95 results about "Timing diagram" patented technology

The invention discloses a water supply pipe network water hammer control method and system based on multifunctional module fusion, and relates to the technical field of data identification. A time sequence diagram neural network prediction and traceability module which realizes water hammer risk prediction and propagation path traceability based on a causal constraint graph neural network model; the reinforcement learning intervention decision module is used for generating an active intervention strategy through a reinforcement learning agent, forming closed-loop control, abstracting a water supply pipe network into a graph structure, and modeling in combination with a time sequence, so that a propagation path of pressure waves can be comprehensively reflected, a blind area of traditional local modeling is overcome, and the comprehensiveness and accuracy of water hammer event detection are improved; and a causal analysis result is input as an adjacent matrix, so that the interference of irrelevant edges on prediction in topology is effectively eliminated, and the learning efficiency and causal traceability of the model are improved.

The invention discloses an APT attack path reconstruction method based on time sequence diagram comparison clustering and a medium. A security event standardized data set is obtained; mapping each security event into a multi-modal node through a heterogeneous time sequence diagram set construction method, and generating a directed edge to construct and complete a heterogeneous time sequence diagram set; a stage embedding time sequence diagram set is obtained through the joint attack stage set; outputting a time sequence diagram similarity matrix by adopting a multi-scale diagram similarity algorithm of time alignment perception; generating an event semantic sparse matrix based on the threat intelligence knowledge graph; obtaining an image clustering result set; uncertain samples in the graph clustering result set are obtained and processed, and an APT attack path reconstruction result is obtained. The problem that an APT attack path reconstruction method mainly depends on rule matching, single-dimensional feature comparison and manual analysis of security logs or alarm streams is solved, the interpretability of APT traceability is greatly enhanced, and subjective errors of manual research and judgment are reduced.

The invention provides a risk conduction prediction method and system based on combined deduction of a time sequence diagram and a large model, and relates to the technical field of artificial intelligence and knowledge engineering, and the method comprises the steps: extracting a prospective time sequence fact based on a hierarchical cue word and a structured constraint mechanism; extracting conflicts and generating a sequential relationship; the historical time sequence knowledge graph is updated based on the time sequence relation and a confidence coefficient weighted updating strategy; determining a central entity, and initiating a structured query to the updated historical time sequence knowledge graph by taking the central entity as a starting point to generate a context knowledge sub-graph; converting the context knowledge sub-graph into a natural language description with a logic relationship, injecting a risk hypothesis event, constructing a cue word as a new input of a large model, and outputting to obtain a structured JSON object containing a complete reasoning chain; and converting the structured JSON object based on a risk path extraction and visualization algorithm influencing weight attenuation to obtain risk early warning information and a visual conduction path diagram.

The invention provides a three-dimensional modeling and construction management method and device and a readable storage medium. The method comprises the steps that multi-professional engineering drawings and unstructured texts related to construction design are acquired; extracting a geometric entity in each professional drawing in the multi-professional engineering drawings, and constructing a target three-dimensional skeleton model; a hierarchical attention mechanism and a time sequence diagram neural network T-GNN are adopted to extract semantic parameters from the unstructured text; fusing geometric data corresponding to the target three-dimensional skeleton model with the semantic parameters to generate a lightweight three-dimensional model; and for the complex conflict in the lightweight three-dimensional model, disassembling the complex conflict into a symbolic logic subtask and a neural network subtask. According to the method, the device and the readable storage medium, the problems of low modeling efficiency, insufficient semantic understanding and limited conflict optimization capability caused by excessive dependence of three-dimensional modeling of multi-specialty engineering drawings on a traditional BIM tool in traditional engineering design can be solved.

The invention provides a calculation and power collaboration system and method, and belongs to the field of calculation and power collaboration, and the system comprises a data collection module which collects power data, calculation power data and price data, and constructs a power supply time sequence diagram and a calculation power demand time sequence diagram; a prediction module which generates a power supply predicted value and a computing power demand predicted value based on the power supply sequence diagram and the computing power demand sequence diagram; the intelligent matching module is used for generating a matching decision scheme based on the power supply predicted value and the computing power demand predicted value; the task scheduling module is used for performing differential scheduling based on the matching decision scheme; and the feedback optimization module is used for evaluating the task scheduling result and feeding back the evaluation result to the prediction module and the intelligent matching module. According to the method, the power supply and the computing power demand are predicted, the active scheduling decision is generated based on the prediction result, and the task scheduling module executes space-time flexible scheduling of the task, so that the consumption utilization rate of the new energy power and the supply stability of the computing power service are remarkably improved.

The invention relates to an open source component multi-modal dependency risk tracing method and device, and the method comprises the steps: employing a mode of combining static analysis and dynamic analysis to analyze a component dependency relationship of software, and combining AST analysis and construction script analysis; function-level features are extracted, a binary fingerprint database is constructed, the similarity between different versions is analyzed through LSH, behavior patterns in binary codes are analyzed, and hidden dependencies or malicious code injection is detected; the version updating history of the dependent component is analyzed and monitored by using a time sequence, and the vulnerability security of the component is evaluated in combination with attack graph analysis; high-risk components on the path are calculated, potential supply chain attack points are identified, and risk points are subjected to cross validation; a time sequence diagram database is used for recording the component dependency relationship, and time backtracking query is supported. According to the method, a multi-mode dependency analysis method is adopted, potential dependency risks are rapidly identified, and potential supply chain attack risks are timely warned.

The invention discloses a computing task scheduling method and system based on time sequence diagram network resource state prediction, and relates to the technical field of computing resource allocation, and the method comprises the steps: collecting computing power resource state data based on computing power nodes, and modeling the computing power resource state data into time sequence heterogeneous diagram data; constructing a time sequence heterogeneous graph attention network THGAT model, and performing offline training on the THGAT model by taking node feature and edge feature prediction loss as a loss function and taking minimization of a prediction error as a target; inputting the time sequence heterogeneous graph data into a THGAT model, and predicting the resource state of a future time window; the calculation task is converted into a calculation demand vector, and a multi-objective optimization resource allocation THGATRM algorithm is adopted to perform calculation power resource allocation on the calculation task according to the resource state of the corresponding time window; and executing the computing task according to the computing power resource allocation scheme. Through the technical scheme of the invention, the accuracy of computing power resource prediction is improved, and efficient allocation and optimization of resources are realized.

The invention provides a training method and system of a network community dynamic graph generation model and a graph generation method, and is applied to the technical field of data mining. The method comprises the following steps: acquiring a historical dynamic time sequence diagram of a target network, and carrying out step-by-step feature extraction on the historical dynamic time sequence diagram by utilizing a feature extraction module according to a time sequence to sequentially obtain a diagram embedding representation, a hidden variable and a hidden state; inputting the graph embedded representation into a community detection module to obtain a community detection result; inputting the hidden state into a community prediction module to obtain a community prediction result of the prediction time step; inputting the hidden variable and the hidden state of the previous time step into a topological structure generation module to obtain a generated network diagram; determining training loss based on the community detection result, the community prediction result of the prediction time step and the generated network diagram; and updating model parameters based on the training loss. According to the method, the problems that application scene requirements sensitive to dynamic changes cannot be met and the quality of generated dynamic graph data is not high in the prior art are solved.

The invention provides a novel traffic information dynamic time sequence diagram topological structure, which relates to the technical field of dynamic time sequence diagrams, and comprises the following steps: configuring a namespace and time granularity system of a traffic information dynamic time sequence diagram, setting a global timestamp rule and a propulsion strategy, defining an information identification bit structure, and setting a visibility and consistency control strategy. And a unified time window scheduling and snapshot life cycle management mechanism is configured. A dynamic extensible time sequence naming system is formed by establishing a unified naming space, time granularity delta t and a global timestamp rule, windowed organization and snapshot chain management of traffic information are achieved, the structure can automatically advance snapshot in a time driving mode and a data driving mode, time sequence consistency and real-time performance are guaranteed, and the time sequence naming efficiency is improved. The problems that node time management is disordered and data cannot be traced in a traditional traffic map model are solved.

The invention discloses a method for constructing vTEE and certification in an AMD SEV environment, provides a refined communication and fusing sequence diagram for the vTEE for the first time, and defines a module interaction sequence under two scenes of normal task execution and anomaly detection. According to the method, a trusted cloud security management platform initiates a request, a protection component coordinates a calculation component to collect an SEV-SNP report and vTEE key data, signature combination and uploading verification are carried out, and finally, the platform compares with a reference value to determine whether the vTEE is trusted or not. According to the method, a double-layer certification mechanism of SEV hardware certification and vTEE equipment measurement is adopted, an SEV-SNP certification report proves that a hardware environment, a VM mirror image and a vTEE operation environment are not tampered, vTEE key data measurement supports complete trust chain verification of equipment firmware, instruction streams and strategy states, and end-to-end certification is achieved. Equipment-level measurement, strategy issuing, abnormal fusing and remote certification are realized through an external protection part, and the system has important commercial landing value and strong system expandability.

The invention discloses an industrial firewall device based on time sequence atlas detection, which relates to the technical field of industrial control network security and comprises the steps of protocol identification, feature extraction, time sequence atlas construction, baseline modeling, online detection and response protection. Extracting time sequence characteristics such as a request-response relationship, a time interval and an event sequence, and converting the time sequence characteristics into a time sequence graph on which nodes correspond to communication events and edges correspond to time sequences; and generating a baseline map based on normal data, comparing the deviation between the current map and the baseline map in real-time communication, and executing a protection action after judging abnormality. The device for realizing the method comprises a data acquisition module, a feature extraction module, an atlas modeling module, a detection analysis module, a protection response module, a man-machine interaction module and a wide-voltage power supply module which are electrically connected in sequence. According to the method and the device, hidden time sequence attacks can be accurately identified, multiple industrial protocols are supported, and the adaptive resource-limited industrial equipment is designed in a lightweight manner.

The invention discloses a dynamic electronic target sequence intention recognition method and system based on a time sequence diagram structure, and the method comprises the steps: obtaining multi-moment target data for a target object which dynamically changes in a real-time scene; performing sequence division on the multi-moment target data by adopting an FP-Growth algorithm to obtain an electronic target sequence set; modeling and coding each electronic target sequence in the electronic target sequence set by adopting a heterogeneous time sequence diagram model to obtain a heterogeneous time sequence diagram sequence; according to the heterogeneous time sequence diagram sequence, time sequence dynamic hidden relation prediction and time sequence dynamic feature prediction are carried out respectively, and a hidden relation and prediction features are obtained; the hidden relation comprises hidden nodes and hidden edges; and according to the hidden relationship and the prediction features, constructing graph data at the next moment, and performing intention recognition through a graph attention convolutional network. The problem that the time-space incidence relation of the target behavior changing along with time is difficult to capture in the prior art is solved, and the prediction and intention recognition precision is improved.

The invention discloses an intelligent lock recognition method based on a time sequence diagram neural network, and the method comprises the following steps: collecting multi-source event data of an intelligent lock, unifying the format, embedding codes, and generating an event feature vector set; constructing a time-varying multi-relation graph, and executing message passing and feature aggregation to obtain a neighborhood aggregation result; constructing a dual-frequency memory time sequence diagram neural network, and extracting long-term and short-term behavior characteristics in slow-frequency and fast-frequency memory units; inputting the slow frequency memory state and the fast frequency memory state into a gating fusion layer, and combining time and scene information to generate a full-time-domain embedded vector; calculating identity matching, abnormal risk and forgery credibility, and outputting a comprehensive identification result; and executing instant response and security processing according to an identification result, and carrying out network updating. According to the method, the sequence diagram neural network and the double-frequency memory mechanism are utilized, multi-element dynamic recognition of the intelligent lock is achieved, and the method has the advantages of being high in self-learning, high in recognition accuracy and excellent in safety robustness.

The invention relates to a natural resource asset management and territorial space planning system fusion method and system. The method comprises the following steps: encoding territorial space planning vector data and natural resource asset registration data to generate a space-time binding code; constructing a spatial topological graph and a time sequence diagram based on the codes, respectively extracting planning constraint feature vectors and asset dynamic feature vectors, and fusing the vectors to generate a planning asset coupling scoring matrix; recognizing a conflict unit set based on the planning asset coupling scoring matrix; based on the conflict unit set and a preset business rule base, performing classification processing on each conflict unit in the conflict unit set to obtain a conflict type; and based on the conflict type, generating a service execution instruction set through a preset reinforcement learning decision model. According to the method, through space-time unified coding and feature fusion processing, the accuracy of data association and the scientificity of decision making are improved, so that collaborative governance of territorial space planning and natural resource asset management is realized.

The invention discloses an intelligent self-adaptive time sequence diagram analysis and structuring method. The method comprises the following steps: preprocessing an input time sequence diagram; identifying and extracting basic structure elements in the time sequence diagram; extracting text information in the time sequence diagram by using an optical character recognition technology; the recognized signal lines are analyzed, and signal state changes and key event points are extracted through a neural network; detecting arrows representing time sequence constraints, and performing arrow detection and classification by using a target detection network; establishing an association relationship between the graphic elements and the text; the association relationship and the identified elements are verified and corrected by using grammar rules and global layout information of the time sequence diagram, and unreasonable results are filtered; converting the time sequence relation model into a standardized structured data format; and the overall recognition performance is continuously optimized based on interactive correction and feedback of confidence. According to the method, the workload of manually converting the time sequence diagrams by engineers is greatly reduced, the accuracy of time sequence constraint extraction is improved, and various time sequence diagram formats and styles are supported.

The invention discloses a power distribution network fault positioning method based on a time sequence diagram convolutional network, and the method comprises the following steps: 1), obtaining the time sequence measurement data of a power distribution network node, and carrying out the preprocessing, and obtaining a plurality of local time sequence segments; 2) performing feature extraction on the local time sequence segments by using a multi-layer one-dimensional convolutional network to obtain time sequence features; 3) processing the time sequence features by using a time sequence diagram neural network to obtain node features; and 4) fusing and mapping the features of the nodes by using a full connection layer, and outputting a fault positioning result. According to the method, the local feature mode and the global fault propagation dynamic state can be captured at the same time, the fault positioning accuracy of the power distribution network is greatly improved, and a solid technical support is provided for safe and stable operation of an intelligent power grid system.

The invention relates to the technical field of energy management, in particular to a multi-source heterogeneous energy load prediction method and system for a manufacturing enterprise cluster, and the method comprises the steps: collecting original energy data from a multi-source heterogeneous system in the manufacturing enterprise cluster and external environment information related to the energy load change, preprocessing the original energy data and the external environment information to obtain standardized energy time sequence data and final external environment information; based on the standardized energy time sequence data, the final external environment information and a pre-established equipment information model, constructing a triple heterogeneous atlas, and extracting structural semantic features of each node in the triple heterogeneous atlas; based on the structure semantic features, constructing a coupling sequence diagram, modeling the coupling sequence diagram, and identifying key factors; and inputting the structure semantic features and the key factors into a multi-scale prediction model, and outputting hourly load prediction curves of various energy media in a future prediction period.

The invention designs and implements a performance test environment construction method oriented to cross-chain transaction of block chains. The system supports a user to customize test scripts and execution parameters according to test requirements, tasks are executed and workloads are initiated at the same time through multiple actuators, a load scene of a tested cross-chain application system is created, the whole process of cross-chain transaction from an initiator to a receiver is tracked at the same time, key data are collected, and performance test indexes are calculated. And a visual result report is generated. Comprising the following steps: analyzing system requirements, and describing and analyzing functional requirements and non-functional requirements of a system; carrying out summary design on the system, and respectively designing and describing the system from a logic view angle, a process view angle, a development view angle and a physical view angle; detailed design is carried out on each module of the system, and a scheme is designed for the modules from two different perspectives of a class diagram and a time sequence diagram; and carrying out system testing and summarization on the system.

The invention provides an unsupervised APT detection method based on a time sequence diagram attention network and a deviation network. The unsupervised APT detection method comprises the following steps: acquiring a system audit log of a fixed-width time window by using a sliding time window; entities related to the events and interaction relations between the entities are extracted, and a dynamic traceability graph is constructed in combination with occurrence time of the events; modeling the interaction behavior of each node adjacent to the node in the time dimension by using a time sequence diagram attention network so as to extract a node time dependent feature representation; predicting the type of an edge between adjacent node pairs by using a multi-layer perceptron to obtain a prediction error; evaluating the degree of deviation between the type of the predicted edge and the corresponding reference standard by using a deviation network; and based on the prediction error and the deviation degree, identifying the time window subjected to the APT attack and the corresponding attack chain. The method is remarkably superior to a traditional detection method in the aspects of detection precision and robustness, and an efficient and extensible solution is provided for APT detection.

The invention relates to a chip time sequence diagram labeling method and device based on a multi-mode large language model, and relates to the field of chip time sequence diagram analysis and automatic labeling, and the method comprises the steps: firstly, carrying out the line and column line recognition of a chip time sequence diagram, and obtaining a signal line block and a time line block; then, detecting an intersection point area of the signal line block and the time line block, and further obtaining an image slice; then, detecting to obtain an annotation state of the image slice; then connecting the continuous annotation states in series to obtain an event, and further obtaining a natural language conforming to the understanding of the large language model; and finally, constructing according to the cue word to obtain a second text description, inputting the second text description into the first large language model to obtain a text response returned by the first large language model, and constructing based on the text response to obtain a sample for training a second large language model. Compared with the prior art, the method has the advantages that the accuracy of label state recognition is improved, the efficiency of training sample generation is improved, and the degree of manual intervention is reduced.

The invention provides a ship information and communication technology double-layer network modeling method based on a unified modeling language. The ship information and communication technology double-layer network modeling method comprises the steps that a ship physical layer deployment graph is constructed according to physical topology, hardware equipment and correlativity in a ship system, the distribution situation of software components on hardware and environmental factors; based on the ship physical layer deployment graph, constructing a ship physical layer class graph according to the ship physical layer dynamic routing relationship; constructing a ship logic layer time sequence diagram according to a self-control signal and a networking structure in a ship system; and based on a unified modeling language, constructing a ship system double-layer network model according to the ship physical layer deployment diagram, the ship physical layer class diagram and the ship logic layer time sequence diagram. According to the method, physical environment parameters, protocol constraints and dynamic fault propagation are introduced into a UML expansion system, and two-way coupling modeling of a physical layer and a logic layer of the ship ICT network is realized by combining deployment diagram three-dimensional space modeling, class diagram redundancy semantic annotation and time sequence diagram dynamic description expansion.

The program causes the assist device (10) to function as: an acquisition unit (120) that acquires a control program (12) executed by the PLC in order to control the device and time length information (114) relating to the time length of the operation of the device specified in the control program; a generation unit (140) that generates a sequence diagram of operations on the basis of the control program (12) and the time length information (114); and an interface unit (170) that outputs display data for displaying the generated time series diagram.

The application discloses a kind of soft measurement modeling methods of fermentation process based on time sequence diagram network, belongs to the technical field of soft measurement of fermentation process.It includes the following steps: (1) data acquisition and integration: the penicillin fermentation process under different conditions is obtained, and the data is divided, collected and integrated;(2) data selection: the data is selected, the redundant useless data is removed and the causal diagram between variables is established;(3) modeling training: algorithm model is constructed, and learning training is carried out;(4) model prediction: the trained algorithm model is used for prediction, and the prediction result is given.The application proposes a kind of soft measurement modeling method of fermentation process based on time sequence diagram network, improves the prediction accuracy of key product quality of fermentation process;The method uses graph convolution network and long short-term memory, extracts data in time and space dimensions, increases the generalization of model;The method can accurately measure the key product quality of different fermentation processes.

This application discloses an interface dependency parsing and automated testing method, device, and storage medium, comprising: extracting the minimum path set of the target medical business scenario from the interface call sequence diagram based on the target medical business scenario; using the minimum path set as the test path, parsing the data dependency relationship of each interface in the test path, extracting data flow rules to generate test data; calling the interfaces of the test path according to the test data to generate integrity verification results; comparing the integrity verification results with the expected behavior of the interface call sequence diagram to locate abnormal test nodes; and identifying changes in the interface dependency relationship of abnormal test nodes. This application automatically constructs and dynamically updates the interface call sequence diagram from real call logs through a time sequence pattern mining algorithm, replacing manual dependency analysis. When business logic changes, the test path and data flow rules are re-extracted to ensure that the test plan is updated synchronously with the business, thereby reducing the maintenance cost of test scripts.

The invention discloses a social network link prediction-oriented time sequence diagram network parallel training acceleration method. The method comprises the following steps: firstly, calculating a redundancy score and an old score for each user interaction edge in a training set, and calculating a comprehensive information loss score according to the redundancy score and the old score; secondly, according to a preset core set retention proportion alpha, selecting all user interaction side comprehensive information loss scores and upper alpha quantiles of repetitiveness as threshold values, and discarding user interaction with the comprehensive information loss scores lower than the threshold values, so that a simplified core set is obtained through single-time preprocessing; then carrying out adaptive batch division on the obtained core set, and dynamically determining an acceptable maximum user interaction number in each batch according to a redundancy and old comprehensive information loss score; and finally, training the time sequence diagram neural network based on the divided batches to realize social network link prediction. The method not only improves the training efficiency, but also greatly improves the precision of the model.

The invention discloses a foundation pit adjacent operation tunnel state prediction method based on a space-time decoupling network, and the method comprises the steps: obtaining historical tunnel change data and foundation pit real-time monitoring data, extracting corresponding foundation pit feature data according to the historical tunnel change moment, and carrying out the prediction of the foundation pit adjacent operation tunnel state; and screening out a key foundation pit feature set having a significant causal relationship with the tunnel state change. And constructing a time sequence diagram network set based on the screened key foundation pit characteristics, the historical tunnel change data and the space relationship and the stress conduction relationship between the foundation pit and the tunnel. And establishing a space-time decoupling network model, training by using the time sequence diagram network set, learning complex space-time coupling characteristics between the foundation pit and the tunnel, and obtaining a trained prediction model. And inputting key foundation pit characteristic data of the foundation pit at the current moment, and realizing prediction of deformation, convergence and displacement of the adjacent operation tunnel at a plurality of moments in the future. The dynamic influence of foundation pit construction disturbance on the tunnel can be effectively captured, and the accuracy and real-time performance of tunnel safety state prediction are improved.

The application discloses an anti-misoperation intelligent control method and system based on integrated control, relates to the technical field of power automation, and comprises the following steps: acquiring first data of a three-party integrated control system, constructing a dynamic instruction pool and an operation time sequence diagram; based on the dynamic instruction pool and the operation time sequence diagram, checking operation consistency through a space-time coupling protocol, combining compliance verification rules and power grid operation data, and constructing a power flow constraint network; according to the power flow constraint network, using a mixed integer programming method and an N-1 risk matrix, generating a risk early warning; according to the risk early warning, generating an anti-misoperation decision, and feeding back to the three-party integrated control system. The application is used to solve the problems that operation instruction management and control of a traditional anti-misoperation system lacks dynamicity and consistency checking is weak.

The application relates to the technical field of agent dynamics, and particularly discloses a multi-agent dynamics modeling method based on PEACE, which comprises the following steps: constructing time sequence diagram data; extracting invariant observation embedding data; constructing agent model data; based on system parameters, generating prompt context through coding and self-attention mechanism, initializing individual prototype prompts, decoupling and fusing the individual prototype prompts and the invariant observation embedding, combining graph ordinary differential equations to construct an individual agent model, and realizing decoupling of the two types of embedding by minimizing mutual information loss; generating enhanced data; cooperatively optimizing and training; and performing dynamics prediction. In this way, the scheme can improve robustness in a distribution shift scenario.

The application provides a risk transmission prediction method and system based on a time sequence diagram and a large model joint deduction, relates to the technical field of artificial intelligence and knowledge engineering, and comprises the following steps: extracting forward-looking time sequence facts based on hierarchical prompt words and a structured constraint mechanism; extracting conflict events and generating time sequence relationships; updating a historical time sequence knowledge graph based on a time sequence relationship and a confidence weighted update strategy; determining a central entity, initiating a structured query to the updated historical time sequence knowledge graph from the central entity, and generating a context knowledge subgraph; converting the context knowledge subgraph into a natural language description with logical relationships, injecting a risk hypothetical event, constructing a prompt word as a new input of a large model, and outputting a structured JSON object containing a complete reasoning chain; and based on a risk path refining and visualizing algorithm with influence weight attenuation, converting the structured JSON object to obtain risk warning information and a visualized transmission path diagram.

This application discloses an AI-based cross-scene furniture e-commerce interaction system and method. The system includes: an event receiving and recording module, a sequence verification module, a representation vector generation module, a credibility calculation module, an attribution classification module, and a sample update module. The system receives interaction events, establishes room scene records and product version records, and generates version influence vectors; it performs sequence verification on the same user events, constructs a time series diagram, and generates representation vectors; it calculates event credibility by combining scene validity period, product version validity period, time deviation, and associated anomalies; it performs replay verification and attribution classification on low-credibility negative feedback events, and updates training samples, user preference states, and product recommendation states accordingly. This system can improve the accuracy of interaction data processing and recommendation updates.