and the attribute of the spatial entity through a graph database; performing time serialization processing on the city information model based on the real-time sensing data, and performing real-time correction on the geometric state and the attribute of the model by adopting an incremental modeling algorithm; performing automatic calibration on the model data in combination with rule reasoning and probability correction methods; predictive calculation is carried out on multiple scenes, and parameterized optimization is carried out on the city information model according to a calculation result; and carrying out adaptive hierarchical abstraction on the city information model, and automatically generating model subsets with different precision levels. According to the method, spatial registration and semantic integration are performed on the data, so that the global consistency of the city information model is realized, and the problems of data inconsistency, repetition and redundancy in a traditional method are solved.

The invention relates to the technical field of laboratory environment control, and discloses an environment parameter control method and system for a complex laboratory, and the method comprises the steps: obtaining environment index data, and carrying out the time sequence processing, and obtaining a real-time change trend; according to the real-time change trend, predicting a future offset by using a prediction model, and performing data fusion when the offset exceeds a threshold value to obtain an accurate environment state estimated value; according to the accurate environmental state estimation value, quantifying the mutual influence among the environmental indexes to obtain the mutual influence relationship of the environmental indexes; determining an environment index priority sequence according to the mutual influence relationship; generating and executing a control instruction according to the priority sequence; and when continuous deviation exists, the prediction model and the mutual influence relation are updated through feedback circulation. According to the method, the dynamic mutual influence among the environmental indexes can be quantified, and accurate and stable cooperative control of multiple parameters in a complex dynamic environment is realized.

The invention relates to the technical field of IT support, and provides a time sequence processing method, device and equipment adopting a quantum pulse neural network, and the method comprises the steps: obtaining time sequence data which comprises network alarm data, network equipment performance index data and network operation and maintenance work order data; encoding the time sequence data into a first quantum state by using a quantum preprocessing layer, inputting the first quantum state into a pulse neural network layer, converting the first quantum state into a time sequence pulse sequence, and processing the time sequence pulse sequence to obtain an output result; by utilizing a quantum attention enhancement mechanism, calculating attention weight of an output result in a quantum state space, and weighting to obtain a second quantum state; and decoding the second quantum state by using the hybrid decoding layer to obtain a final prediction result. The final prediction result is used for realizing fault root cause positioning, abnormal work order identification or network service quality prediction. According to the method, the parallelism of quantum calculation and the superposition characteristic of the quantum state are utilized, and the calculation efficiency can be improved when large-scale time sequence data are processed.

The invention discloses a clothing demand dynamic prediction method and system based on multi-source data fusion and machine learning, and the method comprises the steps: collecting and preprocessing multi-source heterogeneous data, and obtaining basic time sequence features, static attribute features and external situation features containing fashion trend quantification features; inputting the basic time sequence and the static attribute characteristics into a time sequence processing network to obtain a reference trend prediction value; inputting the external situation features into a situation feature processing network to obtain a situation influence vector; generating a dynamic adjustment coefficient by the vector through a gating unit; and finally, performing fusion calculation according to the reference trend prediction value and the dynamic adjustment coefficient to obtain a final demand prediction quantity. According to the invention, through a double-flow network structure and a gating fusion mechanism, effective modeling is carried out on an internal sales law and external situation impact, the prediction accuracy and the response speed to market changes are significantly improved, and accurate and dynamic decision support is provided for a clothing supply chain.

The invention discloses a distributed power supply cooperative control method based on transient dynamic characteristic adaptive driving, which adopts a multi-dimensional transient performance coupling evaluation function, unifies multiple targets including frequency, voltage, power angle and operation economy into a global optimization target, and fundamentally solves the defect of single target in the prior art. A collaborative strategy network based on dynamic context awareness is adopted, and a time sequence processing technology based on an attention mechanism is utilized, so that an intelligent agent can extract key transient dynamic characteristics from a local observation sequence, and the limitation of no memory and non-self-adaption in the prior art is broken through; according to the method, a cooperative control flow of a cooperative control strategy is provided, a high-performance learning type strategy is combined with a high-reliability safety monitor, the problem that safety and performance are difficult to be compatible is solved, fundamental transformation of distributed power supply control from passive, local and static rule-based modes is achieved, and the transient stability margin of a novel power system is remarkably improved.

The invention discloses a triple scene graph video understanding method fused with a time sequence perception moving window, which comprises the following five core steps of: inputting a video clip, acquiring a frame sequence through an encoder, carrying out space division on each frame and adding time embedding; executing multi-head self-attention calculation of space-time offset enhancement in each sliding window, and integrating space structure modeling and time sequence processing; constructing a cross-space-time sliding window based on continuous frames to extract a token subset; compressing the window output into a fixed-length video token (triple scene graph) through a sliding query converter; and inputting the triple scene graph of the video content and a user question into the multi-modal large model to realize semantic analysis and content understanding. According to the method, the video understanding precision is remarkably improved while the space-time relation modeling capability is enhanced, the average recall rate of behavior recognition is improved by 10%, a high-precision video understanding benchmark covering 25 object classes and 26 relation classes is constructed, the labeling error rate is lower than 3%, and the shielding problem and the long tail deviation problem are effectively solved.

The invention discloses an MEMS inertial sensor reliability analysis method based on long and short term memory deep learning, and belongs to the technical field of microelectronic reliability evaluation, and the method comprises the steps: collecting an original output signal and environmental parameter data of an MEMS inertial sensor in a target scene, and recording a collection timestamp; then preprocessing is carried out to obtain a time sequence processing data set; performing feature extraction on the time sequence processing data set to form a multi-dimensional feature vector; carrying out correlation analysis on the multi-dimensional feature vectors, and screening out a key feature set; constructing an LSTM model based on the key feature set, and performing training and parameter optimization on the model to enable the model to capture a time sequence dependency relationship of sensor performance degradation; inputting the real-time key feature set subjected to preprocessing and feature extraction into the trained LSTM model, and outputting probability distribution of the current health state of the sensor; and then reliability evaluation is carried out and an early warning signal is output through the system terminal to prompt maintenance personnel to carry out timely intervention.

The invention discloses a multi-source collaborative dynamic time sequence TCP prediction method and system, a medium and a program product, and the method comprises the steps: collecting multi-source heterogeneous data, including DVH data, CT data, CPP data and longitudinal follow-up data of a plurality of time nodes, of a patient in a whole radiotherapy period; performing hierarchical preprocessing and data format unification on the multi-source heterogeneous data; constructing a dynamic time sequence feature fusion matrix containing a time dependency relationship; constructing a deep learning model integrating a time sequence processing unit and a cross-modal fusion unit, wherein the model supports an incremental learning iteration updating mechanism; using the model to output TCP prediction results of a new patient at different time nodes; and if the new patient is a special case, calling the clinical rule adaptation model, and correcting the TCP prediction result by adopting a mixed correction strategy combining rule matching and doctor experience weight. According to the method, multi-source heterogeneous data can be deeply fused, time dynamic association is captured, modal feature contribution degree is quantified, continuous iterative updating is supported, and individual differences are adapted.

A system includes a machine-learning model implemented on a data processing apparatus, which features a parallel processor with a memory hierarchy. The machine-learning model is a recurrent neural network (RNN) with a multi-head architecture, comprising multiple sub- vectors that process parallel data streams. The RNN's weight matrix is structured as a block-diagonal matrix, allowing for parallel processing of the sub-vectors. A fused computational kernel executes an entire time-series processing loop for the multi-head RNN, maintaining the weight matrix blocks in on-chip memory and performing matrix multiplications and element-wise operations for each sub-vector in a single kernel execution.

The invention relates to a leaf area index time sequence processing method, which comprises the following steps of: inputting a time sequence remote sensing image and suburb forest and economic forest classification data of the same region; calculating a normalized differential vegetation index of the research area and carrying out time sequence sorting; calculating vegetation coverage data of the research area and carrying out time sequence sorting; synthesizing to obtain monthly FVC time sequence data of the suburb forest region and the economic forest region; calculating a suburb forest leaf area index LAI in the research area and performing time sequence sorting; calculating the economic forest leaf area index LAI of the research area and performing time sequence sorting; performing seasonal decomposition on the suburb forest time sequence data after adaptive filtering; performing seasonal decomposition on the economic forest time series data after adaptive filtering; merging the suburb forest time sequence data and the economic forest time sequence data; and outputting to obtain final optimized data. The invention further relates to a leaf area index time sequence processing system. According to the invention, a purification LAI time sequence product which clearly represents long-term trend, mutation and gradual change signals of respective vegetation canopy structures can be output.

Automated forecasting and replenishment planning system for pharmacy stocks, comprising a computer-aided infrastructure for recording dispensing data and stock levels, a computer-based time series processing unit for generating demand forecasts, and a replenishment control unit for automatically triggering replenishment measures based on the forecasts.

The present application relates to a kind of single-station hourly temperature prediction methods based on data fusion and mixed convolution, belong to meteorological prediction field.The present application selects important single-station meteorological observation element, obtains single-station historical observation sequence;Extract multiple-element prediction data, obtain numerical prediction space-time sequence;Data normalization;A three-dimensional convolution module is constructed, input after numerical prediction space-time sequence of normalization, finally produce time series containing spatial features;Build interactive learning model ICM, to single-station historical observation sequence and the time series extracted by three-dimensional convolution processing, generate the time characteristic information of two kinds of data;Build MCNN network model, fuse the two branch time characteristic information obtained in S3, after again interactive learning and fully connected, form prediction result;Temperature prediction is carried out using the model.The time series prediction model built by the present application improves stability and prediction accuracy, and the method is suitable for single-station 72-120 hours of hourly temperature prediction.

The application discloses a kind of GoldenDB database performance bottleneck evaluation method, equipment and medium, related to database data processing technical field, method includes the following steps: the general performance index of each node in cluster and GoldenDB database unique performance index are collected, and the data collected are format unified and time series processing, form standardization data;The standardization data is aggregated under different time dimensions, according to the preset aggregation index, aggregated data is converted into the aggregation data reflecting performance change trend with discrete data points;The aggregation data is respectively input into the corresponding performance evaluation model deployed on each node for analysis, obtains the performance bottleneck evaluation result and performance change trend prediction result of each node and cluster;The performance bottleneck evaluation result and performance change trend prediction result distributed in each node are summarized, and unified database performance bottleneck evaluation and prediction report is generated.

The invention relates to a ferroelectric memristor array and a sequential sequence processing method adopting the ferroelectric memristor array, and the ferroelectric memristor array comprises a bottom array gating structure which is used for providing unit-by-unit addressing and suppressing an undercurrent path of a non-gated unit in the array when the array scale is expanded; the adjustable conductive channel structure is used for generating a continuous photoconductive effect which can be accumulated and attenuated along with time under optical excitation; the in-plane polarization regulation and control structure modifies the conductive degree of the semiconductor channel by regulating and controlling the surface potential barrier of the channel so as to form a non-volatile weight state; and the multi-physical-quantity coupled electrode structure is used as a unified channel for weight regulation and control, conductivity reading and photoelectric response. Compared with the prior art, the semiconductor channel potential barrier is dynamically regulated and controlled through in-plane polarization of the ferroelectric film, non-volatile multi-stage conductance weight is achieved, synaptic-like photoresponse and short-time memory characteristics are obtained through the continuous photoconduction effect of a semiconductor material, and the continuous light input natural accumulation and attenuation process is achieved.

The invention discloses an expansive soil slope hydrothermal control active area identification method. The method comprises the following steps: acquiring a vertical deformation time sequence of each pixel of a target area and a rainfall time sequence of a corresponding time period; processing the two groups of time sequences, and extracting periodic response characteristics representing the moisture-driven deformation process; constructing a hydrothermal transfer model reflecting a swelling soil moisture diffusion mechanism and a depth integral deformation mechanism so as to establish a physical mapping relation between theoretical prediction response characteristics and normalized active area depth; and substituting the periodic response characteristics into a hydrothermal transfer model to carry out inversion matching solution, and calculating the total depth of the active area of each pixel in combination with a pre-calibrated moisture diffusion coefficient. According to the method, the theoretical upper limit of depth inversion of a traditional single-layer time domain model is effectively broken through, and accurate physical quantitative recognition of the depth of the active area under the complex hydrothermal boundary is achieved.

The embodiment of the invention discloses a service data processing method and device, equipment and a medium, and the method comprises the steps: obtaining an incremental data calculation task data packet of a target service, and subscribing a target data theme based on the task configuration information of the incremental data calculation task data packet, so as to start a corresponding incremental data calculation task; obtaining and identifying incremental business data of the target data theme; and performing time sequence processing on the incremental business data, and updating a data chart associated with the incremental business data based on a time sequence processing result so as to complete the incremental data calculation task. According to the technical scheme provided by the embodiment of the invention, the problems that the business data display updating is slow and the calculation amount in the data updating process is large are solved, the real-time dynamic updating aiming at the incremental business data is realized, and the real-time performance and the updating efficiency of the business data chart display updating are improved.

The invention relates to a method suitable for predicting the power of an electric vehicle charging station, belongs to the field of power prediction of charging stations, and solves the problems of the power prediction method of the electric vehicle charging station in the aspects of data acquisition and preprocessing methods, time sequence processing capability, prediction precision, stability and the like. The power prediction refers to predicting the power demand or supply of the charging station at a certain or certain time point in the future through a certain mathematical model and algorithm. The invention provides an electric vehicle charging station power short-term prediction method based on transformer historical data, which is characterized in that historical voltage, current and power data recorded by a transformer are read as prediction input signals, correlation coefficients of input features and output power are calculated, and a prediction model fusing a plurality of deep neural networks is constructed; in the first part, historical data of various transformers are calculated to serve as correlation coefficients of to-be-selected input characteristics and output power, the input characteristics which are most beneficial to improvement of prediction result accuracy are selected, interference is reduced, and calculation overhead is reduced; in the second part, effective representation in an input sequence is captured by using a convolutional neural network, so that the complexity of a prediction model is reduced, and model convergence is accelerated; in the third part, the time dependence characteristic of sequence data is captured by using a long short-term memory network, and information is stored and updated by using a memory unit; and in the fourth part, the attention weight of each time step is calculated for the output result of the previous layer by using an attention mechanism, and the time step which is most important for prediction is highlighted. According to the prediction method, the time sequence processing capability is optimized, and the power prediction precision and stability are improved.

The present invention relates to the technical field of predicting drift trajectories of targets at sea, and in particular to a method and system for predicting the drift trajectory of a man overboard at sea. The method comprises: reading training set data and test set data from drift data; performing time series processing operation on the training set data and the test set data to obtain training set time series data and test set time series data; substituting the training set time series data and the test set time series data into an LSTM model to obtain a training model; and testing the test set data by means of the training model to obtain a trajectory prediction model. The LSTM model can capture a long-term dependency relationship in time series data and is suitable for various sequence prediction tasks, thereby accurately simulating the actual drift of a man overboard under different sea conditions.

The application relates to the technical field of vehicle dynamics control, and discloses a data-driven deep learning vehicle motion state estimation method and system, which comprises the following steps: inputting collected vehicle multi-source sensor data into a pre-constructed TCN-SA-GRU model; and using a whale optimization algorithm to optimize the hyperparameters of the TCN-SA-GRU model, wherein the TCN-SA-GRU model integrates the parallel computing and multi-scale time feature extraction capability of a time convolution network (TCN), the feature extraction capability of a self-attention (SA) mechanism and the time sequence processing capability of a gated recurrent unit (GRU), processes the vehicle sensor data, and obtains vehicle motion state estimation results. The application can realize dynamic and accurate estimation of key motion states such as a vehicle side slip angle and a yaw angular velocity.

Systems and methods for determining wind speed are disclosed. The system includes a video capture device (e.g., a smartphone camera, a drone-mounted camera, or a satellite) to capture a video time-series of a pre-existing flexible object, such as natural vegetation, interacting with a wind flow. A processor is configured to analyze the video time-series to extract kinematic features of the object's motion and determine a quantitative wind speed. In one embodiment, a physics-informed neural network is used, which is trained to infer the object's mechanical properties (such as stiffness or species) from the video to solve for the wind speed. In another embodiment, a neural network is configured to receive a set of statistical feature maps, derived from the video's temporal signals, to determine the wind speed. In a further embodiment, the wind speed is determined by applying a first-principles physical formula based on the discovery that the fluctuating motion of individual leaves becomes decoupled from the branch structure at certain wind speeds, allowing for a quantitative measurement based on leaf size and measured leaf speed.

This invention belongs to the field of power system monitoring technology and discloses a method, system, equipment, and medium for analyzing the icing state of transmission lines based on point cloud modeling and deep learning, thereby overcoming the limitations of existing methods in time series processing. The method includes: dividing the time series point cloud of the transmission line into multiple time subsequences; generating a topological persistence graph for each subsequence through voxelization and persistent coherence analysis; calculating the dynamic topological distance between subsequences based on the persistence graph; extracting topological features to construct a feature matrix, and fusing topological distance, feature space, and feature distribution three views to construct a similarity matrix; using multi-view joint learning to obtain a consensus subspace and performing subsequence clustering within this space to obtain cluster labels; combining physical context information and predefined rules to assign physical pattern labels to the clustering results, generating an icing state classification report and visualizing it. This invention achieves temporal topological quantitative analysis and highly robust state identification of icing morphology.

The present application belongs to the technical field of power metering box state monitoring, and particularly relates to a power metering box state monitoring method based on multi-sensor fusion. The method comprises: acquiring displacement, temperature and humidity, water immersion and infrared sensing data, collecting calibration and time alignment, generating a unified time base and a sensor credibility table; then performing time series processing, feature construction and scene prior association to form a fusion feature set; then generating an evaluation result set through fusion evaluation and anomaly identification; then performing hierarchical early warning and strategy generation to output a evidence trigger list; finally, performing evidence collection and learning update to generate a baseline update package. The beneficial effect is to improve monitoring accuracy, reliability and self-adaptive ability, and to realize closed-loop management and intelligent optimization.

The invention provides a clinical time sequence processing method and system based on a liquid neural network, and is applied to the technical field of medical data processing, and the method comprises the steps: obtaining a plurality of patient trajectories in continuous time, and constructing an input vector; inputting the input vector into a dynamic model, wherein the dynamic model evolves a hidden state based on an ordinary differential equation; the ordinary differential equation comprises a self-adaptive time constant, and the self-adaptive time constant is dynamically adjusted based on an input vector; the hidden state obtained through evolution is mapped into prediction distribution through a probability decoder, and a prediction value is obtained through calculation based on the prediction distribution; constructing a coupled objective function to train the dynamic model; and processing a to-be-processed patient trajectory by using the trained dynamic model, and performing forward integration on the ordinary differential equation from the last observation time of the to-be-processed patient trajectory to generate an anti-fact trajectory with an uncertainty range. According to the method, the data quality can be improved and safer decision support can be realized in a sparse and asynchronous measurement environment.

The invention discloses an FNIRS brain function state decoding method and system based on a graph information bottleneck, and belongs to the technical field of brain-computer interface and neural signal processing, and the method mainly comprises the steps: constructing a brain network graph based on a channel space position of an fNIRS data segment; a data segment is input into a deep learning model which sequentially comprises a graph convolutional network (GCN) module, a graph information bottleneck (GIB) module and a time sequence processing module, a unique cascade processing flow of'spatial modeling-point-by-point compression-time modeling 'is provided, and an information bottleneck mechanism is embedded between spatial feature extraction and time feature extraction. And carrying out instant compression and regularization on the spatial representation of each time point. Through a space-time separation processing flow and embedding a point-by-point information bottleneck, fNIRS space-time feature representation which is most useful and robust for a decoding task can be effectively learned, and the decoding accuracy and the model generalization ability are remarkably improved.

The invention discloses a multi-scale decomposition and abnormal mode detection system for a non-stationary time sequence, and relates to the technical field of earthquake monitoring and early warning, and the system comprises a data obtaining module which obtains earthquake directory data and a spatial topological graph of a target area; the time sequence processing module is used for obtaining the seismic frequency time sequence of each space unit through multi-scale decomposition and screening out components related to seismic precursor; the state vector generation module is used for obtaining a state vector of each space unit based on the space-time diagram neural network; the symbodynamic analysis module is used for calculating a dynamic orderliness index of each space unit; the abnormal flag generation module is used for judging whether the dynamics orderliness is lower than an abnormal threshold value or not and generating an abnormal flag bit; the feature fusion module fuses the flag bit and the state vector to obtain an enhanced state vector; the abnormal propagation recognition module recognizes an abnormal propagation mode based on the enhanced state vector; according to the invention, prospective and high-confidence decision support can be provided for earthquake risk assessment.

The present invention provides an information processing device, an information processing system, an information processing method, and an information processing program that can evaluate the reliability of information regarding moving objects with high accuracy from multiple perspectives. [Solution] The information processing device acquires mobile object-related information from a management device and orders it in a time series based on time information. The processor calculates a first anomaly value based on a feature vector generated from the mobile object-related information using a first model, and calculates a second anomaly value based on the degree of deviation between the predicted work content and the actual work content by predicting the work content from the time series data using a second model. The processor determines the reliability of the mobile object-related information by performing calculations in which a first weight is assigned to the first anomaly value and a second weight different from the first weight is assigned to the second anomaly value. This realizes a multifaceted reliability evaluation that combines anomaly detection based on static features and anomaly detection based on time series.

The application discloses a power equipment inspection device fault detection and diagnosis method based on deep learning, comprising the following steps: collecting multi-modal data to generate an original data set; performing time calibration normalization on the original data set to generate a preprocessed data package; performing abnormality detection on the preprocessed data package to output an abnormal candidate region set and generate a feature slice set; performing group convolution operation on the feature slice set to output a structure preserving representation; performing classification and calibration on the structure preserving representation to output a fault type and a confidence vector; performing risk assessment on the fault type to output a risk level code; performing time series processing on the structure preserving representation to output a fault trend curve and a deterioration rate; organizing the abnormal candidate region set, archiving the risk level code, labeling the fault trend curve and the deterioration rate, and generating a diagnosis report data. The application realizes power equipment inspection device fault detection and diagnosis.

The embodiment of the application discloses a database dynamic query optimization and resource scheduling method, device and medium, belongs to the technical field of databases, and solves the problems that the manual adjustment of PostgreSQL resource parameters is inefficient and causes resource waste. Query feature data and resource state data corresponding to a database are acquired, time series processing is performed on the resource state data, and resource prediction results are obtained; the query feature data and the resource state data are input into a preset AI strategy optimization model to output an optimized execution plan and optimized resource allocation parameters; the optimized execution plan is input into a dynamic injection module; based on the optimized resource allocation parameters and the resource prediction results, the running parameters of PostgreSQL are dynamically adjusted; in response to a plan injection instruction, a required execution plan is selected in the dynamic injection module, and based on the adjusted running parameters and the required execution plan, an optimized query task is executed.

The invention discloses an aircraft fault intelligent diagnosis method, device and equipment and a readable storage medium, and relates to the field of aircraft fault diagnosis, and the method comprises the steps: carrying out the spatial feature extraction of target flight state data corresponding to an aircraft based on a CNN network in a target fault diagnosis model, and obtaining a target feature vector; performing time sequence processing on the target feature vector through a long short-term memory network in the target fault diagnosis model to obtain a target time sequence feature; carrying out feature integration on the target time sequence features according to a full connection layer in the target fault diagnosis model, and outputting a fault gain value used for representing the fault severity of the aircraft execution mechanism; wherein a loss function of the target fault diagnosis model is determined based on a mean square error and an attitude residual error, and the attitude residual error is determined based on an aircraft six-degree-of-freedom model containing a fault gain value parameter. According to the invention, intelligent diagnosis of the fault of the aircraft execution mechanism can be realized, so that the diagnosis reliability, accuracy and adaptability are effectively improved, and the diagnosis difficulty is reduced.