23 results about "Abnormal cell" patented technology

The invention belongs to the technical field of power battery detection, and particularly relates to a power battery abnormal monomer detection method, which comprises the following steps of: completing a data preprocessing step through parameter setting, working condition segmentation and sampling steps; and the model calculation step is completed through the steps of data set setting, value rate calculation, value rate filtering, abnormal coefficient A calculation, Boolean matrix conversion and abnormal monomer judgment. According to the method, the voltage fluctuation degree of each monomer in each message is represented through an abnormal coefficient A, Boolean matrix conversion is used, the voltage fluctuation conditions in the data blocks are aggregated, and finally the judgment of the abnormal monomers is obtained. And the voltage fluctuation condition of each message is analyzed in a detailed manner, so that the accuracy of abnormal monomer judgment can be effectively improved.

Embodiments of the present disclosure provide a software upgrading method, system, electronic device and storage medium. The software upgrading method comprises: adopting a first prediction model to predict an abnormal probability of a cell associated with a first terminal based on first space-time information of the first terminal, wherein the first prediction model is obtained by training a network behavior data collected by a second terminal on a server; and performing a corresponding avoidance operation on an abnormal cell with an abnormal probability greater than a probability threshold.

The embodiment of the invention provides a cell installation detection result display method, system and device and a storage medium, and relates to the technical field of battery manufacturing. The method comprises the following steps: acquiring an installation result and an installation image of a battery cell module; when the installation is normal, generating a correct installation picture based on the state of the battery cell module, the model of the battery cell module, the total number of the battery cell modules and the installation image; when the installation is abnormal, generating an abnormal reason, a solution and the number of abnormal battery cells based on an abnormal type in an installation detection result; marking the battery cell which is installed abnormally to obtain a marked image; and generating an installation abnormity picture based on the abnormity reason, the solution, the number of the abnormal cells, the cell state, the cell model, the total number of the cells and the mark image. Therefore, by marking the abnormal cells, the number of the cells, the polarity arrangement sequence of the cells and the cells with abnormal placement angles are identified, and specific reasons, solutions and the number of the abnormal cells of the abnormality are displayed, so that reasons for abnormal installation of the cell module are conveniently checked.

The invention discloses a battery anomaly detection method, device and equipment based on a sliding-mode observer, and relates to the technical field of batteries, the method comprises the following steps: obtaining real-time operation data of a to-be-detected battery, determining a real-time discharge rate according to the real-time operation data, and determining the real-time discharge rate of the to-be-detected battery according to the real-time discharge rate; calling a matched open-circuit voltage value-battery charge state mapping curve according to the real-time discharge rate; constructing an equivalent model, and constructing a nonlinear system model according to the equivalent model; constructing a sliding-mode observer according to the nonlinear system model, and predicting a predicted value of each state variable of each cell at each time node; and obtaining a measured value of the state variable of each cell at each time node in the operation cycle, marking abnormal cells according to the predicted value and the measured value of the state variable, and determining an abnormal detection result of the to-be-detected battery according to the number of the abnormal cells. According to the invention, the problem of insufficient capability of identifying hidden and slow anomalies such as soft faults and gradual faults can be solved, so that effective early warning of abnormal batteries can be realized.

The application discloses a kind of based on UMLC and ICA's electrochemical energy storage cell health state rapid detection method, medium and program product, belong to electrochemical energy storage power station monitoring and battery management technical field.This method is by BMS real-time acquisition battery cluster in each cell monomer voltage, monomer temperature and battery cluster current and other operating data, constructs the characteristic vector containing voltage and temperature mean, standard deviation and deviation, and is combined with the unsupervised machine learning clustering (UMLC) and incremental capacity analysis (ICA) and other technologies not dependent on historical training data, identify and extract the key features of abnormal cell, to quickly diagnose cell health state.This method does not need historical data, with higher detection efficiency and interpretability, can be directly applied to on-site energy storage power station, realize to cell fast and accurate consistency analysis screening and positioning abnormal cause.

A normal set of cells is characterized using flow cytometry. A centroid and radius are defined for a set of clusters in an n-dimensional space corresponding to a normal maturation for a cell lineage in the normal set of cells. A test set of cells is characterized using flow cytometry and the characterization is compared to the defined set of clusters. Support Vector Machine (SVM) subroutines are employed to identify reference populations of interest by generating multidimensional boundary definitions. These boundary definitions may be used to identify reference populations to use in defining or refining a centroid line or a radius or radii defining a set of normal clusters, and to characterize and compare a test set of cells to the defined set of normal clusters.

The invention relates to a method for identifying abnormal battery cells in a battery pack. The method comprises the following steps: step S1, acquiring accumulated equalization time of each single battery cell in the battery pack in a preset time window; s2, for each single cell, calculating a relative self-discharge rate based on the accumulative equalization time; s3, for each single cell, judging whether the relative self-discharge rate exceeds a preset threshold value or not; and S4, under the condition that the relative self-discharge rate exceeds the preset threshold value, determining that the corresponding single battery cell is an abnormal battery cell. According to some embodiments of the invention, an algorithm for online calculation of the relative self-discharge rate of a single cell based on the accumulative equalization time is provided, so that a suspicious cell can be recognized online in real time, whether the suspicious cell is abnormal can be judged, early warning can be performed in combination with means such as remote diagnosis, and the accuracy and the reliability of the system are improved. Therefore, the abnormal condition of the battery cell is effectively prevented.

The invention provides a cell traffic mutation detection method and device and a medium, and relates to the technical field of networks. The method comprises the following steps: acquiring a same-time characteristic service data sequence of a cell; preliminarily screening out suspected abnormal cells in which abnormal mutation occurs in the same-time characteristic service data sequence based on Pettitt inspection; and judging whether the suspected abnormal cell is a comprehensive abnormal cell or not according to the same-time characteristic service data sequence of the suspected abnormal cell in combination with test related data obtained by Pettit test and associated data of cell service metadata. According to the method and the device, the same-time characteristic service data sequence of the cell is selected, the suspected abnormal cell is quickly and preliminarily screened out by using the Pettitt test, finally, the cell abnormity is comprehensively researched and judged by fusing multi-dimensional information, the Pettitt test is high in speed, small in burden and suitable for full-amount scanning, the data analysis amount of subsequent comprehensive research and judgment can be reduced, the analysis efficiency is improved, and the method and the device are suitable for large-scale popularization and application. Deep analysis is carried out on a small number of suspected targets through comprehensive research and judgment, and the recognition precision is improved.

The present invention relates to automation of urine analysis or urinalysis (UA) with conventional cytopathological assessment tests and methods using an artificial intelligence (AI) based urine diagnostic apparatus or device or system and methods employing said apparatus or device or system for fully automated preparation of raw samples by automating and streamlining conventional laboratory processes and examination of urine samples, automated data collection, automated analysis of data using digital image recognition, and finally, automated screening for cancer suspicion in a non-invasive, non-human or pre-pathologist intervention-based automatic examination and analysis of such samples using AI based on said data analysis for preparing reports intimating to the subject or patient automated negation if no abnormal cells are detected / identified, or to forward suspicious samples from the subject or patient to confirm the possibility of suspicion for cancer risk of cancers routinely and otherwise detected based in examination of urine samples by a trained pathologist.

The invention relates to the field of intelligent medical treatment, and discloses a pathological recognition analysis method and system based on a blood detection sample. The method comprises the following steps: acquiring a blood smear microscopic image and extracting a cell contour; performing texture analysis on the interior of the contour to obtain morphological characteristics; judging a potential abnormal region based on the features; carrying out local enhancement on the abnormal region and extracting subtle difference features; based on the difference features, obtaining quantitative pathological descriptors through a deep learning model; identifying abnormal cells according to a matching result of the descriptors and the feature library; and comprehensively analyzing according to an identification result to obtain pathological quantitative indexes. Through a mode of combining local enhancement, a deep learning model and feature library matching, the problems of low blood smear abnormal cell recognition precision and poor efficiency are solved, and the accuracy and automation level of pathological analysis are improved.

Embodiments of the present disclosure provide a cell avoidance method, system, electronic device and storage medium. The cell avoidance method comprises: using a first prediction model to predict an abnormal probability of a cell associated with a first terminal based on first space-time information of the first terminal, wherein the first prediction model is obtained by training a network behavior data collected by a second terminal on a server; and performing a corresponding avoidance operation on an abnormal cell with an abnormal probability greater than a probability threshold.

The invention relates to a cell switching method and device, electronic equipment and a storage medium. The cell switching method comprises the following steps: determining that a first cell meets a preset condition, wherein the preset condition represents uplink data jamming due to occurrence of advanced timing abnormity; according to the abnormal information of a target cell in a first tracking area, determining a second cell, the first tracking area being a tracking area to which the first cell belongs, and the target cell being a cell having advanced timing abnormity; and switching from the first cell to the second cell. According to the method, switching to other abnormal cells can be avoided while the abnormal serving cell is quitted in time, the problem of uplink data jamming caused by abnormal timing in advance of the serving cell is remarkably solved, and the user experience is improved.

The embodiment of the invention relates to the field of electrolytic cell monitoring, and discloses an electrolytic cell chamber voltage monitoring method, electronic equipment and a storage medium. The method comprises the following steps: acquiring voltage sampling data of each small chamber in the electrolytic cell; performing projection and reverse reconstruction processing on the voltage sampling data by using a pre-established kernel principal component analysis model to obtain a reconstructed voltage value; the reconstructed voltage value is used for indicating the theoretical normal voltage of the cell under the current operation condition; calculating a difference value between the voltage sampling data of each small chamber and the reconstruction voltage value to obtain a reconstruction residual error of each small chamber; performing clustering analysis on the reconstruction residual error of each small chamber to obtain at least two residual error clusters; and determining an abnormal cell according to the residual cluster to which each cell belongs. Therefore, the abnormal small chamber can be positioned.

The invention discloses a cell switching method and device, electronic equipment and a storage medium, and belongs to the technical field of communication, and the cell switching method comprises the steps: determining whether a service cell of a terminal is a redirection abnormal cell or not; under the condition that the service cell is a redirection abnormal cell, whether a measurement event is reported or whether cell switching is carried out is determined based on the signal quality of the service cell; and under the condition that the serving cell is not the redirection abnormal cell and the neighbor cell of the serving cell comprises the redirection abnormal cell, determining whether to report the measurement event based on the cell specific offset of the neighbor cell.

This application relates to a method for identifying abnormal cells in lithium iron phosphate batteries, comprising the following steps: collecting the voltage values ​​of n individual cells after charge and discharge; performing basic statistical calculations, including the mean, standard deviation, median, and quartiles; determining abnormalities using the standard deviation method, the interquartile range method, and the median absolute deviation method, respectively; performing a comprehensive determination: if at least two abnormality determination methods identify an abnormality, the individual cell is confirmed as an abnormal cell; if only one abnormality determination method identifies an abnormality, further analysis is performed; quantifying the degree of abnormality by calculating the abnormality index for each abnormal cell; and classifying the abnormal cells based on their voltage data. This method for identifying abnormal cells in lithium iron phosphate batteries can more accurately screen out cells that affect the overall battery capacity, facilitating targeted maintenance of abnormal cells and thus overcoming the impact of abnormal cells on battery capacity.

The invention provides an abnormal battery cell determination method and electronic equipment. The method includes determining a control limit; wherein the control limit is determined based on the influence factor of the battery cell to be detected and the original electrical performance value of the battery cell, or the control limit is determined based on the electrical performance value after temperature compensation; the influence factor represents a factor capable of influencing the electrical performance value of the battery cell; based on the control limit, screening the battery cells, and determining discrete points in the battery cells; and determining an abnormal battery cell based on the discrete points. The control limit is determined through the influence factor of the to-be-detected battery cell and the original electrical performance value, or the control limit is determined according to the voltage attenuation rate after temperature compensation, the accuracy of the control limit is ensured, the accuracy of determining the abnormal battery cell is improved, and the efficiency of determining the abnormal battery cell can be improved.

The invention discloses an alkaline electrolytic cell performance discreteness Monte Carlo analysis method and system, and relates to the technical field of electrolytic hydrogen production, and the method comprises the following steps: constructing an electrochemical-thermodynamic-hydrodynamic multi-physics field coupling steady-state model of an alkaline electrolytic cell; based on a Monte Carlo method, probabilistic modeling is carried out on manufacturing tolerance, aging effect and operation parameter uncertainty; through large-scale random sampling simulation, quantizing discrete distribution characteristics of stack output voltage of the alkaline electrolytic cell and voltage of each small electrolytic chamber in the alkaline electrolytic cell; high-voltage abnormal cells and the influence of the high-voltage abnormal cells on the system performance are identified; and carrying out parameter sensitivity analysis, and identifying highly sensitive parameters and low sensitive parameters. The method can provide theoretical basis and method support for robust design, health monitoring, operation and maintenance optimization and manufacturing tolerance control of the alkaline electrolytic cell.

This invention belongs to the field of battery health detection technology and discloses a health status detection system and method for new energy vehicle battery packs. It acquires key parameters such as voltage, current, temperature, and high-frequency impedance with nanosecond-level precision through a multi-channel synchronous acquisition unit of the BMS. During the vehicle's idle period after engine shutdown, a low-disturbance DC pulse excitation is injected to complete in-situ benchmark calibration. Simultaneously, it pre-detects the consistency of individual cells and marks abnormal cells, eliminating measurement errors caused by acquisition system drift, environmental disturbances, and initial cell inconsistencies. For three operating conditions—static idle, low-rate stable operation, and high-rate dynamic operation—an adaptive matching denoising algorithm is used. Combined with the mutual information entropy algorithm, core health features are extracted and redundant information is removed to ensure a high signal-to-noise ratio. In the cell estimation stage, a single-particle electrochemical model and a second-order equivalent circuit model are fused, and a lightweight CNN-LSTM network based on an attention mechanism is used for pre-training.

This invention discloses a method and system for detecting abnormal battery cells in an electrochemical energy storage system. The method first collects voltage, current, and temperature data of some individual cells in the battery module. It then uses local inverse distance weighted interpolation to initially estimate the temperature of all cells and dynamically corrects and smooths the temperature using voltage residuals to obtain a calibrated temperature field. Next, it calculates the temperature gradient index based on spatial neighborhood relationships and the voltage gradient index based on electrical connections for each cell. Finally, it integrates the temperature gradient index, voltage gradient index, and temperature change rate to construct a joint criterion, which is compared with an adaptive dynamic threshold to achieve accurate diagnosis and early warning of abnormal cells. This invention effectively overcomes the problems of high false alarm and missed early anomaly rates in existing technologies due to temperature monitoring blind spots and lack of spatial correlation analysis. It significantly improves the sensitivity and reliability of energy storage system safety monitoring without increasing hardware costs.

Embodiments of the present application provide a kind of detection method of abnormal cell, system, storage medium, electronic device and computer program product, the method comprises: obtaining the current user terminal and the RRC connection number of base station of target cell, wherein, user terminal includes first sentry user;First sentry user is specially used to send access network request to base station according to preset time interval terminal;According to RRC connection number, the abnormal situation of target cell is detected, by first sentry user regularly accesses network and maintains RRC connection, and the real-time monitoring of RRC connection number, the fast detection of abnormal state of target cell is realized, to solve the problem that the detection method of abnormal cell in related art is poor in real-time and prone to false detection.

The invention is applicable to the technical field of biomedical engineering, and provides a targeted removal device and removal method for abnormal cells, the device comprises a central control unit, a pulse generation unit, a high-voltage conversion unit, an output control unit and a feedback monitoring unit; the feedback monitoring unit is used for acquiring a voltage value of the first treatment end and / or the second treatment end, generating a feedback signal and outputting the feedback signal to the central control unit; the central control unit adjusts the first pulse control signal and the second pulse control signal based on the feedback signal. The method has the advantages that by applying the electroporation technology to the field of beauty, problem cells such as acnes and the like can be permanently damaged, radical treatment of the acnes and long-acting wrinkle removal are achieved, the problems that existing beauty equipment is limited in effect, short in time efficiency and insufficient in safety are solved, and the method has extremely high application value and industrialization prospects.

This application relates to the field of battery safety management technology, and particularly to a method and system for the safety management of large-capacity batteries in RVs. The proposed solution involves first acquiring operational data from multiple battery cells to construct a measurement matrix. Principal component analysis is used to identify collaboratively changing characteristic values, determining candidate abnormal cells that deviate from these characteristics. Based on trend fitting and disturbance response analysis, the disturbance coupling degree is calculated to determine the final abnormal cell. After identifying the abnormal cell, a hierarchical response management strategy is implemented, including a first scheduling scheme and a second scheduling scheme. The second scheduling scheme calculates the coupling strength between the power-consuming action module and the abnormal cell, and reconstructs the module structure based on electrical feature fingerprints and dependencies. This allows the behavioral unit to be migrated to a more suitable battery pack power supply path, achieving behavior retention and power supply optimization. This method improves the accuracy and scheduling flexibility of the battery system in scenarios involving abnormal individual cells.

The present invention relates to the field of medical biology, and in particular to a polycyclic compound, and a preparation method therefor and a use thereof. Specifically, the present invention relates to a compound, or a pharmaceutically acceptable salt, ester, stereoisomer, polymorph, solvate, nitrogen oxide, isotope marker, metabolite or prodrug thereof, such as a compound of formula (I'), or a pharmaceutical composition thereof, and a preparation method therefor and a use thereof. The compound can be used as a drug for treating diseases related to abnormal cell proliferation.