Remote operation and maintenance management and control method and system for industrial upper computer data acquisition software

Abstract

The invention discloses a remote operation and maintenance management and control method and system for industrial upper computer data acquisition software. According to the method, based on an edge agent and Internet of Things platform collaborative architecture, a lightweight edge agent program is deployed on an industrial upper computer, upper computer management service is expanded on an Internet of Things platform, and full-life-cycle management of upper computer data acquisition software is achieved. The system adopts a containerized packaging technology to ensure the consistency of an operating environment, supports silent upgrade of a program and dynamic loading of configuration through a dual-channel hot update mechanism, and establishes a multi-dimensional health degree assessment rule to realize real-time monitoring and abnormal early warning. The problems that in the prior art, deployment depends on manual work, configuration updating is inconvenient, and the operation state is unknown are solved, the operation and maintenance efficiency and the data collection reliability are remarkably improved, and the method is suitable for large-scale industrial Internet of Things scenes.

Description

Technical Field

[0001] This invention relates to a remote operation and maintenance management method and system for industrial host computer data acquisition software, belonging to the field of industrial Internet of Things technology. Background Technology

[0002] In industrial IoT applications, host computer data acquisition software plays a crucial role in data acquisition and preprocessing, such as PLC data acquisition programs and machine tool monitoring programs. However, existing technologies have serious shortcomings in remote operation and maintenance management, mainly in the following aspects:

[0003] 1. The deployment method is outdated and relies on manual on-site operation.

[0004] Currently, the deployment of host computer programs generally involves on-site installation by engineers or copying via USB drive. This approach has significant drawbacks: high labor costs when deploying across regions, low efficiency in version updates, and difficulty in ensuring version consistency across multiple devices. When a program version upgrade is required, engineers must operate on each device individually, causing a sharp increase in maintenance costs.

[0005] 2. Configuration management is rudimentary and lacks dynamic update capabilities.

[0006] In existing technologies, modifications to data acquisition parameters must be made by manually editing local configuration files. This method has serious drawbacks: configuration modifications require on-site operation, making remote batch updates impossible; program restarts are usually required after configuration changes, affecting the continuity of data acquisition; and the lack of configuration version management makes it difficult to quickly roll back when problems occur.

[0007] 3. Lack of operational status monitoring, resulting in delayed fault detection.

[0008] Traditional solutions lack effective status monitoring mechanisms and have obvious drawbacks: whether the program is running normally cannot be detected in real time; resource anomalies (such as CPU overload and memory leaks) are difficult to be warned in advance; fault detection relies on manual inspection, and response delays increase the risk of data loss.

[0009] 4. Existing remote solutions have incomplete functionality.

[0010] Although some industrial protocol gateways support remote communication, they still have significant shortcomings: they fail to cover the entire lifecycle management of the host computer program; their integration with the Internet of Things platform is insufficient; they cannot adapt to the operation and maintenance needs of large-scale industrial equipment; and they lack a unified health assessment and early warning mechanism.

[0011] These technical deficiencies directly lead to problems such as high operation and maintenance costs, poor data acquisition reliability, and delayed fault response, which seriously restrict the large-scale deployment and stable operation of industrial IoT systems.

[0012] Chinese patent CN120785717A discloses an operation and maintenance method and system for remote management and data collection and analysis based on hybrid networking. The method includes acquiring and preprocessing customer information; deploying and configuring a smart gateway to access the customer network; configuring the smart gateway to establish a secure communication connection between the customer network and the monitoring center; the monitoring center hierarchically recording customer asset information based on the preprocessed customer information; the smart gateway collecting status information of customer assets in the customer network according to a preset collection cycle and transmitting the status information to the monitoring center; the monitoring center updating the status of customer assets based on the received status information and performing operation and maintenance based on the current status of the customer assets. However, this existing technical solution mainly focuses on the smart gateway collecting status information of customer network devices as an information security buffer between the customer network and the monitoring center to achieve secure communication in different scenarios, making it difficult to perform full lifecycle management of the host computer program.

[0013] The aforementioned defects result in high operation and maintenance costs, poor data acquisition continuity, and slow fault response for industrial IoT systems, which restricts the large-scale deployment and stable operation of the systems. Summary of the Invention

[0014] The purpose of this invention is to overcome the shortcomings of the prior art and provide a remote operation and maintenance management method and system for industrial host computer data acquisition software, constructing a two-layer collaborative architecture of "edge agent-IoT platform" to realize full lifecycle management of host computer programs.

[0015] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0016] In a first aspect, the present invention provides a remote operation and maintenance management method for industrial host computer data acquisition software. This method is executed collaboratively by an IoT platform deployed in the cloud and an edge agent program deployed on the industrial host computer. The edge agent program is used to host and run the host computer data acquisition program. The method includes:

[0017] The edge agent program initiates registration with the IoT platform to establish a secure two-way communication channel;

[0018] The IoT platform will distribute a container image containing a data acquisition program and its operating environment to the target edge agent program through the communication channel.

[0019] The edge agent runs the container image in a local container runtime environment to start the data collection program;

[0020] The edge agent program periodically collects local operating status data and reports it to the IoT platform through the communication channel;

[0021] The IoT platform monitors and manages based on the received operational status data.

[0022] The above setup achieves the following effect: By constructing a collaborative execution architecture of "cloud-based IoT platform - industrial host computer edge agent," the data acquisition program is hosted in a lightweight edge agent program, with centralized scheduling and resource distribution handled by the cloud platform. This minimal technical solution establishes a basic closed loop for remote operation and maintenance: device registration and access, remote containerized distribution of programs, local automated operation, status reporting, and cloud monitoring. It fundamentally changes the traditional model that relies on manual on-site operation, providing a framework solution for addressing core technical issues such as low program deployment efficiency and unknown operational status.

[0023] Further configuration: When the edge agent program registers with the IoT platform, it reports the device's unique identifier; after verifying the identifier, the IoT platform issues an identity token for subsequent communication authentication.

[0024] The above settings achieve the following effects: A secure device access and authentication mechanism is established. By reporting and verifying unique device identifiers, the legitimacy of devices accessing the platform is ensured, preventing unauthorized access. The issued identity tokens provide the authentication basis for all subsequent communications, guaranteeing the security of control commands and data reporting channels, and laying the foundation for secure and reliable remote management of large-scale industrial equipment.

[0025] Further settings: The container image is a Docker image; running the container image includes: the edge agent program pulling the image, verifying its integrity, and controlling the Docker daemon to create or replace the container.

[0026] The above settings achieve the following: They clearly define the standardized carrier for program distribution and the specific technical means for local execution. Using Docker images ensures the integrated and consistent deployment of the program and its complex dependencies. The edge agent manages the container lifecycle by controlling a standardized Docker daemon, achieving isolation and standardized management of the program's runtime environment and completely resolving program compatibility and dependency conflicts caused by differences in system environments.

[0027] Further settings: The running status data includes: CPU utilization and memory usage of the host computer system, process status of the data acquisition program, and success rate and latency of the data acquisition task.

[0028] The above settings achieve the following effect: They define a multi-dimensional indicator system for status monitoring, covering the entire chain from infrastructure (CPU, memory), to application processes (program survival), and then to core business (collection success rate, latency). Reporting these specific and quantifiable indicators enables the IoT platform to comprehensively and accurately perceive the actual operating status and business quality of remote data acquisition software, providing a data foundation for refined management and fault diagnosis.

[0029] Further configuration: The IoT platform monitors and manages the edge agent program based on the received operational status data, including: the IoT platform performs health assessment and anomaly detection based on the received operational status data; the health assessment and anomaly detection includes: the IoT platform inputs the operational status data into preset assessment rules, which simultaneously execute threshold comparison and time series anomaly detection algorithms to generate health scores and alarm events.

[0030] The above settings achieve the following effect: This configuration upgrades cloud monitoring from simple data display to intelligent analysis and early warning. By simultaneously executing threshold comparison (suitable for sudden, clear anomalies, such as 100% CPU utilization) and time-series anomaly detection algorithms (suitable for gradual, potential anomalies, such as a slow decline in data acquisition success rate), it enables multi-dimensional and intelligent evaluation of complex operational states. This allows the system to detect potential faults in advance, shifting from "post-event response" to "pre-event warning," significantly improving system reliability and maintainability.

[0031] Further configuration: The IoT platform monitors and manages the edge agent program based on the received operational status data, and also includes: the IoT platform performs version management of the configuration of the data acquisition program, and issues configuration update instructions to the edge agent program through the communication channel; the edge agent program dynamically loads the new configuration according to the configuration update instructions to achieve hot updates; the version management of the configuration includes: the IoT platform stores the configuration reported by the edge agent program in a distributed file system and records the version history; the configuration update instructions include incremental configuration packages generated based on version differences.

[0032] The above settings achieve the following: centralized, versioned, and remote dynamic management of configurations. Versioned management supports traceability and one-click rollback of configuration changes, enhancing operational security. By distributing incremental configuration packages based on version differences, rather than complete files, the amount of data transmitted over the network is greatly reduced, improving update efficiency. Combined with the subsequent hot-reloading mechanism, this solves the technical challenge of service interruption caused by requiring a program restart after configuration modifications.

[0033] Further configuration: The dynamic loading of new configuration refers to the edge agent merging the received incremental configuration package with the current configuration in memory without stopping the data acquisition program, and notifying the data acquisition program to reread the configuration.

[0034] The above settings achieve the following effect: This configuration specifically implements the key technical aspects of hot configuration updates. By merging and applying configurations in memory, it completely avoids the risks of service interruptions and data loss caused by restarting the program or container, ensuring high continuity of data acquisition operations. This provides crucial technical support for industrial scenarios requiring 24 / 7 operation.

[0035] Further configuration: The method also includes a business data processing flow: the edge agent program packages the raw data collected from the industrial equipment and reports it to the IoT platform; the IoT platform parses the data and associates it with the device metadata to perform data quality statistics based on business dimensions.

[0036] The above setup achieves the following effect: It establishes a link between raw equipment-level data and business-level management insights. By associating equipment metadata (such as production line and equipment type), the platform can statistically analyze quality indicators such as the completeness and timeliness of data collection from a business perspective (not just an operations and maintenance perspective). This enables the operations and maintenance system not only to ensure that data is collected but also to evaluate the quality of data collection, providing direct data support for production optimization and decision-making.

[0037] Further configuration: The secure two-way communication channel simultaneously supports at least two of the following protocols: MQTT, Kafka message queue, and HTTPS / RESTful API. MQTT is used for command issuance and heartbeats, while Kafka is used for large-scale status and business data reporting.

[0038] The above configuration achieves the following results: It optimizes communication efficiency and reliability through multi-protocol adaptation. Leveraging the low power consumption, low bandwidth, and persistent connection support of the MQTT protocol, it is ideal for real-time command and heartbeat transmissions with high requirements and small data volumes. Utilizing Kafka's high throughput, persistence, and streaming support, it perfectly meets the needs for reporting large volumes of asynchronous status monitoring and business data. This collaborative protocol design allows the system to flexibly adapt to complex and ever-changing industrial network environments.

[0039] Secondly, this invention provides a remote operation and maintenance management system for industrial host computer data acquisition software. The system includes: an IoT platform device deployed in the cloud for centralized management of program images, configuration policies, and monitoring rules; and an edge agent device deployed on the industrial host computer for hosting and running the data acquisition program, and interacting with the IoT platform device as a client. The edge agent device includes: a communication module for establishing and maintaining a communication connection with the IoT platform device; a container management module for pulling container images from the platform and creating / managing running containers; a configuration hot update module for managing local configurations and executing configuration update commands issued by the platform; and a status acquisition and reporting module for collecting system, program, and business indicators and reporting them to the platform. The IoT platform device includes: a resource distribution module for distributing program container images to designated edge agent devices; a configuration management center for storing and versioning configurations and generating incremental update commands; and a health monitoring and analysis module for receiving and analyzing reported status data, performing health assessments, and issuing anomaly alarms.

[0040] The above setup achieves the following effect: This system architecture provides tangible hardware and software module support for the methods described in the first aspect. It clearly defines the functional division between the cloud and the edge: the IoT platform device acts as the "command center," responsible for resource management, strategy formulation, and intelligent analysis; the edge agent device acts as the "execution terminal," responsible for localized program execution, instruction execution, and data acquisition. Connected through a communication module, the two constitute a complete distributed collaborative management and control system, realizing remote management of the entire lifecycle of industrial host computer data acquisition software, from deployment, configuration, operation to monitoring.

[0041] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0042] 1. Improve deployment and maintenance efficiency: Reduce manual operation time by more than 90% through remote batch deployment and automated upgrades, especially suitable for cross-regional and large-scale industrial equipment scenarios.

[0043] 2. Reduce the risk of data interruption: Real-time monitoring and anomaly response mechanisms can shorten the fault response time from hours to minutes, ensuring the continuity of data collection.

[0044] 3. Enhanced configuration management flexibility: Dynamic hot update technology avoids on-site modification of configuration files, supports version rollback, and reduces downtime losses.

[0045] 4. Unified operation and maintenance management capabilities: Through the IoT platform, the distributed host computer programs are centrally managed to achieve unified management and control of version consistency, configuration compliance, and status visualization.

[0046] 5. Reduce maintenance costs: Reduce the frequency of engineers' business trips and on-site service costs, and the overall operation and maintenance costs are expected to decrease by 40%-60%. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the system architecture of the present invention;

[0048] Figure 2 This is a flowchart of the program release and deployment process for this invention;

[0049] Figure 3 This is a flowchart of the client (edge ​​agent program) registration and heartbeat reporting process of the present invention;

[0050] Figure 4 This is a flowchart of the configuration management and hot update process of this invention. Detailed Implementation

[0051] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0052] Example 1

[0053] This invention provides a remote operation and maintenance management method for industrial host computer data acquisition software, comprising the following steps:

[0054] 1. Specify that all host computer servers should install Docker and other required environments and dependent services, and initialize them all at once using an image.

[0055] 2. Upload and publish the industrial host computer data acquisition software's host computer data acquisition program to the IoT server, including information such as the version number. All data acquisition programs should include functions such as automatic restart upon startup, registration, and heartbeat reporting, in accordance with the constraints.

[0056] Table 1. Main Interactions between the Host Computer Server and the IoT Server

[0057]

[0058] 3. For example Figure 1 As shown, during the first initialization, a remote distribution channel is established to remotely distribute the running program to the designated host computer server, while simultaneously monitoring for the release of the latest version on the server or a manually specified version.

[0059] 4. For example Figure 2 As shown, the host computer data acquisition program is remotely started, the client (edge ​​agent program) information is registered with the IoT server, and a communication channel is established.

[0060] 5. For example Figure 3As shown, the host computer data acquisition client (edge ​​agent program) reports all configuration files of this client (edge ​​agent program), uploads the configurations to the distributed file system Minio according to the configuration categories, and reports heartbeats at regular intervals.

[0061] 6. For example Figure 4 As shown, the IoT server can view and edit all configuration files of the client (edge ​​agent program), and realize bidirectional file updates and program hot updates through the MQTT message mechanism.

[0062] 7. The IoT server parses the collected messages, obtains the device number bound to the message, and matches it with the device information associated in the configuration file reported by the host computer data collection client (edge ​​agent program). This allows the server to calculate the status of all data reported by the client (edge ​​agent program) and to statistically analyze relevant data based on specified business dimensions.

[0063] The core technical principle of this invention is based on the following innovations:

[0064] 1. Principles of Edge-Cloud Platform Collaborative Architecture

[0065] By deploying a lightweight edge agent on the host computer, management services are extended on the cloud-based IoT platform, forming a distributed operation and maintenance management system. The edge agent is responsible for local program execution and status collection, while the cloud platform is responsible for centralized management and policy distribution. The two work together through a secure communication channel.

[0066] 2. Principle of Consistency in Containerized Environments

[0067] By using containerization technologies such as Docker, the host computer program and its dependent environment are encapsulated as a whole, ensuring that the running environment is completely consistent on different devices, thus fundamentally solving the compatibility problem caused by environmental differences.

[0068] 3. Dual-channel hot update principle

[0069] The design incorporates two independent channels: program upgrades and configuration updates. Program upgrades are achieved through container image replacement; configuration updates are achieved through version management and incremental synchronization. Both support hot updates without interrupting data collection operations.

[0070] 4. Principles of Health Assessment

[0071] Health assessment rules are constructed based on multi-dimensional indicators (CPU, memory, network, business data), and combined with threshold detection and time-series anomaly detection algorithms to achieve comprehensive health status assessment from the system layer to the business layer.

[0072] This embodiment provides a remote operation and maintenance management method for industrial host computer data acquisition software. It aims to solve the problems of deployment relying on manual labor, inconvenient configuration updates, and unknown operating status in the prior art through a dual-end collaborative architecture of cloud IoT platform and industrial host computer edge agent.

[0073] 1. Core Architecture Definition

[0074] Edge agent program: A lightweight client program deployed on the industrial host computer. It not only acts as a proxy for communication with the cloud, but also directly hosts and runs the host computer's data acquisition program, executes instructions from the cloud, and collects local status and data.

[0075] IoT Platform: A centralized management service deployed in the cloud, responsible for the unified management and distribution of program images, configuration policies, and monitoring rules, and receiving and analyzing data reported by edge devices.

[0076] 2. Core Methodology and Flow

[0077] The method is based on a collaborative model that separates "management" and "execution," and mainly includes the following processes:

[0078] (1) Establishment of control channel: After the edge agent program starts, it actively registers with the Internet of Things platform to establish a secure and reliable two-way communication channel (such as MQTT).

[0079] (2) Containerized distribution and execution of the program: The IoT platform encapsulates the data acquisition program and its environment into a container image (such as Docker) and distributes it to the target edge agent program through this channel. After receiving the image, the edge agent program starts the image in its local container runtime environment, that is, it runs the data acquisition program.

[0080] (3) Hot configuration update: The edge agent program reports its local configuration to the platform for version management. The platform can edit the configuration and generate incremental update packages, which are then distributed through the command channel. The edge agent program dynamically loads the new configuration, enabling uninterrupted hot updates for business operations.

[0081] (4) Status monitoring and intelligent early warning: The edge agent program periodically collects multi-dimensional indicators such as host resources, program processes, and data collection services, and reports them to the platform. The platform uses health assessment rules (integrating threshold and time-series anomaly detection) for real-time analysis to achieve anomaly detection and early warning.

[0082] (5) Business data flow and statistics: The edge agent program reports the collected industrial equipment data to the platform. The platform associates the data with the equipment metadata and performs quality statistics and analysis from a business perspective.

[0083] Example 2

[0084] This embodiment provides a remote operation and maintenance management method for industrial host computer data acquisition software. The data processing process is completed collaboratively by an edge agent device (deployed on the industrial host computer side) and an IoT platform device (deployed in the cloud).

[0085] The method includes: the edge agent program registering with the IoT platform to establish a secure two-way communication channel; the IoT platform sending a container image containing a data acquisition program and its runtime environment to the target edge agent program through the communication channel; the edge agent program running the container image in its local container runtime environment to start the data acquisition program; the edge agent program periodically collecting local runtime status data and reporting it to the IoT platform through the communication channel; and the IoT platform monitoring and managing the received runtime status data.

[0086] By constructing a collaborative execution architecture of "cloud-based IoT platform - industrial host computer edge agent," the data acquisition program is hosted in a lightweight edge agent program, with centralized scheduling and resource distribution handled by the cloud platform. This minimal technical solution establishes a basic closed loop for remote operation and maintenance: device registration and access, remote containerized distribution of programs, local automated operation, status reporting, and cloud monitoring. It fundamentally changes the traditional model that relies on manual on-site operation, providing a framework solution for addressing core technical issues such as low program deployment efficiency and unknown operational status.

[0087] When registering with the IoT platform, the edge agent program reports the device's unique identifier; after verifying the identifier, the IoT platform issues an identity token for subsequent communication authentication.

[0088] This setup establishes a secure device access and authentication mechanism. By reporting and verifying unique device identifiers, the legitimacy of devices accessing the platform is ensured, preventing unauthorized access. The issued identity tokens provide the authentication basis for all subsequent communications, guaranteeing the security of control commands and data reporting channels, and laying the foundation for secure and reliable remote management of large-scale industrial equipment.

[0089] The container image is a Docker image; the "running container image" includes: the edge agent program pulling the image, verifying its integrity, and controlling the Docker daemon to create or replace the container.

[0090] This setup clearly defines the standardized carrier for program distribution and the specific technical means for local execution. Utilizing Docker images ensures the integrated and consistent deployment of the program and its complex dependencies. The edge agent manages the container lifecycle by controlling a standardized Docker daemon, achieving isolation and standardized management of the program's runtime environment and completely resolving program compatibility and dependency conflicts caused by differences in system environments.

[0091] The operational status data includes: CPU utilization and memory usage of the host computer system, process status of the data acquisition program, and success rate and latency of the data acquisition task.

[0092] This method is described step-by-step according to time sequence:

[0093] Step 1: The edge agent device obtains the device's unique identification data, including the device ID, MAC address, and initial program version number, from the local non-volatile storage medium, encapsulates and encrypts the data, and obtains the registration request data packet.

[0094] Step 2: The edge proxy device sends a registration request to the IoT platform device via HTTPS protocol based on the registration request data packet.

[0095] Step 3: The IoT platform device obtains the registration request data packet from the network interface, performs device legitimacy verification and identity token generation processing, and obtains the device identity token and registration response data.

[0096] Step 4: The edge agent device obtains the registration response data from the IoT platform device, performs token parsing and communication parameter initialization processing, obtains MQTT persistent connection status data, and establishes an instruction channel with the platform.

[0097] Step 5: The edge agent device obtains initial operating status data, including CPU utilization and memory usage, from the system monitoring interface based on the system timer trigger. It performs data formatting and encapsulation processing to obtain the initial heartbeat data packet and reports it to the IoT platform device through the MQTT channel.

[0098] Step 6: The IoT platform device obtains the program release instruction and the corresponding Docker container image data from the administrator operation interface, performs version metadata marking and distribution task scheduling processing, and obtains program distribution task data.

[0099] Step 7: The IoT platform device distributes task data according to the program and sends program upgrade instruction data to the designated edge agent device via the MQTT protocol. The instruction data includes the image storage address, version number, and integrity check code.

[0100] Step 8: The edge agent device obtains the program upgrade instruction data from the IoT platform device, performs local version comparison and remote image retrieval processing, and obtains the new version container image file data.

[0101] Step 9: The edge agent device performs MD5 verification and container runtime replacement processing based on the new version container image file data to obtain program upgrade result status data, and reports it to the IoT platform device through the MQTT channel.

[0102] Step 10: The edge agent device obtains the initial business configuration file from the local configuration file storage path, performs parsing and serialization processing, and obtains configuration snapshot data.

[0103] Step 11: The edge agent device uploads the configuration snapshot data to the distributed file storage system (Minio) integrated into the IoT platform device via the Restful API interface to obtain the configuration version record data on the platform side.

[0104] Step 12: The IoT platform device obtains the configuration modification instruction from the administrator's operation interface, combines it with the currently effective configuration version, performs difference comparison and incremental package generation processing, and obtains incremental configuration update package data.

[0105] Step 13: The IoT platform device sends configuration update notification data to the corresponding edge agent device via the MQTT protocol based on the incremental configuration update package data.

[0106] Step 14: The edge agent device obtains the configuration update notification data from the IoT platform device, performs incremental package download and dynamic merging and loading in memory to obtain the new configuration effective status data, and reports it to the IoT platform device.

[0107] Step 15: The edge agent device obtains multi-dimensional operating indicator data, including CPU utilization, memory usage, data acquisition success rate and network round-trip latency, from the system monitoring interface, process management interface and business acquisition log according to the preset collection cycle. It then performs data aggregation, compression and timestamp marking to obtain status monitoring data packets.

[0108] Step 16: The edge agent device reports the status monitoring data packets in batches to the IoT platform device through the Kafka message queue channel.

[0109] Step 17: The IoT platform device obtains the status monitoring data packet from the Kafka channel, performs data parsing, cleaning and index standardization processing to obtain the raw time-series data of device health.

[0110] Step 18: The IoT platform device inputs the original time-series health data of the device into the preset health assessment rules. The rules execute threshold rule matching and time series anomaly detection algorithms in parallel to perform real-time analysis and scoring to obtain health score data and abnormal alarm event data.

[0111] Step 19: The IoT platform device performs alarm level determination and notification routing and distribution processing based on the abnormal alarm event data, obtains alarm response record data, and triggers platform interface display, email or SMS notification.

[0112] Step 20: The edge agent device obtains raw production equipment data from industrial data acquisition interfaces (such as OPCUA, Modbus), performs timestamp marking, lightweight compression and batch packaging processing to obtain business acquisition data packets.

[0113] Step 21: The edge agent device collects data packets according to the service and reports them to the IoT platform device through the Kafka channel.

[0114] Step 22: The IoT platform device obtains the service acquisition data packet from the Kafka channel, performs data parsing and decoding, and associates and binds it with the device metadata information in the configuration management module to obtain time-series service data with service tags.

[0115] Step 23: The IoT platform device inputs the time-series business data with business tags into the business statistics and analysis module, and performs aggregation calculations and quality analysis processing according to multiple dimensions such as equipment, production line, and time interval to obtain data collection quality statistical report data.

[0116] Through the above sequence of steps, the edge agent device and the IoT platform device work together to complete the entire process of automated data processing, from device access, program deployment, configuration hot update, real-time status monitoring, intelligent health assessment to business data statistics, thereby achieving efficient and reliable remote operation and maintenance management of industrial host computer data acquisition software.

[0117] The data processing procedure of this invention covers the entire lifecycle from program deployment to runtime monitoring:

[0118] 1. Program data flow: Image packaging → Secure storage → Distribution and transmission → Local deployment → Version management;

[0119] 2. Configure data flow: Configure data collection → Version storage → Edit and verify → Incremental distribution → Hot reloading;

[0120] 3. Status data flow: Indicator collection → Data transmission → Real-time analysis → Health assessment → Anomaly detection;

[0121] 4. Alarm data flow: Event generation → Level determination → Notification distribution → Processing tracking → Closed-loop management;

[0122] Through the complete data processing flow described above, this invention achieves fully automated remote operation and maintenance of industrial host computer data acquisition software, significantly improving operation and maintenance efficiency and data acquisition reliability.

[0123] This invention relates to a remote operation and maintenance management system for industrial host computer data acquisition software. Its data processing is mainly completed by the following two core devices: an edge agent program (deployed on the industrial host computer side) and an Internet of Things platform (deployed on the cloud platform side).

[0124] The following describes the data processing steps in detail, using these devices as the main focus:

[0125] 1. Initialization and Connection Establishment Phase

[0126] The edge agent obtains device identification data (including device ID, MAC address, hardware fingerprint, and initial version number) from the local storage device, encapsulates and encrypts the data, and obtains the registration request data packet.

[0127] The edge agent sends a registration request to the IoT platform via HTTPS based on the registration request data packet.

[0128] The IoT platform obtains the registration request data packet from the network interface, performs device legitimacy verification and token generation processing, and obtains the device identity token and registration response data.

[0129] The edge agent obtains registration response data from the IoT platform, performs token parsing and communication channel initialization, and obtains persistent MQTT connection state data.

[0130] The edge agent collects heartbeat data periodically based on the persistent connection status data of MQTT (obtaining operating indicators such as CPU utilization and memory usage from the system monitoring interface), encapsulates it into heartbeat data packets, and reports them to the IoT platform on a regular basis.

[0131] 2. Program Management and Version Control Phase

[0132] The IoT platform obtains program release instructions and Docker image data from the administrator's operation interface, performs version marking and task allocation processing, and obtains program distribution task data.

[0133] The IoT platform distributes task data according to the program and sends upgrade instruction data (including image URL, version number and verification information) to the target edge agent via the MQTT protocol.

[0134] The edge agent obtains upgrade instruction data from the IoT platform, performs version comparison and image download processing, and obtains the new version image file data.

[0135] The edge agent performs MD5 verification and container replacement processing based on the new version image file data, obtains the program upgrade result data, and reports it to the IoT platform.

[0136] The IoT platform obtains program upgrade result data from the edge agent, performs log recording and version status update processing, and obtains device version management data.

[0137] 3. Configuration Management Hot Update Phase

[0138] The edge agent obtains initial configuration data (such as config.json and device_mapping.xml) from the local configuration file storage area, performs configuration parsing and serialization processing, and obtains configuration snapshot data.

[0139] The edge agent uploads the configuration snapshot data to the Minio storage system of the IoT platform via a RESTful interface to obtain the configuration version record data.

[0140] The IoT platform obtains configuration editing instructions from the administrator's operation interface, performs configuration template merging and difference comparison processing, and obtains incremental configuration package data.

[0141] The IoT platform sends configuration update notification data to the edge agent via the MQTT protocol based on the incremental configuration package data.

[0142] The edge agent obtains configuration update notification data from the IoT platform, performs incremental package download and dynamic loading processing, obtains the new configuration effective status data, and reports it to the IoT platform.

[0143] 4. Status Monitoring and Health Assessment Phase

[0144] The edge agent obtains multi-dimensional operational metrics data (including CPU utilization, memory usage, data acquisition success rate, and network latency) from the system monitoring interface, performs data aggregation and formatting, and obtains status monitoring data packets.

[0145] The edge agent monitors data packets based on their status and periodically reports them to the IoT platform via the Kafka channel.

[0146] The IoT platform obtains status monitoring data packets from the edge agent, performs data parsing and indicator aggregation processing, and obtains raw data on device health.

[0147] The IoT platform uses raw device health data and inputs health assessment rules (based on threshold rules and time-series anomaly detection algorithms) to perform real-time analysis and scoring, resulting in health score data and anomaly alarm data.

[0148] The IoT platform performs alarm classification and notification distribution based on abnormal alarm data, and obtains alarm response record data.

[0149] 5. Remote Control and Fault Autonomy Stage

[0150] The IoT platform obtains remote control commands (such as process restart or configuration reset) from the administrator's operation interface, encapsulates the commands and verifies permissions to obtain control command data.

[0151] The IoT platform sends control command data to the edge agent via the MQTT protocol based on the control command data.

[0152] The edge agent obtains control command data from the IoT platform, performs command parsing and local execution processing, and obtains control execution result data.

[0153] The edge agent detects resource overload or failure events from the local monitoring interface, triggers the autonomous response process, performs resource reclamation or process restart, obtains autonomous action log data, and reports it to the IoT platform.

[0154] 6. Data Processing and Business Statistics Stage

[0155] The edge agent obtains raw device data (such as PLC readings or sensor data) from the data acquisition interface, performs timestamp marking and data compression processing, and obtains the acquisition data packet.

[0156] The edge agent uploads the collected data packets in batches to the IoT platform via the Kafka channel.

[0157] The IoT platform obtains data packets from the edge agent, performs data parsing and device metadata binding processing to obtain time-series business data.

[0158] The IoT platform inputs time-series business data into the business statistical model (based on device number and collection rules), performs aggregation calculations and dimensional analysis processing, and obtains business statistical report data (such as data integrity indicators and regional distribution analysis results).

[0159] Through the above data processing flow, the edge agent and the IoT platform work together to realize the remote deployment, hot configuration update, status monitoring and business statistics of industrial host computer data acquisition software, ensuring the efficiency, consistency and reliability of data processing.

[0160] Example 3

[0161] This embodiment provides a remote operation and maintenance management system for industrial host computer data acquisition software. The system includes: an IoT platform device deployed in the cloud for centralized management of program images, configuration policies, and monitoring rules; and an edge agent device deployed on the industrial host computer for hosting and running the data acquisition program, and interacting with the IoT platform device as a client. The edge agent device includes: a communication module for establishing and maintaining a communication connection with the IoT platform device; a container management module for pulling container images from the platform and creating / managing running containers; a configuration hot update module for managing local configurations and executing configuration update commands issued by the platform; and a status acquisition and reporting module for collecting system, program, and business indicators and reporting them to the platform. The IoT platform device includes: a resource distribution module for distributing program container images to designated edge agent devices; a configuration management center for storing and versioning configurations and generating incremental update commands; and a health monitoring and analysis module for receiving and analyzing reported status data, performing health assessments, and issuing anomaly alarms.

[0162] This system architecture provides tangible hardware and software module support for the method described in Example 1. It clearly defines the functional division between the cloud and the edge: the IoT platform device acts as the "command center," responsible for resource management, strategy formulation, and intelligent analysis; the edge agent device acts as the "execution terminal," responsible for localized program execution, instruction execution, and data acquisition. Connected through a communication module, the two constitute a complete distributed collaborative management and control system, enabling remote management of the entire lifecycle of industrial host computer data acquisition software, from deployment, configuration, operation to monitoring.

[0163] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0164] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0165] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0166] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0167] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A remote operation and maintenance management method for industrial host computer data acquisition software, characterized in that, The method, executed collaboratively by an IoT platform deployed in the cloud and an edge agent program deployed on an industrial host computer, wherein the edge agent program is used to host and run the host computer data acquisition program, includes: The edge agent program initiates registration with the IoT platform to establish a secure two-way communication channel; The IoT platform will distribute a container image containing a data acquisition program and its operating environment to the target edge agent program through the communication channel. The edge agent runs the container image in a local container runtime environment to start the data collection program; The edge agent program periodically collects local operating status data and reports it to the IoT platform through the communication channel; The IoT platform monitors and manages the edge agent program based on the received operational status data.

2. The method according to claim 1, characterized in that, When registering with the IoT platform, the edge agent program reports the device's unique identifier; after verifying the identifier, the IoT platform issues an identity token for subsequent communication authentication.

3. The method according to claim 1, characterized in that, The container image is a Docker image; Running a container image involves: the edge agent pulling the image, verifying its integrity, and controlling the Docker daemon to create or replace containers.

4. The method according to claim 1, characterized in that, The operational status data includes: CPU utilization rate and memory usage rate of the host computer system, process status of the data acquisition program, and success rate and latency indicators of the data acquisition task.

5. The method according to claim 1, characterized in that, The IoT platform monitors and manages the edge agent program based on the received operational status data, including: the IoT platform performs health assessment and anomaly detection based on the received operational status data; The health assessment and anomaly detection include: the IoT platform inputs operational status data into preset assessment rules, which simultaneously execute threshold comparison and time series anomaly detection algorithms to generate health scores and alarm events.

6. The method according to claim 5, characterized in that, The IoT platform monitors and manages the edge agent program based on the received operational status data, and also includes: The IoT platform manages the configuration of the data acquisition program in a versioned manner and sends configuration update instructions to the edge agent program through the communication channel; The edge agent program dynamically loads the new configuration according to the configuration update instruction to achieve hot updates; Version management of the configuration includes: the IoT platform stores the configuration reported by the edge agent program in a distributed file system and records the version history; the configuration update instruction includes an incremental configuration package generated based on the version differences.

7. The method according to claim 6, characterized in that, The dynamic loading of new configuration refers to the edge agent merging the received incremental configuration package with the current configuration in memory without stopping the data acquisition program, and then notifying the data acquisition program to reread the configuration.

8. The method according to claim 1, characterized in that, The method also includes a business data processing flow: the edge agent program packages the raw data collected from the industrial equipment and reports it to the IoT platform; the IoT platform parses the data and associates it with the equipment metadata to perform data quality statistics based on business dimensions.

9. The method according to claim 1, characterized in that, The secure two-way communication channel supports at least two of the following protocols: MQTT, Kafka message queue, and HTTPS / RESTful API. MQTT is used for command issuance and heartbeats, while Kafka is used for reporting large amounts of status and business data.

10. A remote operation and maintenance management system for industrial host computer data acquisition software, characterized in that, include: IoT platform devices, deployed in the cloud, are used for centralized management of program images, configuration policies, and monitoring rules; An edge agent device, deployed on an industrial host computer, is used to host and run data acquisition programs and interact with the IoT platform device as a client; The edge proxy device includes: The communication module is used to establish and maintain a communication connection with the IoT platform device; The container management module is used to pull container images from the platform and create / manage running containers; Configure the hot update module to manage local configurations and execute configuration update commands issued by the platform; The status acquisition and reporting module is used to collect system, program and business indicators and report them to the platform. The IoT platform device includes: The resource distribution module is used to distribute program container images to designated edge agent devices; The configuration management center is used to store and version-manage configurations, and generate incremental update commands. The health monitoring and analysis module is used to receive and analyze reported status data, and to perform health assessments and anomaly alarms.

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