358 results about "Logit" patented technology

Provided are a method and an apparatus for constructing a road congestion prediction model, a device, a medium, and a product. A road traffic network is defined as a directed weighted graph. Historical dynamic traffic features of each road segment in the road traffic network are obtained as sample data, including recent dynamic traffic features and periodic dynamic traffic features. The sample data is input into a mixture of adaptive graph learners (MAGL) model for learning, and a probability prediction vector is output. The sample data is input into a trend expert model, and a trend distribution vector of a predicted probability of future traffic conditions is output. The periodic dynamic traffic features are fused to determine a periodicity prediction vector. An aggregated logit vector is obtained. An objective function is determined based on the aggregated logit vector. Congestion prediction training is performed to obtain a road congestion prediction model.

The embodiment of the invention provides a multi-modal data fused postoperative plastic surgery infection early warning method and system. The method comprises the following steps: firstly, performing quality evaluation and restoration on postoperative wound images, clinical texts, wearable vital signs and baseline features; performing double-flow coding and priori segmentation on the repaired image to obtain a coarse mask; constructing a super-pixel graph, obtaining a topological consistent fine mask through a graph neural network, and quantifying the erythema / exudation area; the text and the vital signs are coded respectively and then fused with the image features and the masks through cross-attention and self-attention, and a fusion implicit vector and uncertainty are obtained through quality weight gating; an individualized dynamic threshold value is constructed by combining baseline risk, logarithmic probability calibration and a sliding window trend, a confidence band is output by using conformal prediction, and early reliable infection early warning is realized.

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 provides a civil structure deformation anomaly detection method based on time series data, and relates to the field of civil structure deformation anomaly detection. A disturbance intensity response value, a disturbance curvature and a local disturbance folding feature are constructed, a range adjustment enhancement value is formed by combining a symbol jump mark and a neighborhood disturbance difference value, a latent guide feature is generated based on the range adjustment enhancement value, a disturbance reconstruction feature is constructed through superposition of a diffusion residual error and an asymmetric difference item, normalization mapping is completed to obtain a normalization feature, and the normalization feature is obtained. A disturbance spinor modulation factor is constructed in combination with a nonlinear suppression correlation coefficient, a disturbance spinor tensor is formed through a logarithmic compression channel and a square amplification channel and by applying quadrature phase coding, a tensor potential mapping map is generated under a path coupling and self-coupling mechanism, disturbance energy offset and extreme value deflection are constructed based on the tensor potential mapping map, and disturbance energy offset and extreme value deflection are obtained. And a probability potential index is formed, and civil structure deformation anomaly detection model training is completed based on the probability potential index, so that civil structure deformation anomaly detection is realized.

The invention discloses a multi-modal fusion method based on evidence dual uncertainty, and the method comprises the following steps: S1, constructing a dual uncertainty evidence network according to each modal feature, and modeling randomness and cognitive uncertainty at the same time; s2, predicting uncertainty distribution of a target modal based on a source modal, and designing a cross-modal uncertainty reconstruction mechanism to repair degradation modal information; s3, combining the prediction confidence coefficient and the balance uncertainty of each modal, proposing an adaptive fusion method, and dynamically allocating modal weights by adopting logarithm normalization; and S4, constructing unified uncertainty vector and modal feature joint representation, introducing a layered uncertainty perception gating module, and performing selection according to modal reliability. The method has robustness under noise input, modal missing and distribution offset, and can significantly improve the accuracy and generalization ability of multi-modal classification and prediction.

The invention relates to the technical field of dam safety monitoring, and discloses a multi-source remote sensing data deep learning dam slope monitoring method, which comprises the following steps: resampling each mode to a common ground grid, and calculating robust statistics and a time stability agent on grid and block scales; determining a reference image according to the block-level robust score, and adaptively setting a local displacement search range with a high-intensity centroid difference; evaluating the discrete candidate displacement in a grid neighborhood by using a median absolute difference to obtain a local displacement field and a residual error; three types of subitems are constructed based on robust noise, time stability and registration residual errors, and pixel-level reliability weights are adaptively synthesized through logarithmic variance proportions among modes; analyzing and solving local linear mapping in a neighborhood by using a weight-weighted observation matrix, and calculating a weighted residual error according to the local linear mapping; a binary and probability anomaly graph is generated with a robust threshold.

The invention discloses a context poisoning attack defense method oriented to an RAG application, and relates to the technical field of RAG. the method comprises the following steps: inputting a target query statement, and retrieving the target query statement to obtain multiple pieces of context information; taking representative sentences in the retrieved context information, and identifying and filtering potential malicious template clusters; the big language model gives all candidate answers according to existing context information, the logarithmic probability of all contexts to different candidate answers is calculated, and after the influence of parameter knowledge of the big language model is removed from the logarithmic probability, the support degree of all contexts to different candidate answers is obtained; the whole logarithmic probability vector is used as a support degree distribution condition of the context to the candidate answers; identifying a single piece of harmful information from the support degree distribution condition of the context to the candidate answers through a logistic regression model so as to filter wrong answers; according to the attack defense method provided by the invention, centralized injection of multiple malicious texts and sparse injection of a small number of malicious texts can be defended.

The invention relates to the technical field of intelligent traffic, in particular to a traffic abnormal event dynamic detection method and system based on trajectory analysis, and the method comprises the steps: receiving original space-time trajectory data; preprocessing the original spatio-temporal trajectory data, and mapping the original spatio-temporal trajectory data to a road section sequence of a road network through a map matching algorithm; constructing a behavior model based on the track sequence after map matching, learning the track sequence of the normal behavior mode on the road section, and establishing an observation emission model and a state transition matrix; in the online stage, the log-likelihood value of an observation sequence and / or the similarity between the observation sequence and a normal behavior pattern cluster are / is calculated for a real-time track in a set sliding window, and when the log-likelihood value and / or the similarity exceed a set threshold value, the track is marked as abnormal; performing time-space aggregation on abnormal trajectories of the same road section or intersection in unit time according to single vehicle abnormality judgment, and triggering group abnormal event alarm when aggregation data exceed a preset value.

The invention relates to a time-frequency difference passive positioning method based on an adaptive step cuckoo search algorithm, and belongs to the technical field of passive positioning, and the method comprises the following steps: S1, constructing a time-frequency difference positioning mathematical model; s2, proposing a time-frequency difference positioning algorithm based on an adaptive step cuckoo search algorithm ASCS, and solving the time-frequency difference positioning mathematical model; according to the time-frequency difference positioning algorithm based on the adaptive step cuckoo search algorithm ASCS, an adaptive step strategy and the discovery probability of nonlinear logarithmic decline are introduced on the basis of the cuckoo search algorithm; according to the adaptive step length strategy, the position is adaptively updated by introducing a global historical optimal solution in the optimization process.

The invention provides a text classification method and device, electronic equipment and a storage medium, and relates to the technical field of natural language process.The method comprises the steps that a training sample set is obtained, and the training sample set comprises sample texts and sample classification labels and sample reasoning reasons corresponding to the sample texts; performing fine tuning on a first pre-trained large language model through the training sample set to obtain a text classification model; in response to a text classification request, through the text classification model, based on a preset reasoning constraint parameter, only performing classification processing on a to-be-classified text to obtain a prediction classification label; wherein the preset reasoning constraint parameters comprise an output length limiting parameter and a logit probability intervention parameter. According to the method, the text classification efficiency can be improved while the accuracy and the reliability of a text classification result are improved.

This invention discloses a stepwise data assimilation method for a set subspace in a nonlinear inverse problem, relating to the field of data processing technology. The invention constructs an initial prior physical set based on the physical state variables to be inverted and optimized, and historical observation data. It extracts the static subspace basis anomaly matrix, initializes the latent variable set and particle weights, calculates fractional-step incremental reweighting, evaluates the current likelihood mismatch penalty using predicted data, updates and normalizes the particle weights in the logarithmic domain, obtains the effective sample number, performs system resampling operations in conjunction with a preset resampling tolerance coefficient, eliminates low-weight particles and replicates high-weight particles, synchronously updates the latent variable set and predicted data, executes a dynamic MCMC mutation loop to generate proposed latent state vectors and affinely maps them to a high-dimensional physical space, and updates the latent variable set by evaluating the annealing target energy within the latent variable subspace until all fractional steps are traversed. Finally, it outputs the latent variable set and maps it back to the physical space.

The invention relates to an intelligent simulation assimilation method and device for ponding depth based on a diffusion model, and the method comprises the steps: giving full play to the advantages of the diffusion model based on a Bayesian theory and a data assimilation thought, and achieving the efficient correction of a flood model state through the limited real-time ponding monitoring data; by solving the logarithmic gradient of a posterior distribution function and guiding the sampling process, prior distribution and real-time observation information are effectively fused, so that the regional ponding state is stably estimated under the condition that monitoring points are sparse, error accumulation in model rolling calculation is remarkably inhibited, and the business precision and practicability of urban flood deduction are improved. Therefore, the problems that in the prior art, a systematic intelligent simulation-assimilation fusion system is not formed yet, and error accumulation in model rolling calculation is difficult to restrain are solved.

The invention relates to the technical field of traffic infrastructure operation and maintenance, in particular to a bridge dynamic load intelligent operation and maintenance system based on digital twinning, which comprises a digital twinning modeling module, a dynamic load monitoring module, a data processing and simulation analysis module, a health state evaluation and early warning module and an operation and maintenance decision support module. According to the invention, through multi-source heterogeneous data standardization processing, cross-scale co-simulation fused by a high-fidelity finite element and a reduced-order proxy model, and model dynamic calibration driven by measured data, high-precision and high-efficiency core analysis support is provided for the system; and on the basis of load history inversion, probability fatigue life prediction of a high-speed agent model and failure probability calculation and graded early warning under logarithmic normal distribution, the accuracy of structural performance analysis and the perspectiveness of risk pre-judgment under the dynamic load of the bridge are greatly improved, and a scientific and reliable core basis is provided for subsequent operation and maintenance decisions.

The invention relates to the field of data processing, in particular to a carton production line monitoring method and system, and the method comprises the steps: collecting and preprocessing multi-dimensional time sequence data of a production process; constructing a standard hidden Markov model (HMM) based on historical normal data, and defining the hidden state of the HMM as a microscopic operation mode under a macroscopic process; extracting procedure context features, and constructing a procedure conformity evaluation model (PCAM) to calculate a conformity score; dynamically adjusting the emission probability of the standard HMM in combination with the score to form an improved IHMM; and calculating the log-likelihood probability of the real-time observation sequence by using the improved IHMM, and comparing the log-likelihood probability with a preset threshold to judge the production abnormality. According to the invention, the accuracy and reliability of abnormity monitoring can be effectively improved.

The invention provides a false comment prediction method and system based on a Bayesian multi-scale attention network, and relates to the technical field of network risk prediction, and the method comprises the steps: obtaining network comment core data; feature extraction and covariable design are carried out on the network comment core data, and covariables and time sequence comment data are fused to form a feature matrix; inputting the feature matrix into a comment prediction model, learning an association relationship from different perspectives by using a plurality of attention heads, and generating a context enhancement feature vector containing cross-product information; inputting the context enhancement feature vector into a Bayesian neural network, and outputting a predicted mean value and a logarithm standard deviation of the number of false comments in each time unit; and converting the logarithmic standard deviation into a non-negative standard deviation through an activation function, constructing Gaussian probability distribution of the number of false comments, and realizing prediction uncertainty quantization. The prediction precision and the risk reference value are remarkably improved.

The invention discloses a Beidou inertial cooperative operating vehicle multi-source fusion high-precision positioning method, and relates to the technical field of vehicle-mounted navigation positioning, and the method comprises the steps: S1, completing GNSS / IMU / wheel speed collection, PPS alignment, external parameter calibration and quality control at an end side, and carrying out the uplink at unified event time; s2, constructing a short window factor graph and an IMM, carrying out adaptive switching between RTK / PPP / DR, and outputting HPL by adopting logarithm domain covariance update and RAIM; s3, an out-of-order is rearranged according to event time, and probability association and double-time-axis smoothing are implemented; s4, performing deduplication shaping on the cloud edge stream, introducing an HPL perception fence and performing multi-channel convergence; s5, driving mirror image updating by scene reweighting, soft minimum and tail risk; centimeter / decimeter-level positioning, tunnel exit convergence, fence low false alarm and end-to-end time delay P95 < = 1s can be realized under complex shielding, and playback and statistics are kept consistent.

The invention relates to the technical field of artificial intelligence and power systems, and discloses a power system sample generation method, system and device based on a graph attention network, and a medium, and the method comprises the steps: obtaining historical operation data of a power system, and constructing a graph structure; encoding the graph structure by using a graph attention network encoder to obtain a node embedding representation; performing graph-level aggregation on the node embedded representation to obtain a global representation vector, inputting the global representation vector into a variational auto-encoder, and generating a mean vector and a logarithmic variance vector of potential variables; potential variables are obtained through re-parameterization skill sampling, and model parameters are trained; and based on the trained model, generating a potential vector in a potential space through a sphere center sampling mechanism, inputting the potential vector into a decoder, and generating a new power system operation sample. According to the method, a sample generation scheme with high reliability and high robustness is provided for uncertainty modeling and data-driven optimization scheduling of a power system.

The invention provides a numerical feature embedding method based on a scale perception radial basis function and a situation awareness method for the health degree of power equipment, and the method comprises the steps: obtaining a logarithm of to-be-coded numerical data x, and obtaining a base number D and an index L; performing RBF (Radial Basis Function) expansion on the base number D to obtain the expression of the base number D; carrying out soft sub-bucket distribution on the index L to obtain a distance from the index L to each soft sub-bucket, taking the distance as a coefficient of the soft sub-bucket, and weighting each soft sub-bucket according to the coefficient of the soft sub-bucket to obtain representation of the index L; and converting the representation of the index L into two numbers by adopting a gated linear layer network, and scaling the representation of the base number D to obtain the coded representation of the numerical data x.

This application relates to a method, apparatus, device, and medium for few-sample SAR target recognition. The method includes: dividing SAR samples into support and query sets based on a meta-learning framework; converting the original SAR image into a semantic map and an attribute scattering center topology map; extracting three types of features and calculating class prototypes through three parallel feature branches; obtaining the confidence score of each branch using the Softmax function; assigning teacher and student branches according to the confidence score; minimizing KL divergence to achieve dynamic knowledge transfer; weighted fusion of predicted log odds followed by Softmax output; and backpropagation to optimize the network. This method, through multi-prototype fusion and interactive distillation, mitigates the problems of speckle noise and high intra-class variance in SAR images, maintaining high robustness and generalization ability even in extreme data-scarce scenarios.

The application provides a low-density parity-check code decoding method and device based on a graph neural network, which comprises the following steps: step 1, constructing a factor graph comprising variable nodes, check nodes and edge relationships, and mapping log-likelihood ratio information of a received signal to initial embedding of the variable nodes; step 2, generating message features according to node embedding, node degree and iteration step length, and constructing attention weights for each edge to measure the importance of the message; step 3, inputting the message features and the attention weights into a gated recurrent unit to update the residual of the edge weight, and feeding back the updated edge weight to the graph structure; step 4, weighting and aggregating the messages from the adjacent nodes according to the edge weight, calculating the updated embedding of the variable nodes and the check nodes, and performing gated modulation combined with the check result; step 5, repeating steps 2 to 4 until a preset iteration number or a decoding convergence condition is reached, and mapping the final variable node embedding to a decoding result to realize LDPC code word recovery. By introducing the attention mechanism and the gated residual update into the message passing process, the application realizes adaptive modeling of the contribution degree of different edge messages, dynamically remembers the historical state, thereby improving the decoding performance and the convergence speed; meanwhile, the application can effectively reduce the bit error rate and is suitable for high-speed reliable data transmission scenarios in a 5G / 6G wireless communication system.

This application discloses a method and system for estimating the direction of arrival (DOA) of weak targets based on Riemannian manifold background suppression and adaptive sparse Bayesian learning. The method includes: acquiring time-series signals using a sensor array to construct a series of sample covariance matrices, mapping them to a point sequence on a Hermitian positive definite matrix manifold space; iteratively calculating the background interference covariance matrix using the non-Euclidean geometric properties and logarithmic shielding effect of the Riemannian metric; mapping the background interference covariance matrix back to Euclidean space, adaptively performing background subtraction based on an energy decision mechanism to reconstruct a positive definite covariance matrix to be measured; inputting the covariance matrix to be measured into a sparse Bayesian learning framework, first iteratively recovering the signal power through adaptive mesh refinement sparse Bayesian learning, then performing a closed-loop iteration of subspace noise cleaning while keeping the mesh fixed to recover the sparse spatial spectrum of the target signal; and finally, using local analytical interpolation techniques to eliminate mesh quantization errors and calculate the precise DOA of the target.

The invention provides an agent inference method and a related device. The method comprises the steps of obtaining information of a robot and an environment, and constructing a generation model of an intelligent agent according to the information of the robot and the environment; the intelligent agent interacts with the environment through the generative model, the generative model deduces the real state and outputs the prior state distribution, and the generative model is adjusted according to the prior state distribution to minimize the surprise degree; wherein the free energy is obtained according to the expected value of the logarithm of the KL divergence of the variation distribution and the prior state distribution and the quotient of the variation probability and the conditional probability, and the free energy is minimized to minimize the surprise degree; the intelligent agent has more efficient perception ability in an incomplete information environment, the model training efficiency is improved, environment interaction develops towards an interested state, and falling into a local optimal solution is avoided.

The invention belongs to a price determination method, and provides a method for determining upper and lower limits of contract price fluctuation of a power market and a related device, aiming at the technical problems that a price limit mechanism for setting fixed upper and lower limits of prices is adopted in the current power transaction, a price discovery suppression function exists, and violent price swing within a day cannot be effectively coped with. Calculating a positive excess sample and / or a negative excess sample, then taking the positive excess sample and / or the negative excess sample as input, and obtaining a morphological parameter and a scale parameter when a log-likelihood value of the positive excess sample and / or a log-likelihood value of the negative excess sample converges through generalized Pareto distribution iterative calculation; and finally, positive tail risk measurement index expected loss and / or negative tail risk measurement index expected loss are / is obtained through calculation and serve as an upper limit and a lower limit of contract price fluctuation of the electricity market. And the technical problem of violent price swing within the day can be effectively solved.

The invention discloses a causal graph inference method and a causal graph inference system which are oriented to strong nonlinear continuous variable distribution and based on RealNVP and micro NOTEARS constraints. According to the method, a structure parameter matrix is constructed to represent candidate causal connection, parent variable condition input is constructed for each variable based on structure parameters, and accurate likelihood modeling is performed on the condition density of each variable by adopting a condition RealNVP normalization flow model. By constructing an objective function containing a conditional log-likelihood item, a sparse regular item and a NOTEARS style differentiable acyclic constraint item, a sparse causal structure meeting acyclic constraint is obtained by utilizing gradient optimization, and a causal graph is output. Further, based on the learned causal graph and a conditional RealNVP model, anti-fact inference is executed under a given intervention condition, and an anti-fact result is output. The method is suitable for strong nonlinear relation and complex continuous distribution scenes, and has the advantages of being stable in structure inference, high in interpretability and capable of supporting anti-fact analysis.

The application discloses a data classification prediction method based on covariate and semantic offset and related devices, relates to the technical field of graph data machine learning and data classification prediction, and comprises the following steps: obtaining labeled in-distribution graph data and unlabeled open-world graph data, and uniformly performing standardization processing to obtain standardized graph data; constructing a graph learning model, and outputting a logit based on the graph learning model; calculating an energy score based on the logit, and mapping to obtain a semantic consistency estimation value; constructing a total loss function comprising an in-distribution classification loss, an in-distribution energy upper bound constraint and an open-world energy lower bound constraint, and training a model; obtaining open-world graph data to be measured, inputting standardized graph data of the open-world graph data to be measured into the trained graph learning model, calculating an energy score based on an output logit, combining a preset energy threshold, and performing classification prediction or rejection prediction. The application can accurately distinguish covariate offset and semantic offset, and improves the accuracy of data classification prediction.

The invention relates to the technical field of electronic musical instruments, and discloses an intelligent working mode switching method for a four-fold electric piano based on multi-modal state perception, which comprises the following steps: firstly, acquiring data through angle, pressure, visual and audio sensors, and carrying out fusion and feature extraction to generate multi-modal sensor data; and carrying out standardization processing, feature extraction and time sequence analysis on the data to generate final fusion feature representation. The feature representation is sequentially subjected to full connection layer processing and output layer transformation, and classification logits and multi-level abstract features are obtained. And the classification logits are converted into a five-dimensional probability distribution vector, and multi-level abstract features are introduced to carry out consistency verification and decision optimization, so that a final classification result is output, and confidence and robust probability distribution are predicted. And finally, the system performs reliability evaluation and anomaly detection in combination with robust probability distribution, a classification result, prediction confidence and multi-modal sensor data, and generates a classification reliability score, an anomaly sign and a processing strategy.

The application relates to a Polar code decoding simulation circuit implementation method, system, equipment and medium based on a memory-computing integrated device, and the method comprises the following steps: obtaining a Polar code sparse check matrix and a corresponding probability graph model according to a received Polar code; obtaining an initial log-likelihood ratio of each variable node in the probability graph model; dividing a memory-computing integrated device array into an input part, an output part, an iteration part and a check part according to the Polar code sparse check matrix, and configuring the resistance state of the cross nodes in each part; loading the initial log-likelihood ratio into the input part of the memory-computing integrated device array and keeping it, and the initial input of other parts is 0; checking and iteratively calculating the calculation results output by the iteration part, the check part and the output part in the memory-computing integrated device array by using a peripheral circuit, and outputting a final decoding result after the check condition is met. The application can be widely applied in the field of decoding technology.

The present application belongs to the field of medical information technology and intelligent insurance pricing, and relates to a disease-specific risk financing optimization method and device based on a double-layer value feedback loop, which comprises the following steps: collecting real-time multi-modal clinical data of patients, extracting time series derivatives, embedding semantics and fusing features to construct a global benchmark feature tensor; inputting a deep neural network to derive a high-dimensional clinical risk vector through cross-attention; calculating the actual settlement amount through logarithmic probability transformation, hyperbolic tangent boundary mapping and historical load negative feedback; packaging as a double-domain encrypted certificate and binding with the inpatient medical record number atomization to realize event-level precise mapping; triggering model sandbox retraining and hot loading with residual variance to form the first feedback loop of clinical counter-benefiting; reading the economic relief measurement value to dynamically adjust the message queue, cache preloading and container computing power to form the second feedback loop of operation scheduling. Cross-subsidies can be eliminated, model self-evolution and mathematical-level risk control can be realized, and the method is suitable for commercial health insurance and other scenarios.

Systems, methods, non-transitory computer-readable media to perform operations associated with the storage medium. One system includes a storage medium and an encoding / decoding (ED) system to perform operations associated with the storage medium, the ED system being configured to process a set of log-likelihood ratios (LLRs) and a syndrome vector to obtain a set of confidence values for each bit of a codeword, estimate an error vector based on selecting one or more bit locations with confidence values from the set of confidence values above threshold value and applying hard decision decoding to the selected one or more bit locations, calculate a sum LLR score for the estimated error vector, and output a decoded codeword based on the estimated error vector and the sum LLR score.

This invention provides a peak-scale response analysis method based on nested sub-basin division. By constructing a nested sub-basin structure controlled by area thresholds, and based on a unified design storm conditions and hydrological model parameter system, the method calculates peak discharge under different scale conditions by only changing the sub-basin division scale. It also establishes a logarithmic decay function model between peak discharge and the number of sub-basin units, achieving a quantitative expression of peak-scale response. Furthermore, by comparing and analyzing the scale decay coefficients under different design frequencies, the method quantifies the sensitivity of peak discharge to changes in the division scale. This method forms a complete technical process from data acquisition, structure construction, control variable setting, model running to function modeling and sensitivity analysis. It features a clear logical closed loop, strong repeatability, and good generalization, and does not depend on specific hydrological model software or specific watershed conditions. Under the premise of meeting the unified storm input and nested division principles, it can be widely applied.