167 results about "Time dependency" patented technology

The invention belongs to the technical field of intelligent traffic, and particularly relates to a Mama-based traffic flow prediction method, which comprises the following steps of: collecting and preprocessing a data set; static graph construction and spatial feature extraction; performing space-time dual-token decoupling embedding; carrying out dual-branch Mama modeling; performing space-time-variable feature fusion; and carrying out multi-step prediction, outputting a prediction result, and finally, outputting a prediction value of a multi-step sequence through projection mapping. The static graph convolutional network extracts spatial correlation from the inherent topology of the road network, and the double-branch Mama architecture captures the time dependence and variable correlation of the traffic flow. According to the space-time double-token mechanism, the independent influence and the synergistic effect between variables are explicitly distinguished by decoupling the action boundaries of multi-source factors such as traffic flow, environment variables and event interference, and the modeling robustness and the interpretability in a multivariable coupling scene are remarkably improved.

The invention discloses a multi-device cooperative control method applied to deployment of an embedded real-time operating system, and relates to the field of embedded real-time operating systems, which comprises the following steps of: S1, constructing a real-time dependency relationship graph of a task link, a multi-device cooperative task is disassembled into sub-task link units including an execution path, a real-time constraint threshold and a cross-device communication dependency relationship. According to the multi-device cooperative control method applied to the deployment of the embedded real-time operating system, the problems of task priority conflict and resource utilization imbalance in a multi-device heterogeneous environment are effectively solved through a dynamic priority transmission mechanism and a distributed resource elastic perception model. The system can sense equipment load, communication link stability and task real-time requirements in real time, dynamically adjust sub-task weights and execution paths, ensure conflict-free transmission of end-to-end delay constraints, optimize the global resource utilization rate and avoid the problem of equipment overload or idle caused by traditional static scheduling.

The invention relates to a long-range task-oriented VLA model method for a humanoid robot, which comprises the following steps of: S01, analyzing a natural language instruction through a space-time semantic analyzer to generate an atomic operation sequence with a space-time dependency relationship; s02, maintaining a task state machine by using a dynamic memory network, and tracking the task execution progress in real time; s03, integrating vision, language and sensor data through a multi-modal perception fusion engine; s04, calling a predefined action primitive based on an adaptive execution system and optimizing a motion track; and S05, performing online updating and optimization on the model through a continuous learning mechanism. According to the method, a natural language instruction is analyzed into a structured task sequence with space-time dependence through a space-time semantic analyzer, an execution sequence and preconditions are defined, the semantic understanding ability and the structuring degree of task planning are improved, and task decomposition and replanning in a dynamic environment are supported; according to the method, the LSTM and the knowledge graph are combined, and the task state is maintained in real time.

The invention provides a time sequence anomaly detection method based on time-frequency fusion, and belongs to the field of time sequence data anomaly detection. The invention provides a time sequence anomaly detection method based on time-frequency fusion aiming at the problems that potential correlation exists among variables in multivariable time sequence data and frequency domain characteristics are not considered in a traditional method. According to the method, time domain data and frequency domain data in two different modes are innovatively calculated through Spearman correlation coefficients to obtain a time domain similarity matrix and a frequency domain similarity matrix, then fusion is carried out in a point multiplication mode, and potential relations between different variables are mined by using a graph neural network. The time sequence diagram neural network is used for capturing the time dependence and the space relation at the same time, and the features of the time sequence data can be better extracted. The network model is applied to anomaly detection of multivariable time series data, the purpose of improving the accuracy rate can be achieved, and the effectiveness of the method is verified through experimental results.

The invention discloses a multi-modal abnormal data detection and restoration method and system oriented to a power business scene. The method comprises the following steps: collecting multi-source heterogeneous power data, abstracting a power system into a weighted undirected graph, uniformly mapping the multi-source heterogeneous data into a graph signal, and preprocessing the collected data; extracting spatial features of nodes in a topological structure by adopting a graph convolutional network, and capturing a time dependency relationship in combination with a time sequence encoder; identifying various types of data abnormal points through an abnormal scoring function fusing the time sequence prediction error and the neighborhood consistency; a prediction-reconstruction combined repair strategy is adopted, time sequence prediction and neighborhood diffusion estimation are fused, and a preliminary repair value is generated; a lightweight parameter adapter is introduced, a scene feature vector is used as input, a repair weight and a regularization coefficient are dynamically generated, and a repair strategy is automatically adjusted; and performing physical consistency verification on a data result, wherein the physical consistency verification comprises power injection conservation constraint, voltage amplitude range constraint and time sequence continuity constraint.

The invention provides a traffic flow prediction method based on a space-time sequence, and relates to the technical field of traffic prediction, and the method comprises the steps: obtaining open-source network traffic flow data, and carrying out the preprocessing of the data, and obtaining a traffic flow sequence; inputting a traffic flow sequence into a data embedding layer to obtain high-dimensional representation of input features, performing periodic embedding and adaptive space-time embedding, and connecting the three kinds of embedding to obtain hidden space-time representation; inputting the hidden space-time representation into a space-time encoder for modeling of space dependence and time dependence to obtain a hidden state; and inputting the hidden state into an output layer, and outputting to obtain a traffic flow prediction result. According to the method, the deep features of the traffic flow data can be extracted more effectively, the complex spatial-temporal change rule of the traffic flow is captured, and the accuracy of urban traffic flow prediction is further improved.

The invention relates to an offshore wind power prediction method, and aims to improve the accuracy and reliability of prediction. The method comprises the following steps: (1) a data preprocessing stage: detecting an abnormal value in data by using a DBSCAN clustering algorithm, reconstructing the abnormal value by using a KNN interpolation method, detecting time sequence abnormity through an LSTM automatic encoder, and performing regression reconstruction by using LSTM to ensure the retention of time sequence features; (2) a feature engineering stage: screening out key features through correlation analysis, generating a label column through K-means clustering and wind direction classification, and extracting features such as wind direction change rate, time periodicity and wind speed interaction; and (3) a model construction stage: constructing a composite model in combination with multi-scale convolution (Inception), a bidirectional long short-term memory neural network (BiLSTM) and a multi-head self-attention mechanism, extracting local features through a convolution layer, capturing a time dependency relationship through a bidirectional LSTM layer, enhancing the attention of key features by using the multi-head self-attention mechanism, and finally realizing high-precision wind power prediction.

The invention discloses a universal multi-modal target tracking method based on space-time propagation and modal cooperation, and belongs to the technical field of computer vision. The method comprises the following steps: converting RGB and X modal images into a token form, and constructing initial features in combination with modal specific time tokens; the method comprises the following steps of: extracting a multi-level enhanced feature through a Transform encoder and a Mama collaborative prompt block; generating discriminative fusion features by using a gating fusion and context sensing module; a time-guided attention mechanism is adopted to strengthen search area features, and a result is output through a tracking prediction head; and transmitting the fusion time token as historical information to the next frame, and dynamically updating the template by combining a long-short time template updating strategy. According to the method, complementarity and space-time dependence between modes are effectively mined, tracking robustness and generalization ability in a complex scene are improved, and the method is suitable for various mode combination tasks such as RGB-D, RGB-T and RGB-E.

The invention relates to a spatio-temporal data prediction method based on space grid coding and a large language model, and belongs to the field of artificial intelligence and spatio-temporal data analysis. The method comprises the following steps: according to a spatial range of a geographic area, dividing the area into grid units, processing the grid units by adopting a coding method, and identifying each unit through a unique identifier; constructing a space-time dependency model based on the stay record of the user, representing each stay as a tuple, and predicting the next position of the user by analyzing a historical stay sequence and a context stay sequence; and designing a space-time instruction optimization prompt template in combination with a large language model, guiding the model to analyze historical data and context data by introducing grid coding and context perception reasoning, generating next position prediction, and providing explanation for each prediction. According to the invention, through grid coding and hierarchical modeling, the generalization ability of the model is enhanced, and accurate spatio-temporal behavior prediction is realized.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a root cause analysis method and device based on a space-time dependency graph, equipment and a medium. Comprising the steps of constructing a space-time dependency graph, generating a diagnosis path blueprint, identifying a fault source and a root cause entity type, generating a graph query statement, executing query and performing cause and effect verification, and outputting a root cause analysis report. And the causal reasoning and root cause positioning of the system state change are realized by fusing the graph structure information and the natural language processing capability. Through cooperative processing of a language model and a graph data structure, fault symptom information and system structured state data are deeply fused, a path is generated in the graph structure, and a causal relationship is verified, so that the ability of understanding a complex system state evolution chain is improved, and accurate identification and diagnosis of root causes are realized. And the accuracy and the automation level of root cause analysis are obviously enhanced.

The invention relates to the technical field of multi-agent systems, in particular to a task matching method and system based on a multi-agent system, a master agent is used for analyzing a request of a user, identifying a plurality of corresponding sub-target tasks according to the request, and establishing task mapping with other slave agents according to the identified sub-target tasks; and the slave agent performs corresponding processing according to the relationship between the sub-target tasks, and stores a corresponding processing result to a local knowledge base. According to the method, the newly added task requests are collected to expand and supplement the primary knowledge graph, the expanded knowledge graph is subjected to element analysis, so that the incidence relation between the elements is recognized, then whether the task requests needing to be processed conform to the incidence relation or not is judged, the time dependence relation of the task requests is further judged, and the task requests are processed according to the time dependence relation. And the master agent allocates different slave agents to perform task processing according to different results which are possibly obtained, so that the corresponding slave agents can be allocated more accurately.

The invention provides a multi-step time sequence prediction method of a quantum bidirectional circulation network based on causal convolution. According to the quantum causal convolutional network based on time sequence perception, the quantum causal convolutional network comprises a quantum causal convolutional layer with multiple layers of time sequence dimensions and a residual block formed by a quantum extrusion excitation mechanism layer, longer time dependence can be captured in a shallow network, and by means of the advantages of quantum calculation, the time sequence perception can be improved. The long-time dependency relationship, the causal relationship and the time sequence relevance among the data can be quickly captured and processed; through a quantum bidirectional circulation network guided by an attention mechanism, time sequence state information and time sequence feature information are fused to enhance the expression of time sequence information, the capability of processing a complex time-varying relationship is effectively improved, and a multi-head attention mechanism is utilized to guide updating of hidden layer important information of a bidirectional circulation unit, so that the time-varying relationship processing efficiency is improved. And long-time-span important information is captured and integrated. Compared with the prior art, the performance of multi-step time sequence prediction is effectively improved.

The invention discloses a VAGCN-based complex industrial process operation state evaluation method. The method comprises the following steps: collecting process data and carrying out standardization processing; performing data division on the data by using a sliding window to obtain a time sequence; the method comprises the following steps: converting time sequence data into cross-time-space variable interaction graph structure data through a variable interaction graph structure, and then capturing variable interaction characteristics and local space-time dependency in each time window by using a moving window; time pooling is used, advanced features of variable interaction are extracted and then input into the operation state evaluation model, posterior probabilities of the online data belonging to different operation state evaluation grades are obtained, and a final evaluation result is a state grade corresponding to the maximum posterior probability at the current moment; and performing similarity analysis on the variable interaction graph structure in the non-optimal state and the optimality graph structure in the optimal state so as to evaluate the variable dynamic offset degree in the non-optimal state. According to the method, the interaction among the variables at different time points can be captured, and more accurate process operation state evaluation can be realized.

The invention discloses a hybrid deep learning sea outlet water quality prediction method based on particle swarm optimization, and the method comprises the steps: firstly obtaining time series data through data preprocessing, carrying out the correlation analysis, screening a water quality index, and reducing the data noise; then constructing a water quality prediction model by applying a mixed deep learning mode and combining the advantages of TCN, LSTM and CBAM networks; then, time sequence data is used for training a model type, TCN is used for effectively capturing a long-time dependency relationship in the sequence data, LSTM is used for enhancing the processing capacity of long-term memory, a complex time dependency mode in water quality data is captured, CBAM is used for introducing an attention mechanism to adaptively select important features, and the accuracy of water quality prediction is optimized; and the prediction precision and stability are improved. The method has higher accuracy in daily monitoring and prediction of conventional water quality indexes and early warning and prediction of water quality sudden change under special events, and can meet diversified practical application requirements.

The invention provides a time sequence scene-oriented chip architecture level power consumption evaluation method, which comprises the following steps of: S100, generating an original data set by utilizing micro-architecture design parameters of a chip to be designed and adding simulation excitation; removing redundant data in the original data set, merging to obtain a preprocessed data set, and training a preset LSTM-based power consumption prediction model; and adjusting the micro-architecture design parameters and the ESL model, regenerating input features, and inputting the input features into the trained LSTM-based power consumption prediction model to obtain a power consumption prediction result. According to the method, the multi-dimensional features are introduced, the influence of different workloads and architecture configurations on the power consumption is comprehensively considered, and the time sequence processing capability of the LSTM model is utilized, so that the time dependency relationship in power consumption fluctuation can be effectively captured, and the power consumption prediction precision is remarkably improved, especially in a dynamically changing time sequence scene.

The invention discloses a supply chain network optimization method and system, and the method comprises the steps: obtaining the time consumption data of each node in a supply chain network, and constructing a weighted directed graph of the supply chain network according to the time consumption data; obtaining the longest path length and the average path length of the supply chain network according to the weighted directed graph, and if one path length is greater than a preset path length threshold, obtaining a key path of the supply chain network; calculating the time dependency of each node on the key path according to the weighted directed graph so as to determine the key node of the supply chain network; and performing time optimization on the key nodes according to the structural features and the time consumption features of the key nodes. A graph theory model can be adopted to analyze time dependence among nodes in the supply chain network, the sensitivity of the supply chain network to time delay is identified, and time optimization is carried out on key nodes in the supply chain network to optimize the time delay of the supply chain network.

The invention relates to the technical field of action recognition, in particular to a skeleton action recognition method based on a space-time dependency enhanced network, which comprises the following steps: acquiring a graph containing skeleton action articulation points; constructing an AMGC-IBM-CNN network: establishing a space-time diagram by taking joints as nodes and skeletons as edges, performing global feature vector and local feature vector extraction on the space-time diagram by using the AMGC network, inputting the global feature vector into an IBM module, then inputting the global feature vector into the CNN network, performing addition operation on the output feature vector of the CNN network and the local feature vector, and finally obtaining the AMGC-IBM-CNN network. And performing global average pooling, and outputting an action category through a classifier after FC. According to the method, the problems that time and space convolution layers are separated in a traditional model, cross-space-time information exchange is obstructed, and the cross-space-time dependency relationship of the joint points is difficult to obtain are solved.

The invention discloses a soil moisture content prediction method and device based on deep learning, and the method comprises the steps: carrying out the seasonal trend decomposition of historical soil moisture content time sequence data, and obtaining a trend term, a seasonal term and a residual term; secondly, analyzing a causal relationship between each decomposition item and a meteorological variable in combination with a plurality of causal test methods, and constructing a dynamic causal adjacency matrix to obtain a causal tree structure; for the trend item, the season item and the residual item, designing a causal LSTM sub-model according to the fruit tree structure, inputting a historical decomposition sequence and the screened causal meteorological variables, and carrying out independent prediction; and finally, fusing prediction results of the sub-models through a full connection layer, and outputting a final prediction value of the soil moisture content in a future period. The innovation point of the method is that causal information and time dependence characteristics of decomposition items are fused, and the interpretability of the model and the response capability of the model to long-term trend and short-term mutation are enhanced.

The inventive depth of the method presented here lies in the active modification of a time-dependent signal, enabling its use for particle characterization. The so-called oscillating signal offers advantages in signal analysis because the frequency of the high-frequency component can be specified. This improves signal selection within a continuous signal flow. The low-frequency component of the oscillating signal is derived from its limited edges and can be used to determine the particle property vector by analyzing the amplitude and signal smearing. Furthermore, the combinatorial complexity of the oscillating signals offers a possibility for precise particle classification using artificial intelligence.

Methods and systems use an additional determination to generate predictions of future data processing load predictions. Specifically, the methods and systems generate additional predictions based on other data sets and data calculation methodologies (e.g., data with non-homogeneous time dependencies). The methods and systems then use these additional predictions to determine whether or not the prediction of the real-time average of data processing loads is an outlier. Thus, the methods and systems generate a time-specific data processing prediction.

The invention discloses a day-ahead electricity price prediction method based on multi-source data fusion, and belongs to the technical field of electricity markets. For the limitation of an existing single data source method and the improvement of the problems that complex space-time dependency in time series data is difficult to capture and nonlinear changes in electricity price data are difficult to adapt to in the existing method, the method is based on four key technologies including multi-source data fusion, a convolutional neural network, a long-short-term memory network and an improved Transform multi-attention mechanism. A multi-source data fusion layer, a feature extraction and space-time fusion layer, a long-term dependence fusion layer and a multi-head attention layer are designed, model construction is carried out from four dimensions of integration of multiple data, space-time dependence of a time sequence, a nonlinear mode and external information integration, and prediction accuracy and robustness are improved.

The invention relates to a bearing fault classification and identification method and device and computer equipment. An improved crayfish optimization algorithm is adopted to optimize parameters of variational mode decomposition to extract a more effective intrinsic mode function, so that the feature extraction precision is enhanced, spatial-temporal features, a time dependency relationship and attention to key features are comprehensively considered, and a more accurate and reliable solution is provided for bearing fault diagnosis. Meanwhile, based on a traditional crayfish optimization algorithm, the improved crayfish optimization algorithm is combined with gravity center reverse learning, non-linear control and sparrow warning mechanism strategies, more excellent optimization searching precision and robustness are shown, meanwhile, the convergence speed is higher, parameter optimization of variational mode decomposition is more accurate and reliable, and the method is suitable for large-scale popularization and application. Therefore, the accuracy of feature extraction is improved, and reliable data guarantee is provided for obtaining the accuracy of a fault classification result subsequently.

The invention discloses a dam safety monitoring and crack prediction method and system based on vibration sensing data, relates to the technical field of data processing, and solves the problems of incomplete data acquisition, data processing lag and insufficient prediction capability in existing dam safety monitoring. Adaptive normalization processing based on a dynamic window is carried out on the dam vibration data; a prediction model is constructed, an LSTM network captures time dependence of dam vibration data, an attention mechanism network fuses LSTM network output and external environment variables to extract key time steps, and an output layer predicts the probability of crack generation according to the key time steps; inputting the preprocessed dam vibration data into a prediction model to obtain a crack generation probability; calculating a dynamic threshold according to the external environment factor, and comparing the crack probability to obtain a crack prediction result; by collecting dam vibration data at multiple positions, the crack risk is predicted in advance based on the prediction model.

The invention discloses a rainfall prediction method based on LightGBM and a variable attention mechanism, and belongs to the technical field of weather prediction. The objective of the invention is to solve the problem of poor prediction effect of an existing model when noise or missing exists in data. According to the method, the LightGBM and the variable attention mechanism are combined, the variable attention mechanism is used for accurately capturing cross-variable dynamic association and multi-scale time dependency in meteorological data, the model can flexibly adapt to the relative dependency between different time steps through relative position coding, and the modeling capacity for long-time-sequence data is improved; by adopting a split weighting mechanism based on LightGBM, different types of exogenous variable data can be weighted, large-scale data can be effectively processed, and feature selection can be carried out, so that the model is helped to pay more attention to variables having great influence on meteorological prediction in the training process, and the robustness of the model to noise data and missing data is enhanced. The method can be applied to rainfall prediction.

The invention relates to a load prediction method and system based on an attention mechanism, and the system comprises the steps: 1, carrying out the deep analysis of power utilization data, calculating the importance of each feature, selecting a key feature according to the importance, learning the dependency relationship of long sequence input based on a multi-head probability sparse self-attention model, and carrying out the calculation of the importance of each feature; a relative position coding method is adopted to enhance the internal relation between the data, and a data feature set is obtained; and step 2, training a multi-scale time sequence prediction model based on the data feature set obtained in the step 1 as an input feature set, and predicting a load sequence and peak valleys. According to the method, different parts in the time sequence are dynamically focused through the attention mechanism, the long-time dependency relationship in the time sequence is captured, important features can be extracted more effectively, complex modes in data can be captured more effectively, the accuracy of power load prediction is improved, and decision support is provided for defining the adjustable range of the load and optimizing energy distribution.

The invention provides a multi-step wind speed prediction method based on a variable decomposition depth projection coding echo state network, and the method comprises the steps: 1, obtaining a data set, and carrying out the preprocessing of data; decomposing the wind speed original sequence into a plurality of subsequences with different characteristics by adopting variational mode decomposition (VMD); step 2, establishing a DEESN model; the DEESN model is composed of a plurality of echo state network ESN modules and an extreme learning machine ELM encoder, each ESN module is responsible for asynchronous advanced forecasting, and the ELM encoder between two sub-reservoirs is used for reducing dimensions and optimizing reservoir states; for Q-step advanced wind speed prediction, combining a Qth ESN module and a (Q-1) th ELM module by the DEESN model, and determining parameters of the DEESN model through a trial and error method; and step 3, performing multi-step wind speed prediction by using the DEESN model to obtain a prediction result. According to the method, the VMD-DEESN model is established, the mapping capability is improved, the time dependence between future time step length learning is improved, and then the modeling precision and efficiency are improved.

The invention provides an ROP prediction method based on an LSTM and an ensemble learning algorithm, and the method comprises the steps: carrying out the clustering and denoising: carrying out the denoising of data through employing a DBSCAN clustering algorithm, and removing outliers; data interpolation: applying a Hermite interpolation method to missing data, filling missing values, and ensuring the smoothness of the data; time sequence generation: converting the data into a time sequence format suitable for LSTM, and segmenting the data by using a sliding window; lSTM training: using an LSTM model to train data, learning a time dependency relationship and performing ROP prediction; on the basis of data preprocessing and an LSTM model, regression prediction is carried out by using LightGBM and XGBoost, so that the accuracy of ROP prediction is improved; evaluating a prediction result: evaluating prediction precision on training and measurement data through mean square errors and R2 evaluation indexes; and model optimization: carrying out model optimization according to an evaluation result. According to the method, more accurate prediction can be provided for the drilling operation, and then the efficiency and safety of the drilling operation are optimized.

The invention discloses a figure portraying method based on a combined space-time attention mechanism and a related device, and relates to the technical field of user portray.The method comprises the steps that user behavior attribute data of a target user are acquired, and in order to conveniently capture complex relations and features in the user behavior attribute data, the user behavior attribute data are acquired; the method comprises the following steps of: firstly, converting discrete user behavior attribute data into embedded vector representation by adopting an embedded layer to obtain a comprehensive embedded vector, and then respectively extracting first and second space-time relationship characteristics from the comprehensive embedded vector by adopting a space-time attention and convolution fusion module; according to the method, a global time dependency relationship, a local space dependency relationship, a global space dependency relationship and a local time dependency relationship of user behaviors are extracted, classification and clustering are carried out according to the global time dependency relationship, the local space dependency relationship and the global space dependency relationship, a label of a target user is determined, and an accurate figure portrait is generated. According to the scheme, the local and global spatial-temporal features in the data can be accurately and simultaneously extracted, and a solid foundation is provided for further analysis and decision making.

The invention relates to the technical field of mechanical equipment intelligent operation and maintenance and state monitoring, and discloses a health monitoring method for bearing state evaluation and residual life and degradation trend prediction, which comprises the following steps of: firstly, extracting time domain, frequency domain and time-frequency domain characteristics from original vibration signals of a bearing under different working conditions; according to comprehensive evaluation indexes and known bearing degradation characteristics, features having good characterization capability and trend consistency for bearing degradation performance are screened out, a novel backbone network model is constructed, deep features are mined, effective features are enhanced, meanwhile, a time dependency relationship in a long sequence is captured, and then a multi-task learning mechanism is introduced, so that the bearing degradation performance is evaluated. Through parameter sharing and joint optimization, bearing state identification, residual life prediction and performance degradation trend prediction can be synchronously completed only by training and deploying a single model. According to the method, multi-task collaborative prediction under complex working conditions is realized, and the accuracy and robustness of bearing state recognition and service life prediction are improved.

The invention discloses a time sequence data truth value discovery method based on kernel density estimation, which comprises the following steps of: initializing necessary parameters such as bandwidth and credibility weight for each object and time point; a kernel density estimation stage: performing kernel density estimation on data of each data source at each time point by using a Gaussian kernel function, updating kernel density estimation by using an exponentially weighted recursive formula so as to reflect time dependence of time sequence data, calculating a joint probability of given known data for a missing value, and calculating a kernel density estimation result; kernel density estimation is used to assess the likelihood of missing data. Kernel estimation of multi-source data is carried out, and the credibility of different source data sets is considered; in the truth value estimation stage, an expectation maximization algorithm and a kernel Kalman filtering technology are applied, parameters are optimized, and the most probable truth value is estimated. According to the method, the kernel function and the Kalman filtering algorithm are used, the problem of time sequence data truth value discovery is solved, the truth value discovery effect is optimized, and the method has wide application value and use prospects in the field of big data.