Industrial control system micro-service crowdsourcing cloud test platform

Abstract

According to the method, a micro-service architecture concept and a crowdsourcing test concept are combined, and the purpose is to solve safety problems such as unauthorized access and logic vulnerabilities in the field of industrial control systems through crowdsourcing tasks. The system is composed of five core parts: an industrial control equipment layer, an industrial control data cloud storage layer, an industrial control data transmission layer, an industrial control application service layer and a public test user layer. Firstly, an industrial control equipment layer realizes connection between equipment and a cloud platform through a cloud connection gateway; then, the industrial control data cloud storage layer encrypts the industrial control equipment data and stores the industrial control equipment data in a database; then, the industrial control data transmission layer sends the data to an industrial control application service layer through message middleware; and the industrial control application service layer provides various modules according to the data of the transmission layer: an industrial control module, an identity and authority management module and a crowdsourcing test module. And finally, the public test user layer enables the user to conveniently perform public test tasks and real-time personnel management through a user-friendly interface.

Description

Technical Field

[0001] The present invention relates to the concept of microservices and the field of industrial control crowd testing, and specifically to a system that utilizes microservice architecture and crowdsourcing testing to ensure stable operation and safety of industrial control systems. Background Art

[0002] Microservice architecture, as an innovative architectural pattern in the field of contemporary software development, provides a flexible and efficient development and deployment strategy[1]. This architecture deconstructs complex applications into a series of small, independent service units, each of which executes in its own independent operating environment and is usually built around specific business functions. Interaction between services is achieved through lightweight communication mechanisms[2], such as HTTP RESTful API, to achieve the integrity of application functions. Each microservice instance is essentially self-contained and can be independently developed and deployed using different programming languages ​​and data storage technologies. The decoupled nature of this architecture greatly improves the flexibility of the development process and accelerates the development cycle by allowing different teams to independently update and expand their services at different times. In addition, the microservice architecture supports refined expansion strategies, allowing services to be independently expanded according to demand[3], without the need to expand the entire application like the traditional monolithic application architecture. This not only optimizes resource allocation, but also improves the response speed and operating efficiency of the application[4]. In terms of system stability, the microservice architecture effectively implements fault isolation through isolation between services[5], thereby enhancing the overall availability of the system. The failure of one service will not directly affect other services, which is extremely critical for building a highly available system. With the help of containerization and orchestration tools such as Docker and Kubernetes, microservices have achieved a high degree of automation, thereby further improving the reliability and management efficiency of the system [6]. In recent years, microservice architecture has been widely studied. Regarding the management method of IoT systems, Son et al. [7] proposed the Scalable IoT Microservice Architecture (SIMA), which provides a flexible way to build and manage IoT systems. SIMA achieves system scalability and flexibility by splitting the IoT system into multiple microservices and using containerization technology for deployment and management. Regarding the performance of microservices, Sheikh et al. [8] studied the performance of applications running on the containerized architecture and compared it with the traditional monolithic architecture. The Apache JMeter application was used to measure throughput, latency, packet loss and delay to measure performance. In the present invention, the microservice architecture is applied to the operation scenario of the industrial control system, which not only ensures the system's rapid response capability in various complex environments, but also ensures the stable operation of the system. This architecture enables the industrial control system to effectively cope with the changing and complex industrial environment. Through the design of distributed services, each service module can independently respond to specific operation requirements, thereby improving the efficiency and reliability of the entire system. However, relying solely on microservice architecture cannot fully ensure the security of industrial control systems. Therefore, while using microservice architecture to maintain the stable operation of the system, corresponding security mechanisms should also be established to provide the protection required by the real industrial system environment.

[0003] Therefore, the present invention adopts the concept of crowdsourcing testing to deal with various security risks, aiming to provide an innovative and effective solution to the security challenges faced by industrial control systems [9]. Crowdsourcing testing is a mobilization mechanism involving global information security experts and white hat hackers who participate in the process of software testing and vulnerability discovery in order to identify and fix potential security vulnerabilities. The core of this method is to bring together talents from different cultural backgrounds and professional skills to achieve a comprehensive review and enhancement of the security defense of industrial control systems. Through this global collaboration and intellectual gathering, crowdsourcing testing can reveal security defects that may be overlooked by traditional security review methods, thereby improving the overall security performance of the system. This not only strengthens the response to existing security threats, but also provides a diversified perspective for discovering new vulnerabilities and threats, ensuring that industrial control systems can withstand various complex and evolving security challenges. During the operation of industrial control systems, considering the real-time requirements of data, the stability requirements of the system, and the security requirements of operations, the present invention proposes an industrial control system microservice crowdsourcing cloud testing platform that can cope with a variety of complex industrial control environments while ensuring system stability and real-time performance. [1] Blinowski G, Ojdowska A, A.Monolithic vs.microservicearchitecture:A performance and scalabilityevaluation[J].IEEE Access,2022,10:20357-20374.. [2]Ferreira LCBC, Borchardt ADR, Cardoso GDS,-et al. Edgecomputing and microservices middleware for home energy management systems [J]. IEEE Access, 2022,10:109663-109676. [3] Razzaq A, Ghayyur SA KA systematic mapping stu-dy: The new age of software architecture from monolithic to microservice architecture—awareness and challenges [J]. Computer Applications in Engineering Education, 2023, 31(2): 421-451. [4]Suthendra J A,Pakereng M A I.Implementation of M-icroservicesArchitecture on E-Commerce Web Service[J].ComTech:Computer,MathematicsandEngineeringApplications,2020,11(2):89-95. [5]Gos K,Zabierowski W.The comparison ofmicroservic-e and monolithicarchitecture[C] / / 2020IEEE XVIth International Conference on the PerspectiveTechnologies and Methods in MEMSDesign(MEMSTECH).IEEE,2020:150-153. [6]Lyu Z,Wei H,Bai X,et al.Microservice-based archit-ecture for anenergy management system[J].IEEE Systems Journal,2020,14(4):5061-5072. [7]Son S C,Ko S,Lee H,et al.SIMA:Scalable IoT Mi-croserviceArchitecture With Users and Things Joint L-oadBalancing[J].IEEE InternetofThings Journal,2024,11(5):8170-8184. [8]Shabani I, E,Berisha B,et al.Design of mode-rn distributedsystems based on microservices architect-ure[J].International JournalofAdvanced Computer Sci-ence andApplications,2021,12(2). [9]Vianna FRPM,Graeml AR,Peinado J.The role of crowdsourcing inindustry 4.0:A systematic literature r-eview[J].International Journal of Computer IntegratedManufacturing,2020,33(4):411-427. Summary of the invention

[0004] The present invention aims to provide an industrial control system microservice crowdsourcing cloud testing platform, which can not only effectively coordinate and manage distributed resources to optimize system performance and response speed, but also ensure the stable operation of crowd testing tasks and the security of crowd testing process data. Specifically, in the initial stage, industrial control equipment data is collected through the Internet of Things (IoT) gateway and transmitted to the cloud server after TLS encryption. The uploaded industrial control data is further encrypted and stored in the MySQL cloud database. Through middleware technology, a stable and efficient data transmission channel is established to pass industrial data to the application service layer. The service layer provides industrial control, identity authority management and crowdsourcing testing functions based on the received data. Finally, through an intuitive user interface, the convenience and real-time performance of users in executing crowd testing tasks and personnel management are improved.

[0005] The objective of the present invention is achieved through the following technical solutions: An industrial control system microservice crowdsourcing cloud testing platform, the technology comprising: Step 1: In the initial stage, the industrial control equipment data is collected and transmitted to the cloud server through TLS encryption via the IoT gateway; Step 2: In the industrial control data cloud storage stage, the industrial control data uploaded to the server is encrypted and saved in the MySQL cloud database; Step 3: In the industrial control data transmission stage, the industrial control data transmission layer uses middleware technology to implement the industrial control data transmission channel to ensure stable and efficient transmission of industrial data to the application service layer; Step 4: In the industrial control application service stage, the industrial control application service layer provides multiple modules based on the data of the transmission layer: industrial control module, identity and authority management module, and crowdsourcing test module; Step 5: At the crowd testing user level stage, a user-friendly interface is designed to enable users to easily perform real-time management of crowd testing tasks and personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0007] Figure 1 This is a functional flow chart of the industrial control system microservice crowdsourcing cloud testing platform according to an embodiment of the present invention;

[0008] Figure 2 A schematic diagram of the basic architecture of the industrial control system microservice crowdsourcing cloud testing platform according to an embodiment of the present invention; DETAILED DESCRIPTION

[0009] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments, which does not constitute a limitation of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the protection scope of the present invention.

[0010] Figure 1 This is a functional flow chart of the industrial control system microservice crowdsourcing cloud testing platform according to an embodiment of the present invention, and the steps include: Step 1: In the initial stage, the industrial control equipment data is collected and transmitted to the cloud server through TLS encryption via the IoT gateway; Step 2: In the industrial control data cloud storage stage, the industrial control data uploaded to the server is encrypted and saved in the MySQL cloud database; Step 3: In the industrial control data transmission stage, the industrial control data transmission layer implements the industrial control data transmission channel through middleware technology to ensure stable and efficient transmission of industrial data to the application service layer; Step 4: In the industrial control application service stage, the industrial control application service layer provides multiple modules based on the data of the transmission layer: industrial control module, identity and authority management module, and crowdsourcing test module; Step 5: At the crowd testing user level stage, a user-friendly interface is designed to enable users to easily perform real-time management of crowd testing tasks and personnel.

[0011] Figure 2 A schematic diagram of the basic architecture of the industrial control system microservice crowdsourcing cloud testing platform provided by an embodiment of the present invention, wherein the steps include: System entity: n industrial control devices {DR1, DR2, ...., DR n}; Step 1: During system initialization, the industrial control equipment {DR1, DR2, ...., DR n}Communicate with the IoT gateway through industrial protocols such as Modbus TCP, PROFINET, and EtherCAT to transmit key information such as device status and process parameters. After the IoT gateway receives the data, it first performs protocol conversion to convert the data into the MQTT protocol format that is more suitable for cloud processing. Subsequently, the gateway preprocesses the data, including removing noise, adjusting timestamps, and removing outliers to ensure the accuracy and validity of the data. Before data transmission, the IoT gateway establishes a secure connection with the remote server through the TLS protocol. During the connection establishment process, the gateway and the server will exchange digital certificates and use the public key infrastructure (PKI) for identity authentication to ensure the legitimacy of both parties in communication. Then, RSA asymmetric encryption technology is used to protect the session key exchange during the TLS connection establishment process to prevent potential man-in-the-middle attacks during the negotiation process. After the connection is established, the gateway and the server jointly select the AES algorithm and message authentication code (MAC) algorithm for subsequent data transmission encryption. Through this complete encryption process, industrial control data can be safely and reliably transmitted to the cloud, ensuring a high degree of confidentiality and integrity throughout the process.

[0012] Step 2: During the storage of industrial control data, after receiving the data, the cloud server first uses the AES key to decrypt the data, and then performs a structured conversion on the data of the industrial control equipment and organizes it into JSON format. Next, the system decides whether to perform the addition operation based on whether the corresponding device information already exists in the database. The database mainly records the following types of information: (a) Device category: Industrial control equipment is carefully classified according to its function, purpose and working principle, such as sensor, actuator, controller, etc. This classification helps the microservice layer quickly identify device characteristics and facilitates data processing. (b) Device ID: Each industrial control device has a unique identifier (UniqueIdentifier). (c) Applicability: The applicability of each device is to describe its usage scenarios in certain specific industrial environments. For example, for a voltage and current meter, its applicability is: measuring voltage scenario and measuring current scenario. (d) Constraints: Contains the limiting factors and performance parameters of device operation, such as power requirements, environmental tolerance and maximum operating range. (e) Network interface and protocol support: A detailed description of the network connection capabilities of each device and the supported communication protocols, such as Modbus, PROFINET, EtherNet / IP, etc. The choice of protocol directly affects the mutual communication and data transmission between devices. (f) Data format: Describes the data specifications that various types of industrial control equipment must follow during data transmission. Finally, before writing the sorted data into the database, all data will be encrypted using the SM4 national encryption algorithm. The algorithm first groups the plaintext data into 128 bits, and processes each group of data through multiple encryption rounds to form ciphertext. Each round of encryption operation includes S-box substitution, linear transformation, and XOR operation of the round key. These steps greatly increase the strength of encryption through nonlinear substitution and complex key expansion processes, effectively preventing brute force cracking and differential attacks, and ensuring the security of data storage.

[0013] Step 3: In the industrial control data transmission stage, the industrial control data in the database is transmitted to different topics through Kafka, an efficient message middleware. As a distributed stream processing platform, Kafka has the advantages of high throughput and low latency, and is very suitable for processing large amounts of real-time data streams in industrial control systems. When the data enters the Kafka system, the system will assign it to pre-set topics based on the characteristics of the data. Kafka's topic division mechanism is highly flexible and can accurately classify and organize data according to different equipment categories. After classification and organization, the data is transmitted to the industrial control application service layer to ensure that subsequent application processing and response are more efficient.

[0014] Step 4: In the industrial control application service layer, this layer is mainly responsible for the business logic processing of the system. This layer is divided into three main modules: industrial control module, identity and permission management module, and crowdsourcing test module. The industrial control module provides virtualized industrial control equipment and high-precision simulation environment to ensure that users can design, verify and perform data acquisition tests on control logic in isolation from the real production environment. This module mainly includes the following functions: The virtual device and process simulation function uses Docker containerization technology to build a virtual device environment. By independently encapsulating each virtual industrial control device in a Docker container, the system can effectively isolate the operating environment of different devices. This containerization method ensures the independence of each virtual device, avoids resource competition or environmental conflicts during the simulation process, and thus realizes a highly modular simulation architecture. The automated testing and verification function integrates Robot Framework, a widely used open source automated testing framework, to achieve fast and flexible test case generation and execution. Robot Framework can automatically generate scripts based on user-defined test scenarios, and through integration with virtual device APIs, it can achieve comprehensive verification of device control logic and operating data. The identity and permission management module supports user management and fine-grained permission control, and performs comprehensive identity authentication and permission control by adopting the authentication filter chain of the SpringSecurity framework. The crowdsourcing test module fully supports the release and management of crowdsourcing test tasks, as well as the subsequent task claiming, allocation and acceptance evaluation by adopting the Spring Boot framework. Spring Boot is used to build core operations such as task release, query, modification and deletion, providing a stable and flexible API interface to ensure high performance and scalability of the system. In addition, the module combines MyBatis Plus for data layer operations to achieve efficient query, insertion, update and deletion of task data.

[0015] Step 5: At the crowd-testing user level, the system uses the Ant Design Vue framework to build a Web user interface, which is developed based on the Vue language and provides an intuitive and easy-to-use operating environment, optimizing the user experience. This enables crowd-testing managers to publish, assign, and manage test tasks more efficiently, greatly simplifying the task delivery process. At the same time, testers can easily claim tasks, submit test results, and view task progress in real time through this interface, which significantly improves the overall efficiency and collaborative experience of the crowd-testing process.

[0016] As described above, the industrial control system microservice crowdsourcing cloud testing platform proposed in the present invention has successfully achieved rapid iteration and continuous optimization of industrial control system functions with its highly flexible microservice architecture. Through the crowdsourcing model, the platform fully integrates the innovative resources and technical solutions of developers, significantly improving the testing efficiency of industrial control systems. At the same time, the platform effectively promotes the diversification and richness of system functions, ensuring that while meeting the needs of complex industrial applications, it maintains efficient and secure system operation and maintenance capabilities.

Claims

1. Industrial control system microservice crowdsourcing cloud testing platform, characterized by: The techniques include: Step 1: In the initial stage, the industrial control equipment data is collected and transmitted to the cloud server through TLS encryption via the IoT gateway; Step 2: In the industrial control data cloud storage stage, the industrial control data uploaded to the server is encrypted and saved in the Mysql cloud database; Step 3: In the industrial control data transmission stage, the industrial control data transmission layer implements the industrial control data transmission channel through middleware technology to ensure stable and efficient transmission of industrial data to the application service layer; Step 4: In the industrial control application service stage, the industrial control application service layer provides multiple modules based on the data of the transmission layer: industrial control module, identity and authority management module, and crowdsourcing test module; Step 5: At the crowd testing user level stage, a user-friendly interface is designed to enable users to easily perform real-time management of crowd testing tasks and personnel.

2. According to claim 1, the industrial control system microservice crowdsourcing cloud testing platform is characterized in that: The process of step 1 is specifically as follows: Industrial control equipment transmits data to the IoT gateway through industrial protocols. The gateway is responsible for protocol conversion and data preprocessing, standardizing the data and preparing it for transmission. Before transmission, the gateway and the remote server shake hands through the TLS protocol to verify the identities of both parties and negotiate an encrypted session key. Subsequently, the gateway encrypts the data using the symmetric encryption algorithm AES to ensure the confidentiality and integrity of the data during transmission. The encrypted data is transmitted to the server through the network to achieve secure data collection and transmission.

3. The industrial control system microservice crowdsourcing cloud testing platform according to claim 1, characterized in that: The process of step 2 is specifically as follows: After receiving the industrial control data collected from the IoT gateway, the server encrypts the data using the SM4 national encryption algorithm to ensure the confidentiality of the data during storage. The encrypted data will be directly stored in the MySQL database. During the storage process, the MySQL database further protects data security through user identity authentication and access control mechanisms. The encrypted industrial control data is stored in the corresponding table in ciphertext to ensure that even if the database is attacked, unauthorized personnel cannot read or decrypt the data content.

4. The industrial control system microservice crowdsourcing cloud testing platform according to claim 1, characterized in that: The process of step 3 is specifically as follows: The industrial control data transmission channel uses the message middleware to distribute the data stored in the database to various topic queues, which is extremely critical for real-time data monitoring and analysis of industrial control equipment. In the process of industrial control equipment data transmission, the latest data from the database is first obtained, and then the industrial control data is safely and reliably transmitted to the topic specified in the message middleware through the message middleware. Finally, the industrial control data received by the message middleware is effectively used in the industrial control application service layer, laying a solid foundation for subsequent analysis and processing.

5. The industrial control system microservice crowdsourcing cloud testing platform according to claim 1, characterized in that: The process of step 4 is specifically as follows: The industrial control application service layer can be divided into three main modules: industrial control module, identity and permission management module, and crowdsourcing test module. The industrial control module provides virtualized industrial control equipment and simulation environment to help users perform control logic and data acquisition tests without affecting the real production environment. The main functions are as follows: virtual equipment and process simulation, automated testing and verification. The identity and permission management module supports user management and fine-grained permission control to ensure that the operating permissions of different users on the platform are safe and reasonable. The crowdsourcing test module supports the release and management of crowdsourcing test tasks, the claiming and allocation of tester tasks, and the acceptance and evaluation of test tasks to ensure that the crowdsourcing test module can effectively connect the core functions of the industrial control application service layer with crowdsourcing users, and ensure that crowdsourcing users can efficiently participate in test tasks.

6. The industrial control system microservice crowdsourcing cloud testing platform according to claim 1, characterized in that: The process of step 5 is specifically as follows: Users initiate query requests through these intuitive interactive interfaces, which are then efficiently transmitted to the industrial control application service layer through a carefully designed interface communication mechanism. The Web user interface of the crowd testing user layer not only provides a user-friendly operating environment, but also greatly simplifies the task issuance process for crowd testing managers, allowing them to easily monitor task progress and track crowd testing results in real time.

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