921results about How to "Increase credibility" patented technology

The invention provides a land unmanned equipment semantic preserving large model deployment method and system, and relates to the technical field of navigation control. According to the deployment method, a preliminary control suggestion is directly output through a lightweight fusion model, optimization fusion is carried out through an MPC framework, underlying dynamics and environmental constraints, and seamless connection from semantics to control is achieved; in combination with a VILO odometer and instance segmentation, a global map containing dynamic obstacle semantic information is constructed and updated in real time, and an accurate context is provided for planning and decision making; model pruning, knowledge distillation and edge calculation scheduling are adopted, so that a complex multi-modal semantic model can run in real time on an embedded platform; the decision basis from instruction analysis to control execution is recorded and visualized in the whole process, and the transparency and credibility of the system are improved; on-line re-planning, multi-stage fault detection and switching strategies are integrated, and the robustness of the system in a dynamic environment and an abnormal condition is remarkably improved.

The invention discloses a traceability application-oriented block chain database data management system, particularly relates to the technical field of block chain database data management, and is used for solving the problems of easy data tampering and low verification efficiency caused by traceability data logic association and physical storage splitting. The method comprises the following steps: firstly, matching a parent physical sector offset through an association analysis module, determining a storage partition, and assembling a write-in instruction; then, a topology binding module executes a disk write operation, synchronously reads parent node hash and performs combinatorial operation in combination with a physical address to generate a topology binding check value bound with the physical position; the aggregation anchoring module uses a Merkel tree aggregation check value to generate a root fingerprint and perform uplink, and establishes an anchoring voucher; and finally, a path verification module positions a physical position according to the certificate, executes reverse recursive addressing and step-by-step Hash recalculation, and assembles a pedigree verification packet, so that a closed-loop trust system from physical storage to chain certificate storage is constructed, and the tamper-proof capability and verification efficiency of the traceability data are improved.

The invention discloses an automatic causal structure generation method based on semantic representation and logical reasoning of a large language model. The method comprises the following steps: acquiring an input text; performing semantic coding and clustering on the obtained input text by utilizing a large language model, and establishing a candidate causal variable set; causal relationship detection is carried out on the established candidate causal variable set based on anti-fact intervention and do-calculation; performing causal direction judgment, and generating a directed acyclic causal graph meeting logic consistency; and on the basis of the generated directed acyclic causal graph, natural language interpretation is generated by using a large language model, and logic consistency closed-loop verification is carried out. According to the method, automatic generation from the natural language to the causal structure is realized, the causal variable set is automatically extracted and constructed from the unstructured natural language text, the defects that variables need to be manually defined and modeling depends on field experts in the existing causal modeling process are avoided, and the labor cost and professional threshold of causal structure construction are remarkably reduced.

A converter station AC filter group circuit breaker abnormal arcing modeling analysis method comprises the following steps: firstly, carrying out time mark alignment, amplitude calibration and filtering de-noising on wave recording signals such as AC bus voltage and filter branch current to obtain high-quality input data; then, combining a primary wiring diagram, enabling an alternating current system to be equivalent to an ideal power supply and series impedance, enabling circuit breaker arcing to be equivalent to arc resistance changing along with time, and constructing an equivalent circuit model comprising the power supply, the impedance, a capacitor, an electric reactor and dynamic arc resistance; establishing a state space equation according to a voltage and current constraint relation, and performing time domain solution by adopting a two-step two-order or three-order diagonal implicit Runge-Kutta method to obtain voltage and current responses of the whole process of the fault; on this basis, the arc resistance change is inversed according to the ratio of arc voltage to current, the current zero passage is identified through the resistance symbol change at adjacent moments, and the zero passage section is smoothed; and finally, the inversion result is verified again by means of the simplified equivalent model.

The invention discloses an electric energy metering error evaluation method and system for a direct current charging pile, equipment and a medium, and the method comprises the steps: constructing an electric energy disturbance response function based on transient voltage and transient current, and effectively quantifying the transient metering deviation caused by measurement lag, voltage drop, current peak and other factors in the actual operation of the direct current charging pile. Error reconstruction modeling has higher dynamic adaptive capacity and physical rationality, the limitation that in the prior art, the influence of the high-variable-load working condition on metering cannot be reflected is broken through, and the accuracy and timeliness of direct-current charging pile error monitoring are greatly improved. Besides, a standard error is introduced to carry out fitting calibration on a reconstruction error and carry out energy conservation verification, thereby realizing verifiability on a physical level and controllability in engineering implementation, enhancing credibility and authority of an evaluation result, constructing a closed-loop analysis mechanism from error evaluation to metering correction and then to conservation verification, and improving reliability and reliability of the evaluation result. And the closeness and the adaptive capability of the overall error evaluation are improved.

The invention discloses a material tracking system for battery production, and particularly relates to the technical field of production data traceability, a process topology constraint model is constructed based on a production process, and a material flow expected path is generated; collecting behaviors of the materials in each station to form a material circulation time sequence track; matching the time sequence track with the process topology constraint model to generate a process legality state; controlling association establishment of process data and material identifiers according to a process legality state, and isolating and re-checking data which do not meet constraints; a source identifier is introduced into the process data and is bound with the process legality state, and finally a material production resume record is generated; according to the invention, through process topology constraint and behavior time sequence matching, accurate tracking of material circulation in a dynamic production scene is realized; abnormal path data is prevented from entering associative storage through legality judgment and associative gating; through source identification and resume integration, the traceability and the rechecking reliability capability of the material production process are improved.

The invention discloses a cold-chain pork quality dynamic evaluation method and system based on multi-source data fusion, and relates to the technical field of data processing and intelligent evaluation, and the method comprises the following steps: collecting multi-source signal information of a whole cold-chain transportation process, sorting according to a unified time sequence, continuously arranging all signals on a time axis, and carrying out data processing on all the signals; a complete dynamic record is formed; and extracting each time interval and signal amplitude change track from the formed dynamic record, analyzing a signal section with repeated wave crests in a short time, and generating a preliminary data manuscript for reflection interference judgment. According to the invention, through time sequence arrangement and dynamic association of multi-source signals, a wave crest track analysis mechanism is constructed, real-time identification and correction of reflection interference are realized, and continuity and accuracy of a gas concentration curve are ensured; and by generating a reflection source position list and adjusting numerical value distribution of repeated fragments, false fluctuation is eliminated, and the stability and reliability of cold-chain pork quality evaluation are improved.

The invention relates to the technical field of industrial data processing, in particular to an intelligent safety accident early warning method and system based on risk assessment, and the method comprises the steps: obtaining a mining operation event, extracting response situations before and after the operation event, checking the response situations with a reference response template, and judging a disturbance response according to the deviation degree; a ventilation network model is constructed, and when it is detected that a monitoring point is abnormal, management traceability is carried out along a traversal upstream path in combination with an air volume distribution proportion; predicting the propagation trajectory of the abnormal gas along the wind flow path and the time of reaching a downstream node based on the ventilation network model, and obtaining spatial correlation analysis; analyzing the disturbance response and the spatial correlation, calculating a coupling risk increment, and outputting a comprehensive risk value; judging the critical degree grade of the comprehensive risk value time series data; and determining a scheduling level according to the combination of the comprehensive risk value level and the critical degree level, when the critical index indicates to evolve to a dangerous state, improving the scheduling level in advance, and generating a differentiated resource scheduling suggestion in combination with the propagation trajectory.

The invention discloses a two-stage space-time diagram network and multi-source information fusion time sequence probability prediction method and device, and relates to the technical field of artificial intelligence and big data analysis. The method comprises the following steps: acquiring historical wind speed sequence data and corresponding target variable sequence data; preprocessing the data, and constructing a training sample according to a preset time sliding window; constructing a time sequence probability prediction framework of the two-stage space-time diagram network and multi-source information fusion; based on the fluctuation correlation of the historical sequence data, constructing an adjacent matrix between nodes; inputting the training sample and the adjacent matrix between the nodes into a wind speed prediction model, training the model through a designed threshold perception loss function of a wind speed-power nonlinear relationship, and outputting a multi-node wind speed prediction probability of a first stage; and inputting the multi-node wind speed prediction probability and the multi-source environmental factor data into the gradient boosting tree model for second-stage prediction, and outputting a multi-node wind power prediction result. According to the invention, prediction errors can be reduced.

The invention relates to the technical field of data processing, and provides an information generation method and device, electronic equipment and a storage medium, and the method comprises the steps: retrieving candidate data related to a query request of a user from a multi-source heterogeneous data source, and distributing an authority level for a data source corresponding to each candidate data; determining the data source with the highest authority level from the data sources corresponding to the candidate data with the conflicts, and initiating a verification request to the data source with the highest authority level, so that the data source with the highest authority level returns the latest version data for the candidate data with the conflicts; updating candidate data with conflicts according to the latest version data; and generating business cooperation information of the query request based on the updated candidate data and the candidate data without conflicts. According to the method, the conflict data is updated through the data source with the highest authority level, so that logic contradictions and numerical illusion caused by data timeliness lag or incapability of judging authenticity of the data are avoided.

The invention discloses an enhanced generation query method and system based on multi-granularity retrieval and reinforcement learning strategies. According to the method, semantic analysis is carried out on an original query by utilizing a large language model, the original query is decomposed into a plurality of sub-problems, and each sub-problem generates a query vector; in the multi-granularity vector knowledge base, respectively executing vector similarity retrieval on the query vector of each sub-problem to obtain a related document fragment vector of each sub-problem under each granularity, and further generating a multi-granularity vector set of each sub-problem; weighting the multi-granularity vector set of each sub-problem to obtain a comprehensive context information block, determining the weight of the multi-granularity vector set of each sub-problem through a multi-arm reinforcement learning strategy, and inputting the comprehensive context information block into a large language model to output a query result. According to the method, the retrieval accuracy is improved through semantic decomposition and multi-granularity knowledge collaborative retrieval, results are intelligently integrated through a self-learning mechanism, and lightweight self-adaptive optimization is achieved.

The invention provides a multi-parameter water quality data fusion analysis method and system. The method comprises the steps that water quality parameters are collected to form a three-dimensional data cube; constructing a space-time tensor model by using Tucker decomposition and a graph convolution network, and generating a core tensor matrix; constructing a dynamic constraint library and embedding the generative adversarial network; training a generative adversarial network by using Transform and physical constraint loss, generating synthetic data and verifying the synthetic data; performing space-time fusion by using meta learning weight distribution and Bayesian deep learning to generate a weight matrix and a confidence interval; missing data are restored through physical constraint interpolation and Gaussian process regression, and SHAP and LIME interpretation and path diagrams are generated; and performing real-time analysis by using an edge-cloud collaborative architecture to generate an intelligent report. Through physical constraint modeling, dynamic weight distribution and edge-cloud collaborative architecture, the problems that synthetic data violates physical laws, weight staticization, response lag and insufficient interpretability are solved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a memory analysis question and answer data construction method and device, equipment and a medium. The method comprises the steps of extracting a fact information set containing entities, relationships and values based on structured message representation, generating candidate question and answer pairs by utilizing a language model, executing multi-source verification to form a target question and answer set, performing semantic extension and question method transformation to generate a diversified question and answer set, and completing credibility screening based on a verification result to obtain a memory analysis question and answer set. And generating a memory analysis data set in combination with the user message sequence. According to the method, through multi-stage extraction, generation, verification and screening, reliability and diversity unification of question and answer content are achieved, the constructed memory analysis data set has the advantages of being consistent in context, accurate in fact, rich in expression and the like, and therefore the credibility of memory ability evaluation of the large language model is improved.

The invention provides a multi-source security knowledge fusion driven security risk management large model construction method and application. The method comprises the following steps: constructing a campus security knowledge graph and a semantic vector database based on multi-modal campus security data; performing approximate nearest neighbor search in the semantic vector database based on user query to obtain context evidence related to the user query, splicing the context evidence with the user query to obtain a retrieval enhancement prompt, the user query being campus security risk information of any mode; and training based on the campus security knowledge graph to obtain a security risk management large model, and inputting the retrieval enhancement prompt into the security risk management large model to obtain a risk solution. According to the scheme, the attention confidence, the feed-forward network confidence and the knowledge alignment confidence of all levels are aggregated in the reasoning process to generate the comprehensive confidence score, so that the illusion problem caused by no data reference in the model reasoning process is effectively avoided.

The invention provides an OPC UA information model automatic construction method based on a knowledge graph and a large language model. The method comprises the steps of obtaining an unstructured industrial text, performing semantic analysis on the unstructured industrial text, and extracting a main body entity, an object entity, a method entity and attribute information thereof according to a predefined hierarchical tuple structure; calling an industrial field knowledge graph in real time to perform semantic enhancement on an extraction result; performing similarity calculation and evaluation on an enhanced result, and performing information fusion and duplicate removal based on a similarity evaluation result to obtain an entity-attribute set; generating a tree-shaped hierarchical structure framework by using a large language model, and calling a knowledge graph to perform structural integrity verification; semantic association between the entity-attribute set and the tree structure framework is analyzed, attributes and methods are mapped to corresponding structure nodes, and a mapping reasoning process is output in a natural language form; converting the mapped model structure into a format of XML (Extensive Makeup Language) grammar, and executing automatic compliance check and repair; by designing a progressive task chain of information extraction, semantic fusion, model construction, interpretable mapping and standardized output, a set of complete understanding-enhancement-construction-verification automation system is constructed.

The invention relates to an engineering potential safety hazard detection method based on a multi-modal large model. The method comprises the following steps: acquiring an on-site monitoring image of a target scene; identifying a key entity in the field monitoring image, and extracting positioning information of the key entity; generating a depth map based on the on-site monitoring image; based on the positioning information of the key entity, combining with the depth map, associating the positioning information and the depth information for the key entity, and generating a scene description text; based on the on-site monitoring image, the depth map corresponding to the on-site monitoring image and the scene description text, constructing a multi-modal input packet; inputting the multi-modal input packet into a learned engineering safety domain knowledge base and a visual language large model subjected to instruction fine tuning, and outputting a hidden danger judgment result; the instruction fine tuning of the visual language large model is based on a hidden danger inference chain data set, the data set has hidden danger images corresponding to hidden danger items in an engineering safety field knowledge base and annotation data of each hidden danger image, and the annotation data comprises step result data corresponding to each hidden danger detection step.

The invention discloses a power battery pack health state monitoring system, and relates to the field of battery pack health state monitoring, and the system comprises a vehicle information input module which is used for receiving vehicle unique identification information inputted by a user; the standard data acquisition module is used for acquiring historical operation and charging and discharging data corresponding to the unique identification information of the vehicle from a cloud data source; the data fusion and feature construction module is used for constructing a battery state evaluation feature set; the battery health assessment module is used for constructing a battery pack health state assessment model and outputting a battery health state assessment result; the safety risk grading module is used for carrying out grading evaluation on the potential safety risk of the vehicle battery; and the report generation and display module is used for generating a structured detection report. The method has the advantages that the full life cycle of the power battery pack from real-time health state accurate evaluation to future safety risk grading early warning is realized, and intelligent monitoring can be explained.

The invention discloses an interactive load prediction method based on fusion of prior knowledge and data driving, and the method comprises the steps: reconstructing a load sequence into a trend load sequence and a disturbance load sequence through modal decomposition, and constructing a prior knowledge base of tag-feature weight according to SHAP interpretability analysis; and through a prior knowledge base and feature contribution analysis, channel-level weight learning is carried out by using an automatic correlation determination module, and key input features are obtained. And inputting the reconstructed load sequence and the key input features into an improved Transform model, mapping a priori knowledge base into a priori knowledge matrix, embedding the priori knowledge matrix into the model, and generating a load prediction result. And carrying out interpretability analysis on a prediction result, and feeding back and updating an analysis result to a priori knowledge base to realize dynamic collaborative updating of prediction and the knowledge base. According to the invention, organic combination of expert knowledge and a data-driven model is realized, and the precision and stability of load prediction are improved.

The invention discloses a hydraulic structure risk assessment method based on a multilevel dynamic fuzzy Bayesian network, and relates to the technical field of hydraulic engineering safety monitoring, and the method comprises the following steps: recognizing risk factors of a hydraulic structure, and building a hierarchical hydraulic structure risk assessment system; a Bayesian network risk probability model is constructed based on a risk assessment system, nodes in the model correspond to indexes of each layer of the assessment system, and edges among the nodes are connected according to a causal relationship among the indexes. According to the invention, accurate and dynamic management and control of hydraulic structure risks are realized. In the aspects of data processing and risk judgment, multi-source monitoring data such as displacement, seepage and water level are classified and integrated according to an index layer, a criterion layer and a target layer through hierarchical evaluation system design, the isolation problem of traditional single data monitoring is avoided, the data collaboration value is fully mined through logic association among the layers, and the risk judgment accuracy is improved. And the risk assessment does not depend on one-sided information any more.

The invention discloses a special first-aid medicine and article storage and distribution construction system and method. The system comprises an Internet of Things module, a multi-subject cooperation module, an inter-hospital cooperation module, a cloud computing platform module, a block chain module, an intelligent scheduling center module, a storage network module and an emergency response module. The method comprises the steps of 1, demand integration and joint purchase; step 2, storage network construction and inventory optimization; step 3, intelligent scheduling and path planning; 4, emergency response and cross-mechanism deployment are carried out; 5, whole-process tracing and data uploading are carried out; step 6, performance monitoring and continuous optimization; according to the invention, a special first-aid medicine and article storage and distribution construction system is designed, and standardized data interaction and close cooperation among medical institutions, suppliers and logistics enterprises are realized by establishing a multi-subject cooperation mechanism such as joint purchase, inventory sharing, emergency allocation and performance monitoring. The joint purchasing module gathers the purchasing demands of the medical institutions in the region.

The invention provides a Bayesian-based aviation equipment residual life uncertainty prediction method and system. The method comprises the following steps: S1, constructing an observation function equation and a state parameter description equation of residual life of key or important systems, modules and parts of aviation equipment; s2, constructing an agent model; s3, solving joint posterior probability distribution and performing residual life prediction; and S4, according to the prediction probability distribution of the remaining life of the aviation equipment, generating an optimization control instruction or a maintenance instruction for the aviation equipment, and acting the optimization control instruction or the maintenance instruction on the aviation equipment. The method provided by the invention not only provides a theoretical basis for solving the problem of prediction, analysis and prediction of the service life of the aviation equipment, but also clearly defines how information and uncertainty thereof flow and convert between a physical world and a digital world, and provides a brand new thought for prediction of the uncertainty of the residual service life of the aviation equipment.

The invention discloses a feature analysis and prediction method for drawing a depression patient based on a convolutional neural network, and belongs to the field of artificial intelligence and mental health. The method comprises the following steps: acquiring drawing image data of a depression patient and a healthy control group, and constructing a standardized data set; preprocessing such as denoising, size normalization and edge enhancement is carried out on the image; constructing a convolutional neural network model comprising four convolutional layers, two pooling layers and a feature fusion layer, and automatically extracting key features such as line definition, spatial layout and detail richness by adopting a variable receptive field mechanism and an attention module; correlation analysis is carried out in combination with scores of a clinical evaluation scale, and the model is optimized through transfer learning and a focus loss function; and generating a risk assessment report. According to the method, objectivity and accuracy of depression screening are remarkably improved, subjective evaluation deviation is reduced, visual decision support is provided for clinicians, and early recognition and intervention of depression are achieved.

The invention discloses a remote data management platform for security operation and maintenance service, and belongs to the technical field of computer network security and data operation and maintenance management. The platform comprises a data acquisition module, an access control module, a remote operation and maintenance control module, a data isolation and encryption module, a security auditing and tracing module, an intelligent risk identification module, a strategy decision engine and a visual management interface module. The system adopts a layered deployment architecture, and realizes multi-layer protection, dynamic strategy adjustment and traceable auditing of the whole remote operation and maintenance process through encryption transmission, block chain type log chain, deep learning risk identification and knowledge graph strategy optimization. The platform has the functions of multi-factor authentication, log chain tampering prevention, intelligent risk early warning and strategy self-learning, can effectively prevent unauthorized access and data leakage, improves the safety, intelligence and verifiability of remote operation and maintenance, and is suitable for multi-scene safety operation and maintenance management of medical treatment, energy, government affairs, industrial control and the like.

The application discloses a kind of fresh stewed bird's nest cold chain transport process temperature field monitoring method and system, belong to cold chain monitoring technical field.Through heat inertia pulse test, heat capacity and thermal resistance are obtained, which are respectively taken as node attribute and edge attribute;Optimize sensor distribution based on the principle of random walk entropy minimization;Construct a dynamically updated directed weighted heat propagation path graph;Using sparse deconvolution combined with system impulse response function matrix, separate event excitation and identify abnormal events;Linearize the heat balance inverse problem in the rolling time domain, reconstruct the product temperature sequence and heat flow vector, calculate the equivalent heat damage dose, and generate the solidified causal directed acyclic graph evidence chain.The above scheme integrates heat inertia into the graph model, realizes event and heat response separation, risk quantification and full-process traceability, effectively improves the accuracy and reliability of cold chain temperature monitoring.

This invention provides a phased array testing device and method for fillet welds in pipe fittings, belonging to the field of non-destructive testing technology for pressure-bearing components. It can at least partially solve the problems in existing technologies such as difficulties in pre-treatment of fillet welds in confined spaces, inconsistencies in the positions of the pre-treatment zone and the testing zone, and unstable coupling in composite curvature regions. The invention includes: a shared motion platform with an installation interface at its front end, a guide component on one side, and a support component on the other side; a pre-treatment head and a testing head are respectively connected to the shared motion platform through the same installation interface. The pre-treatment head includes a grinding assembly and a limiting structure, and the testing head includes a phased array sensing unit and a coupling structure. By sharing the same installation interface and guiding reference, this invention achieves positional consistency between the pre-treatment zone and the testing zone, improving the feasibility and repeatability of in-service testing in confined spaces.

The application provides a network security vulnerability automatic management method based on network security intelligence, belongs to the field of network security vulnerability management, and is used for solving the problems of low automation, large risk assessment deviation and poor collaboration in the related art. The method comprises network security intelligence collection, screening, storage and analysis, attack path prediction, key asset identification, hazard scene enhancement and vulnerability management. Through technologies such as dynamic weight, high-order probability graph model and multi-agent game, accurate intelligence processing, dynamic risk quantification, attack forward-looking prediction and cross-enterprise collaborative disposal are realized, the vulnerability management efficiency and accuracy are improved, and the network security of the ECUs is ensured.

The present application belongs to the technical field of concrete preparation detection, and particularly relates to a concrete aggregate water content detection device, which comprises a base and a rack installed above the base. A controller is installed on the side wall of the rack. The device further comprises a through hole opened in the top of the base, a support seat slidably connected inside the through hole, and a weighing mechanism installed inside the through hole for supporting the support seat. A flat hopper is arranged above the base and located on the side of the feed end direction of the support seat. The rack is provided with an adjusting drive mechanism for driving the vertical movement of the flat hopper. A microwave detection unit is arranged on one side of the flat hopper. The present application can perform online detection of the water content of concrete aggregate, improve the detection accuracy by combining weight detection and thickness control, reduce single detection interference error by cross verification of multiple sensing data, and improve data reliability to support subsequent dynamic batching.

The application discloses a kind of based on big language model and ability atlas's wisdom pastor authentication examination method and system, belong to artificial intelligence technical field.The application first constructs the ability atlas based on knowledge, skill, behavior three-dimensional ontology model;Obtain examinee multimodal data and generate initial ability vector;Through double-path dynamic gate graph neural network, real-time update ability vector, the network includes erase gate and increase gate and is updated by graph convolution layer propagation;According to ability short board, determine aggressive factor and combine incidental factor, use big language model to dynamically generate the next logical unique test question;Repeat interaction until the end of examination;Finally, according to all updated ability vector, generate digital ability portrait report.The application realizes dynamic individualized surveying and mapping, anti-cheating and deep characterization of talent ability structure, improves the precision and security of wisdom pastor authentication examination.

The invention relates to the technical field of Internet of Things communication, discloses an Internet of Things multi-Android client state synchronization method, and solves the problems of poor state consistency, high back-end dependence, large delay and insufficient compatibility in the prior art. According to the scheme, the method comprises the steps that after a client completes initialization, MQTT connection with a rear end is established, an MQTT state message pushed by the rear end is monitored, after the message is received, equipment state information is obtained through analysis, local state cache and a UI interface are updated, meanwhile, the client serves as a distribution node, a UDP synchronization message containing the equipment state and the IP of the distribution node is established, and the UDP synchronization message is sent to the client. The UDP synchronization message is broadcasted to the local area network, other clients in the local area network receive the UDP synchronization message and then verify whether the distribution node IP is the client in the local area network, if the verification is passed, the message is analyzed, the state updating timestamp and the local cache timestamp are compared, and the local state cache and the UI interface are updated only when the state updating timestamp is larger.

The invention aims to provide a gas turbine blade unstable flow visualization testing method, and belongs to the technical field of gas turbine experimental testing. Comprising the following steps: inputting boundary conditions and geometric conditions of a calculation model; numerical simulation flow field data under the condition are obtained through CFD calculation; if yes, the next step is carried out, and if not, a higher-quality grid is generated and returned to the encrypted grid, and CFD numerical simulation calculation is carried out again; selecting a proper resolution ratio based on an actual demand; generating a data source quantity and obtaining CFD calculation data according to the data source quantity; and outputting the three-dimensional transient visual flow field at the moment obtained by the checked high-precision CFD numerical simulation. The actual working condition of the gas turbine blade can be simulated, the test error of an actual instrument under the severe working condition is avoided, and the test difficulty is reduced. The precision of CFD numerical simulation is optimized, and the error is reduced, so that the credibility of the three-dimensional visualization cloud picture is improved.