13results about How to "Control complexity" patented technology

This invention discloses a method, system, and storage medium for multivariate time-series feature extraction and grade prediction in flotation processes. The method includes the following steps: Step S1: Raw data input and encoding; encoding and structured input of time-series data composed of various process variables; Step S2: Extracting dynamic features; Step S3: Condition-guided encoding modeling; Step S4: Enhancing the saliency of key variables and important time segments; employing a multi-head attention mechanism to match key-value pairs generated from target-guided query vectors and multi-scale features; Step S5: Outputting a prediction module; performing feature fusion and nonlinear mapping on the attention mechanism output to output the predicted concentrate grade and recovery rate for future times. The system and storage medium are both based on the above method. This invention has advantages such as higher intelligence, better controllability, and improved prediction accuracy and model adaptability for key indicators in the flotation process.

The invention provides an oil-water separation effect prediction method and device based on a Lonion model. The method comprises the following steps: defining an input independent variable vector f (x) = [x1, x2,..., x8] T; performing feature engineering extension on the input independent variable vector to generate an extended feature vector f (z); the extended feature vector f (z) comprises an original feature vector, a polynomial feature vector and a physical interaction feature vector; inputting the extended feature vector f (z) into a pre-trained prediction model to obtain a predicted value, output by the pre-trained prediction model, of the oil content of the effluent of the air flotation synergistic device; inputting the predicted value into an integrated prediction framework to obtain a final predicted value and uncertainty estimation; displaying the final predicted value and the uncertainty estimation to a user through a display interface; according to the technical scheme, the accuracy, robustness and physical consistency of oil content prediction of the effluent can be improved.

The embodiment of the application provides a hydraulic fracturing fracture width profile experiment device and method, the method can reconstruct the continuous distribution of the fracture width profile along the height direction of the fracture by combining a plurality of optical fiber discrete measuring points and vertical interpolation, ensures the spatial resolution, controls the complexity and layout cost of the experiment system, and guarantees the physical rationality of the result by non-negative constraint, and meets the monitoring requirement of the complex expansion scene of the multiple cluster fractures.

The application discloses a design method of gas passage shunting of a gas shunting device, and belongs to the technical field of gas shunting devices. The gas shunting device comprises a plug gas distribution seat, at least one middle layer shunting plate and a bottom layer shunting plate which are sequentially connected and can form multiple shunting layers; at least two plug seats are arranged on the plug gas distribution seat and are in communication with the shunting layers; connectors are arranged on the middle layer shunting plate and the bottom layer shunting plate and extend to the outside of the bottom layer shunting plate; the design method is that the number of connectors is set according to the number of interfaces on one plug seat, and the number of shunting layers is determined according to the total number of interfaces on all plug seats. Through the shunting layout design of multiple layers, the number of shunting layers can be increased to reduce the complexity of gas passage layout as the number of device plug increases, the problem of large size of the device can be effectively controlled, the problems of the prior art, such as the complex single-layer gas passage layout, are overcome, the assembly process is simplified, the connection convenience is improved, the assembly efficiency is improved, and the assembly cost is saved.

The invention provides a distributed photovoltaic low-voltage distribution network voltage estimation method and system, and belongs to the field of distribution network voltage assessment, and the method comprises the steps: 1, inputting the voltage data, time information and space information measured by a measurement device into a learning supervision regression model, the learning supervised regression model maps voltage data measured by the measuring device to adjacent low-voltage power distribution network nodes through a random forest algorithm to obtain voltage data of the adjacent low-voltage power distribution network nodes; step 2, training the learning supervised regression model, and when a regression index is minimum, stopping training to obtain a trained learning supervised regression model; 3, performing voltage estimation on unmonitored nodes in the low-voltage power distribution network by using the trained learning supervision regression model; and based on the measurement data of the limited node, mapping the measurement data of the limited node to the adjacent node without the measurement device, and further carrying out voltage estimation on the unmonitored node in the low-voltage power distribution network containing the distributed photovoltaic.

The invention discloses a user question-oriented rapid feedback method and related equipment, belongs to the technical field of artificial intelligence, and aims at effectively improving the shopping experience by utilizing large-scale question and answer data generated by users to assist the learning of questions and related comment tasks. The rapid feedback algorithm based on the attention model has a remarkable effect in solving the problem positioning in the shopping process, and is superior to similar algorithms. According to the method, the user can be helped to quickly find the answer, the shopping efficiency is improved, and the shopping experience of the user can be further improved. In addition, the purchase demand contained in the questions of the user also provides beneficial information for commodity recommendation, so that the comprehensiveness and individuation degree of shopping experience can be further improved by performing commodity recommendation according to the questions of the user.

The invention discloses a highland barley field carbon-ammonia source sink imbalance risk early warning system, and belongs to the field of gas intelligent early warning, an optical system module comprises a wide temperature range wavelength locking double DFB laser set, a first electric control gradient refractive index liquid crystal lens, a second electric control gradient refractive index liquid crystal lens, an off-axis integral cavity, a convergent lens and a detector, and is used for achieving optical detection of CO2, CH4 and NH3 in a highland barley field; the self-adaptive power supply module provides a stable power supply for the system by adopting a magnetic flux-temperature double feedback and time division multiplexing mechanism aiming at the low-voltage and low-temperature extreme environment of the Tibet Plateau. The temperature control module provides high-precision temperature stability control for the laser based on a bimodal dynamic gain-thermal disturbance prediction composite sliding mode control algorithm; and the main control module is used for generating a laser driving signal and performing early warning on the imbalance risk of the highland barley field by using the rough set-deep belief network hybrid early warning module. The problems of poor system stability, low detection precision and slow early warning response in a plateau extreme environment can be solved.

This invention relates to the field of bearing fault identification. To address the problems of existing technologies where anomaly suppression relies on fixed thresholds and multi-domain feature selection lacks operational stability and redundancy constraints, this invention provides a bearing fault identification method and system based on anomaly suppression and multi-domain features. The bearing fault identification method based on anomaly suppression and multi-domain features includes: cleaning the sample set; extracting candidate features from the cleaned sample set; constructing a multi-objective feature scoring function; forming a multi-domain core feature subset from candidate features with scores not lower than the feature selection threshold; training a fault diagnosis model; preprocessing and feature extraction of the bearing vibration signal under the diagnostic operating condition to obtain multi-domain core features; and then processing these features through the trained fault diagnosis model to obtain the corresponding fault type. This method can still stably output bearing fault diagnosis results with high accuracy and low false alarm rate even under noise and fluctuating operating conditions.

The application provides a data processing method of a laser radar, an electronic device and a storage medium. The method is applied to the technical field of laser radars. The laser radar comprises a transmitting unit and a receiving unit. One transmitting unit corresponds to N receiving units, and N is a natural number greater than 1. The method comprises the following steps: selecting echo signals of M receiving units, wherein M is less than or equal to N; and outputting detection information corresponding to a target object according to the echo signals of the M receiving units. The method is beneficial to reducing the process complexity of the laser radar and improving the detection accuracy and flexibility of the laser radar.

The embodiment of the specification discloses a training method and device of a time series prediction model, and a computer system. The method comprises the following steps: first, performing a target operation on a first input by using a differential attention module in the time series prediction model, wherein the first input is determined according to a first number of index sequences corresponding to a first entity, and each index sequence comprises a historical sub-sequence and a target sub-sequence to be predicted. The target operation comprises the following steps: first, processing a query matrix corresponding to the first input by using a learnable first prototype key matrix and a second prototype key matrix respectively to obtain first and second attention scores; then, determining a comprehensive attention score, which is positively correlated with the first attention score and negatively correlated with the second attention score; next, processing a value matrix corresponding to the first input by using the comprehensive attention score to obtain a first output corresponding to the first entity; then, determining a prediction sequence corresponding to the target sub-sequence based on the first output; and finally, training the time series prediction model according to the prediction sequence.

The application discloses a power grid similar operation section sample set construction method and system, and belongs to the technical field of power system dispatching control. The method comprises the following steps: defining initial electrical characteristics of a power grid operation section; building a feature extraction network comprising a graph neural network, a fusion layer and a time sequence encoding layer; adopting a time sequence comparison learning and node mask reconstruction double-task joint training strategy to realize space-time feature decoupling; preprocessing historical sections and building a feature library; matching the first K similar sections through similarity retrieval and building a labeled sample set. The system comprises corresponding function modules. The application solves the problems of high feature dimension, poor time sequence consistency and dependence on artificial experience in the prior art by deeply fusing graph structure learning and time sequence modeling, realizes automatic and high-precision construction of a power grid historical operation section sample set, and can provide reliable data support for power grid safety analysis and dispatching decision.

The invention provides a visual language model zero sample classification method based on Lasso regularization dynamic integration. The visual language model zero sample classification method specifically comprises the following steps: S1, constructing a model pool comprising a plurality of pre-trained visual encoders; step S2, in a training stage, adaptively screening out a model subset which contributes significantly to a current task from the model pool by using the sparse characteristic of Lasso regularization, and learning the weight of the model subset; s3, introducing a dynamic regularization coefficient lambda adjustment mechanism based on verification loss to balance the complexity and generalization ability of the model; and S4, in a reasoning stage, performing weighted fusion on the prediction result of the selected model by using the sparse weight obtained by training to obtain a final classification result. According to the method, the accuracy and robustness of the visual language model in the zero sample classification task can be effectively improved.

The invention discloses a road disease detection method and device based on a YOLO-DMD model, and the method comprises the steps: constructing the YOLO-DMD model through introducing a C2f-Deform module, an MSDA mechanism and a Dyhead dynamic detection head based on a YOLOv8 algorithm model; acquiring a road streetscape image through streetscape shooting equipment or a vehicle-mounted automobile data recorder; inputting the image into a YOLO-DMD model for size adjustment and normalization processing to obtain a standardized image; performing feature extraction on the standardized image through a backbone network embedded with a C2f-Deform module to obtain an initial disease feature map; based on the initial disease feature map, feature fusion is carried out through an MSDA multi-scale deformable attention mechanism in a neck network, and a fused feature map is generated; and based on the fused feature map, carrying out disease classification and positioning through a Dyhead dynamic detection head, and outputting disease category, confidence and bounding box information to complete road disease detection. According to the method, the problems of insufficient extraction, poor multi-scale fusion suitability, poor detection precision and robustness caused by classification positioning coupling and the like in the prior art are solved.