System simulation efficient data communication method based on heartbeat entropy detection

Abstract

The invention discloses a system simulation efficient data communication method based on heartbeat entropy detection, and belongs to the technical field of distributed simulation. The method comprises the following steps: firstly, designing a non-sensitive communication interface of a heterogeneous platform, defining a communication interface compatible with multi-end sequence identification according to the characteristics of bottom layer drivers under different architecture operating systems, and then designing a distributed probe network based on heartbeat entropy detection, packaging probe information by adopting a TLV format, and constructing a distributed node capability matrix; designing a multi-dimensional communication environment feature sensing algorithm, and evaluating and selecting an optimal communication mode in real time; and finally, designing a dynamic weight distribution QOS strategy self-optimization algorithm, evaluating the stability of data transmission, and dynamically adjusting the serialization mode of each packet of data. According to the invention, the data barrier of the big and small end operating systems is broken through, the complex network initial configuration is simplified, and the memory space and the transmission efficiency are balanced on the basis of ensuring the communication reliability.

Description

Technical Field

[0001] This invention relates to simulation communication technology, specifically to an efficient data communication method for system simulation based on heartbeat entropy detection, belonging to the field of distributed simulation technology. Background Technology

[0002] Currently, mainstream distributed simulation communication is based on the publish / subscribe DDS protocol, with major commercial software including RTI-DDS and OpenDDS. During use, users need to perform complex network configurations, including IP addresses, port numbers, topic data sizes, and communication methods. This leads to low communication stability, poor reliability, and significant difficulties in porting and deployment in practical applications. Furthermore, because mainstream communication protocols offer limited support for user-defined variable-length data, considerable time is still required for debugging, which defeats the original purpose of using DDS communication in distributed simulation.

[0003] Furthermore, with the increasing demand for domestically produced hardware, flexible communication between ARM and x86 architectures is urgently needed. Currently available communication methods cannot achieve seamless communication between both ends and require extensive development and testing for specific hardware and operating system versions. Summary of the Invention

[0004] This invention proposes an efficient data communication method for system simulation based on heartbeat entropy detection, which solves problems such as difficulty in unifying communication protocols between big-endian and small-endian operating systems, complex communication configuration, slow communication speed, unstable communication, and long communication development and debugging cycle in distributed simulation. It breaks down the data barriers between big-endian and small-endian operating systems, simplifies the complex initial network configuration, and balances memory space and transmission efficiency while ensuring communication reliability.

[0005] The above objectives are achieved through the following technical solutions:

[0006] The present invention provides an efficient data communication method for system simulation based on heartbeat entropy detection, comprising the following steps:

[0007] Step 1: Design of seamless communication interface for heterogeneous platforms. Based on the characteristics of the underlying drivers under different architecture operating systems, define a communication interface that is compatible with multi-terminal sequence recognition.

[0008] Step 2: Design a distributed probe network based on heartbeat entropy detection, encapsulate probe information in TLV format, and construct a distributed node capability matrix;

[0009] Step 3: Design a multi-dimensional communication environment feature perception algorithm to evaluate and select the optimal communication method in real time;

[0010] Step 4: Design a self-optimizing algorithm for the QoS strategy with dynamic weight allocation, evaluate the stability of data transmission, and dynamically adjust the serialization method of each data packet.

[0011] Furthermore, the distributed probe network design based on heartbeat entropy detection described in step 2 specifically involves designing a primary communication protocol selection rule based on heartbeat entropy detection according to the topic data relationship mapping table, the target platform type, the size of the topic data, and the importance of the data.

[0012] Furthermore, the primary communication protocol selection rule based on heartbeat entropy detection has the following data settings: whether multi-machine communication is required. Is it important data? Is it high-frequency data? Is it big data? Communication Comprehensive Score The appropriate communication method is determined based on the following evaluation criteria: .

[0013] Furthermore, the multi-dimensional communication environment feature perception algorithm design described in step 3 specifically involves scoring the network environment. ,in These are weighting coefficients and need to be adjusted according to different simulation environments to ultimately maintain the network environment score between 0 and 3. For RTT round trip delay, For packet loss rate, Available bandwidth, combined with comprehensive communication score The selection criteria for the best communication method are as follows: .

[0014] Furthermore, the dynamic weight allocation QoS strategy self-optimization algorithm design described in step 4, in order to cope with the unstable quality during communication, designs a stability evaluation rule, such that the total stability evaluation score is... It is related to real-time packet loss rate, real-time round-trip latency, and real-time available bandwidth, and its normalized value range is: The evaluation rules are as follows: .

[0015] Compared with the prior art, the significant advantages of the present invention are:

[0016] This invention provides an efficient data communication method for system simulation based on heartbeat entropy detection. The core idea of ​​this method is to break down communication barriers between domestically developed operating systems, while significantly simplifying user configuration of network communication. Specific advantages include:

[0017] (1) Good compatibility. It breaks down the data barriers between big-endian and small-endian operating systems, enabling interconnection between ARM and x86 architectures.

[0018] (2) Easy to use. Based on the current hardware, system, network status and subject data type, it intelligently selects the appropriate communication method, avoiding complicated initial network configuration for users.

[0019] (3) Stable and reliable. During operation, the network status is monitored in real time, and the QoS strategy is adaptively adjusted based on the stability assessment results. The underlying serialization method is optimized to balance memory space and transmission efficiency while ensuring communication reliability. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the distributed communication principle proposed in this invention;

[0021] Figure 2 A schematic diagram of the communication node sensing principle;

[0022] Figure 3 A flowchart for network environment awareness;

[0023] Figure 4 Flowchart for configuring adaptive QoS;

[0024] Figure 5 Adjust the process for serialization strategy. Detailed Implementation

[0025] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0026] like Figure 1 The figure shows a system simulation high-efficiency data communication method based on heartbeat entropy detection proposed in this invention. The specific steps are as follows:

[0027] Step 1: Design of seamless communication interface for heterogeneous platforms. Based on the characteristics of the underlying drivers under different architecture operating systems, define a communication interface that is compatible with multi-terminal sequence recognition.

[0028] By identifying the operating systems under different hardware architectures, and based on the system's underlying drivers, a communication interface layer supporting dual-endian order recognition is defined. To identify different platform types, the protocol header first needs to be standardized by incorporating hardware and system information, as shown below:

[0029] Table 1. Bottom header information: Serial Number name type byte length illustrate 1 Protocol identifier uint32_t 4 The fixed value 0x4C595E5B is used to verify the legality of the agreement. 2 Endianness Marking Uint8_t 1 0: Little-endian, 1: Big-endian. Used for subsequent field parsing. 3 Hardware architecture Uint8_t 1 0: x86 architecture; 1: ARM architecture. 4 System Information Uint8_t 1 0: Kylin v4; 1: Kylin v10; 2: Windows. 5 Version number Uint8_t 1 For protocol compatibility, such as 0x01. 6 Total length of Baotou Uint16_t 2 It facilitates dynamic expansion. 7 Reserved fields Uint8_t[2] 2 It facilitates dynamic expansion. 8 Packet header checksum Uint16_t 2 Custom checksum algorithms ensure transmission integrity. .

[0030] The key to this step lies in the underlying automatic byte order conversion. Developers only need to focus on the business logic, without having to manually handle byte order or platform differences, thus shortening the development cycle. During data transmission and reception, the target platform information is determined based on the packet header definition, enabling automatic data conversion. The key implementation process is as follows:

[0031] (1) Communication protocol version matching. If the versions are incompatible, the request will be rejected;

[0032] (2) Automatic byte order conversion. The receiver determines the byte order of the data stream based on the endianness flag. If the byte order is inconsistent with the local endianness flag, the receiver performs automatic byte order conversion on subsequent fields.

[0033] (3) Default byte alignment. The ARM platform requires 4-byte alignment, while x86 can be dynamically adjusted.

[0034] (4) Floating-point number types are uniformly converted to the IEEE 754 standard format for transmission.

[0035] (5) Verification and fault tolerance. The sender calculates and fills the checksum. If the receiver fails to verify the checksum, it requests a retransmission.

[0036] Step 2, as follows Figure 2 As shown, a distributed probe network based on heartbeat entropy detection is designed, using TLV format to encapsulate probe information and construct a distributed node capability matrix; the key implementation process is as follows:

[0037] (1) Quickly establish node probe awareness. During the node discovery phase, use UDP multicast (239.255.0.1) to register and mark all communication nodes in the local area network that conform to the protocol, forming a mapping relationship between target IP and platform information. The mapping relationship will be stored in the memory cache of each communication node in real time, providing a fast query channel for subsequent communication selection, and facilitating information sharing of communication nodes that come online later;

[0038] Table 2 Node Information Table Contents: Serial Number property illustrate 1 Node Unique ID Uses UUID format. 2 Node Name 3 IP address 4 Endianness Marking 0: Little-endian, 1: Big-endian. 5 Hardware architecture 0: x86 architecture; 1: ARM architecture. 6 System Information 0: Kylin v4; 1: Kylin v10; 2: Windows. .

[0039] (2) Establish a topic data relationship mapping. Based on the existing node information table, form a topic-based subscription and release mapping table according to the topic data information (topic name, data packet size) of the node subscription and release. The mapping relationship will be stored in real time in the internal cache of the node related to the current topic, providing a fast query channel for subsequent topic subscription and release of nodes;

[0040] Table 3 Subject Data Relationship Mapping Table Serial Number property illustrate 1 Unique ID for the Theme Uses UUID format. 2 Subject Name 3 Topic data size 4 Order Node List The node ID should be consistent with the node list information. 5 List of published nodes The node ID should be consistent with the node list information. .

[0041] (3) Primary communication protocol selection: Based on the topic data relationship mapping table, the communication protocol is evaluated and selected for the first time according to the target platform type, topic data size and data importance, so as to provide a basis for the preferred communication protocol in step 3;

[0042] The evaluation rules mainly consider four dimensions: whether multi-machine communication is involved. Is it important data? Is it high-frequency data? Is it big data? Each dimension is scored out of 1 point, and the total score is calculated accordingly. Select the corresponding communication method, as shown below: .

[0043] When communicating with the local machine, always choose shared memory communication mode; when the total score is 1, prioritize UDP communication; when the total score is 2 and 3, choose UDP communication mode, and combine it with the traditional retransmission mechanism to ensure data security; when the total score is 4, choose TCP communication mode to ensure absolute data security.

[0044] (4) When a node exits, it first notifies the nodes that have a subscription relationship with it to complete the data loop; then it publishes the offline information in the multicast network, and all nodes cancel the node.

[0045] Step 3, as follows Figure 3 As shown, a multi-dimensional communication environment feature perception algorithm is designed to evaluate and select the optimal communication method in real time; the key implementation process is as follows:

[0046] (1) Network environment awareness: Real-time collection of RTT (round-trip time) by establishing TCP and UDP network communication between local machines. Packet loss rate Available bandwidth Information such as network environment score is provided. .

[0047] 1) Real-time RTT normalization

[0048] RTT normalization is based on a pre-set latency limit. and lower limit For actual collection The numerical values ​​are normalized as follows: .

[0049] 2) Packet loss rate

[0050] Packet loss rate It is based on the number of successfully sent packets. With the number of packets sent The ratio of the difference to the total number of packets sent is shown below: .

[0051] To avoid a sharp drop in score due to a single high packet loss rate, the packet loss rate needs to be optimized. Among other things... It is the packet loss penalty coefficient. It is the maximum penalty limit.

[0052] .

[0053] 3) Available bandwidth normalization

[0054] bandwidth It is based on the theoretical bandwidth limit. Normalization is performed as follows: .

[0055] 4) Network Environment Score

[0056] Network environment scoring is based on round-trip latency. Score, packet loss rate score Available bandwidth The scores are weighted and assigned as follows: , in The weighting coefficients need to be adjusted according to different simulation environments to ultimately keep the network environment score between 0 and 3.

[0057] (2) The communication method optimization algorithm mainly considers two dimensions: network environment score and the total communication score in step 2 The specific rules are as follows: .

[0058] When In all cases, shared memory communication should be selected; when When UDP communication is preferred, then UDP communication is preferred. When UDP communication is selected, a traditional retransmission mechanism is used to ensure data security; when When using TCP communication, ensure absolute data security.

[0059] Step 4, as follows Figure 4 As shown, a self-optimizing algorithm for dynamic weight allocation QoS strategy is designed to evaluate data transmission stability and dynamically adjust the serialization method of each data packet. The key implementation process is as follows:

[0060] (1) During the distributed simulation, the DDS communication has completed its initialization, and the communication method has been mapped and cannot be changed. At this time, in order to deal with the unstable communication quality, the QoS configuration of each packet of topic data needs to be dynamically adjusted. The core is stability assessment, memory management and serialization strategy adjustment.

[0061] (2) Stability assessment, which mainly involves real-time evaluation of runtime communication quality. The assessment method is similar to that of the environment-aware algorithm, and also includes packet loss rate, round-trip time, and available bandwidth. The total stability assessment score is... The normalized range of values ​​is When the score is below 0.8, the memory management mode switches to space mode; when the score is below 0.6, the serialization strategy needs to be adjusted. The specific rules are as follows: .

[0062] (3) Memory management modes are divided into time mode and space mode. When network conditions are good, time mode is preferred, and memory management is relatively relaxed, resulting in a larger memory footprint. When the network environment is unstable, space mode is used. In this case, memory management is streamlined, reducing the size of a data packet and employing a multiple-transmission scheme.

[0063] (4) Adjusting the serialization strategy is done when the network environment is particularly poor. In this case, based on the original spatial pattern, a minimal serialization method is adopted, such as... Figure 5 As shown. The core of the minimal serialization mode is to minimize the memory usage of the data structure. It utilizes attribute reflection to serialize each attribute of the data structure individually, thus simulating a one-byte alignment effect. This mode, due to the special processing required for the data structure, will experience a decrease in efficiency.

[0064] The above are merely preferred embodiments of the present invention, but the present invention is not limited to the above embodiments. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art, under the guidance of the present invention, can make many other forms without departing from the spirit and scope of the claims of the present invention, and all of these are within the protection scope of the present invention.

Claims

1. A system simulation method for efficient data communication based on heartbeat entropy detection, characterized in that, Includes the following steps: Step 1: Design of seamless communication interface for heterogeneous platforms. Based on the characteristics of the underlying drivers under different architecture operating systems, define a communication interface that is compatible with multi-terminal sequence recognition. Step 2: Design a distributed probe network based on heartbeat entropy detection, encapsulate probe information in TLV format, and construct a distributed node capability matrix; Step 3: Design a multi-dimensional communication environment feature perception algorithm to evaluate and select the optimal communication method in real time; Step 4: Design a self-optimizing algorithm for the QoS strategy with dynamic weight allocation, evaluate the stability of data transmission, and dynamically adjust the serialization method of each data packet.

2. The efficient data communication method for system simulation based on heartbeat entropy detection according to claim 1, characterized in that, The design of the distributed probe network based on heartbeat entropy detection described in step 2 specifically involves designing a primary communication protocol selection rule based on heartbeat entropy detection, according to the topic data relationship mapping table, the target platform type, the size of the topic data, and the importance of the data.

3. The efficient data communication method for system simulation based on heartbeat entropy detection according to claim 2, characterized in that, The primary communication protocol selection rule based on heartbeat entropy detection has the following data settings: whether multi-machine communication is required. Is it important data? Is it high-frequency data? Is it big data? Communication Comprehensive Score The appropriate communication method is determined based on the following evaluation criteria: 。 4. The efficient data communication method for system simulation based on heartbeat entropy detection according to claim 2, characterized in that, The multi-dimensional communication environment feature perception algorithm design described in step 3 specifically involves scoring the network environment. ,in These are weighting coefficients and need to be adjusted according to different simulation environments to ultimately maintain the network environment score between 0 and 3. For RTT round trip delay, For packet loss rate, Available bandwidth, combined with comprehensive communication score The selection criteria for the best communication method are as follows: 。 5. The efficient data communication method for system simulation based on heartbeat entropy detection according to claim 1, characterized in that, The dynamic weight allocation QoS strategy self-optimization algorithm design described in step 4, in order to cope with the unstable quality during communication, designs a stability evaluation rule, such that the total stability evaluation score is... It is related to real-time packet loss rate, real-time round-trip latency, and real-time available bandwidth, and its normalized value range is: The evaluation rules are as follows: 。

Images