472 results about "Relationship analysis" patented technology

The invention provides a production line process scheduling optimization method and system based on an agent cluster. The method comprises the steps that the agent cluster comprising a plurality of distributed decision agent nodes is configured according to a production line process scheduling instruction; based on the production line architecture feature graph, a communication protocol between agent nodes is generated through node association relationship analysis, and a cooperation rule set is constructed in combination with a process priority rule and a resource constraint condition; the intelligent agent cluster is instructed to perform scheduling according to a communication protocol and a cooperation rule set, process tasks are dynamically allocated and adjusted through real-time state interaction among nodes, scheduling behavior logs are obtained, a reinforcement learning training sample set is constructed in combination with expected labels, decision parameters of intelligent agent nodes are optimized by using a multi-intelligent-agent reinforcement learning algorithm, and a reinforcement learning training result is obtained. According to the method, the dynamic response capability and the cooperation efficiency of automatic production line scheduling can be improved, and the method is suitable for efficient process scheduling in a complex production environment.

The invention relates to the technical field of distributed agent collaboration and graph database query optimization, in particular to a graph query processing method and system based on multi-agent collaboration, and the method comprises the following steps: reasoning a coordination agent to analyze the semantics and intention of user query by using a large language model; a structured multi-agent execution plan is output, and an entity search agent or a relation analysis agent needing to be called is explicitly called; the agent coordinator receives the multi-agent execution plan and selects agents according to task types and task allocation; obtaining a distributed intermediate result set; starting a result aggregation mechanism, and performing typed processing, priority ranking and deduplication merging on the distributed intermediate result set to obtain an arrangement result; and outputting an answer for answering the initial question of the user. The system comprises an agent execution plan output module, a multi-task execution module and an arrangement result reasoning module. According to the method, efficient, extensible and fault-tolerant graph query processing is realized.

The invention discloses a multi-source field-level blood relationship tracking method and system. The method comprises the following steps: extracting features from a multi-source heterogeneous system, matching templates, triggering a corresponding acquisition strategy, and outputting an original blood relationship log; reading an original blood relationship log to generate an abstract syntax tree, extracting a source / target field set to output a field-level mapping relation, further writing the field-level mapping relation into a graph database to form a blood relationship edge, writing metadata into a relational database, establishing a two-level index, and outputting a blood relationship graph capable of being quickly positioned; and taking the blood relationship map as input, returning a target path with the maximum sum of confidence coefficients by adopting a weighted shortest path algorithm, outputting a link-level traceability result, and displaying the link-level traceability result. According to the method, complete tracking of the field-level blood relationship is realized, the query response time is shortened to a millisecond level, rapid access to multiple types of data sources is supported, and the problems of insufficient blood relationship analysis precision, low query efficiency and weak multi-source adaptability in the prior art are solved.

The invention provides a call center dialogue sentiment analysis method and system fusing voice and text, and relates to the technical field of voice signal processing, and the method comprises the steps: obtaining a voice signal of a call center and a corresponding transliteration text, extracting intonation, speed, sound intensity and pause sentiment features from the voice signal, meanwhile, deep language analysis is carried out on the transliterated text, and semantic emotion features related to context are extracted; and performing cross-modal correlation analysis on the voice emotion features and the semantic emotion features, and generating a time sequence correction coefficient for feature alignment by constructing a corresponding relation analysis framework between feature sequences. According to the method, language emotion information in voice and text is integrated, more accurate and comprehensive recognition of conversation emotion of the call center is realized, customer satisfaction is improved, and reliable emotion analysis basis is provided for efficiently processing customer appeals.

The invention provides a code quality adaptive evaluation and optimization method, system and device and a medium, and belongs to the technical field of computers. The method comprises the following steps: performing redundant information cleaning and standardization processing on an original code; selecting an adaptive parser to convert the processed code into an abstract syntax tree, and optimizing the abstract syntax tree; performing embedding processing on each node of the abstract syntax tree by using a pre-trained deep learning model to generate a node semantic vector, and performing aggregation, feature extraction and vector normalization operation to obtain a semantic vector representing code semantic features; extracting a dependency relationship from the abstract syntax tree to construct a program dependency graph, extracting node features by using a neural network model, and generating a dependency relationship analysis result; based on the semantic vector and the dependency analysis result, identifying a code potential problem by using an optimization algorithm and generating an optimization suggestion; codes are automatically modified according to optimization suggestions, the optimization effect is re-evaluated, and related models and algorithms are iteratively optimized according to feedback information.

The invention provides a large model retrieval enhancement generation method fused with a multi-source knowledge base, and relates to the technical field of large model retrieval enhancement generation. The method comprises the steps that firstly, user query is analyzed, multiple implied independent retrieval intentions are recognized and separated, and a knowledge source identifier is distributed to each retrieval intention subtask based on a mapping rule; according to the method, related evidence fragments are acquired in parallel from a heterogeneous knowledge source library in a targeted manner, multi-level relation analysis is further performed on the original evidence fragments by introducing a cross validation engine, so that a structured evidence conflict graph is constructed, and then the evidence conflict graph is intelligently processed by applying a game theory excitation algorithm. The method comprises the following steps: obtaining an evidence set with internal consistency, forming a guide signal through structured analysis, and finally, inputting original user query, the synthesized evidence set and the guide signal into a large language model in combination with a pre-constructed cue word template to generate a final answer under constraint, so as to realize retrieval enhancement generation fused with multi-source knowledge base retrieval.

The invention discloses a two-dimensional construction drawing exporting method and system based on a three-dimensional BIM model, and the method comprises the steps: analyzing the geometric information and attribute information of a component from obtained three-dimensional BIM model data, and the geometric information comprises pipeline geometric information, equipment geometric information and structure geometric information; on the basis of the analyzed geometric information, automatically identifying a labeling object; carrying out labeling processing on the identified labeling object, calculating an optimal angle and a character avoidance position of a labeling lead in a labeling process, and generating optimized labeling information; on the basis of the analyzed attribute information, automatic layer mapping is conducted on the component, and standardized layer information is generated; on the basis of the analyzed pipeline geometric information, performing axonometric projection transformation on topological relation data obtained through pipeline topological relation analysis, and generating axonometric map information including a pipeline space connection relation and flow direction information; and integrating the labeling information, the layer information and the axonometric drawing information, and outputting a standardized two-dimensional construction drawing.

The invention provides a multi-source heterogeneous data fusion method and device. Equipment state data of a computer monitoring system, equipment ledger and historical operation and maintenance data of a production management system and environmental parameter data of a meteorological system are accessed in real time through a distributed data bus; performing standardization processing on the three types of heterogeneous data based on a unified spatio-temporal index model, and establishing a dynamic association model including a time dimension, a space dimension, an equipment dimension and a working condition dimension; performing causal relationship analysis on the cross-system data by using the dynamic association model to generate a multi-dimensional data association result related to the current accident; and according to a data association result, dynamically generating an emergency disposal scheme and pushing the emergency disposal scheme to a remote centralized control center so as to support real-time decision and disposal operation. According to the method, second-level fusion and intelligent correlation analysis of cross-system heterogeneous data can be realized, the diagnosis efficiency and emergency disposal accuracy of hydroelectric accidents are remarkably improved, the response time is effectively shortened, and the risk of repeated accidents is reduced.

The invention discloses an LCD display screen production whole-process collaborative management method and system based on the Internet of Things, particularly relates to the technical field of production process collaborative management, and is used for solving the problems of circular waiting and resource stiffness caused by lack of global resource allocation dependency relationship analysis in a complex production scene in an existing collaborative management method. Equipment state data and material position data of each process are acquired in real time, a resource allocation relation graph with production equipment as resource nodes and production tasks as task nodes is constructed, a resource access sequence of each production task is analyzed to identify a circular waiting path, and a process association network and a production flow network are combined to evaluate a blockage propagation risk. And finally, on the basis of the relief urgency degree, the production tasks are selected to carry out resource reallocation so as to realize the relief of the circular waiting path, and the overall efficiency of the production line and the resource utilization rate are improved.

The invention discloses a bridge structure risk safety identification method based on artificial intelligence, and relates to the technical field of bridge structure risk identification, and the method comprises the steps: building a topological graph model which reflects the geometric and mechanical connection relation of a bridge, taking piers, main beam sections and supports as graph nodes, and taking the physical connection between components as graph edges; structure response signals and environment load parameters in the service period of the bridge are collected, and the collected data are synchronized according to time and then mapped to corresponding nodes and edges in the topological graph model; based on the mapped data and a topological graph model, establishing a graph neural network embedded with structural dynamic constraints, and outputting the risk probability of a component corresponding to each node by jointly optimizing the consistency of a monitoring data fitting error and a physical rule; and performing causal relationship analysis on the structural response signal and the environmental load parameter, identifying a causal path between environmental interference and structural abnormality, and separating an abnormal component caused by structural degradation from the original response according to an identification result.

The invention discloses a multi-kernel system real-time task scheduling method, system and device based on scene differentiation and a storage medium, and the method comprises the following steps: carrying out the dependency analysis of a task set, and obtaining a task dependency directed graph between tasks; based on the task dependency digraph, determining a regulation and control energy level index corresponding to each task; converting the task dependency directed graph into a task situation graph; determining a scheduling sequence table of the tasks based on the task situation chart and the regulation and control energy level indexes; and allocating real-time tasks to the multi-kernel system based on the scheduling sequence table. The method can be widely applied to the technical field of task scheduling.

The invention discloses a data asset inventory and blood relationship analysis method, and belongs to the technical field of cloud computing and data asset management. The method comprises the following steps: constructing a unified asset information base by deploying a distributed acquisition agent cluster; performing unified semantic modeling on the metadata to generate a standardized data asset description file; analyzing the data processing log to construct a blood relationship map containing time sequence attributes and dynamic relationship strength; performing multi-dimensional anomaly detection and influence propagation analysis based on the atlas; and carrying out value evaluation and classification on the data assets in combination with topological importance, business value and risk level. According to the method, the problems of metadata dispersion, blood relationship static lag, abnormal influence assessment difficulty and the like in the prior art are solved, automatic checking, blood relationship dynamic tracking and asset value intelligent assessment of the data assets are realized, and the accuracy and decision support capability of data asset management are remarkably improved.

The invention relates to the technical field of software engineering, and discloses an assembly line flow adaptive configuration method and system. The method comprises the following steps: obtaining source code data of a software project and performing static analysis and feature extraction to generate structured language description data; querying and matching in a preset template library based on the structured language description data to generate a task template set; performing task dependency relationship analysis and process topology assembly on the task template set to generate initial pipeline data; and performing dynamic strategy adjustment on the execution process of the initial pipeline data by adopting the real-time context data corresponding to the software project to generate target pipeline data. Through automatic language recognition and intelligent process generation, automatic configuration of the continuous integration process is realized, and the technical problem of low automation level caused by lack of automatic recognition and adaptation capability for a project development language environment in a project continuous integration and continuous deployment technology is solved.

The invention discloses a big language model enhanced data consanguinity analysis method and device and a medium, and relates to the technical field of data consanguinity, and the method comprises the following steps: obtaining metadata and a source code, and carrying out data preprocessing on the metadata and the source code to obtain a graph database; based on the graph database, semantic task nodes are determined through data enhancement of LLM interaction; obtaining user query data, and performing query conversion processing on the user query data according to the semantic task nodes to obtain question answers corresponding to the user query data; obtaining abnormal warning data, and performing root cause tracing on the abnormal warning data to determine diagnosis context data; and performing multiple rounds of LLM diagnosis on the diagnosis context data to obtain a repair data stream. Through the method, the technical problem that an existing data blood relationship technology highly depends on manpower is solved.

The invention relates to the crossing field of mineralogy and geochemistry, in particular to a mineral and element relation analysis method for gold-bearing minerals based on image matching and data fusion and application of the mineral and element relation analysis method. Comprising the steps of ore sample processing, gold-bearing mineral image calibration through AMICS, trace element scanning through fixed-point EPMA and EDS, image-level matching and spatial data fusion and gold-bearing mineral-element relation analysis. According to the method, the AMICS mineral map and the EPMA element information are fused, relation analysis of trace elements in the gold ore and visual reconstruction of a mineral-element corresponding mechanism can be achieved, and effective data and image models are provided for microcosmic metallogenic information requirements in digital mine construction.

The invention relates to the field of system optimization, and discloses a Linux-based domestic operating system kernel cutting optimization system, which comprises a hardware baseline identification module used for acquiring a processor architecture, peripheral and bus topology, storage and network resources and firmware capability of target equipment and generating hardware baseline description; the scene portrait construction module is used for statically and dynamically observing a target application and acquiring operation portrait data including system calling, driving access and interruption events to form a scene portrait; a dependency analysis module; a multi-target constraint solving module; configuring a synthesis and construction module; and a verification and difference feedback module. According to the method, portrait analysis is carried out on calling of a target application, driving access and interruption events, a trigger threshold value is set, dependency solving is executed in a high touch module range, scene-driven cutting decision is achieved, the dependency closure calculation range is made to be consistent with an actual operation behavior, redundancy and invalid dependency are reduced, and cutting precision and kernel construction efficiency are improved.

The invention discloses an optical remote sensing ground feature relationship semantic understanding system and method in a localized environment, and the system constructs a remote sensing scene relationship analysis module, a multi-modal scene knowledge base module, a remote sensing scene graph construction module, a remote sensing scene representation module and a multi-modal sample pair construction module. Carrying out programmed modeling on the ground feature relationship by utilizing a code big language model adaptive to a domestic platform, and generating a scene graph triple; through a double-branch double-time phase comparison learning network, time sequence vision and structure knowledge are fused, and cross-modal joint representation learning is realized. According to the method, the problems of incomplete surface feature relationship expression, inaccurate modeling and lack of real-time semantics in the prior art are solved, deep optimization is carried out in the aspects of data annotation, heterogeneous computing power management and the like aiming at the localized environment, and the accuracy and integrity of semantic understanding of the surface feature relationship and the operation efficiency of the semantic understanding on a domestic software and hardware platform are remarkably improved.

According to the task cooperative processing method and device applying the distributed agent network and the medium, the task processing request sent by the user terminal is received, hierarchical semantic analysis and task dependency relationship analysis are carried out to generate the structured atomic task set, the structured atomic task set is input into the distributed agent network, and the task cooperative processing efficiency is improved. And carrying out collaborative decision making and execution intention confirmation on the atomic task units, constructing a dynamic task graph according to a decision making result and a dependency relationship descriptor, and executing the corresponding atomic task units in parallel according to a task distribution relationship in the dynamic task graph. The method comprises the following steps: exchanging task execution state information and intermediate data output in real time through a preset publishing and subscribing communication mode, generating a distributed execution track based on an exchange result, carrying out association fusion processing on execution results of atomic task units, calculating a global consistency weight, carrying out weighted aggregation on the execution results based on the global consistency weight, and generating a distributed execution track; and generating a global task processing result and sending to the user terminal. According to the invention, the task cooperative processing accuracy can be improved.

The invention discloses a method for testing and evaluating safety effectiveness of a digital therapy product for mental diseases, which relates to the technical field of mental diseases and comprises the following steps: S1, evaluating objective indexes of the digital therapy product; s2, testing a man-machine interaction feedback mode; s3, mapping measurement of product output content and patient feedback; and S4, performing real-time adjustment and optimization. According to the method for testing and evaluating the safety effectiveness of the mental disease digital therapy product, aiming at the mental disease digital therapy product, a special testing and evaluating system is constructed, objective indexes and output contents of the digital therapy product are tested, and in the interaction process of a patient and the product, the safety effectiveness of the product is tested. By detecting data such as micro-expression, electroencephalogram signals and eye movement signals of a user in real time, feedback of a patient is obtained in real time, corresponding relation analysis is conducted on the feedback and output content of a digital therapy product, therefore, the treatment effect of the product can be accurately evaluated, and the accuracy of mental disease diagnosis and treatment is improved.

The invention discloses an intelligent service starting arrangement system based on container life cycles, and particularly relates to the field of containerized starting service management. Comprising an arrangement control layer, a pre-detection layer and a life cycle management layer. The arrangement control layer comprises a starting manager module, a dependency relationship analysis sub-module, an arrangement strategy control sub-module and a starting sequence scheduling sub-module; the pre-detection layer comprises a multi-dimensional pre-detection engine, a port availability detection sub-module, a database connection verification sub-module, a configuration integrity verification sub-module and a resource ready state check sub-module. The life cycle management layer comprises a container life cycle monitoring module, a construction stage management sub-module, an operation state tracking sub-module and a stop process control sub-module. A multi-dimensional pre-detection engine, DAG-based complex dependency relationship management, intelligent fault classification and adaptive recovery, container life cycle full-stage perception and dynamic starting of an arrangement strategy are realized.

The invention relates to an intelligent analysis method and system for PCBA production data, and relates to the technical field of PCBA data analysis, and the method comprises the steps: collecting real-time production data in a PCBA production process, associating production batches, and forming a real-time production data sequence; constructing a parameter-defect knowledge graph based on historical production data; after a production defect signal is received, taking a production batch as an index, extracting corresponding real-time production data, taking a corresponding defect type node as a starting point for reverse reasoning, and calculating the contribution degree of each upstream node to the defect type node; and positioning a root cause node, determining a key process parameter node in combination with the type, and outputting a process adjustment suggestion. The method solves the problems that traditional PCBA defect analysis depends on experience judgment and lacks causal correlation analysis among different production key elements in production data, so that root cause positioning is fuzzy, the efficiency is low, passive detection is mostly carried out after defects occur, prevention is difficult to carry out in advance, and then the production quality control effect is affected.

The invention discloses a data governance strategy dynamic execution system based on active metadata and AI recommendation, and relates to the technical field of data governance, and the system comprises an active metadata sensing layer which is used for actively monitoring and capturing metadata real-time changes of various data sources, and reporting the metadata real-time changes in a standardized data format; the intelligent analysis decision-making layer is used for receiving the standardized metadata, taking a preset knowledge base as a basic support, performing metadata association analysis by relying on an AI recommendation module, generating a governance strategy and converting the governance strategy into an executable instruction; the strategy dynamic execution layer is used for receiving an executable instruction, performing task splitting, priority ranking and dependency management through a workflow scheduler, and calling a corresponding executor to automatically complete execution of a governance strategy; and the unified management and control portal layer is used for providing a visual interface for an administrator so as to carry out treatment strategy approval, global monitoring, blood relationship analysis, audit query and manual intervention. According to the invention, intelligent generation and automatic execution of the data governance strategy can be realized.

The invention discloses an urban ecosystem service tradeoff and cooperative relationship analysis method and device. The method comprises the following steps: dividing a target region according to different spatial scales to obtain sample sets corresponding to the spatial scales respectively; acquiring basic geographic information data of the target area in different time periods; according to the basic geographic information data, calculating service evaluation results of each ecological system service in the target area and each sample in different time periods; according to a service evaluation result, calculating a pairwise trade-off cooperative relationship of each ecological system service under a plurality of spatial scales and time sequences; the samples are clustered according to the service evaluation results of the samples to obtain a plurality of service clusters, and the service clusters are used for representing overall tradeoff cooperation features of multiple ecological system services; and determining a management strategy of each ecological system service according to the pairwise trade-off cooperation relationship and the overall trade-off cooperation characteristics. According to the invention, tradeoff and collaboration characteristics of each ecological system service can be identified under multiple spatial scales and multiple time sequences.

The invention discloses an intelligent operation and maintenance alarm causal relationship analysis method based on BERT. According to the method, the semantic similarity of core switch link interruption and access layer equipment offline is improved by 40% through semantic modeling upgrading and joint coding of equipment types and alarm contents, and compared with an independent coding scheme, the misjudgment rate is remarkably reduced. Through organic combination of deep semantic modeling and dynamic time sequence analysis, the crossing of the intelligent operation and maintenance alarm causal relationship from rule driving to data intelligent driving is realized, and an efficient analysis tool is provided for intelligent operation and maintenance of a complex system. According to the method, accurate causal modeling of complex system alarm data is realized through semantic similarity calculation and multi-dimensional weight dynamic fusion under time sequence constraint, and the method is widely applied to large-scale distributed system fault analysis and root cause positioning scenes in industries such as power systems, communication networks, internet platforms, cloud computing infrastructures and industrial Internet of Things.

The invention relates to a data consanguinity analysis method, device and equipment, a medium and a product in the technical field of data processing. The method comprises the following steps: extracting a source path and a processing mode of a field according to an original data script in a data blood relationship link, and constructing a field-level data blood relationship map; matching field contribution degree levels based on the field processing mode and outputting a field level contribution score matrix; and based on the field level contribution score matrix and a preset aggregation rule, outputting a contribution degree result of the field, table level comprehensive contribution and a data blood relationship network. According to the embodiment of the invention, factors such as the source path and the processing mode of the field are combined, the table-level comprehensive contribution value is further summarized and generated through the constructed field-level basic weight model, and quantitative evaluation of the key node in the data source path is realized.

The invention belongs to the technical field of speech analysis, and discloses a spoken English pronunciation quality evaluation method based on multi-modal speech feature analysis, which comprises the following steps: acquiring a spoken English speech signal of a target user, and performing multi-domain decomposition on the speech signal to obtain multi-modal speech features; performing time-frequency domain corresponding relation analysis and feature extraction on the multi-mode speech features to obtain a pronunciation detail feature sequence; performing multi-scale matching on the pronunciation detail feature sequence and a preset standard pronunciation template, constructing a multi-dimensional representation model based on a multi-scale matching result, and calculating a fine-grained quality score of a phoneme unit in each multi-dimensional representation model in combination with rhythm and rhythm parameters in the multi-modal speech features, the rhythm coherence score and the overall fluency score are fused to generate a comprehensive pronunciation quality evaluation result and a visual diagnosis report of pronunciation deviation; the oral English pronunciation evaluation method realizes comprehensive and refined evaluation of oral English pronunciation, and provides a scientific guidance basis for personalized language learning.

The invention provides a data consanguinity management system supporting multi-source heterogeneous data dependency analysis. The system comprises an automatic data analysis module, a consanguinity collecting and updating module, a fine-grained dependency analysis module and an interactive consanguinity analysis module. Wherein the automatic data analysis module is used for acquiring multi-source heterogeneous data and automatically analyzing the multi-source heterogeneous data to generate target standardized metadata; the blood relationship acquisition and updating module generates blood relationship data through static analysis, dynamic analysis and message queue acquisition; the fine-grained dependency analysis module performs dependency analysis from the table level and the field level to generate fine-grained dependency analysis data; and the interactive blood relationship analysis module performs interactive tracing based on the data and constructs a visual interface. Through cooperation of the four modules, automatic processing, fine-grained dependency analysis and real-time dynamic updating of multi-source heterogeneous data are achieved, and the accuracy and efficiency of data management are improved.

The invention discloses a data blood relationship management analysis method, device and equipment and a storage medium, and the method comprises the steps: obtaining original data, carrying out the cleaning, conversion and standardization processing of the original data, and constructing a blood relationship model; storing the blood relationship model based on a JanusGraph graph database, and performing blood relationship analysis through a Gremlin query statement to obtain a blood relationship analysis result; when the change of the data entity is detected, the incremental update of the affected blood relationship is automatically triggered, so that the update delay of the blood relationship information can be effectively compressed from the traditional hour level to the minute level, and the technical bottlenecks of blood relationship information islands, low analysis efficiency and insufficient large-scale data expansibility are solved; the timeliness and the accuracy of data change influence evaluation and the data governance decision support capability are remarkably improved, and the speed and the efficiency of data blood relationship management analysis are improved.

The invention relates to the technical field of intelligent power grids, in particular to an electric power measurement data intelligent management and analysis system which comprises a time registration module, a path verification module, a behavior recognition module, an exception classification module and a permission desensitization module. According to the method, the consistency of a data acquisition time sequence is realized by utilizing dynamic alignment of measurement point signal features, the capabilities of voltage anomaly identification and path traceability between nodes are enhanced in combination with multi-measurement-point voltage change direction and connection relation analysis, peak valley feature extraction and behavior mode linkage are adopted, and the reliability of the system is improved. According to the method, the identification accuracy and the real-time performance of power consumption behavior abnormity are improved, characteristic classification and standard comparison of power abnormity events are used, automatic classification of abnormity types is more accurate, data type sensitivity and authority linkage is combined, multi-role data desensitization control is optimized, the information security risk in the data management process is reduced to a lower level, and the data management efficiency is improved. And full-flow data link closed loop and automatic management and control are realized.

The present application relates to the field of food and beverage technology, and in particular to a food and beverage network sales trend prediction model construction system and method based on multi-source data fusion and deep learning. It includes a data acquisition unit, a data processing unit, a model construction unit, the model construction unit is used for constructing a deep learning prediction model, adopts an improved LSTM-Transformer fusion algorithm, realizes nonlinear mapping modeling of food and beverage sales trend through integrating time sequence feature modeling and global dependence relationship analysis technology, a model training and verification unit, and a prediction output unit. The present application integrates network sales platform data, social media sentiment text, meteorological information and industry information and other multi-source data, extracts time dimension features, text sentiment features and price elasticity-weather influence and other cross-correlation features by combining feature engineering technology, fully covers the influencing factors of food and beverage sales, and solves the problem of single data dimension in the traditional scheme.