Cross-platform safety production data integration sharing system

Abstract

The invention relates to the technical field of industrial safety management, in particular to a cross-platform safety production data integration and sharing system which integrates an Internet of Things sensor, a high-definition monitoring camera, a data flow platform, a real-time data processing module, a rule engine, a data storage module, an API gateway, a data sharing platform, a user interface and a notification system. And a comprehensive, efficient and safe safety production management platform is constructed. The system comprises a plurality of Internet of Things sensors which are used for monitoring temperature, humidity and harmful gas in real time and transmitting data to an Apache Kafka cluster through an MQTT protocol. And the monitoring camera transmits a real-time video stream through an RTSP protocol. And performing data stream processing by using Apache Flink to realize window operation and aggregation analysis. And the rule engine Drools can dynamically adjust the alarm rule to ensure the alarm accuracy. And a unified API interface is provided through the KongAPI Gateway, so that the data access security is ensured. According to the invention, the efficiency and effect of safety production management are obviously improved, and the accident risk is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial safety management technology, and in particular to a cross-platform safety production data integration and sharing system. Background Art

[0002] In industrial production, safety production management is of vital importance. Traditional safety production management systems mainly rely on manual monitoring and simple automation equipment. These systems have the following problems:

[0003] Lack of real-time performance: Traditional systems are often unable to monitor data at the production site in real time, and there is information lag, which leads to the failure to promptly discover and deal with safety hazards when they occur, increasing the risk of accidents.

[0004] Data silos: Data from different sources are scattered in various isolated systems, and it is impossible to form a unified data view, making it difficult for managers to comprehensively monitor and analyze the overall production situation.

[0005] Poor cross-platform compatibility: Many safety production management systems can only run on specific platforms, which limits the choice of usage scenarios and equipment and reduces the flexibility and adaptability of the system.

[0006] Data security issues: In traditional systems, data is at risk of being stolen and tampered with during transmission and access, and there is a lack of effective security protection mechanisms.

[0007] The alarm mechanism is not intelligent: The alarm mechanism of traditional systems is usually based on fixed thresholds and cannot be flexibly adjusted according to specific circumstances, resulting in insufficient accuracy and timeliness of alarms, and prone to false alarms or missed alarms.

[0008] In order to address the above problems, the present invention proposes a cross-platform safety production data integration and sharing system, which constructs a comprehensive, efficient and secure safety production management platform by integrating Internet of Things sensors, high-definition surveillance cameras, data stream platforms, real-time data processing modules, rule engines, data storage modules, API gateways, data sharing platforms, user interfaces and notification systems. Summary of the invention

[0009] In view of this, the purpose of the present invention is to propose a cross-platform safety production data integration and sharing system to solve the above-mentioned problems.

[0010] Based on the above objectives, the present invention provides a cross-platform safety production data integration and sharing system.

[0011] Cross-platform safety production data integration and sharing system, including:

[0012] Multiple IoT sensors installed at production sites to monitor temperature, humidity and harmful gases in real time and transmit data via wireless networks;

[0013] High-definition surveillance cameras installed at the production site are used to monitor the production environment in real time and transmit video streams via the RTSP protocol;

[0014] Data streaming platform, including Apache Kafka cluster, for receiving and transmitting real-time data streams;

[0015] Real-time data processing modules, including Apache Flink, for real-time processing and analysis of data streams;

[0016] The rule engine module includes the Drools rule engine, which is used to detect data anomalies and trigger alarm events based on preset rules;

[0017] Data storage modules, including MongoDB and PostgreSQL, for storing unstructured data and structured data respectively;

[0018] API gateways, including KongAPI Gateway, are used to provide a unified data access interface and perform authentication;

[0019] Data sharing platform, including RESTful API and GraphQL interface, for providing flexible data query services;

[0020] User interfaces, including responsive web interfaces and mobile apps developed using React or React Native, for displaying and manipulating data;

[0021] Notification systems, including Twilio or AWS SNS services, are used to send alarm notifications.

[0022] Furthermore, the IoT sensor sends data to the data stream platform via the MQTT protocol, which has the characteristics of low bandwidth and low latency and is suitable for data transmission of IoT devices.

[0023] Furthermore, the high-definition surveillance camera transmits the video stream to the central server via the RTSP protocol, which supports real-time streaming media transmission and ensures low latency and high reliability of video data.

[0024] Furthermore, the data stream platform is configured with an Apache Kafka cluster for efficiently processing and transmitting real-time data streams from different sources. The Kafka cluster has the characteristics of high throughput and scalability and is suitable for large-scale data processing.

[0025] Furthermore, the real-time data processing module consumes data streams from the Kafka cluster through Apache Flink and performs window operations and aggregation analysis. The window operations include processing methods based on time windows and count windows, and the aggregation analysis includes average value calculation, maximum value calculation, and minimum value calculation.

[0026] Furthermore, the rule engine module configures alarm rules based on the Drools rule engine, and triggers an alarm event when the data exceeds a preset threshold. The alarm rules can be dynamically adjusted according to user-defined conditions.

[0027] Furthermore, the data storage module uses MongoDB to store unstructured data, including sensor data and log data, and uses PostgreSQL to store structured data, including user information and configuration data. Both MongoDB and PostgreSQL databases support high-concurrency data access and query.

[0028] Furthermore, the API gateway uses KongAPI Gateway to provide a unified API interface and authenticates through OAuth2.0 or JWT. The API gateway can route, limit, log and monitor API requests.

[0029] Furthermore, the data sharing platform uses Express.js or FastAPI framework to develop RESTful APIs, and uses Apollo Server to provide GraphQL interfaces. The RESTful APIs and GraphQL interfaces allow users to query and access data on demand, and provide data filtering, sorting, and paging functions.

[0030] Furthermore, the user interface is developed using React or React Native to provide a responsive web interface and mobile application for data display and operation. The user interface supports multiple languages, multiple themes and real-time data update functions. The notification system sends SMS, email and mobile push notifications through Twilio API or AWS SNS to achieve timely notification of alarm events. The notification system supports the configuration of multiple notification channels and notification strategies.

[0031] Beneficial effects of the present invention:

[0032] 1. The present invention uses Apache Kafka and Apache Flink to realize real-time stream processing of data, and can timely discover and issue alarms when safety hazards occur at the production site, thereby reducing the probability of accidents. The system integrates the data of IoT sensors and surveillance cameras, eliminates the data island phenomenon, forms a unified data view, and facilitates managers to comprehensively monitor and analyze the production site. The system adopts a cross-platform design to ensure that it can run on different operating systems and devices, thereby improving the adaptability and flexibility of the system. The KongAPI Gateway and OAuth2.0 or JWT authentication mechanism ensure the security of data access and prevent unauthorized access. The system is deployed in the cloud (such as AWS EC2 instance) and uses Kubernetes for containerized management to ensure that the system has high availability and scalability and can be dynamically expanded according to demand. Intelligent alarms are realized based on the Drools rule engine, and alarm conditions are dynamically adjusted through user-defined rules to ensure the accuracy and timeliness of alarms. RESTful API and GraphQL interface are provided, and users can query and access data on demand, which is flexible and convenient. React and React Native are used to develop responsive Web interfaces and mobile applications to provide users with good visualization effects and operation experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0034] Figure 1 A schematic diagram of the system operation flow structure of an embodiment of the present invention;

[0035] Figure 2 Schematic diagram of the system model architecture of an embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0037] Cross-platform safety production data integration and sharing system, including.

[0038] The specific implementation method is as follows:

[0039] 1. Data Collection and Transmission

[0040] IoT Sensors

[0041] 1.1 Hardware Installation

[0042] First, temperature and humidity sensors (such as DHT11 or DHT22) are installed at key locations on the production site. These sensors are used to detect the temperature and humidity in the environment.

[0043] In areas where harmful gases may exist, install gas sensors (such as MQ series sensors) to monitor the concentration of harmful gases in the air.

[0044] 1.2 Transmission module configuration

[0045] The sensor is connected to a microcontroller (such as ESP8266), which can connect to a Wi-Fi network to transmit data to a central server.

[0046] Use the Arduino IDE programming tool to configure the ESP8266 to ensure it can connect to Wi-Fi and send sensor data to the specified server address.

[0047] 1.3 Data Transmission

[0048] Configure the MQTT protocol to transmit sensor data to the MQTT server through ESP8266. MQTT is a lightweight network protocol that is very suitable for data transmission of IoT devices.

[0049] Surveillance Camera 1.4 Camera Installation

[0050] Install high-definition cameras (such as Hikvision or Dahua cameras) at key locations on the production site to ensure that these cameras can cover all important areas and monitor the production environment in real time.

[0051] 1.5 Video Transmission

[0052] Configure the camera to transmit the real-time video stream to the central server via the RTSP protocol. The RTSP protocol can transmit high-definition real-time video streams.

[0053] 2. Data Stream Processing

[0054] Apache Kafka 2.1Install Kafka

[0055] Install and configure Apache Kafka on a central server. This is a distributed stream processing platform that can efficiently process real-time data streams. Configure the parameters of Kafka Broker to ensure that the system can receive and process large amounts of data.

[0056] 2.2 Theme Configuration

[0057] Create a topic to receive sensor data and video stream data. A topic is the basic unit used by Kafka to organize and store data streams.

[0058] 2.3 Data Producers and Consumers

[0059] Configure sensors and cameras as Kafka producers to send data to corresponding topics.

[0060] Write Kafka consumer applications to read data from topics for processing.

[0061] Apache Flink 2.4 Install Flink

[0062] Install and configure Apache Flink, a distributed computing framework designed for real-time data processing, on the central server. Configure a multi-node cluster to ensure high availability and scalability of the system.

[0063] 2.5 Data Processing

[0064] Write Flink jobs to consume data streams from Kafka. Use Flink's DataStream API to process data, including windowing and aggregation.

[0065] For example, detecting anomalies in sensor data and triggering an alarm if the temperature exceeds a preset threshold.

[0066] 3. Real-time alarm

[0067] Rule Engine 3.1 Rule Configuration

[0068] Install and configure the Drools rule engine, write rule files (.drl), and define alarm conditions. For example, when the temperature exceeds 30 degrees Celsius, an alarm event is generated.

[0069] 3.2 Rule Engine Integration

[0070] Integrate the Drools rule engine into the Flink job, apply the rule engine during data processing, and detect in real time whether an alarm needs to be triggered.

[0071] Notification System 3.3 SMS Notification

[0072] Configure Twilio service and write an API interface for sending SMS. When the rule engine triggers an alarm, call TwilioAPI to send an alarm SMS to relevant personnel.

[0073] 3.4 Email Notification

[0074] Configure the SMTP server (for example, use Gmail SMTP) and write an API interface for sending emails. When the rule engine triggers an alarm, an alarm email is sent to relevant personnel.

[0075] 4. Data storage and integration

[0076] MongoDB 4.1 installation and configuration

[0077] Install MongoDB on a central server and configure a multi-node cluster to ensure high availability and redundancy of data. Configure the mongod.conf file to set cluster parameters.

[0078] 4.2 Data Insertion and Query

[0079] Write scripts for data insertion and querying using MongoDB's JavaScript API or Mongoose (an ORM tool for Node.js). For example, receive sensor data and store it in a MongoDB collection.

[0080] PostgreSQL 4.3 installation and configuration

[0081] Install PostgreSQL on a central server and configure a multi-node cluster to ensure high performance and data consistency.

[0082] 4.4 Complex Queries

[0083] Use SQL to write complex queries and analysis scripts. For example, perform statistical analysis on historical data to identify trends and patterns in safety production.

[0084] 5. Data Sharing and Access

[0085] API Gateway 5.1Kong Installation and Configuration

[0086] Install KongAPI Gateway on the central server, configure services and routes, and ensure unified management of API interfaces.

[0087] 5.2 Authentication

[0088] Configure OAuth2.0 or JWT plug-in to ensure the security of API access. Perform authentication through API gateway to control user access rights.

[0089] Data Sharing Platform 5.3 RESTful API

[0090] Use Express.js or FastAPI framework to develop RESTful API and provide data access interface. Write routes and controllers to handle data query and operation requests.

[0091] 5.4GraphQL Interface

[0092] Use Apollo Server to develop GraphQL interfaces and provide flexible data query services. Define GraphQL schema and resolver to implement data query and operation functions.

[0093] 6. System integration and testing

[0094] CI / CD 6.1Jenkins Configuration

[0095] Install Jenkins on a central server and configure pipeline tasks to automate build, test, and deployment. Write Jenkinsfile to define build and test steps.

[0096] 6.2 Unit Testing and Integration Testing

[0097] Write unit tests (using JUnit or Jest) and integration tests (using Selenium or Cypress) to ensure that the various modules of the system function properly. Integration tests simulate user operations to ensure that the end-to-end functionality of the system is normal.

[0098] 7. Deployment and maintenance

[0099] Cloud Deployment 7.1 AWS Deployment

[0100] Use AWS EC2 instances to deploy various services, configure security groups and load balancers to ensure high availability of the system. Use AWS S3 to store static files and backup data.

[0101] 7.2Kubernetes Deployment

[0102] Install and configure Kubernetes cluster, use Helm Chart to automate application deployment and management. Write Kubernetes deployment files (.yaml) to define the deployment configuration of each service.

[0103] Monitoring and log management 7.3 Prometheus configuration

[0104] Install and configure the Prometheus monitoring system, configure data crawling tasks, and collect system performance indicators. Configure Alertmanager, set alarm rules, and notify operation and maintenance personnel.

[0105] 7.4ELK stack configuration

[0106] Install and configure Elasticsearch, Logstash, and Kibana (ELK stack) to collect, store, and analyze system log data. The Kibana dashboard displays log data intuitively to help operation and maintenance personnel quickly locate problems.

[0107] How it works

[0108] First, the system collects various data in the production environment in real time through multiple IoT sensors and high-definition surveillance cameras installed at the production site. IoT sensors are used to monitor environmental parameters such as temperature, humidity, and harmful gas concentrations. These sensors are connected to a microcontroller (such as ESP8266). When the sensor detects changes in environmental parameters, the microcontroller transmits the data to the Apache Kafka cluster on the central server via Wi-Fi.

[0109] At the same time, high-definition surveillance cameras are installed in key locations to monitor the production site in real time. These cameras transmit video streams to the central server via the RTSP protocol. The Kafka cluster receives and organizes these real-time data streams, and uses topics to classify and store data to ensure efficient processing of large amounts of data.

[0110] Next, Apache Flink consumes data streams from Kafka and performs real-time processing and analysis. Flink jobs process data using techniques such as windowing and aggregation. When Flink detects data anomalies (for example, temperature exceeds a preset threshold), an alarm event is generated.

[0111] The Drools rule engine is integrated into the Flink job to apply preset rules in real time. When the data exceeds the threshold, the Drools rule engine generates an alarm event and passes it to the notification system. The notification system sends alarm notifications through the Twilio API or SMTP server. These notifications are sent to relevant personnel in the form of SMS or email to ensure that they are aware of the safety status of the production site in a timely manner.

[0112] All collected data is stored in MongoDB and PostgreSQL databases. MongoDB is used to store unstructured data (such as sensor data), while PostgreSQL is used to store structured data (such as user information and configuration data). These two databases are adapted to different types of data storage requirements, ensuring efficient data query and analysis.

[0113] To facilitate data access and sharing, the system deploys KongAPI Gateway to provide unified API interface management and authentication services. The data sharing platform provides users with flexible data query and access services through RESTful API and GraphQL interfaces. Users can query and access data on demand through these API interfaces, and the system returns the corresponding data results based on the request parameters.

[0114] In terms of system integration and testing, CI / CD systems (such as Jenkins or GitLab CI) are used to automatically build, test, and deploy applications. After the developer submits the code, the CI / CD system automatically triggers the build and test tasks to ensure code quality and automatically deploys the tested code to the production environment.

[0115] The system is deployed on cloud services (such as AWS EC2 instances) to ensure high availability and scalability. Kubernetes is used to manage and deploy applications in containers, and Helm Chart is used to define the deployment configuration of applications to achieve automated deployment and updates.

[0116] Finally, the system uses the Prometheus monitoring system to collect performance indicators and configure alarm rules, and the ELK stack (Elasticsearch, Logstash, Kibana) is used for log collection and analysis. Through Prometheus and the ELK stack, the system can monitor performance in real time and provide detailed fault diagnosis information to help operation and maintenance personnel quickly locate and solve problems.

[0117] Through the collaborative work of the above-mentioned parts, the entire system realizes real-time monitoring, integration and sharing of production environment data, and improves the efficiency and effectiveness of safe production management.

[0118] The present invention is intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. The cross-platform safety production data integration and sharing system is characterized by: include: Multiple IoT sensors installed at production sites to monitor temperature, humidity and harmful gases in real time and transmit data via wireless networks; High-definition surveillance cameras installed at the production site are used to monitor the production environment in real time and transmit video streams via the RTSP protocol; Data streaming platform, including Apache Kafka cluster, for receiving and transmitting real-time data streams; Real-time data processing modules, including Apache Flink, for real-time processing and analysis of data streams; The rule engine module includes the Drools rule engine, which is used to detect data anomalies and trigger alarm events based on preset rules; Data storage modules, including MongoDB and PostgreSQL, for storing unstructured data and structured data respectively; API gateways, including KongAPI Gateway, are used to provide a unified data access interface and perform authentication; Data sharing platform, including RESTful API and GraphQL interface, used to provide flexible data query services; User interfaces, including responsive web interfaces and mobile apps developed using React or React Native, for displaying and manipulating data; Notification systems, including Twilio or AWS SNS services, are used to send alarm notifications.

2. The cross-platform safety production data integration and sharing system according to claim 1 is characterized in that: The IoT sensor sends data to the data stream platform via the MQTT protocol, which has the characteristics of low bandwidth and low latency and is suitable for data transmission of IoT devices.

3. The cross-platform safety production data integration and sharing system according to claim 2 is characterized in that: The high-definition surveillance camera transmits the video stream to the central server via the RTSP protocol, which supports real-time streaming media transmission and ensures low latency and high reliability of video data.

4. The cross-platform safety production data integration and sharing system according to claim 3 is characterized in that: The data stream platform is configured with an Apache Kafka cluster for efficiently processing and transmitting real-time data streams from different sources. The Kafka cluster has the characteristics of high throughput and scalability and is suitable for large-scale data processing.

5. The cross-platform safety production data integration and sharing system according to claim 4 is characterized in that: The real-time data processing module consumes data streams from the Kafka cluster through Apache Flink and performs window operations and aggregation analysis. The window operations include processing methods based on time windows and count windows, and the aggregation analysis includes average value calculation, maximum value calculation, and minimum value calculation.

6. The cross-platform safety production data integration and sharing system according to claim 5 is characterized in that: The rule engine module configures alarm rules based on the Drools rule engine, triggers an alarm event when data exceeds a preset threshold, and the alarm rules can be dynamically adjusted according to user-defined conditions.

7. The cross-platform safety production data integration and sharing system according to claim 6 is characterized in that: The data storage module uses MongoDB to store unstructured data, including sensor data and log data, and uses PostgreSQL to store structured data, including user information and configuration data. Both MongoDB and PostgreSQL databases support high-concurrency data access and query.

8. The cross-platform safety production data integration and sharing system according to claim 7 is characterized in that: The API gateway uses KongAPI Gateway to provide a unified API interface and authenticates through OAuth2.0 or JWT. The API gateway can route, limit, log and monitor API requests.

9. The cross-platform safety production data integration and sharing system according to claim 8 is characterized in that: The data sharing platform uses Express.js or FastAPI framework to develop RESTful API, and uses Apollo Server to provide GraphQL interface. The RESTful API and GraphQL interface allow users to query and access data on demand, and provide data filtering, sorting and paging functions.

10. The cross-platform safety production data integration and sharing system according to claim 9, characterized in that: The user interface is developed using React or React Native, providing a responsive web interface and mobile application for data display and operation. The user interface supports multiple languages, multiple themes and real-time data update functions. The notification system sends SMS, email and mobile push notifications through Twilio API or AWS SNS to achieve timely notification of alarm events. The notification system supports the configuration of multiple notification channels and notification strategies.

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