This PDF file contains the front matter associated with SPIE Proceedings Volume 12586, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Aiming at the problem that the traditional Faster R-CNN is not sensitive to small targets and occluded targets, this paper submits an improved Faster R-CNN target detection algorithm. In this paper, using PASCAL VOC07+2012 to be the experimental data sample set. For the large differences in the targets to be detected in this set, the general anchor size and dimensions is not often used for detecting multi-category problems. For the purpose of increasing small objects detection accuracy, using K-means to improve this situation, the annotation information is centralized for clustering, and the clustering result is replaced by the anchor scale and size in the original RPN. Finally, missed detection and false detection caused by partial overlap of objects in the image, this paper uses the improved soft-NMS algorithm. The experimental results show that, compared with the traditional Faster R-CNN algorithm, the average mean precision (mAP) of the algorithm under the PASCAL VOC07+2012 dataset can reach 80.7%, and it is enhanced by 6.5 percentage points.

At present, under the background of energy saving and emission reduction, the construction industry, as a typical industry with high energy consumption, has attracted wide attention from all walks of life for its application of energy-saving technology. Many researchers have also carried out a lot of research on energy-saving technologies of green buildings. At present, these studies are mainly concentrated in the field of civil architecture. Specifically, on the one hand, the research on green building energy-saving technology will help to further enrich the research theory in the field of building energy-saving technology, so as to guide the subsequent targeted construction of civil buildings; on the other hand, through this research, we can directly obtain more successful experiences in the design and construction of civil buildings, and provide more practical experience reference for the subsequent green construction of civil buildings. Therefore, in this study, combined with an actual case of a comprehensive building project, the effective application of green building energy-saving technology in carbon emission reduction design of civil buildings is discussed in detail from the perspectives of envelope design, energy saving, water saving and application of new materials, so as to further improve the carbon emission reduction level of civil buildings.

The construction of a smart city is a weather vane for the digital transformation of a city. The manhole covers on the road and the fire hydrants on both sides are most closely related to life and economy. Finding them at all times, and seeing the process by which they give the pulse of the underground pipe network is the key to intelligent transformation. In view of the occasional loss of manhole covers on the current road, the manhole can "eat people" and fire hydrants are often not found in time to supplement the water source during firefighting. In order to prevent this kind of phenomenon from happening again, give an early warning at any time and accurately locate the location of manhole covers and fire hydrants, this paper focuses on the in-depth comparison and analysis of such municipal products that can be found. It is concluded that there are obvious differences in positioning accuracy of current static products under different GPS antenna characteristics.

In order to balance the relationship between D2D users' capacity and energy consumption in a cellular network, an energy-saving transmission mechanism based on non-orthogonal multiple-access (NOMA) enhancement is designed in this paper to increase the number of D2D multiplexing of limited cellular resources. At the same time, to reduce the energy overhead of relay, the idle D2D is used as a relay node and the simultaneous wireless information and power transfer (SWIPT) technology is used to compensate its own energy consumption in the forwarding process. In this paper, a nonlinear fractional programming problem is established to maximize the energy efficiency (EE) of D2D clusters. To overcome the non-convexity of the objective function and the coupling of parameters, we solve the approximation of the original optimization problem based on fractional programming and sequential convex approximation theory. First, the maximum energy efficiency is obtained by dichotomy; Then the sequential convex approximation method is used to solve the corresponding optimal transmitting power; Finally, genetic algorithm is used to get the optimal configuration parameters of the system; The above steps are alternately iterated until convergence to obtain the optimal solution. The simulation results show that the algorithm we proposed has faster convergence and is better than the orthogonal multiple access (OMA) technology in the improvement of energy efficiency performance.

Ever since the invention and massive implementation of telecommunication invited themselves during wartime, it is crucial to keep the message as secret as possible from adversaries. Back in time, the leak of messages meant thousands of potential casualties whereas now enormous costs on fortune and confidential secrets. In this paper, we will dive into the comparison of modern communication apps such as WhatsApp, Messengers, and WeChat with their encryption methods including AES encryption and RSA encryption. Then, we build a front-to-end message app using the RSA encryption method and an easy version of a random number generator to increase the security level. We will discuss the utility of the three apps and their complexity in terms of the input, algorithm, and pros and cons of the features they provide objectively to the average consumer, regardless of occupation or special personalized need. In addition, throughout the studies of the three apps and their flow of design on how to keep the consumers safe during their sharing of messages, we will discover a trend of evolution in how the society treats the idea of information security and what improvements can be done from time to time.

With the continuous promotion and innovation of information technology and application, the governments all around the world begin to build their own data information management system and try to accelerate the implementation of domestic independent iterative upgrade, in which the data migration becomes vitally important to the success of such process. In this paper, we rely on using the dynamic mapping, distributed extensibility and unstructured data processing capabilities of the distributed full-text retrieval framework Elasticsearch, and then propose a heterogeneous data migration method, which can solve the shortcomings of traditional methods that are usually used to process isomorphism data. The application of this method not only meets the needs of the physical migration of historical data to the homemade autonomous controllable ecology, but also supports the more flexible and secondary use of the historical heterogeneous data.

The basic knowledge and theory of wavelet analysis are introduced. The definitions and properties of continuous wavelet transform and discrete wavelet transform are expounded. The Sym8 wavelet used in this paper is briefly explained. Then, the wavelet analysis is applied to the pretreatment of CO concentration time series signal in the return corner of the safety monitoring system. Combining with the characteristics of CO concentration time series signal in the return corner, the wavelet threshold denoising method is used to denoise the CO concentration time series signal, and the appropriate threshold function is selected. Number, wavelet basis function and decomposition level can effectively remove the interference of noise signal.

In this article, it will describe the generation and use of an encryption key named AES, RSA and X3DH. When encrypting or decrypting, their operation processes are similar. That is, they all use a common method and an encryption key. RSA ensures the security of Internet communications, whether it is digital signatures to protect data from tampering or encrypted communications. AES is mainly used in combination with other encryption technologies, such as DH, because AES is symmetric encryption, and DH is a key exchange system, the combination of the two can complement each other. X3DH is a collection of them, including both digital signatures and the ability to encrypt communications. X3DH is simpler, more complicated, and more secure, derived from the invention and application principles of these two keys (AES and RSA). RSA is a new feature that publicly leaks the encryption key without leaking the corresponding decryption key at the same time. For the explanation of the content of this article, many explanations of proper nouns and knowledge are also used; A series of codes derived by me about the knowledge learned by Computer Science, which are generated by the encryption principle, are more clearly explained, and explained the encryption process and operation method of each key. This application uses the x3dh protocol to allow secure conversations between users. X3DH is an end-to-end communication protocol that enables two clients to conduct encrypted conversations, and this process uses the data on the third-party server to sign, so as to verify the communication data between the clients.

The emergence of AmBC (Ambient Backscatter Communication) technology makes it possible for IoT (Internet of Things) devices to complete communication with the help of ambient RF signals. With high domestic coverage and strong stability, DTMB (Digital Terrestrial Multimedia Broadcast) signals have natural advantages in combination with AmBC. However, the existing AmBC signal demodulation algorithm based on DTMB requires the receiver to realize analog-to-digital conversion, which is difficult to be applied to low-power IoT devices. Therefore, this paper provides a design scheme of a low-power demodulation circuit, in which the receiver can identify the frame header and frame body of the DTMB signal, select the corresponding comparison threshold and get the digital demodulation signal. Finally, the scheme is verified by simulation.

MAC protocol design is an important task for safety message broadcasting in VANETs. Previous research works have solved many basic problems. They have a poor performance in the high dynamic and dense vehicle environment. This paper proposes a slotted and OFDM federated protocol in which the control channel of IEEE 1609.4 is divided into many sub-channels for all nodes in the network to avoid conflicts and improve packet delivery ratio. Simulation results show that the proposed protocol has a higher performance of packet delivery ratio.

In order to solve the increasing number of range tests and break the "chimney" between various test sites, the US military first proposed and built TENA (testing and training architecture), which realized the unified test of different geographical distribution and different test sites, greatly shortened the test cost and cycle, and became the benchmark of future joint tests. Among them, the test object is the main body of the joint test site. How to build the test object model quickly and accurately is the key to realize composability, reusability and interoperability among joint test sites. At present, there are many researches on the overall framework of joint test in China, and few researches on the construction technology of test object model. Based on the meta model, this paper designs a set of methods for constructing object model, realizes the design of virtual object model, and constructs it. The simulation verifies the effectiveness of this method, and provides technical reference for the research of multi-regional joint test field in China.

This paper, using the ultra-efficiency DEA-Tobit model, takes 16 listed port enterprises as an example to measure the port efficiency from 2015 to 2021. Based on the unexpected output, the measured port enterprise operation efficiency is more objective, and provides reasonable suggestions for the improvement of the port operation efficiency. The research results show that: (1) the operating efficiency of port enterprises is increasing, but the growth rate is slow; (2) the operating efficiency is limited by the growth of technical efficiency; (3) the fixed assets, management expenses and R & D expenses promote the port operation efficiency, while the paid-in capital, operating cost and carbon emission inhibit the port operation efficiency.

Currently, electric vehicles and fuel vehicles coexist in urban distribution. Given this, this paper focuses on the vehicle routing problem with the mixture of electric and fuel vehicles. Then, a green vehicle routing optimization model with a mixed fleet is proposed. The cost of carbon emissions from the fuel is considered in the model. Then, an improved genetic algorithm is designed to solve the problem. Finally, sensitivity analysis is carried out for key factors such as vehicle composition, battery capacity, and charging rate. Numerical experimental results show that the economic power of logistics enterprises to directly upgrade their fleets from all-fuel vehicles to all-electric vehicles is insufficient. The number of electric vehicles kept in the fleet should be determined according to the combination of vehicle cruising range and customer distribution. Logistics enterprises should comprehensively consider the battery capacity and charging rate to reduce the distribution cost of electric vehicles.

Although the current regulation of user-side voltage takes into account the needs of users, it mostly adopts rigid regulation, which leads to the increase of carbon emissions in energy consumption of the power grid. In order to improve the above defects and maximize the energy saving of the power grid, a flexible adjustment method for the voltage on the large power user side under the constraint of carbon emission is studied. Through the establishment of user side zip model, the user side voltage flexible control load is analyzed. Taking energy consumption, power factor, three-phase imbalance and time sharing as indicators, the carbon emission constraints of power supply are calculated. Based on the actual demand response of the user side, the hierarchical coordinated control of voltage is realized. In the power grid experiment, the regulation method under the constraint of carbon emission can reduce the carbon emission, and the power energy loss during regulation is reduced by about 3/8 on average, and the practical application effect is better.

At present, the communication protocol conversion terminals and conversion methods used by power companies in the networking process of low-voltage power line carriers have some problems, such as packet loss and long delay in uploading and sending messages, which will seriously affect the communication quality of low-voltage power line carriers and the operation safety of the whole distribution network. Based on this, in order to improve the communication quality of low-voltage power line carrier network, a research on communication protocol conversion terminal and conversion method suitable for low-voltage power line carrier network is proposed. First, optimize the communication protocol conversion terminal, and improve its physical interface and software module. Then, the delay of protocol conversion process is controlled, and the flow of wireless communication protocol conversion by carrier protocol is set to realize high-quality communication protocol conversion. Finally, the experimental link is constructed to prove the advanced nature of the research terminal and the proposed method. The experimental results show that this terminal and protocol conversion method can further reduce the packet loss rate, shorten the transmission delay, and improve the communication quality of the low-voltage power line carrier network.

The risk of data leakage and security vulnerabilities exist in digital grid mobile application platforms such as "i State Grid." To address this problem, an application security plug-in for digital grid smart terminals is designed. A lightweight mobile application security protection system architecture is proposed to prevent user data from being damaged and leaked. The system architecture mainly contains three aspects: static environment assessment of mobile applications, dynamic detection of malicious applications, and mobile application risk assessment. That also reduces the security risk from external factors such as the Internet, and effectively ensures the safe and stable operation of the "i State Grid" system.

Chemical products are the cornerstone of agricultural development and provide important raw materials for industrial and agricultural production. Their quality, quantity and price stability are closely watched by upstream and downstream industries. Therefore, it is of great practical and theoretical significance to accurately predict the prices of various chemical products. Due to the influence of various factors on the price of chemical products, they show strong nonlinear, non-stationary and no obvious trend characteristics. It is difficult for the traditional single model to capture the internal hidden laws of their data. Based on this, this paper proposes a combination forecasting model based on data decomposition, which can deeply mine the potential volatility characteristics within the data, so as to better grasp the volatility law and realize its price forecast. Guided by the idea of "decomposing input and combining output," this paper constructs a price forecasting model based on multi factor decomposition and integration, and explains and forecasts it from the factor level.

Personalized recommendation information services in e-commerce platforms can help users get the information they need faster. Still, users also worry about the risk of privacy disclosure and illegal use. Users' willingness to adopt personalized recommendation information on e-commerce platforms and its influence mechanism is unclear. This paper explores how users weigh the conflict between convenience and privacy risks brought by personalized recommendation, builds an influencing factors model of the willingness to adopt personalized recommendation information on ecommerce platforms, and uses structural equation modeling (SEM) for analysis and verification. The results show that the perceived privacy risk negatively influences the adoption willingness, but the impact level is lower than expected. The adoption willingness of personalized recommendation information is more influenced by its usefulness.

As an important part of the power transmission in the power system, the safety and stability of the transmission line directly affect the availability of the power system. With the continuous development of various industries, people's demand for energy is increasing, and the requirements for power supply reliability are also getting higher and higher. Transmission lines are an important part of the power system. Once a major power outage occurs, it will cause huge losses and inconvenience to the national economy. Therefore, the equipment and operation of transmission lines need a practical and effective management system to manage. Clearly, big data analytics has great advantages in this regard.

Aiming at the problem of poor test accuracy of digital integrated circuits at present, this paper puts forward the research on digital integrated circuit test technology under the Internet of things technology, realizes circuit information collection and analysis through analog digital integrated circuit faults, and constructs an abnormal data identification algorithm of digital integrated circuits combined with the Internet of things technology. Finally, it is confirmed by experiments, under the Internet of things technology, digital integrated circuit test technology has high practicability in the process of practical application, which can effectively solve the problem of insufficient test accuracy of digital integrated circuit and fully meet the research requirements.

During the operation of spacecraft control system in orbit, a large amount of telemetry data will be generated. These data can characterize the operation performance of spacecraft control system and have high analytical value, which can identify the health status and operation performance of spacecraft from the telemetry in orbit. This paper presents a cluster storage system and an autonomous health assessment system. Simulation analysis and practical application show that the system supports the efficient storage of more than 800 spacecraft telemetry data, realizes the data elimination of 99.3% accuracy, realizes the fault self-diagnosis of 98.7% accuracy, can complete a comprehensive health assessment of the spacecraft in 3.5 seconds, and automatically generates the evaluation report.

Aiming the potential hazards and risks brought by the analog signal processing module of the vehicle controller, an analog signal processing module based on ISO26262 standard is designed to enhance the safety and reliability of the vehicle controller. Based on the relevant technical design methods in the functional safety standards, this paper performs a hazard and risk assessment on the unexpected acceleration or deceleration of the vehicle caused by the failure of the analog signal processing module, determines that it should achieve the functional safety level ASIL D and functional safety requirements, and then adopted the dual-core principle for redundancy design of the analog signal processing module. The fault injection is carried to simulate the vehicle controller analog signal processing fault. The test and verification results show that the vehicle controller analog signal processing module designed in this paper meets the ASIL D safety level, and the three types of fault injection pass rate are above 99%. It improves the safety and reliability of the vehicle controller.

Aiming at the performance deterioration of traditional OFDM signal demodulation methods under the condition of low signal-to-noise ratio, this paper proposes an OFDM signal enhancement and demodulation method based on intrawell stochastic resonance of tri-stable system. Firstly, the OFDM baseband complex signal model and the mathematical model of the stochastic resonance system in the tri-stable well are given; then, the transient response process and the steady-state response process of the stochastic resonance in the tri-stable well are analyzed, and the system from zero state to the expression of the transient time required for the resonance equilibrium state, and the analytical expression of the steady-state output signal of the stochastic resonance system in the trap is derived. Finally, the OFDM baseband complex signal enhancement and demodulation processing model of the stochastic resonance in the tri-stable trap is given, the received OFDM baseband complex signal is segmented according to the symbol period, the in-phase component and the quadrature component of the two-way signal are processed by in-well stochastic resonance at the same time, and the simulation is carried out. The simulation results show that by choosing appropriate noise intensity and system parameters, the stochastic resonance in the tri-stable well can effectively improve the detection performance of weak OFDM signals.

The effective identification of pedestrian dangerous actions at night was a core task of unmanned driving and intelligent assistant driving system. Limited by the network depth and learning ability of traditional convolutional neural network, the performance of the algorithm and its improvement were still unsatisfactory. Considering the imaging characteristics of the camera at night, this paper proposed an infrared pedestrian dangerous action recognition algorithm based on residual network to recognize pedestrian actions at night. Resnet18 network framework was adopted according to the characteristics of infrared images and the scale of problems. In order to adapt to the network input format, the infrared image in the database were preprocessed. The experimental results in the actual infrared pedestrian dangerous action dataset indicated that the mean precision of the proposed method for six types of dangerous actions was improved to 98.3%, and the average recall rate was improved to 98.1%, which was better than the traditional recognition method.

For green networks, node centrality can be evaluated statically according to certain network topology characteristic. Meanwhile, it can also be evaluated dynamically according to certain network dynamic process. In order to improve the effect of node centrality evaluation, the topology characteristic can be integrated into the dynamic process model of complex networks. Cascading failure model is commonly taken as the dynamic process model of complex networks. To accurately measure the result of cascading failure, a new index is proposed. Based on this index, node centrality can be effectively evaluated. A case demonstrates the superiority and reasonability of this method and this index.

In order to realize the autonomous inspection of Unmanned Aerial Vehicle (UAV) on transmission line and reduce the manual flight control operation, this paper proposes a UAV autonomous inspection system based on accurate positioning of laser point cloud. Through the high-precision and three-dimensional laser point cloud data, we can achieve autonomous route planning, independent generation, so as to finally control the whole process of UAV unmanned patrol flight. The testing results show that the UAV based on accurate positioning of laser point cloud has the ability of space collision detection and automatic wall barrier during autonomous inspection flight, which can effectively ensure the flight safety of UAV, reduce the potential risk of power grid, improve transmission line inspection efficiency and operation reliability, and provide a new development direction for follow-up transmission line inspection exploration.

The distributed big data security risk control model achieves the control of big data security risk by distributed training of data feature vectors. The lack of processing of encrypted data leads to weak generalization ability. In this regard, a big data security risk control model based on federal learning algorithm is proposed. The heterogeneous data is formatted and the original data is preprocessed by data discretization and data scaling. The optimized federation learning algorithm is used to match the encrypted data, and the big data security risk control model is constructed to improve the generalization ability of the model. In the experiments, the proposed model is tested for its generalization ability. The analysis of the experimental results shows that the big data security risk control model constructed by using the proposed method has high data generalization ability.

Timestamps establish a means to dictate the existence of a message at a particular moment in the past. Traditional digital timestamping service utilizes Public Key Infrastructure (PKI) and thus requires the presence of a Time Stamping Authority, whose job is to ensure the message’s validity. However, with the rise of distributed computing and blockchain technology, it has become possible to obtain timestamps in a decentralized manner, eliminating the need for central authorities. Previous research involves sending transactions or utilizing a smart contract mechanism to store hashes on blockchain and validating the outcome using public ledgers. By prototyping a simple digital timestamping protocol on the Solana blockchain, this article explores the natural advantages of the Solana blockchain for timestamping, implements a smart contract of digital timestamping on the Solana blockchain, and measures its latency, costs, and performance. The initial estimation expects the accuracy of the timestamps produced by the prototype to reach the average sub-second slot time. However, due to network propagation delay, it is not always possible to hit this level. The cost incurred during the process is also analyzed and discussed. Finally, this article highlights the potential ability of the blockchain to provide modern decentralized services.

With the rapid development of Internet and the increasing amount of information, it is necessary to use big data technology to solve the bottleneck of processing speed and storage of traditional public opinion monitoring in the era of big data. In this paper, Hadoop open source platform is used to build a big data foundation, realize distributed storage of data, use MapReduce and Spark to realize distributed computing and processing of data, and process the collected data in text. The algorithm model is used to classify and cluster the text information to complete the analysis of text emotional tendency, topic discovery and tracking, and innovatively grasp the public opinion information status of network emergencies. The experimental results from the acquisition rate and average correlation test prove that the algorithm in this paper has higher calculation accuracy. It can provide real-time and effective public opinion analysis service.

Automated driving systems promise low cost and low human consumption. If it is used in mine, canyons and other environments, it will have huge economic benefits. However, in such environments as mines and urban canyons, there is a problem that satellite signals are blocked, leading to the failure of positioning. To solve this problem, we integrate lidar, inertial measurement unit and Real-Time Kinematic Global Position System to achieve high-precision positioning in urban canyon and open environment. Besides, there are many curves on the roads in urban parks, which adds great difficulty to unmanned driving, so we construct a lane-level high-precision environmental map, which realizes path planning based on lane and stable driving of unmanned vehicles. Furthermore, we orderly integrate perceiving, mapping and positioning, path planning and motion control modules to form a lightweight unmanned driving system, which perceive the environment by lidar, inertial measurement unit and Real-Time Kinematic Global Position System, use lightweight SC-LEGO-LOAM to build environment map, use normal distribution transformation to achieve rapid vehicle positioning, and use lane-level high-precision map to achieve global static path planning, use lattice algorithm to realize smooth and stable local path planning, then transmit it to the vehicle site. After real vehicle testing, the vehicle can be driven stably in the complex environment of the park. This automated driving system can be applied in mines and urban parks and can realize unmanned transportation. It has huge economic benefits. The lane-level high-precision map we have built is the development direction of the future driverless electronic map.

The application of Internet of Things technology to agricultural environmental monitoring can not only effectively provide real-time monitoring of the environment, but also provide important data basis for environmental supervision and management through information sharing and auxiliary decision-making. Therefore, in order to improve the reliability of aquaculture environment monitoring, this paper introduces the Internet of Things and develops a new monitoring system for aquaculture environment. The system architecture shall be developed and the monitoring equipment shall be selected according to the actual requirements. With the support of the hardware equipment, the Internet of Things technology shall be introduced to establish the communication link between the sensor, the monitoring equipment and the monitoring environment. The aggregation node of the design information shall be a single node, and all the monitoring information will be forwarded by the Internet of Things node in a multi-hop manner to realize the communication and intelligent transmission of the monitoring environment based on the Internet of Things. By introducing the remote control technology, this paper designs the corresponding monitoring and early warning indicators, starts the sensors and induction devices, carries out the early warning of the aquaculture environment monitoring indicators, and completes the development of the aquaculture environment monitoring system. Design comparison experiments show that the system developed in this paper can not only ensure the continuity of monitoring results, but also reduce the error of monitoring indicators, improve the accuracy of monitoring results, and contribute higher value to the development of aquaculture.

In order to improve the image quality of a specific class of crack images, as well as to solve the problems of insufficient size of the number of crack datasets and small number of complex crack images, a crack image generation model based on DCGAN (Deep Convolutional Generative Adversarial Network, DCGAN) is proposed, which has superior training stability and convergence speed. The experimental results show that DCGAN can generate a large number of real crack images with complex backgrounds more reliably than traditional image augmentation methods, effectively solving the problem of lack of crack images in special cases and greatly reducing the cost of crack image acquisition tasks.

Time-sensitive networking (TSN) meets the needs of industrial internet of things (IIoT). It solves the challenges of deterministic transmission and reliable communication of time sensitive data streams. Traffic scheduling is the core mechanism of time-sensitive networks. Many excellent researches have explored and optimized the method of scheduling time-triggered streams in TSN. However, the existing time-triggered streams scheduling in TSN mostly separates routing and scheduling, which limits the scalability of scheduling. Many researches are based on fixed routing to schedule streams. Due to the interaction between scheduling and routing, the quality of fixed route solution is inferior to that of joint routing and scheduling. In this paper, we propose a meta-heuristic based multipath joint routing and scheduling method for time-triggered traffic in TSN, named MMRS. We set path selection as a variable and consider multipath routing for fault tolerance. At the same time, we establish the integer linear programming formulation and use meta-heuristic to obtain high-quality solutions. The evaluations show that compared with other excellent routing and scheduling methods, the runtime of 60 streams of MMRS method is reduced by 65.6%, and the schedulability is improved by 21% on average. The experiments verify that our proposed scheduling method can obtain high-quality solutions in an acceptable solution time.

A decentralized, unchanging, and verifiable public ledger that can track the exchange of digital assets is provided by the blockchain. After Bitcoin brought out the blockchain, the blockchain is used in new fields such as smart cities and e-health, and plays a very important role. Although the decentralization of the blockchain makes the construction of digital currency possible, its open and transparent design also risks leaking user privacy. In order to fulfill the security needs of blockchain applications for privacy related information, the relevant researchers have recently done research on blockchain privacy protection challenges and posted corresponding protection technologies, a few of which have been also successfully implemented in several prevailing blockchain applications. In view of the blockchain privacy protection technology issues, this paper investigates the latest progress of blockchain privacy protection technology, conducts a comparative analysis, and summarizes at the end, so as to make an outlook on future trends.

In order to apply IIoT in tobacco manufacturing industry, the concept and development history of IIoT were analyzed, and the main contents of IIoT technology in device management was pointed out. This paper constructs the application of IIoT-based device management in tobacco industry, including resource integration, strategic planning, model building, platform integration, data collection improvement, cloud big data analysis, etc., and puts forward the future planning of IIoT in device management application. Through the scheme design and future planning, it is shown that IIoT has innovative ideas for equipment management in tobacco manufacturing industry.

It takes the perspective of big data information sharing to analyze the factors influencing the phenomenon of emotional and behavioural solidification caused by online media information dissemination, taking TikTok App as an example. Based on the SEIR contagion model, the emotional resonance index is introduced and Matlab is used for simulation analysis to verify the key influencing factors of platform users’ emotional and behavioural solidification phenomenon through the establishment of online media information dissemination model. The analysis results show that the phenomenon of emotional and behavioural solidification caused by online media information dissemination is not only related to the topic and media information, but also closely related to the emotional resonance index and behavioural intention execution ability of platform users.

Traditional information technology cannot solve the problems of cheating and false data in the management of safety production in power plant enterprises. Blockchain technology is widely used in the fields of distributed, Internet of Things and trusted supervision because of its characteristics of tamper-proof, traceability and openness and transparency. Taking electric power enterprises as an example, this paper discusses the use of blockchain to supervise the safety production of power plant enterprises, so as to ensure the integrity, authenticity and timeliness of supervision and prevent the occurrence of safety accidents in the electric power industry.

Due to the problems caused by the development of the Internet, such as information redundancy and junk information overflow, it is crucial that e-commercial companies utilize recommendation algorithms to personalize their online shopping system for every user in order to promote sales. After discussing the pros and cons of demographic filtering, content-based filtering and collaborative filtering, the authors mainly focused on collaborative filtering. This article elaborated on how to design the collaborative filtering algorithm and improve its efficiency. Compared to other recommendation algorithms, collaborative filtering can help customers discover potential interests. Moreover, the system only needs feedback matrixes to train the matrix decomposition model and requires no additional relevant features. One major defect of collaborative filtering is called cold start, which means if a new item is added during training, the system cannot create embedding and make a prediction for it. The technology called WALS projection can solve this problem to some degree.

The homogeneity of goods and services currently makes it challenging for the majority of businesses to stand apart in the increasingly tough market rivalry. Enterprises must rely on business model innovation to win the competition. Can businesses leverage big data capabilities to update and combine information and foster business model innovation in the period of the digital economy? In this paper, we introduce knowledge management as the intermediary variable. Using causal models and SPSS26.0, we empirically examine the theoretical model and research hypotheses in this work to determine the relationship between big data capacity and business model innovation. It gives businesses the experience they need to establish big data capabilities, alter their business strategy, and support high-quality development.

With the constant acceleration of urbanization development, the protection and renewal of historical and cultural blocks have become the core elements to improve the quality of urban development and promote the new-type urbanization development. This paper makes scientific analysis of eight historical and cultural blocks in Hankou area of Wuhan based on Analytic Hierarchy Process, combined with conventional data and big data. Explore the construction of the renewal and evaluation system of historical and cultural blocks from the five perspectives of historical and cultural value, economic vitality, urban functions, cultural and nature, and public participation. According to the concrete block to assess the situation put forward the corresponding countermeasure and the suggestion, the historical and cultural blocks management work provide certain reference basis can be better promoted.

The prediction of the volatility of stock has been a topic of great interest in the financial realm. In this paper, we aim to create new trading strategies based on the volatility within the financial market and choose GARCH and LSTM models to forecast the volatility separately. We then discuss and set two methods in the research: one is the strategy of buying at high volatility and selling at low volatility, and the other is a comparison strategy on volatility and implied volatility as our two trading strategies, then both of which are compared with a buy-and-hold strategy. Our two volatility strategies are backtested to determine the relative threshold parameters. The findings of the back-test show that both have satisfactory performance in buying and holding, with the comparison strategy on volatility and implied volatility performing extraordinarily.

Ensuring the distribution of agricultural products is the most central link in the agricultural industry chain, and accelerating the transformation and upgrading of the agricultural products supply chain is the most critical path to enhance the efficiency of agricultural production, processing and distribution. Integrating the agricultural products supply chain is a new challenge in this field. However, the ability of China's agricultural products supply chain system to cope with risks still needs to be improved. Based on the ISM structural model, combined with Matlab software programming calculations and literature and consulting experts, this paper analyses ten major factors, such as consumer preferences and market demand, from the supply risk layer, undertaking risk layer and demand risk layer. Based on the clarification that consumer preferences have a fundamental and deepest influence on the level of agricultural products quality risk supply chain, the logical structure of the other influencing factors and the degree of influence on the agricultural products supply chain quality risk are clarified so as to provide a scientific basis for the transformation and upgrading of agricultural products supply chain and sustainable development for the better.

In recent years, load identification technology has received great attention as the value of real-time load-side electricity information has gradually emerged. There are several ways to precisely identify the different types of loads. However, practical situations with novel load types and little labeled data are seldom considered. For this reason, this paper proposes a few-shot identification method for novel loads based on the Model-Agnostic Meta-Learning (MAML). It uses the Adaptive Weighted Recurrence Graphs (AWRG) model as the base learner, which has the best performance in load identification, and pre-trains the model with existing data. The proposed method uses meta-training to get initial parameters that are generalized across multiple load types to improve the learning ability of the model on few-shot tasks with novel loads. Compared with transfer learning methods commonly used for generalized load identification, the results on the WHITED dataset show that the proposed method can improve the scalability of the load identification for practical applications.

In recent years, with the rapid development of the digital economy and the rise of the rural e-commerce economy, the rural sinking logistics market has gradually demonstrated its potential. However, most of the current rural logistics problems are the low penetration rate of new energy technologies, the widespread idling of vehicles and so on, leading to the high carbon emissions of rural logistics. In this context, based on the consideration of the application of new energy, delivery strategy, vehicle carbon emission calculation and other low carbon optimization methods, this paper establishes a location-routing optimization model with the lowest comprehensive cost of node construction cost, operation cost, transportation cost, carbon emission cost and other optimization goals. Taking Jiangcheng County, Pu'er City as the background, this paper analyzes a calculation example, and compares the carbon emission constraints in the model. The reliability and effectiveness of the model and algorithm are verified.

To reduce the overall application time of the enterprise asset management system, improve the actual application effect, and build a more stable and flexible management and control program, the multi-dimensional asset intelligent management system is designed and researched in combination with RFID technology. First, it is necessary to analyze the system's requirements and carry out the overall frame design. The RFID radio frequency reader is connected to the system circuit, and the storage device is installed to complete the system hardware design; by establishing an interactive asset management and control function module, and connecting to the database, the system software design is completed. The final system test results show that the one-way task management time of the proposed system is controlled below 1.5s, the execution efficiency is relatively high, the control error is small, and it has certain application value.

With the development of space-based Internet of Things (IoT), random multiple-access technology has become a hot topic. This paper examines the impact of the number of time slots per frame and the number of iterations on the performance of Irregular Repetition Slotted ALOHA (IRSA) access technology from the perspective of system throughput rates. Then the concept of packet generation probability is proposed and its relationship with the number of users is investigated. It provides a reference for the communication system of space-based IoT.

In view of the problems of ambiguous symptoms of faults and data and overlapping fault features in the process of mechanical equipment fault diagnosis, this paper will take the deep learning model as the core, adopt the method of BP neural network and information fusion, and complete the construction and training of mechanical equipment fault diagnosis model with the help of class libraries such as Numpy and Matplotlib in Python environment, so as to form an intelligent module that can support the call of Web server. At the same time, this paper will also combine Django framework, use Pycharm tool to complete the development of Web server, improve the definition and deployment of functions and data interfaces, and generate a Web-based online monitoring and fault diagnosis system for mechanical equipment. The overall design of the system chooses B/S architecture, which supports users to remotely operate and visit the Web server to monitor the operation of mechanical equipment, and can classify the historical data of mechanical equipment with the characteristic values of fault frequency domain, and make corresponding predictions to realize the diagnosis of mechanical equipment faults. The construction of the system not only effectively improves the accuracy of mechanical equipment fault diagnosis, but also makes a beneficial attempt for the intelligent reform of the overall operation mode.

Volatility is a measure of the asset return rate's estimated level of uncertainty and may be used to assess the riskiness of financial assets. We use the market capitalization of 200 stocks representing Korea as the primary analytical aim and evaluate the accuracy between them by analyzing the impacts of different hybrid models' hybrid neural networks, which are based on the returns of the KOSPI 200 stock index. By measuring the effectiveness of these models using four dissimilarity measures, we contrasted the performance of hybrid models that combine a single neural network and a single GARCH type model with that of hybrid neural networks that combine multiple GARCH models (MAE, MSE, HMAE, and HMSE). They are applied to anticipate the KOSPI 200 index data's actual volatility. Among these, hybrid neural networks that integrate more than one GARCH-type model have much better forecasting performance than neural network models that mix two or more or more GARCH-type models. GW-LSTM makes the least accurate forecast. We note that the hybrid model combining the three GARCH models shows a minor increase in predicting ability based on merging two and three models.