7results about How to "Data support is good" patented technology

The application discloses a kind of natural gas network steady-state model solving method considering slack node and compressor. The method comprises the following steps: step S1, based on the flow conservation equation at each load node and slack node, establish natural gas network steady-state model;Step S2, the Jacobian matrix corresponding to the natural gas network steady-state model is constructed;Step S3, the pressure of each load node and slack node is obtained by iterative calculation to the natural gas network steady-state model;Step S4, based on the node pressure, the injection natural gas flow at reference node and slack node is calculated.The application can be used to analyze the natural network configured with slack node and compressor, and provide a basis for natural gas network planning and design and operation scheme analysis.

The application relates to the technical field of motor rotor temperature detection, in particular to a motor rotor temperature rise test structure. The motor rotor temperature rise test structure comprises a rotating shaft, a plurality of rotor cores, a rotor pressing plate, a connecting tool and a plurality of temperature sensors, the rotating shaft is provided with a first channel, the rotor core is provided with a plurality of axial through magnetic steel grooves and glue overflow grooves, the glue overflow grooves of adjacent rotor cores are partially overlapped in the axial direction to form a second channel, the rotor pressing plate is provided with a third channel arranged along the radial direction, one end of the third channel is communicated with the first channel, and the other end of the third channel is communicated with the second channel, the connecting tool is fixedly connected with the end of the rotating shaft, the connecting tool is provided with a temperature rise recorder, the plurality of temperature sensors are installed on different circumferential positions in the second channel, and the temperature sensors are connected with the temperature rise recorder. The test tool of the application is simple in structure, can accurately obtain the temperature rise of the rotor assembly under the working state without damaging the rotor assembly.

The application discloses a semi-supervised HRRP noise label filtering method based on early learning guidance, comprising the following steps: constructing an HRRP training data set and a deep neural network model; pre-training the deep neural network model; predicting the HRRP training data set by using the pre-trained network model and dividing the HRRP training data set into two data sets according to the confidence; constructing a known label data set and an unknown label data set to form a semi-supervised training data set and performing data enhancement; semi-supervised training the network model by using the enhanced data; generating the label of the unknown label data set by using the semi-supervised trained network model and fully supervised training the network model; and target recognition of the HRRP data by using the fully supervised trained network model. The application uses a generalized cross-entropy loss function in the early learning training stage of the model, proposes a data enhancement scheme suitable for HRRP, can better reduce the influence of noise labels on the model, and has better stability and implementability.

This invention provides a pseudo-color fusion method and system for images against a dark background based on polarization angle characteristics. The method includes: acquiring the outgoing light intensity of the target scene using a polarization camera, calculating the Stokes parameter image data of the target scene, and parsing the polarization information; mapping the synthesized light intensity I, linear polarization degree P, and polarization angle A to the brightness V, saturation S, and hue H in the HSV model, respectively, to obtain normalized synthesized light intensity, linear polarization degree stretching data, polarization angle preprocessing data, and hue matching parameters; processing the characteristic data of polarization angle A using a preset polarization robustness discrimination logic for the current observation point in the target scene to obtain polarization robustness discrimination parameters; judging the state of polarization noise based on the polarization robustness discrimination parameters and performing pseudo-color model mapping; and converting the pseudo-color HSV image into an RGB color representation image for display. This invention solves the technical problem of unsatisfactory pseudo-color fusion results caused by excessively dark background areas where the target is located.

The present application belongs to the technical field of unmanned aerial vehicle search and rescue, and particularly relates to a method for calculating the same-route dual-aircraft cooperative flight interval for improving dual-aircraft search and rescue efficiency. First, the target effective search leg length is calculated according to the aircraft position, the aircraft searchable distance, the aircraft flight speed and the distance length information of the target being captured by two aircrafts, and then the target effective search leg length is converted into the aircraft cooperative interval. Second, the target positioning accuracy is calculated according to the aircraft position, the aircraft direction-finding error and the distance from the aircraft to the target, and then the target positioning accuracy is converted into the aircraft cooperative interval. Finally, the optimal aircraft cooperative interval general model satisfying the positioning accuracy requirement is constructed, and the dual-aircraft cooperative optimal interval is obtained by solving the highest total value of the dual-aircraft cooperative search target. The present application can solve the dual-aircraft cooperative flight optimal interval for different routes, increase the probability of the target being simultaneously searched by the cooperative aircrafts, and improve the dual-aircraft cooperative search target efficiency.

This invention provides a method, electronic device, and storage medium for office space scene recognition. Multiple IoT devices are installed within the office space. The method includes: acquiring event information from the multiple IoT devices in real time; standardizing the event information of each IoT device into a standard event sequence according to a preset structure, the preset structure including timestamp, device identifier, event type code, event value, and event credibility; aligning each standard event sequence through a time window; constructing a semantic vector for the current moment based on the data in the time-aligned time window; and determining the current spatial state of the office space based on at least some parameter vectors in the current moment's semantic vector, wherein the multiple preset spatial states include at least the following spatial states: vacant, partially occupied, actively occupied, meeting preparation, meeting in progress, meeting adjournment, and nighttime energy saving. This method can improve scene recognition accuracy.

The present application relates to the field of intelligent driving and intelligent networked electric vehicles, in particular to an ECAV multi-vehicle game lane-changing decision-making method based on inverse reinforcement learning, and the specific steps are as follows: S1, a non-cooperative game model of lane-changing decision-making in a multi-vehicle interaction scene is constructed; S2, the state information and road environment information of the lane-changing initiating vehicle and the surrounding associated vehicles are collected by using the vehicle-mounted sensor to construct a data set of the multi-vehicle interaction lane-changing scene; S3, the method of inverse reinforcement learning is used to construct the HDV revenue function with safety and traffic efficiency as the target; S4, the global optimal strategy is obtained and output; S5, the lane-changing decision-making is completed, the lane-changing decision-making method based on inverse reinforcement learning and game theory uses the IRL algorithm to construct the HDV revenue function, predicts the behavior decision-making of the HDV vehicle in the lane-changing process, adjusts the revenue parameters in the non-cooperative game based on the particle swarm optimization algorithm, and obtains the optimal lane-changing decision-making method.