9results about How to "High sampling frequency" patented technology

The application discloses a kind of leakage point positioning method, equipment and medium of water supply network, belong to water supply network operation and leakage control technical field, for solving in existing leakage point positioning, it is easy to rely on single data, equipment and algorithm complex, and also lack high-precision space linkage and to data adaptability and insufficient refinement, it is difficult to realize the technical problems of accurate positioning of water leakage point of water supply network.Method includes: the operating state of water supply network is processed to multi-source data acquisition, obtains water supply network core data;The standardization processing of feature vector is carried out to the corrected water supply network core data after pre-processing, obtains effective water supply network data;Effective water supply network data is screened under the risk value of leakage point area about leakage point, obtains high suspected leakage point area based on water supply network section;Leakage point spatial positioning calculation is carried out to high suspected leakage point area under local topology, obtains the final leakage point area position in water supply network grid.

The present application relates to the technical field of Pickleball, and specifically relates to an intelligent wearable system for monitoring sports posture. The system synchronously collects multi-axis acceleration and angular velocity original signals before and after the player hits the ball through the inertial measurement unit built in the wearable device, generates time series after time window interception and normalization processing; removes noise and baseline drift by wavelet transform; fuses time domain and frequency domain features to construct a high-dimensional comprehensive feature vector; realizes preliminary classification of actions through a long short-term memory network, refines similar action types by combining a convolutional neural network, and finally outputs accurate classification results through a support vector machine; dynamically optimizes model parameters based on a real-time feedback mechanism, and enhances local feature representation through a channel attention mechanism. The system can accurately identify more than fifteen Pickleball special technical actions, has high robustness and self-adaptive ability, can provide real-time posture specification guidance for athletes, and helps to improve training efficiency and prevent sports injuries.

The invention discloses an event-driven adaptive sampling and hierarchical transmission method based on a data change rate. The event-driven adaptive sampling and hierarchical transmission method specifically comprises the steps that S1, an equipment end collects industrial equipment operation data at a fixed frequency and generates a data frame; s2, the server side calculates the data change rate of adjacent data frames from the same equipment, and generates an abnormal score based on the change rate; s3, the server side divides the data frame into event levels at least including a heartbeat level, a conventional level, an abnormal level and an accident snapshot level according to the abnormal score Score; s4, the server performs adaptive adjustment on an issuing strategy to the client according to the event level; s5, the server triggers an accident snapshot level event, and reliably transmits and issues the accident snapshot level event to the client through a TCP; and S6, the server side enables the data of different event levels to enter different priority queues and dispatches and issues the data according to the priorities. According to the invention, through event grading, sampling self-adaption and grading transmission, priority arrival of key abnormal data and complete reservation of accident data are realized.

The present application relates to the technical field of image generation, and particularly relates to an image generation training method for an AIGC large model, comprising: obtaining standard images by preprocessing collected labeled images, injecting the standard images into an initial model for training to obtain a pre-training diffusion model, inputting random noise into the pre-training diffusion model, and generating initial images by reverse diffusion; optimizing the pre-training diffusion model according to the initial images to obtain an AIGC large model with image generation capability, processing image generation requirements by using the AIGC large model to generate target images; determining whether the number of reverse diffusion sampling steps needs to be increased; determining whether the sampling temperature of the image generation process needs to be increased; and determining the residual connection weight of deep features based on the attack iteration decay rate in the optimization process. The present application improves the generation stability of the target images.

The application provides a kind of decommissioned battery remaining life prediction method, system and battery management system, the decommissioned battery remaining life prediction method includes: end side equipment carries out feature extraction and correlation screening to the operation data of decommissioned battery, and battery characteristic data after screening is uploaded to side node;Based on the battery characteristic data after screening, the inference is carried out using battery life prediction model, and the remaining life prediction value is obtained;Wherein, the battery life prediction model uploads the parameters of the battery life prediction model by the side node, the cloud server carries out aggregation optimization to the parameters uploaded by each side node and issues the updated parameters, and the side node receives the updated parameters and updates.The method provided by the application can improve the accuracy and real-time performance of the remaining life prediction of decommissioned battery.

The application discloses a bridge structure self-adaptive sampling monitoring method and device suitable for an extreme road environment, and belongs to the technical field of bridge structure health monitoring.The core target of the application is to solve the problems of repeated arrangement of existing monitoring equipment, road-bridge data fragmentation, and low self-adaptive sampling regulation precision.Through innovative design of a road-bridge multiplexing integrated sensing node, single-node multi-parameter and full-scene monitoring are realized, and the arrangement cost and construction difficulty are reduced; through construction of a self-adaptive sampling strategy based on fuzzy logic control, a feasible algorithm model is given, continuous dynamic regulation of sampling parameters is realized, monitoring precision and low power consumption are taken into account, deep collaborative linkage of road environment monitoring and bridge structure monitoring is realized, and the safe operation of the bridge structure under the extreme road environment is ensured.

The invention provides a fault detection method of an energy storage cabinet, electronic equipment and a system. The method comprises the steps of judging whether an alarm condition is met or not according to a preset fault judgment strategy and electric heat management information acquired by an energy storage module in the energy storage cabinet; when the alarm condition is not met, a second detection module is used for obtaining ventilation and waterproof information of the energy storage cabinet, and the ventilation and waterproof information is used for indicating the pressure difference condition of air flow in front of and behind a filter part and the water accumulation condition of a drainage groove; determining an environmental health degree score based on the ventilation and waterproof information; and when the environmental health degree score is lower than a preset threshold value, adjusting a detection sensitivity parameter of the fault judgment strategy. Compared with a related scheme which only monitors the electrical parameters or the external air temperature environment, the method considers the new detection dimension of the environmental health state of the cabinet body after the waterproof performance is improved, and the characteristic environmental factors corresponding to the ventilation waterproof factors are sensed in advance before the electrical fault is dominated.

The application provides a positioning method and device applied to a ship environment. The method provided by the application comprises the following steps: collecting a group of positioning related data when a current sampling period arrives; determining a joint fluctuation index of an IQ sample according to a magnitude variation and a phase variation of the IQ sample, and determining an updated sampling period of next sampling according to the joint fluctuation index, a preset minimum sampling period and a preset standard sampling period; performing next round of data collection when the updated sampling period arrives, and repeating the above steps; screening and preprocessing a plurality of groups of continuously collected positioning related data to obtain a training sample set; training an initial positioning model based on the training sample set to obtain a trained positioning model; inputting a group of to-be-tested data collected in real time into the trained positioning model, so that the trained positioning model outputs a predicted position of a transmitter corresponding to real-time IQ samples in the group of to-be-tested data based on the group of to-be-tested data.

This invention relates to the fields of data processing and industrial internet technology, and discloses information management methods, systems, electronic devices, and storage media. The methods include receiving heterogeneous data from multiple sources such as carbon emissions and energy consumption, mapping them into holographic data packets containing a unified spatiotemporal reference; dynamically constructing a spatiotemporal causal correlation spectrum based on the correlation weights between data nodes; calculating the topological potential energy value of a physical region, and generating hardware control commands when the potential energy value exceeds a warning threshold to adaptively adjust the sampling frequency of the front-end device; monitoring the correlation entropy of data nodes, and when a phase transition condition is met, serializing and migrating a stable subgraph to a second storage layer and generating a hash index; retrieving data based on the index and performing integrity verification. This invention achieves dynamic adjustment of the sampling frequency through topological potential energy feedback and uses correlation entropy to drive storage evolution, optimizing hardware resource configuration while ensuring the spatiotemporal correlation and reliable traceability of data.