13results about How to "Avoid falling into" patented technology

This utility model discloses a multi-functional fire-fighting intercom, including a main body and an intercom module, a temperature detection module, a control module, a gas detection module, a stationary detection module, an alarm module, and a battery integrated on the main body. The intercom module has a microphone speaker on its left or right side. The temperature display panel of the temperature detection module is located on the top of the main body. The gas detection module has a gas detection probe and an information display window on its front, displaying the concentration of the detected gas. The stationary detection module includes an accelerometer sensor to sense the speed of the main body. The alarm module includes an alarm speaker and several alarm indicator lights. The control module is electrically connected to the gas detection module, the stationary detection module, and the alarm module. The battery is electrically connected to each module. This significantly expands the uses of the intercom and effectively improves the safety of firefighters during fire rescue operations.

The application discloses a point pair registration method and system based on feature guidance and double constraints, and belongs to the technical field of computer vision and robot grabbing. The method aims to solve the problems of recognition failure and insufficient estimation accuracy of traditional methods when parts are blocked and stacked, and point cloud features are not obvious. The scheme adopts a two-stage optimization strategy. Firstly, in the coarse registration stage, an adaptive sampling PPF algorithm guided by geometric features is proposed. By preferentially sampling points in the edge, curvature and other feature obvious areas, the robustness and efficiency of matching are improved. Secondly, in the fine registration stage, an ICP algorithm with multi-constraint optimization is proposed. The pose result obtained by coarse registration is used as the initial value. Through rotation and angle constraint, a multi-objective optimization function is constructed to guide the fine registration process to converge in the correct direction and avoid falling into local optimum, thereby ensuring the accuracy and physical feasibility of the final pose estimation. The application significantly improves the accuracy of industrial part pose recognition.

The embodiment of the application discloses a cluster artificial potential field improvement method and device based on a region threat degree; the method comprises the following steps: acquiring a static obstacle position; establishing a multi-agent cluster structure, and adopting a second-order consistency algorithm to form following; when a moving queue is a desired queue, obstacle avoidance control is performed through an artificial potential field method to obtain an overall following path of the cluster; according to the obstacle avoidance path, the second-order consistency algorithm is adopted to perform path following to control the cluster to avoid obstacles and reach a target point; by introducing second obstacle position information and threat weight information, the size and direction of repulsion force suffered by the cluster are adjusted; by introducing position information and threat weight information of obstacles in a farther range, the size and direction of repulsion force suffered by the cluster are adjusted, so that the moving direction of the cluster is changed, and the cluster is prevented from falling into a local minimum value trap region.

The application belongs to the technical field of power equipment fault diagnosis, and specifically discloses a power transmission line fault identification method and a direct current test instrument. The power transmission line fault identification method comprises the following steps: S1, obtaining fault signals and environmental temperature and humidity data of a to-be-tested line and performing pretreatment to obtain fault characteristic data; S2, inputting the fault characteristic data as test samples into a WOA-KFCM fault detection model, wherein the WOA-KFCM fault detection model comprises a WOA algorithm unit and a KFCM algorithm unit; and S3, calculating the initial clustering center of the KFCM algorithm unit through the WOA algorithm unit, and outputting the line fault category through the KFCM algorithm unit according to the initial clustering center and the test samples. Through the direct current test instrument and the above-mentioned fault identification program, the content of the power transmission line fault can be quickly identified, and the troubleshooting efficiency and the identification accuracy of the complex fault type of the power transmission line are effectively improved.

This invention relates to the field of neural network architecture search and industrial intelligent processing technology, and discloses a structure search method for multi-output dendritic neuron models for industrial classification tasks. The method includes: initializing the synaptic connection weights and dendritic threshold parameters of the multi-input multi-output dendritic neuron model to generate an initial population; uniformly dividing the initial population into several subpopulations of equal size; calculating the temporal and spatial criteria for each subpopulation after task allocation and optimization, and dynamically allocating evolutionary computational resources for each subpopulation based on the fitness change rate represented by the temporal criterion and the population distribution state represented by the spatial criterion; implementing an accelerated sharing penalty mechanism to adjust fitness values ​​according to the crowding degree among individuals to maintain solution set diversity; and updating each subpopulation. This invention solves the problem of low convergence efficiency caused by uniform resource allocation in traditional large-scale multi-objective evolutionary algorithms during neural network architecture search.

The utility model belongs to the field of pesticide spraying device, concretely is a kind of lotus root planting organic fertilizer pesticide spraying device, the bottom of spraying device is provided with locating plate, the upper portion of locating plate is inserted with antiskid rod, the bottom fixedly connected with locating plate has connecting column, the bottom fixedly connected with connecting column has bearing plate, when the spraying device of connecting column upper portion is moved by bearing plate, the pesticide spraying pipe of spraying device side portion can carry out pesticide application to lotus root pond, and the bottom of bearing plate is in contact with lotus root pond silt ground area, avoid spraying device in the process of moving to sink into silt ground, to reduce work intensity, while the end of bearing plate is provided with pry plate, so that in the process of bearing plate movement, when the protruding soil layer is encountered in moving path, it can also pass smoothly, the stop block can prevent steering rod from continuing to rotate when steering rod is in vertical state, so that the steering rod can be kept in place, and multiple antiskid rods can form a fence to avoid the spraying device from falling during movement.

This invention discloses an improved near-infrared spectral wavelength selection and modeling method for artificial bee colony algorithms, comprising the following steps: data preparation, chaotic initialization, fitness evaluation, establishing a PLS model based on wavelength subsets, using the regularized fitness function of RMSECV as the fitness Fitnessxi of nectar source xi; searching for foraging bees; searching for observation bees; searching for scout bees; outputting the optimal wavelength subset Sbest and establishing the final PLS model. The beneficial effects of this invention are: the method effectively improves convergence speed and accuracy; chaotic initialization improves the quality of the initial population, avoiding the algorithm from prematurely falling into local extrema; the adaptive perturbation factor balances the algorithm's global search and local exploitation, improving the ability to find the optimal wavelength combination in high-dimensional discrete space, and the selected wavelength subset has stronger representativeness, resulting in a final PLS model with lower RMSEP (root mean square error of prediction) and better robustness.

The utility model discloses a convenient to install's high -efficient energy -conserving oxygen -increasing machine structure, including floating device, driving device and installation component, driving device adopts frequency conversion drive motor, the driving motor's driving shaft bottom end is installed with helical impeller, floating device adopts three arc float, installation component includes circular protective cover, conical fairing, conical base, triangular support frame, inner support sleeve, the cavity between circular protective cover, inner support sleeve between arc float installation saves space, stable support is not easy to roll over, and arc float automatically adapts to water level, and oxygen -increasing machine installation is convenient, and personnel can complete installation just by placing oxygen -increasing machine in the corresponding position of aquaculture water area and connecting power supply, greatly reduce the labor intensity of user, through the conical base installation in inner support sleeve bottom end center, effectively avoid the water level of aquaculture water area too low and cause helical impeller to sink into silt and sand, and the structural design is reasonable, and the practicability of oxygen -increasing machine has been increased.

This invention discloses an odor source localization method based on adaptive spatial perception information. The method includes the following steps: First, the search environment is divided into a fine grid, and a gas diffusion model that fits the actual scene is established to obtain the predicted concentration for each grid. Then, the real-time gas sampling data from the robot is binarized, and a sensor response model based on Poisson distribution is established to obtain the sampling concentration. The robot performs Bayesian inference using the predicted and sampled concentrations to update the likelihood function, thereby obtaining the posterior probability density function of the entire map. The reward function for each movable direction in the allowable action set is calculated using the posterior probability density function. The robot finds the direction with the largest change in the reward function, calculates the movement step size based on the current information entropy, and moves the robot a specified step size in the direction with the largest change in the reward function. This method solves the problems of existing odor source localization strategies easily getting trapped in local optima and the low search efficiency of fixed step sizes.

This invention proposes a few-shot image segmentation method and system based on cue learning, relating to the field of image processing technology. The method includes: extracting multi-scale features from support set images and query set images using a deep backbone network with shared weights, obtaining support set features and query set features at multiple scales; performing weighted global average pooling on the support set features based on a first mask corresponding to the support set features to obtain image category prototypes; interacting with the adaptively transformed image category prototypes and a preset learnable cue pool through attention, generating enhanced cue vectors through residual fusion; generating prior masks using the similarity between query set features, support set features, and the first mask; and performing interactive processing and cross-scale fusion of prior masks, query set features, and image category prototypes at each scale to obtain enhanced query set features; and performing pixel-level matching using the similarity between the enhanced cue vectors and the enhanced cue vectors to obtain the target segmentation result.

The present application relates to the technical field of atmospheric turbulence, and particularly relates to a turbulence intensity control method in near-ground laser wireless energy transmission based on a KMGA neural network. The method combines a clustering algorithm and a genetic algorithm to generate a KMGA-ANN model to fit the nonlinear relationship between environmental parameters such as temperature, humidity and pressure and atmospheric turbulence intensity, avoids the defect that a traditional neural network model is prone to falling into local optimization, leading to low fitting accuracy of turbulence intensity, and realizes the turbulence generation control function with low repeatability error and low stability error based on a turbulence simulation generator through the nonlinear relationship; solves the problem that a traditional turbulence simulation generation device uses an empirical formula to realize Kolmogorov turbulence but cannot accurately characterize dynamic non-Kolmogorov atmospheric turbulence; and at the same time solves the defects that an indoor turbulence simulation generation device cannot simulate normal low-altitude atmospheric environment with low repeatability error and low stability error and needs to be matched with corresponding turbulence test equipment.

This invention proposes an optimization method and system for energy storage systems based on a multi-objective ant colony optimization algorithm, belonging to the field of grid regulation technology for energy storage systems. It aims to efficiently solve the coordinated optimization problem of energy source, grid, load, and storage. The core of this method lies in constructing multiple heterogeneous heuristic information sets with different optimization biases, and dividing the ant colony into different groups. Each group searches for a scheduling scheme in parallel based on a specific heuristic information. This mechanism breaks through the limitations of traditional single search, greatly improves the diversity of global search, and can generate higher-quality and more widely distributed trade-off solution sets, thereby achieving optimal coordination between economic and environmental goals.

The application relates to a PID controller parameter optimization method of a water turbine speed regulation system, which comprises the following steps: according to a parallel PID controller structure, a three-order nonlinear object is adopted to establish a water turbine speed regulation system mathematical model; according to the water turbine speed regulation system mathematical model, an optimization objective function of the water turbine speed regulation system under no-load working conditions or load working conditions is established; according to the optimization objective function and the constraint range of the PID controller parameters, an improved sparrow search algorithm is adopted to optimize the PID controller parameters, and optimal PID controller parameters are output, wherein the improved sparrow search algorithm introduces a water hammer coefficient in the producer search step length of a basic sparrow search algorithm and adopts boundary adsorption combined with gradient correction instead of random disturbance. The application realizes global optimization and engineering adaptation of PID parameters, and effectively improves the control performance and operation stability of the speed regulation system under all working conditions.