778 results about "Scale model" patented technology

The invention relates to the technical field of low-altitude safety emergency, and discloses a low-altitude safety emergency system based on large model application. The system comprises a low-altitude situation awareness module which constructs a dynamic situation prediction model based on historical flight data, collects a low-altitude target track coordinate sequence, radar echoes and communication signal characteristics in real time, and outputs a theoretical security situation value. The multi-source data fusion analysis module performs three-dimensional difference analysis on theoretical and actually measured situation values, which relates to time domain trend deviation, space domain coverage offset and event sequence matching degree, and generates a target-level difference feature matrix; and the risk propagation path deduction module inputs the matrix into a geographic topology analysis network, generates a probability thermal distribution diagram of an abnormal risk propagation path in combination with airspace node communication parameters and a target position, and locates a risk influence area. And the intelligent disposal strategy generation module configures parameters according to the graph, starts high-frequency monitoring for a high-probability area, and performs communication disturbance test on an adjacent target.

The invention discloses a spatial registration method for tunnel multi-source heterogeneous data, and the method comprises the steps: constructing a lightweight digital twinborn model based on a design parameter library, a construction log library and a real-time monitoring library; mapping the multi-source sensor data in the real-time monitoring library to a digital twin model space node through a space-time synchronization algorithm; probability distribution model-based abnormal value elimination is performed on the geological exploration data, the while-drilling parameters and the geological sketch, and a discrete smooth interpolation algorithm is adopted to generate a surrounding rock mechanical parameter three-dimensional distribution field; the surrounding rock mechanical parameter three-dimensional distribution field serves as input, and front surrounding rock classification probability distribution is dynamically predicted through a machine learning model optimized through transfer learning; based on the three-dimensional point cloud data of the tunnel face in the digital twin model, extracting geometric parameters of the structural plane and constructing a fracture network model constrained by a normal vector; according to the method, the surrounding rock risk location is accurately positioned based on double-threshold triggering, and multi-scale model seamless fusion is realized through gradient constraint topology fusion.

The invention provides a large model autonomous evaluation method, device and equipment based on a multi-agent technology, and a storage medium. The method comprises the following steps: generating an evaluation task set covering multiple scenes and grading multiple difficulties by setting dimension weights of basic indexes and extension indexes; a multi-agent evaluation system of a questioner, an evaluator and a reference agent is constructed based on the task set, a parallel interaction mechanism is adopted to simultaneously initiate tasks to a to-be-evaluated model and the reference agent, the evaluator agent compares the to-be-evaluated model and the reference agent according to a multi-dimensional scoring standard and outputs the two agents, and task instances, output results and scores are constructed into a structured data set; the subsequent task set is dynamically optimized through analysis of the data set, comprehensive and iterative evaluation of the capacity of the large model is achieved, and the problem that an existing evaluation mode cannot comprehensively reflect the real capacity level of the large model is solved.

The invention provides an assembly line parallel division and memory optimization method for large-scale model training. The method comprises the following steps: S1, identifying and dividing model operators; s2, calculating operator memory occupation and numbering; s3, constructing a target function, and minimizing a memory difference value through dynamic programming; s4, starting from a single operator, gradually constructing and searching an optimal division strategy; and S5, analyzing and optimizing memory occupation, and balancing the memory of the equipment in each stage. According to the method, automatic division of the model is realized by using a dynamic programming algorithm, and the problem of unbalanced memory occupation in each stage in pipeline parallelism is solved. And the search space of a dynamic programming algorithm is reduced by utilizing the isomorphism between model layers, so that the time required for searching an optimal solution is reduced. And the minimum unit of model division is reduced to operators, so that the model division fineness is improved, and the algorithm can search a better division strategy.

The invention relates to the technical field of nuclear power internal disaster analysis, and discloses a nuclear power plant internal flooding experiment verification method, device, equipment and medium, and the method comprises the steps: carrying out the modeling analysis of a target plant prototype, and determining a plurality of model parameter values of an experiment scaling model; constructing an experiment scaling model according to the plurality of model parameter values, and determining experiment parameters of an internal water logging experiment; performing an internal water logging experiment on the experimental scale model by using the experimental parameters to obtain an internal water logging experiment result of the experimental scale model; and obtaining an internal water logging simulation result of the numerical model, comparing the internal water logging simulation result with an internal water logging experiment result, and verifying the accuracy of the internal water logging simulation result according to a comparison result. According to the method, the experiment scale model of the nuclear power plant prototype is constructed, the flooding spreading conditions under different working conditions are simulated and compared with the flooding simulation result, the accuracy of flooding simulation calculation is verified, and more accurate guidance is provided for follow-up nuclear power project internal flooding analysis.

The invention discloses a knowledge-based visual question-answering method and device based on big and small model collaboration and a medium, belongs to the technical field of visual question-answering, and solves the problem of how to improve the accuracy of complex questions faced by a visual question-answering technology. In the image description extraction step, an image description and key object label set which is highly associated with a natural language problem is generated, and in the entity enhancement processing step, key entities are extracted and a clarity problem and an entity example which are used for resolving semantic ambiguity are generated. In the candidate answer generation step, a plurality of semantically complementary answers are generated under different reasoning dimensions on the basis of a multi-round generation strategy, optimized candidate answers are screened on the basis of a model built-in scoring mechanism, in the context example retrieval step, examples most related to current input are retrieved, and finally, the candidate answers are obtained. And the large language model receives a unified input prompt formed by splicing output results in the steps, and inference is performed in an autoregression mode to generate answers, so that the accuracy of the visual question-answering technology facing complex questions is effectively improved.

The invention belongs to the field of automobile evaluation, and provides an automobile live risk evaluation system and method based on a large model, and the method comprises the steps: receiving voice input information of an evaluator in a vehicle on-site inspection process; extracting component state information of the target vehicle based on the structured check item text; acquiring historical maintenance record data of the target vehicle, and performing association fusion on the historical maintenance record data and the component state information; based on a large language model, performing joint reasoning on a currently input inspection label and the historical maintenance record, constructing a causal chain related to an accident risk, and generating at least one subsequent to-be-inspected item; generating voice guide content and feeding back the voice guide content to the evaluator so as to prompt the evaluator to continue to check the target item of the target vehicle; gradually perfecting the risk check path of the target vehicle until the risk chain closed loop is completed; and calling an estimation formula to calculate a residual estimation value of the target vehicle, and outputting a structured risk assessment report.

The invention discloses a method and a system for predicting the lumpiness of open-air throwing blasting, and relates to the technical field of rock blasting engineering. The invention discloses a new calculation relation about a lumpiness similarity proportion, relates to the technical field of open-air loose blasting mining, and can predict blasting lumpiness of projects such as mines, tunnels and the like by scaling a lumpiness distribution proportion. The method comprises the following steps: firstly, acquiring a rock sample from a blasting site, measuring parameters such as rock density and fracture toughness, and acquiring blasting parameter information of a to-be-blasted area; and then, a numerical simulation model under an open-air throwing blasting similar material is constructed by using a numerical simulation method, the change rule of blasting fragmentation lumpiness along with explosive unit consumption in a scale model test is studied, and the blasting lumpiness in a field blasting area is predicted according to the blasting lumpiness similarity ratio. According to the method, the change rule along with the unit consumption of the explosive is analyzed, the utilization rate of crushing energy of the explosive in surface blasting engineering is improved, reference is provided for blasting lumpiness control, and prediction of field blasting lumpiness before blasting is achieved.

The invention relates to the field of carbon fiber composite material mechanics, in particular to a fine evaluation method for the strength of a small-size large or full-opening CFRP (carbon fiber reinforced plastic) shell, which comprises the following steps: establishing a microscopic damage model containing material anisotropy, defining damage variables and evolution equations, uniformly quantifying multi-scale damage behaviors such as fiber fracture, matrix cracking and layered expansion, and evaluating the strength of the small-size large or full-opening CFRP shell. The problem that a traditional single-scale model cannot represent the multiphase coupling effect is solved; through cross-scale transmission of damage variables, whole-process prediction from microscopic damage initiation to macroscopic failure is realized, and the precision of strength evaluation of the complex opening structure is significantly improved. The method realizes refinement, reliability and engineering practicability of strength evaluation of the small-size large-opening or full-opening CFRP shell, is particularly suitable for the fields of aerospace and the like with extremely high requirements on structural weight and safety, and has remarkable technical leading and application value.

The invention provides a digital twin hydraulic engineering large language model reasoning method based on tensor parallel processing. The method sequentially comprises the following steps: S1, gradient calculation optimization; s2, carrying out self-attention tensor dimension reduction blocking; s3, normalizing and parallelizing a low-dimensional layer; s4, inter-layer overall weak extension resource scheduling processing is carried out on the low-dimensional layer after middle-layer normalization processing in the step S3; and S5, converting an intermediate tensor result of a decoder into a specific predicted value. According to the method, the parameters and the input data of the large-scale language model are subjected to tensor segmentation and are distributed to the computing nodes for parallel processing, so that the reasoning efficiency of the large-scale model is remarkably improved. By using the method, the calculation efficiency and the real-time performance of the hydraulic engineering large language model can be greatly improved, the calculation nodes can work simultaneously by processing the reasoning task of the large-scale model in parallel, the calculation burden of a single node is remarkably reduced, larger-scale hydraulic engineering data can be processed, and the calculation efficiency and the real-time performance of the hydraulic engineering large language model are improved. And efficient and accurate risk prediction capability can be maintained.

The invention provides a physical-numerical hybrid scale model test method for a floating fan, and belongs to the technical field of offshore wind power generation. The test method comprises the steps that the floating fan is modeled into a physical model and a numerical model, and besides the physical model and the numerical model, a test platform further comprises a measuring system and a loading system; real-time interaction is carried out on actual measurement signals of the measurement system and calculation results of the numerical model through the computer, loading of the signals is achieved through the loading system, and building and closed-loop control of the hybrid test platform are achieved. And finally, required information is extracted through visual interface monitoring. According to the method, the dynamic response performance of the key structure can be accurately captured, and a series of problems of scale effect, scale criterion incompatibility caused by the scale effect, difficulty in indoor test simulation of the offshore floating fan and the like can be solved.

The invention discloses a wind driven generator structure high-cycle fatigue cross-platform simulation method based on multi-scale coupling modeling, and aims to accurately evaluate a fatigue damage state of a wind driven generator structure under the action of a high-cycle low-amplitude load. Firstly, a multi-scale model based on coupling of a fan overall structure finite element model unit and a local cracking area near-field dynamic model is established, and fan power time-history analysis is carried out based on a finite element analysis platform; then response data are transmitted to a processing platform, a rain flow counting method and a linear damage accumulation criterion are combined, the fatigue damage state of the connection key is determined, a fatigue life attenuation value is calculated, the fatigue cycle number needed by local crack propagation is determined, and the residual fatigue life of the unbroken key is updated; and finally, correcting the fan model, removing a newly added fracture key at the tip of the crack, and repeating dynamic time-history analysis and damage updating until the structure fails. According to the method, whole-process simulation and fatigue behavior analysis of fan fatigue crack propagation are realized, and important reference is provided for fan design optimization and maintenance.

The invention relates to the technical field of artificial intelligence, and discloses a multi-level large model scheduling and knowledge base adaptive optimization method and system, and the method comprises the steps: obtaining a to-be-processed task, carrying out the preprocessing of the to-be-processed task, and calculating the complexity of the to-be-processed task; processing the to-be-processed task by using the small model to obtain a test processing result, and calculating the maturity of the test processing result; determining a scheduling strategy of the large model according to the complexity and the maturity, and processing the to-be-processed task by using the scheduling strategy to obtain a processing result; and optimizing a small model or a knowledge base related to the scheduling strategy based on a processing result. By calculating the complexity of a to-be-processed task and testing the maturity of a processing result, models of different scales are selected accordingly, calling of a super-large model is reduced, the calculation cost is reduced, the system response efficiency is improved, a small model or a knowledge base is optimized through a historical processing result, the processing capacity of the small model and the knowledge base is improved, knowledge is continuously accumulated, and the processing efficiency is improved. And repeated calculation is reduced.

The invention discloses an underground comprehensive pipe gallery electric power cabin plugging selection method based on a fire situation, and relates to the technical field of urban underground management. According to the method, FDS fire dynamics simulation software is utilized to construct a blocked underground utility tunnel electric power cabin full-size model and a non-blocked underground utility tunnel electric power cabin full-size model, fire spreading conditions under the two models are simulated, extinguishing time and fire spreading images are recorded, and fire evolution under different conditions is determined; based on the extinguishing time and the fire spreading image, whether plugging is carried out or not is selected, the safety and fireproof performance of the underground comprehensive pipe gallery power cabin are improved, further spreading of the fire is stopped in time, the resident safety is improved, social resources are guaranteed, follow-up reconstruction is facilitated, and resource waste is reduced.

The invention discloses a railway investigation and design standard specification retrieval method based on a large model. The railway investigation and design standard specification retrieval method based on the large model comprises the steps that 1, the railway investigation and design standard specification retrieval method based on the large model is provided; s2, utilizing the large model to construct a knowledge graph for railway investigation and design standard specification retrieval; s3, using a vector database to carry out vectorization storage on the article information; s4, extracting key information in the user question by using the large model; s5, screening the standard specification articles through a multi-path recall mechanism; and S6, performing secondary sorting by using the large model and outputting a result. According to the method, accurate retrieval of railway investigation and design standard specifications is realized, and effective technical guarantee is provided for improving the quality of railway investigation and design results; constructing a knowledge graph for railway survey design standard specification retrieval; and a multi-path recall and large model secondary sorting method is adopted, so that the comprehensiveness, the accuracy and the stability of standard article retrieval are improved.

The invention provides a civil engineering field knowledge large model retrieval enhancement generation method. The method comprises civil engineering domain knowledge processing and vectorization storage, civil engineering domain knowledge capability evaluation and adaptation of a large language model, a civil engineering domain knowledge vector database efficient retrieval mechanism and knowledge enhancement generation guidance based on retrieval results. According to the method, knowledge in the civil engineering field is encoded and established into a vector database through preprocessing processes such as data cleaning and text segmentation; a general large language model suitable for civil engineering field knowledge retrieval enhancement generation is selected through a semantic confusion index, target knowledge is retrieved through similarity calculation to form cue words, and the civil engineering field professional knowledge generation ability of the large language model is enhanced.

The invention relates to the technical field of wind tunnel tests, in particular to a large-scale model wind tunnel test device and a debugging method thereof. The scale model wind tunnel test device comprises a supporting device which comprises an upper supporting truss, a lower supporting truss and a plurality of supporting stand columns, the upper supporting truss and the lower supporting truss are composed of main beams and anchoring structures, the supporting stand columns are connected with the upper supporting truss and the lower supporting truss, and sliding rails are symmetrically arranged on the upper supporting truss and the lower supporting truss. The large-scale model is connected between the two sliding rails through a line spacing adjusting device and an inclination angle adjusting device, large-scale model assemblies are fixed to the upper end and the lower end of the large-scale model through detachable force transmission plates, and the force transmission plates are connected with a force measuring balance; the large-scale model is provided with a pressure measuring hole, and the scale ratio of the large-scale model is greater than 1: 20; a wind pressure sensor is arranged in the data acquisition device, the data acquisition device is connected with the pressure measuring hole of the large-scale model through a pressure measuring pipe, and wind pressure is transmitted to the wind pressure sensor of the data acquisition device through the pressure measuring pipe. The large-scale model wind tunnel test device and the debugging method thereof are convenient and fast to install and flexible to adjust.

The invention provides a matching recommendation method and system based on large-model digital talent inventory, relates to the technical field of artificial intelligence and human resource management, integrates large-model analysis, employee portrait modeling, intelligent matching recommendation and visual display of comparative analysis, has the characteristics of automation, high precision and interpretability, and is suitable for popularization and application. The problems of difficult man-post matching, inaccurate talent evaluation, opaque selection process and the like are solved, and digital talent selection and intelligent decision making are realized.

The invention relates to the technical field of data testing, in particular to a testing method and system based on large model multi-modal generation and dynamic verification, and the testing method based on large model multi-modal generation and dynamic verification comprises the following steps: initializing a testing system, deploying a pre-trained large model, and generating testing data through the large model; dynamically detecting the generated test data through a preset abnormal mode knowledge base, and feeding back an abnormal result; and according to an abnormal result, reasoning and positioning an abnormal root cause through a preset causal reasoning model, and carrying out interpretable causal analysis output. According to the method, an iteration mechanism of test data generation, detection and feedback can be established, the generation work of the test data is reversely optimized through the abnormal result, all the generated test data can be ensured to pass basic verification, the compliance of the test data is ensured, dynamic detection is performed through the preset abnormal mode knowledge base, and the test efficiency is improved. Compared with a traditional static model, the method is higher in detection accuracy and processing efficiency.

The invention discloses a video question answering method for specific domain fine-grained heuristic prompt. The method comprises the following steps: 1, designing a prompt template of a specific domain through data engineering; performing space-time cutting processing on the original video data; the space-time cutting treatment is carried out; 2, taking the prompt template and the clipped video data as input of an entity prompter and an action prompter to obtain a heuristic label; and 3, through a heuristic enhanced answer reasoning scheme, collaborative interaction between the visual language basic model and the entity prompter and between the visual language basic model and the action prompter is enhanced, that is, a label output by the visual language basic model is compared with a heuristic label, and the ability of the visual language basic model to accurately deduce answers is improved. According to the method, a large-scale model is converted into a dynamic knowledge engine, complex content can be efficiently and accurately analyzed, and attention to basic cross-modal elements is enhanced by integrating a fine-grained inspiration method in a specific field into design of a prompt template.

The invention belongs to the technical field of intelligent manufacturing of aeronautical manufacturing enterprises, and relates to an assembly process procedure auxiliary compilation method based on a large model, which comprises the following steps: data preparation and processing: collecting and preprocessing an assembly process procedure text file; carrying out algorithm model selection and fine tuning, using an assembly process procedure text as a training data set and a test data set, and selecting different types of large models to carry out fine tuning; service publishing: deploying a plurality of large models with the best evaluation indexes as services, publishing the services, and externally providing available interface services; constructing an RAG knowledge base, vectorizing the assembly process procedure text, and constructing a vector knowledge base; intelligent question and answer: performing knowledge question and answer based on the RAG knowledge base; and assembly process procedure file generation: constructing the assembly process procedure file through text generation. According to the method, the functions of semantic retrieval intelligent question answering of the assembly technological procedure knowledge base, automatic assembly technological procedure file generation, information recording and the like are achieved.

The invention discloses a fluid distribution dynamic regulation and control method and device based on a shale reservoir microstructure, and relates to the technical field of oil and gas exploitation. The method comprises the steps that microstructure parameters of a target shale reservoir are obtained through a core experiment, and a multi-scale model is constructed based on the microstructure parameters; determining control equations of different physical fields of the multi-scale model; coupling the control equations of different physical fields, and resolving through a finite element method; performing a numerical simulation experiment based on the multi-scale model and the resolving result to determine regulation and control parameters; the regulation and control parameters are input into the multi-scale optimization model to optimize the regulation and control parameters, and a regulation and control strategy is obtained. The problems that an existing displacement experiment is single, the complex interaction process under the actual mining environment is not comprehensively considered, and the guiding effect on mining work is limited are solved. Business personnel can be guided to formulate a development scheme more scientifically and accurately, the mining efficiency and the success rate are improved, the development cost can be reduced, and meanwhile economic benefits are improved.

The invention provides a work order generation method and device based on large model intelligent classification, equipment and a medium, and the method comprises the steps: constructing a plurality of first labels and second labels according to first-level classification, second-level classification and third-level keywords, the third-level keywords being classification subjects of classification description information; when the work order description information is obtained, candidate tags are determined from the second tags based on a classification model, and the corresponding classification description information is configured as model cue words to determine identification tags; and determining the first tag corresponding to the secondary classification included in the identification tag as a target tag, and generating a target work order based on the target tag and the work order description information. According to the technical scheme, each scene classification only corresponds to one second label, the detailed classification description information serves as additional information of the three-level keywords, the number of the classification labels is reduced, illusion classification is avoided by taking the classification description information as the model cue word, and the recognition efficiency and accuracy of the large model are improved.

The invention discloses a design method and system of a cylindrical ring rib pressure-resistant shell ultimate bearing capacity similar model, and relates to the field of cylindrical ring rib pressure-resistant shells. The problem of design of an existing ultimate bearing capacity similar model of the pressure-resistant shell structure is solved. The method comprises the following steps: S1, determining size parameters of a pressure-resistant shell prototype, and establishing a finite element model of the pressure-resistant shell prototype; s2, defining the relation between the scale ratio of the grid width of the cylindrical ring rib pressure-resistant shell and the scale ratio of the radius and the scale ratio of the plate thickness of the cylindrical ring rib pressure-resistant shell; s3, calculating the scale ratio of the shell plate grid width of the cylindrical ring rib pressure-resistant shell; s4, calculating a relationship between parameters such as the scale ratio of the geometric length of the cylindrical ring rib pressure-resistant shell and the number of ring ribs of the scale model; s5, calculating the number of ring ribs of the reduced scale model based on S3 and S4; s6, determining size parameters of the cylindrical ring rib pressure-resistant shell reduced scale model, and establishing a finite element model of the reduced scale model; and S7, respectively analyzing the buckling strength and the ultimate strength of the pressure-resistant shell prototype and the pressure-resistant shell reduced-scale model, verifying the accuracy of the reduced-scale model, and completing the design of the model.

The invention discloses a steel surface defect detection method based on a YOLOv8 multi-scale convolution attention mechanism, and relates to a steel surface defect detection method. Firstly, a multi-scale convolution attention mechanism MSCA module is introduced into a backbone network, accurate expression of small target features is enhanced, and the detection precision is improved; besides, a brand-new deformable convolution C2fDCNv2 module is provided to replace an original C2f module, so that the capability of capturing complex shape and irregular target features by the model is enhanced, and the efficiency of feature extraction is further improved; and finally, replacing the original loss function with a Wise-IoU (WIoU) loss function, accurately measuring the similarity between the target frames, and improving the defect detection performance of the model. According to the model, an experiment is carried out on an NEU-DET data set, an experiment result is compared with different-scale models of YOLOv8, the steel defect detection precision is effectively improved, and the industrial detection requirement is better met.

The invention discloses a target prediction method for small sample data, and the method comprises the steps: extracting a target small sample time sequence data set, taking moments as groups, constructing a phase distribution sequence, and constructing a phase distribution transfer matrix; constructing a position generation method according to the phase distribution sequence and the small sample time sequence data set; then generating random phase distribution information by the system, and rapidly obtaining a large-scale model prediction training data set in combination with a phase distribution transfer matrix and a position generation method; finally, according to the model prediction training data set, an intelligent prediction model is constructed, model parameters are updated by pre-training the intelligent prediction model, and target intelligent prediction oriented to small sample data is achieved. The method can effectively solve the problem of state transition between adjacent moments, further generates large-scale high-quality training data, remarkably improves the accuracy of an intelligent prediction model, and solves the problem of audio and video generation in a real scene such as a small sample scene.

The invention provides a large model evaluation method, a large model optimization method and device, electronic equipment, a storage medium, an intelligent agent and a program product, and relates to the technical field of artificial intelligence, in particular to the technical field of large models, the technical field of evaluation analysis, the technical field of optimization and the like. According to the specific implementation scheme, a dialogue log is obtained, wherein the dialogue log comprises dialogue content and an intermediate result; based on the dialogue content features of the dialogue content, determining evaluation nodes and a plurality of evaluation indexes belonging to the evaluation nodes, the evaluation nodes being used for evaluating sub-processes executed by the large model; based on the dialogue log, performing evaluation analysis on the large model, and determining respective evaluation results of the plurality of evaluation indexes; and based on the respective evaluation results of the plurality of evaluation indexes, determining a target evaluation result for the large model.

When a sliding main bearing of a wind driven generator is under a heavy-load working condition, a lubricating oil film in a starting stage is not enough to form a complete fluid lubricating film, namely, a friction surface forms a boundary lubrication state, and friction in the stage seriously affects the service life of the bearing. In this way, a macroscopic model and a microscopic model are coupled, and a multi-scale model is verified through experiments. The macroscopic model is established based on an actual structure of a wind power main shaft sliding bearing, and is equivalent to a three-layer structure of a rigid bearing base, an elastic surface supporting layer and a main shaft; a three-layer microcosmic molecular model representing'main shaft-lubricating oil-elastic supporting layer 'is established by the microcosmic model facing the boundary lubrication state of a friction pair. Wherein the thickness of the supporting layer is changed by the macroscopic model, the components of the supporting layer material are changed by the microscopic model, the friction coefficient of the tribological interface under different supporting layer thicknesses and material components is analyzed through the macro-micro cross-scale analysis model, and the optimal thickness and the optimal material component proportion suitable for the service condition of the wind power main shaft sliding bearing are obtained.

The invention discloses a real-time intelligent prediction method for fatigue life of a mine hoist based on digital twinning. A physical scale model is established; establishing a digital twinborn model; aggregating the multi-source monitoring data and the elevator use information from the physical scale model and the simulation data from the elevator finite element model to obtain original aggregated data, and extracting key features by adopting a feature selection model; the method comprises the following steps of: predicting the service life of an entity elevator, training an elevator service life prediction model by using historical characteristic data to obtain a trained elevator service life prediction model, receiving multi-source monitoring data from a physical scale model in real time, predicting the service life of the elevator, and obtaining a prediction result and a corresponding adjustment control decision. And then an adjustment control decision is carried out on the finite element model of the elevator, the prediction precision is adjusted according to a simulation result, and finally a real-time intelligent prediction model of the fatigue life of the elevator is obtained. The method can effectively predict the fatigue life of the mine hoist in real time.

The invention relates to the field of large model security testing, and particularly provides a large model output content security testing method and device, and the method comprises the following steps: S1, preparing and managing a test set, a sensitive word library and a regular expression which are required by testing; s2, reading a test set, and obtaining a large model output result according to the test set and the large model interface information; s3, judging whether the output content of the large model is safe or not according to the sensitive lexicon and the regular expression; s4, extracting semantic risk features according to the output content of the large model by using the large model and the oriented Prompt, and automatically storing the semantic risk features after confidence verification; and S5, storing the information result of each request in a file. Compared with the prior art, the test time can be shortened, and the evaluation efficiency can be improved; and the security of the output content of the large model can be effectively evaluated by using a method for dynamically constructing the sensitive word bank by using the output result of the large model.