42 results about "Computation history" patented technology

The invention relates to the technical field of hydrological forecasting, and discloses a medium-and-long-term runoff intelligent forecasting method, which comprises the following steps: collecting drainage basin data, preprocessing, extracting features, generating a feature vector sequence, and dividing a training set; constructing a physical constraint recurrent neural network module, and training based on a total loss function of physical constraint loss; constructing a sliding window online self-adaptive correction module, and initializing a sliding window to store actually measured runoff values and initial runoff forecast values at the latest O moments; inputting the feature vector at the current moment into a trained physical constraint recurrent neural network module to generate a preliminary runoff forecast value, calculating a historical average deviation according to a window state and correcting the historical average deviation to obtain a final forecast result; and finally, forming a new sample pair by the actually measured runoff value and the initial runoff forecast value, adding the new sample pair into the window, and removing the oldest sample to realize dynamic updating of the window. According to the invention, medium and long term runoff intelligent forecasting with high precision, high physical consistency and on-line adaptive capability is realized.

The invention provides a causal reasoning and confidence-driven gas overrun risk judgment method and device, and the method comprises the following steps: firstly constructing a scenario semantic context based on underground monitoring data and an equipment state, and inputting the scenario semantic context into a fine-tuned large language model to generate a risk evolution causal chain containing hidden intermediate nodes; then, respectively calculating a historical case support degree, a real-time data goodness of fit and a knowledge fragment matching degree, and obtaining a comprehensive confidence coefficient based on arithmetic average logic; and the system automatically executes a hierarchical response strategy of full-automatic confirmation, man-machine collaborative research and judgment or low-confidence suppression according to a comparison result of the comprehensive confidence and a preset threshold value, and performs model closed-loop optimization by using feedback data. According to the method, by introducing a recessive logic completion and three-dimensional confidence verification mechanism, interpretability analysis and reliability quantification of the gas over-limit risk are achieved, and the problems that a traditional method is high in false alarm rate and lacks physical basis are effectively solved.

The invention discloses a load curve decomposition method and system based on a curvature equipartition rational B-spline basis function, and the method comprises the steps: calculating the curvature of a historical load sequence, generating a continuous curvature function, determining a group of B-spline node sequences meeting the cumulative absolute curvature equipartition condition, and enabling the node distribution to be adaptive to the load fluctuation intensity; a B-spline primary function is generated based on the node sequence, and a rational B-spline primary function is constructed through weight optimization so as to finely describe a continuous time fluctuation mode; a fitting model is established in combination with time period virtual variables, and a load curve is decomposed into two parts, namely a time period characteristic load level representing different time period reference power consumption intensity and a time characteristic load curve representing a continuous change fluctuation form. The defect that a traditional uniform spline is poor in adaptability to a non-uniform load form is overcome, self-adaptive decomposition with definite physical significance is achieved, and a better-quality data basis is provided for load analysis and prediction.

The invention provides a channel prediction method and system based on a multi-cycle feature convolutional network. According to the method, context sensing weighted input and a dynamic multi-model pool mechanism are introduced. A terminal device obtains environmental context features in real time, dynamically calculates the weight of each time step in a historical channel state information sequence based on the features, and generates a weighted optimized input sequence. Meanwhile, the network equipment pre-trains and maintains a plurality of multi-period feature convolutional network models specific to different environments, and the network can effectively capture the complex time-varying rules in the period and during the weeks in the channel, so that the accuracy of channel state information prediction is improved. And the terminal equipment immediately selects and activates the optimal sub-model in the model pool for prediction according to the real-time context features, and locally performs increment fine adjustment. According to the invention, through management of the data input end and rapid and adaptive switching of the prediction model end, the precision of channel prediction and the adaptive capacity to environmental sudden change are effectively improved.

The invention relates to the technical field of software operation and maintenance simulation, and discloses a software operation and maintenance full-process simulation system based on digital twinning. According to the system, a data acquisition module is used for acquiring a historical operation data set and a real-time operation data set of a software system; the feature parameter extraction module performs feature extraction on the historical data, generates a historical feature parameter set and establishes a dynamic change feature matrix; the digital twin state modeling module constructs a multi-dimensional virtual state space according to the matrix, and determines a key early warning index set by calculating the state distribution density of historical fault events; the real-time risk analysis module extracts real-time characteristic parameters to form a state vector, and performs spatial correlation calculation on the state vector and the key early warning index set to generate a real-time risk correlation value; and the full-process simulation engine module generates an operation and maintenance risk prediction model based on the data, simulates a fault probability and outputs operation and maintenance optimization configuration, so that precise simulation and optimization of the software operation and maintenance full process are realized.

The present application relates to the technical field of communication detection, and discloses a training method and device of a border gateway protocol anomaly detection model and computer equipment, wherein the historical route statistical features and historical graph topology features corresponding to historical BGP data are extracted, and the features are trained by using the border gateway protocol anomaly detection model, the drift detection score between the historical anomaly detection result and the historical BGP data is calculated in the training process, and the drift detection threshold is used as a reference benchmark to adaptively update the multiple parameter values of the border gateway protocol anomaly detection model in combination with the drift detection score. Therefore, even if the BGP abnormal condition caused by the dynamic change of the network environment is faced, the BGP abnormal data can be accurately detected, and the high efficiency of the BGP abnormal data anomaly detection can be ensured.

The application discloses a kind of based on multi-source data's electric quantity abnormality monitoring method and system, method includes: calculating historical load fluctuation coefficient and carrying out first level division, obtain user terminal set;Real-time load data sequence is obtained and real-time load feature is extracted;Combining static attribute data and real-time load feature, user terminal set is carried out second level division, and obtain user terminal sub-set;Based on historical load fluctuation coefficient interval, target user terminal is selected in sub-set and electric quantity abnormality detection is carried out, and obtain abnormality degree set;Calculate dispersion and adopt screening strategy to optimize abnormality degree set, and obtain target abnormality degree set;According to target abnormality degree, the electric quantity abnormality degree of each user terminal in sub-set is determined.The application realizes the accuracy and high efficiency of electric quantity abnormality monitoring by the synergistic mechanism of double-layer classification, representative selection, dynamic detection, dispersion optimization and result inference.

The invention relates to the technical field of legal artificial intelligence, and discloses a legal contract term AI examination method, which comprises the following steps: acquiring a contract to be examined, historical contract data and user game parameters, analyzing a contract text into a structured graph containing legal functional units, generating vectorized representation, and calculating the evolution distance between historical contracts to obtain the legal contract term AI. The method comprises the following steps: constructing a historical contract evolutionary tree structure, combining an evolutionary tree, a commercial risk scene and user game parameters, performing simulation deduction by applying a genetic algorithm, evaluating advantages and disadvantages of various strategy combinations through a payment function, guiding a mutation direction by utilizing evolutionary fitness scores of historical contracts, and searching an optimal strategy combination. And by comparing the difference between the optimal strategy combination and the original contract, an accurate modification suggestion or a complete contract draft is generated. According to the method, evolutionary intelligence of historical contracts is combined with game theory simulation, the limitation of static and template review in the prior art is overcome, and the strategic and personalized level of review is improved.

The application discloses a media creation task distribution method and system based on multi-dimensional feature matching, belonging to the field of data processing. The method first receives a multi-modal task requirement containing a text report, a reference material set and a structured constraint, and generates a multi-dimensional structured task feature vector group through joint semantic understanding and feature decomposition. Relying on the extraction of features from the author's historical works and the calculation of the historical originality index, a static capability portrait is constructed. Through the release of style transfer innovation challenge tasks, the creation output is evaluated and the innovation index is calculated to generate a dynamic capability vector. Combined with the task feature vector, the author's static portrait and dynamic vector, the original adaptation score is obtained through the prediction network, and the risk is corrected according to the two types of original innovation indexes to generate an author ranking recommendation list. In the task execution, the similarity of the creation content, the reference material and the copyright library features is compared in real time, and a hierarchical copyright guidance prompt is triggered, which can improve the efficiency of media creation task distribution and control the copyright risk.

The invention relates to an intelligent question and answer interaction method and device for psychological health education, equipment and a medium. According to the method, historical dialogue data of a user is collected and subjected to text preprocessing and serialization, and an instant emotion vector is extracted based on a pre-training language model to capture current emotion expression of the user; a potential emotional state is decoupled from a dialogue history by using a time sequence model constructed by a variational auto-encoder and a recurrent neural network, a cross-dialogue evolution rule of the potential emotional state is tracked to generate a state vector sequence, a historical emotional baseline is calculated on the basis, and a risk detection threshold and a response generation strategy are dynamically adjusted through statistical deviation analysis; finally, a personalized response is generated in combination with real-time user input and the emotional state vector, continuous tracking of the long-term emotional state of the user and personalized security strategy self-adaption in a pure dialogue environment independent of biological signals are achieved, and the accuracy of emotion recognition and the security protection capability of a mental health interaction system are effectively improved.

The present application relates to the field of cable temperature monitoring, more particularly, the present application relates to a new energy load cable temperature monitoring method and system. The method comprises: obtaining a temperature sequence of a preset period; calculating a first-order difference sequence of the history sequence, iteratively segmenting the first-order difference sequence, constructing a segmentation evaluation function, obtaining a plurality of sub-difference sequences, and constructing a network model for any sub-difference sequence; inputting the to-be-predicted sequence into any network model to output the corresponding temperature prediction value, taking the importance of the calculated network model as the weight, calculating the product of the weight and the temperature prediction value, taking the sum of the products of each network model as the final temperature prediction value, and completing the temperature monitoring. Through the technical scheme of the present application, the accuracy of temperature prediction can be improved, and the response speed and stability of temperature monitoring can be improved.

The present invention provides a method, program, and apparatus for efficiently compressing historical data, which is data recording the past price movements of financial instruments, as well as a method, program, and apparatus for decompressing compressed historical data. [Solution] Various differences are calculated from the preceding and succeeding records of the historical data (however, to ensure that data close to 0 appears as frequently as possible, the difference between the opening price and the closing price of the previous data is calculated for the opening price, and the difference between the high price, low price, and closing price and the opening price of the current data is calculated for the high price and the closing price). The numerical values ​​related to price are converted to integers, and for integers that may be negative, both positive and negative numbers close to 0 are mapped to non-negative integers close to 0. By representing the non-negative integers with the fewest possible octets, the historical data can be represented with less data than before.

The application provides a simulation method and system of a dual active bridge converter with an invariant circuit admittance matrix, wherein the method comprises the following steps: equivalent the dual active bridge converter to an equivalent circuit in which each branch is a parallel branch of a resistor and a historical current source; calculating a system admittance matrix according to each circuit parameter; checking a switching change condition, and calculating the historical current source according to circuit information of a previous time step; and solving a new node voltage according to a system node voltage equation, and then solving a branch voltage and a branch current until iteration is completed. The application calculates the system admittance matrix which does not change with a system state, and reflects the switching change in the equivalent current source of the switching branch. In the simulation process, only the equivalent current source of the switching branch needs to be updated, the overhead of recalculating the system admittance matrix is avoided, and a large memory required for precalculating a large number of system admittance matrices is also avoided.

The invention belongs to the technical field of optimization scheduling, and provides a source network load storage cooperative control method and system based on learning type adaptive model prediction control, and the method employs a learning type prediction method to continuously calculate a historical prediction error, and is used for optimizing an actual value. Obtaining instantaneous power surplus according to the optimized actual value; obtaining a dynamic electricity price signal according to the instantaneous power surplus and the reference electricity price; according to the dynamic electricity price signal, establishing a mathematical model comprising a source network load storage unit, and solving the mathematical model to carry out optimization control by taking the maximum operator income as an objective function and taking the power balance constraint, the energy storage SOC dynamic constraint and the abandoned electricity quantity constraint as constraint conditions; according to learning type prediction correction, source network load storage collaborative optimization is realized, users are guided to adjust loads through dynamic electricity prices, renewable energy sources and energy storage utilization rates are improved, and the operator income of source network load storage element-containing projects is maximized.

A deep reinforcement learning optimization compensation method for nonlinear intermittent processes, which expands a three-dimensional input data matrix into a two-dimensional matrix; performs standardization processing; constructs a JY-KPLS model; constructs a JY-KPLS model optimization problem; solves the optimization problem; calculates the similarity of historical and query data; according to the similarity, m old data and n new data are selected from the old and new process data sets respectively, and the difference is calculated with the current query data; the deviation sample is used as the data set to establish a JITL-JYKPLS local model to solve the mismatch problem; the compensated model and the optimization system are interacted and trial-and-error trained; if the total reward value of the current batch training exceeds the total reward value of the previous batch training, the optimized system of the current training is used for batch-to-batch optimization; otherwise, the optimization system of the previous batch is used for batch-to-batch optimization; the final product quality is output. The method can significantly improve the quality of the final product.

The application discloses a load curve decomposition method and system based on curvature equalization rational B-spline basis functions, and the method comprises the following steps: calculating the curvature of a historical load sequence, generating a continuous curvature function, and determining a set of B-spline node sequences meeting the cumulative absolute curvature equalization condition according to the continuous curvature function, so that the node distribution is adaptive to the load fluctuation intensity; generating B-spline basis functions based on the node sequences, and constructing rational B-spline basis functions by optimizing the weights, so as to finely depict the continuous time fluctuation mode; and combining time period virtual variables to establish a fitting model, and decomposing the load curve into two parts, i.e., a 'time period characteristic load level' representing the reference electricity intensity in different time periods and a 'time characteristic load curve' representing the continuous change fluctuation mode. The method overcomes the defect that the traditional uniform spline has poor adaptability to non-uniform load forms, realizes adaptive and clear physical meaning decomposition, and provides a higher-quality data basis for load analysis and prediction.

The invention relates to the technical field of human resources, in particular to a human resource demand prediction method and system, and the method comprises the steps: collecting internal data and external environment data of an enterprise, and obtaining standardized feature data after processing; a dual-channel weight calculation module constructed based on an attention mechanism respectively calculates a dependency weight of historical time sequence data and an influence weight of external environment data, and fuses the dependency weight and the influence weight to obtain a total weight of each data; based on the standardized feature data and the total weight of each data, a preset basic time sequence model is adopted to capture a periodic trend, an enhanced neural network is combined to learn a long-term and short-term dependency relationship, and meanwhile, a causal inference engine is constructed to quantify causal association between variables to obtain an initial demand prediction result; the dynamic error between the initial prediction result and the actual demand is calculated, when the error exceeds a preset adjustment threshold value, incremental learning is triggered to update the model parameters, and the calibrated demand prediction result is obtained, and the method has the advantages of being high in dynamic adaptability and high in prediction precision.

A processing device obtains an input (302), where the input specifies a set of devices to be placed and routed for a circuit design. In response to the input, the processing device executes a machine learning model (304) to calculate a probability distribution function on a historical device placement library, the probability distribution function estimates the suitability of each historical device placement in the library of historical device placement for placing and routing the set of devices specified in the input. The processing device presents (306) a graphical representation of a defined number of historical device placement from the library of historical device placement, the historical device placement being estimated to be suitable for placement and routing of the set of devices based on the probability distribution function.

The application provides a shared energy storage resource configuration method and device, computer equipment and medium, and belongs to the field of energy configuration. The method comprises the following steps: obtaining a power demand prediction value sequence and a capacity demand prediction value sequence in a continuous unit time length according to a multiple relationship between a unit time length of a load side resource regulation system participating in operation and a minimum selling time length of a shared energy storage right, and then determining a power configuration amount and a capacity configuration amount; calculating a tail risk quantitative value of a yield distribution in a historical market scenario, and taking a weighted combination of the tail risk quantitative value and an expected yield as a target function to be optimized, so as to maximize the target function to obtain a power calling price and a capacity calling price of the load side resource regulation system in a time period corresponding to the minimum selling time length. Thus, the peak power demand in the entire service period can be covered, the operation risk caused by insufficient power configuration can be avoided, and the stability and economy of system operation are enhanced.

To visualize a relationship between design and manufacturing standards and product manufacturing performances without specifying parameters or keywords.SOLUTION: A design and manufacturing support device includes: a language extraction unit configured to extract product language information from product design and manufacturing performance information; a standard application range DB that stores application range language information indicating an application range of each of multiple accumulated design and manufacturing standards; an applicability calculation unit configured to refer to the standard application range DB and calculate, for each of the design and manufacturing standards, a similarity between the application range language information and the product language information extracted from the design and manufacturing performance information corresponding to each of multiple products designed or manufactured during a specified period, to obtain a calculated statistical value of the similarity as applicability; and an applicability history DB that stores a history of the calculated applicability for each design and manufacturing standard.SELECTED DRAWING: Figure 1

The invention relates to the technical field of data processing, and discloses an information security risk management method and system based on big data, and the method comprises the steps: firstly integrating and cleaning multi-source security data through the method, and enhancing each record through calculating a historical risk cumulant; subsequently, it identifies risk nodes and scores, further divides time windows, dynamically tracks total risk values, fluctuations and peak values within each window to describe risk states. Based on this, the system analyzes the change speed and acceleration of the risk, detects the abnormal evolution trend, predicts the risk level and trend of the next stage, and marks the potential high-risk stage. And dynamically triggering graded early warning response according to a prediction result. Finally, the system adaptively adjusts the key score and the threshold value according to the early warning effect feedback, and forms a continuously optimized closed loop, thereby comprehensively improving the perception, prediction and active prevention capabilities of security threats.

This invention discloses a method for invoking tools from a large language model based on multi-stage adaptive and hard-negative sample contrastive learning. The method includes: encoding user queries, tool descriptions, and server text descriptions into vectors and performing L2 normalization, followed by contrastive learning training; pruning the processed server and tool vectors using hierarchical clustering to obtain cluster prototype vectors, with adaptive updates to cluster centers during the hierarchical clustering process; generating standardized description vectors for the cluster prototype vectors using the large language model based on the user query vectors; dynamically adjusting the enhancement rate based on the semantic similarity between the standardized description vectors and the cluster prototype vectors; calculating historical relevance scores and a final matching score; and selecting the optimal output based on the final matching score. This invention enables large language models to efficiently and accurately select and invoke appropriate tools from a large-scale tool library.

The application discloses a front-end exception monitoring method, system and device based on an EMA algorithm and a medium, wherein the front-end exception monitoring method based on the EMA algorithm comprises the following steps: receiving exception information sent by a front-end page; collecting the exception information according to a time sequence to obtain a data source; regarding the number of the exception information in a preset time period as a detection point according to the time sequence, the detection point comprising a historical detection point and a target detection point, and arranging the historical detection points according to the time sequence to obtain a historical detection sequence; predicting a target standard deviation corresponding to the target detection point based on the historical detection sequence; calculating an EMA baseline of the historical detection point; and determining that the front-end page is abnormal when the deviation of at least one target detection point and the corresponding EMA baseline is greater than N times the target standard deviation. The application combines the EMA baseline and the standard deviation to determine whether the detection point is abnormal, thereby reducing the false alarm probability and improving the probability that the front-end abnormality can be learned by a developer.

The invention provides a power grid equipment inspection decision autonomous optimization method based on a reinforcement learning agent, and belongs to the technical field of intelligent operation and maintenance of a power system, and the method comprises the steps: constructing a power grid equipment state prediction model based on an LSTM network, and carrying out the training, and obtaining a trained prediction model; predicting the state of each power grid device in a future time period by using the trained prediction model, and calculating a historical prediction confidence coefficient; constructing a reinforcement learning agent, and optimizing an inspection task scheduling strategy of the power grid equipment; based on the prediction state and the historical prediction confidence of each power grid device, constructing a state space of a reinforcement learning agent, interacting with the environment through a deep reinforcement learning algorithm, and training an agent optimization inspection scheduling strategy; and generating an inspection task scheduling scheme according to the trained intelligent agent. According to the invention, through combination of power grid equipment state prediction and optimization scheduling of the reinforcement learning agent, the efficiency of the inspection task is effectively improved, and the risk of fault occurrence is reduced.

The application relates to the field of software security, and particularly discloses a software supply chain security analysis method, which comprises the following steps: S1: collecting data of components of a software supply chain, and calculating historical security risk weights of each component in the supply chain; S2: collecting behavior data of the software in runtime, and performing correlation analysis in combination with supply chain data; and S3: pre-processing the collected behavior data and supply chain data, and extracting key features. The software supply chain security analysis method can discover runtime threats that cannot be detected by static analysis, such as zero-day vulnerabilities and hidden backdoor threats, through dynamic behavior analysis, significantly improves the coverage rate of threat detection, introduces historical security risk weights to quantify historical security problems of components of the supply chain, and realizes intelligent risk assessment and threat tracing in combination with knowledge graph and graph neural network technology.

The application relates to the field of power system line fault monitoring, in particular to a power system line fault detection and discrimination method, which comprises the following steps: step one: obtaining historical data X and online data Y of a power system; step two: respectively calculating a singular value matrix Sigma 2 of the historical data X and a singular value matrix Sigma 1 of the online data Y; step three: calculating divergence distance D of the historical data X and the online data Y; and step four: judging whether a high-resistance fault occurs in a line based on a discrimination condition. div The method is suitable for complex fault environments of actual power system lines, can realize detection and discrimination without understanding the actual fault point environment, and is cheap and efficient.

A continual learning system learns a prediction model that performs prediction on input data, acquires additional data, learns the prediction model, calculates information on past data to be used in a next learning stage, and stores the learned prediction model and the calculated information on the past data. The continual learning system calculates the prediction model and the information of the past data, and calculates statistics of the past data. The statistics provides a learning result equivalent to a learning result obtained when the acquisition unit uses the past data acquired as the additional data in past by the acquisition unit.

The embodiment of the application provides a kind of to the score modification method, system and terminal equipment of evaluation text, belong to artificial intelligence technical field.The method includes: obtaining historical consumption information and initial evaluation text and initial score information under historical consumption information;Text sentiment consistency analysis is carried out to initial evaluation text to obtain target analysis result;According to target analysis result, initial score information is determined in combination with target data state;The information correlation degree between historical consumption information and initial evaluation text is calculated;According to target data state and information correlation degree, initial evaluation text is deleted to obtain target evaluation text, and associated score information is obtained from initial score information;Target evaluation text is carried out data clustering to obtain target clustering result, and according to target clustering result, associated score information is modified to obtain target score information.

The invention provides a document generation method, and relates to application of artificial intelligence, large models and the like in the field of financial science and technology. The method comprises the following steps: in response to detection that the number of historical cue words processed by a dialogue model in a current session window reaches a first preset threshold value, calculating an importance evaluation value of the historical cue words for each round of historical cue words in the current session window; according to at least one round of historical cue words of which the importance evaluation values meet a preset condition in the current session window, constructing a first round of cue words in a new session window; and in the new session window, generating a document required by the user according to the first-round cue word and the real-time cue word input by the user. The invention further provides a document generation device, a storage medium and a program product.

The embodiment of the invention provides a distribution network line fault determination method and device, equipment and a medium. The method comprises the following steps: firstly, acquiring an operation state data set and a historical fault data set of a target distribution network line; then, on the basis of a feature selection algorithm, the dependency degree of the historical fault data set relative to the operation state data set is calculated; secondly, optimizing the objective function according to the dependency degree and a preset criterion, and determining a fault criterion subset; and finally, based on the fault criterion subset, determining the fault type of the target distribution network line. Through the method, the accuracy and applicability of fault judgment are improved.