7 results about "Breakdown point" patented technology

The invention discloses a wire harness processing control method and system based on artificial intelligence, and the method comprises the steps: collecting sensor array data, carrying out the normalization processing, forming a standardized parameter set, precisely recognizing an abnormal mode through a classification algorithm, marking a potential fault point, extracting a time sequence feature sequence, and generating a prediction deviation vector through a prediction algorithm, analyzing dominant influence factors, grouping similar deviation modes through a clustering algorithm, and judging process adjustment requirements; and an optimization instruction sequence is generated, a production scene is matched based on historical data, the adjusted parameter configuration is dynamically injected into a control system, and the parameter model is continuously optimized through real-time monitoring and feedback circulation. According to the method, through data-driven anomaly detection and prediction adjustment, the stability and efficiency of the manufacturing process are remarkably improved, the fault rate is reduced, and intelligent machining state optimization is achieved.

The invention discloses an IDC machine room fault point prediction method and system based on artificial intelligence and the Internet of Things, relates to the technical field of computers, and aims to solve the problems that hidden risks cannot be identified before temperature exceeds a limit, fault response is passive, and fault cause interpretation ability is lacked in the aspects of heat dissipation and airflow organization management of an existing IDC machine room. According to the method, data of a PDU, a UPS, an air conditioner, a temperature and humidity probe, a BMC, an entrance guard and the like are analyzed and standardized through data access of the Internet of Things, and a unified time axis is constructed to carry out quality marking and credible normal fragment screening; based on cabinet topology and an air supply partition map, effective air volume and theoretical temperature rise are estimated, a cabinet-level air supply temperature field is solved under anchoring constraint and spatial smooth constraint, and the short-circuit air return proportion and a vertical hot spot height section are further inverted; therefore, early prediction, accurate positioning and explainable output of fault points are realized, and the stability and the operation and maintenance efficiency of the machine room are improved.

The application belongs to the field of power system fault diagnosis, and discloses a single-phase grounding fault determination method for a small current grounding system based on Bi-LSTM, which comprises the following steps: analyzing a model of a power system and actual dispatching data, building a computer numerical simulation model through the analyzed data, labeling fault data and non-fault data in the simulation data and the actual dispatching data, normalizing simulation and real-time dispatching characteristic data, making a training set and a test set of a Bi-LSTM model, and designing a fault line selection model based on Bi-LSTM and training and testing of the model. In the case of single-phase grounding, the application can quickly determine a fault line according to discrete dispatching data; compared with existing zero sequence current identification methods, the application does not require zero sequence current data, and has a higher identification rate in the case of neutral point grounding through an arc suppression coil or a fault point with high transition resistance, thereby realizing simple and efficient small current grounding fault line selection.

The invention provides a line fault positioning method and device, equipment, a medium and a computer program product. The method comprises the following steps: analyzing acquired pipeline information to obtain a resource mapping relationship; performing multi-dimensional analysis on the fault original data according to the resource mapping relation to obtain a pre-positioned pipeline section set; converting the pipeline segment set to obtain a weighted undirected graph, and converting the weighted undirected graph to obtain an adjacency list; and determining a post-positioned target fault path based on the weighted undirected graph, the adjacency list and the OTDR measurement distance. The method comprises the following steps: performing multi-dimensional analysis on line fault information to obtain the breakpoint probability of each pipeline section in a fault area, and screening according to the probabilities to obtain a pre-positioned pipeline section set; and then, by inputting OTDR positioning conditions and results, marking out a route on a fault point path, displaying a pipeline position where a fault point is located, and improving the optical cable fault positioning precision through path visualization.

Aspects discussed herein may relate to methods and techniques for using a multi-step approach to automatically analyze the computer architecture using an iterative process to inject error conditions. After determining an ordered graph indicating possible failure points, the system may then inject one or more error conditions (e.g., increased latency) to determine possible effects and / or points of failure. As a result of this analysis, the system may modify analysis regarding possible error points and / or take remedial actions to avoid failure.

The embodiment of the application provides a power distribution network-multi-microgrid collaborative optimization method and related equipment, and belongs to the field of power system optimization scheduling. The method comprises the following steps: each operation subject constructs a chaotic polynomial proxy model of operation cost based on a random response surface method, and quantitatively obtains an expected independent operation cost of each subject considering uncertainty risk; the cost is used to reconstruct a negotiation breakdown point of a cooperative game; a Nash bargaining model is established based on a new benchmark, and is decomposed into power and price optimization sub-problems, distributed iterative solution is carried out by using an alternating direction multiplier method, and an optimal collaborative transaction strategy is obtained; and finally, the strategy is executed and dynamically updated under an intra-day rolling framework. The application realizes endogenous quantification and fair allocation of risk cost by deeply integrating the random response surface method and the game theory, overcomes the defects of the traditional "risk-game separation" paradigm, and effectively improves the overall economy, operation stability and benefit distribution fairness of the system.

The application provides a line loss cause analysis and positioning method, system and medium based on artificial intelligence, relating to the technical field of power system management, which comprises the following steps: when the total line loss rate of a target substation exceeds a preset line loss threshold, an excitation sequence is issued to the total meter, circuit breaker and user meter of the target substation, corresponding transient response data are obtained, and then a dynamic topology graph of the target substation is constructed; then the topology structure is optimized and the abnormal connection point is detected in combination with a graph neural network and an attention mechanism. Accordingly, the downstream associated user meters are determined, and the electricity abnormality score is calculated based on the load data. The steepest front wave signal is injected into the meter with the highest score to obtain the reflection characteristic parameters. The Double DQN reinforcement learning algorithm is used for iterative analysis to determine the fault point, line loss cause type and confidence, so that efficient and accurate line loss cause positioning and analysis are realized. The application realizes efficient and accurate investigation of line loss causes.