A data flow control system for hardware board card state management

Abstract

The application provides a data flow control system for hardware board card state management, which comprises a serial port data receiving and analyzing module, a resource utilization rate obtaining module, a data processing module, a data compression and optimization module, a data transmission module, a health information uploading module, a health information collecting module, an IIC module, a data frame analyzing module, a serial port configuration and management module, the data compression and optimization module and the serial port data receiving and analyzing module are in data interaction, the data transmission module and the serial port configuration and management module are in data interaction, the health information collecting module and the health information uploading module are in data interaction, the IIC module and the health information collecting module are in data interaction, the health information collecting module and the Ethernet data frame analyzing module are in data interaction, and the serial port configuration and management module and the IIC module are in data interaction.

Description

Technical Field

[0001] This invention belongs to the field of data flow control, and specifically relates to a data flow control system for hardware board status management. Background Technology

[0002] Currently, data flow control systems for hardware board status management are a key component of modern information technology infrastructure, widely used in data centers, communication networks, industrial automation, and other fields. Their main function is to ensure efficient system operation and stability by monitoring and managing the data flow of hardware boards in real time. With the increasing complexity of hardware boards and the growing demands for data processing, the performance requirements of data flow control systems are also rising. However, despite significant advancements in related technologies in recent years, existing data flow control systems still have many shortcomings in practical applications, which limit their further performance improvement and wider application.

[0003] Existing data flow control systems have limitations in terms of accuracy and real-time performance in flow monitoring. Data flow from hardware boards is typically characterized by high speed and high concurrency. Existing systems often employ fixed sampling frequencies or simple flow statistics methods, making accurate monitoring of data flow difficult. For example, under high load conditions, existing systems may fail to capture flow peaks or abnormal fluctuations in a timely manner, leading to the failure of flow control strategies. Furthermore, existing systems lack the ability to perform in-depth analysis of flow data, failing to identify flow patterns or predict flow trends in real time, further reducing the accuracy and real-time performance of flow control.

[0004] Existing data flow control systems lack flexibility in flow scheduling and resource allocation. Data traffic from hardware boards is typically diverse and dynamic, and existing systems, which often employ static flow scheduling strategies or fixed resource allocation schemes, struggle to adapt to dynamic traffic changes. For example, in cases of traffic bursts or load imbalances, existing systems may fail to adjust resource allocation in a timely manner, leading to overload on some boards while others remain idle, thus reducing overall system efficiency. Furthermore, existing systems lack fine-grained management of traffic priority and Quality of Service (QoS), making it difficult to meet the flow control requirements of different application scenarios.

[0005] Existing data flow control systems have low levels of intelligence, making automated control and optimization difficult. With the development of artificial intelligence and machine learning technologies, intelligence has become a crucial development direction for data flow control systems. However, existing systems typically rely on simple rules or preset parameters for flow control, lacking the ability to analyze and dynamically adjust flow data in real time. For example, in cases of traffic anomalies or network congestion, existing systems cannot automatically adjust flow control strategies or trigger emergency measures, leading to performance degradation or even system failure. Furthermore, existing systems lack the ability to coordinate with hardware board status management, hindering the achievement of intelligent management across the entire system.

[0006] Existing data flow control systems suffer from shortcomings in scalability and compatibility. As the types and quantities of hardware boards continue to increase, existing systems often struggle to support the flow control requirements of various boards. For example, hardware boards from different manufacturers may employ different communication protocols or data formats, and existing systems lack compatibility support for these differences, making it difficult to implement flow control strategies uniformly. Furthermore, existing systems perform poorly in terms of scalability, failing to adapt to the flow control needs of a large number of hardware boards, thus increasing system deployment and maintenance costs.

[0007] Existing data flow control systems have security and reliability vulnerabilities. Data flow from hardware boards often involves sensitive information or critical business processes, and existing systems may face security threats such as data leakage, tampering, or denial-of-service attacks during flow control. For example, existing systems lack encryption and authentication mechanisms for traffic data, potentially leading to data theft or alteration during transmission. Furthermore, existing systems perform poorly in fault recovery and tolerance, struggling to cope with hardware board or network device failures, resulting in decreased system reliability.

[0008] In summary, although data flow control systems play a crucial role in hardware board status management, existing technologies still have significant shortcomings in terms of flow monitoring accuracy, scheduling flexibility, intelligence level, scalability, security, and reliability. These deficiencies limit the performance improvement and widespread application of data flow control systems. Therefore, developing a novel data flow control system to overcome the shortcomings of existing technologies has become an important research direction in the field of information technology. Summary of the Invention

[0009] This invention proposes a data flow control system for hardware board status management. This data flow control system solves problems such as low data transmission efficiency, unbalanced resource load, and inaccurate health status monitoring in hardware board status management by using technologies such as dynamic weight coefficient table, compression algorithm switching, and fuzzy logic controller, and achieves efficient and stable data flow control.

[0010] The technical solution of this invention is implemented as follows: A data flow control system for hardware board status management includes a serial port data receiving and parsing module, a resource utilization acquisition module, a data processing module, a data compression and optimization module, a data transmission module, a health information uploading module, a health information collection module, an IIC module, a data frame parsing module, and a serial port configuration and management module. The data compression and optimization module interacts with the serial port data receiving and parsing module; the data transmission module interacts with the serial port configuration and management module; the health information collection module interacts with the health information uploading module; the IIC module interacts with the health information collection module; the health information collection module interacts with the Ethernet data frame parsing module; the serial port configuration and management module interacts with the IIC module; and the data compression and optimization module interacts with the data transmission module. The serial port data receiving and parsing module classifies data in real time, the resource utilization acquisition module binds priority and resource status to generate a dynamic weight coefficient table, and the data compression and optimization module dynamically switches compression algorithms according to priority and resource load, and adjusts the compression intensity in combination with the sliding window algorithm. The health information collection module generates a health index after obtaining the hardware status through the IIC module. The data transmission module dynamically adjusts the transmission rate based on the health index and bandwidth utilization using a fuzzy logic controller. The serial port configuration and management module dynamically adjusts the baud rate and data bits according to the historical error rate and health index value. The IIC module calibrates the serial port clock to eliminate timing deviations.

[0011] Current data flow control systems for hardware board status management typically employ fixed compression algorithms and transmission strategies, lacking the ability to dynamically adjust resource load and health status, resulting in low data transmission efficiency and uneven resource utilization. This technical solution, however, significantly improves the system's flexibility and adaptability by introducing dynamic weight coefficient tables, dynamic switching of compression algorithms, and fuzzy logic controllers.

[0012] Existing compression algorithms are typically fixed and cannot dynamically adjust compression intensity based on resource load and priority, leading to decreased data transmission efficiency under high load. This technical solution, however, utilizes a data compression and optimization module combined with a sliding window algorithm to dynamically switch compression algorithms and adjust compression intensity based on priority and resource load, thereby maintaining high data transmission efficiency even under high load.

[0013] Many solutions typically use static adjustments based on simple bandwidth utilization for transmission rate control, failing to incorporate dynamic optimization based on hardware health status. This solution, however, acquires hardware status data and generates a health index through a health information collection module. Combined with a fuzzy logic controller, it dynamically adjusts the transmission rate based on the health index and bandwidth utilization, ensuring stable and efficient data transmission.

[0014] Traditional serial port configurations are typically static, unable to dynamically adjust the baud rate and data bits based on historical error rates and health status. This technical solution, however, utilizes a serial port configuration and management module to dynamically adjust the baud rate and data bits based on historical error rates and health index values. Simultaneously, it uses an IIC module to calibrate the serial port clock, eliminating timing deviations and thus improving the reliability and stability of serial communication.

[0015] Existing resource utilization management technologies often lack priority binding and dynamic weight adjustment mechanisms, leading to uneven resource allocation. This technical solution, however, uses a resource utilization acquisition module to bind priorities to resource status and generate a dynamic weight coefficient table, thereby achieving dynamic and balanced resource allocation and avoiding resource waste and uneven load. Current health status monitoring typically relies on a single data source, lacking the fusion and comprehensive analysis of multi-source data. This technical solution, through a health information collection module, can acquire multi-source data via an IIC module and an Ethernet data frame parsing module to generate a comprehensive health index, thus improving the accuracy and comprehensiveness of health status monitoring.

[0016] In a preferred embodiment, the serial port data receiving and parsing module is used to receive the raw data stream from the hardware board in real time and classify and parse the data; by receiving the serial port data stream from the hardware board, it identifies the header marker of the data packet and dynamically divides the priority according to the data type; the classified data is transmitted to the data compression and optimization module, and the priority tag is sent to the resource utilization acquisition module as the basic data for generating the dynamic weight coefficient table.

[0017] In a preferred embodiment, the resource utilization acquisition module collects CPU, memory, and bandwidth usage in real time, combines the priority tags sent by the serial port data receiving and parsing module, calculates the dynamic weight coefficients of each priority data, and sends the weight coefficient table to the data compression and optimization module for the selection of compression algorithms and adjustment of compression intensity.

[0018] In a preferred embodiment, when the data compression and optimization module dynamically selects a compression algorithm and adjusts the compression intensity based on priority and resource load, it selects a compression algorithm by receiving classified data from the serial port data receiving and parsing module and combining it with the dynamic weight coefficient table of the resource utilization acquisition module. It also dynamically adjusts the compression intensity according to the sliding window algorithm, and reduces the compression level to reduce CPU usage when resources exceed a set threshold, and increases the compression level to optimize data volume when resources are below the set threshold.

[0019] In a preferred embodiment, the data transmission module is used to dynamically adjust the transmission rate based on the health index and bandwidth utilization. By receiving the health index and current bandwidth utilization from the health information collection module, the upper limit of the transmission rate is calculated using a fuzzy logic controller. When the health index is <0.5 and the bandwidth utilization is >70%, a degraded transmission mode is triggered, and the rate is reduced to 50% of the nominal value. When the health index is ≥0.8 and the bandwidth idle is >40%, a burst transmission mode is activated, and the rate is increased to 150%.

[0020] In a preferred embodiment, the health information collection module reads the temperature and voltage parameters of the hardware board through the IIC module, calculates the health index by combining the network latency data from the Ethernet data frame parsing module, and sends the health index to the data transmission module and the serial port configuration and management module for dynamically adjusting the transmission rate and serial port parameters.

[0021] In a preferred embodiment, the serial port configuration and management module dynamically adjusts the serial port parameters based on the historical error rate and health index. The serial port configuration and management module receives the health index and historical transmission error rate from the health information collection module to dynamically adjust the baud rate and data bits, and calibrates the serial port clock frequency through the IIC module to eliminate timing deviations caused by external interference, thereby ensuring the stability and reliability of data transmission.

[0022] The beneficial effects of this invention after adopting the above technical solutions are as follows: This data flow control system significantly improves data transmission efficiency and resource utilization through technologies such as dynamic weight coefficient tables, compression algorithm switching, and fuzzy logic controllers. The data compression and optimization module can dynamically switch compression algorithms based on priority and resource load, and adjust the compression intensity using a sliding window algorithm to ensure high data transmission efficiency even under high load. The health information collection module acquires multi-source data through the IIC module and Ethernet data frame parsing module, generates a comprehensive health index, and dynamically adjusts the transmission rate using a fuzzy logic controller, ensuring the stability and efficiency of data transmission. The serial port configuration and management module dynamically adjusts the baud rate and data bits based on historical error rates and health index values, and calibrates the serial port clock through the IIC module, eliminating timing deviations and improving the reliability and stability of serial communication. The resource utilization acquisition module generates a dynamic weight coefficient table by binding priority and resource status, achieving dynamic balanced resource allocation and avoiding resource waste and uneven load. Overall, through the collaborative work of multiple modules, this system achieves efficient, stable, and precise control of hardware board status management, significantly improving data transmission efficiency, resource utilization, and system reliability, and providing an efficient and intelligent solution for hardware board status management. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of the system framework in an embodiment of the present invention; Figure 2 This is a flowchart of the serial port data receiving and parsing module of the present invention; Figure 3 This is a flowchart illustrating the IIC module parsing process of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example

[0026] like Figures 1-3As shown, a data flow control system for hardware board status management includes a serial port data receiving and parsing module, a resource utilization acquisition module, a data processing module, a data compression and optimization module, a data transmission module, a health information uploading module, a health information collection module, an IIC module, a data frame parsing module, and a serial port configuration and management module. The data compression and optimization module interacts with the serial port data receiving and parsing module; the data transmission module interacts with the serial port configuration and management module; the health information collection module interacts with the health information uploading module; the IIC module interacts with the health information collection module; the health information collection module interacts with the Ethernet data frame parsing module; the serial port configuration and management module interacts with the IIC module; and the data compression and optimization module interacts with the data transmission module. The serial port data receiving and parsing module classifies data in real time, the resource utilization acquisition module binds priority and resource status to generate a dynamic weight coefficient table, and the data compression and optimization module dynamically switches compression algorithms according to priority and resource load, and adjusts the compression intensity in combination with the sliding window algorithm. The health information collection module generates a health index after obtaining the hardware status through the IIC module. The data transmission module dynamically adjusts the transmission rate based on the health index and bandwidth utilization using a fuzzy logic controller. The serial port configuration and management module dynamically adjusts the baud rate and data bits according to the historical error rate and health index value. The IIC module calibrates the serial port clock to eliminate timing deviations.

[0027] In the specific implementation scenario of this data flow control system, assume a data center needs to monitor and manage the status and data flow of multiple hardware boards in real time to ensure efficient system operation and stability. The system achieves precise control of data flow, dynamic optimization of resource utilization, and real-time monitoring of hardware health status through the collaborative work of multiple modules. First, the serial port data receiving and parsing module connects to the hardware boards via a serial port, receiving and classifying data streams in real time. Data is divided into high-priority (e.g., critical business data) and low-priority (e.g., log data), and the classification results are transmitted to the resource utilization acquisition module. The resource utilization acquisition module generates a dynamic weight coefficient table based on the hardware board's resource status (e.g., CPU utilization, memory usage) and data priority, providing a decision-making basis for the data compression and optimization module. The data compression and optimization module dynamically switches compression algorithms (e.g., LZ77, Huffman coding) based on the weight coefficient table and resource load, and adjusts the compression intensity using a sliding window algorithm to ensure efficient transmission of high-priority data even under resource constraints, while reducing bandwidth consumption of low-priority data.

[0028] The health information collection module communicates with the hardware board via the IIC module to acquire real-time hardware status information (such as temperature, voltage, and fan speed) and generate a health index (e.g., the health index decreases when the temperature is too high). The data transmission module dynamically adjusts the transmission rate based on the health index and current bandwidth utilization using a fuzzy logic controller. For example, it reduces the transmission rate when the health index is low or the bandwidth utilization is high to avoid hardware overload or network congestion. The serial port configuration and management module dynamically adjusts the serial port baud rate and data bits based on historical error rates (such as the number of data verification failures) and the health index value. For example, it reduces the baud rate to improve transmission stability when the error rate is high, and simultaneously calibrates the serial port clock via the IIC module to eliminate timing deviations and ensure the accuracy and reliability of data transmission.

[0029] Throughout the workflow, the modules collaborate through data interaction: the serial data receiving and parsing module classifies data in real time; the resource utilization acquisition module generates a dynamic weight coefficient table; the data compression and optimization module dynamically adjusts the compression strategy based on weights and resource load; the data transmission module dynamically adjusts the transmission rate based on health index and bandwidth utilization; the health information collection module acquires hardware status and generates a health index through the IIC module; the serial port configuration and management module dynamically adjusts serial port parameters based on historical error rate and health index; and the IIC module calibrates the serial port clock to eliminate timing deviations. Finally, the system transmits the processed data to the upper-layer management system through the Ethernet data frame parsing module, achieving comprehensive monitoring and management of hardware board status and data traffic. In this scenario, the serial data receiving and parsing module ensures the accuracy of data classification, the resource utilization acquisition module provides dynamic weight support, the data compression and optimization module achieves efficient data compression, the data transmission module dynamically adjusts the transmission rate through a fuzzy logic controller, the health information collection module monitors hardware status in real time, the serial port configuration and management module optimizes serial port parameters, the IIC module ensures timing accuracy, and the Ethernet data frame parsing module standardizes data transmission. Through modular design and collaborative operation, the overall system achieves precise control of data traffic, dynamic optimization of resource utilization, and real-time monitoring of hardware health status, providing a reliable guarantee for the efficient operation of the data center.

[0030] like Figure 1 As shown, in the specific implementation, data acquisition from the MCU is achieved through serial port interaction between the CPU and the MCU, receiving and sending relevant data. The CPU first opens the serial port for communication with the MCU, setting parameters such as baud rate and parity according to the protocol. The serial port continuously receives data and places it into a circular queue for processing. All data received from the serial port is placed into the circular queue, where the data processing thread parses the data. Based on the frame header, frame length, and checksum, a complete packet of health management data from the MCU is obtained, and valid hardware information such as current and voltage is extracted from the data.

[0031] The serial port data receiving and parsing module receives raw data streams from the hardware board in real time and classifies and parses the data. By receiving the serial port data stream from the hardware board, it identifies the header markers of the data packets and dynamically prioritizes them according to data type. The classified data is then transmitted to the data compression and optimization module, while priority tags are sent to the resource utilization acquisition module as the basis for generating a dynamic weight coefficient table. This solution dynamically classifies data types and assigns priorities by identifying header markers of data packets, ensuring that critical business data is processed first. In specific working scenarios, such as high-load environments in data centers, the system can receive raw data streams from the hardware board in real time and dynamically classify them according to data content (such as control commands and log information). High-priority data is transmitted to the data compression and optimization module, while priority tags are sent to the resource utilization acquisition module, providing basic data support for subsequent resource allocation. This dynamic priority division significantly improves the system's response speed and data processing efficiency.

[0032] The resource utilization acquisition module collects CPU, memory, and bandwidth usage in real time, and combines this with priority tags sent by the serial port data receiving and parsing module to calculate dynamic weight coefficients for each priority data. This weight coefficient table is then sent to the data compression and optimization module for selecting compression algorithms and adjusting compression intensity. When hardware resources are scarce, the system can prioritize resource allocation to high-priority data based on the weight coefficient table, ensuring the stable operation of critical services. Conversely, when resources are idle, the system optimizes the processing efficiency of low-priority data, maximizing resource utilization. This dynamic weight allocation mechanism significantly improves the system's resource management capabilities.

[0033] The data compression and optimization module dynamically selects a compression algorithm and adjusts the compression intensity based on priority and resource load. It selects the algorithm by receiving categorized data from the serial port data receiving and parsing module and combining this with the dynamic weight coefficient table from the resource utilization acquisition module. The compression intensity is dynamically adjusted using a sliding window algorithm. When resources exceed a set threshold, the compression level is reduced to decrease CPU usage; when resources are below the set threshold, the compression level is increased to optimize data volume. When CPU utilization exceeds a set threshold, the system automatically reduces the compression level to decrease resource consumption; conversely, when resources are idle, the system increases the compression level to optimize data volume. This dynamic compression strategy not only improves data transmission efficiency but also reduces the load on hardware resources.

[0034] The data transmission module dynamically adjusts the transmission rate based on a health index and bandwidth utilization. It receives the health index and current bandwidth utilization from the health information collection module and uses a fuzzy logic controller to calculate the upper limit of the transmission rate. When the health index is <0.5 and bandwidth utilization is >70%, a degraded transmission mode is triggered, reducing the rate to 50% of the nominal value. When the health index is ≥0.8 and bandwidth idle time is >40%, a burst transmission mode is activated, increasing the rate to 150%. In specific operating scenarios, for example, when the hardware health index is below 0.5 and bandwidth utilization exceeds 70%, the system automatically triggers a degraded transmission mode, reducing the rate to 50% of the nominal value to avoid hardware overload and network congestion. Conversely, when the health index is above 0.8 and bandwidth idle time exceeds 40%, the system activates a burst transmission mode, increasing the rate to 150% to maximize data transmission efficiency. This dynamic rate adjustment mechanism significantly improves the system's adaptability and stability.

[0035] The health information collection module reads the temperature and voltage parameters of the hardware board through the IIC module, calculates a health index by combining it with network latency data from the Ethernet data frame parsing module, and sends the health index to the data transmission module and the serial port configuration and management module for dynamic adjustment of transmission rate and serial port parameters. In specific operating scenarios, such as when the hardware temperature is too high or the voltage is abnormal, the system can adjust the transmission rate and serial port parameters in real time to avoid hardware failure and data loss. This real-time health monitoring mechanism significantly improves the reliability and security of the system.

[0036] The serial port configuration and management module dynamically adjusts serial port parameters based on historical error rates and health indices. It receives health indices and historical transmission error rates from the health information collection module to dynamically adjust the baud rate and data bits, and calibrates the serial port clock frequency via the IIC module to eliminate timing deviations caused by external interference, ensuring the stability and reliability of data transmission. In specific operating scenarios, such as when the historical error rate is high or the health index is low, the system automatically reduces the baud rate to improve transmission stability; while when the health status is good, the system optimizes serial port parameters to improve transmission efficiency. This dynamic parameter adjustment mechanism significantly improves the stability and reliability of data transmission.

[0037] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A data flow control system for hardware board card state management, characterized by, The system includes a serial port data receiving and parsing module, a resource utilization acquisition module, a data processing module, a data compression and optimization module, a data transmission module, a health information uploading module, a health information collection module, an IIC module, a data frame parsing module, and a serial port configuration and management module. The data compression and optimization module interacts with the serial port data receiving and parsing module; the data transmission module interacts with the serial port configuration and management module; the health information collection module interacts with the health information uploading module; the IIC module interacts with the health information collection module; the health information collection module interacts with the Ethernet data frame parsing module; the serial port configuration and management module interacts with the IIC module; and the data compression and optimization module interacts with the data transmission module. The serial port data receiving and parsing module classifies data in real time, the resource utilization acquisition module binds priority and resource status to generate a dynamic weight coefficient table, and the data compression and optimization module dynamically switches compression algorithms according to priority and resource load, and adjusts the compression intensity in combination with the sliding window algorithm. The health information collection module generates a health index after obtaining the hardware status through the IIC module. The data transmission module dynamically adjusts the transmission rate based on the health index and bandwidth utilization using a fuzzy logic controller. The serial port configuration and management module dynamically adjusts the baud rate and data bits according to the historical error rate and health index value. The IIC module calibrates the serial port clock to eliminate timing deviations. When the data compression and optimization module dynamically selects a compression algorithm and adjusts the compression intensity based on priority and resource load, it selects a compression algorithm by receiving classified data from the serial port data receiving and parsing module and combining the dynamic weight coefficient table of the resource utilization acquisition module. It also dynamically adjusts the compression intensity according to the sliding window algorithm, and reduces the compression level to reduce CPU usage when resources exceed a set threshold, and increases the compression level to optimize data volume when resources are below a set threshold. The data transmission module is used to dynamically adjust the transmission rate based on the health index and bandwidth utilization. It calculates the upper limit of the transmission rate by receiving the health index and current bandwidth utilization from the health information collection module and using a fuzzy logic controller. When the health index is <0.5 and bandwidth utilization is >70%, a degraded transmission mode is triggered, reducing the speed to 50% of the nominal value; when the health index is ≥0.8 and bandwidth idle time is >40%, a burst transmission mode is activated, increasing the speed to 150%. The serial port data receiving and parsing module is used to receive the raw data stream from the hardware board in real time, and to classify and parse the data; by receiving the serial port data stream from the hardware board, it identifies the header markers of the data packets and dynamically assigns priorities according to the data type. The categorized data is transmitted to the data compression and optimization module, while the priority label is sent to the resource utilization acquisition module as the basis for generating the dynamic weight coefficient table.

2. The data flow control system for hardware board status management as described in claim 1, characterized in that: The resource utilization acquisition module collects CPU, memory, and bandwidth usage in real time, and calculates the dynamic weight coefficient of each priority data by combining the priority tags sent by the serial port data receiving and parsing module. The weight coefficient table is then sent to the data compression and optimization module for the selection of compression algorithms and the adjustment of compression intensity.

3. A data flow control system for hardware board status management as described in claim 1, characterized in that: The health information collection module reads the temperature and voltage parameters of the hardware board through the IIC module, calculates the health index by combining the network latency data from the Ethernet data frame parsing module, and sends the health index to the data transmission module and the serial port configuration and management module for dynamic adjustment of the transmission rate and serial port parameters.

4. A data flow control system for hardware board status management as described in claim 1, characterized in that: The serial port configuration and management module dynamically adjusts the serial port parameters based on the historical error rate and health index. The serial port configuration and management module receives the health index and historical transmission error rate from the health information collection module to dynamically adjust the baud rate and data bits, and calibrates the serial port clock frequency through the IIC module to eliminate timing deviations caused by external interference, ensuring the stability and reliability of data transmission.

Images