95 results about "Computation history" patented technology

The invention discloses a machine learning virtual power plant load prediction method based on multi-factor fusion, and the method comprises the following steps: S1, collecting multi-source heterogeneous data, including historical load data, meteorological data, calendar information and social activity data, and carrying out the preprocessing of the historical load data and the meteorological data; s2, a multi-head attention mechanism and a cross-modal learning technology are adopted to construct a multi-factor fusion model, and fusion vectors of historical load data, meteorological data, calendar information and social activity data are calculated; s3, constructing a multi-scale load prediction model, and constructing a training set for the multi-scale prediction model; s4, training the multi-scale prediction model by using the training set to obtain a trained multi-scale prediction model; and S5, performing power load prediction according to the trained multi-scale prediction model. The method not only can effectively process complex and changeable influence factors, but also has high-precision prediction and high adaptability.

The invention provides a road line selection optimization method, system and device based on AI and quantitative parameter constraint, and a storage medium. The method comprises the following steps: establishing a road line selection initial preset parameter library; a high-precision remote sensing technology, an unmanned aerial vehicle, a GIS database and a traffic monitoring system are utilized to collect quantitative data, and data preprocessing is automatically carried out; a deep learning model is developed and trained, parameter values in historical cases are automatically identified and calculated, the identified and calculated parameters are trained and learned, and a deep learning result is fed back to a quantitative parameter set; a multi-factor target evaluation index system is constructed, and evaluation index values of all candidate schemes are obtained; and providing a decision support tool to help a user to understand the advantages and disadvantages of each scheme and recommend an optimal line scheme meeting all quantitative parameter constraints. According to the invention, the line selection efficiency and accuracy can be improved, the compliance of the line selection scheme in the aspects of technology, safety, environment and the like is ensured, and an efficient, economic and environment-friendly line selection scheme is provided for a road construction project.

The invention provides a large language model long-term memory method based on human cognition inspiration, which comprises the following steps of: calculating semantic similarity distribution of historical interaction text vectors according to a sliding window, and calculating local information entropy of the window; determining an event boundary based on the local information entropy difference of the adjacent windows; taking each discrete event as the memory of the model, and performing partition management on the memory; when the event memory partition is full, the memory event with the low retention rate is transferred to a long-term memory area; when the interactive content relates to historical information, retrieving related historical events in the event memory partition by using a contextualized memory retrieval method; and the large language model generates content for the user in combination with the current context and the retrieved related historical events. According to the method, memory coding and storage, multi-level dynamic memory management and event-level situational memory retrieval methods based on the event cutting theory are adopted, and therefore the problem that an existing large language model long-term memory method lacks dynamic memory and situational memory is solved.

The invention relates to an electric power internet of things operation and maintenance management method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining sensor data, carrying out the cleaning and time alignment of the sensor data, and obtaining standard operation data; based on a historical data window corresponding to the standard operation data, calculating a mean value and a standard deviation of the historical data, and further generating a dynamic detection threshold value; comparing the standard operation data of the current time node with a dynamic detection threshold value, and screening out potential abnormal data; calculating an abnormal trend factor corresponding to the potential abnormal data, and determining an abnormal development trend of the potential abnormal data according to the abnormal trend factor; and determining a corresponding causal influence weight based on a preset causal relationship network, and calculating a fault score according to the causal influence weight and the abnormal trend factor, so as to generate an early warning report according to the fault score. The method has the effect of improving the management efficiency of the power equipment.

The invention discloses a multi-round dialogue method and device based on a large model, equipment and a storage medium, and relates to the field of natural language processing, and the method comprises the steps: screening out a target round dialogue corresponding to a current round dialogue from historical dialogues; extracting triple information of the current round of dialogue and the target round of dialogue, and constructing a target cue word based on the domain data and the triple information corresponding to the current round of dialogue; the triple information comprises intention information, entity information and slot position information; inputting the target cue word into a preset dialogue large model, and determining a target key value cache of the target cue word and a historical key value cache corresponding to the target round of dialogue; and generating a dialogue reply of the current round of dialogue based on the target key value cache and the historical key value cache. By calculating the semantic similarity of the historical dialogue and the current round, combining entity recognition and only retaining part of content, the memory used by the dialogue is effectively reduced, logic breakage is avoided, and the use efficiency of the video memory is effectively improved through key value cache multiplexing.

The invention belongs to the field of traffic control systems, and relates to a vehicle driving track prediction system and method based on an improved graph neural network model, and the system comprises an FPGCN model and a Social LSTM model. The FPGCN model comprises a graph convolution layer, a Dropout layer, a differential pooling layer, a feature fusion module and a full connection layer; obtaining node feature vectors from graph structure data through a graph convolution layer, a Dropout layer and a differentiable pooling layer, then dividing and conquering node features, generating global features through a dense attention mechanism and principal component analysis, and generating local features through virtual filling and matrix calculation; after global features and local features are obtained, a feature fusion module is used for fusion, then the features are input into a full connection layer, and the influence score of a historical trajectory diagram on a current state diagram is calculated; integrating the preprocessed driving data and the data of which the score is calculated by the FPGCN model, and inputting the integrated data into a Social LSTM model; according to the system, while the model expression ability is maintained, the parameter quantity is remarkably reduced, the calculation logic is optimized, and the prediction accuracy is improved.

The invention provides an electric power information multi-modal data dynamic integration method and system, relates to the technical field of electric power information data integration, and realizes multi-modal data fusion through a technical chain of asynchronous receiving, feature extraction, elastic alignment, dynamic verification and intelligent compensation. The method comprises the following steps: acquiring a sensor numerical value, an infrared image and an environment audio heterogeneous data stream in real time; extracting each modal feature and calculating a historical deviation coefficient; sensor data is used as a reference axis, and a multi-modal maximum feature coincidence interval is determined through sliding window matching; performing cross-modal feature cross validation in the extended dynamic interaction interval, and marking credible fusion features; redundant feature compensation is triggered for the modals which do not pass verification, and an integrated feature vector is generated; and if continuous failure occurs, marking the data source as a low-confidence data source. Through elastic time alignment and a dynamic verification mechanism, the problem of native asynchronization of power multi-modal data is solved, the feature fusion reliability is improved, and accurate fault information can be provided.

The embodiment of the invention provides a merchant comprehensive value evaluation method and system based on artificial intelligence and terminal equipment, and belongs to the technical field of artificial intelligence. The method comprises the steps of obtaining historical consumption information and an initial evaluation text and initial score information under the historical consumption information; performing text emotion consistency analysis on the initial evaluation text to obtain a target analysis result; determining a target data state according to the target analysis result in combination with the initial score information; calculating an information association degree between the historical consumption information and the initial evaluation text; deleting the initial evaluation text according to the target data state and the information association degree to obtain a target evaluation text, and obtaining association score information from the initial score information; performing data clustering on the target evaluation text to obtain a target clustering result, and correcting the associated score information according to the target clustering result to obtain target score information; and determining a merchant value evaluation result according to the historical consumption information and the target evaluation text in combination with the target score information.

The invention relates to the technical field of data management, in particular to a government affair data operation and maintenance intelligent management method, which comprises the following steps of: 1, determining a space boundary value based on the data processing speed of each storage node value, and setting a space state for a data storage space; 2, setting a data label for the collected government affair data, storing the data according to the data label, detecting the space state of the data storage space at the moment, and generating a space management signal; 3, based on the space management signal, acquiring and analyzing a data use record of the historical storage data, and determining an inertia use period and a data use value; and 4, calculating a data evaluation value of the historical storage data according to the inertial use cycle and the data use value, determining expired data according to the data evaluation value, and performing operation and maintenance management on the expired data, thereby optimizing the utilization resources of the government affair data storage space, further reducing the load of the storage equipment, and saving the energy cost.

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.

A computing system for filtering indirect tax legislation includes processing circuitry that implements an indirect tax legislation filtering program. The processing circuitry imports customer sales tax calculation history data from a customer sales tax calculation history database, imports customer configuration data from a customer configuration database, and generates a product category report indicating taxable products sold by a customer. New and / or changed tax rules from the monthly data update are imported, and, in response to receiving an instruction to identify products affected by a monthly data update of indirect tax rule changes, the product category report is filtered with the monthly data update of the tax rule changes. A legislative change report indicating product categories and associated products affected by the tax rule changes is generated and output.

An example operation may include one or more of computing historical patterns related to fraudulent attempts from a transaction log, predicting future fraud attempts from public data, correlating the historical patterns and the predicted future fraud attempts, modifying one or more first endorsement policies based on the correlations, preventing modifying one or more second endorsement policies, the one or more second endorsement policies providing read-only access, and adding the modified one or more first endorsement policies to a smart contract.

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 relates to the field of data analysis, in particular to a power distribution system data analysis processing method and system, and the method comprises the steps: constructing a time sequence segment which comprises a plurality of samples which comprise a plurality of features representing monitoring parameters; a plurality of historical time sequence segments are obtained, the historical time sequence segments are labeled, and labels are fault types; the matching degree of the historical time sequence segments is calculated, the historical time sequence segment with the maximum matching degree serves as the optimal matching time sequence segment, and the fault type corresponding to the optimal matching time sequence segment serves as the fault type of the real-time time sequence segment. The method has an effect of accurately extracting feature expression differences of different faults.

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.

This invention discloses a method for recommending points of interest (POIs) based on user spatiotemporal behavior and social information. The method includes constructing a spatiotemporal knowledge graph based on user POI visit sequences and social relationships; learning POI representations in the spatiotemporal knowledge graph and constructing a POI transfer matrix; updating POI features through graph convolution using the POI transfer matrix as weights; calculating a user preference representation based on the user POI visit sequence and POI features; calculating the weights of historically visited POIs based on the spatiotemporal distances between POIs and the distances between POIs and user preferences; inputting the POI sequence into a recurrent neural network and updating a hidden representation using the weights of historically visited POIs; and connecting the hidden representation with the user preference representation, inputting the result into a recommendation model to generate the next POI that matches the user's preferences. This method can effectively model user spatiotemporal behavior, capturing behavioral patterns and user preferences, and making POI recommendations more accurate.

The embodiment of the present application belongs to the field of artificial intelligence, and relates to a method for replenishing items, including obtaining the inventory quantity, demand quantity, and order quantity of historical items at different stages, and calculating the historical state variables according to the inventory quantity, demand quantity, and order quantity; obtaining the reward function and the target constraint capacity, and constructing a basic deep neural network according to the target constraint capacity and the reward function; training the basic deep neural network based on the historical state variables, and when the loss function of the basic deep neural network converges, determining that the training of the basic deep neural network is completed, and obtaining the target deep neural network; obtaining the current state variable of the target item, and calculating the predicted reward value of the target item under different replenishment quantities according to the target deep neural network; selecting the replenishment quantity corresponding to the predicted reward value with the maximum value as the target replenishment quantity. The present application also provides an item replenishment device, a computer device, and a storage medium. The present application realizes the accurate prediction of the replenishment quantity of items.

The present invention relates to a method, device, terminal and medium for avoiding data loss in a peering process. Before an OSD in a down state in a placement group starts up and enters the peering process, a historical epoch sequence is calculated, and by judging whether the historical epoch sequence contains an abandoned OSDmap version, it is determined whether data writing occurs in the placement group during the period of the historical epoch sequence, thereby avoiding the situation where data writing cannot be judged when data is written to the abandoned OSDmap version. The analyzed historical epoch sequence of data writing is not less than the actual situation, thereby avoiding missing data writing of the placement group. When an OSD in the placement group that has data writing during the down state starts up and enters the peering process, the status of the remaining OSDs in the placement group is detected. If the remaining OSDs are all in the down state, the peering process of the OSD is suspended, and the peering process is completed only after the remaining OSDs are started up, thereby avoiding data loss caused by the OSD completing the peering process alone when data is written to the placement group during the down state.

The invention relates to the technical field of fuel contract templates, and discloses an AI-based fuel contract template collaborative editing recommendation method and system, and the method comprises the steps: obtaining historical contract editing data of each contract editor in a contract editing team, and determining a plurality of historical contract editing data sequences; dividing the historical contract editing data sequence into a score-adding and score-reducing historical contract editing data sequence, and calculating a score-adding contract editing factor and a score-reducing contract editing factor; calculating a historical contract editing data recommendation factor of the contract editing team according to the score adding contract editing factor and the score reducing contract editing factor; and selecting the contract editing team corresponding to the maximum historical contract editing data recommendation factor, carrying out collaborative editing on the fuel contract template, calculating the historical contract editing data recommendation factors to improve the recommendation precision and recommendation efficiency of the contract editing team, and editing and recommending a low-risk and high-efficiency contract editing team for the fuel contract template. And the comprehensiveness and safety of contract collaborative editing are ensured.

The invention discloses an electric power prediction method and device, equipment, a storage medium and a product, and relates to the technical field of data processing, and the method comprises the steps: extracting time sequence information from a historical electric power feature sequence obtained in advance, and calculating the time sequence features of the historical electric power feature sequence according to the time sequence information; time coding is carried out according to the sampling date of the time sequence feature and historical power feature sequence and the sampling date of a preset prediction sequence, and an input sequence time code and a prediction sequence time code are obtained; a correlation matrix is constructed based on input sequence time coding and prediction sequence time coding, power prediction is carried out based on the correlation matrix, a power prediction result is obtained, and the problems that a traditional prediction model is insensitive to a time sequence, position coding cannot represent complex periodic characteristics and the like are solved. Therefore, precise modeling of the power data time sequence and the complex period is realized, and the prediction precision and practicability are remarkably improved.

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.

A computer system comprising of an orchestration engine that allows for optimizing of investment allocation across plural levers, the orchestration engine configured to: receive input data that includes historic data regarding pricing and sales according to plural levers; prepare the received input data by converting the plural levers in the received input data into a common currency; calculate historical return on investment based on the common currency; simulate return on investment using the calculated historical return on investment and a machine learning model of a given campaign based on historical sales, campaign definitions, and investment metrics; optimize the simulated return on investment from the campaign with respect to one or more optimization goals and constraints; and output from the optimizer an optimized, campaign plan.

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 relates to the technical field of data processing, and discloses a method for ensuring data consistency and transaction management based on event-driven. It obtains the data distributed event information of a data distributed processing system, calculates the constraint coefficient of the data distributed processing system, and generates a data consistency guarantee transaction management group; generates a historical distributed data generation efficiency sequence; calculates the historical distributed data generation efficiency coefficient to obtain high- and low-efficiency data distributed processing system groups; calculates the constraint coefficient of the high-efficiency data distributed processing, constrains the data update time interval, obtains the constrained data update time interval, and performs data consistency guarantee transaction management. Through system constraints, it effectively ensures the prerequisite conditions for data consistency processing. Through time constraints, it balances the data processing time intervals of different distributed systems, avoids the imbalance of data processing order, eliminates data conflict and inconsistency problems, and ensures the dynamics, coordination, and real-time nature of data consistency processing.