814 results about "Stream processing" patented technology

The invention provides a financial statement generation method and system based on cloud computing, and the method comprises the steps: collecting multi-source data of an internal business system of an enterprise through a centralized data integration platform based on a cloud computing environment, carrying out the classified storage, and constructing a data set; verifying the data set, and obtaining key index fields related to financial statement generation in the verified data set; performing grouping calculation on the key index fields by adopting a data aggregation algorithm to form a financial data summarization result; dynamically mapping the financial data summarization result by adopting a report generation engine, and matching with a preset financial report template to obtain a report data framework; and performing secondary verification and encryption on the report data framework to obtain a final financial report. According to the technical scheme, automation, real-time performance and unmanned performance of financial statement generation are realized through full-link innovation of edge intelligent acquisition, streaming processing, incremental aggregation and zero-trust security.

The invention discloses an intelligent data stream processing system based on Flink and an implementation method thereof, and belongs to the technical field of distributed streaming data processing, and the implementation method comprises the following steps: a data source access layer uniformly accesses multi-source data; the entity monitoring layer captures service entity change in a non-intrusive manner based on JPA, generates a standardized message body and asynchronously delivers the standardized message body to double channels; the unified event model layer maps the data into a standardized event and expands the standardized event; the Flink stream processing engine layer is used for complex event processing, state management and window calculation, and supports declarative pipeline definition and dynamic construction; the anomaly detection layer performs multi-dimensional anomaly detection; the distributed task scheduling layer schedules tasks according to a DAG model, and realizes load balancing through hotspot splitting and state transition; and the result storage and visualization layer adopts a cold and hot separation strategy to store data, and provides a dynamic visualization interface. The real-time performance, the consistency and the intelligent level of data processing are remarkably improved, and the development and maintenance cost is reduced.

The invention provides a financial data intelligent quality inspection method and system, and the method comprises the steps: S1, accessing a real-time transaction data flow through a dynamic rule engine, and enabling the dynamic rule engine to dynamically adjust the rule weight through a Bayesian network and reinforcement learning hybrid model; s2, calling a multi-modal LLM verification framework, performing joint semantic analysis on the text, the image and the time series data, and generating a risk early warning signal; s3, identifying a cross-entity risk path based on the financial knowledge graph, and converting the identified risk path into a structured risk report; and S4, a closed loop iteration system is formed according to the weight of the early warning feedback optimization rule. According to the method, full-life-cycle quality management and control of financial transactions can be realized through technical collaboration of real-time data stream processing, multi-dimensional semantic verification and cross-entity risk tracking.

The invention relates to the technical field of computer video processing, and discloses a low-delay video stream real-time processing method and device, and the method comprises the steps: obtaining original video stream data, and processing the original video stream data through employing a lightweight motion prediction method; processing the macro block data set and the predicted coding configuration parameter by adopting multi-thread assembly line coding to obtain a coded data block; establishing a data transmission mechanism to perform data flow control on the unified memory access interface; a heterogeneous task scheduling strategy is adopted to distribute task division results; a lightweight neural network is adopted to carry out parameter adaptive adjustment, and an optimized video stream processing result is obtained; according to the method, a zero-copy data transmission technology is adopted, and optimal configuration and efficient utilization of computing resources are achieved.

The invention provides an urban space intelligent processing method based on multi-modal fusion, and the method comprises the steps: taking multi-modal data as input, and constructing a unified data stream processing and feature alignment mechanism; a physical space is used as a core framework, and the multi-modal data is converted into a space behavior graph with space-time position semantics; constructing an entity attribute-relation type-influence weight ternary interaction model on the basis of an interaction layer of the spatial behavior map, and analyzing a human, object and environment ternary interaction relation in the city and the park based on the ternary interaction model; designing a space intelligent engine with time sequence modeling and dynamic prediction capabilities; and constructing a task processing system. According to the invention, through deep combination of multi-modal fusion and a space intelligent technology, full-link upgrading of urban space from data perception to intelligent decision is realized, and powerful technical support is provided for fine management, efficient operation and safety guarantee of complex space scenes.

The invention relates to the technical field of edge computing, and discloses an intelligent edge computing co-processing system based on an integrated circuit, which comprises a heterogeneous computing cluster module, a hardware computing unit set, a multi-core control processor based on RISC-V, a programmable pulse tensor computing array and a reconfigurable engine oriented to streaming processing, according to the intelligent edge computing cooperative processing system based on the integrated circuit, zero-delay data exchange is realized through silicon intermediate layer integration of the heterogeneous computing cluster module and a snakelike data channel, and traditional bus arbitration delay is eliminated through real-time operation code analysis and optimal optical communication path mapping of the hardware task routing matrix (TRF) module; the priority path distribution of the task scheduling subsystem is synchronously coordinated with the time-sensitive task, so that the collaborative efficiency of the computing unit is improved in multiple dimensions, the non-blocking transmission of the high-priority task is ensured, and the effect of enhancing the overall processing capability and response speed of the intelligent edge computing is achieved.

The invention provides a parallel computing method and device, electronic equipment and a storage medium, and relates to the technical field of parallel computing, and the method comprises the steps: carrying out the first protocol operation of a target tensor based on each computing core in each stream processor cluster, and generating a data block containing the computing result of each computing core; writing a data block generated by each stream processor cluster into a shared cache; under the condition that each stream processor cluster completes the first protocol operation, reading a data block written by each stream processor cluster from the shared cache; and executing a second protocol operation on the data block read from the shared cache to generate a calculation result of the target tensor. According to the method and device provided by the invention, the parallel architecture and memory access characteristics of the artificial intelligence chip can be better matched, the unnecessary calculation delay and synchronization overhead of the cross-flow processor cluster in the parallel calculation process are reduced, the bandwidth utilization rate of the shared cache is improved, and the overall performance and calculation efficiency of parallel calculation are remarkably improved.

The invention discloses an equipment digital inspection evaluation method and system based on big data, and belongs to the field of artificial intelligence, and the method comprises the steps: S1, collecting multi-source heterogeneous data; s2, unifying timestamps by adopting a time synchronization technology, and realizing accurate alignment of multi-source data; s3, cleaning and structuring data by using a distributed stream processing framework, eliminating invalid redundancy, and completing coding standardization; s4, constructing a graph neural network model based on equipment topology, and extracting a state feature vector; s5, combining with historical fault labels, applying a reinforcement learning training evaluation strategy, and improving prediction accuracy and inspection efficiency; s6, dynamically sorting the inspection tasks according to a strategy, and determining a priority and a path in combination with the risk and the dependency relationship; s7, generating a digital inspection evaluation report fusing real-time analysis and a model result; and S8, performing digital signature and timestamp registration through an intelligent contract uplink evidence storage, and ensuring that a result is verifiable, traceable and non-tampered. The beneficial effects are that inspection efficiency is improved and data safety and reliability are ensured.

The invention provides an industrial equipment real-time health monitoring system based on multi-modal sensor fusion, which comprises a multi-modal sensor module for acquiring equipment operation parameters, and an edge computing gateway for cleaning, preprocessing, feature extraction and preliminary anomaly detection of sensor data, the MQTT data acquisition and transmission module is used for receiving data released by the edge computing gateway and forwarding the data; the Kafka real-time stream processing module is used for receiving mass real-time data streams forwarded by the MQTT; the data fusion and feature engineering module is used for enabling the data transmitted by the Kafka real-time stream processing module to form a unified multi-dimensional feature vector; the LSTM time prediction module is used for carrying out time sequence prediction by respectively utilizing the fused multi-modal feature data, the random forest classification module is used for carrying out classification diagnosis, the auto-encoder anomaly detection module is used for carrying out abnormal behavior identification, and the digital twinning module is used for carrying out visual display and interactive query through real-time data streams. According to the system, comprehensive and accurate evaluation and fault early warning of the operation state of the industrial equipment can be realized.

The invention relates to the technical field of obstacle prediction, and provides a vehicle fault real-time diagnosis method based on multi-source heterogeneous data and a dynamic stream processing system, and the method comprises the steps: obtaining multi-source heterogeneous operation data from a vehicle sensor, a vehicle-mounted bus and an external environment; based on historical fault samples, determining a parameter critical value interval of each fault for each historical fault sample through reverse reasoning, and constructing a fault critical value library; performing dynamic streaming processing on the multi-source heterogeneous operation data, extracting time sequence features, comparing the time sequence features with a fault critical value library in real time, and outputting early warning through a multi-source voting mechanism; and when the operation data is close to or exceeds the corresponding critical value interval, outputting a fault prediction result, and triggering a dynamic updating mechanism to correct the critical value library. According to the method, the accuracy, timeliness and interpretability of fault identification are improved, meanwhile, the system resource consumption and real-time requirements are considered, and the method is suitable for long-term health state monitoring of large-scale vehicles.

The invention provides a data driving and multi-element cloud edge cooperative computing method for intelligent workshop construction, which comprises the functions of multi-protocol heterogeneous equipment sensing, edge node adaptive load balancing, lightweight real-time edge computing processing, cloud centralized management control, industrial intelligent application service deployment scheduling, cloud edge task unloading and the like. The system comprises a data acquisition and transmission middleware, an edge stream processing engine and a cloud service center. The method has the characteristics of real-time performance, low delay, flexibility, expandability, loose coupling, low resource occupancy, high availability and privacy security.

The invention relates to the field of financial science and technology, and particularly discloses a cross-platform financial data integration and real-time analysis system and method. The system is characterized in that a distributed heterogeneous data acquisition module accesses multi-source data in parallel through a pluggable protocol adapter; the streaming data cleaning engine realizes field-level normalization based on a dynamic template and semantic mapping; the hybrid storage architecture forms a three-level system by a memory database, a time sequence database and distributed file storage; the stream processing core engine adopts an elastic sliding window to execute CEP complex event processing; and the dynamic analysis decision module integrates online machine learning to generate a real-time transaction signal. The method comprises the steps of multi-source concurrent acquisition and microsecond-level timestamp injection, context sensing data standardization, dual-channel data storage, event time window aggregation, dynamic risk calculation and block chain audit evidence storage. The defects of low data fusion efficiency, high real-time analysis delay and the like in the prior art are overcome, and cross-platform data processing timeliness and decision accuracy are remarkably improved.

The invention relates to the technical field of cross-border advertisement putting, and provides a cross-border e-commerce advertisement accurate putting method and system based on a user portrait, and the method comprises the steps: building a unified data flow through multi-source data collection and standardization processing; calculating and fusing the real-time statistical features, the historical interest vectors and the context features in real time by using a stream processing engine to generate multi-dimensional feature vectors; inputting the feature vector into a multi-modal time sequence neural network model which captures a user behavior rule through time sequence coding and an attention mechanism and outputs a user behavior weight and an interest attenuation coefficient; dynamically updating the real-time interest score of the user by adopting an exponential decay model; and when the interest score reaches a threshold value, triggering a reinforcement learning agent to make a decision according to the comprehensive state information, and dynamically outputting an action strategy including release triggering, a bidding coefficient and a creative type. The problems that in the prior art, user interest modeling lags behind, and the self-adaptive capacity of the putting strategy is poor are effectively solved.

The invention relates to the technical field of cognitive inference, and discloses a cognitive inference system fusing a dynamic knowledge graph and deep learning. The system comprises a cognitive event stream processing module which is used for acquiring a real-time event stream and a cognitive target stream, identifying entity state change and extracting a reasoning intention; the graph mapping generation module is used for receiving the entity state change and the reasoning intention, constructing a mapping relation with knowledge graph topology and generating a multi-dimensional graph mapping library; the cognitive path decision-making module is used for extracting key topological characteristics from the multi-dimensional map mapping library, calculating a reasoning path migration probability and generating an initial cognitive path set; the time sequence evolution prediction module is used for carrying out time sequence evolution modeling on the knowledge graph topology based on the initial cognitive path set, identifying node state offset and updating a multi-dimensional graph mapping library; and the reasoning optimization module is used for analyzing a reasoning stage and a reasoning dependency relationship of the cognitive target flow according to the updated multi-dimensional map mapping library, and generating a dynamic cognitive reasoning library.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a knowledge graph updating method, electronic equipment and a storage medium, and the method comprises the steps: obtaining a target new literature, and extracting a candidate triple; performing multi-dimensional conflict detection on the candidate triple; when the candidate triad does not pass the multi-dimensional conflict detection, determining a corrected triad based on the large model, the external knowledge base and the candidate triad; determining a target triple based on the corrected triple and an existing triple in the knowledge graph to be updated; and updating the to-be-updated knowledge graph based on the target triple. According to the technical scheme provided by the invention, stream processing, multi-modal verification and dynamic weight technologies are fused, triples are extracted in real time through an RAG model, a triple verification mechanism of semantic embedding, an aging range and an external knowledge base is combined, conflicts are accurately recognized, a correction strategy is generated by utilizing a large model, and confidence coefficients of new and old knowledge are balanced by adopting a weighted fusion algorithm, so that the reliability of the new and old knowledge is improved. Second-level updating is achieved, and the efficiency and accuracy of knowledge graph updating are improved.

The invention relates to the technical field of spatial data analysis, and particularly provides a commercial vitality space-time prediction and dynamic evaluation method based on multi-modal feature fusion, which comprises the following steps: collecting geographic space, text semantics, real-time behavior and economic attribute multi-modal heterogeneous data, and cleaning and desensitizing to obtain a structured data set; features are extracted from the single-modal data, and commercial vitality core feature vectors are obtained through cross-modal attention fusion in combination with an emotion-vitality association model; inputting the result into a space-time sequence prediction model, and outputting a commercial activity prediction result in a future preset time period; combining real-time updated data to construct a quantization-quality two-dimensional evaluation system, and performing real-time calculation by using a stream processing framework to obtain a dynamic evaluation result; and displaying and outputting a structured evaluation report through a visual panel. According to the method, timely and reliable decision support is provided for scenes such as business district operation optimization, business investment decision and regional economy monitoring, and the precision and real-time requirements of business vitality analysis are comprehensively met.

The invention discloses a real-time data stream processing method for multi-device collaborative management of a dome cinema, which relates to the technical field of cinema device data management, and comprises the following steps of: establishing communication connection between a central management node and a plurality of playing devices, collecting device identifiers, projection area numbers and geometric position parameters of the playing devices, and establishing a central management node; generating an equipment mapping relation table; realizing local clock correction of the playing equipment based on a unified time synchronization protocol; the central management node generates a control instruction stream according to the equipment mapping relation and the playing content, the instruction stream comprises space frame distribution information and fusion control parameters, and the space frame distribution information and the fusion control parameters are distributed to the playing equipment through communication channel numbers; and the playing device completes image geometric transformation and edge fusion processing according to the received control instruction and then executes synchronous playing. According to the invention, multi-device automatic mapping is realized, the time synchronization precision is improved, the image playing is coordinated and consistent, and the collaborative display capability and the playing stability of the dome cinema are improved.

A real-time anomaly detection framework for embedded BI systems (100) using LLMs, consisting of: (a) a data collection and pre-processing module configured to collect, cleanse, normalise and encrypt structured and unstructured data from multiple enterprise data sources in real time; b) a contextual embedding and feature extraction module configured to transform processed data into semantically rich, domain-aware embeddings that capture temporal, correlational, and contextual features; c) an LLM-based anomaly detection engine configured to analyze said embeddings using fine-tuned large language models to identify anomalies based on semantic inference, historical trends, and adaptive learning; d) a real-time stream processing and low-latency pipeline configured to perform high-throughput parallel in-memory computations to deliver anomaly detection results with minimal latency; (e) a root cause analysis and interpretability module configured to identify probable causes for detected anomalies and generate human-readable explanations, confidence scores and causal narratives; (f) an alerting and visualisation module configured to deliver real-time notifications via embedded BI dashboards, messaging platforms and graphical representations such as heat maps and trend charts; and g) a continuous learning and feedback optimization module configured to refine the accuracy of anomaly detection by incorporating user feedback, updated historical data, and evolving business rules.

The invention discloses a graphic processing unit cooperative processing method and system for accelerating a three-dimensional generative model. The model comprises a diffusion processing stage and a Gaussian splash rendering stage. In the diffusion processing stage, the time step number and the data precision calculated by the neural network are dynamically adjusted according to the visual angle sensitivity, and a specific graphic processing unit core is scheduled for execution. In the Gaussian splash rendering stage, pixel block tasks are predicted and intelligently scheduled to a stream processor based on historical data; and in the stream processor, the processing sequence of the Gaussian ball by the rendering unit is optimized according to the geometric distance, and the close-range unit is preferentially processed. During back propagation, the built-in logic of the stream processor preaggregates the gradient contributions of the same Gaussian ball, and then updates the gradient contributions through a single write operation. Through collaborative optimization of software and hardware, the efficiency bottleneck of the 3D generation model on a graphic processing unit is effectively solved, the calculation speed, the throughput and the hardware utilization rate are remarkably improved, and the model generation time is shortened.

The invention relates to the technical field of big data processing, and particularly discloses a computer data management system based on big data. The system comprises a distributed data acquisition module which captures multi-source data through an API gateway and a crawler and embeds a quality label; the intelligent storage scheduling engine is used for realizing automatic migration of cold and hot data by adopting a column-type and distributed hybrid architecture; fusing a computing framework, and dynamically coordinating flow processing and batch processing resource execution feature extraction; the self-adaptive strategy center is used for generating data partitions and encryption strategies based on reinforcement learning; and a multi-level security protection system is adopted, and attribute base decryption and dynamic desensitization are deployed. The problems of resource scheduling rigidity and security protection lag in the prior art are solved, the storage cost is reduced by more than 40%, the query delay is compressed to be within 200ms, and the method is suitable for real-time decision-making scenes in the fields of e-commerce and finance.

The invention relates to the technical field of data processing, in particular to an auxiliary driving device and system based on video image processing, and the device comprises a frame buffer queue with a multi-channel video collection module aligned with timestamps; a hierarchical calculation task decoupling module identifies task types in the frame buffer queue and marks priority labels, including a first priority label of a radar data analysis and distance superposition task, a second priority label of a vehicle profile generation task and a third priority label of a video fusion task, and outputs a data stream with priority labels; the calculation module generates a task processing result; the genetic algorithm optimization scheduling module generates a resource allocation strategy and controls the calculation module to execute task processing; and the output synchronous control module synthesizes a high-priority task result and performs frame rate synchronous output on a low-priority task. According to the method, the problem of real-time degradation caused by multi-path high-definition video stream processing is solved through task grading and dynamic resource scheduling, and the image output delay of the auxiliary driving scene is reduced.

The invention discloses a unified streaming processing method, system and device for multi-modal AI interactive content, a medium and a program product, and the method comprises the steps: receiving a request which is sent by a user and comprises an image and a text, and constructing a request context; extracting image features; carrying out image analysis and outputting an image description text; performing intention recognition and outputting an intention; multi-stage reasoning is carried out, and thinking content fragments are generated in a streaming mode; buffering and releasing thinking content fragments; generating text content fragments based on multi-stage reasoning; buffering and releasing the text content fragments; performing recommendation triggering based on the image description text and the intention; recommending content fragments; inserting a recommended position, buffering and issuing; and process event management: issuing event notifications when the beginning, any step fails and the end. The method is a universal method capable of transmitting different types of outputs in a single ordered stream, the analysis cost can be reduced, and the interaction experience and expansibility are improved.

The invention relates to the field of data processing, in particular to a distributed flow limiting method and device, equipment and a medium, which are used for realizing distributed flow limiting control on a service request. The method comprises the following steps: receiving business requests of business servers connected with a cache server, and clustering the business requests based on multi-dimensional object information of the business requests to obtain request clusters; aiming at each request cluster, respectively executing the following steps: sorting the service requests in the request cluster to obtain a request sequence; aiming at each service request in the request sequence, sequentially executing the following steps: acquiring the count of a counter corresponding to the multi-dimensional object information of the service request, comparing the count with a first quantity threshold value, and judging whether the count is updated or not based on a first comparison result; and sending the first comparison result to a service server corresponding to the service request to enable the service server to analyze the first comparison result, and if a return value in an analysis result is a first set value, performing flow limiting processing on the service request.

An audio synthesis method, an audio synthesis model training method, an apparatus, an electronic device, a computer-readable storage medium, and a computer program product, which relate to artificial intelligence technology. The method includes: invoking an audio synthesis model based on language information and preset style information of a target text to perform following processing, the audio synthesis model including a prior encoder and a waveform decoder: generating audio features corresponding to the target text based on the language information and the preset style information by using the prior encoder; performing normalizing flow processing on the audio features by using the prior encoder, to obtain a hidden variable of the target text; and performing waveform decoding on the hidden variable of the target text by using the waveform decoder, to obtain a synthetic waveform conforming to an audio style described in the preset style information and corresponding to the target text.

The invention relates to the technical field of big data storage, and discloses a multi-level data storage and mixed query method and system, a storage medium and equipment, and the method comprises the steps: collecting original buried point data, carrying out real-time stream processing, storing the processed real-time data in a hot storage layer, carrying out timing offline batch processing on historical buried point data, and storing the processed real-time data in a hot storage layer; converting into an optimized storage format and storing in an object storage system; an extended metadata management system is constructed, and structure information, partition information and access frequency information of data in the object storage system are automatically extracted and managed; executing query, and reducing the data scanning amount through a query optimization strategy; automatically migrating data among different storage layers through a dynamic cold and hot data layering mechanism; a unified query interface is provided, a query request is distributed to a corresponding real-time processing system or an offline query system based on an intelligent routing strategy, and a query result is cached and returned, so that high-performance and low-cost storage and second-level query of data are realized.

The invention discloses a multi-task parallel data stream processing method and device based on an FPGA (Field Programmable Gate Array), which are applied to the technical field of data processing. Carrying out normalization processing on the decoded data to generate a data stream in a unified format; performing coding and ID identification code embedding processing on the normalized data to generate to-be-stored data with an identifier; carrying out FIFO (First In First Out) buffering and write-in priority judgment on the coded data to generate priority queue data; dDR writing and BANK scheduling processing are carried out on the priority queue data, and stably stored parallel data are generated; carrying out reading and output coordination processing on DDR storage data to generate a continuous output data stream, and triggering a reading application by decoding ID identification code positioning data, reading FIFO according to depth scheduling and monitoring data volume by channel temporary storage FIFO; and performing dynamic bandwidth allocation and energy efficiency optimization on the whole data stream processing process to generate an efficient parallel processing result.

The invention provides a dynamic risk control method and system based on streaming processing, and the method comprises the steps: carrying out the rule configuration operation for a graphic rule configuration interface to configure a risk control rule, and generating a risk analysis code of a risk control task based on a preset template engine and the risk control rule; and then compiling and packaging the risk analysis code to generate a program package of a risk control task, pushing the program package to a preset distributed stream processing cluster, obtaining an event message generated by a service system from a target data source through the preset distributed stream processing cluster, and running the program package to realize risk analysis of the event message. And storing the risk analysis result through the preset memory database, so that the business system asynchronously obtains the risk analysis result from the preset memory database. By adopting the risk control system, the problems of high programming capability requirement and complicated operation of the existing risk control system in the system configuration level can be relieved.

The invention discloses a strong real-time signal processing method based on multi-stream processing, and the method comprises the steps: carrying out the remainder of a preset stream number of received coherent processing interval data through a CPU according to a CPI number, obtaining a stream number, distributing the coherent processing interval data to each stream distributed calculation program corresponding to the stream number, each stream distributed computing program comprises a CUDA stream and a CPU processing thread which are executed in sequence; the GPU and the CPU execute each stream distributed calculation program to overlap the calculation time and the data copying time of the CUDA stream and the CPU processing thread to obtain each trace point condensation data with different completion time; the CPU obtains the corresponding report to be sent according to the trace point condensation data, and orderly sends the report to be sent to the target program based on a report waiting synchronization mechanism, and the application can complete front-end downloading data receiving, detection processing and report sending in strong real time, thereby improving the development efficiency, shortening the product development period, and reducing the project cost.

The invention discloses a real-time anti-fraud transaction intelligent analysis system and method based on multi-chain collaboration, and the system comprises a dynamic federated learning module which is used for dynamically loading and updating a global model during the operation of a JVM (Java Virtual Machine) by adopting a Java Agent bytecode enhancement technology; the multi-chain collaborative verification engine comprises an alliance chain and a private chain, the alliance chain is formed by taking a plurality of banks as organization nodes and is used for distributed evidence storage of transaction characteristics among financial institutions, and the private chain is maintained by a supervision mechanism and is used for auditing and compliance verification; and the real-time stream processing pipeline processes the transaction request in a stream processing mode to obtain feature data, and makes a decision based on the feature data and the global model output by the dynamic federation learning module. The intelligent risk control system which supports cross-mechanism collaboration, privacy protection, high throughput and low delay is constructed, and the problems that in the prior art, model updating is lagged, a verification mechanism is not transparent, and processing delay is high are solved.

The invention discloses a road crack segmentation model based on Transform enhancement, a construction method and a use method, and relates to the technical field of image processing. According to the method, a multi-scale cross-level module (MSAF) is provided, and overall image global semantics and 2 * 2 local block features are explicitly fused by embedding a multi-head attention mechanism of two-dimensional sine position coding. The module not only retains edge details of the crack, but also captures long-range context information, so that the crack detection capability of the model is remarkably improved, and additional parameters do not need to be added; a global-local double-flow coding architecture, namely MSAFNet, is designed, a parallel double-flow coding architecture is adopted, global flow processing is performed on the whole image, local branch processing is performed on 2 * 2 cutting, and seamless recombination is performed after the features of the whole image and the local branch processing are fused through an MSAF module, so that fine-grained texture information of cracks is reserved, and global context information is captured; therefore, the high-precision crack segmentation capability can be maintained in different scenes.