Robustness Capacity Research Articles

Estimation of Mango Fruit Production Using Image Analysis and Machine Learning Algorithms

Mango production is fundamental to the agricultural economy, generating income and employment in various communities. Accurate estimation of its production optimizes the planning and logistics of harvesting; traditionally, manual methods are inefficient and prone to errors. Currently, machine learning, by handling large volumes of data, emerges as an innovative solution to enhance the precision of mango production estimation. This study presents an analysis of mango fruit detection using machine learning algorithms, specifically YOLO version 8 and Faster R-CNN. The present study employs a dataset consisting of 212 original images, annotated with a total of 9604 labels, which has been expanded to include 2449 additional images and 116,654 annotations. This significant increase in dataset size notably enhances the robustness and generalization capacity of the model. The YOLO-trained model achieves an accuracy of 96.72%, a recall of 77.4%, and an F1 Score of 86%, compared to the results of Faster R-CNN, which are 98.57%, 63.80%, and 77.46%, respectively. YOLO demonstrates greater efficiency, being faster in training, consuming less memory, and utilizing fewer CPU resources. Furthermore, this study has developed a web application with a user interface that facilitates the uploading of images from mango trees considered samples. The YOLO-trained model detects the fruits of each tree in the representative sample and uses extrapolation techniques to estimate the total number of fruits across the entire population of mango trees.

Coordination of NPCs in multi-agent systems based on behavior trees

Abstract. This paper addresses the design and coordination challenges of controlling non-player characters' (NPCs) behaviors in multi-agent systems (MAS) using behavior trees (BTs), which are preferred over finite state machines (FSMs) due to their hierarchical structure and ease of maintenance. While BTs effectively resolve the question of "What to do next?" for individual NPCs, their application in MAS, particularly with the integration of a blackboard system for central control, reveals limitations in efficiency, robustness, and heuristic capacity as system complexity increases. To explore solutions to these challenges, this study analyzes various algorithms that enhance the functionality of behavior trees within MAS. The research focuses on three primary areas: the optimization of behavior trees, the development of behavior tree search algorithms, and the improvement of communication algorithms within BTs. Methods involve a comparative analysis of existing and new algorithmic approaches to identify and address inefficiencies in NPC coordination. The findings indicate that advanced behavior tree configurations, when combined with innovative search and communication strategies, significantly improve the coordination, robustness, and operational efficiency of MAS. These enhancements allow for more dynamic and responsive NPC interactions in complex gaming environments.

An adaptive robust watermarking scheme based on chaotic mapping

Digital images have become an important way of transmitting information, and the risk of attacks during transmission is increasing. Image watermarking is an important technical means of protecting image information security and plays an important role in the field of information security. In the field of image watermarking technology, achieving a balance between imperceptibility, robustness, and embedding capacity is a key issue. To address this issue, this paper proposes a high-capacity color image adaptive watermarking scheme based on discrete wavelet transform (DWT), Heisenberg decomposition (HD), and singular value decomposition (SVD). In order to enhance the security of the watermark, Logistic chaotic mapping was used to encrypt the watermark image. By adaptively calculating the embedding factor through the entropy of the cover image, and then combining it with Alpha blending technology, the watermark image is embedded into the Y component of the YCbCr color space to enhance the imperceptibility of the algorithm. In addition, the robustness of the algorithm was further improved through singular value correction methods. The experimental results show that the average PSNR and SSIM of the watermarking scheme are 45.3437dB and 0.9987, respectively. When facing various attacks, the average NCC of the extracted watermark reaches above 0.95, indicating good robustness. The embedding capacity of this scheme is 0.6667bpp, which is higher than other watermarking schemes, and the average running time is 1.1136 seconds, which is better than most schemes.

Bivariate BMM-based hybrid domain image watermark detector

Robustness, imperceptibility, and watermark capacity are three indispensable and contradictory properties for any image watermarking systems. It is a challenging work to achieve the balance among the three important properties. In this paper, by using the bivariate Beta mixture model (Bivariate BMM), we present a statistical image watermark scheme in nonsubsampled Contourlet transform (NSCT)-fractional order Jacobi Fourier moments (FJFMs) amplitude hybrid domain. The whole watermarking algorithm includes two parts: watermark embedding and detection. NSCT is firstly performed on host image to obtain the frequency subbands, and the NSCT subbands are divided into non overlapping blocks. Then, the significant NSCT domain blocks are selected using local binary patterns (LBP). Meanwhile, for each selected NSCT coefficient block, FJFMs are calculated to obtain the NSCT-FJFMs amplitude. Finally, watermark signals are inserted into the amplitude hybrid domain of NSCT-FJFMs. In order to detect accurately watermark signal, the statistical characteristics of NSCT-FJFMs magnitudes are analyzed in detail. Then, NSCT-FJFMs magnitudes are described statistically by Bivariate BMM, which can simultaneously capture the marginal distribution and strong dependencies of NSCT-FJFMs magnitudes. Also, Bivariate BMM parameters are estimated accurately by the rough-enhanced-Bayes mixture estimation & expectation–maximization (REBMIX&EM) approach. Finally, a statistical watermark detector based on the locally optimum (LO) decision rule and Bivariate BMM is developed in NSCT-FJFMs magnitude hybrid domain. Also, the closed-form expressions on the LO statistic is derived and the receiver operating characteristic (ROC) about our detector is analyzed. Extensive experimental results show the superiority of the proposed image watermark detector over some state-of-the-art statistical watermarking methods.

An optimized watermarking scheme based on genetic algorithm and elliptic curve

Digital watermarking serves as a crucial tool for tracing copyright infringements and ensuring the authenticity and integrity of sensitive information. The fundamental concept involves embedding a watermark in the host information, ensuring its undetectability by unauthorized parties. The efficacy of a watermarking scheme mainly depends on achieving high levels of imperceptibility, robustness, and embedding capacity. These attributes are intricately linked to both the selection of the host information segment and the embedding factor. Existing schemes often (i) employ the entire host information for embedding, incurring computational expenses, and (ii) optimize the embedding factor without considering imperceptibility, robustness, and embedding capacity simultaneously, resulting in less secure watermarks. To address these limitations, we introduce a novel watermarking scheme leveraging elliptic curves (ECs) and genetic algorithms (GA). We model the choice of the embedding part by generating pseudo-random numbers over ECs, taking advantage of their proven sensitivity, security, and low computational complexity. Due to parallel search and adaptability to non-linear relationships of GA, the scheme employs genetic optimization with a multivariate objective function to establish a balance between imperceptibility, robustness, and embedding capacity for optimal watermarked generation. Rigorous analysis and comparisons demonstrate that our proposed scheme attains significantly higher imperceptibility, robustness, and embedding capacity compared to existing optimized schemes. Furthermore, our scheme exhibits a speed advantage, being up to 278 and 21 times faster than optimized and non-optimized schemes, respectively, thereby affirming its practical applicability.

Semi-IPN polysaccharide-based hydrogels for effective removal of heavy metal ions and dyes from wastewater: a comprehensive investigation of performance and adsorption mechanism.

The escalating issue of environmental pollutants necessitates efficient, sustainable, and innovative wastewater treatment technologies. This review comprehensively analyzes the mechanisms and isotherms underlying the adsorption processes of semi-interpenetrating polymer network (semi-IPN) polysaccharide-based hydrogels to remove heavy metal ions and dyes from wastewater. Polysaccharides are extensively utilized in hydrogel synthesis due to their biocompatibility, cost-effectiveness, and non-toxic nature. The synthesis of these hydrogels as semi-IPNs enhances their mechanical and structural robustness and adsorption capacity. This review explores the key parameters affecting adsorption performance, including pH, temperature, contact time, and adsorbent dosage. Findings highlight that semi-IPN polysaccharide-based hydrogels exhibit remarkable adsorption capabilities through electrostatic interactions, ion exchange, and surface complexation. Furthermore, this review highlights the distinct advantages of semi-IPNs over other polymer networks. Semi-IPNs offer improved mechanical stability, higher adsorption efficiencies, and better reusability, making them a promising solution for wastewater treatment. Detailed isotherm models, including Langmuir and Freundlich isotherms, were studied to understand these hydrogels' adsorption behavior and capacity for different pollutants. This study highlights the potential of semi-IPN polysaccharide-based hydrogels as effective adsorbents forheavy metals and dyes and as a promising solution for mitigating environmental pollution. The insights provided herein contribute to developing advanced materials forenvironmental remediation, aligning with global sustainability goals, and advancing wastewater treatment technology.

Ultrahigh Responsivity and Robust Semiconducting Fiber Enabled by Molecular Soldering-Governed Defect Engineering for Smart Textile Optoelectronics.

Semiconducting fibers (SCFs) are of significant interest to design next-generation wearable and comfortable optoelectronics that seamlessly integrate with textiles. However, the practical applications of current SCFs are always limited by poor optoelectronic performance and low mechanical robustness caused by uncontrollable multiscale structural defects. Herein, a versatile in situ molecular soldering-governed defect engineering strategy is proposed to construct ultrahigh responsivity and robust wet-spun MoS2 SCFs, by using a π-conjugated dithiolated molecule to simultaneously patch microscale sulfur vacancies within MoS2 nanosheets, diminish mesoscale interlayer voids/wrinkles, promote macroscale orientation, build long-range photoelectron percolation bridges, and provide n-doping effect. The derived MoS2 SCFs exhibit over two orders of magnitude higher responsivity (144.3 A W-1) than previously reported fiber photodetectors, 37.3-fold faster photoresponse speed (52ms) than pristine counterpart, and remarkable bending robustness (retain 94.2% of the initial photocurrent after 50000 bending-flattening cycles). Such superior robustness and photodetection capacity of MoS2 SCFs further enable large-scale weaving of reliable smart textile optoelectronic systems, such as direction-identifiable wireless light alarming system, modularized mechano-optical communication system, and indoor light-controlled IoT system. This work offers a universal strategy for the scalable production of mechanically robust and high-performance SCFs, opening up exciting possibilities for large-scale integration of wearable optoelectronics.

Assessing the Sustainability of County-level Rural E-commerce Ecosystems in China: A Multi-dimensional Framework

2. Enhancing the sustainability of the rural e-commerce industry chain and achieving sustainable development within County-level Rural E-commerce Ecosystems (CREE) is a crucial endeavor in China's rural revitalization phase. This study aims to design an assessment tool named SREEM to evaluate the sustainability of China County-level Rural E-commerce Ecosystem. it provides a comprehensive exposition of SREEM, elucidating its foundational principles and indicator origins. From the dimensions of Robustness, Niche Creation, and Endogenous Development Capacity, the study explicates the various levels of indicators within the preliminary SREEM framework. Employing a combined approach of Modified Delphi and AHP, the study screens SREEM assessment indicators and determines indicator weight. The final iteration of SREEM includes 3 first-level indicators, 8 second-level indicators, 23 third-level indicators, and 35 fourth-level indicators.

High-Performance Optimised Porosity Iron Electrode for Hybrid Battery Electrolyzer

The growing demand for efficient, cost-effective and sustainable energy systems has prompted significant research into the development of advanced electrochemical devices. Hybrid battery electrolyzer, integrating the capabilities of both battery and electrolyzer, is a groundbreaking technology aiming to enhance both energy conversion efficiency and storage capacity. [1] Iron with large earth-abundance, low-cost, robustness, and high capacity is a promising electrode material for such hybrid energy systems. [2-4]This study focuses on the design, fabrication, and performance evaluation of a novel porous iron electrode tailored for use in a hybrid battery electrolyzer system. The proposed electrode exhibits superior electrocatalytic activity, enhanced mass transport properties, and increased surface area, contributing to improved overall performance. The electrode's porosity and pore structure are tuned to facilitate efficient mass transport and electrolyte penetration, addressing common challenges associated with conventional electrodes. The electrochemical performance of the optimised porosity iron electrode is investigated through comprehensive characterization techniques, including scanning electron microscopy (SEM), X-ray diffraction (XRD), electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and charge-discharge cycling tests. Results indicate superior electrochemical properties, showcasing enhanced specific capacity, faster charge/discharge kinetics, and prolonged cycle life compared to conventional electrodes. The incorporation of conducting nanocarbon and sintering at optimized temperature of 800 ℃ led to the achievement of a stable and elevated capacity exceeding 500 mAh/g for these advanced iron electrodes. The iron electrodes exhibit outstanding porosity (65 - 70%), along with a pore size of 5-10 µm. This configuration results in exceptional current rate performance, achieving a discharge capacity of 350 mAh/cm2 at a current density of 35 mA/cm2 and 245 mAh/cm2 at 50 mA/cm2. Furthermore, the iron electrodes exhibit superior performance in high-current electrolysis, showcasing an HER potential of -1.25, -1.34, and -1.50 V (vs Hg/HgO) at a current density of 200, 500 and 1000 mA/cm2 respectively.These findings highlight the potential of optimised porosity iron electrodes as a key component in the development of high-performance and cost-effective hybrid battery electrolyzer systems. The integration of such advanced materials holds promise for unlocking new frontiers in energy storage and green hydrogen technology, contributing to the realization of efficient and sustainable energy systems.

A Hybrid Metadetector for Measuring Bell States of Optical Angular Momentum Entanglement.

High-dimensional entanglement of optical angular momentum has shown its enormous potential for increasing robustness and data capacity in quantum communication and information multiplexing, thus offering promising perspectives for quantum information science. To make better use of optical angular momentum entangled states, it is necessary to develop a reliable platform for measuring and analyzing them. Here, we propose a hybrid metadetector of monolayer transition metal dichalcogenide (TMD) integrated with spin Hall nanoantenna arrays for identifying Bell states of optical angular momentum. The corresponding states are converted into path-entangled states of propagative polaritonic modes for detection. Several Bell states in different forms are shown to be identified effectively. TMDs have emerged as an attractive platform for the next generation of on-chip optoelectronic devices. Our work may open up a new horizon for devising integrated quantum circuits based on these two-dimensional van der Waals materials.

Commercial Carbon Fibers as Host for Sodium Deposition to Achieve High Volumetric Capacity

AbstractThe advancement of flexible electronic devices necessitates the utilization of electrode materials that offer robustness and high capacity. In this paper, it is revealed that commercially available carbon fibers with specific microcrystalline structures not only have high mechanical strength but also a high volumetric capacity of up to 300 mAh cm−3, surpassing conventional carbon materials. When multiple structural parameters of carbon fiber reach certain thresholds, a breakthrough in sodium storage capacity and rate performance can be achieved. This study further elucidates the mechanism whereby this specific carbon fiber primarily utilizes an all‐plateau sodium deposition mechanism, which occurs in pore‐like grain boundaries. Through in situ spectroscopy and synchrotron techniques, the reversible deposition process of metallic sodium has been revealed at different scales. Theoretical calculations and thermodynamic principles further confirm the desolvation and deposition mechanisms in carbon fibers. As a result, this research discovers the modulating effects and patterns of crystallinity, defect, and orientation of carbon materials on sodium storage sites and diffusion kinetics, thereby achieving controlled sodium storage. This work shows that commercial carbon fibers can serve as robust hosts for sodium deposition and enhances the theoretical understanding of how the microcrystalline structure of carbon materials relates to sodium storage properties.

Vision-language constraint graph representation learning for unsupervised vehicle re-identification

Existing vehicle re-identification methods rely on visual features to extract vehicle identity information. However, while individual visual features enable the model to learn limited semantic information, multimodal representations are difficult to extract. A vision-language constraint graph representation learning method guided by textual descriptions is proposed to exploit the cross-modal robustness capacity. Initially, the training set creates the unique conditional prompts for textual feature extraction. These prompts are employed to improve the understanding of visual modalities. We subsequently designed a vision-language constraint graph topology, where each training sample is considered a node in the graph. Under the dual constraints of visual and textual features, the relationship between graph nodes is further explored to construct more reliable positive and negative sample pairs for graph representation learning. Then, neighboring node label smoothing is introduced to mitigate label noise generated by visual feature clustering and achieved by combining pseudo-label assignment results from neighboring node pairs in the graph topology. Extensive experiments have confirmed that the proposed method achieves state-of-the-art performance by combining salient information from visual and textual modalities.

Alternative proton exchange membrane based on a bicomponent anionic nanocellulose system

As integral parts of fuel cells, polymer electrolyte membranes (PEM) facilitate the conversion of hydrogen's chemical energy into electricity and water. Unfortunately, commercial PEMs are associated with high costs, limited durability, variable electrochemical performance and are based on perfluorinated polymers that persist in the environment. Nanocellulose-based PEMs have emerged as alternative options given their renewability, thermal and mechanical stability, low-cost, and hydrophilicity. These PEMs take advantage of the anionic nature of most nanocelluloses, as well as their facile modification with conductive functional groups, for instance, to endow ionic and electron conductivity. Herein, we incorporated for the first time two nanocellulose types, TEMPO-oxidized and sulfonated, to produce a fully bio-based PEM and studied their contribution separately and when mixed in a PEM matrix. Sulfonated nanocellulose-based PEMs are shown to perform similarly to commercial and bio-based membranes, demonstrating good thermal-oxidative stability (up to 190 °C), mechanical robustness (Young's modulus as high as 1.15 GPa and storage moduli >13 GPa), and high moisture-uptake capacity (ca. 6330 % after 48 h). The introduced nanocellulose membranes are shown as promising materials for proton-exchange material applications, as required in fuel cells.

Digital Steganography : A Comprehensive Study on Various Methods for Hiding Secret Data in a Cover file

This research paper offers an in-depth examination of digital steganography, with a focus on the diverse methodologies utilized for embedding secret data within cover files. Steganography, the practice of concealing information within other non-secret data, ensures the hidden message remains undetectable to unauthorized observers. This study systematically reviews both traditional and modern steganographic techniques, dissecting their fundamental mechanisms, advantages, and weaknesses. Techniques explored include Least Significant Bit (LSB) insertion, discrete cosine transform (DCT), discrete wavelet transform (DWT), and innovative methods leveraging deep learning and adaptive algorithms. Each method is assessed for its imperceptibility, robustness, and data capacity, offering a comparative analysis to underscore their respective practical applications and limitations. Additionally, the paper delves into steganalysis—methods for detecting hidden information—to provide a comprehensive perspective on the field. Through experimental evaluation and theoretical analysis, this study seeks to enhance the understanding of digital steganography, presenting insights that could inform future research and the development of more secure data hiding techniques.

Towards real-world state of health estimation, Part 1: Cell-level method using lithium-ion battery laboratory data

Accurate and rapid state of health (SOH) estimation is crucial for battery management systems (BMS) in lithium-ion batteries (LIBs). Given the variability in battery types and operating conditions, along with limited data samples, conventional data-driven methods are inadequate to meet the requirements, especially in real-world applications, e.g., electric vehicles and energy storage systems. To this end, we develop a meta-learning-based method with a Gated Convolutional Neural Networks-Model-Agnostic Meta-Learning (GCNNs-MAML) model to seek proper initial parameters that can rapidly adapt to new given teat samples with few-shot training. It uses multiple existing historical datasets for meta-training, and then the initial parameters of the trained model are used for meta-testing on new small-scale data. With only random 800 s charging segments from 5% of the cycling data employed for training, the GCNNs-MAML model yields a SOH estimation with a mean RMSE of 1.8% and a minimal RMSE of 1.3% on the remaining 95% testing samples. The results indicate that it remarkably outperforms the feature-based and learning-based methods. The meta-learning-based method exhibits high precision, robustness, and strong generalization capacity, implying its enormous potential for real-world applications and few-shot conditions.

Multiple-image encryption based on authenticable phase and phase retrieval under structured light illumination

In this paper, a multiple-image encryption method is proposed based on authenticable phase and phase retrieval under structured light illumination. In the encryption process, each plaintext image is firstly encrypted into two phase masks by using phase retrieval algorithm under structured light illumination with the help of encryption authenticable phase, and then the final ciphertext is generated by using phase mask multiplexing technology, overcoming the trade-off relationship between encryption capacity and decryption quality, thereby the decryption quality is improved meanwhile the encryption capacity is expanded. Furthermore, the optical parameters of structured phase mask in the structured light illumination are introduced as supplementary digital keys, expanding the key space and enhancing the security of encryption method. In the decryption process, the decryption authenticable phase needs to pass authentication before it is used to decrypt the ciphertext, leading to a further improvement for the level of security of encryption method. Numerical simulations are performed to demonstrate the feasibility of the proposed multiple-image encryption method, and the simulation results show that the proposed method exhibits high feasibility and security as well as strong robustness and large capacity.

QSAR Modeling of Sphingomyelin Synthase 2 Inhibitors for Their Potential as Anti-Atherosclerotic Agents

Sphingomyelin synthase 2 (SMS2) has emerged as a promising target for atherosclerosis threatment. However, the availability of selective SMS2 inhibitors and their associated pharmacological properties remains limited. This research paper explores various QSAR modeling techniques applied to a range of compounds acting as SMS2 inhibitors. Multiple distinct QSAR modeling methodologies were employed, including conformation-independent, GA-MLR and 3D based QSAR modeling, and their mutual correlations were investigated, Various statistical methods were applied to assess the quality, robustness, and predictive capacity of these developed models, yielding favorable results. Furthermore, molecular fragments derived from SMILES notation descriptors, which account for the observed changes in the evaluated activity, were defined. The methodology presented in this research holds potential for identifying novel agents for atherosclerosis treatment by targeting sphingomyelin synthase 2.

A novel hybrid STL-transformer-ARIMA architecture for aviation failure events prediction

Accurate prediction of aviation failure events helps to anticipate future safety situations and protect against further uncontrollable accidents. However, the large sample size, complex temporal characteristics, and significant long-term correlation of aviation failure events increase the operational cost of accurate prediction. To address these challenges, this paper proposes a novel approach involving seasonal-trend decomposition using Loess (STL) and a hybrid prediction model consisting of a transformer and autoregressive integrated moving average (ARIMA). First, STL decomposition is utilized to isolate trend, seasonal, and remainder components, contributing to a comprehensive understanding of the events sample characteristics. The trend component is then trained and predicted using transformer, solving the vanishing gradient problem and improving computational efficiency. ARIMA is employed to train and predict the seasonal and remainder components, maintaining accuracy while reducing complexity. Finally, a comparative evaluation between the proposed and multiple existing approaches is conducted using Aviation Safety Reporting System (ASRS) data. The results demonstrate that the STL-transformer-ARIMA provides more accurate predictions of failure events than single model. It also exhibits significant advantages in robustness and generalization capacity compared to single transformer-based predictors. This revealed that the proposed approach performed better in predicting aviation failure events.

Signal processing collaborated with deep learning: An interpretable FIRNet for industrial intelligent diagnosis

Due to the neglect of prior characteristics and the lack of explicit constraints on fault knowledge, conventional intelligent diagnosis methods suffer from great hardships in exacting fault-sensitive information and making explainable decisions, resulting in poor interpretability and inferior robustness. Motivated by the excellent multiscale analysis performance of signal processing and the powerful feature mining ability of deep learning, this study proposes an interpretable FIRNet for equipment intelligent diagnosis under strong noise environments. It consists of a well-crafted FIRLayer and a deep learning backbone. Inspired by the modulation principle that fault-sensitive components are normally modulated into multiscale mode characteristics, multiple ex-ante interpretable filters with two learnable parameters, including center frequency and bandwidth, are analytically designed to process the sequence signal sets, represented as signal-processing-based FIRLayer where the extracted multiscale feature maps are taken as an interpretable status information expression. Subsequently, multiscale convolutional kernels are established to extract the high-level feature maps and further make the final diagnostic decisions, represented as the deep learning backbone. The simulated and experimental results show that the proposed FIRNets have higher identification precision compared to the other nine deep learning models. Specially, three aspects, including model interpretability, noise robustness capacity and edge intelligent diagnosis, are further analyzed to illustrate the interpretable advantages of FIRNets. Hereinto, feature visualizations of the FIRLayer and mode decision contributions of the deep learning backbone are in-depth analyzed to verify the interpretable feature representation and decision-making principle of FIRNets. The results indicate that FIRNets have superior ex-post interpretability compared to other methods. Focusing on industrial practices, an efficient edge diagnosis system based on a pruned FIRNet is established, and an online diagnosis accuracy of more than 99% has been achieved. It can be foreseen that the proposed FIRNets show great potential and competitiveness to promote the edge computing application of equipment intelligent diagnosis.

Commutative encryption and watermarking algorithm based on compound chaotic systems and zero-watermarking for vector map

Commutative encryption and watermarking (CEW) is an efficient and safe protection technology combining cryptography and digital watermarking. It features the dual functions of secure transmission and copyright safeguarding. In traditional CEW algorithms for vector map, the watermark is embedded by modifying the host data, which cannot meet the needs of high precision vector map, especially for vector maps applied in military and navigation positioning fields. To fill this gap, a CEW algorithm for vector map based on zero-watermarking and permutation encryption is proposed. Firstly, compound chaotic systems are applied to permutation encryption to encrypt the storage order and position of the vertex coordinates respectively. The key is generated by the SHA-256 hash method, which has higher security. Secondly, the number of vertex features is regarded as the feature invariants to establish the watermark information and watermark index. After that, the zero-watermarking image is constructed through the XOR operation of the feature matrix and the copyright information matrix. Experiments show that the combination of permutation encryption and zero watermark can better meet the needs of secure transmission and copyright protection of high-precision vector graphics. The security, robustness and watermark capacity of the algorithm have been improved, making it more practical.