320 results about "Sample sequence" patented technology

The invention discloses a flow field measurement method based on an event camera, and the method comprises the steps: generating a PIV data set, each time sequence sample sequence comprising a plurality of frames of continuous particle images, a corresponding velocity vector field, and particle event data at all moments; establishing a flow field data acquisition device based on an event camera and a high-speed camera, acquiring real event data and real image data which are synchronous in time so as to adjust parameters of an event simulator, and verifying and updating particle event data in the PIV data set according to the adjusted event simulator so as to obtain a flow field data acquisition result; obtaining the updated PIV data set as a training data set; building an event camera optical flow method model, and training by adopting the training data set; and on the basis of the trained event camera optical flow method model, event sequences in two adjacent time periods are used as inputs to calculate a velocity vector field corresponding to a middle moment. According to the invention, the flow field velocity field at the required moment can be obtained based on the event data within a period of time.

The invention relates to the technical field of data processing, and discloses a secondary equipment hidden danger mining method and system based on a wave recording file and monitoring data. The method comprises the steps of calculating a conditional probability and intervention probability difference identification causal relationship by analyzing a recording file in combination with equipment topology and protection logic to construct a correlation graph, and performing Fourier transform and wavelet decomposition on a sampling sequence to extract multi-scale features; small signal test excitation is injected, a system identification estimation transfer function is fused with monitoring data to form a health feature vector to judge a health state, and a correlation map and the health state are input into a map neural network to calculate weighted attention coefficients and aggregate neighbor information to predict a fault propagation path; and establishing a degradation model, correcting the failure rate, predicting the residual life and generating a graded early warning report. According to the method and the device, the transformation from passive post analysis to active predictive maintenance is realized, and the timeliness, accuracy and systematicness of hidden danger identification of the secondary equipment are improved.

The invention relates to the technical field of urban rail transit station-city collaborative planning, in particular to an ontology-based station-city collaborative data integration and planning prediction method, which comprises the following steps of: obtaining rail transit station passenger flow data, resident travel behavior data and station periphery built environment index data; forming a space-time sample sequence according to the unified space-time granularity of the site walking service area; constructing an urban rail transit station-city cooperation ontology, and carrying out semantic annotation and semantic fusion on the space-time sample sequence to generate a feature sequence; inputting the feature sequence into a multi-task space-time diagram convolutional neural network prediction model to output a passenger flow prediction result and establish an environment index prediction result; and calculating a feature contribution degree based on a Shapley additive interpretation value, optimizing a background sample set by using a genetic algorithm to determine a key action element set, outputting a planning index threshold and an intervention measure parameter, and realizing an interpretable station-city collaborative prediction and planning decision closed loop.

The invention discloses a PCB component layout method, device and equipment based on a multi-modal large model, and relates to the technical field of electronic design automation, the method comprises the following steps: carrying out quality screening on a plurality of PCB design files which have been laid out to obtain a qualified design file set; constructing a serialized training sample set based on the qualified design file set; each training sample sequence comprises a plurality of training samples arranged according to a layout logic sequence; each training sample comprises multi-modal data representing a current layout state and a real position coordinate of a next component to be laid out; based on the serialized training sample set, training the open-source multi-modal large model to obtain a PCB component layout model; and iteratively generating a layout file of the target PCB based on the netlist file of the target PCB and the PCB component layout model. The method is high in generalization capability and low in cost based on the PCB component layout model trained by the layout data, and can realize rapid layout of components of a new PCB.

The embodiment of the invention provides a model training method, a carbon emission prediction method, a device and equipment. The method comprises the following steps: firstly, obtaining carbon emission sample sequence data; then, preprocessing the carbon emission sample data sequence to obtain preprocessed carbon emission sample sequence data; further, according to the preprocessed carbon emission analysis sample sequence data, determining a time sample feature and an interaction feature sample sequence; and then. Processing the sample time feature and the preprocessed power consumption sample sequence data to obtain a sample time feature component and a sample power consumption feature component; and finally, inputting the sample time characteristic component, the sample power consumption characteristic component, the interaction characteristic sample sequence and the preprocessed carbon emission sample sequence data into an initial Transform model for optimization training, and obtaining an improved target Transform model. In this way, the prediction precision of the prediction model and the generalization ability of the model are improved, and therefore accurate prediction of carbon emission data is achieved.

The invention discloses a distribution line early-stage fault time sequence hidden Markov modeling and identification method and system based on multistage evolution characteristics, and belongs to the field of distribution line early-stage fault identification. Comprising the steps of obtaining a current waveform sample sequence of an early fault in a distribution line; for each current waveform sample in the sequence, extracting a multi-dimensional time-frequency feature, and constructing a feature vector of each sample; performing fault stage identification on each sample by using the first-level hidden Markov model group, and outputting a fault stage tag sequence corresponding to each sample; combining the fault stage label sequences of the current sample and a plurality of previous historical samples to form a stage label sequence window; and respectively inputting the stage label sequence window into a second-level tree line fault hidden Markov model and a second-level non-tree line fault hidden Markov model, calculating a corresponding first average log-likelihood value and a corresponding second average log-likelihood value, comparing the two average log-likelihood values, and judging whether a current sample belongs to a tree line early fault or not.

Methods, systems, and devices for wireless communications are described. A transmitter may modulate a set of bits into an on-off keying (OOK) sample sequence for wireless transmission to a receiver in a set of frequency resources. The transmitter may apply a transform (e.g., a discrete Fourier transform (DFT)) to the OOK sample sequence to generate a frequency domain representation of the OOK sample sequence. In some cases. the transmitter may, using an orthogonal frequency division multiplexing (OFDM) waveform generator, generate an OFDM waveform based on mapping the frequency domain representation of the OOK sample sequence to a set of resource elements of the set of frequency resources. In some cases. the transmitter may transmit the OFDM waveform to the receiver via the set of frequency resources.

The invention relates to the technical field of time sequence data processing and deep learning, in particular to a time sequence filling method and system based on a coarse-to-fine filling normal form. In the invention, a preprocessing module is used for sampling an input sequence to obtain a subsequence rk with the length of Lk, and a regression prediction module is combined with a causal mask to generate a prediction value r'k with the same structure and the same length as the rk based on all subsequences {r1... rk} generated by the preprocessing module; the correction module performs up-sampling on the predicted value r'k to obtain an up-sampling sequence r ''k with the length of T; supplementing missing data of the original sequence based on the r ''k to obtain a corrected sequence X (k + 1); and traversing k = 1... K to obtain a correction sequence X (K + 1) as a final repair completion time sequence. The method overcomes the defects that the time sequence filling mode in the prior art does not consider the unfixed missing rate and missing value block distribution, and is beneficial to improving the filling accuracy.

The invention relates to a Parkinson's disease treatment effect prediction method based on a multi-modal image model and a related device. The method comprises the following steps: acquiring a multi-modal vector of a Parkinson's disease patient; aligning the multi-modal vectors on a time axis, and constructing a time point data element sample sequence; deploying a double-flow cross attention encoder for a sample sequence in each time point data element, and outputting a fused multi-modal feature through the double-flow cross attention encoder; and connecting the fused multi-modal features with digital clinical treatment scheme vectors corresponding to corresponding time points to form time point comprehensive feature vectors, inputting the time point comprehensive feature vectors to a time sequence information aggregation gating circulation unit, outputting final time point aggregation features, and inputting the final time point aggregation features to a multi-task adaptive prediction head. A UPDRS total score or a specific sub-scale score for the patient at a future preset point in time is predicted. According to the method, time sequence modeling is carried out on multi-mode and multi-time-point data, so that the accuracy and interpretability of Parkinson's disease treatment effect prediction are improved.

The invention relates to the technical field of artificial intelligence application, and discloses an enzyme EC number prediction method, and the method comprises the steps: obtaining the sample sequence characteristics of a to-be-predicted sample containing a substrate SMILES sequence and a product SMILES sequence through a target BERT model; constructing a molecular object and feature coding based on atom mapping, atom truncation and sequence analysis, constructing a reaction graph of a to-be-predicted sample, inputting the reaction graph into a target graph isomorphic neural network, and constructing molecular graph features of the to-be-predicted sample based on a recursive neighborhood aggregation mechanism; and fusing the sample sequence features of the to-be-predicted sample with the molecular map features by using a bidirectional cross attention mechanism to obtain multi-modal features, inputting the multi-modal features into the multi-layer perceptron, and obtaining the prediction probability of the enzyme EC number of the to-be-predicted sample. According to the method, efficient and accurate end-to-end prediction of enzyme EC numbering is realized through the multi-dimensional chemical spatial characteristics of the collaborative modeling reaction.

The invention relates to the technical field of anomaly prediction, in particular to a transformer operation anomaly prediction method and system, and the method comprises the following steps: obtaining the temperature, current, oil and vibration parameters of a transformer, aligning a sample sequence, calculating the Pearson correlation of the parameters, analyzing the mutation of the time sequence correlation, and extracting a feature group; and judging temperature difference and current variability, marking abnormal points, constructing a trend sequence to evaluate a risk trend, and outputting an abnormal trend prediction result. According to the method, by capturing linkage features between multiple parameter pairs in a time window, sudden change nodes can be identified, statistical features of multiple parameters in a time interval before and after an abnormal time point can be extracted, a composite feature set representing sudden change behaviors can be constructed, and an abnormal state can be judged under the combination condition of a maximum difference value and a variable coefficient. Risk evolution identification is realized by combining trending time sequence construction and abnormal frequency accumulation, and the stability and foresight of transformer operation abnormity prediction are improved.

The invention provides a model training method for a transverse mixed attention mechanism, a medium, equipment and a program product, and the method comprises the steps: obtaining a data set containing a plurality of sample sequences, each sample sequence in the data set being formed by arranging a plurality of Token sequences obtained through word segmentation; constructing a to-be-trained model based on the pre-trained full attention model, and adding newly added parameters for linear attention calculation; in the same transverse mixed attention layer, executing total attention calculation on a Token set in a preset total attention calculation range, executing linear attention calculation on all Tokens, and fusing results of the total attention calculation and the linear attention calculation to obtain transverse mixed attention output used for forward reasoning and loss calculation; and based on the output and prediction result, only updating the newly added parameters to optimize the to-be-trained model until the to-be-trained model converges. According to the method, the calculation complexity and video memory occupation of long text sequence processing are reduced, and the reasoning speed and the resource utilization rate are improved.

The invention discloses an intelligent acquisition risk identification system based on deep learning, and the system comprises a time sequence sample construction module which is used for building a time sequence sample sequence; the hierarchical attention structural feature coding module is used for carrying out structural feature coding on the sequential sample sequence by utilizing a hierarchical attention network; the time sequence feature extraction module is used for inputting the structural feature vector into an ETSform model to perform time sequence feature extraction; the hierarchical time sequence collaborative attention adaptive fusion module is used for performing bidirectional attention interaction and dynamically generating a hierarchical weight and a time sequence weight through a meta-learning controller; the improved CatBoost risk identification module is used for outputting a risk score and a risk type label; and the system fusion module is used for summarizing and merging the risk scores and the risk type labels. The method and the device are suitable for merchant transaction risk identification in an acquiring business scene.

The invention discloses a self-adaptive virtual synchronous generator control method of an energy storage converter, and relates to the technical field of self-adaptive adjustment. The method comprises the following steps: S1, generating an electrical comprehensive state set based on three-phase voltage and three-phase current of a grid-connected point; s2, constructing a bearing trigger function and evaluating to generate a bearing trigger identifier; s3, obtaining a bearing fault diagnosis result based on the constructed diagnosis sample sequence; s4, according to the bearing fault diagnosis result and the diagnosis sample sequence, an adjustment parameter set is obtained, and an adjustment instruction is issued to the virtual synchronous generator; s5, updating the virtual power angle and the frequency reference; extracting a current vector and remodeling an original voltage reference according to an admittance parameter group; adjusting a power droop target and power angle evolution of a virtual synchronous generator of the energy storage converter according to a projection active instruction and a projection reactive instruction obtained through projection operation; the self-adaptive virtual synchronous generator control method realizes self-adaptive virtual synchronous generator control which keeps voltage shaping and virtual power angle continuity under current bearing constraint.

The invention discloses a radio frequency chip phase deviation calibration method based on multichannel sampling data analysis, and relates to the technical field of radio frequency signal processing and multichannel data synchronous calibration. Processing the sampling sequence of each channel by adopting a phase estimation algorithm to calculate a phase value to obtain initial phase distribution; according to the initial phase distribution, if the number of detection channels is increased or decreased, the reference phase is recalculated by fusing the time sequence relation adjustment information to obtain an updated reference phase; acquiring an updated reference phase, and extracting a reference signal from the residual channels and compensating a missing part through an adaptive filtering algorithm to obtain compensated phase distribution if a failure channel signal is lost according to partial channel failure judgment; the radio frequency chip phase deviation calibration method based on multichannel sampling data analysis has long-term self-calibration and self-adaption capabilities, and the robustness, reliability and signal processing precision of a system are remarkably improved.

The invention discloses a wind power gear box variable speed fault diagnosis method based on LMSRCT and a medium, and belongs to the field of wind power fault detection.The method comprises the following steps that a vibration acceleration sensor is installed on a wind power gear box, an original vibration signal x (t) of the gear box in the running state is collected at the sampling frequency f s, and meanwhile a rotating speed pulse signal is collected; obtaining accurate rotation frequency f < r > (t) and shaft rotation angle information theta (t); preprocessing the collected original vibration signal x (t) to obtain x pre (t); the x pre (t) is converted to an angle domain through an LMSRCT algorithm, and a one-dimensional angle domain sequence signal s (theta) is generated; the s (theta) is segmented into a sample sequence with a fixed length L for embedded encoding, the sample sequence is input to a four-layer Transform encoder, and probability distribution of different fault types is output; and taking the fault type corresponding to the maximum probability value as a final diagnosis result. According to the method, the LMSRCT algorithm is adopted to convert the vibration signal from the time domain to the angle domain, the problem of diagnosis failure caused by spectrum aliasing is solved, and the accuracy of fault diagnosis is improved by combining the LMSRCT algorithm with a Transform model.

The invention relates to the technical field of power failure prediction, and discloses a power failure prediction system and method, which is used for solving the problem that a failure sample is not representative during power failure prediction, and comprises the following steps: obtaining an initial historical sample sequence, judging whether the failure sample needs to be supplemented according to the initial historical sample sequence, and if yes, judging whether the failure sample needs to be supplemented; if it is judged that the fault samples need to be supplemented, generating a synthetic fault sample set, averagely dividing a historical operation cycle into a plurality of historical sub-cycles, obtaining sub-cycle sample enhancement evaluation parameters of each historical sub-cycle, performing evaluation to obtain a sample enhancement judgment index, and judging whether the fault samples need to be supplemented in the historical sub-cycles or not; if it is judged that the fault samples need to be supplemented in the historical sub-periods, the fault samples are supplemented in the historical sub-periods, all the historical sub-periods are traversed, a supplemented historical sample sequence is obtained, an actual power fault prediction model is constructed according to the supplemented historical sample sequence, and the accuracy of power fault prediction is effectively improved.

The invention discloses an AI-based multimodal transport collaborative optimization method and system, and relates to the technical field of intelligent transportation, and the method comprises the steps: obtaining a temperature sampling sequence of cold-chain goods in a non-temperature-control region, building a self-adaptive baseline, and recognizing a temperature peak factor and a change trend; aiming at the temperature deviation cumulant with the rising risk, constructing a temperature deviation safety window through inertia correction and an internal loss dynamic model; identifying the intermodal transport risk based on the security window, and if the intermodal transport risk exceeds a threshold, constructing a matching function to perform intermodal transport capacity unit matching; monitoring transport capacity temperature deviation in real time after matching, and regulating and controlling a refrigeration strategy in a grading manner based on a corrected urgency coefficient; the system comprises a trend identification module, a window determination module, a combined transport matching module and a model optimization module. According to the method, dynamic tracking, transport capacity matching and model self-optimization of the cold chain transport temperature deviation risk are realized, and the multimodal transport temperature control efficiency and the goods damage prediction accuracy are improved.

The invention provides an industrial product quality prediction method based on geometry preserving cross-scale difference, and relates to the technical field of industrial product quality prediction.The method comprises the steps that collected time sequence data of industrial process variables are preprocessed, a sample sequence is constructed through a sliding window, and the sample sequence is divided into a training set, a verification set and a test set according to the time sequence; the method comprises the following steps: constructing a double-branch coding architecture to independently process trend features and differential features; a geometric perception attention mechanism is introduced into each branch encoder, it is ensured that hidden layer representation and output target space keep geometric consistency, and the stability and interpretability of the model are enhanced; and deep interaction and adaptive fusion of double-branch information are further realized by adopting cross-scale cross attention, so that the comprehensive modeling capability of long-term trend and short-term dynamic in the industrial process is remarkably improved.

The invention discloses a data processing method and device, equipment and a medium. Comprising the steps that a training sample set is obtained, the training sample set comprises a plurality of sample pairs, each sample pair comprises an image sample and a text sample, and through a to-be-trained model, prediction is carried out based on the image samples and the text samples to obtain an output lexical element sample sequence, determining a first target lexical element with relatively high confidence and a second target lexical element with relatively low confidence from a plurality of output lexical elements contained in the output lexical element sample sequence; filtering the loss value corresponding to the first target lexical element to obtain a first target loss value; performing upper limit constraint on the loss value corresponding to the second target lexical element to obtain a second target loss value; and training based on the first target loss value and the second target loss value to obtain a visual language model. According to the technical scheme, the reliability of data processing in a visual language model scene is improved.

The invention relates to the technical field of precision shell contour metering, and discloses an online metering method for a precision shell contour, and the method comprises the steps: obtaining an original sampling point cloud and a feature point group of a to-be-measured workpiece in a conveying state; resolving a real-time pose matrix of the to-be-measured workpiece relative to a preset measurement reference by using a spatial topology constraint relationship of the feature point groups; carrying out differential processing on a real-time pose matrix in the sampling sequence, and synthesizing an instantaneous motion vector corresponding to a sampling moment; in combination with a signal response time delay constant of the sampling system, phase compensation is carried out on a motion displacement deviator induced by signal conversion delay, and a corrected sampling point cloud is generated; according to the method, the geometric distortion of the point cloud generated by response delay of the sensor is solved, the problem of instantaneous drift of the measurement reference in an unsteady state conveying state is solved, and the contour reduction precision in a complex working condition is improved.

The invention relates to the technical field of oil and gas drilling and completion engineering, in particular to a rock core hydration strain monitoring method and device based on multi-source fusion data, and the method comprises the steps: collecting first hydration strain data generated in the hydration process of a rock core through a rock core hydration strain monitoring system; inputting the first hydration strain data into a noise reduction model to obtain second hydration strain data after noise reduction; the second hydration strain data are converted into hydration stress data through an evolution model, distribution and evolution prediction results of the hydration stress in time and space are obtained, and the evolution model is obtained based on strain sample sequence data and stress label training and used for representing the mapping relation between the hydration strain and the hydration stress. The method accurately identifies the core hydration expansion evolution characteristics and predicts the well wall stability state, improves the experimental analysis efficiency and the result reliability, and has an important value for researching well wall stability monitoring and prevention and control.

The invention relates to a low-voltage active transformer area line loss rate prediction method and system based on an improved GAN, belongs to the technical field of data prediction, and solves the problem of inaccurate line loss rate prediction caused by lack of sample data in the prior art. Comprising the steps of constructing a historical sample sequence and putting into a historical sample set; constructing a generative adversarial network comprising a generator, a feature extractor and a discriminator; the generator obtains a plurality of generated sample sequences according to each historical sample sequence and a sample generation strategy; a feature extractor extracts real features and multiple generation features according to each historical sample sequence and the corresponding multiple generation sample sequences; the discriminator discriminates the real features and the generated features; and obtaining a generation sample sequence corresponding to the generation feature which is judged to be true, putting the generation sample sequence into a historical sample set, constructing a prediction data set together with the rated capacity of the equipment, training the time sequence neural network model to obtain a line loss rate prediction model, and further obtaining the line loss rate at the prediction moment. And accurate prediction of the line loss rate is realized.

The invention provides a model training method and device, electronic equipment, a storage medium and a program product, and relates to the technical field of data processing, and the method comprises the steps: carrying out the dynamic masking processing of a voice feature sample sequence corresponding to a voice signal sample, and obtaining a voice feature sample sequence after the masking processing, under the condition that parameters of a quantizer in the self-supervised learning model are kept fixed, training an encoder in the self-supervised learning model based on the voice feature sample sequence and the quantization tag sequence after masking processing, and obtaining a first self-supervised learning model under the condition that a first preset training condition is met; and training the quantizer in the self-supervised learning model on the basis of the voice feature sample sequence and the quantization tag sequence after masking processing under the condition that the parameter fixed limitation of the quantizer is removed and the parameter of the encoder is kept fixed, and obtaining a second self-supervised learning model under the condition that a second preset training condition is met. And the model stability and the training efficiency are improved.

The invention provides an industrial multi-axis servo motor discrete temperature health assessment method based on a cloud model. The method comprises the following steps: acquiring a temperature discrete sample sequence of each servo motor; performing preprocessing operation on the temperature discrete sample sequence to obtain a preprocessed temperature sample sequence; when the temperature change rate of the pre-processing temperature sample sequence is lower than a set threshold value and lasts for a preset time length, dividing the pre-processing temperature sample sequence into heat balance windows corresponding to the servo motors, and respectively calculating an expected value, an entropy value and a super entropy value by taking the heat balance windows as input and adopting a reverse cloud generator; inputting the expected value, the entropy value and the hyper-entropy value obtained for each servo motor into a forward cloud generator to generate a cloud droplet set corresponding to the servo motor; and calculating a cloud droplet condensation degree based on the cloud droplet set, and generating a temperature health assessment level corresponding to each servo motor according to a preset judgment rule. According to the invention, the uncertainty of the discrete temperature data of the servo motor can be quantified in a unified manner, and the multi-axis temperature health level is generated.

The invention discloses a large model security evaluation method based on multi-dimensional adversarial attack, and the method comprises the steps: generating an adversarial sample sequence through constructing multi-dimensional attack scene description and combining the characteristics of data availability damage and system integrity violation, optimizing sample parameters in a black box attack mode, and precisely positioning a weak link of a model. And meanwhile, based on a risk quantized value sequence and a safety portrait mechanism, reinforcing demand data is extracted and a protection path is generated through reverse optimization, and finally, the protection capability of the model is remarkably improved. Through a closed-loop mechanism of scene generation, sample optimization and risk assessment, a complex attack environment is effectively dealt with, and the safety and stability of the system are guaranteed.

A distance estimation method comprising: transmitting, by a transmitter, a radiation wave modulated in a frequency domain to a scene; receiving a reflection of the transmission wave from the scene; and causing a copy of the transmission wave to interfere with the received reflections to generate a sequence of samples of beat signals having wrapped phases in the time domain. The method further includes iteratively estimating the frequency of the beat signal in the time domain until a termination condition is satisfied. The frequency of the beat signal is iteratively estimated based on a phase unwrapping of the beat signal samples affected by a correlated phase error derived from a phase noise statistical characteristic of the transmitter and a linear regression that fits the frequency of the beat signal into the unwrapped phase of the beat signal.

The invention discloses a cloud resource use prediction method and system based on wavelets and an attention mechanism. The method comprises the following steps: firstly, acquiring a multi-dimensional resource use sequence of a target server and dividing the multi-dimensional resource use sequence into a training subset and a test subset; generating an input sample set by adopting a sliding window mechanism; then constructing a cloud resource prediction model comprising a wavelet attention network, a multi-attention module, a wavelet reconstruction module and a memory enhancement self-attention module; decomposing the time window sample into a low-frequency feature component and a high-frequency feature component through a wavelet attention network; respectively generating a low-frequency feature vector and a high-frequency feature vector by using a multiple attention module; fusing into a reconstructed sample sequence through a wavelet reconstruction module; generating enhanced feature representation through a memory enhanced self-attention module; and finally, outputting a cloud resource use prediction sequence in a future time period through a full-connection projection layer. According to the method, the multi-frequency-domain time sequence characteristics can be effectively separated, the interaction relationship among the multi-element resources can be modeled, and the prediction precision and generalization ability are remarkably improved.

This invention discloses a method and system for real-time analysis of transformer operating status oriented towards edge computing, specifically relating to the field of power equipment condition monitoring and edge computing data processing technology. The method includes acquiring load values, oil temperature values, winding temperature values, cooling status values, and status monitoring values ​​collected from the transformer site. It generates three types of change symbols (increase, decrease, and remain unchanged) for each type of value according to the sampling order, and binds each change symbol to its corresponding sampling sequence number, outputting ordered transformer operating data. By converting the load values, oil temperature values, winding temperature values, cooling status values, and status monitoring values ​​collected from the transformer site into change symbols with sampling sequence numbers, it identifies load change segments or cooling change points and extracts the current temperature rise response segment. Then, it compares the current temperature rise response segment with existing temperature rise response segments in response order, and generates transformer operating status analysis results based on the order differences formed by oil temperature reversal, winding temperature changes, and status monitoring value changes.