1109 results about "Relationship analysis" patented technology

The invention belongs to the technical field of bridge diagnosis, and discloses a bridge crack intelligent diagnosis system based on multi-modal data fusion. By fusing multi-source data such as visual images, sound wave detection and vibration signals, comprehensive perception and characterization of crack features are realized; constructing a bridge crack characteristic spectrum diagram by adopting a cross-modal feature extraction and heterogeneous feature coding technology; generating a crack evolution situation map based on space-time correlation analysis and knowledge graph construction; the robustness of the system in a complex environment is improved through environmental adaptability feature enhancement and multi-scale characterization; constructing a bridge safety risk hypergraph in combination with multi-dimensional risk analysis and multi-agent collaborative diagnosis; analyzing and revealing a crack evolution mechanism by applying a causal relationship; and finally, through dynamic fusion and uncertainty quantification, a crack intelligent diagnosis comprehensive report is generated. According to the system, the limitation of traditional single-mode diagnosis is broken through, and dynamic prediction and accurate risk assessment of fracture evolution are realized.

The invention relates to a power equipment fault prediction method based on multi-source heterogeneous data and a twinborn model, and the method comprises the following steps: S1, obtaining multi-modal data related to the operation of power equipment, constructing a joint feature encoder through an attention mechanism in deep learning, and carrying out the joint modeling of multi-modal information, and obtaining a joint feature representation; s2, based on joint feature representation, adopting a dynamic twin model of self-supervised learning and fusion reinforcement learning, and dynamically updating the equipment health state; s3, constructing a complete-cycle prediction framework based on Transform, capturing long-term dependency and dynamic change characteristics in equipment operation, and identifying trigger factors of faults in combination with causal relationship analysis; and S4, based on the equipment health state and the fault prediction result, optimizing equipment operation parameters by adopting an intelligent operation and maintenance optimization engine, and dynamically generating an optimal operation and maintenance plan in combination with a genetic algorithm. The reliability and the operation efficiency of the power equipment are remarkably improved.

The invention discloses a pharmaceutical quality traceability decision-making method and system based on a dynamic knowledge graph, and relates to the technical field of pharmaceutical quality control. The method comprises the following steps: defining a dynamic knowledge graph containing quality-related entities, wherein the dynamic knowledge graph at least comprises a quality event entity, a production element entity and a time sequence causal relationship between the quality event entity and the production element entity; capturing production data related to quality in real time, and generating event data with a quality rule identifier based on a preset triggering condition; and carrying out dynamic state modeling on the production element entity to generate a state vector containing a time sequence attenuation characteristic and a quality influence weight. By constructing the dynamic knowledge graph and designing the time sequence causal reasoning algorithm, real-time capture, dynamic modeling and causal relationship analysis of production data are achieved, the defects of the traditional technology in real-time performance and relevance are overcome, and an efficient and accurate intelligent solution is provided for pharmaceutical quality traceability.

The invention provides an electric power Internet of Things security response method and system based on risk assessment, and the method comprises the steps: obtaining a heterogeneous security data set, and carrying out the data processing of the heterogeneous security data set based on a preset data processing rule, and obtaining a standardized data set; according to ATTamp; according to a CK causal relationship analysis method, pattern matching is carried out on the standardized data set, and whether a potential threat scene exists or not is recognized; if the potential threat scene exists, performing time sequence correlation analysis on the threat scene according to a preset correlation graph to obtain a correlation analysis result; performing link analysis on the correlation analysis result to obtain a key attack path; inputting the key attack path and the Internet of Things terminal access behavior data in the standardized data set into a preset abnormal behavior classification model, so that the abnormal behavior classification model judges whether an abnormal behavior exists or not; if it is judged that the abnormal behavior exists, safety response is carried out based on preset response measures, and the operation safety of the Internet of Things terminal is improved.

The invention provides a private domain social relation mining method and system based on a graph neural network, and relates to the technical field of social relation mining, and the method comprises the steps: carrying out the entity alignment and relation mapping of historical behavior data of a private domain platform user and an external knowledge base through employing a knowledge graph technology, generating user behavior feature data, and constructing a user relation graph; processing the user data by using a federal map neural network model containing a space-time attention layer to obtain a user node representation vector; and according to the user node representation vector, calculating a social influence index and a relationship strength index between the users, determining a final social circle layer structure by adopting a spectral clustering method and causal intervention analysis, and generating a social relationship analysis report. According to the method, complex user social relations in a private domain platform can be effectively mined, the accuracy and stability of social circle layer identification are improved, and technical support is provided for precise marketing and personalized recommendation.

The invention relates to a membrane pool optimization control method, system and equipment based on multi-agent collaborative decision-making and a medium, and the method comprises the steps: abstracting each membrane pool into an agent, building a state space and an action space, and creating a global reward function; applying hard constraint and dynamic soft constraint to the action space to obtain a limited action space; an agent relation analysis layer is embedded in the evaluation network, behavior characteristics and cluster cooperation intensity distribution of all agents are obtained through high-dimensional characteristic mapping and a multi-head attention interaction mechanism, and global value evaluation is generated by combining rewards of a global reward function and through cooperation intensity distribution correction; and synchronously optimizing strategy parameters of each agent based on global value evaluation by adopting a double-stage collaborative training framework, and injecting controllable exploration noise to drive collaborative convergence of the membrane pool cluster. The purposes of saving energy, reducing emission, reducing cost, improving efficiency, reasonably distributing productivity and prolonging the average service life of the membrane module are achieved, and water plant operation is obviously promoted to develop towards the intelligent and sustainable direction.

The invention relates to the technical field of part typesetting, in particular to a nesting typesetting mixed common-edge analysis method and system based on laser cutting. The method comprises the following steps: obtaining initial typesetting part structure data, and carrying out part common-edge relation analysis to obtain part common-edge relation data; common-edge path optimization is carried out according to the initially-typeset part structure data, and optimized part cutting path data is obtained; performing laser cutting simulation based on the optimized part cutting path data to obtain laser cutting simulation data; performing typesetting heating balance optimization according to the laser cutting simulation data so as to obtain optimized part typesetting data; and performing cutting waste rate evaluation on the optimized part typesetting data to obtain typesetting cutting waste rate data, and performing minimum cutting waste rate optimization on the optimized part typesetting data based on the typesetting cutting waste rate data to obtain optimal part typesetting data. The cutting efficiency and precision are improved.

The invention relates to the technical field of data monitoring, in particular to an automatic monitoring system for plasma meltallizing coating process data, which is characterized in that a parameter association relationship analysis module performs association analysis based on real-time parameters in a plasma meltallizing coating process, identifies an association relationship among the parameters and generates a process parameter association analysis result; according to the method, through multi-dimensional correlation analysis and path screening of the technological parameters, key factors influencing the coating quality and action links of the key factors are defined, and the abnormal fluctuation state and the transfer path can be accurately predicted in combination with parameter priority dynamic adjustment and real-time data feedback, so that the stability and continuity in the technological process are ensured. In the scheme, a dynamic weight distribution strategy is adopted, so that key parameters in an abnormal state occupy a higher priority position, parameter distribution is rebalanced when the normal state is recovered, and excessive interference of a single parameter on the whole process is avoided.

The invention discloses a text knowledge automatic extraction and mapping method based on deep learning, which optimizes a semantic understanding algorithm by combining a deep learning model, enhances the perception ability of the model for the internal structure and external relationship of a text, realizes comprehensive extraction and detailed relationship analysis of equipment fault knowledge, and utilizes a knowledge mapping technology to realize automatic extraction and mapping of equipment fault knowledge. According to the method, an automatic graph construction process is implemented to improve the query efficiency and analysis capability of the graph, and the method comprises the following steps: S1, performing structured processing on an original document, segmenting the original document into independent case units, and extracting key information related to an equipment fault; s2, key entities in the document and mutual relations of the key entities are recognized and marked; and S3, based on the extracted entities and relationships, constructing and updating a knowledge graph of the fault case. The system has high automation, intellectualization, expandability and modular design, can be easily integrated into an existing industrial informatization system, and assists an enterprise in realizing intellectualized transformation and upgrading.

The invention relates to the technical field of thermal power production, artificial intelligence and time sequence analysis, in particular to a measuring point time sequence anomaly analysis method and system based on a multi-modal large model. Calculating a cross-modal feature similarity weight based on a self-attention mechanism to obtain a fusion feature vector; comparing the fusion feature vector with the normal mode feature representation through a self-supervised learning algorithm to realize anomaly detection, wherein a result comprises anomaly time, a fault image area and a text keyword; and a result is input into a multi-modal large model, cross-modal attention mechanism association data is utilized, fault reasoning and causal relationship analysis are carried out, a visual diagnosis report is generated, and then the accuracy of measuring point screening and time sequence anomaly analysis is improved.

The invention provides a primitive recognition method and device for a wiring diagram, electronic equipment and a storage medium, and relates to the technical field of image recognition, and the method comprises the steps: constructing a training data set according to the wiring diagram; training the residual network model and the semantic segmentation network model by adopting the training data set to obtain a primitive segmentation network model; detecting a to-be-identified wiring diagram according to the primitive segmentation network model to obtain a primitive mask area; and extracting edge point coordinates according to the primitive mask area to obtain a primitive segmentation result. According to the method, the advanced semantic segmentation network technology is combined, pixel-level fine segmentation is carried out on the primitive area of the line graph, the positioning detection and recognition capability of all types of primitives of the line graph is remarkably improved, and powerful support is provided for subsequent tasks such as primitive relation analysis and connection line optimization.

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 AGV scheduling control, and discloses an AGV scheduling control method and system for automobile carrying, and the method comprises the steps: carrying out the data collection of a plurality of AGVs in a stereo garage, and obtaining a state information data set; performing causal relationship analysis on each AGV motion track to obtain a dynamic space-time causal reasoning graph; performing trajectory feature extraction and operation state prediction through a time convolutional neural network to obtain an operation trajectory prediction sequence of each AGV; carrying out deadlock loop identification in a local adjacent AGV and global garage range to obtain a deadlock risk state information matrix; according to the multi-AGV cooperative scheduling optimization method, the multi-AGV vehicle carrying task allocation and the driving path selection are subjected to anti-deadlock optimization to obtain a multi-AGV cooperative scheduling optimization strategy, the efficient cooperative execution of the multi-AGV vehicle carrying task in the stereo garage is realized, and compared with the prior art, the conflict and waiting time between the AGVs can be obviously reduced, and the operation efficiency and the service quality of the stereo garage are improved.

The invention relates to the technical field of equipment monitoring and management, and particularly discloses an AI oT-based distributed industrial equipment monitoring and management system and method, and the system comprises a distributed equipment monitoring module which collects operation parameters, performance indexes and environment data in real time through a plurality of types of sensors disposed on industrial equipment, a dynamic hierarchical edge architecture is utilized to process data and evaluate a static health state, and task intelligent layering and cloud collaboration are supported. And the multi-modal perception analysis module generates an equipment association graph based on the collaborative task, extracts multi-modal features by using a cross-node collaborative algorithm, and realizes dynamic health assessment by combining a CNN-LSTM fusion model and equipment implicit dependency relationship analysis. And the distributed equipment management module fuses equipment dynamic and static health data, cooperates with a task association graph and a fault tracing model, and realizes efficient resource configuration and fault prevention. The system guarantees intelligent monitoring and management of the whole life cycle of industrial equipment through a cooperation mechanism of edge intelligence and cloud deep analysis.

The invention relates to the technical field of heart data analysis, in particular to an AI (artificial intelligence)-based heart disease prediction system, which comprises a feature relationship modeling module for mapping each type of information into a multi-dimensional feature space based on collected heart data, analyzing the condition correlation degree between feature nodes, connecting the feature nodes through correlation information, and establishing a model; and quantifying a linkage effect between the features to obtain a feature condition dependency relationship analysis result. According to the method, various types of heart health data are noded by constructing a multi-dimensional feature space, and a correlation structure is established according to condition correlation among data features. And a synergistic effect and an interdependence relationship among the features are disclosed. By analyzing the joint state probability of the feature nodes, the linkage effect of the feature combination on the overall risk is quantified, and the analysis depth of the disease risk is enhanced. In addition, by comparing the independent state probability of a single feature with the joint state probability of the feature combination, a feature combination that makes a significant contribution to the risk can be identified.

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 micro-pressure oxygen cabin control system and a control method thereof, and relates to the technical field of medical treatment, and the system comprises an oxygen cabin operation monitoring module which is used for monitoring and obtaining information data of a micro-pressure oxygen cabin in real time during working, comprise the running state of the micro-pressure oxygen cabin, the air pressure value in the oxygen cabin, the oxygen concentration in the oxygen cabin and the vital sign state of a user in the oxygen cabin; and a data processing and analyzing module. According to the micro-pressure oxygen cabin control system and the control method thereof, a data processing and analysis module, especially a coupling relation analysis unit, is introduced, an oxygen and pressure dynamic change model is constructed by utilizing an algorithm, the safety of equipment and a user is protected, and the safety of the equipment and the user is improved through an intelligent and automatic control means. The problems of an existing micro-pressure oxygen cabin are effectively solved, the treatment effect and the use safety are improved, and the more advanced and reliable micro-pressure oxygen cabin control system and the control method thereof are provided for the medical field.

The invention discloses an outbound intelligent routing service system, relates to the technical field of routing service, and aims to solve the problem that data quality monitoring is incomplete in a calling process. According to the method, the calling queues are sequenced, and the outbound request is initiated according to the priority, so that the high-priority task can be preferentially processed. The call-out efficiency is improved, the overall service performance is improved, when a quality problem occurs in the call process, active adjustment is carried out by adopting a jitter buffering technology, the intelligent adjustment mode can quickly respond and improve the call quality, and when the quality problem is monitored, future call parameters of the client are automatically adjusted, so that the call-out efficiency of the client is improved. The adaptive adjustment strategy can ensure that the service quality of important customers is always kept at a relatively high level, the data analysis stage comprises descriptive analysis, causal relationship analysis and trend analysis, and the analysis methods can deeply mine information and rules in data from different angles.

The invention relates to the technical field of natural language processing, in particular to a hypergraph neural network-based multi-modal entity relationship extraction method, which comprises the following steps of: acquiring a text-image pair and inputting the text-image pair into a preset hypergraph construction model, acquiring node characteristics and a multi-modal hypergraph structure of the text-image pair, the hypergraph structure comprises a group of hyperedge sets, each hyperedge in the hyperedge sets can be connected with a plurality of nodes at the same time, and the number of the nodes connected with the hyperedges is not limited; the node features and the multi-modal hypergraph structure are input into a preset hypergraph neural network model, the multi-modal entity relation in the text-image pair is output, and the hypergraph neural network model is used for analyzing hypergraph node information propagation from the semantic and contextual relation and giving corresponding weights to semantic information between modals in combination with an attention mechanism. According to the method, the multi-modal information can be effectively fused, and the entity category and the entity relationship of the multi-modal information can be identified.

The invention discloses a decorative paper weather resistance evaluation method and system, and relates to the technical field of weather resistance evaluation, and the method comprises the steps: determining the structural composition information and target application space of target decorative paper; determining an aging influence factor of the decorative paper; determining a weather resistance evaluation index; performing relation analysis, and establishing a weather resistance evaluation model; establishing a closed bias feedback layer and an open bias feedback layer; and performing dynamic characteristic analysis, inputting a dynamic characteristic analysis result into the optimized weather resistance evaluation model for analysis, and generating a target weather resistance evaluation result corresponding to the weather resistance evaluation index. The technical problems that in the prior art, decorative paper weather resistance evaluation is difficult to consider different scenes and the accuracy is insufficient are solved, the effect of accurately evaluating the weather resistance of the decorative paper is achieved, and the technical effects of improving the accuracy and reliability of aging performance evaluation of the decorative paper in different application scenes are achieved.

The invention relates to the technical field of water level and water surface relation analysis, in particular to a lake water level and water surface relation analysis method based on a DEM and a remote sensing image, which comprises the following steps of: based on DEM and remote sensing image data, analyzing a space relation between measured data and the DEM, correcting abnormal elevation and adjusting original data, screening boundary points with small elevation difference, and calculating the water level and water surface relation of a lake; and performing statistics on water surface areas under different water levels to obtain a water level area change sequence. According to the method, through cooperative adjustment and automatic fusion based on multi-source spatial data, through elevation correction, spatial anomaly screening and time sequence change tracking, the consistency of spatial data is improved, elevation anomaly information is dynamically corrected, the discrimination capability of a water body spatial distribution boundary is enhanced, and dense water surface change information extraction is realized based on spatial overlapping data; the space node and area change trend under the water level grading condition can be automatically concluded, and the dynamic response characteristics of the lake space structure can be systematically reflected.

The invention provides a bank anti-money laundering data model construction method based on privacy computing, and the method comprises the steps: recognizing node role diversity and topology mutation characteristics through an encryption topology framework, extracting transaction paths and a multi-layer nesting relation between nodes, analyzing the role distribution of a transaction main body, and obtaining a dynamic path mutation set; according to the dynamic path mutation set, accounts with short use cycles and accounts with frequently changed transaction subjects are screened out and marked as potential suspicious transaction subjects, and a preliminary suspicious transaction subject list is obtained; identifying transaction records of the preliminary suspicious transaction subject list, screening out high-risk transaction subjects with false registration information or key identity information missing, and obtaining a high-risk transaction subject subset; and counting a plurality of small transactions of the high-risk transaction subject subset, identifying distribution characteristics and multi-layer transfer paths of the small transactions, and extracting fund splitting modes and hidden flow direction characteristics to obtain a high-risk transaction path characteristic graph.

The invention relates to a multi-dimensional data blood relationship tracking method. The method comprises the following steps: S1, data blood relationship meta-information collection; s2, constructing a multi-dimensional blood relationship; s3, blood relationship analysis; s4, an intelligent early warning mechanism; s5, effect verification is realized; a multi-dimensional data blood relationship tracking system comprises a meta-information acquisition module, a blood relationship construction module, a blood relationship analysis module, an intelligent early warning module and an effect verification module. The method has the advantages of high blood relationship tracking accuracy and high analysis capability.

The invention relates to the technical field of big data management and data analysis, and discloses a visual data platform full-link data flow traceability method, system and device and a medium, and the method comprises the steps: monitoring a job scheduling process submission event, analyzing the input and output configuration information of each job node, converting the information into a data flow model, and storing the data flow model in a traceability database; static analysis is conducted on node scripts and codes, and analysis results are stored in a traceability database; generating a unique identifier for each data record, and recording related information in a data circulation process; and writing the data flow relationship into a graph database Neo4j through a Kafka asynchronous message, and performing data traceability query by a user through a visual interface. The method has the beneficial effects that the data quality and safety are improved, and the limitation and complexity of a mainstream open source tool in the aspect of a data blood relationship analysis function are solved.

The embodiment of the invention provides a knowledge graph construction method and device, and the method comprises the steps: carrying out the knowledge point analysis of a target document through a knowledge point analysis model, obtaining a plurality of initial document knowledge points in the target document, carrying out the relation recognition of the plurality of initial document knowledge points, and carrying out the relation recognition of the plurality of initial document knowledge points; obtaining an initial association relationship between the initial document knowledge points; performing knowledge point merging on the plurality of initial document knowledge points to obtain a plurality of target document knowledge points, and performing relationship analysis on the plurality of target document knowledge points and the initial association relationship by using a relationship analysis model to obtain a target association relationship among the target document knowledge points; based on the multiple target document knowledge points and the target association relationship among the target document knowledge points, constructing a target knowledge graph; the problems of low efficiency and poor accuracy in the knowledge graph construction process are avoided, and efficient and accurate construction of the knowledge graph is realized.

The embodiment of the invention discloses a data consanguinity analysis method and device, a storage medium and electronic equipment, and the method comprises the steps: firstly, obtaining structured query processing information corresponding to a first target table to which a first target field belongs for the initial first target field, and then obtaining structured query processing information corresponding to a second target table to which the first target field belongs according to the structured query processing information; judging whether a termination condition is met at present or not, if not, performing upstream source disassembly on the structured query processing information to obtain the latest structured query processing information corresponding to the upper layer, and so on, if yes, constructing a data blood relationship tree, then explaining the data blood relationship tree to generate corresponding prompt information, and sending the prompt information to the upper layer of the structured query processing information to the upper layer of the structured query processing information to the upper layer of the structured query processing information. And inputting the prompt information into the trained data blood relationship analysis large model to obtain a data blood relationship analysis result. According to the scheme, the intricate data blood relationship can be linearized and standardized, and the risk interpretation is humanized.

The invention relates to the technical field of food and beverage, 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. Comprising a data acquisition unit; a data processing unit; the model construction unit is used for constructing a deep learning prediction model, and an improved LSTM-Transform fusion algorithm is adopted to realize nonlinear mapping modeling of the food and beverage sales trend by integrating time sequence feature modeling and a global dependency relationship analysis technology; a model training verification unit; and a prediction output unit. According to the method, multi-source data such as network sales platform data, social media emotion texts, weather information and industry information are integrated, cross-correlation features such as time dimension features, text emotion features and price elasticity-weather influence are extracted in combination with a feature engineering technology, influence factors of food and beverage sales are comprehensively covered, and the sales quality is improved. The problem that a traditional scheme is single in data dimension is solved.

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 discloses a tidal reach water level and flow relation analysis method based on tidal level harmonic analysis. The method comprises the steps of collecting actually measured water level data, basin river topography and digital elevation data of a tidal reach hydrologic station; characteristic parameters and water surface elevation change characteristics of all partial tides of the tide level of the tidal river reach are obtained on the basis of tide level harmonic analysis, and then independent tide flow and river runoff flow are obtained; establishing a tidal reach one-dimensional hydrodynamic model, and analyzing a flow change process and a water level flow change relation under different water level change scenes; and analyzing the contribution degree of the tidal flow and the river runoff to the actual flow and the change process by utilizing a multivariate regression analysis and drift diameter analysis method to obtain the change characteristics of the water level and flow relationship of the tidal reach. The method has the advantages that the water level and flow relation of the tidal reach can be rapidly analyzed, complex estuary hydrodynamic process simulation is not needed, and the method is also applicable when actually measured flow data is lacked.

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.