4 results about "Similarity test" patented technology

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 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 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.