133 results about "Stream analytics" patented technology

The invention provides a credit risk dynamic assessment and early warning method based on multi-dimensional data analysis, and relates to the technical field of financial risk control and big data analysis, and the method comprises the steps: obtaining multi-dimensional data from borrower basic information, financial statements, credit records and an external market environment, carrying out cleaning, standardization processing and storage on the multi-dimensional data; based on the multi-dimensional data, through a credit scoring model, cash flow analysis, liability ratio analysis and market environment risk assessment, generating a financial risk level of the borrower; the financial and market data of the borrowers are updated in real time, the dynamic change of the risk state of the borrowers is monitored in combination with a time sequence prediction model and risk correlation analysis, and a dynamic monitoring result is generated; and according to the risk level and the dynamic monitoring result, triggering a grading early warning mechanism, and generating a corresponding control measure suggestion so as to adjust a credit strategy or adopt a risk slow release means.

The invention relates to a house building construction stage carbon emission dynamic estimation method and system based on full life cycle data, and the method comprises the steps: dividing a construction region based on a BIM model, laying a multi-source sensor network, fusing static BIM data and a dynamic Internet of Things monitoring flow, building a four-dimensional space-time label data set, and carrying out the construction stage carbon emission dynamic estimation based on a double-layer dynamic correction model. Kalman filtering is utilized to eliminate instantaneous interference, LSTM transfer learning is combined to predict mechanical efficiency attenuation, a dynamic correction coefficient matrix of a mechanical aging rate, operator skills and environmental factors is embedded, and a carbon emission value is calibrated in real time; when abnormal emission is detected, a key pollution source is traced and positioned through a graph neural network, a three-dimensional thermodynamic diagram is generated in the digital twinborn model, and component-level carbon flow analysis and early warning are performed, so that the carbon emission estimation precision is remarkably improved, the abnormal traceability time consumption is shortened, and the timeliness is remarkably improved.

The invention relates to the technical field of urban traffic management and carbon emission reduction accounting, and provides a bus travel carbon emission reduction accounting method fused with Beidou spatio-temporal data, which comprises the following steps: acquiring spatio-temporal trajectory data based on a Beidou satellite navigation system, and constructing a passenger-trajectory-station three-dimensional correlation model for passenger flow analysis; constructing a passenger transfer analysis system based on the passenger flow analysis result; constructing a dynamic'perception-prediction-optimization 'dynamic scheduling optimization system for passenger flow detail analysis on the basis of space-time trajectory data and a reinforcement learning algorithm; and constructing a mileage-frequency distribution model and a station efficiency evaluation model based on the spatio-temporal trajectory data, respectively generating a trajectory matching optimization strategy and a differentiated station optimization scheme, and realizing adaptive optimization of the public transportation system under dynamic demand change. Compared with a traditional static data dependence method, the method achieves the dynamic and fine-grained monitoring of the operation process of the public transportation system, and effectively improves the timeliness and integrity of data.

The invention discloses a deep reinforcement learning optimization method for injection molding process parameters, and belongs to the technical field of intelligent manufacturing. The method comprises the following steps: constructing a dynamic causal graph network through information entropy flow analysis and transfer entropy calculation, and revealing a causal relationship and time delay characteristics among process parameters; manifold learning is adopted to map a high-dimensional parameter space to a low-dimensional manifold, and Riemannian metric guide optimization search is constructed based on the quality gradient; generating enhanced state representation fusing causal association and manifold geometric information; identifying a production element state and selecting a corresponding optimization strategy; a geodesic line is planned in a manifold space to obtain an optimal parameter adjustment path; historical experience is utilized through memory retrieval and case adaptation; cross-task knowledge migration is realized; adopting a depth deterministic strategy gradient algorithm to optimize the decision; and online learning is realized through elastic weight consolidation. According to the method, the problems of black box decision, slow convergence, difficulty in knowledge reuse and the like in the prior art are solved, the optimization efficiency and the interpretability are improved, and the method has the capability of quickly adapting to new tasks.

The invention relates to the technical field of public transport organization, in particular to an urban traffic station connection evaluation method based on multi-source data, which comprises the following steps of: performing passenger flow analysis on an upstream public transport section of a station, dividing passenger flow time periods, and acquiring multi-source connection situation data by adopting a sampling or real-time monitoring mode based on different time periods; calculating a transport capacity supply saturation index based on the station upstream public traffic section passenger flow data, calculating a dynamic transport capacity service efficiency index based on the mobile passenger-carrying end parking characteristic data, weighting to obtain a comprehensive connection bearing coefficient, estimating the real-time passenger flow arrival flow based on the arrival shift data of the traffic station and the number of resold tickets, and determining the real-time passenger flow arrival flow. And evaluating the urban traffic connection capability of each station by generating a real-time connection efficiency value. According to the method, the quantitative evaluation of the operation state of the connection system of the urban traffic station is realized, the defects of a traditional method in the aspect of multi-source data fusion are overcome, and the analysis precision of the connection capability of the urban traffic is improved.

The application provides a multi-source data automatic acquisition method based on a large language model, which comprises the following steps: analyzing the feature distribution of historical acquisition data, combining the state of the current data source, and dynamically generating an optimal acquisition strategy; scheduling the acquisition task in real time to obtain a dynamically scheduled data stream; performing real-time anomaly detection and adaptive compensation on the dynamically scheduled data stream to obtain a self-fault-tolerant data stream; analyzing the entity association and business logic of the self-fault-tolerant data stream, predicting potential data demand points; automatically expanding the data acquisition range to obtain knowledge-enhanced data assets; identifying the protocol characteristics of the data transmission stream of the data assets, parsing the data based on a protocol characteristic mapping parser to obtain protocol-independent standardized data; performing semantic noise filtering and cross-modal cleaning to obtain semantic pure data. In the application, the defects that the existing multi-source data acquisition technology cannot perform protocol adaptation and adaptive strategy optimization are overcome.

The invention provides a college affair assistant construction method and system based on an agent technology. The method comprises dynamic memory flow analysis: (1) an agent memory flow mechanism; (2) memory module interaction; and (3) carrying out personalized analysis. (4) carrying out multi-source information fusion analysis; and (5) multi-agent collaboration and retrieval enhancement. (6) performing multi-agent collaboration; (6) constructing a multi-agent collaboration workflow; (7) a dynamic memory flow energizing agent; and (8) an intelligent agent fusion mechanism. (9) predicting a dual-stage load condition; (10) carrying out progressive load recovery; and (11) a decision is recovered in combination with a multi-dimensional load. The system comprises a personalized analysis module based on dynamic memory flow and student information, an RAG retrieval strategy module based on student information enhancement, an agent design strategy module and a dynamic load recovery mechanism module. The invention provides a high-performance efficient transaction assistant which meets individual requirements for users.

The invention discloses a medical image recognition method and system based on artificial intelligence, and the method comprises the steps: obtaining CT image data, pathological section data and PET metabolic activity data, introducing a GNN graph neural network to model the semantic association between multi-modal features, and outputting a fusion feature vector; inputting the fusion feature vector into a DSRN double-flow space-time recursive network, extracting 3D lesion morphological features of a single image by using a spatial flow, analyzing lesion growth kinetic parameters of a historical image sequence through a time flow, and generating a lesion malignancy probability index based on a gating fusion unit; based on the focus malignancy probability index, Monte Carlo Dropout sampling is utilized to generate a confidence interval, a clinically interpretable credibility score is output, and when the confidence is smaller than a threshold value, a low-confidence area is displayed for a doctor to check. The deep fusion of multi-source information is realized, the one-sidedness of a single mode is avoided, and the recognition accuracy and efficiency are improved.

The invention discloses a meeting place people flow analysis, statistics and prediction system, and relates to the technical field of intelligent people flow management, and the system comprises a face recognition verification terminal, a people flow statistics terminal, a prediction control terminal, an early warning prompt module, an emergency control module, a data storage module and a visualization module. Statistical errors caused by repeated entering and exiting are avoided, the number of people in the museum is calibrated in real time, the problems of identity confusion and insufficient precision existing in traditional manual statistics and single sensor counting are solved, the accuracy and reliability of people flow statistics are improved, and a high-quality data basis is provided for subsequent prediction and control. Historical activity data, real-time reservation information and environment variable factors are fused, a prediction model is constructed to pre-judge the peak period and the peak scale of people flow in advance, the management and control lagging risk caused by a traditional afterward response mechanism is reduced, and intelligent conversion from passive interception to active dredging is achieved in combination with a gate flow limiting and shunting guide strategy.

Analytics equipment (14) for a communication network (10) stores a stream (18) of data records (20) from the communication network (10). The analytics equipment (14) samples the stored stream (18T) to obtain a sampled stream (18S) that includes fewer data records (20) than the stored stream (18T). The analytics equipment (14) explores the sampled stream (18S) to identify characteristics (28) of data records (20) to be used for insight creation. Based on the identified characteristics (28), the analytics equipment (14) filters the stored stream (18T) to obtain a filtered stream (18E) that includes data records (20) with the identified characteristics (28). The analytics equipment (14) then creates one or more insights (16) about the communication network (10) using the filtered stream (18E).

The invention relates to a scale-aware geographic flow clustering method and device, electronic equipment and a storage medium, and the method comprises the steps: introducing a pre-constructed scale factor to construct a relation mapping between a spatial search neighborhood of a flow and a flow length, and generating a to-be-processed flow connection cluster set based on the relation mapping and a plurality of flows; any one to-be-processed stream connection cluster is selected from the to-be-processed stream connection cluster set, and whether any one to-be-processed stream connection cluster is a strong connection stream cluster is judged; if any to-be-processed stream connection cluster is a strong connection stream cluster, adding any to-be-processed stream connection cluster into a stream clustering cluster set, otherwise, taking any to-be-processed stream connection cluster as a weak connection stream cluster, and processing the weak connection stream cluster to obtain a potential strong connection stream cluster meeting a preset condition; and obtaining all strong connection flow clusters and all potential strong connection flow clusters in the to-be-processed flow connection cluster set to generate an actual flow clustering result of the flow. Therefore, the technical problems that local spatial distribution characteristics of stream data are neglected when spatial heterogeneous stream data are clustered, accurate perception of stream analysis scales is lacked, stream clusters with different densities cannot be correctly divided and the like in related technologies are solved.

The invention relates to the technical field of spacecraft design, in particular to an aircraft information flow analysis, recheck and recalculation system, which comprises an information large graph design module used for automatically extracting design data such as an interface data list and a cable network contact table, generating a static information large graph through data check and analysis, and supporting signal management, layered display and export; the model library construction module is used for constructing a hierarchical information professional model library based on a Modelica language and covering multi-level models from a basic library, a universal library to a model customization library; and the information flow simulation verification analysis module is used for mapping the static information large graph into a simulation model, carrying out dynamic simulation verification on a system network, a communication network and a load network, and realizing multi-dimensional visual display of a result. According to the method, the information system design is rechecked and recalculated from static state to dynamic state and from manual operation to automatic operation, and the design efficiency, accuracy and verification sufficiency are effectively improved.

Based on demographic information of the area being analyzed, the system estimates the distribution of people with user-specified attributes at a given location within that area. [Solution] The human flow information estimation device 2000' includes a calculation device 2100 that performs human flow information analysis based on predetermined demographic information, which includes the number of people located in each of a plurality of predetermined area meshes and information on the attributes of those people. The human flow information estimation unit 2103 identifies a set of unit sub-regions that cover the area to be analyzed without overlapping each other, and based on demographic information and predetermined lifestyle information, it distributes the number of people from the demographic information to the unit sub-regions at predetermined time intervals, based on the type of place and the size of the place corresponding to the classification of people's behavior in each area mesh, and estimates aggregated data for each unit sub-region. Based on the aggregated data, the human flow information estimation unit 2103 calculates a human absorption capacity value, which is the ratio of the number of people with the analyzed attributes for each unit sub-region to a predetermined value.

The application provides a two-stage electric-thermal coupling system energy flow calculation method and system based on hybrid driving, belongs to the technical field of energy flow calculation of comprehensive energy systems, and comprises the following steps: constructing a heterogeneous graph structure for the electric-thermal coupling comprehensive energy system, and building heterogeneous graph data based on the characteristics of the heterogeneous graph structure; inputting the heterogeneous graph data into a graph neural network model based on a relationship-aware grouping graph attention mechanism; taking the prediction result output by the graph neural network model based on the relationship-aware grouping graph attention mechanism as an initial value, and quickly correcting the initial value; and outputting complete system multi-energy flow analysis results based on the correction result.

The invention discloses a power system load flow analysis training data enhancement method and related equipment, and the method comprises the following steps: taking all buses in a power grid as nodes in a topological graph, taking all connection equipment as middle edges of the topological graph, and obtaining a structure graph of the power grid; the sample enhancement method is recorded as a sample enhancement method based on subgraph sampling and / or a sample enhancement method based on subgraph grafting based on the structure graph, and samples are generated to enhance the training data. According to the power system load flow analysis training data enhancement method based on subgraph sampling and subgraph grafting, node scale change is carried out on the actual operation scene of a power grid, the number of training samples is increased, the number of nodes of the samples is increased, and the richness of a topological wiring mode is improved. The invention provides a sample expansion method based on sub-graph sampling and sub-graph grafting, and the adaptability of the model to a node number reduction scene caused by starting and stopping of a generator and a node number increase scene caused by power grid expansion can be improved respectively.

A threat analysis method includes acquiring system configuration information indicating hardware components of a system for which threat analysis is performed, function allocation information indicating functions allocated to the hardware components, an asset information indicating assets used in each function, and asset input / output information indicating hardware components of input / output sources and input / output destination of the asset; deriving a physical data flow that indicates a flow of the asset corresponding to each hardware component and a logical data flow that indicates a flow of the asset corresponding to each function in accordance with the acquired information and analyzing a likelihood of an attack on the asset and an impact of the attack on the asset for each of the hardware components and each of the functions in accordance with the physical data flow and the logical data flow; and outputting a result of the analysis.

The invention belongs to the field of computer vision and data analysis, and particularly relates to a passenger flow statistical system and statistical method based on deep learning. A dynamic target tracking module; a space-time passenger flow analysis module; and a feedback optimization module. The defects of an existing passenger flow statistical method in the aspects of accuracy, adaptability, density sensitivity, management and control disjunction and the like are overcome.

The application discloses a multi-screen fusion multi-information source intelligent push playing management method and system, which is connected with an airport multi-information screen cluster, comprises a data fusion module, a passenger portrait and state tracking module, an information strategy decision engine, a multi-level screen management module, a content generation and distribution module, a distributed screen control module and a man-machine interaction management interface; the data fusion module is used for accessing and processing multi-source heterogeneous data from an airport operation database, a passenger processing system, an indoor positioning system and a real-time passenger flow analysis system, and generating a unified airport real-time state view; the application allows an operator to conveniently make and regularly publish rich media content through a self-produced program template configuration and push module, supports batch management of a device group, planned on-off and state monitoring, perfectly unifies two functions of airport key operation information publishing and daily media content playing, and forms a complete, efficient and reliable multimedia information publishing and management platform.

This invention relates to the field of transformer monitoring technology, specifically to an online monitoring device and handling system for grounding current of transformer core and clamps. The system includes a data acquisition module, a current analysis module, a linkage analysis module, an early warning determination module, and a fault determination module. First, it analyzes the current surge and relative stability of the current data to determine the linkage characteristic of the current surge; then, it removes background current to obtain the true fault current; it determines the synchronization of the linkage response time between current and temperature; combining the linkage characteristic of the current surge and the synchronization of the linkage response time, it determines the early warning value for a metallic grounding fault; when the early warning value for a metallic grounding fault exceeds the preset normal early warning range, it compares the characteristics of different fault locations at the current moment with those in the historical database to obtain the fault location corresponding to the current moment. This invention achieves intelligent fault location determination.

This invention provides a bitstream analysis method, apparatus, storage medium, and electronic device. The method includes: acquiring the raw bitstream reported by a target camera device accessing a server, wherein the server has an analysis function to analyze the raw bitstream, and the analysis function is configured in the server through an acquired configuration file, which is pre-acquired from camera devices that have historically accessed the server; determining, based on the capabilities of the target camera device, whether the target camera device has performed target analysis on target bitstreams included in the raw bitstream; extracting the target bitstream from the raw bitstream and performing target analysis on the target bitstream. This invention solves the problem of poor intelligent analysis capabilities of camera devices in related technologies, achieving the goal of enhancing the intelligent analysis capabilities of camera devices and improving the efficiency of intelligent analysis.

The invention discloses an intelligent scenic spot management method, device and equipment based on cloud computing, and the method comprises the steps: collecting scenic spot tourist flow data in real time through scenic spot equipment and / or scenic spot equipment of the Internet of Things, and carrying out the data exchange and integration with other tourism information systems or data sources through data exchange and integration; tourist portraits of tourism scenes are constructed for data processing and tourism passenger flow monitoring and analysis, and tourist passenger flow data and subsystems of the smart scenic spot are displayed on a scenic spot map through layer loading; tourist flow analysis, passenger flow public opinion monitoring and scenic spot resources are integrated on a scenic spot data acquisition platform of edge computing nodes, the scenic spot data acquisition platform is transmitted to a comprehensive management platform through a communication network, data of various data sources is subjected to data heterogeneous processing, and then the acquired scenic spot tourist data is subjected to integrated analysis; the scenic spot tourist data is monitored through the visual platform, and the scenic spot tourist flow is displayed in real time according to the internal data. The scenic spot comprehensive monitoring analysis is realized, and the user experience is optimized.

The application belongs to the technical field of broadcast television network, and is a DVB full-signal source fusion intelligent network management system, which comprises a three-dimensional integrated no-reference evaluation system of a protocol layer, a content layer and a device layer, and comprises the following steps: S1: performing multi-dimensional no-reference intelligent perception of multicast signal quality, deeply analyzing the protocol layer and monitoring core indexes, and simultaneously analyzing audio and video content based on a single stream to identify implicit faults; S2: performing DCM device full-life-cycle white-box monitoring, collecting device hardware states and service information through deep fusion of a standard protocol and a management information base, and realizing intelligent correlation analysis of device layer alarms and signal layer alarms; S3: performing intelligent closed-loop linkage control, constructing an alarm strategy engine, and designing a safe switching control process based on a finite state machine; and S4: deploying a distributed micro-service architecture, splitting the system into multiple micro-services and decoupling through a message queue, and realizing dynamic balanced scheduling and fault migration of tasks.

The present application relates to the technical field of underground pipe network safety monitoring and artificial intelligence image processing, and discloses a third-party construction intelligent discovery and closed-loop supervision method and system, comprising the following steps: firstly, coordinate mapping is performed on multi-source video images to obtain geographic location information; a pre-trained industry large model is used to perform double-flow analysis, identify static construction elements and dynamic operation behaviors, and determine suspected construction events in combination with time sequence characteristics; then, based on the spatial distance of the construction center point and the pipe network and the pipe property, the risk level is calculated, and a differentiated graded early warning strategy is executed; finally, the on-site disposal feedback result is received, and the weight parameters of the industry large model and the determination threshold in the risk rules are dynamically updated. Through multi-modal feature fusion and self-adaptive closed-loop mechanism, the present application effectively solves the problems of high construction identification false alarm rate and supervision lag in complex scenes, and significantly improves the intelligent level of safety protection of energy pipe networks.

The invention discloses a cascade reservoir outflow analysis and simulation method based on a GraphDB knowledge graph, and the method comprises the steps: carrying out the relation recognition and knowledge reasoning of related entities of reservoir outflow through a GraphDB graph database constructed based on a large amount of data, mining, and determining the influence of each entity on the outflow flow, thereby carrying out the accurate outflow simulation based on the knowledge graph. Comprising the following steps: demand analysis and data collection; carrying out data modeling; integrating and loading data; constructing a knowledge graph; knowledge reasoning; querying and analyzing; and displaying and applying a result. The cascade reservoir outflow analysis and simulation method based on the GraphDB knowledge graph not only can visualize the complex relation between the input and output of the cascade reservoir and the upstream and downstream water levels and the like, provides convenience for knowledge sharing cooperation of different departments and institutions, but also can analyze and simulate the outflow of the cascade reservoir on the basis of a large amount of historical data. Historical decisions or optimization strategies under different scenes are quickly derived, and data-driven decision support is provided for flood control and disaster reduction of a reservoir, benefit-making water supply and the like.

The application discloses a physical information neural network-based island micro-grid safety analysis method, and steps include: step 1, establishing a micro-grid power flow analysis model; step 2, constructing a physical information neural network; step 3, constructing a static safety analysis model based on the physical information neural network, including: 3.1) data processing; 3.2) constructing a basic framework of a deep learning model; 3.3) constructing an overall framework of static safety analysis, and completing static safety analysis.The application belongs to the technical field of power system automation and artificial intelligence, and solves the problem that the existing PINN lacks a systematic method system in the application in the power system, and the evaluation efficiency and results are difficult to meet the requirements.

The application provides an intelligent closed-loop production system and process optimization method for glass aspheric molding. The application constructs a closed-loop system of "intelligent design-real-time mold control-result iteration", and realizes the full-link digitalization and intelligent optimization of aspheric molding production by combining multi-domain coupling simulation, multi-source perception dynamic control and AI-driven knowledge iteration technology. The aspheric surface precision CPK value is improved from 0.7-1.2 of the traditional process to 1.33 or more, which meets the strict requirements of precision manufacturing and effectively reduces the precision fluctuation in batch production. The new product test molding cycle is shortened by more than 50%, the product changeover time of the same production line is reduced by 40%, and the rapid switching of products with different specifications and difficulty levels is adapted. Through the analysis of the whole process data flow, the production process state visualization and predictive optimization of equipment / mold maintenance are realized, and the service life of the production system is prolonged.

The invention relates to the technical field of data processing, in particular to an intelligent question answering system and method based on multi-modal feature fusion, and the method comprises the steps: a quantization behavior collection module generates a non-Euclidean space-time behavior feature tensor; the neural radiation field simulation module outputs an enhanced space-time sequence; the meta learning feature coding module outputs a 128-dimensional super vector; the game reasoning engine module simulates a group interaction strategy to generate a group behavior probability matrix; the chaos rule evolution module analyzes the stability of the decision trajectory and outputs a dynamic decision rule; the cross-modal consensus module outputs a grading early warning signal, and the signal feeds back and adjusts the sampling frequency of the quantum sensor to form a closed loop. According to the method, the input data dimension is expanded under the privacy compliance condition through non-biological feature collection and multi-modal fusion, and the abnormal behavior recognition accuracy and decision reliability in real-time passenger flow analysis are improved in combination with virtual scene enhancement and dynamic decision optimization.

The application provides a precursor filling and sampling analysis system, and relates to the technical field of detection, comprising a collection module, a sampling and detection module and an analysis module. The collection module pre-processes the system pipeline and the container before obtaining the precursor, generates a pre-processing result, and obtains the precursor to be analyzed after meeting the preset conditions. The sampling and detection module detects the precursor to be analyzed online, generates a real-time heterogeneous data stream including first data of particulate matter information and second data of fluid state. The analysis module identifies and marks abnormal data points in the first data, constructs a causal association neighborhood based on the first data around the abnormal data points and the second data before the occurrence, extracts multi-dimensional physical features, and calculates a artifact confidence weight accordingly. When the confidence weight is higher than a preset threshold, the abnormal data points are reconstructed as baseline noise values, the real-time heterogeneous data stream is corrected, and a precursor quality analysis result is generated based on the corrected data.

The invention relates to a multi-target tracking and region dynamic counting method and device based on edge calculation, equipment and a medium. The method comprises the following steps: acquiring a real-time video stream through a high-resolution camera, transmitting the real-time video stream to an edge computing box through network transmission, and carrying out hard decoding and size adjustment on the video stream; inputting the processed video frame into an fp16 quantized YOLOv8 model to carry out efficient target detection; then, based on the optimized DeepSORT algorithm, in combination with Kalman filtering and appearance feature matching, multi-target tracking is executed at the edge end, and a unique ID is allocated to ensure identity consistency; and the system records a target motion track, and dynamically defines a counting line and a polygonal monitoring area through JSON configuration, so that accurate area dynamic counting and in-out direction analysis are realized. According to the invention, dependence on cloud computing resources is reduced, real-time performance and computing efficiency are improved, and the method is suitable for scenes such as intelligent security and protection, traffic monitoring and passenger flow analysis.

This invention discloses a method and system for predicting the quality of injection molded products based on an XGBoost-LSTM ensemble model. The method includes the following steps: S1, designing the gating and cooling systems according to the requirements of the injection molded product, determining key injection molding process parameters, and performing mold flow analysis to simulate actual production; S2, collecting Moldflow simulation analysis data; S3, preprocessing the simulation dataset and dividing it into training and test sets; S4, training the XGBoost-LSTM ensemble model using the training set; S5, verifying the prediction accuracy of the XGBoost-LSTM ensemble model using the test set. The system includes a mold flow analysis module, a simulation data acquisition module, a data preprocessing module, a model construction module, a model training module, and a model testing module. This invention accurately predicts product quality based on set injection molding parameters, reducing trial-and-error costs and improving production efficiency.