526 results about "Sequence model" patented technology

The invention provides a construction scene prediction method and device fusing an image, a text and a BIM modal, and relates to the technical field of intelligent construction prediction management. According to the method, the BIM semantic graph is constructed by extracting the BIM semantic information of the BIM model, and the target detection recognition of the construction site video is carried out in combination with the YOLO model to obtain the target detection result; performing cross-modal alignment with the CLIP to realize deep fusion of the multi-modal data of the image, the text and the BIM to obtain a multi-modal heterogeneous graph; and inputting the multi-modal heterogeneous graph into a space-time sequence model for prediction, outputting prediction results of construction scenes at a plurality of moments in the future, and dynamically mapping the prediction results to a digital twinborn platform to realize risk early warning and visual display. The construction dynamic change can be captured in real time, the construction progress and risk can be accurately predicted, and the intelligent level of construction management is improved.

The invention discloses a fault early warning method and system based on an AI large model, and the method comprises the steps: obtaining multi-source heterogeneous data, and carrying out the denoising and standardization processing, and obtaining fusion data; inputting into a feature extraction model, and outputting a feature vector set; identifying the dynamic operation mode based on a K-means algorithm to obtain an operation mode baseline; inputting a feature sequence model, and outputting a precursor feature sequence; calculating an abnormal score according to the precursor feature sequence, marking as abnormal if the score is greater than or equal to a threshold value, otherwise, marking as normal, and obtaining an abnormal detection result; evaluating a risk level according to a detection result; inputting the risk level into a fault analysis model to obtain fault cause distribution; determining optimized operation mode parameters according to the fault cause distribution; and performing deviation analysis on the data and the optimized parameters, and if a deviation value is greater than a threshold value, triggering an early warning signal. The method can solve the problem of insufficient recognition capability in a scene with variable fault types.

The invention relates to a remote education data processing system which comprises the following steps: under a remote teaching task, pre-defining a task intention and a data expectation point; a semantic timestamp and a task binding label are printed on each data fragment; mapping the collected confusion data including silence, eye movement drift and prediction into a unified learning semantic vector; a micro-expression + interactive behavior + time sequence decision path ternary modeling mode is introduced, and a potential cognitive intention corresponding to the feature combination is recognized; teaching context information is fused; constructing a cognitive state mapping model; reasoning a current cognitive state label from multi-modal sensing data; searching intervention track VS effect feedback data in a historical database; generating a predicted intervention behavior sequence by using a sequence modeling algorithm; a dynamic combination suggestion chain including light prompt, content reconstruction, personalized practice and tutoring invitation is adopted; and superposing the cognitive state sequences of all students into a group cognitive trajectory map.

The invention discloses a welded pipe conveying abnormity prediction method and system based on large model reasoning, and aims to solve the problems that multi-source data is difficult to align, cross-station false correlation is caused, prediction lacks executable positioning and time sequence, and linkage control reliability is insufficient. Event alignment is carried out by taking a controller edge signal and an encoder zero position as time anchor points, a production line topology semantic graph containing time delay, capacity and interlocking attributes is constructed, and topology reachability and physical time delay constraints are applied in a self-attention long sequence model to carry out multi-step rolling prediction. And outputting a risk probability, refining the risk probability to spatial positioning of a roller way section or a shaft and the minimum executable intervention time, and generating a risk interval in combination with uncertainty estimation and calibration so as to drive an upstream beat self-adaptive speed reduction, shunting or stopping strategy. The technical effects of improving accuracy and interpretability, reducing false alarm and missing alarm, ensuring that linkage can be executed in advance and meeting edge time delay budget are achieved.

The invention relates to the technical field of e-commerce, in particular to an e-commerce promotion method and system based on cloud computing, and the method comprises the following steps: building a user behavior sequence through log collection, extracting a sudden change point through dynamic time warping, generating a dynamic parameter through segmented integral, calculating a difference degree through gray correlation analysis, and reconstructing a weight set. And flexibly scheduling advertisement resource distribution, and generating a delivery instruction. According to the method, user click stream data is collected, a behavior sequence model is constructed, continuous dynamic characteristics of user browsing tracks are captured, time sequence relevance is analyzed, fluctuation peak points of user behavior paths are aligned, interest mutation nodes are captured, abnormal behavior modes are recognized, and implicit preferences and explicit feedback parameters in the interaction process are quantified. Establishing an associated feature analysis mechanism, resetting advertisement weight parameters, matching real-time requirements of users with putting strategies, mapping weights to distributed advertisement nodes, dynamically scheduling computing resources, adapting display requirements and resource allocation, and forming a closed-loop optimization link.

The invention discloses a composite structure damage form monitoring method and system based on deep learning, and the method comprises the following steps: collecting multi-source monitoring data of a composite structure in a loaded state, and carrying out the preprocessing; reconstructing a damage evolution trajectory in a high-dimensional phase space by adopting a delay coordinate embedding method, and executing dimension reduction to generate a chaotic dynamics low-dimensional trajectory; extracting singular attractor features, and generating a singular attractor feature set; carrying out sequence modeling through an improved Linformer damage identification network, and generating a prediction vector; training an improved Linformer damage identification network based on the prediction vector, and introducing nonlinear dynamic constraints to generate a damage identification network of the nonlinear dynamic constraints; and performing damage form classification and damage evolution prediction. According to the method, dynamics and deep learning are fused, composite structure damage monitoring is achieved, and the method has the advantages of being high in accuracy, high in stability and reliable in early warning.

The invention relates to the technical field of electric power engineering purchase demand prediction, in particular to an electric power engineering purchase demand prediction system based on machine learning, and the system comprises the steps: obtaining historical purchase data, construction progress information and electric power engineering design parameters, carrying out the standard stage division and time alignment, and constructing a stage sequence model reflecting the material use rhythm; and a coupling factor matrix is generated based on the material co-occurrence frequency and the stage position relationship, and the modeling capability of the model for the material cooperation relationship is enhanced. And the stage time sequence features, the coupling information and the structured engineering parameter vectors are fused and input into a regression prediction model, so that accurate mapping of material demands and multi-dimensional engineering features is realized, and the purchase prediction precision in a target period is improved. A deviation sequence is constructed based on historical prediction errors, and error correction is performed through a feedforward neural network, so that prediction accuracy and response capability are effectively improved, and resource waste and construction delay are reduced.

A process assessment platform for automatic query calibration in sequenced models can be used to generate system actions in management of process deployments. System actions can be generated by using a set of input data from one or more process management systems that define a workflow. The input data can include a set of observed task board information item properties for a particular structured process of the workflow. Using the set of input data, a first AI model can dynamically identify a set of actions from the structured process to be executed. Using the identified set of actions, a second AI model can dynamically classify particular dimensions of the set of task board information items and determine a degree of completion therefor.

The invention provides a mediation document generation method and system, relates to the technical field of data processing, and comprises the step of forming structured case data through text preprocessing, named entity recognition and legal information association based on case text data and legal corpus information. Then, generating a preliminary mediation document with placeholders by utilizing case classification, semantic matching and a sequence-to-sequence model; after standard auditing and logic auditing are carried out on the documents, optimization is carried out based on reinforcement learning, language style migration and a text generative adversarial network, and the structural rationality, law term normativity and semantic integrity are improved. And finally, a mediator preference template is constructed through Few-shot Learning and meta learning, the format and language style of the document are adjusted in a personalized manner, and a mediation document conforming to the habits of a mediator is generated. According to the method, the intelligence and the accuracy of mediation document generation are improved, the law compliance and the logic preciseness are ensured, and the mediation working efficiency is improved.

The invention discloses an airport hub construction intelligent regulation and control method and system based on BIM + 5G, and relates to the technical field of construction scheduling optimization, and the method comprises the steps: collecting on-site real-time data to construct a path construction sequence model, constructing a dynamic construction state set with consistent space and time sequence through component coding and semantic attribute mapping, and constructing a dynamic construction state set with consistent time sequence; and constructing a minimum disturbance optimization algorithm of four-dimensional disturbance cost based on the construction disturbance mapping graph, outputting procedure sequence adjustment, resource rearrangement, path decoupling and environment avoidance intervention suggestions, and tracking a construction response state in real time based on an intervention execution feedback mechanism. According to the method, unified modeling of construction plans, resource allocation and green indexes is achieved by constructing a ternary structure and a construction disturbance mapping graph, the field state is dynamically collected in combination with a 5G and edge sensing system, process abnormity and resource conflicts are accurately recognized, an efficient and executable regulation and control strategy is generated based on a multi-target disturbance optimization algorithm, and the efficiency and the reliability of the system are improved. And the intelligence, responsiveness and energy-saving level of the construction process are obviously improved.

The invention provides an intelligent customer service dynamic intention recognition system based on a semantic analysis model, and the system collects the multi-dimensional data of a user through a data collection module, constructs a user portrait through a feature extraction module, extracts the portrait features, carries out the semantic analysis of multiple rounds of historical dialogue data, and extracts preference features. And constructing a dynamic user-entity association graph to extract GNN node features. Multi-modal data of a user is analyzed through an emotion recognition module, emotion features are recognized, portrait features, preference features and emotion features are fused and analyzed based on an MLP model to obtain a final emotion state, and the portrait features, the preference features, GNN node features and the emotion features are fused through an intention recognition module to obtain a final emotion state. According to the method, dynamic intention analysis is carried out on the basis of the Transform sequence-to-sequence model, the current intention of the user is recognized, the recognition accuracy is high, the dynamically changing intention is adjusted in real time, and more accurate and targeted answers or services are provided for the user.

The invention discloses a railway bogie bearing fault diagnosis method and equipment based on a feature extraction network, and relates to the field of intelligent diagnosis and maintenance guarantee of urban rail trains. The method comprises the following steps: data acquisition: acquiring vibration signals of a bogie axle box bearing under the same rotation speed and load combination; performing data preprocessing: performing frequency spectrum adaptive decomposition on each section of signal based on improved empirical wavelet transform, introducing a Fisher score and frequency spectrum entropy joint scoring mechanism, screening out key modal signals, and then performing image mapping to reconstruct a diagnosis sequence; model construction: introducing a topology perception attention mechanism module on the basis of the lightweight convolutional neural network, and constructing a fault diagnosis model; model training: training the fault diagnosis model to obtain an optimized fault diagnosis model; and diagnosis result output: using the optimized fault diagnosis model to diagnose the fault signal of the bogie axle box bearing, and outputting the diagnosis result. The method can improve the accuracy of fault diagnosis.

The invention discloses an integrated airport foreign object detection system and method, and the system comprises a multi-source data collection module which is combined with a self-adaptive triggering mechanism, and dynamically adjusts the working mode of a sensor according to a flight timetable and meteorological data; the edge calculation processing module is used for extracting a foreign object candidate region by adopting a multi-modal data fusion technology, carrying out confidence coefficient weighted decision based on a D-S evidence theory and outputting accurate position and size information of a foreign object; the automatic submission module is used for establishing a grading alarm mechanism, pushing high-risk foreign objects to a control tower terminal in real time through a 5G / wireless private network, and synchronously generating an encrypted standardized report; and the intelligent analysis platform predicts a foreign object high-incidence area by using an LSTM time sequence model, traces foreign object causes in combination with a knowledge graph technology, and provides management decision support through a visual interface. According to the invention, full-process optimization is realized through a closed-loop architecture of multi-source data acquisition, edge calculation processing, automatic submission and intelligent analysis.

The invention discloses an island shoreline identification method and system based on artificial intelligence, and relates to the technical field of shoreline change monitoring. According to the invention, through fusion modeling of the optical image, the SAR image and the topographic data, the limitation of a single data source is broken through; a vegetation coverage area and a non-vegetation shoreline can be accurately distinguished through the multispectral characteristics of the optical image, and the problem of monitoring blind areas in scenes such as cloud and mist and nighttime is solved through the all-weather penetrating power of the SAR image; a random forest classifier can effectively distinguish bedrock steep cliff, sandy sand beach and the like in combination with superpixel unit feature vectors constructed by topographic data, and accurate partitioning of a target island shoreline is completed; moreover, a dynamic global correction mechanism further eliminates space-time error accumulation, a standard deviation mean value is calculated through cross-season historical data, accidental errors such as typhoon and rainstorm are filtered, a future global correction coefficient is predicted in combination with a time sequence model, and the reliability of long-term monitoring is remarkably improved.

The invention provides an industrial equipment residual service life prediction method based on a DMM-JA model, and relates to the technical field of industrial equipment predictive maintenance, and the DMM-JA model comprises a dynamic bimodal fusion and multi-scale feature extraction module DBF-MSFEModule, an LSTM-Mama mixed sequence modeling module, a jump perception attention module and an output layer. The method comprises the following steps: preprocessing bimodal sensing data of industrial equipment; the preprocessed bimodal sensing data is processed through a dynamic bimodal fusion and multi-scale feature extraction module DBF-MSFEModule, and multi-scale fusion features are obtained; the multi-scale fusion features are input into an LSTM-Mamba mixed sequence modeling module, and joint time sequence features are obtained; the joint time sequence features are corrected through a jump perception attention module, and robustness features are obtained; and inputting the robustness characteristics into an output layer to obtain an RUL prediction result of the industrial equipment. Key features in the equipment degradation process can be accurately captured, and the accuracy of residual service life prediction is improved.

The invention relates to the technical field of monitoring behavior analysis, and discloses an intelligent behavior analysis system and method for a monitoring camera. The system obtains video stream data through a video data acquisition module, extracts a video frame sequence and a timestamp, identifies a moving target and a position coordinate, associates behavior segments and calculates an active value, and generates a target behavior active set; a behavior sequence modeling module extracts active values and coordinates, sorts adjacent behavior segments and marks consistent and conflict sections to obtain a behavior consistency partition marking set; the environment mapping module obtains consistent section target behaviors, extracts illumination and shielding time sequences, evaluates the influence intensity of environment interference on the behaviors in combination with behavior types, and generates an overlay analysis result; the exception screening module recognizes exceptional points with response values larger than the average reference value and located in the conflict section, and a behavior exceptional point set is formed; and the risk output module obtains abnormal point information, marks diffusion risk point locations, and generates behavior detection and risk early warning results.

The embodiment of the invention discloses a traffic state prediction method based on multi-modal data and adaptive topology modeling. The method comprises the following steps: dynamically determining an adjacent matrix between roads according to the type and historical traffic flow of each road, and respectively inputting a GCN model and a GAT model according to an adjacent matrix dynamic traffic network diagram; extracting global spatial features by the GCN model according to the weight of each edge and the current feature representation of each node; strengthening the effect of a key node by the GAT model to obtain a local attention space feature; performing short-time timing sequence modeling and long-time timing sequence modeling on the time sequence of the multi-modal traffic data of each node in the latest period of time to obtain a short-time feature and a long-time feature respectively; and predicting a future traffic state according to the global spatial features, the local attention spatial features, the short-time features and the long-time features. According to the embodiment, the traffic state prediction accuracy is improved.

The invention relates to an event-based lip reading method and device, equipment and a storage medium. The method comprises the following steps: collecting an original event stream of a lip sequence image through an event camera; therefore, the brightness change of each pixel can be asynchronously recorded with microsecond-level time resolution, and ultra-low delay, high dynamic range and sparse data representation are realized. Converting the original event stream into a frame-shaped event tensor based on a voxel representation method to obtain a voxelized event body; performing spatial feature extraction on the voxelized event body through a front-end network in an event-based lip reading model to obtain multi-scale spatial features; performing time-dependent modeling on the multi-scale spatial features through a rear-end sequence model in an event-based lip reading model to obtain a sequence code; therefore, space and time features can be fused, and the accuracy and stability of sequence coding are improved. And determining the lip reading recognition content according to the sequence code. Therefore, stable recognition of lip movement can be realized, and lip reading accuracy is improved.

The invention discloses a multi-source heterogeneous engineering monitoring data fusion method based on deep learning, and relates to the technical field of engineering monitoring and artificial intelligence crossing. Collecting multi-source heterogeneous time series data; inputting the multi-source heterogeneous time sequence data into a dynamic space-time alignment module, performing time sequence alignment to obtain alignment data, and extracting time sequence characteristics of the alignment data through a sequence-to-sequence model; constructing a sensor topological graph through an adjacent matrix based on the sensor data, determining node features of the multi-source heterogeneous time series data through a statistical feature method, and inputting the sensor topological graph and the node features into a graph convolutional network for processing to obtain spatial features; inputting the structural crack image sequence data into the residual convolutional network to extract visual semantic features; and dynamically fusing the time sequence features, the spatial features and the visual semantic features through a space-time cross attention mechanism to obtain fused features. According to the method, the compatibility of engineering monitoring data fusion can be improved.

The invention discloses a depression recurrence risk intervention method, device and equipment and a medium, and belongs to the technical field of medical treatment. The method comprises the following steps: integrating a gene risk score, neuroimaging brain region characteristics and clinical medical history data through a multi-modal fusion neural network, and generating an individual baseline risk score; the baseline score is dynamically corrected based on the self-assessment data, and the real-time performance of risk assessment is enhanced; analyzing time sequence characteristics of the physiological and behavior data by using an LSTM time sequence model, and outputting a short-term recurrence early warning label; and in combination with emotion knowledge graph analysis and dynamic risk grading of real-time voice / text data, a hierarchical intervention strategy is triggered. Through multi-dimensional data fusion and a dynamic calibration mechanism, the problems that a traditional method is single in evaluation dimension and lags in response are solved, closed-loop management from risk early warning to accurate intervention is achieved on the premise that direct clinical diagnosis is avoided, and comprehensiveness and timeliness of prevention and control of depression recurrence are improved.

The invention discloses a wearable fatigue monitoring and feedback method based on deep learning. The method comprises the steps that a heart rate variability signal, a gamma wave band electroencephalogram signal and body movement posture information of a user are collected in real time; denoising, normalizing and synchronously fusing the acquired multi-mode signals; extracting a fatigue state representation vector in real time by adopting a Mamba linear state space sequence model; estimating a user fatigue index in real time based on a lightweight full-connection neural network decoder and constructing an individual fatigue threshold dynamic model; calculating a phase synchronization index of the electroencephalogram signal in real time; and generating and outputting an individualized 40Hz gamma wave band sensory nerve stimulation feedback signal in real time based on the fatigue index and the phase synchronization index. According to the invention, high-robustness fatigue identification and low-delay feedback adjustment in a complex motion noise environment are realized.

The invention belongs to the technical field of intelligent traffic, and discloses a multi-scale space-time fusion image feature extraction method based on traffic flow, which comprises the following steps of: 1, constructing a dynamic image generation module; a self-adaptive adjacency matrix is generated in combination with historical traffic data, spatial embedding and time embedding, the matrix is used for spatial-temporal feature extraction of a GCN layer, and the generated adjacency matrix can flexibly capture static and dynamic relationships; 2, constructing a learnable weighting module; according to the module, the weights of different features are adaptively adjusted, so that various feature information is effectively fused; 3, constructing a sequence feature mapper; through a recurrent neural network (RNN) and a sequence compression-expansion mechanism, dynamic features of time sequence signals are effectively extracted and enhanced. According to the method, adaptive adjustment of an adjacent matrix is realized through a dynamic graph generation module, the multi-feature fusion capability is improved in combination with a learnable weighting mechanism, and the long sequence modeling capability of an RNN layer is optimized by adopting a sequence compression and expansion strategy.

The invention provides a user behavior analysis and interest recommendation method which fuses time-assisted features, introduces comparative learning and uses meta-learning optimization. According to the method, a user interaction sequence is enhanced by using a time homogenization strategy, and optimization is performed by using contrast learning, so that a next interested article is recommended for a user. The specific process comprises the following steps: preprocessing original interaction data, and performing time-level data enhancement on a user interaction behavior sequence, so that the original interaction sequence becomes a more uniform sequence; and then constructing a comparative learning framework, performing comparative training on the original sequence and the enhanced sequence, and optimizing a comparative learning result by constructing a positive and negative sample pair, combining the original loss and the comparative loss and adopting a meta-learning mechanism. Moreover, a multi-head self-attention mechanism is introduced to model a context relationship between nodes, user behavior features in time evolution are captured, and the discrimination capability of feature representation is improved. According to the method, on the basis of fusing time information, contrast learning and meta learning, the sequence modeling capability in a data sparse environment is effectively improved, and accurate description of user interest dynamics is realized.

The invention relates to a big data security management system, and provides a security management system covering a whole life cycle aiming at the problems of untrusted source end, link leakage, centralized storage of single points, extensive authorization, disposal lag and the like. The system comprises an acquisition security module, a transmission encryption module, a storage security module, an access control module, a threat detection module, a situation awareness module, a traceability module and an emergency response module. A session basis is generated through multi-factor authentication, a dynamic key is derived according to time slices, and data fragments are encrypted one by one; distributed storage is adopted, and metadata is chained to be non-tampered; implementing fine-grained authorization by taking the attribute as a core; utilizing a sequence model linkage knowledge graph to identify abnormity and calculate a risk index; and when the threshold is exceeded, automatically reconstructing a log chain and executing isolation, alarm or recording. According to the system, trusted acquisition, confidential transmission, traceable storage, fine authorization and automatic disposal are realized, and the safety and compliance are remarkably improved.

The invention discloses a deep learning model for picking up a seismic phase from a seismic signal with a low signal-to-noise ratio. The deep learning model comprises a feature extraction trunk, a bidirectional time sequence-channel attention module (BTCA) and a multi-scale dilated convolutional layer module (DSCN). According to the feature extraction trunk, a cascaded LiteMobileBlock module is used, shallow high-resolution features are extracted from an original waveform step by step, and an initial feature map is generated; the bidirectional time sequence-channel attention module integrates the time sequence modeling capability of the bidirectional LSTM and the spectrum sensing capability (emphasizing importance of different sensors / directions) of a channel attention mechanism, and outputs fusion features; and the multi-scale cavity convolution layer module utilizes parallel cavity convolution modules with different expansion coefficients to synchronously capture a local mutation and global oscillation mode of a waveform to generate multi-scale enhancement features, further enhance the capture capability of the model on long-range time dependence in seismic signals through LSTM, and output time sequence enhancement features. According to the model, a multi-scale cavity convolution module, a bidirectional time-frequency attention mechanism module and an LSTM enhanced sequence modeling module are fused, so that the feature extraction and time sequence modeling capability of a seismic signal with a low signal-to-noise ratio is improved, and robust pickup of a seismic phase is realized.

The invention discloses a road network trajectory generation method based on a sequence model and a diffusion model, and relates to the technical field of trajectory processing, and the method comprises the following steps: S1, collecting an original road trajectory, and processing the original road trajectory by using a sequence model to obtain the output of the sequence model; s2, taking the output of the sequence model as a guide condition, and performing recovery processing by using a diffusion model; and S3, after recovery processing, training the sequence model and the diffusion model, and determining a prediction result of the road prediction task by using the trained sequence model and diffusion model. According to the method, the sequence model and the diffusion model are combined through a conditional diffusion model structure, and the consistency, regularity and diversity of the generated trajectory can be met at the same time.

The invention discloses a landslide image instance segmentation method based on a dual adaptation mechanism, relates to the technical field of image processing, and ensures that a model can have a better effect and numerical stability on data from different sources through a complete process from multi-source data collection to data normalization processing. The designed model is based on a multi-scale state alignment mechanism, in the forward process of the model, exponential moving average fusion is carried out on feature information in the same level, transmission fusion is carried out on feature information of different levels, feature robustness is enhanced, and error accumulation caused by local deviation is reduced. A meta-context incremental learning mechanism is designed, and input data are dynamically converted into a series of key value vector sequences. In the reasoning process of the model, data distribution different from a training domain is dynamically recognized, a gradient descent process is implicitly executed according to the characteristics of data, and model parameters are finely adjusted, so that the characterization capability is greatly improved, and efficient and robust instance segmentation is realized.

The invention discloses a bill recognition method and system based on artificial intelligence image enhancement, and relates to the field of image recognition. The method comprises the following steps: S1, extracting multi-dimensional quality features based on an original image of a bill and calculating a scene consistency factor; s2, adjusting a global enhancement weight according to a scene consistency factor, adjusting a local gain in combination with a detail fidelity factor, and performing adaptive enhancement on the original image to generate an enhanced image; s3, establishing an optical flow field model to analyze geometric deformation of the enhanced image, and performing adaptive correction in combination with local deformation rigidity to generate a corrected image; and S4, analyzing gradient features and character confidence of the corrected image, extracting a candidate character region, and performing context recognition by adopting a sequence model to obtain a text field set. Scene complexity is quantified through multi-dimensional quality features, and detail fidelity self-adaptive enhancement and optical flow deformation correction of high-frequency character distinguishing are combined, so that bill character definition and recognition accuracy are remarkably improved.

The invention relates to the technical field of computer processing, in particular to a Kafka cluster configuration parameter adjusting method and device, equipment and a medium, and is used for relieving problems caused by a fixed change means of code modification. The method comprises the following steps: according to related historical data of a Kafka cluster, predicting a first predicted load index in a future time period through a first time sequence model, and predicting a second predicted load index in the future time period through a second time sequence model; weighting the first predicted load indicator; weighting the second predicted load index; determining a predicted load index according to the weighted first predicted load index and the weighted second predicted load index; determining a plurality of first candidate adjustment actions according to the predicted load index; selecting a first candidate adjustment action from the plurality of first candidate adjustment actions as a first adjustment action; and adjusting a partition allocation index and a copy allocation index of the Kafka cluster configuration parameters by using the first adjustment action.