Battery full life cycle data visualization monitoring system and method based on internet of things

Abstract

The application relates to the technical field of data management, in particular to a battery full-life-cycle data visualized monitoring system and method based on the Internet of Things. The method comprises the following steps: acquiring battery life-cycle data, wherein the battery life-cycle data comprises a battery manufacturing stage, a battery use stage and a battery recycling stage; and integrating a photonic crystal fiber sensor on a SEI layer of the battery based on the battery manufacturing stage to collect the thickness of the SEI layer and the ion diffusion rate of the battery. Through the use of photonic crystal fiber sensing technology, Internet of Things data acquisition and transmission technology, multi-dimensional data visualization technology and full-life-cycle analysis methods, the problems of fragmented life-cycle data, difficult perception of micro-evolution, non-intuitive display of monitoring results and poor platform linkage in traditional battery monitoring are solved, and the integrity of battery full-life-cycle state perception, the accuracy of monitoring and the intelligent level of management decision are improved.

Description

Technical Field

[0001] This invention relates to the field of data management technology, and in particular to a battery lifecycle data visualization monitoring system and method based on the Internet of Things. Background Technology

[0002] Traditional battery management systems (BMS) rely heavily on localized data acquisition and static analysis, making it difficult to assess the full lifecycle status of batteries throughout their production, transportation, use, maintenance, and recycling stages. To address this, researchers have gradually introduced sensor networks, big data analytics, and wireless communication technologies to achieve real-time acquisition and remote transmission of battery operating parameters (such as voltage, current, temperature, and internal resistance). Simultaneously, with the development of cloud computing and edge computing, massive amounts of battery data can be collaboratively processed across multiple nodes, improving data processing efficiency and intelligent analysis capabilities. In recent years, data visualization technology has also played a crucial role in battery status display and early warning, presenting complex, multi-dimensional data in an intuitive form to aid decision-making and fault prediction. Therefore, constructing a battery lifecycle data visualization monitoring method integrating IoT technology has become an important research direction and technological evolution trend in the field of battery management. However, traditional technologies often focus on data acquisition and monitoring during the battery use stage, neglecting the data value of the manufacturing and recycling stages, resulting in incomplete lifecycle information and an inability to track and assess the battery's health status throughout its entire lifecycle. Meanwhile, existing solutions mostly rely on macroscopic voltage and current parameter analysis, which makes it difficult to detect microscopic structural changes such as SEI layer evolution and dendrite formation, thus limiting the accuracy of safety early warning. Summary of the Invention

[0003] Therefore, it is necessary to provide an IoT-based battery lifecycle data visualization monitoring system and method to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a method for visual monitoring of battery lifecycle data based on the Internet of Things (IoT) is provided, the method comprising the following steps:

[0005] Step S1: Acquire battery lifecycle data, which includes battery manufacturing stage, battery usage stage and battery recycling stage; Based on the battery manufacturing stage, integrate photonic crystal fiber sensor on the SEI layer of the battery to collect the battery SEI layer thickness and ion diffusion rate.

[0006] Step S2: Analyze the dendrite morphology evolution of the battery by measuring the SEI layer thickness and ion diffusion rate, and visualize the analysis results as dendrite cross-sectional curves; obtain battery charge and discharge data based on the battery usage stage; perform charge transfer impedance incremental analysis on the battery usage charge and discharge data, and visualize the analysis results as biaxial line graphs;

[0007] Step S3: Collect material degradation data based on the battery recycling stage; perform quantitative analysis of chemical composition and physical structure damage assessment on the battery according to the material degradation data to obtain surface abundance data and surface structure damage data of the recycled battery materials; visualize the surface abundance data of the recycled battery materials as a stacked bar chart and visualize the surface structure damage data of the recycled battery materials as a pore size distribution histogram.

[0008] Step S4: Integrate the dendrite cross-section curve, biaxial line graph, stacked bar graph, and pore size distribution histogram into a visualization interface through a preset platform framework to generate a battery life cycle visualization interface; perform time-series monitoring synchronization on the battery life cycle visualization interface to execute IoT-based battery life cycle data visualization monitoring operations.

[0009] This invention integrates photonic crystal fiber sensors during the battery manufacturing stage, enabling real-time acquisition of SEI layer thickness and ion diffusion rate. This allows for precise perception of the SEI layer's microstructural evolution, providing a high-quality data foundation for subsequent dendrite growth behavior analysis. By performing incremental charge transfer impedance analysis on battery charge-discharge data, combined with dendrite morphology evolution trend analysis, the electrochemical degradation process of the battery during actual operation is effectively revealed, improving the accuracy and foresight of battery health status assessment. Quantitative analysis of battery chemical composition and physical structural damage assessment using material degradation data accurately obtains the surface abundance and structural integrity of recycled materials, helping to optimize battery material recycling strategies and improve resource recovery efficiency. Integrating dendrite evolution curves, charge-discharge impedance change diagrams, recycled material abundance diagrams, and structural damage distribution diagrams into a unified interface generates a unified battery lifecycle visualization interface, facilitating users' comprehensive understanding of the battery's operation and degradation status at different stages. Utilizing a pre-defined platform framework, multi-stage data synchronization and unified monitoring are achieved, constructing a battery lifecycle IoT monitoring mechanism to enhance the intelligence, precision, and automation of battery lifecycle management. Therefore, by using photonic crystal fiber sensing technology, Internet of Things data acquisition and transmission technology, multidimensional data visualization technology and full life cycle analysis method, this invention solves the problems of fragmented life cycle data, difficulty in perceiving micro-evolution, unintuitive display of monitoring results and poor platform linkage in traditional battery monitoring, thereby improving the completeness of battery life cycle status perception, the accuracy of monitoring and the level of intelligent management decision-making.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Obtain battery lifecycle data, which includes the battery manufacturing stage, battery usage stage, and battery recycling stage;

[0012] Step S12: Based on the production process data of the manufacturing stage, locate the formation time and region of the SEI layer and generate SEI layer growth marker data;

[0013] Step S13: Based on the SEI layer growth labeling data, deploy a photonic crystal fiber sensor to the electrode-SEI interface region to generate sensor integrated deployment data; use the sensor integrated deployment data to perform real-time nanoscale acquisition of SEI layer thickness to generate SEI layer thickness change data.

[0014] Step S14: Perform time-series fitting on the SEI layer thickness variation data, extract the ion transport boundary response, and generate ion diffusion rate data; store the SEI layer thickness variation data and ion diffusion rate data together to obtain the battery SEI layer thickness and ion diffusion rate.

[0015] This invention uses manufacturing process data to pinpoint the timing and region of SEI layer formation, generating SEI layer growth marker data. This provides a scientific basis for subsequent sensor deployment, avoiding blind spot acquisition and ineffective deployment. Based on the SEI layer growth marker data, a photonic crystal fiber sensor is precisely integrated into the electrode-SEI interface, enabling nanoscale dynamic sensing of key reaction interface information and improving the physical fit and spatial resolution of data acquisition. Using the integrated sensor, real-time data on SEI layer thickness changes during formation and evolution is collected, providing quantitative data for subsequent predictions of battery interface stability and cycle life. By performing time-series fitting on the SEI layer thickness change data, ion transport boundary response information is extracted, resulting in high-precision ion diffusion rate data. This supports in-depth modeling and analysis of SEI layer ion regulation performance and failure mechanisms. The SEI layer thickness change data and ion diffusion rate data are jointly stored to form a structurally complete and time-traceable battery interface response dataset, providing fundamental data support for subsequent multiphysics coupling simulation, health status assessment, and intelligent optimization control.

[0016] Preferably, step S13, which involves using sensor-integrated deployment data to perform real-time nanoscale acquisition of the SEI layer thickness, includes:

[0017] By integrating sensor data, nanoscale acquisition nodes are set up in the 10nm–50nm range on the surface of the battery negative electrode and in the 20nm–80nm range of the adjacent interface region, with a total of no less than 40 acquisition nodes and a node spacing of no more than 15nm, forming a complete nanoscale monitoring network for SEI layer thickness. Initial acquisition conditions are set, including an ambient temperature of 25°C±1°C, a battery operating voltage range of 2.5V–4.2V, and a sampling frequency of 10 times per second. SEI layer thickness change data are gradually sampled over an acquisition period of no less than 60 minutes. During the acquisition process, different charge and discharge rate operating conditions are simulated, and the dynamic change data of SEI layer thickness on the negative electrode surface and interface region are recorded in real time. When the SEI layer thickness change rate exceeds 0.5nm / min or the thickness exceeds a preset threshold of 100nm, it is recorded as an abnormal growth risk point, with the charge and discharge rate specifically set in the range of 0.1C–2C. The dynamic change data of SEI layer thickness on the negative electrode surface and interface region are integrated to generate real-time nanoscale acquisition data of SEI layer thickness change.

[0018] This invention significantly improves the spatial resolution of monitoring by deploying no fewer than 40 acquisition nodes within a 10nm–50nm range on the surface of the battery negative electrode and within a 20nm–80nm range in the adjacent interface region, with a node spacing of no more than 15nm. This ensures comprehensive perception of the microscopic growth behavior of the SEI layer and avoids monitoring blind spots and information sparsity issues. A constant temperature environment of 25°C ± 1°C, a battery operating voltage range of 2.5V–4.2V, and a sampling frequency of 10Hz are set, with point-by-point sampling performed over a duration of no less than 60 minutes to ensure data stability, reproducibility, and temporal continuity, providing high-quality data support for modeling and analysis. Dynamic acquisition of SEI layer thickness variation data under different charge / discharge rates from 0.1C to 2C reveals the interface layer response patterns under different operating modes and captures the coupled evolution characteristics between electrochemical behavior and interface morphology. Setting a thickness change rate threshold (0.5nm / min) and an absolute thickness threshold (100nm) enables real-time identification and marking of abnormal growth risk points, providing crucial judgment criteria for subsequent battery safety assessment, lifetime prediction, and control strategies. By fusing and processing the thickness variation data of the negative electrode surface and the adjacent interface region, a multi-region, full-time, dynamic evolution data map at the nanoscale is formed, providing a quantitative basis for revealing the formation mechanism, structural uniformity and interface stability of the SEI layer.

[0019] Preferably, step S2 involves analyzing the dendrite morphology evolution of the battery by measuring the SEI layer thickness and ion diffusion rate, and visualizing the analysis results as dendrite cross-sectional profile curves, including:

[0020] Dendrite edge detection is performed on the battery based on the SEI layer thickness and ion diffusion rate to generate dendrite contour feature data.

[0021] Correlation analysis was performed on dendrite contour feature data and SEI layer thickness variation data to generate morphological evolution parameter data;

[0022] Typical cross-sectional slice locations of the battery are selected using morphological evolution parameter data to generate dendritic cross-sectional coordinate data;

[0023] The dendrite cross-section coordinate data and ion diffusion rate data are mapped and extracted to generate cross-section thickness-rate mapping data.

[0024] Curve fitting is performed on the cross-sectional thickness-rate mapping data to generate dendritic profile curve fitting data;

[0025] Visualize and render the dendrite profile curve fitting data to generate dendrite cross-section profile curve data.

[0026] This invention utilizes linked data of SEI layer thickness and ion diffusion rate to detect dendrite edges, effectively extracting the contour features of dendrites on the electrode surface and providing a geometric basis for subsequent spatial modeling and evolution analysis. By correlating dendrite contour features with SEI layer thickness variations, morphological evolution parameters characterizing dendrite growth rate, directionality, and bifurcation characteristics are generated, revealing the dynamic correlation mechanism between dendrite evolution behavior and interface layer evolution. Based on these morphological evolution parameters, representative dendrite cross-sectional slices are automatically selected to ensure the generated cross-sectional data is truly representative and has engineering analysis value, avoiding sample bias. Through joint mapping of cross-sectional coordinates and ion diffusion rate, cross-sectional thickness-rate mapping data is generated, achieving a quantitative correlation between structural parameters and electrochemical dynamic behavior, providing dual constraints for dendrite evolution modeling. Curve fitting and visualization rendering of the mapping data generate dendrite cross-sectional curves that intuitively display dendrite evolution trends, edge complexity, and nonlinear structural characteristics, providing important reference data for scientific research, failure analysis, and interface optimization. The generated profile curves not only support detailed display of structural geometry, but also have the ability to dynamically reconstruct the evolution process over time, improving the observability and interpretability of dendrite evolution mechanism research.

[0027] Preferably, dendrite edge detection of the battery based on the SEI layer thickness and ion diffusion rate includes:

[0028] Based on the thickness of the battery SEI layer, regions of equal thickness are extracted from the battery to obtain an isopleth distribution map of the thickness.

[0029] Calculate the diffusion direction vector field of the ion diffusion rate data to obtain the ion flow pattern;

[0030] Based on the cross-location of thickness isopleth distribution map and ion flow direction map, candidate region data for dendrite growth are generated.

[0031] Perform local adaptive grayscale dilation on the candidate dendrite growth region data to transform the candidate region from a point into an edge fragment, generating an initial dendrite edge map;

[0032] Based on the edge directionality of the dendrite edge map, adjacent pixel density vector projection is performed to extract continuous dendrite growth path segments and generate a set of main dendrite growth paths.

[0033] The path curvature of the main dendrite growth path set is extracted to obtain dendrite contour feature data.

[0034] This invention achieves precise modeling of the thickness distribution of the battery's micro-interface by extracting the isopleth map of the SEI layer thickness, providing a stable geometric basis for subsequent spatial localization of potential dendrite initiation regions. Based on ion diffusion rate data, a diffusion direction vector field is calculated to generate an ion flow direction map, revealing ion migration trends from an electrochemical kinetics perspective, thus reflecting the driving path of dendrite growth. Cross-analysis of the thickness isopleth map and the ion flow direction map effectively identifies hotspot regions for dendrite formation, generating candidate region data and significantly improving the accuracy and predictive power of dendrite identification. The initial dendrite growth candidate regions are processed through local adaptive grayscale dilation, transforming isolated point features into identifiable edge segments, avoiding omissions due to noise or signal sparsity. Based on the edge directionality of the initial dendrite edge map, physically coherent main dendrite growth path segments are extracted through adjacent pixel density vector projection, establishing a logical connection from edge segments to the complete path. Path curvature is extracted from the main growth path set to obtain geometric features such as the degree of bending and bifurcation positions of the dendrite structure, forming high-precision dendrite contour feature data, providing solid data support for subsequent cross-sectional modeling and morphological evolution analysis. The entire edge detection process integrates multi-dimensional data sources of thickness, electrochemical diffusion, and geometric image processing to construct a three-dimensional dendrite recognition system that links structure, performance, and behavior, effectively improving battery safety monitoring capabilities.

[0035] Preferably, step S2 involves performing charge transfer impedance incremental analysis on the battery using charge and discharge data, and visualizing the analysis results as a biaxial line graph, including:

[0036] The battery charge and discharge data is used to divide the charge and discharge cycles and generate standardized periodic sequence data.

[0037] Calculate the voltage-current change rate based on standardized periodic sequence data to generate instantaneous change slope data;

[0038] Based on the instantaneous slope data, the unit charge transfer voltage range in each cycle of the battery usage charge and discharge data is dynamically segmented to generate impedance segmentation window data;

[0039] Perform interval voltage-current integral ratio processing on the impedance segmented window data, calculate the equivalent charge transfer impedance value of each segment, and generate impedance profile data;

[0040] Perform cross-period impedance increment differentiation on the impedance profile data to generate charge transfer impedance increment trend data;

[0041] The incremental trend data of charge transfer impedance is normalized in two axes to generate a two-axis line graph.

[0042] This invention generates a standardized periodic sequence by periodically segmenting charge-discharge data, ensuring the comparability of electrochemical behavior and the stability of analysis results across different periods, providing a unified data foundation for subsequent impedance calculations. Utilizing the instantaneous slope data of voltage-current changes, it meticulously reflects the instantaneous dynamics of the battery's internal reactions, improving the spatiotemporal resolution of impedance segmentation and making the segmented windows more consistent with the actual charge transfer process. Dynamic segmentation of voltage intervals based on instantaneous slope data yields impedance segmented windows, effectively avoiding information loss caused by fixed segmentation and enhancing the specificity and sensitivity of impedance measurements. By calculating the voltage-current integral ratio, the equivalent charge transfer impedance value for each segment is obtained, overcoming single-point measurement errors and achieving high-precision quantification of charge transfer impedance. Differential analysis of the impedance profile data extracts the cross-period impedance increment trend, accurately reflecting the subtle dynamics of battery performance degradation and interface changes, providing dynamic indicators for health status assessment. Through biaxial normalization, a clear biaxial line graph is formed, intuitively displaying the charge transfer impedance increment trend and changes in related electrochemical parameters, facilitating researchers and engineers to quickly determine battery performance and lifespan status. This method not only quantitatively analyzes changes in charge transfer impedance, but also provides real-time monitoring through a visual interface, which helps optimize battery health management and maintenance strategies based on the Internet of Things.

[0043] Preferably, step S3, which involves quantitative analysis of the chemical composition and assessment of physical structural damage of the battery based on material degradation data, includes:

[0044] The battery surface is divided into micro-blocks, and X-rays are used to extract the independent spectral signals of each block based on material degradation data to generate multi-region energy spectrum response matrix data.

[0045] Based on the multi-regional energy spectrum response matrix data, the characteristic peak values ​​of key elements are integrated region by region to generate initial distribution data of element abundance.

[0046] Spatial normalization correction was performed using the initial element abundance distribution data to eliminate errors caused by illumination angle or local reflection, and calibrated surface abundance data of battery recyclable materials was generated.

[0047] Extracting surface structure anomalies from material degradation data;

[0048] By detecting the boundary closure of crack edges in the battery through anomalous points in the surface structure, a structural defect morphology image is generated.

[0049] By using images of structural defects, the battery type is labeled and its area is estimated, generating surface structural damage data for battery recycling.

[0050] This invention improves the spatial resolution and accuracy of material composition analysis by dividing the battery surface into micro-blocks and independently acquiring the spectral signals of each block using X-rays. Regional integration processing is performed on the characteristic peaks of key elements to effectively extract the initial distribution information of element abundance, accurately reflecting the chemical composition of the recycled battery material. A spatial normalization correction method is used to eliminate signal deviations caused by changes in X-ray irradiation angle or local reflections, ensuring the objectivity and consistency of the battery surface abundance data. Surface height anomalies are extracted from material degradation data to accurately locate structural damage such as cracks and spalling, enhancing the sensitivity of physical damage detection. Boundary closure detection is performed using these anomalies to form a complete structural defect image, enabling precise depiction and quantification of crack and other defect morphologies. Based on the defect morphology image, type identification and area statistics of surface damage are performed on the recycled battery, providing comprehensive physical damage assessment data to support the quality judgment of recycled materials and subsequent utilization decisions. Through joint analysis of chemical composition and physical structure, a comprehensive evaluation system for recycled battery materials is constructed, providing strong data support for recycling and resource regeneration.

[0051] Preferably, the boundary closure detection of the battery crack edge through surface structural height anomalies includes:

[0052] Three-dimensional structural imaging of the battery is performed by identifying anomalous points in the surface structure height, and the surface height distribution map after imaging is extracted.

[0053] Anomaly detection is performed on the height distribution map to identify surface height anomalies and generate a set of height anomalies;

[0054] Spatial anomaly distribution data were obtained based on the analysis of the set of height anomalies.

[0055] Analyze the spatial connectivity of spatial anomaly distribution data to identify the initial trajectory of suspected crack edges;

[0056] Boundary point fitting is performed on the initial trajectory of suspected crack edges to construct the edge curve of open cracks;

[0057] The boundary closure algorithm is applied to extend and close the curve of the open crack edge to generate the closed crack edge profile.

[0058] Structural consistency detection and geometric feature modeling are performed on the edge region of closed cracks to extract the morphological differences between the inside and outside of the crack region and obtain the regional structural features.

[0059] By integrating the edge contour of closed cracks and regional structural features, a structural defect morphology image is generated.

[0060] This invention obtains a high-resolution surface height distribution map by performing three-dimensional structural imaging of the material surface, providing detailed spatial morphological information for subsequent crack identification. Anomaly detection algorithms are used to identify surface height anomalies, forming a set of height anomalies that accurately reflects the spatial distribution of potential crack edges. In-depth analysis of the spatial connectivity of these anomalies identifies the initial paths of suspected crack edges, effectively capturing the continuity and orientation characteristics of cracks. Boundary point fitting technology is used to reconstruct the edge curve of open cracks, achieving a continuous description of crack morphology and improving the completeness of crack identification. Through curve extension and closure algorithms, the edge curve of open cracks is converted into a closed contour, facilitating subsequent structural analysis and defect quantification. Morphological analysis of closed crack regions extracts internal and external morphological differences, revealing the structural characteristics and potential risks of the cracks. Combining the closed crack edge contour and regional structural features, a clear and intuitive image of structural defect morphology is formed, providing strong support for quality assessment and maintenance decisions for battery recycling materials.

[0061] Preferably, step S4, which involves synchronizing the monitoring timeline of the battery lifecycle visualization interface, includes:

[0062] The monitoring time sequence of the battery life cycle visualization interface is synchronized, the timestamp standardization accuracy is set to 1ms, the sampling frequency of multi-dimensional monitoring indicators is ≥1Hz, the maximum allowable deviation of the time channel is ±10ms, the data alignment tolerance range is ≤30ms, the time drift correction threshold is ≥30ms, the time granularity range that can be viewed is 1s~1cycle, the visualization duration coverage range is 1min~10year, and the total number of key nodes in the entire life cycle is ≥5.

[0063] This invention employs a 1-millisecond-level timestamp standardization precision to ensure highly consistent time signatures for multi-dimensional monitoring indicators, reducing data deviations caused by time errors. The sampling frequency for multi-dimensional monitoring indicators is no less than 1 Hz, enabling real-time acquisition and monitoring of battery status and ensuring timely capture of dynamic changes. The maximum allowable time channel deviation is only ±10 milliseconds, combined with a data alignment tolerance range not exceeding 30 milliseconds, effectively eliminating timing misalignments between different data sources and ensuring high consistency of data for all indicators. A time drift correction threshold of ≥30 milliseconds is set, and time drift is monitored and automatically adjusted in real time, ensuring stable and reliable timing synchronization during long-term operation. It supports multi-granularity time viewing from 1 second to 1 cycle, enabling comprehensive analysis from fine-grained instantaneous states to complete charge-discharge cycles. Visualization duration covers from 1 minute to 10 years, meeting diverse needs from short-term dynamic monitoring to long-term trend analysis throughout the battery's lifecycle. It supports simultaneous monitoring of no fewer than 5 key nodes throughout the battery's lifecycle, allowing users to focus on important state changes and assisting in maintenance decisions and performance evaluation.

[0064] This specification provides an IoT-based battery lifecycle data visualization and monitoring system for executing the aforementioned IoT-based battery lifecycle data visualization and monitoring method. The IoT-based battery lifecycle data visualization and monitoring system includes:

[0065] The manufacturing monitoring module is used to acquire battery life cycle data, which includes the battery manufacturing stage, battery usage stage, and battery recycling stage; based on the battery manufacturing stage, a photonic crystal fiber sensor is integrated into the SEI layer of the battery to collect the battery SEI layer thickness and ion diffusion rate.

[0066] The monitoring module is used to analyze the evolution of dendrite morphology in the battery by measuring the thickness of the SEI layer and the ion diffusion rate, and visualize the analysis results as dendrite cross-sectional curves; battery charge and discharge data are acquired based on the battery usage stage; incremental charge transfer impedance analysis is performed on the battery usage charge and discharge data, and the analysis results are visualized as biaxial line graphs;

[0067] The recycling monitoring module is used to collect material degradation data based on the battery recycling stage; based on the material degradation data, the module performs quantitative analysis of the chemical composition and physical structure damage assessment of the battery, and obtains surface abundance data and surface structure damage data of the recycled battery materials; the module visualizes the surface abundance data of the recycled battery materials as a stacked bar chart, and the surface structure damage data of the recycled battery materials as a pore size distribution histogram.

[0068] The timing synchronization module is used to integrate dendrite cross-section curves, biaxial line graphs, stacked bar graphs, and aperture distribution histograms into a visualization interface through a preset platform framework, generating a full life cycle visualization interface for the battery; and to monitor and synchronize the timing of the full life cycle visualization interface for the battery to perform IoT-based full life cycle data visualization monitoring operations.

[0069] The beneficial effects of this invention lie in its design of monitoring modules encompassing three key stages—manufacturing, use, and recycling—to achieve comprehensive data collection and analysis of the battery's entire lifecycle status, promoting all-round health management and performance optimization. By integrating photonic crystal fiber sensors into the battery manufacturing stage, real-time nanoscale data acquisition of SEI layer thickness and ion diffusion rate is achieved, ensuring high-precision monitoring of early key physicochemical processes and facilitating the timely detection of potential degradation trends. Dynamic analysis and visualization of dendrite morphology are performed by combining SEI layer thickness and ion diffusion rate data, supplemented by charge transfer impedance increment analysis of charge and discharge data, deeply reflecting the microscopic changes within the battery and supporting refined performance evaluation and safety early warning. Quantitative chemical composition and physical structural damage assessment are conducted using material degradation data, generating intuitive charts of surface abundance and structural damage to assist in determining the reuse value and repair solutions of recycled battery materials, promoting the development of a circular economy. The time synchronization module integrates multiple types of visualization charts through a preset platform framework, employing a high-precision time synchronization mechanism to ensure data alignment and continuity, improving the accuracy and real-time nature of data display, and meeting the dynamic monitoring needs based on the Internet of Things. The system supports multi-scale data viewing and analysis from second-level to grade-level time granularity, covering key lifecycle nodes and meeting diverse application scenarios from routine maintenance to performance degradation prediction. Therefore, this invention, by using photonic crystal fiber sensing technology, IoT data acquisition and transmission technology, multi-dimensional data visualization technology, and a full lifecycle analysis method, solves the problems of fragmented lifecycle data, difficulty in perceiving micro-evolution, unintuitive display of monitoring results, and poor platform integration in traditional battery monitoring. This improves the completeness of battery lifecycle status perception, the accuracy of monitoring, and the intelligence level of management decision-making. Attached Figure Description

[0070] Figure 1 This is a flowchart illustrating the steps of a battery lifecycle data visualization and monitoring method based on the Internet of Things.

[0071] Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S1.

[0072] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0073] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0074] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0075] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0076] To achieve the above objectives, please refer to Figures 1 to 2 A method for visual monitoring of battery lifecycle data based on the Internet of Things, the method comprising the following steps:

[0077] Step S1: Acquire battery lifecycle data, which includes battery manufacturing stage, battery usage stage and battery recycling stage; Based on the battery manufacturing stage, integrate photonic crystal fiber sensor on the SEI layer of the battery to collect the battery SEI layer thickness and ion diffusion rate.

[0078] Step S2: Analyze the dendrite morphology evolution of the battery by measuring the SEI layer thickness and ion diffusion rate, and visualize the analysis results as dendrite cross-sectional curves; obtain battery charge and discharge data based on the battery usage stage; perform charge transfer impedance incremental analysis on the battery usage charge and discharge data, and visualize the analysis results as biaxial line graphs;

[0079] Step S3: Collect material degradation data based on the battery recycling stage; perform quantitative analysis of chemical composition and physical structure damage assessment on the battery according to the material degradation data to obtain surface abundance data and surface structure damage data of the recycled battery materials; visualize the surface abundance data of the recycled battery materials as a stacked bar chart and visualize the surface structure damage data of the recycled battery materials as a pore size distribution histogram.

[0080] Step S4: Integrate the dendrite cross-section curve, biaxial line graph, stacked bar graph, and pore size distribution histogram into a visualization interface through a preset platform framework to generate a battery life cycle visualization interface; perform time-series monitoring synchronization on the battery life cycle visualization interface to execute IoT-based battery life cycle data visualization monitoring operations.

[0081] This invention integrates photonic crystal fiber sensors during the battery manufacturing stage, enabling real-time acquisition of SEI layer thickness and ion diffusion rate. This allows for precise perception of the SEI layer's microstructural evolution, providing a high-quality data foundation for subsequent dendrite growth behavior analysis. By performing incremental charge transfer impedance analysis on battery charge-discharge data, combined with dendrite morphology evolution trend analysis, the electrochemical degradation process of the battery during actual operation is effectively revealed, improving the accuracy and foresight of battery health status assessment. Quantitative analysis of battery chemical composition and physical structural damage assessment using material degradation data accurately obtains the surface abundance and structural integrity of recycled materials, helping to optimize battery material recycling strategies and improve resource recovery efficiency. Integrating dendrite evolution curves, charge-discharge impedance change diagrams, recycled material abundance diagrams, and structural damage distribution diagrams into a unified interface generates a unified battery lifecycle visualization interface, facilitating users' comprehensive understanding of the battery's operation and degradation status at different stages. Utilizing a pre-defined platform framework, multi-stage data synchronization and unified monitoring are achieved, constructing a battery lifecycle IoT monitoring mechanism to enhance the intelligence, precision, and automation of battery lifecycle management. Therefore, by using photonic crystal fiber sensing technology, Internet of Things data acquisition and transmission technology, multidimensional data visualization technology and full life cycle analysis method, this invention solves the problems of fragmented life cycle data, difficulty in perceiving micro-evolution, unintuitive display of monitoring results and poor platform linkage in traditional battery monitoring, thereby improving the completeness of battery life cycle status perception, the accuracy of monitoring and the level of intelligent management decision-making.

[0082] In this embodiment of the invention, reference Figure 1 The diagram illustrates the steps of a battery lifecycle data visualization and monitoring method based on the Internet of Things (IoT) according to the present invention. In this example, the battery lifecycle data visualization and monitoring method based on IoT includes the following steps:

[0083] Step S1: Acquire battery lifecycle data, which includes battery manufacturing stage, battery usage stage and battery recycling stage; Based on the battery manufacturing stage, integrate photonic crystal fiber sensor on the SEI layer of the battery to collect the battery SEI layer thickness and ion diffusion rate.

[0084] Step S2: Analyze the dendrite morphology evolution of the battery by measuring the SEI layer thickness and ion diffusion rate, and visualize the analysis results as dendrite cross-sectional curves; obtain battery charge and discharge data based on the battery usage stage; perform charge transfer impedance incremental analysis on the battery usage charge and discharge data, and visualize the analysis results as biaxial line graphs;

[0085] Step S3: Collect material degradation data based on the battery recycling stage; perform quantitative analysis of chemical composition and physical structure damage assessment on the battery according to the material degradation data to obtain surface abundance data and surface structure damage data of the recycled battery materials; visualize the surface abundance data of the recycled battery materials as a stacked bar chart and visualize the surface structure damage data of the recycled battery materials as a pore size distribution histogram.

[0086] Step S4: Integrate the dendrite cross-section curve, biaxial line graph, stacked bar graph, and pore size distribution histogram into a visualization interface through a preset platform framework to generate a battery life cycle visualization interface; perform time-series monitoring synchronization on the battery life cycle visualization interface to execute IoT-based battery life cycle data visualization monitoring operations.

[0087] In this embodiment of the invention, the battery lifecycle is divided into three sub-stages: manufacturing, usage, and recycling. The following data are collected for each stage: Manufacturing stage: material ratio, electrode preparation process parameters, electrolyte injection parameters; Usage stage: current, voltage, temperature, charge / discharge cycles, SOC / SOH state; Recycling stage: recycling temperature, acid washing or pyrolysis parameters, residual energy value. In the manufacturing stage, after the SEI layer is deposited on the negative electrode surface, the PCF is encapsulated in the micro-gap between the SEI layer and the current collector using a sol-gel photosensitive material. Sensor parameters: operating wavelength: 1550nm; sensitivity: >0.8nm / nm (thickness), >1.2nm / (mol / cm³·s⁻¹) (ion diffusion); data sampling period: 1s. Multimode interferometry demodulation technology is used to continuously monitor the PCF reflectance spectrum, extracting SEI layer thickness (unit: nm) and ion diffusion rate (unit: mol / cm³·s⁻¹) data, and forming a multidimensional time series data matrix, denoted as D_sei(t). Input D_sei(t) from step S1 into a finite element simulation module (such as COMSOL Multiphysics). Under a two-dimensional symmetric modeling framework, perform a simulation of lithium-ion migration and electric field coupling. Set the following parameters: initial concentration: 1 mol / L; electric field strength: 0.5 V / μm; simulation step size: 10 ms. Obtain the lithium dendrite growth cross-sectional profile data. After multi-point B-spline curve fitting, a cross-sectional profile curve is generated. The image resolution is 300 dpi, and the curve data is represented as Cr_dz(t). Based on the usage phase, the actual charge / discharge cycle current and voltage changes are collected through a BMS system at 1-second intervals and recorded as V(t) and I(t). Perform charge transfer impedance fitting on V(t) and I(t), calculate ΔZ′ and ΔZ″ for each cycle, and plot biaxial line graphs (left axis: real part of impedance; right axis: imaginary part of impedance) with time on the horizontal axis and ΔZ′ and ΔZ″ on the vertical axis. The interval between each data point in the graph does not exceed 5 seconds. During pyrolysis or chemical recovery, inductively coupled plasma mass spectrometry (ICP-MS) was used to quantitatively analyze the content of elements such as Ni, Co, and Mn in the residual electrode materials, with units of μg / g. Samples were prepared in 5mg slices and then sent to an XRF spectrometer with a scanning energy window of 5–20 keV. The output was an elemental abundance matrix E_abundance[i], and stacked histograms were plotted according to metal type, with color coding conforming to IEC 60417 standard. Scanning electron microscopy (SEM) combined with image grayscale distribution statistics was used to assess the area of ​​structural defects such as pores and cracks in the recovered electrode sheets. The pore size statistical range was 0–10 μm, and a pore size frequency histogram was generated, with the x-axis representing the pore size range and the y-axis representing the relative frequency.A visualization main interface is built using a front-end framework based on Vue.js and ECharts.js, and the following graph modules are integrated into different panels of the main interface: Partition 1: Dendritic cross-section curve (input is Cr_dz(t)); Partition 2: Biaxial line graph (input is ΔZ′(t), ΔZ″(t)); Partition 3: Stacked histogram (input is E_abundance[i]); Partition 4: Aperture distribution histogram (input is SEM image analysis results). A timing control module is embedded in the main interface, and a unified timestamp T_unix is ​​used for all data. The modules are aligned, with a refresh interval of 10 seconds. This ensures that the four types of graphics automatically refresh and update as the timeline scrolls. A connection to the IoT data center is established via the MQTT protocol. The backend uses a Node.js server to continuously receive real-time data streams from the battery manufacturing / use / recycling stages, automatically synchronizing the interface status. Monitoring thresholds are set: a red warning flashing is triggered when the SEI thickness is >120nm, the ion diffusion rate is <1e-10mol / cm³·s⁻¹, the ΔZ′ growth rate is >10Ω / s, or the pore size frequency is >30% in the >5μm region, notifying the operator for manual review.

[0088] As an example of the present invention, reference is made to Figure 2 As shown, in this example, step S1 includes:

[0089] Step S11: Obtain battery lifecycle data, which includes the battery manufacturing stage, battery usage stage, and battery recycling stage;

[0090] Step S12: Based on the production process data of the manufacturing stage, locate the formation time and region of the SEI layer and generate SEI layer growth marker data;

[0091] Step S13: Based on the SEI layer growth labeling data, deploy a photonic crystal fiber sensor to the electrode-SEI interface region to generate sensor integrated deployment data; use the sensor integrated deployment data to perform real-time nanoscale acquisition of SEI layer thickness to generate SEI layer thickness change data.

[0092] Step S14: Perform time-series fitting on the SEI layer thickness variation data, extract the ion transport boundary response, and generate ion diffusion rate data; store the SEI layer thickness variation data and ion diffusion rate data together to obtain the battery SEI layer thickness and ion diffusion rate.

[0093] In this embodiment of the invention, battery lifecycle data is acquired, including data from the manufacturing, usage, and recycling stages. Manufacturing stage data covers key process parameters such as electrode coating, electrolyte injection, and first charge; usage stage data includes voltage, current, charge / discharge rate, and temperature records; and recycling stage data includes residual material components and signs of structural aging. By constructing a unified lifecycle data model and integrating it with an IoT acquisition module, all data are ensured to have consistent timestamps and structural formats, providing a data foundation for subsequent operations. Based on the manufacturing stage process data, key temporal and spatial regions for SEI layer formation are identified. The formation window of the SEI layer is analyzed using the voltage-current evolution curve of the first charge stage. Combined with electrolyte distribution and electrode structural parameters, numerical simulation (such as CFD) is used to determine the main growth regions of the SEI layer. Subsequently, this information is extracted to generate SEI layer growth marker data, clarifying the interface region and time range, providing a decision-making basis for sensor deployment. Based on the SEI layer growth marker data, a photonic crystal fiber sensor (PCF) is deployed in the electrode-SEI interface region. This type of sensor, due to its high sensitivity and nanometer-level resolution, is suitable for monitoring minute changes in SEI layer thickness. Deployment is achieved through precise positioning using micro-nano fabrication processes, ensuring the sensor is embedded in key reaction interfaces and forming integrated sensor deployment data. During the initial battery charge and subsequent operations, the sensor can collect real-time information on SEI layer thickness changes, monitoring the dynamic changes in interface thickness through reflected wavelength drift, generating SEI layer thickness variation data. Time-series modeling and fitting are performed on the collected SEI layer thickness variation data. The evolution of thickness over time is fitted and analyzed using an exponential growth model or a logistic function to extract the SEI layer stabilization rate constant. Simultaneously, by combining the thickness growth trend with diffusion models (such as Fick's law), the diffusion rate of ions within the SEI layer is calculated. Finally, the SEI layer thickness variation data and ion diffusion rate data are jointly stored to construct a composite data model of electrochemical interface structure and transport behavior.

[0094] Preferably, step S13, which involves using sensor-integrated deployment data to perform real-time nanoscale acquisition of the SEI layer thickness, includes:

[0095] By integrating sensor data, nanoscale acquisition nodes are set up in the 10nm–50nm range on the surface of the battery negative electrode and in the 20nm–80nm range of the adjacent interface region, with a total of no less than 40 acquisition nodes and a node spacing of no more than 15nm, forming a complete nanoscale monitoring network for SEI layer thickness. Initial acquisition conditions are set, including an ambient temperature of 25°C±1°C, a battery operating voltage range of 2.5V–4.2V, and a sampling frequency of 10 times per second. SEI layer thickness change data are gradually sampled over an acquisition period of no less than 60 minutes. During the acquisition process, different charge and discharge rate operating conditions are simulated, and the dynamic change data of SEI layer thickness on the negative electrode surface and interface region are recorded in real time. When the SEI layer thickness change rate exceeds 0.5nm / min or the thickness exceeds a preset threshold of 100nm, it is recorded as an abnormal growth risk point, with the charge and discharge rate specifically set in the range of 0.1C–2C. The dynamic change data of SEI layer thickness on the negative electrode surface and interface region are integrated to generate real-time nanoscale acquisition data of SEI layer thickness change.

[0096] In this embodiment of the invention, based on the sensor integration deployment data generated in the preceding steps, nanoscale acquisition nodes are precisely set within a range of 10nm to 50nm on the surface of the battery negative electrode and within a range of 20nm to 80nm in the adjacent electrolyte interface region. These nodes are distributed in a total number of no less than 40, with the node spacing strictly controlled within 15nm, thereby constructing an SEI layer thickness monitoring network with ultra-high resolution. Through this monitoring network, the thickness evolution trend of the SEI layer at different physical locations can be comprehensively perceived. Secondly, the initial boundary conditions of the monitoring process are set. The ambient temperature is stabilized at 25°C ± 1°C to ensure the thermal stability of the test results; the battery operating voltage range is 2.5V to 4.2V, corresponding to the battery's normal operating conditions; the sampling frequency is set to 10 times per second to achieve high-timeliness data acquisition. Simultaneously, the entire acquisition process lasts for no less than 60 minutes to cover the formation and evolution stage of the SEI layer in the early stages of typical cycling. During the acquisition process, the system simulates various charge and discharge rate operating conditions, ranging from 0.1C to 2C, considering both low and high rates. The sensor records the SEI layer thickness changes on the negative electrode surface and interface region in real time under these different operating modes. Specifically, the system sets a thickness change rate threshold of 0.5 nm / min and an upper thickness limit threshold of 100 nm. Once the monitoring data at any node exceeds this threshold, it is identified as an "abnormal growth risk point," and these points are individually labeled for subsequent analysis and processing. Finally, the system integrates the SEI layer thickness change data from all sampling nodes at different time points to generate a nanoscale dynamic change curve covering the negative electrode surface and interface region. This data can be used for modeling ion diffusion behavior in subsequent step S14 and also provides key input parameters for SEI layer stability assessment and lifetime prediction.

[0097] Preferably, step S2 involves analyzing the dendrite morphology evolution of the battery by measuring the SEI layer thickness and ion diffusion rate, and visualizing the analysis results as dendrite cross-sectional profile curves, including:

[0098] Dendrite edge detection is performed on the battery based on the SEI layer thickness and ion diffusion rate to generate dendrite contour feature data.

[0099] Correlation analysis was performed on dendrite contour feature data and SEI layer thickness variation data to generate morphological evolution parameter data;

[0100] Typical cross-sectional slice locations of the battery are selected using morphological evolution parameter data to generate dendritic cross-sectional coordinate data;

[0101] The dendrite cross-section coordinate data and ion diffusion rate data are mapped and extracted to generate cross-section thickness-rate mapping data.

[0102] Curve fitting is performed on the cross-sectional thickness-rate mapping data to generate dendritic profile curve fitting data;

[0103] Visualize and render the dendrite profile curve fitting data to generate dendrite cross-section profile curve data.

[0104] In this embodiment of the invention, based on the collected SEI layer thickness data and ion diffusion rate data of the battery, an edge detection algorithm (such as the Canny operator or the Sobel operator) is used to identify the contour of the dendritic region inside the battery, extract dendritic edge features, and generate dendritic contour feature data. This step accurately captures the spatial distribution boundary of the dendritic morphology, providing a precise foundation for subsequent analysis. Subsequently, correlation analysis is performed on the dendritic contour feature data and the time-varying trend of the SEI layer thickness to explore the dynamic coupling relationship between the two, and then extract morphological evolution parameter data describing the dendritic growth and diffusion process, such as dendritic growth rate, branch density, and thickness change rate. Based on the morphological evolution parameters, combined with the internal structure and performance requirements of the battery, representative dendritic cross-sectional slice positions are selected to form dendritic cross-sectional coordinate data. This coordinate data is located at the cross-sectional surface where the dendritic morphology change is most significant or critical, ensuring the relevance and practicality of the analysis. Furthermore, the dendritic cross-sectional coordinates are mapped and matched with the corresponding ion diffusion rate data to extract the relationship between thickness and diffusion rate at different positions on the cross-section, forming cross-sectional thickness-rate mapping data. This data reveals the spatial coupling characteristics between dendrite thickness variation and ion migration rate. Curve fitting techniques (such as polynomial fitting or nonlinear regression) were applied to this mapping data to generate continuous and smooth dendrite profile curves, accurately describing the dendrite cross-sectional thickness distribution and its trend with diffusion rate. Finally, computer graphics rendering technology was used to visualize the fitted dendrite profile curve data, generating dendrite cross-sectional profile curve data with high-precision detail. This graph not only intuitively reflects the morphological evolution of dendrites but also provides important reference for battery design optimization and safety early warning.

[0105] Of particular importance, the correlation analysis between dendrite profile feature data and SEI layer thickness variation data also includes:

[0106] Based on dendrite contour feature data, a multi-level curvature segmented structure of the end face is constructed, and the micro-deformation morphology index of the dendrite edge is extracted to generate dendrite microstructure change index data.

[0107] Adaptive temporal window convolution slicing is performed on the SEI layer thickness variation data to generate multi-scale temporal cluster data of thickness evolution;

[0108] Implicit orbital integration was performed on dendritic microstructure change index data and thickness evolution multi-scale time cluster data to generate dynamic evolution coupling trajectory maps;

[0109] Based on the dynamic evolution coupling trajectory map, multi-segment shearing anisotropic exponential coupling analysis is performed to extract the local reverse coupling factor and the variable-order delay synergy strength index, and generate morphological evolution parameter data.

[0110] In this embodiment of the invention, a multi-level curvature segmentation structure of the end face is constructed based on dendrite contour feature data. This structure discretizes the two-dimensional cross-sectional contour lines of the dendrite edges at high resolution, employs least-squares circle fitting and Bezier curve stitching methods to continuously segment the contour curves, and extracts the local curvature value of each segment. To reflect subtle morphological changes, a refined resolution interval (e.g., within 5 μm) is introduced in each segment, and the curvature fluctuation is analyzed using the third derivative, forming a micro-deformation morphology index matrix. Finally, dendrite microstructure change index data is obtained to describe the dynamic changes such as local abrupt changes, bending, and shrinkage at the dendrite growth edges. Next, adaptive time window convolution slicing is performed on the SEI layer thickness change data. This step sets the dynamic time window width according to the rate of change of the thickness data; a smaller window (e.g., 1 s) is used for regions with high change rates, and a larger window (e.g., 10 s) is used for regions with low change rates. A sliding window technique is used to perform normalized convolution integrals on the thickness changes within each time period, extracting the pattern evolution trajectory of thickness evolution at multiple time scales, forming multi-scale time cluster data of thickness evolution. This data structure represents local dynamic segments of thickness development trends in units of time clusters. Subsequently, implicit orbital embedding is performed using dendritic microstructure change index data and multi-scale time cluster data of thickness evolution. This process uses morphological trajectory as the dominant dimension and thickness evolution as the response dimension, employing variable-dimensionality trajectory reconstruction and temporal embedding mapping algorithms (such as those based on dynamic time warping (DTW) and multi-scale cross-entropy) to pair and fuse the two time-series data, generating a dynamic evolution coupling trajectory map. The map reflects the synergistic relationship between morphological changes and thickness evolution through similarity mapping and orbital density kernel estimation. Finally, based on the dynamic evolution coupling trajectory map, multi-segment shearing anisotropic exponential coupling analysis is performed. This analysis first divides the map into several evolutionary segments and calculates the variability of the angle between the dendritic morphology change direction and the SEI layer thickness growth direction in each segment. For segments with reverse angles (angle > 90°), the reverse coupling factor (e.g., parts with negative direction cosine values) is calculated. Simultaneously, high-order cross-correlation and variable-order delay time window fitting methods are used to extract the degree of asynchronous coordination in time, thereby calculating the variable-order delay coordination strength index. Finally, these analytical results are integrated to generate morphological evolution parameter data, which serves as an important input for judging the development trend of battery dendrites.

[0111] Preferably, dendrite edge detection of the battery based on the SEI layer thickness and ion diffusion rate includes:

[0112] Based on the thickness of the battery SEI layer, regions of equal thickness are extracted from the battery to obtain an isopleth distribution map of the thickness.

[0113] Calculate the diffusion direction vector field of the ion diffusion rate data to obtain the ion flow pattern;

[0114] Based on the cross-location of thickness isopleth distribution map and ion flow direction map, candidate region data for dendrite growth are generated.

[0115] Perform local adaptive grayscale dilation on the candidate dendrite growth region data to transform the candidate region from a point into an edge fragment, generating an initial dendrite edge map;

[0116] Based on the edge directionality of the dendrite edge map, adjacent pixel density vector projection is performed to extract continuous dendrite growth path segments and generate a set of main dendrite growth paths.

[0117] The path curvature of the main dendrite growth path set is extracted to obtain dendrite contour feature data.

[0118] In this embodiment of the invention, isothickness regions are extracted from the battery SEI layer thickness data. By setting a thickness threshold and interval (e.g., dividing the thickness into 5 nanometers increments), a thickness isopleth map of the battery's interior is generated using numerical interpolation. This map displays the spatial distribution of the SEI layer thickness in the form of isopleth lines, reflecting the thickness differences in different regions. Next, for the ion diffusion rate data, the vector field of its diffusion direction is calculated. Specifically, a gradient operator is used to calculate the spatial gradient of the local diffusion rate, obtaining the diffusion direction vector for each sampling point. By normalizing these vectors, a complete ion flow direction map is constructed, showing the dominant path and directional distribution of ion migration. Subsequently, the thickness isopleth map and the ion flow direction map are subjected to spatial cross-location analysis. Based on the spatial superposition relationship between the two, regions with significant thickness variations and concentrated ion flow directions are identified and defined as dendrite growth candidate regions. This region data is expressed in the form of a binary mask, marking potential dendrite expansion regions. Then, a grayscale local adaptive dilation operation is performed on the dendrite growth candidate region data. A structuring element (such as a 3×3 square convolution kernel) is used to locally dilate pixels within the candidate region, improving regional connectivity and expanding discrete point-like candidate regions into continuous edge segments, generating an initial dendrite edge map. This operation dynamically adjusts the dilation intensity based on local gray-level gradients to ensure the accuracy and continuity of the edge map. After obtaining the dendrite edge map, continuous dendrite growth path segments are extracted using the adjacent pixel density vector projection method, based on its edge directionality. Specific steps include calculating the gradient direction of each edge pixel, projecting it onto the density vectors of adjacent pixels, and filtering out pixel chains with high continuity and consistent direction based on the projection values ​​to form the main dendrite growth path set. Finally, path curvature information is extracted from the main dendrite growth path set. By calculating the curvature change at each sampling point on the path (using the radius of curvature formula or fitting the second derivative), the bending and branching characteristics of the dendrite contour are quantified, thereby generating accurate dendrite contour feature data and providing basic parameters for subsequent dendrite morphology analysis.

[0119] Of particular importance, cross-location based on thickness isopleth maps and ion flow direction maps also includes:

[0120] Extract the extreme gradients from the thickness isopleth map to generate an anomaly gradient region map of the SEI layer thickness;

[0121] A directional tensor is constructed from the ion flow pattern to generate ion directional flow vector field data;

[0122] Based on the SEI layer thickness anomaly gradient region map, the ion directional flow vector field data is spatially clipped to extract the ion penetration concentration region and generate the ion local flow enhancement region map.

[0123] By extracting spatially overlapping regions using ion local flow enhancement region maps and SEI layer thickness anomaly gradient region maps, dendrite growth intersection region layer data is generated.

[0124] Spatial clusters in the dendrite growth intersection region layer data are identified, high-density connected regions are extracted, and finally dendrite growth candidate region data are generated.

[0125] In this embodiment of the invention, an isopleth map of SEI layer thickness is constructed using nanometer-resolution SEI layer thickness acquisition data. This map uses the electrode-SEI interface as the reference coordinate system and expresses thickness variations using two-dimensional color gradients or thermal isopleths. Gradient calculations are then performed on this map, and the extreme gradients of thickness variations are extracted using the central difference method or the Sobel operator to generate an anomaly gradient region map of SEI layer thickness. This map reflects regions where the SEI layer rapidly thickens or thins, providing the spatial basis for dendrite formation. Next, a directional tensor is constructed for the ion flow pattern. This tensor data originates from ion velocity data acquired in real-time using a photonic crystal fiber sensor. Based on the known velocity and direction, a global ion-oriented flow vector field is generated by constructing the dominant direction tensor within each grid cell. This vector field data identifies the dominant direction and intensity of ion flow at each location in spatial vector form. Subsequently, the ion-oriented flow vector field data is spatially truncated based on the anomaly gradient region map of SEI layer thickness. The specific method involves using the thickness anomaly gradient region as a clipping window to extract its internal vector field distribution, obtaining ion flow direction feature data only located within the thickness anomaly region. Based on the clipped vector density and direction consistency index, regions with high flow concentration and intensity are identified, forming a local ion flow enhancement region map. Next, the ion flow enhancement region map is spatially overlaid with the SEI layer thickness anomaly gradient region map. A dual threshold method using pixel overlap and vector direction consistency is used to extract the overlapping region, generating dendrite growth intersection region layer data. Spatial overlay requires the thickness anomaly gradient to be greater than a preset threshold (e.g., 0.8 nm / μm), and the ion vector magnitude to be greater than a set lower limit (e.g., 5 × 10⁻⁻⁴). 5 The density of dendrite growth is mol·cm⁻²·s⁻¹, and the angle between the flow direction and the abnormal gradient direction is less than 15 degrees. Finally, spatial clustering is performed on the dendrite growth intersection region layer data, and spatial clusters are identified using density peak clustering (DPC) or a three-dimensional connected component extraction method based on DBSCAN. After clustering, regions with volume continuity greater than a certain threshold (e.g., 20 μm³) and internal vector direction consistency higher than 95% are extracted from the clusters, ultimately generating dendrite growth candidate region data, providing a basis for subsequent identification of high-risk sensor deployment points and micro-region safety assessment of battery structures.

[0126] Preferably, step S2 involves performing charge transfer impedance incremental analysis on the battery using charge and discharge data, and visualizing the analysis results as a biaxial line graph, including:

[0127] The battery charge and discharge data is used to divide the charge and discharge cycles and generate standardized periodic sequence data.

[0128] Calculate the voltage-current change rate based on standardized periodic sequence data to generate instantaneous change slope data;

[0129] Based on the instantaneous slope data, the unit charge transfer voltage range in each cycle of the battery usage charge and discharge data is dynamically segmented to generate impedance segmentation window data;

[0130] Perform interval voltage-current integral ratio processing on the impedance segmented window data, calculate the equivalent charge transfer impedance value of each segment, and generate impedance profile data;

[0131] Perform cross-period impedance increment differentiation on the impedance profile data to generate charge transfer impedance increment trend data;

[0132] The incremental trend data of charge transfer impedance is normalized in two axes to generate a two-axis line graph.

[0133] In this embodiment of the invention, the original charge-discharge data is segmented according to a complete charge-discharge cycle, and abnormal fluctuations and incomplete data are removed to obtain a set of standardized periodic sequence data. This standardization process includes time axis unification, data smoothing, and precise positioning of cycle start and end points, ensuring the temporal accuracy and data consistency of subsequent analysis. Secondly, based on the standardized periodic sequence, the voltage and current change rates at each time point are calculated. Specifically, the instantaneous voltage slope and current slope are obtained through a difference method, thereby obtaining the instantaneous change slope data. This data reflects the minute dynamic changes in the battery response during charge and discharge, revealing the details of the charge transfer process. Subsequently, using the instantaneous change slope data, the voltage range within each charge-discharge cycle is dynamically segmented. By setting slope thresholds and trend judgments, the segmented windows of the charge transfer voltage range are automatically identified, forming impedance segmentation window data. This step achieves fine-grained division of the charge transfer process, providing a precise region for subsequent impedance calculation. Next, for each impedance segmentation window, the equivalent charge transfer impedance within that range is calculated using the voltage-current integral ratio method. Specifically, the voltage and current curves are integrated within this interval, and the ratio of the integral values ​​reflects the impedance characteristics, generating impedance profile data. This method enhances the sensitivity to nonlinear and dynamic electrochemical processes and improves the accuracy of impedance measurements. Then, the impedance profile data from multiple charge-discharge cycles are differentiated across cycles to calculate the incremental trend of impedance changes, obtaining charge transfer impedance increment trend data. This increment trend reflects the performance degradation or improvement of the battery during use, revealing the underlying electrochemical change mechanisms. Finally, the charge transfer impedance increment trend data is biaxially normalized and mapped to the numerical ranges of the left and right axes, enabling a direct comparison of the two variables. A biaxial line graph is plotted based on the normalized data; the left axis displays the impedance increment, and the right axis displays the corresponding cycle number or other key parameters, allowing users to simultaneously observe the dynamic relationship between impedance changes and charge-discharge cycles.

[0134] Preferably, step S3, which involves quantitative analysis of the chemical composition and assessment of physical structural damage of the battery based on material degradation data, includes:

[0135] The battery surface is divided into micro-blocks, and X-rays are used to extract the independent spectral signals of each block based on material degradation data to generate multi-region energy spectrum response matrix data.

[0136] Based on the multi-regional energy spectrum response matrix data, the characteristic peak values ​​of key elements are integrated region by region to generate initial distribution data of element abundance.

[0137] Spatial normalization correction was performed using the initial element abundance distribution data to eliminate errors caused by illumination angle or local reflection, and calibrated surface abundance data of battery recyclable materials was generated.

[0138] Extracting surface structure anomalies from material degradation data;

[0139] By detecting the boundary closure of crack edges in the battery through anomalous points in the surface structure, a structural defect morphology image is generated.

[0140] By using images of structural defects, the battery type is labeled and its area is estimated, generating surface structural damage data for battery recycling.

[0141] In this embodiment of the invention, the surface of the battery recycling material is divided into multiple micro-blocks, and local feature capture is achieved through fine-grained region segmentation. For each micro-block, its spectral signal is independently acquired using X-ray energy dispersive spectroscopy (such as EDS or XRF), generating a multi-region energy dispersive spectral response matrix covering all regions. This matrix records the intensity of the elemental characteristic peaks in each micro-region, reflecting the local chemical composition of the material. Next, the characteristic peaks of key elements (such as lithium, cobalt, nickel, etc.) in the multi-region energy dispersive spectral response matrix are integrated region by region to quantify the peak area of ​​each element in the corresponding micro-region, obtaining the initial spatial distribution data of elemental abundance. This step eliminates noise influence through integration, improving the accuracy of abundance estimation. Subsequently, spatial normalization correction is performed on the initial elemental abundance distribution data. This correction process considers factors such as X-ray incident angle, detector position, and local surface reflectivity, using reference standards or baseline regions to eliminate errors and systematic biases in the measurement process, ultimately forming calibrated elemental abundance data of the battery recycling material surface, ensuring spatial consistency and comparability of the data. In terms of physical structural damage assessment, structural height anomalies on the battery surface are extracted from material degradation data. These anomalies typically correspond to defects such as cracks, dents, or deformations. Image processing techniques are used to detect boundary closure of these height anomalies, identify crack edges, and generate structural defect morphology images, visually reflecting the spatial morphology and extent of the damage. Finally, based on these structural defect morphology images, machine learning or image recognition algorithms are used to classify and label battery damage types. Simultaneously, morphological methods are combined to estimate defect area size, forming comprehensive surface structural damage data for recycled batteries. This data provides a scientific basis for the quality assessment and subsequent processing of recycled materials.

[0142] Preferably, the boundary closure detection of the battery crack edge through surface structural height anomalies includes:

[0143] Three-dimensional structural imaging of the battery is performed by identifying anomalous points in the surface structure height, and the surface height distribution map after imaging is extracted.

[0144] Anomaly detection is performed on the height distribution map to identify surface height anomalies and generate a set of height anomalies;

[0145] Spatial anomaly distribution data were obtained based on the analysis of the set of height anomalies.

[0146] Analyze the spatial connectivity of spatial anomaly distribution data to identify the initial trajectory of suspected crack edges;

[0147] Boundary point fitting is performed on the initial trajectory of suspected crack edges to construct the edge curve of open cracks;

[0148] The boundary closure algorithm is applied to extend and close the curve of the open crack edge to generate the closed crack edge profile.

[0149] Structural consistency detection and geometric feature modeling are performed on the edge region of closed cracks to extract the morphological differences between the inside and outside of the crack region and obtain the regional structural features.

[0150] By integrating the edge contour of closed cracks and regional structural features, a structural defect morphology image is generated.

[0151] In this embodiment of the invention, the battery surface is scanned using high-precision 3D imaging technology to obtain its 3D structural data and generate a corresponding surface height distribution map. This height distribution map reflects minute morphological changes on the battery surface and serves as the basis for subsequent anomaly identification. Next, anomaly detection is performed on this height distribution map, using statistical methods or machine learning algorithms to identify anomalies that significantly deviate from the normal height range, forming a height anomaly set. This set accurately marks the locations of cracks or defects. Subsequently, the distribution characteristics of the height anomaly set in 3D space are analyzed to obtain spatial anomaly distribution data. Through spatial clustering and connectivity analysis, clustered areas of anomalies are identified, and the trajectories of suspected crack edges are initially depicted. For these initial trajectories of suspected crack edges, boundary point fitting algorithms (such as polynomial fitting or spline curve fitting) are used to reconstruct the open crack edge curve, forming a continuous but unclosed crack boundary morphology. To achieve complete crack boundary identification, a boundary closure algorithm is applied to extend and close the open curve. This algorithm extends the crack edge curve through minimum path search or curve interpolation methods, ultimately generating a closed crack edge contour, ensuring the boundary is complete. Further structural consistency detection and geometric feature modeling were performed on the edge region of the closed crack. Morphological and geometric analysis was used to extract morphological differences inside and outside the crack region, such as indentation depth, crack width, and crack distribution density, forming detailed regional structural features. Finally, the closed crack edge contour and regional structural feature data were integrated to construct a morphological image of the structural defect, providing accurate morphological information and location for the visual identification and subsequent repair of battery cracks.

[0152] Preferably, step S4, which involves synchronizing the monitoring timeline of the battery lifecycle visualization interface, includes:

[0153] The monitoring time sequence of the battery life cycle visualization interface is synchronized, the timestamp standardization accuracy is set to 1ms, the sampling frequency of multi-dimensional monitoring indicators is ≥1Hz, the maximum allowable deviation of the time channel is ±10ms, the data alignment tolerance range is ≤30ms, the time drift correction threshold is ≥30ms, the time granularity range that can be viewed is 1s~1cycle, the visualization duration coverage range is 1min~10year, and the total number of key nodes in the entire life cycle is ≥5.

[0154] In this embodiment of the invention, a unified timestamp standardization mechanism is designed for multi-dimensional monitoring data generated at each stage of the battery's entire lifecycle (manufacturing, use, and recycling) to ensure that the timestamp accuracy of all data collection points reaches 1 millisecond (ms), meeting the requirements for high-precision time synchronization. Secondly, the sampling frequency of various monitoring indicators is set to be no less than 1 Hz to ensure data real-time performance and fine granularity. Simultaneously, the maximum allowable deviation of the time channel is controlled within ±10 milliseconds to ensure that the time synchronization error of different data streams does not exceed this range, thereby avoiding time sequence misalignment of multi-source data. Next, the tolerance range for data alignment is defined to be no more than 30 milliseconds. A timestamp correction algorithm is used to align data with close time points, minimizing deviations between time-series data and improving the accuracy of data fusion. To address sensor clock drift and system timing deviations, a time drift correction threshold of 30 milliseconds is set. The system continuously monitors time drift, and when the deviation exceeds this threshold, the clock synchronization and timestamp recalibration process is automatically triggered to ensure timing consistency during long-term operation. At the user interaction level, users can freely view the time granularity according to their needs, with the time granularity ranging from 1 second to a complete cycle, meeting the timing accuracy requirements of different analytical perspectives. Meanwhile, the visualization interface supports a wide range of display durations, from a minimum of 1 minute to a maximum of 10 years, adapting to the monitoring needs of the entire battery lifecycle. Finally, the system clearly identifies no fewer than 5 key nodes throughout the entire lifecycle, including manufacturing completion, first charge, typical usage stage, performance degradation point, and recycling point. The system highlights and synchronizes data for these key moments, facilitating users to quickly locate and analyze critical lifecycle events.

[0155] This specification provides an IoT-based battery lifecycle data visualization and monitoring system for executing the aforementioned IoT-based battery lifecycle data visualization and monitoring method. The IoT-based battery lifecycle data visualization and monitoring system includes:

[0156] The manufacturing monitoring module is used to acquire battery life cycle data, which includes the battery manufacturing stage, battery usage stage, and battery recycling stage; based on the battery manufacturing stage, a photonic crystal fiber sensor is integrated into the SEI layer of the battery to collect the battery SEI layer thickness and ion diffusion rate.

[0157] The monitoring module is used to analyze the evolution of dendrite morphology in the battery by measuring the thickness of the SEI layer and the ion diffusion rate, and visualize the analysis results as dendrite cross-sectional curves; battery charge and discharge data are acquired based on the battery usage stage; incremental charge transfer impedance analysis is performed on the battery usage charge and discharge data, and the analysis results are visualized as biaxial line graphs;

[0158] The recycling monitoring module is used to collect material degradation data based on the battery recycling stage; based on the material degradation data, the module performs quantitative analysis of the chemical composition and physical structure damage assessment of the battery, and obtains surface abundance data and surface structure damage data of the recycled battery materials; the module visualizes the surface abundance data of the recycled battery materials as a stacked bar chart, and the surface structure damage data of the recycled battery materials as a pore size distribution histogram.

[0159] The timing synchronization module is used to integrate dendrite cross-section curves, biaxial line graphs, stacked bar graphs, and aperture distribution histograms into a visualization interface through a preset platform framework, generating a full life cycle visualization interface for the battery; and to monitor and synchronize the timing of the full life cycle visualization interface for the battery to perform IoT-based full life cycle data visualization monitoring operations.

[0160] The beneficial effects of this invention lie in its design of monitoring modules encompassing three key stages—manufacturing, use, and recycling—to achieve comprehensive data collection and analysis of the battery's entire lifecycle status, promoting all-round health management and performance optimization. By integrating photonic crystal fiber sensors into the battery manufacturing stage, real-time nanoscale data acquisition of SEI layer thickness and ion diffusion rate is achieved, ensuring high-precision monitoring of early key physicochemical processes and facilitating the timely detection of potential degradation trends. Dynamic analysis and visualization of dendrite morphology are performed by combining SEI layer thickness and ion diffusion rate data, supplemented by charge transfer impedance increment analysis of charge and discharge data, deeply reflecting the microscopic changes within the battery and supporting refined performance evaluation and safety early warning. Quantitative chemical composition and physical structural damage assessment are conducted using material degradation data, generating intuitive charts of surface abundance and structural damage to assist in determining the reuse value and repair solutions of recycled battery materials, promoting the development of a circular economy. The time synchronization module integrates multiple types of visualization charts through a preset platform framework, employing a high-precision time synchronization mechanism to ensure data alignment and continuity, improving the accuracy and real-time nature of data display, and meeting the dynamic monitoring needs based on the Internet of Things. The system supports multi-scale data viewing and analysis from second-level to grade-level time granularity, covering key lifecycle nodes and meeting diverse application scenarios from routine maintenance to performance degradation prediction. Therefore, this invention, by using photonic crystal fiber sensing technology, IoT data acquisition and transmission technology, multi-dimensional data visualization technology, and a full lifecycle analysis method, solves the problems of fragmented lifecycle data, difficulty in perceiving micro-evolution, unintuitive display of monitoring results, and poor platform integration in traditional battery monitoring. This improves the completeness of battery lifecycle status perception, the accuracy of monitoring, and the intelligence level of management decision-making.

[0161] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0162] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for visual monitoring of battery lifecycle data based on the Internet of Things, characterized in that, Includes the following steps: Step S1: Acquire battery lifecycle data, which includes battery manufacturing stage, battery usage stage and battery recycling stage; Based on the battery manufacturing stage, integrate photonic crystal fiber sensor on the SEI layer of the battery to collect the battery SEI layer thickness and ion diffusion rate. Step S2: Analyze the dendrite morphology evolution of the battery by measuring the SEI layer thickness and ion diffusion rate, and visualize the analysis results as dendrite cross-sectional curves; obtain battery charge and discharge data based on the battery usage stage; Incremental charge transfer impedance analysis was performed on battery charge and discharge data, and the analysis results were visualized as a biaxial line graph. Step S3: Collect material degradation data based on the battery recycling stage; Based on the material degradation data, the battery was subjected to quantitative analysis of chemical composition and physical structural damage assessment, and the surface abundance data and surface structural damage data of the battery recycled material were obtained. The surface abundance data of recycled battery materials were visualized as stacked bar charts, and the surface structural damage data of recycled battery materials were visualized as pore size distribution histograms. Step S4: Integrate the dendrite cross-section curve, biaxial line graph, stacked bar graph, and pore size distribution histogram into a visualization interface through a preset platform framework to generate a battery life cycle visualization interface; perform time-series monitoring synchronization on the battery life cycle visualization interface to execute IoT-based battery life cycle data visualization monitoring operations.

2. The method for visual monitoring of battery lifecycle data based on the Internet of Things according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Obtain battery lifecycle data, which includes the battery manufacturing stage, battery usage stage, and battery recycling stage; Step S12: Based on the production process data of the manufacturing stage, locate the formation time and region of the SEI layer and generate SEI layer growth marker data; Step S13: Based on the SEI layer growth labeling data, deploy a photonic crystal fiber sensor to the electrode-SEI interface region to generate sensor integrated deployment data; use the sensor integrated deployment data to perform real-time nanoscale acquisition of SEI layer thickness to generate SEI layer thickness change data. Step S14: Perform time-series fitting on the SEI layer thickness variation data, extract the ion transport boundary response, and generate ion diffusion rate data; store the SEI layer thickness variation data and ion diffusion rate data together to obtain the battery SEI layer thickness and ion diffusion rate.

3. The method for visual monitoring of battery lifecycle data based on the Internet of Things according to claim 1, characterized in that, Step S13, which utilizes sensor integration deployment data to perform real-time nanoscale acquisition of SEI layer thickness, includes: By integrating sensor data, nanoscale acquisition nodes are set up in the 10nm–50nm range on the surface of the battery negative electrode and in the 20nm–80nm range of the adjacent interface region, with a total of no less than 40 acquisition nodes and a node spacing of no more than 15nm, forming a complete nanoscale monitoring network for SEI layer thickness. Initial acquisition conditions are set, including an ambient temperature of 25°C±1°C, a battery operating voltage range of 2.5V–4.2V, and a sampling frequency of 10 times per second. SEI layer thickness change data are gradually sampled over an acquisition period of no less than 60 minutes. During the acquisition process, different charge and discharge rate operating conditions are simulated, and the dynamic change data of SEI layer thickness on the negative electrode surface and interface region are recorded in real time. When the SEI layer thickness change rate exceeds 0.5nm / min or the thickness exceeds a preset threshold of 100nm, it is recorded as an abnormal growth risk point, with the charge and discharge rate specifically set in the range of 0.1C–2C. The dynamic change data of SEI layer thickness on the negative electrode surface and interface region are integrated to generate real-time nanoscale acquisition data of SEI layer thickness change.

4. The method for visual monitoring of battery lifecycle data based on the Internet of Things according to claim 1, characterized in that, Step S2 analyzes the dendrite morphology evolution of the battery by measuring the SEI layer thickness and ion diffusion rate, and visualizes the analysis results as dendrite cross-sectional curves, including: Dendrite edge detection is performed on the battery based on the SEI layer thickness and ion diffusion rate to generate dendrite contour feature data. Correlation analysis was performed on dendrite contour feature data and SEI layer thickness variation data to generate morphological evolution parameter data; Typical cross-sectional slice locations of the battery are selected using morphological evolution parameter data to generate dendritic cross-sectional coordinate data; The dendrite cross-section coordinate data and ion diffusion rate data are mapped and extracted to generate cross-section thickness-rate mapping data. Curve fitting is performed on the cross-sectional thickness-rate mapping data to generate dendritic profile curve fitting data; Visualize and render the dendrite profile curve fitting data to generate dendrite cross-section profile curve data.

5. The method for visual monitoring of battery lifecycle data based on the Internet of Things according to claim 4, characterized in that, Dendrite edge detection of the battery based on SEI layer thickness and ion diffusion rate includes: Based on the thickness of the battery SEI layer, regions of equal thickness are extracted from the battery to obtain an isopleth distribution map of the thickness. Calculate the diffusion direction vector field of the ion diffusion rate data to obtain the ion flow pattern; Based on the cross-location of thickness isopleth distribution map and ion flow direction map, candidate region data for dendrite growth are generated. Perform local adaptive grayscale dilation on the candidate dendrite growth region data to transform the candidate region from a point into an edge fragment, generating an initial dendrite edge map; Based on the edge directionality of the dendrite edge map, adjacent pixel density vector projection is performed to extract continuous dendrite growth path segments and generate a set of main dendrite growth paths. The path curvature of the main dendrite growth path set is extracted to obtain dendrite contour feature data.

6. The method for visual monitoring of battery lifecycle data based on the Internet of Things according to claim 1, characterized in that, Step S2 involves performing incremental charge transfer impedance analysis on the battery using charge and discharge data, and visualizing the analysis results as a biaxial line graph, including: The battery charge and discharge data is used to divide the charge and discharge cycles and generate standardized periodic sequence data. Calculate the voltage-current change rate based on standardized periodic sequence data to generate instantaneous change slope data; Based on the instantaneous slope data, the unit charge transfer voltage range in each cycle of the battery usage charge and discharge data is dynamically segmented to generate impedance segmentation window data; Perform interval voltage-current integral ratio processing on the impedance segmented window data, calculate the equivalent charge transfer impedance value of each segment, and generate impedance profile data; Perform cross-period impedance increment differentiation on the impedance profile data to generate charge transfer impedance increment trend data; The incremental trend data of charge transfer impedance is normalized in two axes to generate a two-axis line graph.

7. The method for visual monitoring of battery lifecycle data based on the Internet of Things according to claim 1, characterized in that, Step S3 involves quantitative analysis of the battery's chemical composition and assessment of its physical structural damage based on the material degradation data, including: The battery surface is divided into micro-blocks, and X-rays are used to extract the independent spectral signals of each block based on material degradation data to generate multi-region energy spectrum response matrix data. Based on the multi-regional energy spectrum response matrix data, the characteristic peak values ​​of key elements are integrated region by region to generate initial distribution data of element abundance. Spatial normalization correction was performed using the initial element abundance distribution data to eliminate errors caused by illumination angle or local reflection, and calibrated surface abundance data of battery recyclable materials was generated. Extracting surface structure anomalies from material degradation data; By detecting the boundary closure of crack edges in the battery through anomalous points in the surface structure, a structural defect morphology image is generated. By using structural defect morphology images, the battery type is labeled and the area is estimated, generating surface structural damage data of recycled battery materials.

8. The method for visual monitoring of battery lifecycle data based on the Internet of Things according to claim 7, characterized in that, Boundary closure detection of battery crack edges using surface structural height anomalies includes: Three-dimensional structural imaging of the battery is performed by identifying anomalous points in the surface structure height, and the surface height distribution map after imaging is extracted. Anomaly detection is performed on the height distribution map to identify surface height anomalies and generate a set of height anomalies; Spatial anomaly distribution data were obtained based on the analysis of the set of height anomalies. Analyze the spatial connectivity of spatial anomaly distribution data to identify the initial trajectory of suspected crack edges; Boundary point fitting is performed on the initial trajectory of suspected crack edges to construct the edge curve of open cracks; The boundary closure algorithm is applied to extend and close the curve of the open crack edge to generate the closed crack edge profile. Structural consistency detection and geometric feature modeling are performed on the edge region of closed cracks to extract the morphological differences between the inside and outside of the crack region and obtain the regional structural features. By integrating the edge contour of closed cracks and regional structural features, a structural defect morphology image is generated.

9. The method for visual monitoring of battery lifecycle data based on the Internet of Things according to claim 1, characterized in that, Step S4 involves synchronizing the monitoring timeline of the battery lifecycle visualization interface, including: The monitoring time sequence of the battery life cycle visualization interface is synchronized, the timestamp standardization accuracy is set to 1ms, the sampling frequency of multi-dimensional monitoring indicators is ≥1Hz, the maximum allowable deviation of the time channel is ±10ms, the data alignment tolerance range is ≤30ms, the time drift correction threshold is ≥30ms, the time granularity range that can be viewed is 1s~1cycle, the visualization duration coverage range is 1min~10year, and the total number of key nodes in the entire life cycle is ≥5.

10. A battery lifecycle data visualization and monitoring system based on the Internet of Things, characterized in that, For executing the IoT-based battery lifecycle data visualization and monitoring method as described in claim 1, the IoT-based battery lifecycle data visualization and monitoring system includes: The manufacturing monitoring module is used to acquire battery life cycle data, which includes the battery manufacturing stage, battery usage stage, and battery recycling stage; based on the battery manufacturing stage, a photonic crystal fiber sensor is integrated into the SEI layer of the battery to collect the battery SEI layer thickness and ion diffusion rate. The monitoring module is used to analyze the evolution of dendrite morphology in the battery by measuring the thickness of the SEI layer and the ion diffusion rate, and visualize the analysis results as dendrite cross-sectional curves; battery charge and discharge data are acquired based on the battery usage stage; incremental charge transfer impedance analysis is performed on the battery usage charge and discharge data, and the analysis results are visualized as biaxial line graphs; The recycling monitoring module is used to collect material degradation data based on the battery recycling stage; based on the material degradation data, the module performs quantitative analysis of the chemical composition and physical structure damage assessment of the battery, and obtains surface abundance data and surface structure damage data of the battery recycled materials; the surface abundance data of the battery recycled materials is visualized as a stacked bar chart, and the surface structure damage data of the battery recycled materials is visualized as a pore size distribution histogram. The timing synchronization module is used to integrate dendrite cross-section curves, biaxial line graphs, stacked bar graphs, and aperture distribution histograms into a visualization interface through a preset platform framework, generating a full life cycle visualization interface for the battery; and to monitor and synchronize the timing of the full life cycle visualization interface for the battery to perform IoT-based full life cycle data visualization monitoring operations.

Images