12results about How to "Good adaptability" patented technology

The invention belongs to the technical field of data processing, and particularly relates to a peptide milk powder activity quality detection method based on wet process production, which comprises the following steps: S1, acquiring fluid time sequence data of a production node and an original reflection spectrum sequence of a spray drying node, performing smooth filtering operation on the original reflection spectrum sequence to obtain a standardized smooth spectrum; s2, importing the smooth spectrum into a pre-configured partial least square regression algorithm model, and calculating and outputting the apparent chemical concentration of the peptide component in the milk powder of the current batch; and S3, based on background features of the smooth spectrum and fluid mechanics features of the fluid time sequence data, constructing an operation index with causal logic to complete depth correction operation on the apparent chemical concentration, and outputting real activity retention concentration with both concentration attribute and activity attribute. According to the invention, the optical measurement error is effectively eliminated, and the real biological value is accurately evaluated.

The utility model relates to an automatic water supply control device, it includes: water pool, the depth of water pool is H, first liquid level switch sets up in water pool, and the height of distance water pool's bottom is L, second liquid level switch sets up in water pool, and the height of distance water pool's bottom is S, first relay, first liquid level switch's output end is connected with first relay's trigger input end, alarm device, second liquid level switch's output end is connected with alarm device, inlet pipe, one end is connected with water source, and the other end is led into water pool, electromagnetic control valve sets up on inlet pipe for controlling the on-off of water route in inlet pipe, the control end of electromagnetic control valve is connected with first relay's output end, wherein, 0.2H <= L <= 0.3H, 0.7H <= S <= 0.8H. The application can realize the water level monitoring for factory production water pool, and can realize automatic water supply, and work is safer and more reliable.

The application discloses an agent autonomous decision-making method and system based on embedded edge computing, comprising the following steps: constructing an initial probability decision space according to an observation state, and performing localized simplification processing on state characteristic parameters under the calculation power and storage constraints of an embedded edge device; sampling the initial state value by using an improved Posen sampling algorithm, generating a decision value update sequence, and iteratively obtaining an updated state value; dynamically adjusting the sampling intensity according to the convergence trend of the updated state value to obtain a state value threshold; correcting the decision value update sequence according to the state value threshold and generating a decision path; feeding back the decision path to the initial probability decision space to calibrate the state transition probability; and finally obtaining a real-time optimal action strategy based on the calibrated state transition probability. The application effectively improves the real-time performance and reliability of the autonomous decision-making process in the embedded resource-constrained environment.

The application discloses a kind of multi-scene switch cabinet partial discharge simulation and fault reproduction system, including partial discharge signal acquisition module, data preprocessing module, multi-stage second-order attention twin feature extraction module, equipment topology structure modeling module, graph neural network fusion module and fault reproduction module, constructs the fine feature of partial discharge signal by multi-scale second-order attention twin network, and carries out graph neural network analysis by fusing switch cabinet electrical topology structure, obtains partial discharge fault characteristic mode and accurately generates multi-scene discharge simulation signal.The application significantly improves the precision and scene adaptability of partial discharge simulation, and enhances the accurate positioning ability of fault position.

The application discloses an environment-friendly eutectic solvent anti-friction and anti-wear additive and a preparation method and application thereof, and belongs to the field of lubricating additives.The application uses cetyltrimethylammonium chloride / cetyltrimethylammonium bromide and natural plant source thymol as raw materials, mixes them in a molar ratio of 1:1 to 1:3, stirs them at a constant temperature of 90 DEG C for 2 hours, and thus a sulfur-free and phosphorus-free transparent stable eutectic solvent is prepared without an organic solvent and a catalyst; the solvent is used as an anti-friction and anti-wear additive, is added into lithium-based lubricating grease in an addition amount of 0.5wt% to 2.0wt%, is uniformly dispersed through three-roll grinding, and thus high-performance modified lubricating grease suitable for severe working conditions is obtained.The raw materials are green and renewable, the preparation process is mild and simple, and the environment compatibility is excellent; a low addition amount can significantly reduce a friction coefficient and wear volume, a dense adsorption film is formed on a metal friction surface, and the application is particularly suitable for long-acting anti-friction and anti-wear lubrication under high-speed and boundary lubrication working conditions such as cotton picker picking spindles.

This invention discloses a pressure differential-driven passive discharge valve, belonging to the field of valve technology. The discharge valve includes a discharge valve pipe, a valve, a vent, a pneumatic component, an elastic rope, a metal bellows, a slider, a trigger, a trigger shaft, and an anti-rotation rod. The valve is rotatably mounted inside the discharge valve pipe. The vent connects the discharge valve pipe to the pneumatic component. The elastic rope connects the valve to the inner wall of the discharge valve pipe for energy storage. The pneumatic component is driven by the metal bellows. The slider engages with the lever-shaped trigger, which rotates around the trigger shaft and engages with the valve's unlocking part. When the discharge valve pipe is breached, air enters the pneumatic component through the vent. When the pressure inside the cavity reaches a set value, the metal bellows is compressed, causing the slider to actuate the trigger and unlock the valve. The elastic rope drives the valve to rotate to the normally open position defined by the anti-rotation rod. This invention requires no external power, relying on pressure differential to achieve passive opening, resulting in reliable operation and rapid response.

The invention discloses an anti-surge interference method for an offshore rocket launching platform based on reinforcement learning. The anti-surge interference method comprises the following steps of collecting multi-source data of the platform in real time; carrying out fusion processing on the collected data to obtain a platform position, a posture and a motion state; estimating a longitudinal surge disturbance state of the platform; a real-time state feature vector is constructed after a disturbance state estimation result is processed; inputting the state feature vector into a soft actor commentator algorithm controller to obtain a platform control action suggestion value; checking and adjusting a control action suggestion value according to a safety rule; and executing a final control action to suppress longitudinal surge interference in real time. According to the invention, the precision and adaptability of surge disturbance resistance of the marine rocket launching platform are effectively improved, and the stability of the position and attitude of the platform is remarkably improved.

The invention discloses a snow melting and temperature rising process disaster chain hidden danger recognition method based on big data. The method comprises the following steps that remote sensing snow covers, radar wet snow lines, daily temperature, flow and ground surface deformation data of a research area are obtained and preprocessed; an improved Bayesian online change point detection algorithm is adopted to determine a real-time node triggered by snow melting temperature rise; calculating a snow cover area change rate and a wet snow line change rate to identify a snow melting sensitive area; analyzing the surface deformation data to determine a surface acceleration sensitive area; analyzing the flow data to determine a runoff mutation sensitive area; constructing a disaster chain hidden danger transmission space-time structure model of snow melting triggering, surface acceleration and runoff response; and identifying a disaster chain hidden danger level in real time based on a space-time structure model. According to the invention, real-time and quantitative recognition of regional scale disaster chain hidden dangers is realized, and the accuracy and timeliness of disaster early warning are improved.

The invention discloses a magneto-rheological-electromagnetic cooperative driving intelligent vibration reduction control system, which relates to the technical field of vibration control, and comprises a physical actuation module for providing variable damping force and active actuation force; the digital twinning and prediction module is used for receiving and processing the intrinsic state signal of the physical actuation module; the intelligent cooperative control module is used for predicting the state and an external vibration signal; and the power driving module is used for driving the physical actuation module to work according to the cooperative control instruction. According to the invention, direct sensing of the intrinsic state is realized through the piezoelectric fiber sensor network integrated in the actuator, and the future state and the nonlinear dynamic state of the system are accurately predicted in combination with a high-fidelity digital twin model based on online real-time identification and calibration; an adaptive cooperative control algorithm fusing predictive feedforward compensation and real-time feedback is adopted to generate an accurate cooperative instruction, and high-speed and high-fidelity execution is carried out through a customized special power driving unit.

The application provides a single-cell transcriptome basic model construction method based on a Mamba-Transformer hybrid architecture, and belongs to the technical field of artificial intelligence and bioinformatics. The technical scheme is as follows: quality control and standardization are performed on single-cell transcriptome data to obtain a standardized gene expression matrix; an input embedding containing gene identification, expression value marking and state label is constructed to form a cell-level continuous vector representation; a hybrid network architecture in which Mamba, Transformer and sparse hybrid experts are alternately stacked is constructed, combined with residual connection and layer normalization; joint pre-training is performed in combination with gene expression value prediction and cell representation learning tasks to obtain a basic model. The beneficial effects are as follows: the application overcomes zero inflation and technical noise by learning global context, improves signal-to-noise ratio and feature robustness, constructs a unified representation space to effectively strip batch effects, realizes precise alignment across platforms and modalities, and significantly reduces the dependence on artificial annotation for downstream tasks and saves computing resources by virtue of strong knowledge transfer capability.

The application discloses a kind of multi-task data analysis systems based on hybrid expert model, comprising: data input module is standardized to multi-task original data;Task feature extraction module extracts the corresponding feature expression data in multi-task data set;Hierarchical gated routing module determines the expert path of task using hierarchical routing algorithm;Hierarchical expert network module executes feature fusion according to routing result;Multi-task prediction module executes task prediction to the fused features;Multi-task loss optimization module optimizes model parameters based on gradient normalization and uncertainty weighting algorithm;Model parameter update module realizes parameter synchronous update;Multi-task result output module carries out final correction of prediction result.The application effectively improves the stability and accuracy of multi-task data analysis.