148 results about "Interval prediction" patented technology

The invention discloses a long-tail SKU sales prediction method and system, and belongs to the technical field of sales prediction, and the method comprises the following steps: obtaining SKU sales feature data, and carrying out the preprocessing of the sales feature data; obtaining a multi-dimensional feature table based on the standardized training data set; a rule engine and a lightweight classification model are used for joint judgment to classify the multi-dimensional feature table, and four SKU category labels and probability vectors thereof are output; differentiated training models of the four SKU category labels are established, different types of SKUs are routed to the corresponding training models, and SKU sales volume point predicted values are output; probability calibration is carried out, and a plurality of quantiles are calculated to form interval prediction; establishing an SKU multi-hierarchy relationship, and performing hierarchy consistency solution on prediction results among multiple hierarchies; in the online operation process of the prediction model, input data distribution drift is monitored, and model updating is triggered when the input data distribution drift exceeds a threshold value. According to the method, the prediction precision and stability of the long-tail SKU are improved, and rapid adaptive prediction of sudden and off-peak demands is realized.

The invention relates to the technical field of ship energy consumption prediction, and discloses a ship energy consumption interval prediction method and system based on a Gaussian quantile regression model.The method comprises the steps that firstly, a ship navigation historical data set is preprocessed, a model input feature set is screened, a Gaussian process quantile regression model with a radial basis function as a kernel is constructed, and hyper-parameters are optimized; and after the prediction performance of the subset evaluation point is verified through the model, an upper quantile prediction model and a lower quantile prediction model are respectively established according to a target confidence level, and finally a prediction interval is synchronously output and an evaluation report is generated. According to the method, through combination of Gaussian process processing nonlinear relation and quantile regression estimation condition distribution, high-precision point prediction is provided, meanwhile, prediction uncertainty can be quantized, an energy consumption prediction interval corresponding to a target confidence level is output, and more comprehensive and reliable information support is provided for ship energy efficiency management and operation decision making.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a wind power prediction method based on multi-objective optimization, and the method comprises the steps: wind power data collection and training data set construction, adaptive sliding window and dynamic fluctuation decomposition of wind power time series data, construction of a short-term power prediction model, and short-term power prediction. According to the method, the historical window length and the decomposition scale can be autonomously adjusted according to the inherent fluctuation characteristics of the data, and multi-scale accurate characterization of the non-stationary power sequence is realized; according to the method, a dynamic space-time diagram fusing geographic distance and instantaneous power correlation is constructed, and a diagram attention network combined with trend similarity gating is designed, so that dynamic refined modeling of a space incidence relation is realized; according to fluctuation intensity self-adaptive loss function dynamic balance point prediction precision and interval prediction reliability, synchronously outputting deterministic and probabilistic prediction results; the rated power limit and the ramp rate constraint are embedded into the model in a soft mode, and it is ensured that the prediction result conforms to the actual operation rule of the wind turbine generator.

The invention relates to the technical field of power battery life prediction, and discloses a power battery residual life prediction method based on sensor fusion. The method comprises the steps of collecting and processing multi-dimensional electrical sensor signals of historical operation of a battery, and generating a voltage platform change characteristic spectrum, a current stress characteristic spectrum and an internal resistance component evolution spectrum. And inputting the maps into a feature space alignment network for space-time dimension registration and feature cross validation to generate a unified electrochemical state panoramic feature map. Based on the atlas, multi-stage battery life state backtracking and deduction are realized by constructing a dynamic attenuation trajectory tree, and a battery aging state vector set is output. After working condition constraint correction, the set drives a residual life interval prediction module, boundary estimation and probability density propagation calculation are fused, and finally a confidence interval of the residual cycle life and probability density distribution of the confidence interval are generated. According to the method, the precision of multi-source information fusion and the reliability of life prediction are improved.

The invention belongs to the technical field of electric power transaction, and particularly relates to an electric power spot transaction clearing and bidding optimization method based on computing power prediction, which comprises the following steps: collecting and preprocessing multi-source data, and screening a core feature set through mutual information entropy, Pearson's correlation coefficients and variance expansion factors; a CNN-LSTM attention model is built, and output load, power output and electricity price fluctuation interval prediction results are calibrated through error feedback; a dynamic bidding decision mathematical model is constructed, and elastic constraints are set in combination with prediction parameters; solving by adopting an improved particle swarm algorithm, and outputting an optimal bidding strategy; a node marginal electricity price mechanism is fused for accurate clearing; and verifying a clearing result in a layered manner, and performing deviation feedback iterative optimization. According to the method, a whole-process closed-loop mechanism is constructed, the prediction precision and the bidding and clearing collaboration are improved, the system operation cost is reduced, the transaction real-time requirement is met, the power grid safety and the market subject income are guaranteed, and the method has important practical value.

The invention discloses an electric power spot day-ahead electricity price prediction method based on bidding space analysis, and the method comprises the steps: constructing input features, obtaining electric power market data, meteorological data and date data, and constructing a multi-dimensional input feature system; data preprocessing: carrying out missing value processing, abnormal value elimination, feature normalization and category feature coding on the data in the multi-dimensional input feature system to form a standardized feature matrix; model training: carrying out training by adopting a deep learning model, extracting a multi-scale nonlinear relationship between the bidding space and the electricity price, and fusing time sequence dependence and global feature interaction; and electricity price prediction: outputting a point prediction result and an interval prediction result of the electricity price by using the trained model. According to the method, a multi-dimensional feature system is constructed, and a model is used for training after preprocessing so as to synchronously output day-ahead electricity price point prediction and interval prediction. And high-precision and self-adaptive high-value electricity price prediction with risk assessment capability is realized.

The invention relates to the technical field of power system optimization scheduling, and discloses a source network load storage optimization scheduling method suitable for flexible supply and demand balance in extreme weather. The method comprises the following steps: firstly, acquiring new energy power interval prediction data which aims at typical extreme weather scenes and is subjected to error correction; on the basis, the flexibility requirement of the system and the supply capability of multi-type resources are quantified; and furthermore, with maximization of the comprehensive benefit of the system as a target, a flexible vacancy virtual penalty term for stabilizing fluctuation risks caused by extreme weather is particularly introduced, an optimal scheduling model is constructed and solved, and a scheduling plan is generated. According to the method, by fusing extreme weather exclusive prediction information and an operation risk hedging mechanism, efficient coordination of source network load storage resources in extreme weather is realized, the new energy power abandoning rate is effectively reduced, the dependence on traditional thermal power is reduced, and the comprehensive operation benefit and safety margin of the system are improved.

The invention relates to the technical field of energy consumption interval prediction, and particularly discloses a building energy consumption interval prediction method, system and equipment, and the method comprises the steps: collecting historical energy consumption data, meteorological data, building characteristic data and use mode data of a target building, and carrying out the hierarchical processing, thereby obtaining a hierarchical data set; performing wavelet transform decomposition on the historical energy consumption data, and decomposing the energy consumption time sequence into a trend term, a periodic term and a random term to obtain multi-scale decomposition features; constructing a feature evaluation model, calculating a contribution degree and dynamically allocating weights to obtain a weighted feature set; constructing an integrated prediction framework based on the weighted feature set, and generating a prediction interval through quantile regression; and performing adaptive adjustment according to the historical prediction deviation and the environmental factor change to obtain a final prediction interval. Through multi-scale feature processing, dynamic weight optimization and prediction interval adaptive calibration, the accuracy and reliability of the prediction interval are improved, and more accurate support is provided for building energy management.

The invention discloses a power grid state prediction method under high wind power permeability based on multi-source data fusion, and the method comprises the steps: firstly analyzing the correlation between variables through feature engineering and a Pearson's correlation coefficient, and expanding a data set through a sliding time window to mine time domain information; establishing a graph convolution network model of an adaptive adjacency matrix, dynamically capturing the relationship between fans, and extracting spatial features through multilayer convolution to realize information transmission; and carrying out probability density analysis on the wind power prediction error by adopting a kernel density estimation method, determining an error interval boundary value, and realizing interval prediction by combining a deterministic prediction result. The system based on the method comprises a data preprocessing module, a model building module and an uncertainty analysis module which are matched with a processor and a memory to operate. According to the scheme, the accuracy and stability of power grid state prediction in a high wind power permeability scene can be effectively improved, the certainty and the interval prediction result are considered, and a reliable basis is provided for power grid dispatching and optimized operation.

The invention relates to an interval value carbon price prediction method based on multi-source data fusion and deep learning integration, and the method comprises the following steps: obtaining the actual historical transaction data of a carbon market, collecting the maximum price and the minimum price of the daily actual transaction of the carbon market, and determining an interval value carbon price sequence; key external driving factors influencing the carbon transaction price are screened through feature importance analysis, and data alignment is conducted on the key external driving factors of different market transaction days through a cubic spline interpolation method; cEEMDAN-VMD dual decomposition is adopted, and higher-precision multi-scale characterization of an original interval value carbon price sequence is achieved; and by taking the decomposition result and the determined external key driving factor as input and the carbon price prediction interval as output, constructing a carbon price interval prediction model, and finally outputting to obtain the carbon price prediction interval containing upper and lower boundary values. The method can effectively cope with the non-stationary and non-linear characteristics of the carbon valence sequence, and is good in expansibility and adaptability.

The invention discloses a power load probability prediction method and system based on a neural network quantile regression model and multiple linear regression, and belongs to the technical field of power system load prediction. The method comprises the following steps: obtaining standardized data by using a longitudinal data analysis method; identifying key influence factors of the power load in the standardized data based on a Pearson correlation analysis method, and constructing a factor analysis model to quantify influence weights of the key influence factors on the power load; constructing a neural network quantile regression model based on seasonal trend decomposition to fit the key influence factors with different influence weights to obtain a quantile prediction result; based on the quantile prediction result, estimating a continuous probability distribution curve of a load common factor by adopting a non-parametric kernel density technology to obtain an interval prediction result; and constructing a multiple linear regression model to predict the load scale change, and adjusting the interval prediction result. According to the invention, the precision and calculation efficiency of load prediction are effectively improved.

The invention provides a clearing price interval prediction method based on an LSTM network and a similar point matching algorithm, and the method comprises the following steps: carrying out the optimization of an independent variable feature set, taking a real-time clearing price as a target variable based on the historical data of an electricity market at an interval of 15 minutes, constructing a point prediction model based on the LSTM network, and carrying out the calculation of the point prediction model. On the basis of optimization of an independent variable feature set, a long-short-term memory neural network is adopted to construct a real-time clearing price point prediction model, high-precision point prediction, similar point matching and statistical feature extraction are realized, feature vectors of to-be-predicted time points are constructed to be used for matching similar time points in a historical data set, and a real-time clearing price point prediction model is obtained. And constructing upper and lower limits of the prediction interval. A real-time clearing price point prediction model is constructed based on an LSTM network, and an upper limit and a lower limit of a real-time clearing price prediction interval are formed in combination with a similar point matching algorithm and an interval feature statistical analysis method, so that uncertainty in an electricity market is quantified better, and more decision bases are provided for market participants.

The invention discloses a cross-scene self-adaptive flexible load dynamic interval prediction method. The method comprises the following steps of multi-modal scene identification, dynamic interval prediction and cross-scene self-adaptive migration. According to the cross-scene adaptive flexible load dynamic interval prediction method provided by the invention, the cross-scene adaptive capability is realized, automatic identification of industrial, commercial and resident scenes is realized, cross-scene prediction MAPE errors are reduced to be within 3%, which is better than that of a traditional model, the interval reliability is improved, quantile non-cross constraint enables PICP to be stabilized to be more than 95%, PINAW is reduced to be 5%-8%, and the interval reliability is improved. According to the method, the requirement of power grid dispatching for a narrow and reliable interval is met, knowledge migration is efficient, through field adaptive migration learning, the new scene model training time is shortened by 60%, the data demand quantity is reduced by 50%, and the engineering application cost is reduced.

This invention relates to the field of subway equipment fault prediction technology. It provides a method for adaptive threshold early warning of subway equipment fault intervals. The method includes: organizing original fault events, calculating the time interval between two adjacent faults to obtain a fault interval sequence, and cleaning the fault interval sequence; converting the cleaned fault interval sequence into a dimensionless health index sequence, and generating two adaptive threshold sequences on the health index sequence using a fixed-length sliding window; performing ordinary least squares regression on the health index sequence to obtain the slope and intercept, simultaneously calculating the Theil-Sen slope, and then performing a Mann-Kendall trend test to calculate the statistic, variance, standardized value, and two-tailed values ​​to obtain a trend conclusion; based on fault interval prediction, combined with hysteresis window determination, event proportion threshold, and cooling step size mechanism, generating a final alarm, and outputting corresponding maintenance suggestions based on the trend conclusion. This invention achieves adaptive early warning for non-stationary drift.

The invention relates to the technical field of capacitor voltage transformers, in particular to a CVT state online monitoring method and device, electronic equipment and a storage medium, and the method comprises the steps: synchronously collecting secondary voltage signals of a three-phase CVT during normal operation; based on a Pisareenko harmonic decomposition method, main components of each phase of secondary voltage signal are extracted; acquiring secondary voltage characteristic data of each phase as a training sample according to the secondary voltage signal of each phase and the main component of the secondary voltage signal, and training a single-class support vector machine model according to the training sample; obtaining secondary voltage characteristic data of each phase of CVT in the current time period as a plurality of test samples of a primary test, and identifying an abnormal ratio of abnormal samples in the plurality of test samples based on a trained single-class support vector machine model; performing interval prediction on the abnormal ratio of multiple tests by adopting a linear regression algorithm to obtain a prediction interval; and giving an alarm for the abnormal ratio which is not in the prediction interval. The technical scheme is used for on-line monitoring of the CVT state, and monitoring can be carried out accurately and effectively.

The invention belongs to the technical field of data processing, and provides a power market price prediction method and system based on two-stage optimization, and the method comprises the steps: determining an initial generating capacity node value and an initial unit price node value of each generator set, and carrying out the interpolation refining operation of the initial generating capacity node value and the initial unit price node value, obtaining a refined generating capacity node value and a refined unit price node value; establishing an output interval prediction 0-1 planning model of the generator set; performing discrete optimization solution on the output interval prediction 0-1 planning model to obtain a predicted generating capacity interval of each generator set; according to the predicted generating capacity interval, the refined generating capacity node value and the refined unit price node value, establishing an average electricity price prediction quadratic programming model; and performing continuous optimization solution on the average electricity price prediction quadratic programming model to obtain an average electricity price prediction value. The scheme can adapt to market dynamic changes, and prediction precision, flexibility and reliability are effectively improved.

This invention relates to a method for predicting robot position errors and their intervals. The steps include: acquiring the joint angle vectors of an industrial robot during operation and calculating the difference between the theoretical and actual positions to construct a dataset containing the joint angle vectors and position errors; inputting the dataset containing the joint angle vectors and position errors into a neural network-based prediction model; predicting the position error interval by calculating the predicted interval coverage probability and the average predicted interval width, while ensuring the predicted interval coverage probability meets the standard and minimizing the average predicted interval width; generating relative weights for the predicted interval boundaries by defining learnable parameters and using mean squared error loss to regress the predicted values ​​to predict the position error; and outputting the position error value and its interval prediction result from the prediction model. Compared with existing technologies, this invention can not only predict position errors but also predict the error range, improving the applicability and effectiveness of position error prediction.

To provide methods and devices for decoding video signals using inter prediction.SOLUTION: A method comprises constructing a reference picture list of a current picture in the video signal, and performing a prediction for a current picture by using the reference picture list. The step of constructing the reference picture list comprises, if a first entry of the reference picture list corresponds to an STRP entry, obtaining a POC difference value between a picture related to the first entry and another picture, and if a second entry of the reference picture list corresponds to an LTRP entry, obtaining a POC modulo value of a picture related to the second entry. A reference picture list for identifying a picture may be generated in a simple and effective manner, and thus, the compression performance may be increased and the computation time may be decreased.SELECTED DRAWING: Figure 2

This invention provides a method for predicting the relative safety (RUL) of an aircraft system based on combined probability density. The specific steps are as follows: In the training module, preprocessed training data is input into an FFT model, and different quantile loss functions are set. After the loss functions converge, the predicted values ​​at each quantile are obtained. In the testing module, processed test data is input into a trained QRFFT model to obtain multi-quantile RUL prediction results. These results are then used as input to a KDE (Knowledge-Defined Allocation) model, and the RUL probability density distribution (PDF) is obtained through a Gaussian kernel function and optimal bandwidth. This model combines RUL point prediction, interval prediction, and probability density prediction functions. Experiments using real aircraft flight path data are conducted, and a new probability prediction evaluation index is introduced. Comparison with existing QR models in terms of point prediction accuracy and interval prediction performance shows that this invention has higher effectiveness and superiority.

The application discloses a double-reliability RVFL bat optimization wind power interval prediction method and system, specifically for: preprocessing wind farm historical power data, constructing a data set; establishing a basic RVFL, a cascaded enhanced RVFL and a direct connection enhanced RVFL candidate model, expressing the upper and lower bounds of the prediction interval as a linear combination of enhanced features and output weights; in the first stage, the hidden layer parameters are fixed, the output weights are solved by using an opportunity constraint optimization model containing a training set and a validation set coverage constraint, and the optimal basic model is selected according to the validation set SCORE index; in the second stage, the model parameters are used as elite prior solutions, a mixed initialization bat population is constructed by combining random exploration individuals, and the global network parameters are optimized; and the prediction interval is output on the test set and evaluated. The application improves the generalization coverage ability by double-reliability constraint, enhances the scene adaptability, and optimizes the network parameters by using a mixed initialization bat algorithm.

This invention discloses a water-energy coupled water resources planning method, apparatus, equipment, and medium. The method includes: collecting and processing historical index values ​​corresponding to water demand prediction for each water-using sector to obtain the upper and lower limits of each index for the planning year, and inputting them into a trained water demand prediction model to obtain the water demand interval prediction results for each water-using sector; collecting and preprocessing historical water resource availability and energy availability data; constructing a water-energy system coupling risk analysis model based on the Copula function to quantitatively analyze the coupling risk of the water-energy system, and setting four coupling risk scenarios: low, medium-low, medium-high, and high; constructing a multi-objective interval stochastic chance constrained programming model, which is decomposed into two deterministic sub-models; solving the two deterministic sub-models using a hybrid intelligent algorithm, and integrating the solution results to obtain and output the regional water resources optimization allocation results adapted to the four risk scenarios of low, medium-low, medium-high, and high.

The short-term photovoltaic power interval prediction method based on multi-dimensional clustering and secondary decomposition can fully mine the periodic similarity and meteorological correlation of the photovoltaic power time sequence by obtaining the accurate similar day photovoltaic power data in history of the target prediction day; then the photovoltaic power data in the accurate similar day sequence is decomposed once to obtain high-frequency modal components and low-frequency modal components, and the modal components after the second decomposition of the high-frequency modal components are combined to construct a feature vector to obtain the prediction interval of the photovoltaic power of the target prediction day. Through the screening of the similar day, the data amount of subsequent model training is reduced; the secondary decomposition accurately splits different frequency components, helps the model to focus on the component law, reduces the model learning complexity, can avoid excessive calculation, the training and prediction time is shorter, and the real-time scheduling demand of the power system can be met.

The invention discloses a real-time optimization scheduling method for opposite conflict of long-tunnel single-lane construction vehicles, and the method comprises the steps: extracting a standard deviation corresponding to a state covariance matrix eigenvalue outputted by a UWB / IMU fusion positioning algorithm, and introducing the standard deviation into dynamic travel time prediction and dynamic safety boundary calculation. The interval prediction of the conflict probability and the self-adaptive adjustment of the security boundary along with the change of the positioning precision are realized; establishing a multi-objective optimization dynamic avoidance decision model which integrates multiple rigid constraints of vehicle staggering platform safety, transportation material timeliness, TBM key process progress, heavy-load vehicle starting and stopping and buffer area smoothness and comprehensively considers three objectives of minimization of construction progress influence, minimization of transportation total delay and maximization of load throughput; the problem that tunnel construction requirements cannot be dynamically met due to the fact that current tunnel vehicle scheduling research is limited in positioning precision, rigid in safety boundary, lack of probability-based conflict advanced early warning capability and dependence on fixed rules when facing a GNSS rejection environment is solved.

The present application relates to the technical field of hydraulic engineering safety monitoring, in particular to a soil and rock dam multidimensional displacement interval prediction integrated model and state identification method, comprising: determining soil and rock dam displacement key influence factors; constructing NGO-GRU soil and rock dam multidimensional displacement prediction model; constructing pseudo data set; point prediction result analysis and calculation system error variance; calculating random error variance; constructing multidimensional displacement prediction interval; identifying soil and rock dam displacement state. The present application takes into account the point prediction accuracy and interval prediction reliability, can adaptively optimize model parameters, quantify prediction uncertainty, dynamically identify soil and rock dam multidimensional displacement state, and effectively improve the intelligentization and refinement level of soil and rock dam safety monitoring and early warning.

This application provides a PWM control method for avoiding eddy current losses in LED power supply cores, comprising: generating a basic modulation sequence from an asymmetric pulse width modulation chaotic mapping; extracting the main frequency bands of harmonic energy distribution of the basic modulation sequence; determining the degree of overlap between the main frequency bands of harmonic energy distribution and the expected position range; if the overlap ratio exceeds a preset threshold, activating a frequency avoidance mechanism; comparing the spectrum of the offset modulation sequence with that of the basic modulation sequence to determine the degree of preservation of spectrum broadening characteristics and the change in energy distribution; if the degree of preservation of spectrum broadening characteristics is lower than a preset preservation range, reducing the offset and re-executing the frequency avoidance mechanism; and feeding back ripple data and temperature increments collected during actual operation to the sensitive interval prediction model to update the internal state of the sensitive interval prediction model.

The invention provides a soft measurement modeling method based on a VMD-BiLSTMA-GPR model, and relates to the field of artificial intelligence, and the method comprises the steps: S1, obtaining the direct measurement data of a process variable and the offline detection data of a target variable; s2, performing variational mode decomposition on the data of the target variable to obtain a series of intrinsic mode function subsequences and a residual sequence; s3, adopting a bidirectional long short-term memory network and self-attention mechanism fusion model to train and predict each intrinsic mode function sub-sequence and the residual sequence obtained in the step S2; s4, superposing the prediction results of the sequences obtained in the step S3, and reconstructing to obtain a point prediction value of the target variable; and S5, based on the point prediction value obtained in the step S4, adopting a Gaussian process regression algorithm to carry out probability modeling on the prediction residual error, and outputting an interval prediction result with a specific confidence interval. According to the method, high-precision point prediction and reliable interval prediction of variables difficult to measure in a complex industrial process are realized.

The present application provides a rotary kiln sintering temperature probability interval prediction method, system and device, which introduces a parallel multi-head self-attention mechanism. By fusing the multi-head self-attention feature with the context vector of the sintering temperature prediction model, more abundant information is provided for the decoder, thereby improving the understanding ability of the complex dynamic relationship in the input sequence and enhancing the modeling ability of the long-term dependence relationship. In addition, the present application also adopts a Gaussian process regression (GPR) method to predict the probability interval of the sintering temperature. GPR can infer the posterior distribution and establish a probability prediction model according to the prior distribution of the prediction and the existing data set. Compared with the deterministic method, the GPR prediction contains the uncertainty of parameter estimation, provides a probability prediction interval, and better guides the process control decision.

The invention provides an electric power spot market price prediction system and method based on artificial intelligence. The system comprises a market boundary prediction analysis module, a spot market price prediction module, an agent electricity purchase decision optimization module and a prediction result redisk analysis module. According to the system, a deep learning model fused with a CNN-MLP-Attention algorithm is adopted, kernel density estimation is combined to carry out electricity price interval prediction, meteorological data weighting processing, feature engineering, rolling training and multi-day prediction functions are integrated, and 1-7-day high-precision determinacy and uncertainty prediction of the electricity price of the electric power spot market is achieved. The method solves the problems of low electricity price prediction precision, large uncertainty, lack of scientific support of electricity purchasing strategies and the like in the existing electric power spot market, can effectively improve the decision scientificity of power grid enterprises in the spot market, reduces the transaction risk, and improves the efficiency of the power grid enterprises. And full-process data support and strategy simulation capability are provided for a power grid enterprise to participate in an agent power purchase transaction mode.

This invention discloses an interpretable bidding space prediction method based on multimodal data analysis, comprising the following steps: S1, acquiring multi-source time series data and processing it to form standardized input data; S2, constructing training samples and generating lower bound labels, upper bound labels, and boundary information; S3, dividing the data into external driving forces, system endogenous inertia, and market operating state feature groups; S4, inputting an improved boundary-aware temporal Kolmogorov-Arnold network model to obtain boundary response temporal mapping features and boundary response intensity; S5, fusing and generating bidding space fusion features; S6, generating a prediction lower bound and a prediction upper bound, and updating the model parameters; S7, outputting the bidding space prediction interval and multidimensional interpretation. This invention uses an improved boundary-aware temporal Kolmogorov-Arnold network model to achieve interpretable interval prediction of the bidding space.

The present application relates to a data product pricing trend prediction method based on transaction behavior clustering analysis, aiming to solve the technical problems of complex transaction behavior causal relationship identification, causal mechanism dynamic evolution and trend prediction accuracy improvement. The core scheme is: through transaction behavior clustering and multi-dimensional unsupervised correlation measurement to generate a coarse-grained causal graph, combined with causal stability evaluation, conditional independence test and graph fusion to realize dynamic updating of causal relationship; further using semantic annotation path set, path distillation encoder and overall trend prediction link to form an end-to-end price interval prediction and model dynamic closed-loop optimization. This technology can effectively mine the causal structure of transaction behavior, enhance the stability of causal inference, improve the accuracy of trend prediction, and realize adaptive parameter optimization, thereby improving the intelligent level of market trend analysis.