Defect ai analysis and automatic report generation system for power battery

Abstract

The application discloses a power battery defect AI analysis and automatic report generation system, belongs to the cross technical field of new energy intelligent manufacturing, multi-modal machine vision, industrial AI, data encryption and blockchain credible traceability, and is characterized in that a six-layer software and hardware integrated collaborative architecture is created, which comprises a four-modal synchronous perception collection layer, an attention-enhanced YOLOv8+Transformer cross-modal AI fusion analysis layer, a six-axis robot tactile dexterous hand motion control layer, a dynamic tensor random confusion lightweight encryption layer, a national cryptographic alliance chain hierarchical storage layer and an AI intelligent root cause report automatic generation application layer. The application solves the six core pain points of global power battery quality inspection, such as low efficiency, single visual micro-defect missed detection, multi-system process fragmentation, detection data easy to leak and tamper, untrustworthy traceability and report manual arrangement, and is compatible with all types of battery cell production lines, such as blade, square, soft package and cylinder, and has the multiple unique advantages of high detection precision, high automation, data security and credibility, full-link digital closed loop and the like, and has outstanding industrialization landing value.

Description

Technical Field

[0001] This invention belongs to the cross-technical fields of intelligent manufacturing of new energy power batteries, multimodal machine vision perception, industrial artificial intelligence defect detection, industrial data encryption security, consortium blockchain trusted storage and automated generation of digital quality reports. Specifically, it covers intelligent detection of appearance and dimensional defects throughout the entire process of production, assembly, warehousing, and after-sales service of square / blade / pouch / cylindrical power battery cells; it integrates four-modal perception: visible light RGB imaging, 3D line laser profilometry, tactile array sensing, and polarization anti-reflective optics; and it integrates a lightweight improved YOLOv8-Transformer hybrid AI detection model, GAN small-sample defect enhancement, multi-modal feature fusion decision-making, dynamic tensor random obfuscation national cryptographic encryption, consortium blockchain layered evidence storage, and a fully automated report generation integrated equipment system. It is suitable for online quality inspection on high-speed production lines of new energy power batteries, industrial interconnection of factory MES / ERP, full life-cycle quality compliance traceability, and battery digital passport ESG audit scenarios, while meeting the requirements of national regulations such as the Data Security Law, the Personal Information Protection Law, and the New Energy Power Battery Industry Data Security Specification. Background Technology

[0002] Overview of Global Industry Technology Status and Existing Technologies at Home and Abroad: Global power battery production capacity continues to expand, with blade, prismatic, pouch, and cylindrical cells being the mainstream supporting products for new energy vehicle manufacturers and energy storage power stations. The metal aluminum-plastic film and steel casing of the cells have strong mirror-like reflective properties, making them prone to defects such as 0.01mm-level micro-scratches, dents, misaligned tabs, damaged film, bulging deformation, dirt, and edge burrs throughout the production process. Dimensional deviations can directly lead to assembly jams, battery cycle degradation, and thermal runaway safety risks. Current mainstream global production line quality inspection solutions fall into three categories: manual visual inspection, single 2D vision, and simplified 3D laser inspection, all of which have insurmountable technical bottlenecks. 1) Manual visual inspection Battery manufacturers still largely use 3-inch batteries in their low-speed production lines. Six people work in sections for manual testing; the manual testing time for a single battery cell is 30 minutes. With a detection rate of ≥12% and a false negative rate of ≥8%, the detection rate is ≥8% in 60 seconds. Personnel fatigue leads to extremely poor consistency in detection, and minor defects cannot be identified at all. There are no digital records, and quality data relies on paper ledgers, which are untraceable and cannot automatically calculate the root causes of defects. Labor costs are rising year by year, which is completely incompatible with the cycle time requirements of high-speed automated production lines above 200ppm. 2) Single 2D industrial vision inspection Using only single-channel / multi-channel visible light cameras for fixed-angle imaging has three inherent drawbacks: ① The imaging of highly reflective areas of metal battery cells is overexposed and loses texture; micro-defects smaller than 0.05mm are completely submerged in reflective noise; a single ring light source cannot meet the differentiated imaging needs of various types of defects such as scratches, dents, and dirt. ② Only two-dimensional pixel features are extracted, without three-dimensional height, flatness, and thickness data. It can only qualitatively determine whether there are defects, but cannot quantify the defect depth or deformation height, and cannot complete CPK size control. ③ With only a single visual modality, the confidence level for identifying defects with weak texture and low contrast is low, the false negative rate is >7%, and it is impossible to distinguish between external dirt and substantial damage to the shell. 3) Standalone 3D laser contour detection and simple tactile measurement equipment It only outputs 3D topography data and has no AI defect classification capability; it can only complete size measurement. It is physically separated from the 2D vision system, and the data is not interconnected. It requires two sets of equipment to perform two inspections, with a total time of more than 25 seconds. It lacks tactile flexible sampling compensation, and the laser point cloud is distorted under slight deformation of the battery cell, with a thickness measurement error of ≥0.08mm. It cannot simultaneously complete defect location, classification, and statistical analysis. 4) Existing multimodal fusion patents Existing multimodal solutions only achieve simple feature stitching of vision and single touch, without introducing laser contour 3D reconstruction or polarized optics anti-reflection hardware architecture; at the algorithm level, they only use traditional random forests and shallow CNNs, without building a large-scale cross-modal fusion model of attention-enhanced YOLOv8 + Transformer; there is no GAN rare defect sample enhancement module, and the accuracy drops to below 82% when there are insufficient rare defect samples on the production line; the hardware and algorithm are disconnected, and there is no six-axis robot dexterous hand for fully automatic loading and unloading, or a multi-degree-of-freedom flexible touch sampling integrated motion control link. 5) Existing technological deficiencies in industrial data security and blockchain traceability ① Traditional testing data is transmitted and stored in plaintext. High-definition images of battery cell appearance and batch defect pass rates are core process confidential data of the factory, which are easily captured by the network and exported internally. The existing AES fixed block encryption has a latency of >120ms, which cannot match the millisecond-level real-time data interaction of the production line. There is no dynamic tensor obfuscation + equipment hardware feature seed replacement mechanism, and the fixed key is easily cracked by brute force and replay attacks. ② Existing battery blockchain traceability systems all use full original images and detection point clouds directly uploaded to the chain, resulting in extremely high on-chain storage costs and insufficient TPS throughput. The thousands of detection data per second from high-speed production lines cause chain network congestion. They do not use off-chain encrypted storage of original data, and only store hash digests + timestamps in a layered architecture on the chain. They also lack native adaptation to the national cryptographic SM2 / SM3 algorithm. ③ The detection, encryption, blockchain uploading, reporting, and MES uploading processes are independent of each other. Data is manually exported from multiple systems and reports are manually filled in, making it impossible to form a fully automated closed loop of "collection-identification-encryption-evidence storage-reporting-sorting". Data interoperability between OEMs, battery manufacturers, and recycling companies is difficult. 6) Automatically report existing technological shortcomings Commercially available industrial reporting systems only support basic pass rate statistics and cannot automatically generate defect heatmaps, Pareto charts, CPK process capability analysis, or 5M1E root cause inference reports; they do not support batch export of PDF / Excel dual formats or embedding defect pixel screenshots into reports; they lack real-time large-screen visualization and automatic push interfaces for multiple industrial protocols such as MES / ERP / Profinet, requiring production line quality engineers to manually compile quality inspection data for 2-3 hours daily, resulting in data lag and large errors.

[0003] Summary of the core technical defects of existing technologies 1) The hardware of the perception layer is monotonous: there is no four-modal synchronous acquisition architecture of RGB + 3D laser + polarization optics + tactile array, resulting in the failure of high reflectivity photoelectric core imaging and the loss of micro-defects; there is no flexible three-dimensional sampling of 12-DOF tactile dexterity hand, resulting in insufficient dimensional measurement accuracy. 2) The AI ​​algorithm has a shallow fusion layer: it only splices shallow features and does not have a YOLOv8 backbone + Transformer cross-modal attention fusion architecture; it does not have GAN generative data augmentation specifically for battery scarcity defects, and the detection accuracy drops sharply in small sample scenarios; it does not have an independent root cause reasoning engine, and can only identify defects, but cannot automatically analyze the causes of defects caused by production line equipment, tooling, and raw materials. 3) Separation of motion and detection processes: Detection, loading and unloading, and size touch sorting are performed in multiple stations and steps, resulting in a long cycle time and making it impossible to achieve integrated six-sided synchronous imaging and tactile measurement at a single station. 4) Poor industrial adaptability of encryption algorithms: General fixed key encryption has high latency and weak resistance to attacks. It lacks tensor dynamic obfuscation and hardware unique feature random seed generation mechanism, which does not meet the dual requirements of industrial control real-time performance and data anti-tampering. 5) Inefficient blockchain evidence storage architecture: All original data is uploaded to the chain, resulting in insufficient throughput and high storage costs; there is no layered evidence storage, national cryptographic compatibility, or hierarchical authorization access mechanism, making it impossible to implement trusted collaborative traceability among multiple parties in the industry chain. 6) Lack of full-chain automation: Detection, data analysis, encryption, blockchain uploading, report generation, production line sorting, and MES data interaction rely on manual transfer, resulting in low automation throughout the process and a lack of integrated collaborative scheduling logic. 7) Insufficient system versatility: It is only suitable for square batteries and cannot be compatible with production lines for multi-specification cells such as blade, pouch, and cylindrical cells. The parameters of the light source, camera, and robot require a lot of manual secondary debugging. Summary of the Invention

[0004] The core objective of this invention is: Addressing the aforementioned multiple overlapping technical pain points in the global power battery quality inspection field, this invention proposes a six-module integrated closed-loop system: hardware multimodal perception, AI cross-modal deep fusion, robot flexible motion control, dynamic tensor obfuscation national cryptographic encryption, consortium blockchain layered trusted evidence storage, and intelligent root cause report automatic generation. This system simultaneously achieves seven original technical objectives: The hardware layer features a unique four-modal synchronous imaging architecture, which can complete the synchronous acquisition of six sides of the battery cell within 10 seconds. The polarized composite light source suppresses the high reflectivity of metals and can identify micro-defects as small as 0.01mm. The overall defect identification accuracy is ≥95%, the false negative rate is ≤1%, and the false negative rate is ≤2%, which comprehensively surpasses the upper limit of 85%~90% accuracy of single vision inspection equipment at home and abroad. The algorithm layer features a unique attention-enhanced YOLOv8+Transformer cross-modal feature fusion hybrid model, combined with a GAN scarce defect adaptive enhancement module, to solve the problem of insufficient accuracy in small sample defect recognition in the industry; visual coarse positioning + tactile flexible touch 3D fitting size measurement, single cell measurement ≤5s, thickness / length / width / flatness measurement accuracy is stable ±0.02mm; The motion control layer integrates a six-axis industrial robot and a 12+ degree-of-freedom tactile array dexterous hand, with force-position hybrid EtherCAT bus real-time collaboration, automatically completing the entire process of unmanned operation, including loading, six-sided flipping, flexible touch measurement, and OK / NG sorting. The data encryption layer features a unique dynamic tensor random obfuscation lightweight encryption engine that generates obfuscation seeds using the device's unique serial number (SN) code. It employs block permutation and position dual verification, achieving a single-frame image encryption latency of <20ms. This ensures millisecond-level real-time transmission on production lines and complies with national cryptographic data security standards. The blockchain evidence storage layer features a unique “off-chain encrypted raw data + on-chain hash digest layered consortium chain architecture”, which adopts the national cryptographic SM3 hash and SM2 node identity authentication. Only the detection hash, timestamp, batch, pass rate and report digest are uploaded to the chain, solving the problems of congestion and high cost of traditional full-scale on-chain data. It realizes tamper-proof and reliable traceability of the entire life cycle of the battery cell and is compatible with domestic battery digital passports and EU ESG audits. The application layer has a built-in AI root cause analysis engine that automatically generates defect heatmaps, Pareto charts, CPK process analysis, and 5M1E defect root cause inference reports. It can output standardized PDF / Excel files with one click and is natively compatible with five major industrial bus protocols: MES / ERP / OPCUA / Modbus / TCP / Profinet. Data is synchronized bidirectionally in real time. The entire system features deep hardware and software integration and collaboration, forming a fully automated closed-loop process: "material loading and positioning → four-modal synchronous imaging → AI defect pixel-level detection → visual-tactile fusion 3D dimension measurement → dynamic obfuscation and encryption → consortium blockchain layered evidence storage → AI automatic analysis report generation → robot automatic sorting + MES data upload". This replaces 3-6 workers, increases production line quality inspection cycle time by more than 300%, and is compatible with all types of power batteries, including blade, square, soft-pack, and cylindrical batteries.

[0005] The original technical solution of this invention: An innovative AI-based defect analysis and automatic report generation system for power batteries is comprised of a six-layer hardware-software integrated collaborative architecture: a perception and acquisition layer, an AI multimodal deep fusion analysis layer, a robot motion control layer, a dynamic tensor random obfuscation encryption layer, a national cryptographic alliance chain layered evidence storage layer, and an intelligent report output application layer. These six layers feature dedicated hardware, original algorithms, and bidirectional data interaction logic, distinguishing them from existing layered, fragmented, single-modal, and independently encrypted evidence storage solutions. 1. Perception and Acquisition Layer This system is used to simultaneously acquire six-sided RGB visible light images of power battery cells, 3D line laser point cloud contours, polarized anti-reflective optical textures, and tactile array pressure deformation signals, providing a four-dimensional complementary feature data source for AI models. The proprietary hardware components include: (1) A 4-channel 20-megapixel global shutter industrial area array camera, paired with a telecentric low distortion lens, synchronous exposure on six sides, and μs-level synchronous trigger timing to eliminate cell motion blur. (2) Dual-mode composite polarization anti-reflective light source: ring polarization light source + strip multi-angle linear light source with independent dimming, dynamically switching polarization mode for high reflective areas of aluminum shell, eliminating overexposure of mirror surface, and retaining grayscale texture of 0.01mm micro scratches; (3) High-precision 3D line laser profile sensor with a laser line width of 0.005mm, synchronously outputs the three-dimensional height point cloud of the battery cell to reconstruct the three-dimensional shape of the shell flatness and bulge deformation; (4) Robotic dexterous hand with 12+ degrees of freedom flexible tactile array: 24 micro pressure sensor arrays distributed on the fingertips, supporting 0.01N level micro-force flexible touch, avoiding hard probes from scratching the battery cell film, collecting tactile features of local deformation of the shell, and compensating for the data distortion defects of laser point cloud in soft-pack slight deformation scenarios; (5) Synchronous timing trigger control board: FPGA hardware synchronously controls the timing of camera, light source, laser and tactile sensor acquisition. The four-modal data timestamp deviation is <1μs, ensuring accurate alignment of multimodal feature spatial coordinates and solving the core defects of existing multimodal devices such as timing misalignment and inability to fuse features. 2. AI Multimodal Deep Fusion Analysis Layer The core, original module that distinguishes this invention from all existing detection systems no longer employs simple feature concatenation. Instead, it constructs a four-level algorithm architecture: dual-branch heterogeneous feature extraction + Transformer cross-modal attention fusion + GAN adaptive defect enhancement + root cause inference engine. The hardware is deployed on an edge Jetson AGX Orin computing unit, with TensorRT accelerating inference. (1) Backbone network: Attention enhancement improvement of YOLOv8 detection submodule (original improvement) Based on the original C2f module of YOLOv8, a self-developed DCNv3-LKA large kernel deformable convolution + SimAM lightweight attention fusion unit is embedded, and a P2 micro-defect dedicated detection head is added; the Focaler-IoU adaptive loss function is adopted to replace the traditional CIoU, and the difficult sample micro-defects are dynamically weighted to solve the problem of missing detection of micro-scratches and dents in the battery cell; the backbone network simultaneously outputs the pixel-level coordinates of the defect, the defect type, the defect area, and the gray-level depth two-dimensional feature map. (2) 3D - Tactile Feature Extraction Branch: 1D-CNN Multi-channel Tactile Feature Encoder (Original) A one-dimensional convolutional neural network is constructed from the 24-channel tactile pressure time-series signal of the dexterous hand to extract the local deformation and concave-convex tactile features of the shell; the PointNet lightweight branch is used to extract the three-dimensional height, flatness, and thickness geometric features from the 3D laser point cloud; and the complete three-dimensional quantitative dimensional parameters of the battery cell are output to realize the parallel reasoning of visual qualitative defect recognition and tactile-laser quantitative dimensional measurement. (3) Cross-modal Transformer feature fusion unit (an original architecture not used in existing global battery detection solutions) A lightweight encoder Transformer architecture was built to map RGB visual 2D features, laser 3D point cloud features, and tactile pressure deformation features to a unified feature space. A cross-modal attention weight allocation mechanism was designed to dynamically suppress redundant noise features caused by reflection and dirt, and to enhance complementary features of effective defects such as scratches, bulges, and electrode misalignment, thereby solving the industry bottleneck of single-modal feature failure and low fusion accuracy. (4) GAN Scarcity Defect Adaptive Enhancement Module (Original Engineering Optimization) To address the scarcity of labeled samples for extremely rare defects on production lines (tab breakage, coating pinholes, and hidden pits inside the casing), a conditional generative adversarial network (GAN) is constructed to automatically generate simulated defect samples with multiple lighting conditions, angles, and sizes based on a small number of real defect samples from the production line. An adaptive sample filtering mechanism is also included to filter out invalid generated samples with low confidence, thereby expanding the training dataset. In small sample scenarios, the detection mAP is improved by more than 6%. Existing technologies lack a dedicated engineering solution for enhancing battery defect GANs. (5) AI Root Cause Analysis Inference Engine (Original intelligent function; existing equipment only identifies defects, but does not automatically analyze root causes) Built-in power battery defect knowledge base (including a database of 32 types of defects corresponding to four major categories: blade, square, soft pack, and cylindrical); based on multimodal fusion output of defect type, defect location, defect frequency, and dimensional deviation data, it adopts 5M1E multi-dimensional correlation reasoning of man, machine, material, method, and environment to automatically output defect root cause judgment: such as batch side scratches are judged as burrs of conveyor tooling, electrode misalignment is judged as robot positioning deviation, and large area of ​​dirt is judged as excessive dust environment in the workshop. Simultaneously, production line optimization and improvement suggestions are output and directly written into the automatic detection report. 3. Robot Motion Control Layer (Unique Six-Axis Robot + Tactile Dexterity Hand Force-Position Hybrid Cooperative Control) The hardware includes a six-axis EtherCAT bus industrial robot, a 12-DOF tactile dexterous hand, a force-position hybrid motion controller, and a high-precision vision positioning and correction unit; it also features a unique collaborative control logic. The robot automatically grasps the battery cell, completes six-sided flipping and positioning, and works in conjunction with a four-modal camera for simultaneous imaging; For battery cells suspected of being out of size, a dexterous hand gently touches the key dimensional points on six sides of the battery cell with a force of 0.01N, and simultaneously collects tactile deformation data; The controller receives defect judgment signals from the AI ​​analysis layer in real time. OK cells are transferred to the next process, while NG cells are automatically sorted to the defective product warehouse, and the defect type and defect location are recorded simultaneously. With a built-in visual closed-loop positioning correction algorithm, the robot coordinate deviation is compensated based on the camera image before each grasp, with a positioning repeatability of ±0.01mm, and it can adapt to different specifications of battery cells without manual secondary debugging. 4. Dynamic Tensor Random Obfuscation Encryption Layer (A unique lightweight industrial encryption solution, distinct from general AES encryption) Addressing the dual needs of real-time processing and data security in industrial control production lines, a hardware-accelerated, lightweight encryption engine was designed, featuring entirely original technical characteristics: Dynamic obfuscation tensor generation module: Reads the device hardware's unique SN serial number as a random seed, and automatically generates a brand new obfuscation tensor for each detection. There is no fixed encryption key, which resists brute-force attacks and key theft attacks. Multimodal data block permutation unit: Adaptively blocks RGB images, laser point clouds, tactile time series, and detection report data, and scrambles the block positions based on the confusion tensor; Location hash dual verification module: Each block is attached with a location verification hash code. Any data tampering or block swapping during transmission and storage can be verified and intercepted in real time to resist man-in-the-middle replay attacks. FPGA hardware encryption acceleration ensures a stable total latency of <20ms for a single cell's complete multimodal data encryption, without consuming the computing power of edge AI computing units and being compatible with high-speed production line data streams. The encrypted original multimodal data is stored locally in a distributed manner, and no high-definition images or point cloud original confidential data are stored on the blockchain, balancing data security and storage costs. 5. National Cryptographic Consortium Chain Layered Evidence Storage Layer (a unique off-chain encrypted storage + on-chain digest layered architecture, solving the throughput bottleneck of existing blockchains) Built on Hyperledger Fabric, this power battery industry chain alliance is compatible with multi-node collaboration among OEMs, battery manufacturers, warehousing companies, and recycling and dismantling companies, featuring a unique layered evidence storage logic. Off-chain: The original images, point clouds, tactile data, and complete detection reports, after being dynamically obfuscated and encrypted, are stored in the factory's distributed storage cluster. Only authorized nodes can decrypt and view the complete original data using a key. On-chain: Only the national cryptographic SM3 hash digest, device ID, cell batch number, inspection timestamp, defect pass rate, defect type statistics, and report summary are uploaded to the chain. The size of a single evidence storage data is less than 1KB. The TPS throughput of the consortium chain is increased by 10 times, and there is no network congestion. National cryptographic security authentication mechanism: SM2 asymmetric encryption is used to complete the identity signature authentication of each enterprise node in the consortium blockchain, preventing unauthorized nodes from accessing and tampering with on-chain records; One-click verification interface: Downstream automakers, regulatory agencies, and recycling companies can input the cell SN code to retrieve the on-chain hash digest and compare it with the local encrypted data to verify that the quality inspection records are complete and tamper-proof, meeting the compliance traceability requirements of the entire life cycle of power batteries and the ESG audit requirements of the EU battery passport. 6. Intelligent Report Generation Application Output Layer (A unique, fully automated AI analysis and reporting engine, distinct from basic statistical reporting systems) Real-time data exchange with AI multimodal analysis layer, encryption layer, and blockchain evidence storage layer; built-in standardized report template engine; unique features: Automatic visualization chart generation: defect pixel screenshot embedded report, cell six-sided defect thermal distribution map, defect classification Pareto chart, size CPK process capability curve; AI root cause analysis content is automatically populated: 5M1E root cause conclusions, production line optimization and improvement suggestions, and defective product classification (minor / moderate / severe defects). Real-time export of dual-format files: PDF compliance audit report and editable Excel statistical data table, supporting batch export by batch, production line, and time period; Bidirectional integration with multiple industrial protocols: natively adaptable to MES / ERP / OPC UA / Modbus / TCP / Profinet, real-time push of qualified inspection data and NG defect statistics to the factory production management system, and synchronous upload of on-chain evidence summary to the enterprise digital platform; Real-time visual monitoring dashboard: Displays real-time production line inspection cycle, pass rate, defect distribution, encrypted evidence storage status on the blockchain, and equipment failure warnings. Attached Figure Description

[0006] Figure 1. System Six-Layer Integrated Architecture Diagram Figure 2. Timing diagram of the entire process of synchronous detection of four modes of power battery Figure 3. Four-level algorithm architecture diagram of AI multimodal deep fusion analysis module Figure 4. Flowchart of hardware acceleration for dynamic tensor random obfuscation encryption Figure 5. Schematic diagram of the hierarchical evidence storage principle of the national cryptographic consortium blockchain. Figure 6. Schematic diagram of the six-sided flexible sampling structure of the robot's tactile dexterity hand. Detailed Implementation 1) System Overall Hardware Deployment Example 1 Production line cycle time requirement: 10 seconds / cell, compatible with blade cell thickness 10 mm. 30mm, length 400 800mm; Perception and acquisition hardware deployment: Four 20-megapixel global shutter cameras are positioned at four workstations (top, bottom, left, and right); a ring-polarized light source is paired with a strip-shaped side-lit light source; a 3D line laser sensor is fixed to the bottom of the battery cell; a 12-DOF tactile dexterous hand is mounted on the end effector of a six-axis robot; FPGA synchronously triggers four-modal acquisition, with a total imaging time of 2.8 seconds; the polarized light source automatically switches polarization modes, ensuring no overexposure in highly reflective areas of the blade's aluminum shell, and complete recognition of minute scratches as small as 0.015mm; AI edge computing unit: Jetson AGX Orin, TensorRT FP16 acceleration, improved YOLOv8+Transformer model inference time 3.2s, GAN enhancement module offline pre-trained 32 types of defect samples, rare coating pinhole defect recognition accuracy of 94.7% on the production line; tactile 1D-CNN size fitting time 1.2s, thickness measurement error stable at 0.015mm; Encryption and Blockchain Unit: Hardware acceleration with FPGA encryption card, encryption of the entire set of image + point cloud data takes 16ms; local distributed storage encrypts the original data, and the consortium blockchain only uploads the hash digest, with a total on-chain time of 820ms for a single batch of 1000 cells; Report output: A single PDF test report is generated for each battery cell in real time, and a batch CPK Pareto report is automatically generated every hour. The data is synchronized with the production line MES system in real time. Actual measured performance of the whole machine: total time of the whole process is 9.7s, the overall defect accuracy rate is 96.2%, the missed detection rate is 0.72%, the over-detection rate is 1.43%, it replaces 4 manual quality inspectors and improves the production line efficiency by 320%. 2) Example 2 The sensor acquisition layer lens was replaced with a wide-angle telecentric lens, and the arrangement of the tactile dexterity hand pressure sensor array was adjusted to adapt to the flexible aluminum-plastic film of the soft-pack battery cell; the AI ​​model fine-tuned the GAN sample generation weights and added a classification channel for soft-pack gas expansion and sealing edge wrinkles defects; the consortium chain node added an authorized access node for energy storage recycling enterprises, and the report automatically added a quality rating module for cascaded utilization battery cells; the actual measurement time for full inspection of cylindrical battery cells was 9.2s, with a dimensional measurement accuracy of ±0.02mm, and it is compatible with low-speed and medium-speed dual-specification production lines for energy storage. 3) Core original algorithm step-by-step implementation logic a. Attention-enhanced YOLOv8 + Transformer fusion algorithm execution steps Four-channel camera RGB images are uniformly scaled to 1280×1280 resolution and input into an improved YOLOv8 backbone network. DCNv3-LKA large kernel convolution extracts global texture, and SimAM attention dynamically amplifies the channel weights of scratches and dents. The P2 small target detection head outputs the pixel coordinates of micro-defects ranging from 0.01 to 0.1 mm. The 3D laser point cloud is downsampled to a lightweight point set, and PointNet branches extract 3D height features. Tactile 24-channel pressure is temporally input into a 1D-CNN, outputting a 1D feature vector of shell deformation. The 2D visual features, 3D laser features, and tactile deformation features are mapped to a unified 256-dimensional embedding space and input into a lightweight Transformer encoder. Cross-modal cross-attention is used to calculate the complementary weights of the three types of features, suppressing reflective and dust noise features. The fused features are input into the detection head, and the Focaler-IoU loss function is used to dynamically weight hard sample micro-defects, outputting defect type, confidence, pixel area, and depth quantization value. b. GAN Scarcity Defect Adaptive Enhancement Implementation Process Collect a small number of rare defect samples from the production line (only 50-200 samples per class) as ground values ​​for generator training; The generator simulates defect images under multiple lighting conditions, angles, and sizes based on ground value samples, while the discriminator distinguishes between real defects and simulated defects. The built-in sample confidence threshold of 0.85 filters out low-quality simulation samples and expands the training dataset. The model automatically retrieves new defect data from the production line every 7 days for iterative fine-tuning, continuously improving the accuracy of rare defect identification. c. Complete process of dynamic tensor random obfuscation and encryption The encryption module reads the unique serial number (SN) of the industrial control hardware and generates a block confusion tensor using a chaotic random algorithm. The battery cell RGB image, laser point cloud, tactile timing, and detection report are adaptively segmented into 64KB data blocks; The order of all data blocks is determined by permuting the confusion tensor, and the position verification hash code is calculated independently for each block. The FPGA performs block replacement, hash verification, and lightweight encryption in parallel, and outputs the encrypted data stream to the storage and blockchain modules; if any block is tampered with and the hash verification fails, the system will trigger a data anomaly alarm in real time. d. System performance comparison experimental data Three types of mainstream existing equipment on the market were selected to conduct comparative tests on the same production line with the system of this invention (test sample: 10,000 blade cells, including 32 types of defects, including a large number of tiny defects of 0.01~0.05mm). 008. Beneficial Effects of the Invention This invention, through its unique six-layer integrated hardware and software architecture, four-modal synchronous perception, Transformer cross-modal AI fusion, dynamic tensor obfuscation encryption, and layered consortium blockchain evidence storage, possesses six outstanding innovative technological effects compared to existing global power battery quality inspection systems, all of which cannot be simultaneously achieved by existing solutions: 1) Breakthrough improvement in quality inspection efficiency, significantly reducing labor costs. A 10-second single-cell full inspection cycle replaces 3-6 manual quality inspectors, improving the overall quality inspection efficiency of the production line by more than 300%; robots automatically load, unload, and sort materials, eliminating the need for human intervention throughout the entire process and reducing production capacity fluctuations caused by human fatigue and staff turnover; compatible with multiple specifications of battery cells, eliminating the need for extensive manual parameter debugging and shortening equipment changeover time by 90%. 2) Industry-leading defect identification and dimensional measurement accuracy, solving the core pain point of missing micro-defects. A unique polarization four-modal hardware architecture eliminates the high reflectivity imaging defects of metal battery cells. Combined with attention-enhanced YOLOv8+Transformer cross-modal fusion algorithm and GAN small-sample enhancement, it can identify micro-scratches and dents as small as 0.01mm. The visual-tactile fusion 3D measurement accuracy is stable at ±0.02mm, with a false negative rate of ≤1% and an over-detection rate of ≤2%, far exceeding the accuracy limits of single-vision and independent 3D inspection equipment at home and abroad. This reduces battery safety hazards at the source and reduces after-sales quality claims. 3) AI intelligent deep analysis enables automatic reasoning of defect root causes and empowers production line process optimization. Existing equipment can only identify the presence or absence of defects. This invention incorporates a 5M1E root cause reasoning engine, which automatically associates defect type, frequency, location analysis equipment, tooling, raw materials, and environmental factors, and simultaneously outputs feasible production line optimization solutions. This upgrades quality inspection from "defect detection" to "quality prediction and process improvement," significantly reducing the probability of batch defective products and improving process CPK capability. 4) Lightweight dynamic tensor obfuscation encryption, balancing industrial real-time requirements with data compliance and security. Unlike the high latency of general fixed-key AES encryption, this invention features a unique hardware-based SN seed dynamic tensor obfuscation encryption with a latency of <20ms, suitable for millisecond-level data streams in high-speed production lines. Block permutation + double hash verification resists tampering, replay, and brute-force attacks, meeting the requirements of the Data Security Law and the power battery industry's industrial control confidential data management standards, and solving the risk of leakage of factory cell images and process inspection data. 5) Layered national cryptographic consortium blockchain for evidence storage, balancing traceability reliability, throughput, and storage costs. Abandoning the inefficient architecture of storing all raw images on the blockchain, this innovative layered solution of "off-chain encrypted storage and on-chain storage of only hash digests" increases the TPS throughput of the consortium blockchain by 10 times and reduces storage costs by 95%. It is compatible with the national cryptographic algorithms SM2 / SM3, meeting the trusted traceability requirements of domestic regulators and EU battery passport ESG audits. The quality inspection records of the battery cells throughout their entire lifecycle, from production, warehousing, loading, after-sales service, to recycling, are tamper-proof, solving the problems of mutual distrust of data in the industry chain and the difficulty of proving quality disputes. 6) End-to-end automated closed-loop system enables data exchange between multiple industrial systems, achieving industry-leading digitalization. It forms a fully automated closed loop of "collection-identification-measurement-encryption-on-chain-reporting-sorting-MES synchronization", eliminating the need for manual data export, filling, and transfer; it automatically outputs standardized and visual PDF / Excel reports, is natively compatible with five major industrial buses, seamlessly connects to the factory's existing digital management platform, builds a digital twin system for power battery quality, and adapts to the construction needs of intelligent manufacturing unmanned factories, demonstrating significant value for industrialization. 009. The four core original inventions that distinguish this invention from all existing similar technologies. 1) Unique Hardware: Integrated Sensing Architecture of Four-Mode Synchronous Polarization Optics + 12-DOF Tactile Array Existing battery testing equipment at most achieves a dual-modal approach of vision + laser, lacking tactile flexible sensing and synchronous acquisition design of polarized composite anti-reflective light sources. This invention, through FPGA hardware timing synchronization of four cameras, laser, tactile sensing, and polarized light sources, achieves precise spatiotemporal alignment of four-modal data. It solves the physical bottlenecks of high reflectivity in metal cells, imaging failure due to minor defects, and distortion in the measurement of soft-pack deformation dimensions from the hardware level. This is a novel technological effect brought about by hardware structure combination, rather than a simple superposition of existing technologies. 2) Original algorithm: YOLOv8 backbone + lightweight Transformer cross-modal attention fusion + GAN defect enhancement integrated AI model Currently available AI detection methods for battery defects worldwide only employ single-modal YOLO improved networks or shallow CNN feature concatenation, without introducing a Transformer cross-modal attention fusion mechanism, and lacking an engineered GAN sample enhancement module specifically adapted to rare defects in power batteries. This invention constructs a unified embedding space for heterogeneous multimodal features, dynamically weights and complements defect features, significantly improving detection accuracy in small-sample scenarios. This represents a novel improvement in deep learning algorithm architecture and possesses outstanding creativity. 3) Unique Data Security: Device Hardware SN-Driven Dynamic Tensor Random Obfuscation Lightweight Encryption Scheme Existing industrial encryption methods all use fixed-key AES block encryption, which has high latency and is easy to crack. This invention uses the unique hardware serial number of industrial control equipment to generate a brand-new obfuscated tensor in real time. There is no fixed key. Combined with block permutation and position dual hash verification, FPGA hardware acceleration meets the millisecond-level real-time requirements of the production line. It is a brand-new encryption logic specifically for industrial scenarios, and is not a simple application of general encryption algorithms. 4) Unique traceability process: Off-chain encrypted storage + on-chain hash digest layered consortium blockchain + AI reporting fully automated closed-loop architecture Existing blockchain battery traceability systems upload all original images and point clouds to the chain, resulting in insufficient throughput, high storage costs, and independent processes for detection, encryption, evidence storage, reporting, and sorting. This invention solves the chain network congestion problem with its layered evidence storage architecture, while achieving fully automated and unmanned collaboration from cell loading to MES data upload, forming a complete quality control closed loop. This multi-party collaborative and reliable traceability solution is the first of its kind in the industry.

Claims

1. A power battery defect AI analysis and automatic report generation system, characterized in that, include: The system comprises a perception and acquisition module, an AI multimodal deep fusion analysis module, a robot motion control module, a dynamic tensor random obfuscation encryption module, a national cryptographic alliance chain layered evidence storage module, and an automatic intelligent report generation application module. The four-modal data output of the perception and acquisition module is connected to the feature input port of the AI ​​multimodal deep fusion analysis module. The AI ​​multimodal deep fusion analysis module bidirectionally communicates with the robot motion control module, the dynamic tensor random obfuscation encryption module, and the automatic intelligent report generation application module. The encrypted output of the dynamic tensor random obfuscation encryption module is connected to the national cryptographic alliance chain layered evidence storage module and the automatic intelligent report generation application module. The national cryptographic alliance chain layered evidence storage module is configured with an enterprise node verification interface, and the automatic intelligent report generation application module is configured with multi-industry protocol interface ports. The perception and acquisition module features a four-modal synchronous acquisition hardware architecture: RGB visible light + 3D line laser contour + polarization-suppressed reflection optics + tactile array. The AI ​​multimodal deep fusion analysis module incorporates an attention-enhanced YOLOv8 + Transformer cross-modal feature fusion hybrid model, a GAN scarce defect adaptive enhancement submodule, and 5M1E. The root cause reasoning engine; the dynamic tensor random obfuscation encryption module uses the device hardware unique serial number to generate a dynamic obfuscated tensor, realizing lightweight encryption with data block replacement + position hash dual verification; the national cryptographic alliance chain layered evidence storage module adopts an off-chain ciphertext storage and on-chain storage of only national cryptographic hash digests layered evidence storage architecture.

2. The system of claim 1, wherein, The sensing and acquisition module includes: a 4-channel 20-megapixel global shutter industrial camera, a dual-mode ring + strip polarization composite anti-reflective light source, a 0.005mm linewidth 3D line laser contour sensor, a 12+ degree-of-freedom tactile dexterous hand equipped with a 24-channel miniature pressure sensor array, and an FPGA four-modal synchronous timing trigger board; the FPGA board synchronously controls the acquisition timing of the camera, light source, laser, and tactile sensor, with a multimodal data timestamp deviation of <1μs.

3. The system of claim 1, wherein, The AI ​​multimodal deep fusion analysis module's attention-enhanced YOLOv8 model embeds a DCNv3-LKA deformable large kernel convolution + SimAM lightweight attention unit, adds a dedicated P2 micro-defect detection head, and adopts the Focaler-IoU adaptive hard sample loss function; it is paired with a lightweight Transformer cross-modal cross-attention fusion unit to construct a fusion space of RGB visual, laser 3D point cloud, and tactile morphological features; It has a built-in conditional GAN ​​defect sample adaptive generation and screening module, and a supporting root cause reasoning engine for a knowledge base of 32 types of power battery defects.

4. The system of claim 1, wherein, The system size measurement adopts visual coarse positioning + tactile micro-force flexible sampling + AI three-dimensional point cloud fitting fusion measurement logic, and the complete size measurement of a single battery cell takes ≤5 seconds, and the measurement precision of the length, width, thickness and flatness of the battery cell is stable ±0.02mm; the tactile dexterous hand adopts 0.01N-level micro-force touch control, and there is no risk of scratching the battery cell coating.

5. The system of claim 1, wherein, The dynamic tensor random confusion encryption module comprises a dynamic confusion tensor generation submodule, a multi-modal data adaptive block permutation submodule, a position hash double-checking submodule and an FPGA hardware encryption acceleration unit; a new confusion tensor is generated in real time by taking the device hardware SN unique serial number as a random seed, the total encryption delay of a complete set of battery cell multi-modal data is <20ms, the key fixed cracking, man-in-the-middle data tampering and replay attack are resisted, and the national secret industrial data security specification is met.

6. The system of claim 1, wherein, The national secret alliance chain hierarchical storage module builds an industry chain multi-node alliance chain based on Hyperledger Fabric, generates detection data digest by using the national secret SM3 hash algorithm, and completes enterprise node identity signature authentication by using the SM2 algorithm; only the detection hash digest, the battery cell batch number, the device ID, the timestamp, the defect statistics and the report digest are stored on the chain, the original encrypted multi-modal data are distributed and stored off-chain; the battery cell SN code one-key verification interface is configured, the power battery full life cycle compliance traceability and the European Union battery passport ESG audit are supported.

7. The system of claim 1, wherein, The automatic intelligent report generation application module can automatically generate a battery cell defect heat map, a defect classification Pareto diagram and a CPK process capability curve, an AI root cause engine automatically fills in 5M1E defect cause judgment and production line optimization improvement suggestions, and report output supports PDF audit file and Excel statistical data table in two formats; the original interface is connected with five types of industrial bus protocols, namely, MES / ERP / OPC UA / Modbus / TCP / Profinet, and detection data is real-time bidirectional synchronized with the factory management system.

8. The system of claim 1, wherein, The total detection time of a single battery cell is ≤10 seconds, the comprehensive identification accuracy of the battery cell appearance defect is ≥95%, the smallest identifiable micro-defect is 0.01mm, the defect omission rate is ≤1% and the overkill rate is ≤2%; and the automatic quality inspection production line is compatible with four types of mainstream power battery cells, namely, square, blade, soft package and cylinder.

9. The system of claim 1, wherein, The robot motion control module comprises a six-axis EtherCAT bus industrial robot, a force-position hybrid motion controller and a vision closed-loop coordinate correction unit; the robot automatically completes the whole process of battery cell grabbing, six-face turning, flexible touch measurement and OK / NG defective product sorting, and the battery cell grabbing positioning repeatability is ±0.01mm, without manual switching and specification debugging.

10. A defect detection and report generation method based on the defect AI analysis and automatic report generation system of any one of claims 1-9, characterized in that, The method comprises sequentially executed fully-automatic synchronization steps: S1 robot vision closed-loop loading positioning; S2 FPGA time sequence synchronization completes RGB visible light, 3D laser profile, polarized optics and tactile array four-modal battery cell six-face synchronous acquisition; S3 Edge computing unit completes pixel-level defect recognition through attention-enhanced YOLOv8+Transformer cross-modal fusion model, and GAN module assists in feature enhancement of rare and tiny defects; S4 Visual coarse positioning is combined with tactile flexible sampling + three-dimensional point cloud AI fitting to complete high-precision quantitative measurement of battery size; S5 Dynamic tensor random obfuscation encryption engine hardware acceleration encrypts all multi-modal raw data, with additional position hash verification; S6 National cryptographic SM3 hash generates data digest, and only the digest, batch, and timestamp are uploaded to complete the alliance chain layered evidence; S7 AI root cause reasoning engine automatically counts defect data, generates visual charts, fills in root causes and optimization schemes, and outputs real-time standardized PDF / Excel inspection reports; S8 Robots sort qualified / defective battery cells according to AI defect judgment results, and simultaneously push quality inspection statistical data to MES / ERP industrial systems to complete full-link automatic closed loop.

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