14results about How to "Good predictive accuracy" patented technology

The invention relates to the technical field of seat cushion framework detection, in particular to an automobile seat cushion framework fatigue test method and system. The method comprises the following steps: acquiring information data of an automobile seat cushion framework sample; performing skeleton key part screening on the automobile cushion skeleton sample information data to obtain skeleton key part screening data; performing sensor data acquisition on the automobile seat cushion framework sample based on the framework key part screening data to obtain stress sensor acquisition data and deformation sensor acquisition data; and carrying out loading mode design on the information data of the automobile seat cushion framework sample to generate fatigue loading mode data of the automobile seat cushion framework. According to the invention, the accuracy and reliability of the fatigue test of the automobile seat cushion framework are improved by accurately screening key parts of the framework, designing a real loading mode, deeply detecting damage, predicting the service life and optimizing materials.

The invention discloses a multi-element aluminum alloy quenching sensitivity prediction and optimization model based on phase diagram calculation. According to the model, mass fraction distribution diagrams of eta phases with different Zn and Mg total amounts and Zn / Mg ratios under the condition that the Cu content is determined in the aluminum-rich angle multi-element aluminum alloy are calculated through a CALPHAD method, then correlation models of M eta, CZn + Mg and RZn / Mg are established for the M eta distribution diagram through a Radial Taylor model, distribution diagrams of M eta and # imgabs0 # under different CZn + Mg and RZn / Mg are drawn, then the model is optimized through a quenching sensitivity experiment, and the aluminum-rich angle multi-element aluminum alloy is obtained. According to the model, the quenching sensitivity of the multi-element aluminum alloy is predicted by constructing a correlation model of M eta, CZn + Mg and RZn / Mg, a predicted value of the model is verified in combination with a TTP curve and a TTT curve, and the model is optimized, so that the accuracy and rapidity of predicting the quenching sensitivity of the multi-element aluminum alloy by the obtained model are ensured.

The utility model discloses a portable intelligent substation microstation equipment inspection device, including front end equipment, back end equipment and cloud equipment. Front end equipment is handheld inspection terminal, and integrated main control board, camera, display screen, fingerprint identification, communication interface etc. parts are used for information collection and interaction, and cloud equipment includes equipment information database, fault log record library, solution library, and is connected with front and back end equipment through information interaction module, realizes data storage and scheme calling, and back end equipment includes video processing, state monitoring, fault prediction, personnel scheduling and information transmission etc. module, forms the automation inspection and fault processing process. The device has the advantages of flexible deployment, accurate identification, efficient response, and can improve the intelligent degree of inspection and the substation operation and maintenance efficiency.

The present invention relates to the field of seat cushion frame detection technology, and in particular to a method and system for automobile seat cushion frame fatigue testing. The method comprises the following steps: obtaining automobile seat cushion frame sample information data; screening key parts of the frame for the automobile seat cushion frame sample information data to obtain key part screening data for the frame; collecting sensor data for the automobile seat cushion frame sample based on the key part screening data for the frame to obtain stress sensor collection data and deformation sensor collection data; designing a loading mode for the automobile seat cushion frame sample information data to generate automobile seat cushion frame fatigue loading mode data. The present invention improves the accuracy and reliability of automobile seat cushion frame fatigue testing by accurately screening key parts of the frame, designing a true loading mode, conducting in-depth damage detection, life prediction, and material optimization.

The present application relates to the technical field of scraper conveyor, and particularly relates to a chain breakage fault pre-judgment device and method of a scraper conveyor and the scraper conveyor, the device comprising: a controller, a first plug plate and a first magnetic sensor fixed to the first plug plate; a conveying groove of the scraper conveyor is provided with a first opening, the first plug plate is inserted into the first opening and located at a side of the chain close to a chain channel, and the first magnetic sensor is fixed to the first plug plate and opposite to one side of the chain; in the process of running of the chain of the scraper conveyor, the first magnetic sensor collects a first magnetic characteristic signal of a chain link passing through the first magnetic sensor; the controller is connected with the first magnetic sensor, the damage state of the chain link is analyzed according to the first magnetic characteristic signal, and whether the chain link is a chain breakage fault risk point is pre-judged based on the damage state. In this way, the present application pre-judges the chain damage of the scraper conveyor in advance, so as to realize the early warning and preventive maintenance of the chain breakage fault, and the operation reliability and safety of the scraper conveyor are improved.

The invention relates to the field of early diagnosis of deciduous tooth caries of preschool children, in particular to a marker and a model for diagnosing and / or predicting caries of preschool children and a construction method thereof, the marker comprises age, urban and rural classification of residence, feeding modes of children within 6 months after birth, whether toothache experiences exist or not, and whether teeth are seen or not. Discrimination indexes of the constructed prediction model comprise AUC, sensitivity and specificity, the AUC, the sensitivity and the specificity are respectively 0.746, 0.765 and 0.624 in a model development set, 0.717, 0.680 and 0.678 in an internal verification set and 0.804, 0.807 and 0.660 in an external verification set, a calibration curve shows that the model prediction accuracy is good, and the model prediction accuracy is high. Clinical decision analysis and a clinical influence curve show that the prediction model conforms to clinical application conditions within a reasonable threshold range.

The present invention discloses a method, device, equipment and readable storage medium for optimizing horizontal federated modeling, the method comprising: using a preset local data set to perform parameter update operation on a local basic model, obtaining parameter update information corresponding to a private part model and a public part model in the local basic model, wherein the public part model structure of each participating device is the same; sending the parameter update information corresponding to the public part model to a coordination device participating in horizontal federated modeling, so that the coordination device can perform federated fusion on the parameter update information sent by each participating device to obtain global update information; using the global update information received from the coordination device to update the model parameters in the public part model, and using the parameter update information of the private part model to update the model parameters in the private part model, so as to update the local basic model. After the present invention realizes horizontal federated modeling, each participating device can flexibly adjust its own local model.

The invention relates to the technical field of food biology, in particular to a method for predicting the activity of amylase subjected to an inactivation process or inactivated amylase. The invention provides a method for predicting an inactivation process based on required amylase activity, which comprises the following steps: providing the required amylase activity, presetting an inactivation temperature, and calculating the inactivation time by adopting an amylase activity-inactivation time prediction model according to the required amylase activity. The invention also provides a method for predicting the activity of the inactivated amylase based on the inactivation process, which comprises the following steps: providing the inactivation temperature and the inactivation time, and calculating the activity of the inactivated amylase by adopting the amylase activity-inactivation time prediction model according to the inactivation temperature and the inactivation time. The method disclosed by the invention has relatively good predictive capacity, and is beneficial to accurately controlling the inactivation process conditions; by optimizing the inactivation process, the production time and cost can be reduced, and the production efficiency is improved; the enzyme activity is strictly controlled or the enzyme is inactivated, so that the food quality safety is ensured.

This invention discloses a numerical method for transparent characterization of stress fields in mining engineering, belonging to the field of numerical simulation. The method includes: constructing a standardized numerical computation environment and determining the target processing area; establishing a three-dimensional rock mass mesh model and performing mechanical equilibrium solutions, simultaneously acquiring stress component and elastic strain energy data to achieve stress-energy dual-dimensional characterization; based on preset target center coordinates, real-time analysis and direct storage of spatial distance and angle information of each mesh point; writing node coordinates, displacement, stress, energy, and element connection relationships into a unified ASCII universal format file to generate a cross-platform readable data file; and finally, data verification and output. This invention overcomes the limitations of traditional methods, such as single characterization dimension, reliance on third-party processing for spatial analysis, and difficulties in cross-platform data interaction, significantly improving the efficiency and accuracy of stability analysis in mining engineering.

The invention relates to the technical field of anti-compression peptides, in particular to a blood vessel anti-compression peptide recognition method combining prior knowledge embedding and multi-view enhancement, which comprises the following steps: constructing an ACEIP sequence data set, a nonACEIP sequence data set and an ACE sequence data set, and preprocessing; constructing feature vector codes of the peptide sequences by utilizing the priori knowledge similarity and the molecular docking adaptation degree; and taking coded vectors of ACEIP and nonACEIP sequences as input, and constructing a Deep4ACE model which comprises a feature enhancement module, a model learning module and a model prediction module and is used for predicting the ACEIP and the nonACEIP. The method solves the problems that an existing method utilizes sequence similarity search, molecular modeling and the like, focuses on a primary structure of the peptide, neglects interaction between a space structure and a receptor, and is high in false positive rate of the identified compression-resistant peptide.

The present application relates to a kind of antibody viscosity prediction method and its application, the method includes: (1) extracting physical and chemical characteristics from the variable region sequence and structure of antibody;The physical and chemical characteristics include net charge, hydrophobicity, DeepSCM space charge chart score, space aggregation attribute based on DeepSP prediction, local SCM based on DeepSP prediction, surface hydrophobic region, positive charge region, negative charge region, variable region charge symmetry parameter and length parameter;(2) the features and viscosity value extracted in step (1) are used to train viscosity prediction model;(3) the viscosity prediction model obtained in step (2) is used for prediction;The viscosity judgment method is: greater than 20 cP, high viscosity, less than or equal to 20 cP, low high viscosity.The present application extracts 43 physical and chemical characteristics from the variable region sequence and structure of antibody, and the accuracy of detection is greatly improved compared with the existing prediction method by the viscosity prediction model obtained by training.

The invention discloses a wireless network node working mode adjusting method and device, a medium and a product, and relates to the technical field of wireless network communication, and the method comprises the steps: obtaining the current residual energy of a wireless network node and a solar radiation value at the starting moment of a current time slot, according to the current residual energy and a corresponding solar radiation prediction method, determining a solar radiation prediction value of the wireless network node at the starting moment of the next time slot, and according to the solar radiation value of the wireless network node at the starting moment of the current time slot and the solar radiation prediction value of the wireless network node at the starting moment of the next time slot, determining an energy collection curve of the wireless network node, based on the energy collection curve of the wireless network node, a working mode adjustment model is constructed with the maximum information collection frequency as a target, the working mode adjustment model is solved, the final information collection frequency of the current time slot is obtained, and the environmental energy utilization rate is improved while the service life of the wireless network node is guaranteed.

The invention discloses an anti-aging peptide prediction model and a construction method and application thereof. The construction method comprises the following steps: acquiring a training data set containing positive and negative samples; extracting a plurality of initial features from the peptide sequence; based on a weighted fusion evaluation and greedy search strategy of four models of a random forest, a gradient boosting decision tree, a lightweight gradient elevator and XGBoost, screening out an optimal feature combination composed of CKSAAGP, k-mer, AAindex, secondary structure and tertiary structure features; and by taking the optimal feature combination as input, constructing and training a neural network model which combines a bidirectional long-short-term memory network (BiLSTM) and a global predictor. According to the method, high-precision and high-efficiency prediction of the anti-aging peptide is realized through the optimized feature combination and the model structure.

The embodiment of the invention provides an increment prediction method and device for multiple business targets. The method comprises the steps of obtaining user features of a target user and current intervention features; inputting the user features into an expert module, and separating a first feature related to the first business target, a second feature related to the second business target and a shared feature related to both the first business target and the second business target through the expert module; inputting the first feature and the shared feature into a first gating unit to obtain a first target feature; inputting the first target feature and the intervention feature into a first target tower to obtain a first increment value with intervention relative to non-intervention under the first business target; inputting the second feature and the shared feature into a second gating unit to obtain a second target feature; and inputting the second target feature and the intervention feature into a second target tower to obtain a second increment value with intervention relative to non-intervention under the second business target. The complexity is low, and the prediction accuracy is good.