12 results about "Similarity test" patented technology

The invention provides a rub-impact fault nonlinear characteristic evolution law analysis method based on peak statistics in a period, and the method comprises the steps: firstly building a rub-impact fault model, and selecting two kinetic parameters in the model as analysis parameters; according to the selected dynamic parameter value interval range, a two-dimensional parameter space is established, grid division is carried out, a plurality of grid points are obtained, and each grid point corresponds to one parameter combination; performing numerical integration on each parameter combination to obtain a converged model output steady-state track; and continuously capturing a plurality of extreme points in the steady-state track to perform cycle similarity test, counting the number Nspike of spike pulses in a single cycle as a characteristic quantity, mapping the characteristic quantity to a two-parameter plane to generate an isoperidic map, and obtaining a distribution range of a chaos interval in the isoperidic map. According to the method, the calculation complexity of parameter sensitivity analysis can be remarkably reduced on the premise of keeping accurate identification of chaotic threshold parameters by counting the peak characteristics of the system in periodic oscillation.

This invention discloses a physical similarity test method for roof and floor deformation during mining based on distributed optical fibers. It constructs an integrated optical fiber observation network for the roof and floor within a similar physical model, supplemented by low-temperature lattice coding calibration and orthogonal strain coupling at intersection points. This invention achieves full-field, continuous, and high-resolution acquisition of the roof and floor deformation field and fracture development process under mining conditions, significantly improving the spatial resolution and strain measurement accuracy of roof and floor deformation monitoring during mining. It enables synchronous distributed monitoring and unified spatial coordinate calibration of the roof and floor at different height interfaces. Furthermore, it achieves continuous and detailed observation of the entire roof and floor deformation process during mining, providing reliable experimental evidence and data support for evaluating the stability of the roof and floor in coal mines.

The invention discloses a reduced scale similarity test platform and method for slope type gravity energy storage, and relates to the technical field of energy storage, and the platform comprises an upper warehousing system, a slope transportation simulation system, a lower warehousing system, an energy storage load-carrying vehicle, an intelligent monitoring and control system, and a multi-parameter test analysis system. Through combination of the height adjustable characteristic of the upper warehousing system and the fixed foundation of the lower warehousing system, different terrain height differences and gradient conditions can be flexibly simulated, the energy storage load-carrying vehicle is strictly designed based on the physical similarity theory, accurate correspondence of dynamic characteristics between a reduced scale model and a prototype is ensured, and the dynamic performance of the energy storage load-carrying vehicle is improved. Through cooperative work of the intelligent monitoring and control system and the multi-parameter test analysis system, real-time sensing, comprehensive data acquisition and deep analysis of the operation state of the platform are achieved, and the automation degree, the measurement precision and the research efficiency of the test are greatly improved.

The application discloses a self-propelled model power similarity design method of a super-high-speed planing boat, and relates to the ship technology field.The method estimates a model power system according to static water towing test data after a self-propelled model is manufactured, then carries out static motion test and dynamic planing test on the self-propelled model loaded with the model power system to obtain a change curve of a throttle lever operating position with time, a change curve of a speed with time and a relationship curve of the throttle lever operating position and static thrust, and then determines a static thrust correction coefficient through a data analysis method to correct the static thrust of the self-propelled model, and directly carries out power similarity design according to a thrust similarity criterion, which is favorable to improving the precision and practicability of the self-propelled model power similarity test of the super-high-speed planing boat, and can meet the design requirements of the power system of the super-high-speed planing boat and the self-propelled model.

The invention relates to the field of network public opinions, and discloses a hot event discovery method, device and equipment based on a large model. The method comprises the following steps: acquiring text data, calculating Hash fingerprints and repetition times to identify potential hotspots, carrying out preliminary de-duplication, and carrying out BM25 and SimBERT similarity judgment on the potential hotspots and a historical event library to realize preliminary merging; and for inconsistent data, further calling a multi-stage large language model to carry out deep semantic understanding and event attribute judgment so as to exclude non-event information, supplementing networking information to generate a strong general title, and carrying out secondary BM25 comparison and judgment based on the supplemented title so as to finish accurate event aggregation and new event discovery. Through deep semantic understanding of a large language model and a multi-stage collaborative filtering mechanism, the limitation of a traditional method in processing complex or emerging events is effectively overcome, and the accuracy of event aggregation and the completeness of information supplementation are ensured.

The application discloses a rotor system similarity test model design method considering gravity acceleration distortion, and the method comprises the following steps: determining the similarity parameters of a substructure on a target rotor system according to a preset similarity relationship, wherein the preset similarity relationship comprises the similarity relationship of the mass of a rotating shaft, the mass of a rotating disc and the mass of a supporting structure, the similarity relationship of the polar moment of inertia of the rotating shaft and the polar moment of inertia of the rotating disc, and the substructure comprises the rotating shaft, the rotating disc and the supporting structure; calculating experimental parameters based on the similarity parameters and actual parameters of the target rotor system; and establishing a similarity test model corresponding to the target rotor system based on the experimental parameters. The rotor system is divided into three substructures of the rotating shaft, the rotating disc and the supporting structure, and it is proposed that the similarity of the substructures can guarantee the similarity of the entire rotor system, the obtained similarity test model guarantees that the inherent frequency and the vibration response are completely consistent with the target rotor system while the size is reduced, and the cost, period and risk of the similarity model experiment can be further reduced.

The present application relates to the multi-dimensional fusion test evaluation method and system for the deterministic heavy precipitation forecast, comprising the following: obtaining the model precipitation forecast data and the site live data of the target evaluation area as the to-be-evaluated data; inputting the to-be-evaluated data into the pre-established heavy precipitation multi-dimensional fusion test evaluation system to obtain the evaluation result; and visually displaying the evaluation result; the present application supplements the model precipitation forecast process stability evaluation method and the spatial test similarity test method suitable for the heavy precipitation characteristics on the basis of the traditional test method, and adds more test dimensions for the model precipitation forecast. The present application further constructs the multi-dimensional fusion test evaluation method, the multi-dimensional test index can be displayed through the graph, the model development and the user can intuitively understand the prediction ability of each dimension of the model; and the comprehensive test evaluation index (CEI) formed reflects the comprehensive evaluation result of the six core precipitation prediction abilities of the model.

The present application relates to a kind of automatic discovery data quality inspection rule method.The present method includes that for the data table that has been entered in the warehouse, system automatically executes rule discovery task, and forms single table rule;Management personnel according to system current standard and experience judge whether the single table rule formed in the previous step has secondary cascade relationship, if the single table rule has secondary cascade relationship, then manually confirm, then using the single table rule after confirmation to the data table for discovering the single table rule carries out quality inspection, and after quality inspection is completed, the single table rule changes into quasi rule;All quasi rules found in multiple data tables are subjected to similarity check, and the same or similar quasi rules of multiple data tables are combined to generate general rule, and the generated general rule is used for the quality inspection of all data tables.The present method can be based on the discovery of single table rule, and the combination of multi-table quality inspection rule is carried out by similarity test, so as to provide the basis for the discovered general data quality inspection rule applicable to all data tables.

The application discloses a kind of high density pool water treatment dosing quantity prediction method, in method, the water inflow of high density pool, water inflow turbidity, water inflow pH, water inflow temperature, raw water conductivity, raw water dissolved oxygen, raw water COD (chemical oxygen demand), raw water ammonia nitrogen, pre-coagulation dosing flow are collected and recorded, data are analyzed and pretreated to analyze the correlation of characteristic and target data Test and similarity test, predict via PCA_LSTM_ELM residual, and compare its error range with true value, long short memory neural network LSTM is used to predict extreme learning machine ELM residual value, use the first 50% of ELM residual value as test set, use the last 50% of ELM residual value as validation set, calculate and determine to obtain final prediction value, compare final prediction value with true value in this time interval.

The invention relates to the field of data processing, and discloses a vehicle test problem similarity matching method and a computer readable storage medium, and the vehicle test problem similarity matching method comprises the steps: obtaining a vehicle problem; determining hierarchical information of a main object of the vehicle problem in a whole vehicle system; performing term standardization processing on the vehicle problem to obtain a matched problem phenomenon; according to the hierarchical information and the problem matching phenomenon, a high-similarity test problem list is obtained from a preset test problem library, noise interference caused by vehicle problem description differences can be reduced, the matching accuracy of the vehicle problems in the test problem library is improved, and the vehicle problem matching accuracy is improved. And the main object of the vehicle problem is subjected to hierarchical division, so that the matching range is narrowed, and the efficiency and accuracy of test problem similarity matching on the vehicle problem are further improved.

The application provides a track static irregularity trend prediction method, system, electronic equipment and storage medium. The method comprises the following steps: performing similarity test on a plurality of acquired historical data by using a dynamic time warping algorithm, and representing the similarity test result by using a path matrix; fitting and correcting the path matrix by using a robust regression method to obtain fitting data, and repeatedly performing similarity test on the fitting data until the fitting data meets a preset condition, and then ending the fitting and correction to obtain a dynamic warping path matrix; rechecking necessary mileages in the path matrix and the dynamic warping path matrix by using a correlation coefficient method; aligning the plurality of historical data when the rechecking requirement is met; and training the aligned plurality of historical data by using a long short-term memory network model with preset hyperparameters. The application can make accurate prediction on small-scale static single data in the track irregularity degradation state within a range of several meters.

Method and servers for performing a privacy-preserving distribution similarity test between first and second data being available to a first and a second client respectively. The method includes generating a target upper boundary value, a target lower boundary value, and a target step size based on first and second boundary values and step-sizes received from the first and second clients receptively, generating non-sensitive testing values, causing generation of first and second Empirical Cumulative Distribution Function (eCDF) values by the first and second client respectively based on the non-sensitive testing values and on the first and second data respectively, acquiring first encrypted eCDF values from the first client, providing encrypted statistic data to the first client, acquiring a result of a privacy-preserving distribution similarity test from the first client, and identifying the first client and the second client as suitable participants for the federated learning task.