A fuzzy dynamic scheduling data communication detection system based on virtual-reality interaction scenario

Abstract

The present application relates to the field of data communication detection technology, and in particular to a fuzzy dynamic scheduling data communication detection system based on a virtual-real interaction scenario, wherein the system comprises a hardware interface layer, a data interaction layer, and a simulation application layer; a hardware abstraction interface module is deployed in the hardware interface layer; a dynamic priority scheduler, a lightweight binary encoder, and an adaptive transmission controller are set in the data interaction layer, and the simulation application layer is connected to the simulation platform through a timestamp synchronization mechanism; wherein the data flow is designed based on a bidirectional ring buffer structure, the raw data received by the hardware interface layer is stored in the input buffer after verification, the data interaction layer extracts the data from the buffer, processes it, and sends it to the simulation application layer, and the feedback instruction is sent to the actuator through the reverse path. Thus, the problems of protocol redundancy, low resource efficiency, and insufficient dynamic adaptability in the prior art are solved.

Description

Technical Field

[0001] The present application relates to the technical field of data communication detection, and in particular to a fuzzy dynamic scheduling data communication detection system based on a virtual-reality interaction scenario. Background Art

[0002] With the continuous advancement of technology, the intelligent and automated development of construction machinery continues to accelerate. Against this backdrop, hardware-in-the-loop simulation, with its exceptional capabilities in verifying the reliability and real-time performance of control systems, has become an indispensable key technology in this field, enjoying widespread and in-depth application. This technology cleverly integrates real-world hardware with virtual models for real-time interaction, accurately and efficiently simulating the actual operating behavior of construction machinery under complex operating conditions, significantly reducing the cost and potential risks associated with physical testing.

[0003] However, although semi-physical simulation technology has made certain progress in the field of engineering machinery, the existing semi-physical simulation system still faces a series of technical bottlenecks that need to be broken through in actual application.

[0004] Traditional communication protocol stacks are typically designed to fully implement the OSI seven-layer model. This requirement forces data to undergo numerous layers of processing during encapsulation and decapsulation, resulting in a significant amount of redundant operations. For example, in actual testing of a certain excavator control system, single-transmission data transmission delays reached as high as 2.8ms. In demanding real-time applications like hydraulic control, system response times must reach 1ms to meet these requirements. Clearly, the real-time performance of existing systems is insufficient for these demanding scenarios, severely limiting overall system performance.

[0005] In the transmission of high-frequency, small data packets, the fixed header design of existing communication protocols (typically 8-16 bytes) results in a low payload rate, typically less than 40%. This means that a large amount of valuable network bandwidth is consumed by invalid data. Furthermore, the conversion between heterogeneous protocols relies heavily on complex middleware, further exacerbating CPU resource consumption. Typically, CPU utilization exceeds 30%, significantly depleting resources required for the normal execution of control algorithms, thereby affecting system response speed and stability.

[0006] During actual operation, construction machinery often exhibits multiple complex operating modes. For example, an excavator may simultaneously perform complex actions, such as digging and traveling. Switching between different operating modes is frequent and uncertain. However, traditional static priority scheduling mechanisms are unable to cope with this multi-modal switching. When burst control instructions compete with regular data streams for bandwidth resources, the packet loss rate of critical data can rise sharply, exceeding 5%. This phenomenon directly leads to cumulative errors in the simulation model, reaching over 3%, seriously impacting system reliability.

[0007] Engineering machinery systems usually integrate a large number of multi-source heterogeneous devices from different manufacturers using different interface protocols, such as devices based on different interfaces such as CAN protocol, EtherCAT protocol, Modbus protocol, etc. When connecting these devices to the existing semi-physical simulation system, it is necessary to develop a customized protocol adaptation layer for each device. This process is not only cumbersome and complicated, but also leads to a significant extension of the system deployment cycle, usually increasing the time cost by more than 60%. In addition, the existing system has obvious performance shortcomings when dealing with harsh environmental conditions such as a wide temperature range (-40℃~85℃), making it difficult to ensure the stable operation of the system under complex working conditions, thereby limiting its application in a wider range of scenarios.

[0008] A common hardware-in-the-loop simulation system based on a virtual serial port is currently available. This solution utilizes a host computer PLC as the core of the hardware architecture, connected to a PC via a converter, and employing the Mitsubishi non-sequential protocol for communication. On the slave computer side, a virtual 3D model is constructed on the PC, and the device's motion control is implemented using the Unity3D engine. Data exchange between the host and slave computers is accomplished via a virtual serial port, with register data transmitted using a round-robin mechanism. However, this solution suffers from several significant drawbacks. For example, the system response latency is as high as approximately 14ms, far from meeting the stringent real-time requirements of highly dynamic control scenarios. The fixed 8-byte message format results in low bandwidth utilization and poor scalability, making it difficult to adapt to changing system requirements. Unity3D's multi-threaded processing mechanism consumes a significant amount of CPU resources, further reducing overall system performance. Furthermore, the lack of an effective dynamic scheduling mechanism significantly increases packet loss in bursty traffic, severely impacting system reliability and stability. Summary of the Invention

[0009] The present application provides a fuzzy dynamic scheduling data communication detection system based on a virtual-reality interaction scenario to solve the problems of protocol redundancy, low resource efficiency, and insufficient dynamic adaptability in the prior art.

[0010] The first embodiment of the present application provides a fuzzy dynamic scheduling data communication detection system based on a virtual-real interaction scenario, the system includes a hardware interface layer, a data interaction layer and a simulation application layer, wherein a hardware abstract interface module is deployed in the hardware interface layer, a multi-protocol driver is integrated, the original electrical signal is converted into a standard data frame through a physical layer signal conversion circuit, and the device protocol type is analyzed in real time based on a sliding window matching algorithm through a built-in protocol identification unit; a dynamic priority scheduler, a lightweight binary encoder and an adaptive transmission controller are set in the data interaction layer, wherein the dynamic priority scheduler is based on a dynamic priority algorithm of fuzzy control, according to the data The transmission priority is adjusted in real time based on the timeliness, criticality, data volume and system load of the flow; the lightweight binary encoder adopts a variable length compression protocol to encode and compress the data; the adaptive transmission controller dynamically adjusts the data packet size through a bandwidth prediction model and adopts a two-level retransmission mechanism; the simulation application layer is connected to the simulation platform through a timestamp synchronization mechanism to keep the virtual and real data consistent; wherein, the data flow is designed based on a bidirectional ring buffer structure, the original data received by the hardware interface layer is stored in the input buffer after verification, the data interaction layer extracts the data from the buffer, processes it and sends it to the simulation application layer, and the feedback instruction is sent to the actuator through the reverse path.

[0011] Optionally, the multi-protocol driver includes CAN, EtherCAT, and Modbus protocol drivers, and the time consumed by the protocol identification unit to identify the device protocol type is controlled within 200ms.

[0012] Optionally, a dynamic priority algorithm based on fuzzy control includes: obtaining four-dimensional characteristic parameters, wherein the four-dimensional characteristic parameters include timeliness coefficient, criticality level, data volume and system load rate; constructing a fuzzy rule base, inputting the four-dimensional characteristic parameters into the fuzzy rule base to obtain the target priority weight; designing a double-buffer queue structure, wherein the high-priority queue in the target priority adopts preemptive scheduling, and the regular queue adopts weighted round-robin scheduling.

[0013] Optionally, a variable-length compression protocol is used to encode and compress the data, wherein the encoding process is divided into two stages: protocol header optimization and data compression, including: the protocol header adopts a three-layer nested structure, the basic header is fixed to 3 bytes, and includes the protocol version, data type, timestamp and data length; the extension header is dynamically added according to demand and is used to store encryption identification or QoS parameters; the data compression adopts a lossy-lossless hybrid strategy, retains the first 16 valid bits for floating-point sensor data, and applies an improved LZ4 algorithm to compress only continuous similar data.

[0014] Optionally, the adaptive transmission controller dynamically adjusts the data packet size through a bandwidth prediction model, including: predicting the network bandwidth based on an exponentially weighted moving average algorithm; when the predicted network bandwidth is higher than a first threshold, adopting large packet transmission; when it is lower than a second threshold, switching to small packet transmission; and calculating the target packet length in the intermediate state through linear interpolation.

[0015] Optionally, a two-level retransmission mechanism is adopted, including: high-priority data in the target priority level is immediately retransmitted after timeout, and regular data adopts a binary exponential backoff strategy.

[0016] Optionally, the bidirectional ring buffer structure includes an input buffer, an output buffer and a buffer, wherein the input buffer is used to store original data from the hardware interface layer; the output buffer is used to store data processed by the data interaction layer; and the buffer uses a dual-port RAM to achieve zero-copy transmission.

[0017] The second aspect of the present application provides a fuzzy dynamic scheduling data communication detection method based on a virtual-reality interaction scenario, including: obtaining the original data stream; storing the original data in an input buffer and performing feature extraction; judging the data after feature extraction, wherein if the priority is greater than or equal to the priority threshold, entering the preemptive channel; otherwise, entering the regular queue; then performing data compression processing to obtain compressed package data; obtaining a feedback instruction based on the compressed package data, issuing and executing the feedback instruction, and at the same time, monitoring the compressed package data, and issuing an alarm if the compressed package data exceeds the packet loss threshold, and optimizing the dynamic weight coefficient in time.

[0018] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to perform the fuzzy dynamic scheduling data communication detection method based on the virtual-reality interaction scenario as described in the above embodiment.

[0019] The fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to perform the fuzzy dynamic scheduling data communication detection method based on the virtual-reality interaction scenario as described in the above embodiment.

[0020] Therefore, this application has at least the following beneficial effects:

[0021] (1) The hardware abstract interface module deployed in the hardware interface layer integrates multi-protocol drivers and uses the physical layer signal conversion circuit to quickly and accurately convert the original electrical signal into a standard data frame to ensure the timeliness of data acquisition. The built-in protocol identification unit analyzes the device protocol type in real time based on the sliding window matching algorithm, and can accurately identify the device communication protocol in a very short time, saving valuable time for subsequent data processing and transmission, and effectively reducing the delay in the data acquisition link;

[0022] The dynamic priority scheduler set up in the data interaction layer is based on a dynamic priority algorithm of fuzzy control. It comprehensively considers multiple factors such as the timeliness, criticality, data volume and system load of the data flow, and adjusts the transmission priority in real time to ensure that critical data can be transmitted preferentially and quickly through the communication link. This avoids the delay problem caused by critical data waiting for low-priority data transmission under the traditional fixed priority scheduling mechanism, significantly improving the real-time performance of data transmission. At the same time, the adaptive transmission controller dynamically adjusts the data packet size through the bandwidth prediction model, so that the data packet can be adaptively optimized according to the current network bandwidth conditions, further improving data transmission efficiency and reducing transmission time.

[0023] The simulation application layer seamlessly connects with the simulation platform through a high-precision timestamp synchronization mechanism, ensuring high temporal consistency between virtual and real data, with minimal errors. This precise time synchronization mechanism enables the simulation platform to respond promptly and accurately to changes in the state of actual hardware devices, providing strong support for the highly dynamic control of construction machinery and meeting the requirements of scenarios with extremely high real-time requirements, such as hydraulic control.

[0024] (2) The hardware interface layer integrates multiple protocol drivers and realizes unified management and support of multiple industrial protocols through the hardware abstract interface module. The built-in protocol identification unit analyzes the device protocol type in real time based on the sliding window matching algorithm. There is no need to develop an adapter interface for each protocol separately, which greatly shortens the development cycle of new equipment access system and reduces development costs. At the same time, this architecture can quickly adapt to different manufacturers and different types of industrial equipment, improves compatibility and scalability, and can easily cope with the complex needs of multi-source heterogeneous equipment access in the field of engineering machinery;

[0025] The lightweight binary encoder at the data exchange layer uses a variable-length compression protocol to encode and compress data, compressing the protocol header to an extremely small size. This significantly reduces protocol header overhead, increases payload rate, reduces data transmission volume, and reduces network bandwidth requirements. This allows for more effective data to be transmitted within limited bandwidth resources, improving network utilization. Furthermore, compressed data occupies fewer network resources during transmission, further reducing data transmission latency and improving overall system performance.

[0026] The adaptive transmission controller dynamically adjusts packet size using a bandwidth prediction model and employs a two-level retransmission mechanism based on data priority. This ensures reliable data transmission while rationally allocating network resources and avoiding network congestion. The fast retransmission mechanism for high-priority data prevents critical data from being lost for extended periods due to transmission errors, ensuring the system's real-time and stability. The exponential backoff retransmission mechanism for regular data, while ensuring data transmission reliability, reduces unnecessary retransmissions, lowers CPU utilization and network bandwidth consumption, and enables more efficient use of system resources.

[0027] (3) The data flow designed based on the bidirectional ring buffer structure realizes the orderly transmission and efficient processing of data between the hardware interface layer, the data interaction layer and the simulation application layer. The original data received by the hardware interface layer is stored in the input buffer after verification, ensuring the integrity and accuracy of the data. The data interaction layer extracts data from the buffer in an orderly manner for processing, and then sends the processed data to the simulation application layer, avoiding data loss and disorder problems. Feedback instructions are sent to the actuator through the reverse path, forming a complete and stable data interaction closed loop, ensuring the consistency and real-time performance of virtual and real data;

[0028] During data transmission, each layer implements corresponding verification and fault-tolerance mechanisms. The hardware interface layer verifies the received raw data to ensure accuracy during data acquisition. The data interaction layer utilizes error detection and correction technology during data processing and transmission to promptly detect and correct data transmission errors. The simulation application layer re-verifies received data to ensure the quality of data entering the simulation platform. This multi-layered verification and fault-tolerance mechanism effectively reduces data transmission error rates, improves reliability and stability, and ensures system operation even in complex industrial environments.

[0029] (4) The hardware interface layer integrates wide-temperature power management and anti-interference circuits, and can operate stably in harsh environments of -40°C to 85°C, breaking the limitations of traditional equipment on the working environment. The communication architecture of the embodiment of the present application can be widely used in various engineering machinery operation scenarios under extreme temperature conditions, such as mining in cold areas and construction in high-temperature areas, providing reliable guarantees for the research and development, testing and operation of engineering machinery in different environments; the communication architecture of the embodiment of the present application has the ability to quickly adapt to protocols, and the protocol extension development cycle is significantly shortened from 6 months in traditional solutions to 2 weeks. The adaptation can be completed quickly, reducing the upgrade cost and risk caused by protocol incompatibility.

[0030] This solves the problems of protocol redundancy, low resource efficiency, insufficient dynamic adaptability, etc. in the prior art.

[0031] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0033] Figure 1 A schematic diagram of a fuzzy dynamic scheduling data communication detection system based on a virtual-reality interaction scenario provided according to an embodiment of the present application;

[0034] Figure 2 A flowchart of dynamic priority scheduling provided according to one embodiment of the present application;

[0035] Figure 3 A schematic diagram of a fuzzy dynamic scheduling data communication detection system based on a virtual-reality interaction scenario according to an embodiment of the present application;

[0036] Figure 4 Flowchart of a fuzzy dynamic scheduling data communication detection method based on a virtual-reality interaction scenario provided by an embodiment of the present application;

[0037] Figure 5 This is a flowchart of a fuzzy dynamic scheduling data communication detection method based on a virtual-reality interaction scenario according to one embodiment of the present application;

[0038] Figure 6 A schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0039] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0040] The following describes a fuzzy dynamic scheduling data communication detection system based on a virtual-real interaction scenario with reference to the accompanying drawings. In response to the problem of low resource efficiency mentioned in the above background technology, the present application provides a fuzzy dynamic scheduling data communication detection system based on a virtual-real interaction scenario. In this system, at the hardware interface layer, a hardware abstract interface module with integrated multi-protocol drivers quickly converts electrical signals and identifies device protocols, reduces acquisition delays, and also improves compatibility and scalability, adapts to multi-source heterogeneous device access, and can operate stably in a wide temperature range; the dynamic priority scheduler of the data interaction layer adjusts the transmission priority in real time based on multi-dimensional factors, the lightweight binary encoder compresses the protocol header and improves the payload rate, and the adaptive transmission controller dynamically adjusts the packet size and adopts a two-level retransmission mechanism, which improves the real-time, efficiency and reliability of data transmission and reduces resource consumption; the simulation application layer uses a high-precision timestamp synchronization mechanism to ensure consistency between virtual and real data and meet high dynamic control requirements; the bidirectional ring buffer structure realizes orderly data transmission and closed-loop interaction, and the verification and fault tolerance mechanism of each layer reduces the error rate. Thus, the problems of protocol redundancy, low resource efficiency, and insufficient dynamic adaptability in the prior art are solved.

[0041] The following describes, with reference to the accompanying drawings, a fuzzy dynamic scheduling data communication detection system based on a virtual-reality interaction scenario according to an embodiment of the present application.

[0042] Specifically, Figure 1 This is a structural diagram of a fuzzy dynamic scheduling data communication detection system based on a virtual-reality interaction scenario provided by an embodiment of the present application.

[0043] like Figure 1 As shown, the fuzzy dynamic scheduling data communication detection system 10 based on the virtual-reality interaction scenario includes: a hardware interface layer 100, a data interaction layer 200 and a simulation application layer 300.

[0044] The hardware interface layer 100 deploys a hardware abstraction interface module, integrating multi-protocol drivers. Physical layer signal conversion circuits convert raw electrical signals into standard data frames, and a built-in protocol identification unit uses a sliding window matching algorithm to analyze device protocol types in real time. The data interaction layer 200 includes a dynamic priority scheduler, a lightweight binary encoder, and an adaptive transmission controller. The dynamic priority scheduler, based on a fuzzy control-based dynamic priority algorithm, adjusts transmission priorities in real time based on the timeliness, criticality, data volume, and system load of the data stream. The lightweight binary encoder uses a variable-length compression protocol to encode and compress data. The adaptive transmission controller dynamically adjusts packet size using a bandwidth prediction model and employs a two-level retransmission mechanism. The simulation application layer 300 connects to the simulation platform through a timestamp synchronization mechanism to ensure consistency between virtual and real data. The data flow is designed based on a bidirectional ring buffer structure. Raw data received by the hardware interface layer 100 is verified and stored in an input buffer. The data interaction layer 200 extracts data from the buffer, processes it, and sends it to the simulation application layer 300. Feedback instructions are then sent to the actuator via a reverse path.

[0045] It can be understood that the embodiment of the present application realizes millisecond-level protocol adaptation (protocol identification <10μs), deterministic real-time scheduling (key data delay <0.2ms) and microsecond-level virtual-real alignment (time synchronization error <0.05°) of industrial equipment communication through the coordination of the three-layer architecture of hardware interface layer (protocol abstraction analysis) → data interaction layer (fuzzy scheduling compression) → simulation application layer (virtual-reality closed-loop synchronization). Relying on the four core technologies of sliding window protocol identification, fuzzy control dynamic priority, variable-length lightweight encoding, and bidirectional ring buffer, resource utilization is increased by 3 times, cross-domain collaboration error is compressed to 1mm level, and extreme environment stability (MTBF 100,000 hours) and fault prediction closed-loop verification efficiency are improved.

[0046] In an embodiment of the present application, the multi-protocol driver includes CAN, EtherCAT, and Modbus protocol drivers, and the time consumed by the protocol identification unit to identify the device protocol type is controlled within 200ms.

[0047] In an embodiment of the present application, a dynamic priority algorithm based on fuzzy control includes: obtaining four-dimensional characteristic parameters, wherein the four-dimensional characteristic parameters include timeliness coefficient, criticality level, data volume and system load rate; constructing a fuzzy rule base, inputting the four-dimensional characteristic parameters into the fuzzy rule base, and obtaining the target priority weight; designing a double-buffer queue structure, wherein the high-priority queue in the target priority adopts preemptive scheduling, and the regular queue adopts weighted round-robin scheduling.

[0048] The bidirectional ring buffer structure includes an input buffer, an output buffer and a buffer.

[0049] It is understandable that the embodiment of the present application comprehensively and accurately evaluates the data priority by obtaining the four-dimensional characteristic parameters of timeliness coefficient, criticality level, data volume and system load rate, and dynamically adjusts it according to the system operation status. Preemptive scheduling is used for high-priority data to reduce the waiting time for critical data, improve the system response speed and real-time performance, and meet the needs of scenarios such as industrial automation control; weighted polling scheduling is used for conventional data to reasonably allocate resources based on changes in system load to avoid resource waste and data backlog. The double-buffered queue structure is combined with dynamic priority scheduling to avoid data loss and backlog. When the system encounters sudden traffic, processing bottlenecks or abnormal situations, it can quickly resume normal operation and enhance system stability and fault tolerance.

[0050] Specifically, the dynamic priority algorithm based on fuzzy control dynamically adjusts the transmission priority by quantifying the timeliness, criticality, data volume and system load of the data stream in real time. The algorithm first extracts four-dimensional feature parameters from the data packet header: the timeliness coefficient (T) is calculated by the countdown of the data validity period, the criticality level (C) is determined by the device security preset value, the data volume (S) directly reads the packet header length field, and the system load rate (L) is obtained by the weighted value of CPU occupancy and memory usage. Subsequently, a fuzzy rule base containing 25 rules is constructed, for example, "If the timeliness is high and the criticality level is 5, the priority is set to the highest." The membership function uses a Gaussian distribution, and the output priority is divided into five levels (VLOW to VHIGH). The final priority weight is obtained by the formula P = 0.4*T -1 +0.3*C+0.2* -1 +0.1*(1-L), and the weight coefficient is optimized jointly by offline training and online gradient descent.

[0051] In order to ensure the real-time transmission of high-priority data, a double-buffer queue structure is designed, such as Figure 2 As shown, the high-priority queue (8KB capacity) uses preemptive scheduling, immediately interrupting the current transmission task if VHIGH-level data arrives. The regular queue (32KB capacity) uses weighted round-robin scheduling, allocating bandwidth based on priority. The queue management module monitors memory usage in real time and automatically triggers a data discard strategy (preferentially discarding VLOW-level data) when buffer utilization exceeds 90%. This algorithm improves the transmission success rate of key instructions to 99.8% in a hydraulic excavator's complex motion scenario, while keeping latency fluctuation within ±0.2ms.

[0052] It should be noted that the input buffer is used to store the original data from the hardware interface layer; the output buffer is used to store the data processed by the data interaction layer; the buffer uses dual-port RAM to achieve zero-copy transmission.

[0053] The embodiment of the present application uses an input buffer to temporarily store the original data of the hardware interface layer and an output buffer to store processed data, and uses a dual-port RAM to achieve zero-copy transmission. Data is read and written bidirectionally concurrently within the ring area, avoiding data copy overhead and reducing transmission delay. Input and output can be processed in parallel and efficiently scheduled, thereby improving data throughput and system response speed.

[0054] In an embodiment of the present application, a variable-length compression protocol is used to encode and compress data, wherein the encoding process is divided into two stages: protocol header optimization and data compression, including: the protocol header adopts a three-layer nested structure, the basic header is fixed to 3 bytes, including the protocol version, data type, timestamp and data length; the extended header is dynamically added according to demand to store encryption identification or QoS parameters; the data compression adopts a lossy-lossless hybrid strategy, retains the first 16 valid bits for floating-point sensor data, and applies the improved LZ4 algorithm to compress only continuous similar data.

[0055] It is understood that the protocol header in the embodiments of this application utilizes a unique three-layer nested structure. A 3-byte base header stores core information, and extended headers are dynamically added as needed, allowing for flexible storage of encryption identifiers or QoS parameters. Data compression utilizes a hybrid lossy-lossless strategy, retaining the first 16 significant bits for floating-point sensor data and combining it with an improved LZ4 algorithm to compress consecutive similar data packets. This effectively optimizes data transmission and storage, ensuring data accuracy and security while improving transmission efficiency and reducing data redundancy.

[0056] Specifically, to address the redundancy issue of traditional protocol headers, a variable-length compression protocol (VLCP) was designed. Its encoding process consists of two stages: header optimization and data compression. The protocol header adopts a three-layer nested structure: a fixed 3-byte base header containing the protocol version (4 bits), data type (4 bits), timestamp (12 bits), and data length (10 bits). An extended header, up to 2 bytes, is dynamically added as needed to store encryption identifiers or QoS parameters. Compared to the traditional 8-byte protocol header, VLCP improves the payload rate from 37.5% to 78.4% for 256B packets. The data compression stage utilizes a hybrid lossy-lossless strategy: for floating-point sensor data, the first 16 significant bits are retained (with a precision loss of <0.1%). Subsequently, a modified LZ4 algorithm is applied, compressing only consecutive similar data (with a difference less than a preset threshold). This method achieves a 58% compression rate in pressure sensor data testing, a 23% improvement over traditional schemes.

[0057] In an embodiment of the present application, an adaptive transmission controller dynamically adjusts the data packet size through a bandwidth prediction model, including: predicting the network bandwidth based on an exponentially weighted moving average algorithm; when the predicted network bandwidth is higher than a first threshold, using large packet transmission; when it is lower than a second threshold, switching to small packet transmission; the intermediate state calculates the target packet length through linear interpolation.

[0058] The first threshold value can be specifically calibrated, such as 80%, and the second threshold value can be specifically calibrated, such as 40%.

[0059] It is understood that the embodiments of this application use an exponentially weighted moving average algorithm to predict network bandwidth and optimize data transmission strategies based on the predicted results. When the predicted bandwidth exceeds a first threshold, large packets are selected for transmission to reduce protocol header overhead. When it falls below a second threshold, small packets are switched to improve transmission success rate. In intermediate states, the target packet length is calculated through linear interpolation. This method can dynamically adjust the packet size based on network conditions, improve data transmission efficiency, and ensure the rational use of network resources.

[0060] Specifically, based on the exponentially weighted moving average (EWMA) algorithm, real-time network bandwidth prediction (Bpred = 0.7*Bcurrent + 0.3*Bhistory) is performed. When the predicted bandwidth exceeds a threshold of 80%, large 4KB packets are used to reduce protocol header overhead. When it falls below 40%, smaller 256B packets are used to improve the success rate. In between, the optimal packet length is calculated using linear interpolation.

[0061] In the embodiment of the present application, a two-level retransmission mechanism is adopted, including: high-priority data in the target priority level is immediately retransmitted after timeout, and the conventional data adopts a binary exponential backoff strategy.

[0062] Among them, the two-level retransmission mechanism is a fault-tolerant mechanism that ensures reliable data arrival through two different levels of retransmission strategies during data transmission.

[0063] It can be understood that the embodiments of the present application adopt immediate retransmission upon timeout for high-priority data and a binary exponential backoff strategy for regular data, so that critical data can be quickly recovered to avoid delays affecting the real-time nature of virtual-real interaction. The backoff strategy is used to reduce the probability of retransmission conflicts for regular data, reduce network congestion, improve data transmission efficiency, perform differentiated transmission of data of different priorities, ensure the timeliness of critical data, and at the same time, optimize network resource allocation and transmission efficiency.

[0064] Specifically, the retransmission mechanism is designed as a two-tiered model: high-priority data is immediately retransmitted after a 50μs timeout, while regular data uses a binary exponential backoff strategy with a maximum of three retries. This mechanism has increased network utilization from 65% to 92% and reduced packet loss to below 0.3%.

[0065] It should be noted that the system hardware is deployed on a customized embedded industrial control platform. The main control unit uses an ARM Cortex-A72 dual-core processor and is equipped with a hardware encryption engine to accelerate data verification. The interface expansion board integrates physical interfaces such as CAN FD and EtherCAT, and supports a wide operating temperature range of -40°C to 85°C. The ring buffer is implemented as a dual-port RAM with a capacity of 64KB, and read and write operations are performed in parallel to avoid conflicts. A multi-threaded architecture is implemented at the software level: the protocol parsing thread has the highest priority to ensure timely processing of real-time data; the encoding and compression thread is synchronized with the transmission thread via semaphores to prevent data contention.

[0066] For example, in a real-world test of a hydraulic excavator control system (at an ambient temperature of -20°C), the system connected 12 heterogeneous devices (including four hydraulic valves, three inclination sensors, and five pressure sensors). Test data showed a stable end-to-end transmission delay of 1.2ms (for hydraulic valve commands), a payload rate of 78.4%, protocol conversion time of 0.28ms / frame, and a maximum packet loss rate of 0.15%. Compared with traditional solutions based on virtual serial ports, CPU utilization dropped from 34% to 19%, reducing hardware costs by 62%. The system operated continuously for 72 hours in an 85°C environment without any performance degradation, validating its robustness and reliability.

[0067] The embodiment of the present application proposes a fuzzy dynamic scheduling data communication detection system based on a virtual-real interaction scenario. At the hardware interface layer, a hardware abstract interface module with integrated multi-protocol drivers quickly converts electrical signals and identifies device protocols, reducing acquisition delays, improving compatibility and scalability, adapting to multi-source heterogeneous device access, and being able to operate stably over a wide temperature range. The dynamic priority scheduler at the data interaction layer adjusts the transmission priority in real time based on multi-dimensional factors. The lightweight binary encoder compresses the protocol header and increases the payload rate. The adaptive transmission controller dynamically adjusts the packet size and adopts a two-level retransmission mechanism, improving the real-time, efficiency, and reliability of data transmission and reducing resource consumption. The simulation application layer uses a high-precision timestamp synchronization mechanism to ensure consistency between virtual and real data and meet high dynamic control requirements. The bidirectional ring buffer structure realizes orderly data transmission and closed-loop interaction, and the verification and fault tolerance mechanisms at each layer reduce the error rate. Thus, the problems of protocol redundancy, low resource efficiency, and insufficient dynamic adaptability in the prior art are solved.

[0068] The following will describe a fuzzy dynamic scheduling data communication detection system based on a virtual-real interaction scenario through a specific embodiment. Figure 3 Shown, including:

[0069] It adopts a layered collaborative architecture, consisting of a hardware interface layer, a data interaction layer, and a simulation application layer. The hardware interface layer deploys a unified hardware abstract interface module, integrates multi-protocol drivers, and converts raw electrical signals into standard data frames through physical layer signal conversion circuits. Each interface module has a built-in protocol identification unit, which parses the device protocol type in real time based on a sliding window matching algorithm, and the identification time is controlled within 200ms. The data interaction layer serves as the core processing engine and includes three subsystems: a dynamic priority scheduler (DPS), a lightweight binary encoder (LBE), and an adaptive transmission controller (ATC). The simulation application layer connects to simulation platforms such as MATLAB / Simulink through a timestamp synchronization mechanism (accuracy of ±10μs) to ensure the consistency of virtual and real data.

[0070] The data flow design utilizes a bidirectional ring buffer structure. Raw data received by the hardware interface layer is stored in the input buffer after CRC verification. The data exchange layer extracts data from the buffer and performs dynamic priority calculation. High-priority data (such as hydraulic valve control instructions) enters the encoding and compression module via a fast channel, while regular data (such as temperature sensor readings) enters a weighted polling queue. The encoded data packets are then length-adjusted by the adaptive transmission controller and sent to the simulation application layer. Feedback instructions are then sent to the actuator via a reverse path. The entire process utilizes dual-port RAM for zero-copy transmission, reducing memory duplication overhead.

[0071] In summary, the dynamic priority scheduling algorithm based on fuzzy control integrates four-dimensional feature parameters to construct a fuzzy rule base, and combines double-buffered queues to realize dynamic resource allocation to solve data congestion; the variable-length lightweight binary coding protocol (VLCP) adopts a nested protocol structure and lossy-lossless hybrid compression to reduce protocol header overhead and improve payload rate; the adaptive transmission control mechanism based on bandwidth prediction uses the EWMA model to predict bandwidth in real time, dynamically adjusts the data packet length and designs a two-level retransmission strategy to improve network utilization; the multi-protocol compatible unified hardware abstraction interface (UHAI) supports automatic identification of 12 industrial protocols, integrates protocol feature libraries and temperature compensation circuits to achieve rapid protocol adaptation over a wide temperature range and reduce conversion time; the layered collaborative system architecture constructs a three-level architecture of "hardware interface layer-data interaction layer-simulation application layer", and uses the dual-port RAM ring buffer zero-copy transmission and timestamp synchronization mechanism to ensure real-time interaction of multi-source heterogeneous devices on the embedded platform.

[0072] Next, a fuzzy dynamic scheduling data communication detection method based on a virtual-reality interaction scenario proposed according to an embodiment of the present application is described with reference to the accompanying drawings.

[0073] Figure 4 A flowchart of a fuzzy dynamic scheduling data communication detection method based on a virtual-reality interaction scenario provided by an embodiment of the present application.

[0074] like Figure 4 As shown, the fuzzy dynamic scheduling data communication detection method based on the virtual-real interaction scenario includes the following steps:

[0075] In step S101, an original data stream is obtained.

[0076] Among them, the original data stream is the multi-source heterogeneous real-time data of the hardware interface layer and the virtual control instructions generated by the simulation application layer.

[0077] It can be understood that the embodiment of the present application integrates dynamic information from both the virtual and real ends to form a complete data set covering the state of the physical world and virtual control requirements, supporting the fuzzy dynamic scheduling algorithm to perform priority division and resource allocation based on four-dimensional characteristic parameters such as timeliness and criticality, ensuring real-time aggregation and two-way flow of data in virtual and real interaction scenarios.

[0078] In step S102, the original data is stored in an input buffer for feature extraction.

[0079] The input buffer is used to temporarily store raw data received from external data sources such as the hardware interface layer, providing a temporary storage buffer area for subsequent encoding, processing or transmission.

[0080] It can be understood that the embodiments of the present application achieve data caching and buffering through temporary storage, ensuring the complete reception and orderly transmission of multi-source heterogeneous raw data, providing continuous and stable input for feature extraction, and at the same time decoupling the asynchronous operation of the hardware interface layer and the data processing layer, thereby improving the system's adaptability to bursty data and ensuring the real-time and integrity of data collection in virtual-reality interaction scenarios.

[0081] In step S103, the data after feature extraction is judged, wherein, if the priority is greater than or equal to the priority threshold, it enters the preemptive channel; otherwise, it enters the regular queue; and then data compression processing is performed to obtain compressed package data.

[0082] Among them, preemptive channel refers to a mechanism that allows high-priority tasks, data or devices to use communication channels or shared resources first by interrupting the current transmission or occupancy status of low-priority tasks or devices during data communication or resource allocation.

[0083] It can be understood that the embodiments of the present application allow high-priority data to interrupt low-priority transmission and occupy resources first, thereby opening up a fast channel for critical data, avoiding congestion, reducing transmission delays, ensuring timely response and reducing system risks, and at the same time achieving optimal resource allocation through priority differentiation, balancing the immediacy and orderliness of multimodal data transmission.

[0084] In step S104, a feedback instruction is obtained based on the compressed package data, and the feedback instruction is issued and executed. At the same time, the compressed package data is monitored. If the compressed package data exceeds the packet loss threshold, an alarm is issued and the dynamic weight coefficient is optimized in time.

[0085] The packet loss threshold is the critical value of the maximum number or ratio of packet loss allowed in data transmission. When this value is exceeded, the system will trigger corresponding processing mechanisms such as retransmission and error reporting.

[0086] It can be understood that the embodiments of the present application set quantitative standards to accurately identify and promptly respond to data transmission anomalies, thereby avoiding the loss of critical data or system failure due to continuous packet loss. At the same time, combined with the dynamic weight optimization mechanism, it can adaptively adjust the data scheduling strategy, while ensuring transmission reliability, improving the system's robustness to network fluctuations, and ensuring the stability and efficiency of data communication in virtual-reality interaction scenarios.

[0087] The embodiment of the present application proposes a fuzzy dynamic scheduling data communication detection method based on the virtual-real interaction scenario. At the hardware interface layer, the hardware abstract interface module with integrated multi-protocol drivers quickly converts electrical signals and identifies device protocols, reducing acquisition delays, improving compatibility and scalability, adapting to the access of multi-source heterogeneous devices, and being able to operate stably over a wide temperature range; the dynamic priority scheduler of the data interaction layer adjusts the transmission priority in real time based on multi-dimensional factors, the lightweight binary encoder compresses the protocol header and improves the payload rate, the adaptive transmission controller dynamically adjusts the packet size and adopts a two-level retransmission mechanism, improving the real-time, efficiency and reliability of data transmission and reducing resource consumption; the simulation application layer uses a high-precision timestamp synchronization mechanism to ensure the consistency of virtual and real data and meet the requirements of high dynamic control; the bidirectional ring buffer structure realizes orderly data transmission and closed-loop interaction, and the verification and fault tolerance mechanism of each layer reduces the error rate. Thus, the problems of protocol redundancy, low resource efficiency, and insufficient dynamic adaptability in the prior art are solved.

[0088] The following will describe the fuzzy dynamic scheduling data communication detection method based on the virtual-real interaction scenario through a specific embodiment. In a virtual-real interaction scenario of intelligent industrial manufacturing, such as Figure 5 Shown, including:

[0089] First, when the system is started, the "driver loading module" begins to work. For example, it loads the corresponding driver for different types of sensors (such as thermocouple temperature sensors, strain gauge pressure sensors, etc.) to ensure that the system can recognize and communicate with these sensors.

[0090] Then, go to "Initialize Double-Buffered Memory Pool." Since sensors continuously generate data, a double-buffered memory pool prevents conflicts during data reading and writing. It's like having two storage areas: while one receives new data, the other is available for subsequent modules to read. These two areas alternate, ensuring smooth data processing.

[0091] Data collected by various sensors (such as temperature and pressure) enters the system through the "Hardware Interface Layer Multi-Protocol Data Receiving" module. Because different sensors may use different data protocols (such as Modbus and CAN), this module can identify and receive data from these different protocols, forming a raw data stream. For example, a temperature sensor using the Modbus protocol and a pressure sensor using the CAN protocol can both be successfully received by this module.

[0092] The "dynamic scheduler" processes the raw data stream. It prioritizes data based on pre-set rules. For example, temperature data from critical equipment components might be assigned a high priority (HIGH), as excessive temperatures could cause equipment failure. Meanwhile, data on the operating status of auxiliary equipment might be assigned a normal priority.

[0093] If the data priority is "HIGH" or higher, it will enter the "Encoding and Compression Module" directly through the "preemptive channel." This is like how, in a traffic jam, emergency vehicles (high-priority data) can use the emergency lane (preemptive channel) to pass quickly. If the data priority is lower, it will enter the "regular queue" and then the "Encoding and Compression Module."

[0094] The "Encoding and Compression Module" processes incoming data. It compresses the data using specific encoding algorithms (such as Huffman coding or the LZ77 algorithm) to reduce the amount of data for more efficient transmission. For example, a sequence of continuously repeating temperature values ​​can be compressed and encoded.

[0095] The compressed data enters the "adaptive transmission controller." This controller adaptively adjusts the data transmission strategy based on current network conditions (such as bandwidth and latency) and the needs of the virtual model. For example, if the network bandwidth is low, the controller will reduce the data transmission rate to ensure data is not lost. If the virtual model urgently needs certain critical data, it will prioritize the transmission of this data. The data is then transmitted to the "virtual model."

[0096] After receiving the data, the "virtual model" performs data analysis and simulation, and then provides "control instructions" based on the results. For example, if the virtual model finds that the device temperature is approaching a critical value, it will provide control instructions to the front-end device, such as adjusting the cooling system power.

[0097] The "Real-time QoS Monitoring" module continuously monitors the quality of service during data transmission, such as latency, packet loss rate, and other indicators. Once these indicators exceed the preset threshold (such as latency exceeding 50ms or packet loss rate exceeding 1%), an "over-threshold alarm" will be issued.

[0098] Upon receiving an alert, the "Parameter Adjustment Module Dynamic Weight Coefficient Optimization" adjusts certain system parameters, such as reallocating data priority coefficients. For example, it prioritizes device security-related data while reducing the weight of less important data. This optimizes the entire data communication and processing process, making it more adaptable to the needs of virtual-reality interaction scenarios.

[0099] In summary, a data communication detection method based on fuzzy dynamic scheduling is implemented in the virtual-reality interaction scenario of intelligent industrial manufacturing. At startup, the sensor driver is loaded and the double-buffered memory pool is initialized. The hardware interface layer then receives multi-protocol raw data streams, which are then prioritized by the dynamic scheduler. High-priority data flows through the preemptive channel, while regular-priority data flows through the regular queue. Both flows enter the encoding and compression module. The compressed data is then transmitted to the virtual model by an adaptive transmission controller based on network and virtual model requirements. The virtual model then provides feedback on control instructions. Simultaneously, service quality is monitored in real time, and parameter optimization processes are adjusted when thresholds are exceeded to ensure efficient data processing and transmission, and that the virtual model accurately reflects device status and makes decisions.

[0100] Compared with traditional solutions (such as semi-physical simulation systems based on virtual serial ports) with end-to-end delays exceeding 5ms, the present invention uses a three-level streamlined protocol stack and a dynamic priority scheduling algorithm to compress transmission delays to less than 2ms, with a key instruction delay fluctuation of ±0.2ms. In resource-constrained embedded environments, it achieves a better real-time-cost balance than the 10μs real-time accuracy of Kaiyun Technology's ETest_RT platform, meeting millisecond-level requirements such as hydraulic control.

[0101] Unlike existing technologies like DDS and OPCUA, which have protocol headers of 8-16 bytes and payload rates of less than 40%, the variable-length compression protocol (VLCP) of this invention compresses the protocol header to 3 bytes, achieving a payload rate of 78.4%. Furthermore, the improved LZ4 hybrid compression algorithm increases the data compression rate by 23%, reduces CPU usage from 34% to 19%, and reduces resource consumption.

[0102] In view of the defects of traditional static priority scheduling in multimodal working conditions, the present invention adopts a dynamic scheduling algorithm based on fuzzy control, which calculates the priority in real time according to the four-dimensional characteristic parameters of timeliness, criticality, data volume, and system load, so that the success rate of key data stream transmission reaches 99.8%, and the burst traffic packet loss rate is reduced from 5.7% to 0.3%. It is more adaptable to the dynamic working condition switching of construction machinery than the fixed test scripts of the ETest platform.

[0103] Compared to existing solutions like ETest_PT, which rely on middleware and extend development cycles by 60%, this solution automatically identifies 12 industrial protocols through a unified hardware abstraction interface (UHAI), converts protocols in 0.28ms per frame, and supports hot swapping. Its layered architecture and ring buffer design adapt to both single-machine and multi-machine distributed deployments, reducing hardware costs by 62%.

[0104] Breaking the 0°C-50°C temperature range limitations of traditional desktop simulation equipment, this invention's hardware interface layer integrates wide-temperature power management and anti-interference circuitry, enabling stable operation between -40°C and 85°C. The protocol expansion development cycle is reduced from six months to two weeks, making it easier to adapt to new industrial protocols than the fixed protocol library on the ETest platform.

[0105] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0106] A memory 601 , a processor 602 , and a computer program stored in the memory 601 and executable on the processor 602 .

[0107] When the processor 602 executes the program, the fuzzy dynamic scheduling data communication detection method based on the virtual-reality interaction scenario provided in the above embodiment is implemented.

[0108] Furthermore, the electronic device further includes:

[0109] The communication interface 603 is used for communication between the memory 601 and the processor 602 .

[0110] The memory 601 is used to store computer programs that can be run on the processor 602 .

[0111] The memory 601 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0112] If the memory 601, the processor 602, and the communication interface 603 are implemented independently, the communication interface 603, the memory 601, and the processor 602 can be connected to each other via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0113] Optionally, in a specific implementation, if the memory 601, the processor 602 and the communication interface 603 are integrated on a chip, the memory 601, the processor 602 and the communication interface 603 can communicate with each other through an internal interface.

[0114] The processor 602 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0115] An embodiment of the present application also provides a computer-readable storage medium having a computer program or instruction stored thereon. When the computer program or instruction is executed by a processor, a fuzzy dynamic scheduling data communication detection method based on a virtual-reality interaction scenario as described above is implemented.

[0116] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0117] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this application, "N" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0118] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing a custom logical function or process step, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed in a different order than shown or discussed, including performing functions in a substantially simultaneous manner or in a reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0119] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiment, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, it can be implemented using any one or a combination of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0120] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

Claims

1. A fuzzy dynamic scheduling data communication detection system based on virtual-reality interaction scenarios, characterized in that: The system includes a hardware interface layer, a data interaction layer and a simulation application layer, wherein: Deploy a hardware abstract interface module in the hardware interface layer, integrate multi-protocol drivers, convert the original electrical signal into a standard data frame through the physical layer signal conversion circuit, and use the built-in protocol identification unit to analyze the device protocol type in real time based on the sliding window matching algorithm; A dynamic priority scheduler, a lightweight binary encoder, and an adaptive transmission controller are provided in the data exchange layer. The dynamic priority scheduler is based on a dynamic priority algorithm of fuzzy control and adjusts the transmission priority in real time according to the timeliness, criticality, data volume, and system load of the data stream; the lightweight binary encoder uses a variable length compression protocol to encode and compress the data; the adaptive transmission controller dynamically adjusts the data packet size through a bandwidth prediction model and adopts a two-stage retransmission mechanism; The simulation application layer is connected to the simulation platform through a timestamp synchronization mechanism to keep the virtual and real data consistent; The data flow is designed based on a bidirectional ring buffer structure. The original data received by the hardware interface layer is stored in the input buffer after verification. The data interaction layer extracts data from the buffer, processes it, and sends it to the simulation application layer. The feedback instruction is sent to the actuator through the reverse path.

2. The fuzzy dynamic scheduling data communication detection system based on the virtual-reality interaction scenario according to claim 1 is characterized in that: The multi-protocol driver includes CAN, EtherCAT, and Modbus protocol drivers, and the time consumed by the protocol identification unit to identify the device protocol type is controlled within 200ms.

3. The fuzzy dynamic scheduling data communication detection system based on the virtual-reality interaction scenario according to claim 1 is characterized in that: Dynamic priority algorithm based on fuzzy control, including: Acquiring four-dimensional characteristic parameters, wherein the four-dimensional characteristic parameters include timeliness coefficient, criticality level, data volume, and system load rate; Constructing a fuzzy rule base, inputting the four-dimensional feature parameters into the fuzzy rule base to obtain a target priority weight; A double-buffered queue structure is designed, wherein the high-priority queue in the target priority level adopts preemptive scheduling, and the regular queue adopts weighted round-robin scheduling.

4. The fuzzy dynamic scheduling data communication detection system based on the virtual-reality interaction scenario according to claim 1 is characterized in that: The data is encoded and compressed using a variable-length compression protocol. The encoding process is divided into two stages: protocol header optimization and data compression, including: The protocol header adopts a three-layer nested structure. The basic header is fixed to 3 bytes and contains the protocol version, data type, timestamp and data length; the extended header is dynamically added according to demand and is used to store encryption identification or QoS parameters; The data compression adopts a lossy-lossless hybrid strategy, retains the first 16 valid bits of floating-point sensor data, and applies an improved LZ4 algorithm to compress only continuous similar data.

5. The fuzzy dynamic scheduling data communication detection system based on the virtual-reality interaction scenario according to claim 1 is characterized in that: The adaptive transmission controller dynamically adjusts the data packet size through a bandwidth prediction model, including: Predict network bandwidth based on exponentially weighted moving average algorithm; When the predicted network bandwidth is higher than a first threshold, large packets are used for transmission; when it is lower than a second threshold, small packets are used for transmission; the target packet length is calculated by linear interpolation in the intermediate state.

6. The fuzzy dynamic scheduling data communication detection system based on the virtual-reality interaction scenario according to claim 1 is characterized in that: A two-level retransmission mechanism is adopted, including: High-priority data in the target priority level is retransmitted immediately after timeout, and regular data adopts a binary exponential backoff strategy.

7. The fuzzy dynamic scheduling data communication detection system based on the virtual-reality interaction scenario according to claim 1 or 3 is characterized in that: The bidirectional ring buffer structure includes an input buffer, an output buffer and a buffer, wherein: The input buffer is used to store the original data from the hardware interface layer; The output buffer is used to store data processed by the data interaction layer; The buffer area uses dual-port RAM to achieve zero-copy transmission.

8. A method for a fuzzy dynamic scheduling data communication detection system based on a virtual-reality interaction scenario according to any one of claims 1 to 7, characterized in that: The method comprises: Get the original data stream; Storing the raw data in an input buffer for feature extraction; The data after feature extraction is judged, wherein if the priority is greater than or equal to the priority threshold, it enters the preemptive channel; otherwise, it enters the regular queue; then data compression processing is performed, wherein the incoming data is processed and compressed using a specific encoding algorithm to obtain compressed package data; A feedback instruction is obtained based on the compressed package data, and the feedback instruction is issued and executed. The compressed data enters the "adaptive transmission controller", which adaptively adjusts the data transmission strategy according to the current network status and the needs of the virtual model, and then transmits the data to the "virtual model". After receiving the data, the "virtual model" performs data analysis and simulation, and feeds back the "control instruction" based on the processing results. At the same time, the compressed package data is monitored. If the compressed package data exceeds the packet loss threshold, an alarm is issued and the dynamic weight coefficient is optimized in time.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the fuzzy dynamic scheduling data communication detection method based on a virtual-reality interaction scenario as claimed in claim 8.

10. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, it is used to implement the fuzzy dynamic scheduling data communication detection method based on the virtual-reality interaction scenario as described in claim 8.

Images