83 results about "Quantile regression" patented technology

The invention relates to the technical field of power system operation and control, and particularly discloses a time-sharing electric quantity prediction method based on a logarithmic load density growth curve, which comprises the following steps of: firstly, performing causal detection and dynamic time-delay optimization on historical load and multivariate external data through convergence cross mapping and mutual information technologies, and constructing a causal time-delay feature set; and the problems of multi-element coupling and time-delay effect quantization are solved. Secondly, fitting a load trend by using time-frequency decomposition in cooperation with a segmented logistic model, extracting dynamic parameters representing a growth rate and a saturation capacity, and endowing the model with a sensing ability for a load evolution stage; then, causal features, growth parameters and load components are deeply fused through cross-domain modulation and a gating mechanism, the nonlinear modulation effect of an external environment on a load mode is explicitly modeled, and finally, a probability interval is generated in combination with quantile regression and residual error correction. According to the scheme, accurate and probabilistic prediction of the time-sharing electric quantity in a complex scene is realized, and the scientificity of an agent electricity purchase decision is improved.

The invention discloses a vehicle-mounted power supply short-circuit heat prediction method based on big data analysis, and the method comprises the following steps: collecting real-time operation data, and storing the data in a data cache, thereby obtaining a cache time sequence signal; filtering the cached time sequence signal, and extracting a basic feature sequence; constructing an RC network model based on the basic feature sequence, and converting a fractional order operator into an equivalent integer order model; performing parameter estimation on the equivalent integer order model to generate a thermal prediction model; taking the thermal prediction model and the basic feature sequence as joint input and output power loss features; inputting the power loss characteristics into a quantile regression forest model to form a heat prediction result; comparing the heat prediction result with a heat threshold value and a temperature rise threshold value, and outputting a control trigger signal; and calling parameters in a control strategy library according to the control trigger signal to generate a control strategy. According to the invention, vehicle-mounted power supply short-circuit heat prediction is realized.

The invention relates to the technical field of equipment life prediction, and provides an equipment life prediction method based on a mixed attention enhancement time sequence convolutional network, which comprises the following steps of: preprocessing original test data, extracting 10 types of time domain statistical characteristics from the preprocessed original test data, screening high-importance feature data as model input through a random forest algorithm; a life prediction model is constructed, an encoder adopts a stacked expansion causal convolutional layer and a self-attention layer, and a decoder fuses historical features and exogenous variables through cross attention; training a life prediction model by using a mixed attention enhancement time sequence convolutional network, wherein a composite loss function synchronously optimizes point prediction and multi-quantile regression loss; and inputting sensor data collected in real time into the trained model, outputting a prediction result, and generating a 95% confidence interval based on nonparametric probability prediction. The overall reliability and accuracy of equipment life prediction can be effectively improved.

The invention relates to the technical field of network monitoring, in particular to a computer network data flow monitoring system and method, and the system comprises a data flow collection module, a protocol layer analysis module, a time sequence feature extraction module, a defect compensation reconstruction module and a monitoring result output module. According to the method, a continuous difference track can be formed by extracting protocol fields and marking change time points in data stream acquisition, a cross-layer synchronization index is generated by calculating the interval and the sequence of adjacent fields, so that a cross-layer relationship is quantized in a dynamic process, quantile regression is adopted in feature processing to unify a numerical interval, and the accuracy of the cross-layer relationship is improved. Compared with the prior art, the method guarantees the comparability under different time scales, can rapidly identify an abnormal section when cross-layer synchronization is broken, deduces a potential mode in combination with feature values before and after abnormity, reconstructs defect information in a probability mode, enables the result to have the abnormity recognition precision and the flow monitoring integrity, and improves the network management efficiency and safety.

The invention discloses an environmental benefit and resource load evaluation method for an urban sewage treatment plant, and belongs to the field of sustainable evaluation. The implementation method comprises the following steps: training a random forest model by taking pollutant effluent concentration, energy consumption intensity and medicament consumption intensity of a national town sewage treatment plant as output variables and taking other historical operation information as input variables; on the basis, quantile regression is used for calculating quantiles of actual values of three operation indexes of each factory under the original processing condition; constructing an environmental benefit evaluation index system represented by pollutant removal efficiency and a resource load evaluation system represented by consumption efficiency of resources such as energy and chemicals; through obtaining environmental benefit and resource load grading benchmark values of the town sewage treatment plant, core functions and necessary input of each plant are accurately quantified, and real environmental contribution of the town sewage treatment industry is clarified, so that evaluation of sustainability of the town sewage treatment plant is more objective and fair, and sustainable development of urban environment infrastructures is facilitated.

The invention discloses a risk indicator construction method and system for new energy output prediction, and belongs to the technical field of data processing and power system management, and the method comprises the steps: obtaining the preprocessing data of a plurality of new energy stations, carrying out the spatial-temporal feature analysis, and generating a spatial-temporal feature set; obtaining meteorological prediction data, and performing medium and long term output prediction through an extreme gradient boosting tree based on the space-time feature set; comparing the output prediction result with historical real output data to obtain a prediction error, and performing probability coupling modeling and quantile regression prediction in combination with a meteorological element evolution sequence to generate a dynamic quantile value; and determining a dynamic risk interval, and constructing a graded early warning index. According to the method, probabilistic coupling modeling and quantile regression prediction are adopted, prediction errors are deeply associated with dynamic evolution of meteorological elements, predicted uncertain risks can be quantized and graded, and decision support is provided for optimal scheduling and risk management of a power grid.

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 provides an optical access network PON port fault prediction and operation and maintenance support method and system, and the method comprises the steps: collecting real-time multi-index sequences of optical power, bit error rate, temperature, frame loss rate, business load and the like, automatically extracting a long-term trend based on variational mode decomposition, constructing a residual sequence, generating a state transition map through employing a Markov transition field algorithm, and carrying out the fault prediction and operation and maintenance support of the PON port. And future operation state distribution prediction is realized through the LSTM neural network. And after an abnormal threshold is dynamically adjusted in combination with quantile regression, a time sequence prediction result and a threshold response are fused to output a comprehensive fault probability score, and an early warning signal is triggered through exponential smoothing and continuity check, so that the progressive fault detection accuracy and advancement can be improved, and an efficient and steady prediction and early warning basis is provided for intelligent operation and maintenance of the PON port.

The invention belongs to the field of geological disaster prediction, and particularly provides a reservoir landslide displacement prediction method based on dynamic lag recognition and fuzzy entropy optimization, which comprises the following steps: acquiring and preprocessing landslide time sequence monitoring data; combining the distributed lag nonlinear model with the maximum information coefficient, dynamically analyzing the lag relationship between the displacement and the rainfall and reservoir water level through a sliding window, outputting a self-adaptive lag stage and constructing a lag feature set; adaptively decomposing the displacement sequence by using variation mode decomposition of fuzzy entropy optimization, determining the optimal mode number according to the minimum fuzzy entropy, and reconstructing the intrinsic mode function into trend, period and random items; the method comprises the following steps: extracting local features of a multi-lag feature space through CNN, inputting reconstructed displacement components into GRU to capture time dependence, introducing an attention mechanism to weight a key time step, and outputting a predicted value and a confidence interval through quantile regression; according to the method, dynamic lag capture, adaptive decomposition and CNN-GRU-Attention are fused, and high-precision and high-robustness prediction is realized.

This invention relates to the field of power system operation and planning technology, specifically to a short-term wind power segment probability prediction method. It utilizes deep learning to mine implicit information in data and nonlinear features in wind power sequences to generate prediction probability intervals. Simultaneously, a nonlinear weighting method is selected to improve the optimization performance of the particle swarm optimization algorithm (IPSO), addressing some problems of traditional algorithms and improving convergence speed. Furthermore, a hybrid artificial intelligence algorithm, CNN-LSTM, is selected to construct a prediction model based on a combination of SVM and quantile regression (IPSO-CNN-LSTM). After training, short-term wind power probability prediction is completed. The CNN network can extract latent features from sample data using convolutional kernels, while the Long Short-Term Memory (LSTM) network can capture long-term components, avoiding gradient vanishing and exploding phenomena found in some existing algorithms, thus improving the efficiency of wind power probability prediction.

The application is a kind of biomarker detection data processing method and system, relating to data classification technical field, including acquiring multi-source biomarker data, using wavelet-quantile regression to denoise, constructing sample correlation graph, and filling in missing values by graph attention network.Deep forest combines causal graph to learn features, cascades multi-granularity scanning to capture nonlinear interaction, combines permutation importance and SHAP value to screen markers.Multi-modal encoder extracts high-dimensional features, multi-modal attention fusion network fuses features, cross-modal attention mechanism aligns, and generates joint representation vector.Multi-task contrast learning optimization introduces contrast loss function to obtain cross-modal fusion features.Input classification model prediction obtains biomarker classification results.

This application provides a method, system, and storage medium for quantitatively assessing a building's flexible energy utilization capacity. The method includes: collecting historical building operation data and constructing input features; based on the input features, establishing a building load behavior baseline prediction model using quantile regression to predict the typical load operation baseline and its upper and lower fluctuation amplitudes at each time point; constructing a similar sample set based on samples from the historical operation data sample that have similar operation attributes to the time point to be assessed; taking the high and low quantile values ​​of the load distribution from the similar sample set as the original upper and lower boundaries of the load at the time point to be assessed, and correcting the upper and lower fluctuation amplitudes of the typical load operation baseline at the time point to be assessed to obtain the final feasible upper and lower boundaries; calculating the instantaneous downward and upward adjustment power based on the load behavior baseline prediction model and the feasible upper and lower boundaries; calculating the continuous downward and upward adjustment energy within the control time window; and obtaining the building's flexible energy utilization capacity index.

The invention provides a new energy power system reliability evaluation method based on environment variables, and relates to the technical field of power systems. According to the method, a non-parametric self-adaptive quantile regression forest model is adopted to perform modeling on wind speed and sunlight intensity, and online incremental updating is triggered based on Kolmogorov-Smirnov statistics; setting a cross-scale state coupling interface layer in the double-layer nested reliability evaluation architecture, defining a shared state vector including an energy storage charge state, an equipment aging index and an available reserve capacity, and applying Lyapunov stability constraint; a period economic value dynamic pricing module based on Q-learning is integrated in a short-term evaluation layer, and a real-time electricity price, a user interruption contract and a load elastic coefficient are used as state spaces to correct a load shedding strategy. According to the scheme, through adaptive modeling of environment variable distribution and cross-scale state stable transmission, the problem of evaluation result lag is effectively solved, and the real-time performance and economical efficiency of reliability evaluation are improved.

This invention belongs to the field of job scheduling, and relates to a method, apparatus, computer equipment, and storage medium for scheduling jobs in a computing cluster. The method includes: acquiring multi-source job data; preprocessing and constructing multi-dimensional features from the multi-source job data; constructing a heterogeneous ensemble learning model and performing quantile regression training; based on the quantile regression training results, quantifying prediction uncertainty and generating job runtime estimates and probabilistic prediction intervals; transforming the job runtime estimates and probabilistic prediction intervals into a set of budget uncertainties and establishing a robust optimization scheduling model; transforming the robust constraints in the model into equivalent deterministic linear constraints; embedding the robust optimization scheduling model into a rolling time-domain control framework, and combining it with a prediction error feedback mechanism to achieve online adaptive scheduling. This achieves the proactive quantification and utilization of job runtime prediction uncertainty; improves the reliability and performance stability of the system under uncertain environments; and enhances reliability.

The invention belongs to the field of job scheduling, and relates to a computing cluster job scheduling method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining multi-source job data, and carrying out the preprocessing and multi-dimensional feature construction of the multi-source job data; constructing a heterogeneous integrated learning model, and carrying out quantile regression training; on the basis of a quantile regression training result, uncertainty quantization is predicted, and a job operation time point estimation and probabilistic prediction interval is generated; the job operation time point estimation and probabilistic prediction interval is converted into a budget uncertainty set, and a robust optimization scheduling model is established; robust constraints in the model are converted into equivalent deterministic linear constraints; and the robust optimization scheduling model is embedded into a rolling time domain control framework, and online adaptive scheduling is realized in combination with a prediction error feedback mechanism. The active quantization and utilization of the operation running time prediction uncertainty are realized; the reliability and the performance stability of the system in an uncertain environment are improved; and the reliability is enhanced.

The invention provides an output lexical element prediction method oriented to a multi-agent large language model application. Comprising a multi-agent behavior acquisition module for executing light intrusion behavior acquisition, a feature construction module for executing feature construction, a single-agent high-quantile output length prediction module for executing single-agent high-quantile output length prediction, a model online self-adaptive adjustment and optimization module for executing online self-adaptive updating, and a model online self-adaptive adjustment and optimization module for executing online self-adaptive updating. The multi-agent execution portrait construction module is used for executing the application-level execution portrait; a two-stage hybrid prediction architecture (intention classification + quantile regression) adopted by the method can effectively adapt to behavior differences of different roles (such as translation, writing and reasoning) in a multi-agent system. Meanwhile, the prediction overhead is extremely low, and the low-delay requirement of a real-time scheduling system is met.

The application provides a kind of urbanization threshold determination method based on multi-objective optimization under water resource constraint, which determines water intensity under different efficiency scenarios through panel quantile regression, reduces the subjective setting degree of water demand parameters, and improves the accuracy of water demand parameter identification under different efficiency states.In addition, urban population, added value of the secondary industry and added value of the tertiary industry are taken as decision variables, and a multi-objective optimization function is constructed under the constraints of water resource rigidity, urban development boundary and variable boundary, realizing the collaborative solution of the relationship between urbanization scale expansion and water resource constraint.The scheme determines the urbanization threshold range based on the multi-objective optimal solution set, combined with ideal point distance identification, inflection point identification and marginal cost jump verification, which can more truly reflect the multi-objective trade-off relationship between non-agricultural comprehensive output, water resource pressure and regional development imbalance, and improve the stability and planning applicability of threshold identification.

The invention discloses a transformer area short-term load and overload prediction method and system, a medium and a processor, and relates to the technical field of load prediction. According to the method, a dynamic topological graph is constructed by sensing switch state changes in real time, space-time fusion features are extracted based on a space-time diagram Transform model, load probability distribution is generated in combination with quantile regression, error drift is calibrated through KS inspection and a lightweight gating micro-model, and finally the equipment overload probability is calculated and a hierarchical regulation and control instruction is generated. The system comprises association, fusion, prediction, calibration, calculation modules, a medium and a processor for executing the method. According to the method, the problems of topology mismatch, large error, slow response and the like of a traditional method are solved, second-level real-time control is realized, load prediction and overload risk assessment precision is improved, and support is provided for safe and stable operation of a power distribution network.

The invention discloses a power grid dispatching optimization method for predicting power grid load change based on an AI algorithm. The method comprises six steps of data preparation, feature engineering, AI model prediction based on quantile regression, robust optimization dispatching based on prediction results, rolling updating and closed-loop feedback. According to the method, an end-to-end hybrid prediction model is adopted, quantile regression is innovatively introduced, a prediction interval is output, and the prediction uncertainty is quantized; according to the method, a prediction interval is directly converted into a robust optimization constraint, a system is forcibly required to reserve up-regulation reserve and down-regulation reserve to cover the interval, and a scheduling scheme with the optimal cost can be found in a known uncertainty range; the method has the advantages of being high in prediction precision, high in uncertainty coping capacity, low in total cost and high in practicability, and power grid dispatching can be changed from empirical decision making based on certainty to intelligent decision making based on probability and data driving.

The application discloses a power load prediction method and system based on quantile regression, a medium and equipment, and relates to the technical field of power system load prediction.The method comprises the following steps: acquiring historical power load data and corresponding time characteristic data of coal mine power load; preprocessing the historical power load data to construct a power load feature set; constructing a learning prediction model based on quantile regression; iteratively training the learning prediction model according to a preset quantile and a loss function in a quantile regression output layer based on a training set; inputting corresponding historical power load values and time characteristic data at a to-be-predicted moment into the trained learning prediction model, outputting a load prediction value in the quantile regression output layer, and constructing a probability prediction interval according to multiple quantiles.The application focuses on the key time point that has the greatest influence on the prediction result in the historical power load data, dynamically allocates feature weights, and significantly improves the accuracy of point prediction.

This invention relates to the field of environmental protection and data processing technology, and discloses a method and system for determining emission rights quotas based on multi-source data fusion. The method includes a multi-source emission data acquisition step, a time scale normalization step, an evidence conflict detection and fusion step, and a quota determination and allocation step. By simultaneously accessing four types of heterogeneous data sources—online monitoring, material balance theoretical emissions, emission factor estimated emissions, and statistical annual reports—and employing an evidence theory framework for conflict detection and fusion, the invention determines the enterprise's industry emission efficiency position based on quantile regression, and calculates differentiated quota allocations in conjunction with regional total emission targets. This invention achieves a comprehensive characterization of enterprise emissions and improves the reliability and accuracy of emission estimation.

The invention discloses a reinforcement learning-based house resource dynamic market valuation prediction optimization system, which comprises a data processing module for collecting real estate transaction, geographic information, economic indicators and policy event data and generating a house resource state vector; the expert data module is used for constructing a weighted expert track set according to expert valuation records and transaction prices; the state recognition module is used for executing point change detection and determining market state identification; the confrontation signal module is used for generating a hetero-variance weighted confrontation reward signal based on the improved GAIL model; the risk constraint module introduces conditional value risk constraints based on quantile regression to generate risk weighted signals; the strategy optimization module is used for updating valuation strategy parameters by the risk weighting signals and distilling the valuation strategy parameters into a lightweight reasoning model; and the deployment updating module is used for executing incremental training and parameter updating based on the newly added data. According to the invention, robust optimization and risk adaptive adjustment of the valuation strategy are realized.

The application relates to the technical field of network monitoring, in particular to a computer network data flow monitoring system and method, which comprises a data stream collection module, a protocol layer analysis module, a time sequence feature extraction module, a loss compensation reconstruction module and a monitoring result output module.In the application, protocol fields are extracted and change time points are marked in data stream collection, a continuous difference track is formed, a cross-layer synchronization index is generated by calculating the interval and the sequence of adjacent fields, the cross-layer relationship is quantified in a dynamic process, a quantile regression is adopted in feature processing to unify the numerical interval, the comparability under different time scales is ensured, an abnormal section can be quickly identified when the cross-layer synchronization is broken, potential patterns are deduced by combining the feature values before and after the anomaly, loss information is reconstructed in a probabilistic manner, the result has both anomaly recognition accuracy and flow monitoring integrity, and the network management efficiency and safety are improved.

This invention discloses a multi-dimensional information security risk dynamic assessment system and method for detection platforms, relating to the field of information security technology. It collects multi-dimensional operational parameters such as assets, vulnerabilities, and attacks, converting them into risk factors. An initial comprehensive risk index is calculated using a radial basis function algorithm. Real-time business load is sensed to calculate weight offsets, and risk weights are redistributed and normalized, updating the risk index. A sliding assessment cycle is used to clean the sequence, and quantile regression is applied to determine a basic threshold. This basic threshold is then adjusted using historical statistical data of the load status to generate a dynamic risk threshold. When the risk index exceeds the dynamic threshold, a risk is identified and a response is triggered. This achieves accurate risk fusion and adaptive judgment, reduces response lag, and improves assessment accuracy, aiming to solve the problems of insufficient risk perception and high false alarm rates in complex environments.

This invention discloses a quality control method for minute-by-minute temperature variability at automatic weather stations based on a lightweight gradient booster. The quality control method includes: P1, Data Acquisition: Collecting multi-source data from automatic weather stations and standardizing the data; P2, Data Preprocessing: Cleaning and performing preliminary quality checks on the acquired multi-source data to obtain a preprocessed dataset; P3, Feature Extraction: Extracting multi-dimensional features and performing spatiotemporal matching on the preprocessed dataset to obtain a feature vector dataset; P4, Model Training and Evaluation: Constructing an adaptive automatic weather station temperature variability quality control model based on the LightGBM quantile regression algorithm and completing model training and quantitative evaluation; P5, Automatic Weather Station Temperature Variation Quality Control: Using the constructed adaptive automatic weather station temperature variability quality control model, outputting the temperature variability results of the target automatic weather station, and performing real-time quality control and judgment on the temperature of the target automatic weather station. This invention can solve the problem that traditional fixed threshold methods cannot adapt to complex climatic environments.

This invention belongs to the field of lithium-ion battery safety early warning technology, specifically involving a two-layer thermal runaway early warning method for lithium-ion batteries. The method includes: collecting voltage data of the battery pack during each charging cycle; obtaining a voltage matrix after linear interpolation and resampling; constructing a voltage envelope using quantile regression; determining a deviation matrix based on the voltage matrix and the lower envelope; extracting deviation statistical features from the deviation matrix to construct a multi-dimensional feature matrix; performing static anomaly detection using a density-based clustering algorithm after dimensionality reduction of the multi-dimensional feature matrix to identify statically abnormal batteries; performing dynamic anomaly detection based on the maximum voltage drop amplitude in the deviation statistical features to identify dynamically abnormal batteries; and fusing the two-layer detection results to output a thermal runaway early warning signal and an abnormal battery number. This invention effectively captures weak early signals of sudden-death thermal runaway by constructing deviation features through quantile regression to extract the voltage envelope and combining a two-layer mechanism of static outlier identification and dynamic cumulative amplification.

This invention discloses a method and system for intelligent load forecasting and optimized scheduling in power systems, relating to the field of power system technology. Through refined data cleaning and multi-dimensional feature engineering, this invention significantly improves data quality using the modified Z-score method and manifold learning, effectively uncovering the nonlinear correlation between meteorological conditions and load. A multi-timescale forecasting model, combined with attention mechanisms and quantile regression, accurately captures time-series dependencies and quantifies forecast uncertainty, improving load forecasting accuracy under extreme scenarios. An improved intelligent optimization algorithm, through adaptive particle swarm optimization and deep reinforcement learning, achieves a dynamic optimal balance between renewable energy consumption and operating costs while satisfying security constraints. A fully closed-loop adaptive mechanism triggers incremental model learning or reconstruction based on real-time error grading, combined with multi-timescale MPC feedback correction, ensuring that the scheduling strategy evolves in real-time with changing operating conditions. This method improves the safety margin and economy of the power grid, meeting the real-time scheduling requirements under high-proportion renewable energy integration.

The invention belongs to the technical field of traffic signal control, and relates to a single intersection signal control method based on a DDQN-PER algorithm. Traffic lights are used as intelligent agents, and a binary action space is adopted; state representation is a combination of a position matrix, a speed matrix and a phase vector; a Dueling DDQN network processing state containing a SENet channel attention mechanism is adopted, and a SENet module carries out channel-level adaptive weighting on the convolution feature map; the reward function is composed of weighted items of the vehicle accumulated waiting time difference, the average speed and the queuing length; during training, a loss function combining priority experience playback and quantile regression Huber loss is adopted, and sampling deviation is corrected through importance sampling weight. According to the method, through collaborative optimization of feature extraction, reward design and a training mechanism, the technical problems of insufficient feature expression, Q value over-estimation and unstable convergence of an existing method under different traffic flows are solved, and the traffic efficiency and robustness of single intersection signal control are remarkably improved.

The application discloses a high-performance file hash calculation method based on double-engine adaptive switching, relates to the technical field of data security, and aims to solve the problems that the existing scheme is mostly heuristic scheduling based on static threshold values or empirical rules, lacks residual time estimation and interval control based on online statistical learning, and lacks real-time detection of residual drift and adaptive scheduling based on evidence; the method comprises the following steps: constructing a high-dimensional vector by mapping observation characteristics, adopting joint modeling of recursive least squares with ridge regularization and quantile regression, providing point estimation and upper and lower bounds of single fragmentation and residual time consumption; fusing the extreme values of residual variance and quantile interval to obtain a robust interval, and using Page-Hinkley / CUSUM for mutation detection to trigger adaptive adjustment of fragmentation size, concurrency and engine type; and before engine switching, serializing internal working vectors, processed bytes and intermediate summaries and performing mirror checking to ensure consistency of cross-engine results.

This invention discloses a method for calculating the probability of high-intensity forest fires by combining BiLSTM and kernel density estimation, belonging to the field of remote sensing inversion technology. The method for assessing the probability of high-intensity forest fires described in this invention is simple to operate, requiring only input of information on combustible materials, meteorology, topography, and human activities. It can quickly assess the probability of high-intensity forest fires based on BiLSTM quantile regression and kernel density estimation. This method fully considers key risk factors for high-intensity forest fires, incorporating the temporal variation characteristics of combustible materials and meteorological factors. It is well-suited for estimating the probability of high-intensity forest fires in complex forest areas and is of great significance for high-intensity forest fire risk monitoring and early warning.