A dynamic networking multi-node distributed data sharding scheduling optimization system

Abstract

The application relates to the technical field of digital information transmission and discloses a dynamic networking multi-node distributed data fragment scheduling optimization system, which comprises a local network data flow intrinsic parameter extraction module, a sliding window response count increment and an output queue discrete consumption variable rate are extracted; an endogenous channel prediction adaptive fragment control module, a local channel carrying capacity state vector is calculated according to the increment and the variable rate, and a data fragment target logical cascade size is determined, or an equal distribution blind flow transfer instruction is output when the channel is interrupted; and a distributed fragment cache topology reorganization module, memory pointer movement step length is adjusted according to the size or the instruction, original multi-source flow data is divided into a data fragment set and is pressed into a target sending buffer. The application eliminates global routing metadata synchronization overhead, feeds forward from a local endpoint to hedge topology evolution fluctuation, reduces information transmission delay and reduces fragment packet loss, and avoids scheduling deadlock caused by feedback failure.

Description

Technical Field

[0001] This invention relates to the field of digital information transmission technology, and more specifically, to a dynamic networking multi-node distributed data sharding scheduling optimization system. Background Technology

[0002] Currently, in distributed storage and digital information transmission systems, the common approach is to divide continuous data streams into discrete fragments to improve channel throughput. Traditional solutions rely on centralized coordinators or global hash rings to complete routing. The design is based on the premise that the network topology is quasi-static. The system maintains the accuracy of routing information by periodically synchronizing the global topology view, thereby ensuring the throughput performance of fragment distribution between nodes.

[0003] When relying on static routing views to ensure link control, optimizing packet slicing through control methods has also attracted attention. For example, Chinese invention patent CN1917476B discloses a fragmentation adjustment method and device. By detecting whether the local transmission delay of real-time packets exceeds the preset tolerance delay, the fragmentation size of non-real-time packets is dynamically reduced or increased backward. When the network is deployed in a wireless environment with nodes having high-frequency mobility and transient disordered interconnection characteristics, the static view assumption and the control reference based on the backward adjustment feedback loop that has already experienced delays fail. High-frequency sudden changes in nodes trigger high-frequency reconstruction of the global routing table, causing large-scale routing metadata update data flows on the line, occupying available transmission bandwidth. The system lacks a mechanism to feedforward and extrapolate the path carrying capacity at the initial endpoint of the data flow, and can only rely on passive backward adjustment after delay congestion occurs. When a large area of ​​response packets is lost due to sudden physical obstruction of local paths, the backward feedback loop fails to converge and the scheduling diverges due to the loss of the timestamp reference.

[0004] Therefore, the technical problem to be solved by this invention is how to get rid of the dependence on external detection signals and global view, determine the matching target logic cascade size and trigger multi-path routing allocation before data slicing, so as to solve the technical problem of transmission bandwidth erosion and fragmentation reassembly failure caused by topology transient distortion without broadcasting routing metadata. Summary of the Invention

[0005] To address the problems in the background art, this invention provides a dynamic networking multi-node distributed data sharding scheduling optimization system, the system comprising:

[0006] The local network data stream intrinsic parameter extraction module is used to continuously monitor the transmit register status of the local network interface controller and extract the sliding window response count increment and output queue discrete consumption rate for each candidate receive path within a set discrete time window.

[0007] The intrinsic channel prediction adaptive fragmentation control module is connected to the local network data stream intrinsic parameter extraction module. It is used to calculate the local channel carrying capacity state vector and the data fragmentation target logic concatenation size of each candidate receiving path based on the sliding window response count increment and the output queue discrete consumption rate. The intrinsic channel prediction adaptive fragmentation control module is also used to shut down the conventional calculation of the data fragmentation target logic concatenation size and output a multi-path evenly distributed blind transmission flow instruction when the local channel carrying capacity state vector corresponding to a specified receiving path in the candidate receiving path continuously outputs zero values.

[0008] The distributed fragmented cache topology reorganization module is connected to the endogenous channel prediction adaptive fragmentation control module and receives the input raw multi-source stream data. It is used to adjust the memory pointer movement step size according to the data fragment target logical cascade size or the multi-path evenly distributed blind transmission flow instruction, and to divide the raw multi-source stream data into a variable-length data fragment set and push it into the target transmission buffer.

[0009] Preferably, the system further includes a reverse congestion suppression channel module connected to the distributed fragmentation cache topology reassembly module; when the queue depth of the intersection node corresponding to the specified receiving path exceeds the set congestion threshold, the reverse congestion suppression channel module sets a fixed sliding window marker in the reverse data packet header so that the endogenous channel prediction adaptive fragmentation control module can reduce the traffic allocation weight of the corresponding specified receiving path by 50% when parsing and capturing the sliding window marker.

[0010] Preferably, the intrinsic channel prediction adaptive fragmentation control module includes a discrete event determination unit. The discrete event determination unit is used to output a data distribution instruction when a specified receiving path in each candidate receiving path satisfies the condition that the ratio of its queue length value to the magnitude of its corresponding local channel carrying capacity state vector is less than or equal to a set throughput threshold constant. Both the queue length value and the magnitude of the local channel carrying capacity state vector are normalized dimensionless values. The intrinsic channel prediction adaptive fragmentation control module controls the distributed fragmentation buffer topology reassembly module to push the variable-length data fragment set into the target transmission buffer according to the data distribution instruction.

[0011] Preferably, the endogenous channel prediction adaptive fragmentation control module includes a dynamic redundancy fault-tolerant coding module. The dynamic redundancy fault-tolerant coding module is used to perform a horizontal XOR operation on the regular data fragments in the variable-length data fragment set to generate redundant companion check fragments when the differential rate of change of the local channel carrying capacity state vector exceeds the set slope threshold for three consecutive sampling periods.

[0012] Preferably, the local network data stream intrinsic parameter extraction module includes the following steps during operation: monitoring the status of the local network interface controller's transmit register and reading the status of the full storage flag and the number of idle descriptors in the transmit register; at the end of each set discrete time window, obtaining the sliding window response count increment by performing a differential operation on the number of response data packets at the current time and the end time of the previous discrete time window.

[0013] Preferably, the local network data stream intrinsic parameter extraction module includes a queue variability analysis module. The queue variability analysis module is used to collect discrete samples of the length of the transmission hardware buffer queue in real time and calculate the quadratic difference value between adjacent discrete samples to generate the output queue discrete consumption variability.

[0014] Preferably, the endogenous channel prediction adaptive fragmentation control module includes the following steps during operation: inputting the sliding window response count increment and the output queue discrete consumption rate into a nonlinear mapping matrix and stripping the channel time-varying noise to generate a local channel carrying capacity state vector characterizing the transmission capability of each candidate receiving path; when the local channel carrying capacity state vector is greater than a set saturation threshold, determining that the data fragmentation target logic concatenation size is proportional to the magnitude of the local channel carrying capacity state vector.

[0015] Preferably, the endogenous channel prediction adaptive fragmentation control module further includes an anti-divergence robust control module. The anti-divergence robust control module is used to shut down the conventional formula calculation path for the data fragmentation target logical cascade size when the local channel carrying capacity state vector outputs zero values ​​for four consecutive sampling periods, and send a multi-path equal-distribution blind transmission flow instruction to the distributed fragmentation cache topology reorganization module.

[0016] Preferably, the distributed sharded cache topology reorganization module includes the following steps during runtime: receiving the target logical concatenation size of the data shards and synchronously modifying the movement step of the memory pointer to a byte length consistent with the target logical concatenation size of the data shards; segmenting and truncating the original multi-source stream data cached in memory according to the modified movement step to generate a variable-length data shard set.

[0017] Preferably, the distributed fragmentation cache topology reassembly module further includes a multi-path routing distribution module. The multi-path routing distribution module is connected to the endogenous channel prediction adaptive fragmentation control module and is used to receive multi-path equal-sharing blind transmission instructions. It divides the variable-length data fragment set generated by the distributed fragmentation cache topology reassembly module into equal shares according to the number of each candidate receiving path. Without relying on the feedback response from the receiving end, it pushes each equal-share data fragment into the hardware transmission buffer corresponding to each candidate receiving path in parallel, so that the fragment size is stabilized within the set minimum boundary range for transmission.

[0018] The embodiments of the present invention have at least the following beneficial effects:

[0019] 1. In the dynamic networking multi-node distributed data fragmentation scheduling optimization, the local network interface controller continuously counts the transmission queue bit depth change trajectory and the number of acknowledgment packets. It directly feeds forward to deduce the local channel carrying capacity state of candidate paths before continuous stream data slicing, and establishes an inverse monotonic function relationship between the carrying capacity state vector and the slice target size, so that the logical boundary of the variable-length slice fluctuates with the channel state. This method completely transforms the global topology view maintenance overhead into the intrinsic transformation of local single-source features. Nodes do not need to broadcast any routing metadata. Feedforward from the original endpoint in place to offset the neighbor topology evolution trend, avoid the erosion of available channel bandwidth by high-frequency route reconstruction in dynamic networking, and eliminate the control disorder problem caused by data transmission lag adjustment.

[0020] 2. By directly coupling multi-path dispatch routing with the underlying transmission register hardware buffer, and combining the monotonicity of the local channel carrying capacity state vector to introduce discrete event judgment, a dispatch command is output when the ratio of the candidate node's mounted queue length to the carrying capacity vector meets the throughput threshold constant, so that heterogeneous variable-length data streams are accurately pushed into the target transmission buffer; when a sudden physical blockage causes the carrying capacity vector to continuously output zero values, the system automatically activates the secure and robust communication anti-divergence truncation rule, shuts down the logical size formula calculation and opens the multi-path equally divided blind transmission flow loop, reduces the probability of data block loss, and avoids closed-loop deadlock caused by feedback convergence failure.

[0021] 3. The reverse damping response channel integrated into the routing loop directly marks a specific sliding window in the reverse data packet header when the hardware queue depth of the converging node exceeds the preset congestion threshold. This allows downstream source nodes to parse and capture the marked bit as a discrete trigger condition, driving the adaptive splitting arbitration module to unconditionally reduce the allocation weight parameter of the corresponding path by half. This mechanism implicitly embeds flow control adjustment into the existing reverse transmission link, eliminating the need for external heterogeneous monitoring components and forming a self-smoothing negative feedback loop. This allows for the rapid elimination of nonlinear congestion without warning when multi-source flow data converges to the common converging node, and achieves traffic stability of multi-path topology under the perspective of decentralized scheduling. Attached Figure Description

[0022] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings, in which several embodiments of the invention are illustrated by way of example and not limitation, wherein:

[0023] Figure 1 This is a flowchart illustrating the data flow and module logic interaction of the system of the present invention;

[0024] Figure 2 This is a topological diagram of the hierarchical connection of the functional modules of the system of the present invention. Detailed Implementation

[0025] The principles and spirit of the present invention will now be described with reference to several exemplary embodiments in conjunction with the accompanying drawings. It should be understood that these embodiments are provided merely to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0026] A dynamic network multi-node distributed data sharding scheduling optimization system, the system comprising:

[0027] The local network data stream intrinsic parameter extraction module is used to continuously monitor the transmit register status of the local network interface controller and extract the sliding window response count increment and output queue discrete consumption rate for each candidate receive path within a set discrete time window.

[0028] The intrinsic channel prediction adaptive fragmentation control module is connected to the local network data stream intrinsic parameter extraction module. It is used to calculate the local channel carrying capacity state vector and the data fragmentation target logic concatenation size of each candidate receiving path based on the sliding window response count increment and the output queue discrete consumption rate. The intrinsic channel prediction adaptive fragmentation control module is also used to shut down the conventional calculation of the data fragmentation target logic concatenation size and output a multi-path evenly distributed blind transmission flow instruction when the local channel carrying capacity state vector corresponding to a specified receiving path in the candidate receiving path continuously outputs zero values.

[0029] The distributed fragmented cache topology reorganization module is connected to the endogenous channel prediction adaptive fragmentation control module and receives the input raw multi-source stream data. It is used to adjust the memory pointer movement step size according to the data fragment target logical cascade size or the multi-path evenly distributed blind transmission flow instruction, and to divide the raw multi-source stream data into a variable-length data fragment set and push it into the target transmission buffer.

[0030] Preferably, the system further includes a reverse congestion suppression channel module connected to the distributed fragmentation cache topology reassembly module; when the queue depth of the intersection node corresponding to the specified receiving path exceeds the set congestion threshold, the reverse congestion suppression channel module sets a fixed sliding window marker in the reverse data packet header so that the endogenous channel prediction adaptive fragmentation control module can reduce the traffic allocation weight of the corresponding specified receiving path by 50% when parsing and capturing the sliding window marker.

[0031] Preferably, the intrinsic channel prediction adaptive fragmentation control module includes a discrete event determination unit. The discrete event determination unit is used to output a data distribution instruction when a specified receiving path in each candidate receiving path satisfies the condition that the ratio of its queue length value to the magnitude of its corresponding local channel carrying capacity state vector is less than or equal to a set throughput threshold constant. Both the queue length value and the magnitude of the local channel carrying capacity state vector are normalized dimensionless values. The intrinsic channel prediction adaptive fragmentation control module controls the distributed fragmentation buffer topology reassembly module to push the variable-length data fragment set into the target transmission buffer according to the data distribution instruction.

[0032] Preferably, the endogenous channel prediction adaptive fragmentation control module includes a dynamic redundancy fault-tolerant coding module. The dynamic redundancy fault-tolerant coding module is used to perform a horizontal XOR operation on the regular data fragments in the variable-length data fragment set to generate redundant companion check fragments when the differential rate of change of the local channel carrying capacity state vector exceeds the set slope threshold for three consecutive sampling periods.

[0033] Preferably, the local network data stream intrinsic parameter extraction module includes the following steps during operation: monitoring the status of the local network interface controller's transmit register and reading the status of the full storage flag and the number of idle descriptors in the transmit register; at the end of each set discrete time window, obtaining the sliding window response count increment by performing a differential operation on the number of response data packets at the current time and the end time of the previous discrete time window.

[0034] Preferably, the local network data stream intrinsic parameter extraction module includes a queue variability analysis module. The queue variability analysis module is used to collect discrete samples of the length of the transmission hardware buffer queue in real time and calculate the quadratic difference value between adjacent discrete samples to generate the output queue discrete consumption variability.

[0035] Preferably, the endogenous channel prediction adaptive fragmentation control module includes the following steps during operation: inputting the sliding window response count increment and the output queue discrete consumption rate into a nonlinear mapping matrix and stripping the channel time-varying noise to generate a local channel carrying capacity state vector characterizing the transmission capability of each candidate receiving path; when the local channel carrying capacity state vector is greater than a set saturation threshold, determining that the data fragmentation target logic concatenation size is proportional to the magnitude of the local channel carrying capacity state vector.

[0036] Preferably, the endogenous channel prediction adaptive fragmentation control module further includes an anti-divergence robust control module. The anti-divergence robust control module is used to shut down the conventional formula calculation path for the data fragmentation target logical cascade size when the local channel carrying capacity state vector outputs zero values ​​for four consecutive sampling periods, and send a multi-path equal-distribution blind transmission flow instruction to the distributed fragmentation cache topology reorganization module.

[0037] Preferably, the distributed sharded cache topology reorganization module includes the following steps during runtime: receiving the target logical concatenation size of the data shards and synchronously modifying the movement step of the memory pointer to a byte length consistent with the target logical concatenation size of the data shards; segmenting and truncating the original multi-source stream data cached in memory according to the modified movement step to generate a variable-length data shard set.

[0038] Preferably, the distributed fragmentation cache topology reassembly module further includes a multi-path routing distribution module. The multi-path routing distribution module is connected to the endogenous channel prediction adaptive fragmentation control module and is used to receive multi-path equal-sharing blind transmission instructions. It divides the variable-length data fragment set generated by the distributed fragmentation cache topology reassembly module into equal shares according to the number of each candidate receiving path. Without relying on the feedback response from the receiving end, it pushes each equal-share data fragment into the hardware transmission buffer corresponding to each candidate receiving path in parallel, so that the fragment size is stabilized within the set minimum boundary range for transmission.

[0039] Example 1: In a continuously operating distributed multi-node wireless network digital information transmission environment, the distributed data storage and digital information transmission system faces the challenges of high-frequency movement of network nodes and transient disordered interconnection. Sudden node additions and removals cause high-frequency reconstruction of the global routing topology table, triggering large-scale routing metadata synchronization storms on online lines, resulting in excessive occupation of available channel transmission bandwidth. Furthermore, due to the implicit reliance of traditional control methods on static hash views or passive backward adjustment after channel congestion, when a local link encounters sudden physical obstruction leading to a large-scale loss of acknowledgment and response data packets, the feedback loop often falls into a deadlock state of convergence failure and scheduling divergence due to the loss of timestamp reference, leading to the risk of high-frequency loss of variable-length data fragments and receiver reassembly failure.

[0040] To address the aforementioned local topology collapse and bandwidth loss, a dynamic networking multi-node distributed data sharding scheduling optimization system runs on the standard kernel protocol stack and processor of a general-purpose network host. The system includes a local network data flow intrinsic parameter extraction module that continuously monitors the transmit register status of the local network interface controller. By reading the full flag status and the number of idle descriptors in the transmit register, it performs data sharding within a set discrete time window period of 5ms. At the end, a single-source differential statistical count is performed on the number of response data packets between the current time and the end time of the previous discrete time window, thereby directly extracting the sliding window response count increment for each candidate receiving path. Simultaneously, the local network interface controller continuously collects discrete samples of the transmitted hardware buffer queue length, calculates the quadratic difference between adjacent discrete samples, and calculates the discrete consumption rate of the output queue used to characterize the output queue bit depth variation trajectory. .

[0041] The intrinsic channel prediction adaptive fragmentation control module of the local network data stream intrinsic parameter extraction module receives sliding window response count increments. With output queue discrete consumption rate As initial information input, it is fed into a nonlinear mapping matrix to eliminate time-varying noise in the channel, and the local channel carrying capacity state vector, characterizing the transmission capability of each candidate receiving path, is calculated online in the form of data stream feedforward. The state vector is stored in the one-dimensional data register of the intrinsic channel prediction adaptive fragmentation control module and has two-dimensional data component attributes. The first component is the sliding window response count increment for a specific receiving path, statistically analyzed within a 5ms discrete time window. The second component is the output queue discrete consumption rate calculated by quadratic difference. The specific online calculation action is as follows: the main control operator of the control module retrieves the first component and the second component, performs a product operation, and then divides the result by the system's preset normalized dimensionless reference queue bit depth constant of 64. This performs scalar algebraic convergence superposition of the two components, and finally outputs a dimensionless scalar composite value between 0 and 5, representing the comprehensive transmission capability of the specific path, as the deterministic scalar modulus state of the vector. After this state is output, the system performs a boundary threshold comparison, i.e., when the local channel carrying capacity state vector is... When the data fragmentation exceeds the set saturation threshold, the intrinsic channel prediction adaptive fragmentation control module determines the target logic cascade size for data fragmentation. Proportional to the local channel carrying capacity state vector The modulus value, specifically corresponding to the unsigned integer byte length value stored in the memory pointer step size register of the distributed fragmentation buffer topology reorganization module; the specific reshaping and reconstruction control flow steps are as follows: the arithmetic logic unit of the endogenous channel prediction adaptive fragmentation control module retrieves the scalar modulus value and adds 1, and then performs a multiplication calibration operation with the system's preset basic fragmentation step size constant 512B, generating a discrete integer byte value between 512B and 3072B online, which serves as the single memory pointer movement step size for the master control read pointer to continuously advance and segment and truncate data in the original multi-source stream data buffer, thereby eliminating any semantic ambiguity of topology cascade entities, thus establishing an inverse monotonic function relationship between the carrying capacity state vector and the slice target size before the stream data slice, so that the variable length slice logic boundary can achieve in-situ feedforward and frequency-fluctuation changes with the channel state.

[0042] The distributed fragmentation buffer topology reassembly module, which connects to the intrinsic channel prediction adaptive fragmentation control module and receives the input raw multi-source stream data, receives the data fragmentation target logical cascade size at runtime. The memory pointer's movement step size is synchronously modified to match the logical cascade size of the data fragment target. With a consistent byte length, the original multi-source stream data cached in memory is segmented and truncated according to the modified movement step size to obtain a variable-length data fragment set; the discrete event determination unit integrated within the intrinsic channel prediction adaptive fragmentation control module obtains the current mounted queue length value of each candidate receiving path in real time. When the queue length value corresponding to the specified receiving path is met. Its corresponding local channel carrying capacity state vector The ratio of the magnitudes is less than or equal to the set throughput threshold constant. The event-driven decision condition, i.e., satisfying the inequality constraint condition. The data distribution instruction is output at the time. The throughput threshold constant specifically corresponds to the dimensionless integer limit 12 fixed in the hardware comparator of the endogenous channel prediction adaptive fragmentation control module. Its specific dimensionality simplification and discrete event judgment action sequence is as follows: the dimensionality conversion calibration interface of the endogenous channel prediction adaptive fragmentation control module calls one reference hardware clock cycle of the local network interface controller, directly multiplies the value of the local channel carrying capacity state vector generated online by a fixed time-domain square calibration constant 25ms², and uses the time-domain square reference to offset and eliminate the time square dimension introduced by the second difference in the underlying digital space, and converts the state vector into a virtual data packet depth unit that is equivalent to the static hardware queue depth.Subsequently, the main control operator controls the hardware comparison circuit to directly divide the normalized dimensionless current mounted queue length by the virtual data packet depth unit. The hardware comparison circuit generates a level transition and triggers an internal discrete event interrupt only if the ratio is less than or equal to the constant 12. This drives the system to execute the subsequent distribution process. The endogenous channel prediction adaptive fragmentation control module, according to the data distribution instructions, controls the distributed fragmentation buffer topology reassembly module to directly push the segmented variable-length data fragment sets into the corresponding target transmission buffer. This utilizes the numerical monotonicity of the local channel carrying capacity state vector to drive the distributed reassembly of data fragments of different sizes. Before determining the ratio in the location-based route distribution, the system introduces a dimension conversion calibration interface to unify the physical dimensions. Specifically, the local channel capacity state vector, as the acceleration of queue consumption changes, needs to have its time-domain quadratic dimension aligned with the static queue length. To achieve this, the system retrieves the reference hardware clock cycle of the local network interface controller and multiplies the calculated local channel capacity state vector by a fixed time-domain squared calibration constant. The constant's value is equal to 25 square milliseconds, thus completely eliminating the time dimension in the denominator and converting it into a virtual packet unit of pure data packet depth equivalent to the numerator. Through this dimension mapping rule, Ultimately, both parameters involved in the inequality comparison operation are normalized to the same dimensionless logical dimension before processing, eliminating cross-dimensional logical breaks and ensuring the physical rigor and closed-loop stability of the throughput threshold comparison operation. When the network topology dynamically changes, the discrete time window period monitored by the local network data flow intrinsic parameter extraction module is adjusted stepwise according to the relative movement speed of the nodes. When the movement speed varies between 10m / s and 40m / s, the discrete time window period is incremented within 2ms to 20ms, ensuring that the local channel carrying capacity state vector has constant dimensionless physical properties across different time window spans. The time-domain squared scale in the dimension conversion interface... The constant is updated in tandem with the change of the discrete time window period. The time-domain squared calibration constant is set to the square of the discrete time window period. When the discrete time window period is shortened to 5ms due to high-speed movement, the dimension conversion interface synchronously overwrites the time-domain squared calibration constant in the register to 25 square milliseconds. The dynamically evolved time-domain squared reference is used to cancel the quadratic time dimension in the denominator, converting the calculated local channel carrying capacity state vector into a virtual data packet depth unit with a value equivalent to the current mounted queue length. This ensures that the two parameters participating in the inequality comparison operation remain in the same dimensionless logical dimension before processing, eliminating cross-dimensional logical breaks caused by dynamic adjustment of the time window.

[0043] When the wireless networking environment experiences a decrease in local channel transmission capability due to sudden physical obstruction or external frequency sweeping interference, the locally extracted sliding window response count increment... The occurrence of a zero-valued state vector indicates that the local channel carrying capacity state vector corresponding to the specified receiving path has been reduced. When the output is zero for two consecutive discrete time window periods, the anti-divergence control unit integrated within the intrinsic channel prediction adaptive sharding control module determines that the feedforward control has reached the boundary limit and shuts down in situ the logic cascade size targeting the data sharding target. The formula calculation path skips the cascade size formula calculation and instead segments the data to target the logical cascade size. The hard safety boundary constant is set to 128 bytes. Simultaneously, a multi-path equal-distribution blind transmission instruction is sent to the distributed fragmented cache topology reassembly module. Upon receiving the instruction, the multi-path routing distribution module within the distributed fragmented cache topology reassembly module, without relying on feedback from the receiving end, divides the segmented variable-length data fragments into equal shares according to the number of candidate receiving paths and pushes them into the corresponding sending buffers of each candidate receiving path. This stabilizes the fragment size within the set boundary range for a downgraded blind transmission process until the sliding window response count increment returns to a non-zero state. During the downgraded blind transmission process, to avoid pointer movement conflicts or data readouts during multi-path parallel distribution from a single memory buffer, multi-path routing distribution... The module employs a time-division reusable serial truncation read mechanism. Specifically, the memory pointer does not move in parallel multi-threaded motion. Instead, a master read pointer advances continuously in the original multi-source stream data buffer according to a hard step size of 128 bytes. After each 128-byte data segment is truncated, the system pushes the segment data directly into the hardware transmit buffer of the corresponding path in an atomic operation based on the circular index number of the current candidate receive path. When the path index has traversed all available candidate receive paths, the master read pointer moves forward by another 128 bytes. This eliminates the synchronization overhead and deadlock conflicts when multiple paths and multiple pointers coexist, ensuring the safe operation of cache topology reorganization under extreme interrupt conditions.

[0044] To address the risk of sudden congestion and physical disconnection when multi-source data converges to a common rendezvous node, the reverse congestion suppression channel module connected to the distributed fragmentation cache topology reassembly module directly sets a fixed sliding window flag to 1 in the reverse transmission packet header when the queue depth of the rendezvous node corresponding to a specified receiving path exceeds the set congestion threshold of 48. When the downstream source node captures the sliding window flag as a discrete trigger condition while parsing the received packet, it drives the endogenous channel prediction adaptive fragmentation control module to reduce the flow allocation weight parameter of the corresponding specified receiving path by 50%, embedding flow control adjustment into the existing reverse transmission link to form a self-smoothing negative feedback loop. The fixed ratio of 50% selected here is determined based on the critical damping boundary of the double linear decreasing control law in the decentralized network. This was verified by testing 16 computational... Extreme stress tests were conducted on routing nodes. The results showed that if the weight reduction ratio was less than 30%, it was unable to quickly suppress sudden nonlinear congestion, causing the hardware queues of the convergence nodes to overflow rapidly within 10 milliseconds. If the weight reduction ratio was greater than 70%, it would cause severe step oscillations and routing queuing shifts in multipath traffic, increasing the average delivery latency of the entire network by more than 45%. However, an unconditional reduction of 50% of the traffic weight could ensure that when the queuing depth of the current convergence node exceeded the upper limit threshold of 48 packets, the path traffic was suppressed to within the core carrying capacity boundary of the system within 2 round-trip cycles. This allowed the entire multipath traffic allocation topology to automatically converge to an asymptotically stable state without external coordination. At the same time, the dynamic redundancy fault-tolerant coding module integrated within the intrinsic channel prediction adaptive fragmentation control module continuously monitored the local channel carrying capacity state vector. The differential rate of change is used to determine if the physical link is on the verge of disconnection. When the differential rate of change exceeds the set slope threshold of 2.5 for three consecutive sampling periods, the XOR cascade erasure logic is automatically activated. This logic performs a horizontal XOR operation on the regular data fragments within the current processing block and calculates the redundant accompanying check fragments with algebraic correlation online. ,in, For the calculated first A redundant companion check fragment, For the first regular data fragment in the current processing block, For the second regular data fragment within the current processing block, For the first in the current processing block A regular data shard, in which To verify the sharded index, The total number of regular data fragments is represented by the plus sign (XOR operator) and the equals sign (Assignment operator). The redundant accompanying verification fragments are redirected to backup path nodes orthogonal to the physical spatial topology of the regular data fragments via a multi-path routing distribution module. This controls the additional overhead of redundant verification calculations to within 3% of the chip's baseline computing power, enabling the system to self-repair under physical disconnection conditions. While the surface multipath Rayleigh fading coherence time is shortened to less than 10 milliseconds under high-speed movement conditions, the core cause of physical disconnection is usually due to overall physical occlusion or large-scale shadow fading. The time span of the overall fading is typically between 200 and 500 milliseconds. The differential monitoring used by the system, with three consecutive sampling periods totaling 15 milliseconds, is essentially a sliding window filtering mechanism. It triggers a slope evaluation immediately at the end of each 5-millisecond window, without waiting for the 15-millisecond absolute delay. The sliding window differential trend can accurately capture the initial falling edge of the overall large-scale shadow fading waveform, providing an overall trend prediction chain on top of the surface fluctuations controlled by coherent time. This ensures that the XOR cascaded erasure logic can withstand substantial large-scale disconnections in the physical link. Deployment was completed more than 50 milliseconds in advance, ensuring complete self-consistency in the overall spatiotemporal scale and eliminating the risk of failure caused by control latency. The reverse congestion suppression channel module runs at the kernel network driver layer, employing a low-level injection method combining the extended Berkeley packet filter and the extreme data path. When the hardware queue depth in the ring buffer of the network card at the specified receiving node exceeds the congestion threshold of 48 packets, the extended Berkeley packet filter program mounted on the network card driver layer intercepts and parses the header structure of the reverse return data packet at the extreme data path layer, and uses the special flags used as sliding window markers within it. The value of a fixed field is forcibly overwritten and set. The kernel's built-in checksum update helper function is called to incrementally overwrite and correct the header checksum of the reverse transmission data packet. This avoids direct tampering with the header data, which could cause checksum errors and packet loss in the network protocol stack. When the downstream source node detects that the sliding window flag is set through the hook function mounted in the kernel network layer, it triggers the internal traffic allocation adjustment mechanism. This unconditionally reduces the traffic allocation weight of the corresponding specified receiving path by 50%. Under the premise of compatibility with existing standard network hardware components, the path traffic is controlled to converge to within the preset bearer boundary within two round-trip cycles.

[0045] The data flow intrinsic feedforward adaptive reconstruction mechanism, constructed using the aforementioned local network data flow intrinsic parameter extraction module, intrinsic channel prediction adaptive fragmentation control module, and distributed fragmentation cache topology reorganization module, transforms the global topology view maintenance overhead into intrinsic transformation of local single-source features. Nodes do not need to broadcast any routing metadata, and the synchronous traffic of fragmentation routing metadata across the entire network decreases. This allows the system to offset the dynamic changes in neighboring topology evolution from the original endpoint in advance, avoiding the occupation of available channel transmission bandwidth due to high-frequency route reorganization. It also controls the end-to-end average delivery delay under high dynamic conditions to within 25ms, reducing the probability of data blocks being broken or lost in the air, avoiding the risk of closed-loop deadlock and scheduling divergence caused by conventional feedback convergence failure, and maintaining the underlying continuity and reorganization integrity of digital information transmission links under complex mobile conditions.

[0046] Example 2: In a distributed data storage and digital information transmission physical hardware environment with network topology changes, a dynamic networking multi-node distributed data sharding scheduling optimization system is deployed and runs on a hardware test platform containing 16 distributed computing routing nodes. The hardware test platform runs on Linux 5.15 kernel firmware, and each distributed computing routing node establishes a wireless data communication link through a channel simulator with time-varying fading simulation function. The channel simulator supports a radio frequency range of 2.4GHz to 2.5GHz, a maximum multipath delay spread specification of 500ns, and a delay test accuracy of 0.1ns, used to present multipath fading and Doppler shift interference; the local network data stream intrinsic parameter extraction module sets a discrete time window period. The operating state is constrained by the relative moving speed of the computational routing nodes and the load on the control chip. When the relative moving speed of the computational routing nodes is 30m / s, the corresponding channel coherence time is shortened to 10ms. If the discrete time window period... If the time window period is greater than 20ms, the calculated local channel carrying capacity state vector will lag behind the changes in the physical channel state, resulting in fragment size mismatch and data loss. If the time is less than 2ms, the interrupt frequency generated by the hardware timer increases, causing the processor overhead to rise to 45%. Therefore, an upper limit constraint is determined based on a fixed proportion of the channel coherence time. Under the 200Hz Doppler spread state simulated on the test platform, the discrete time window period is... The time was set to a fixed constant of 5ms to ensure dynamic response while keeping processor overhead below 5%.

[0047] In a channel simulator, Gaussian noise with a signal-to-noise ratio of 20dB and random packet loss characteristics conforming to Rayleigh fading distribution are superimposed onto the physical test signal source. The system is divided into the present invention sample group, a control group using a hash mapping pathfinding mechanism, and a partially missing control group that lacks local channel carrying capacity state vector calculation but maintains a reverse feedback channel. Correspondingly, it is divided into low-speed, medium-speed, and high-speed levels with relative movement speeds of 10m / s, 25m / s, and 40m / s. Multi-source service data streams are continuously input to each test entity. At the high-speed level with a relative movement speed of 40m / s, the local network data stream intrinsic parameter extraction module monitors the status register of the network interface controller and obtains the sliding window response count increment. The output queue discrete consumption variability is calculated by sending queue buffer samples after the value decreases from 45 in the static state to a measured value of 12. The value is changed to 3.2. At this point, the endogenous channel prediction adaptive fragmentation control module retrieves the sliding window response count increment. With output queue discrete consumption rate Input it into the linear formula Obtain the local channel carrying capacity state vector The modulus is 0.60 for the data point, where, In order to target the The local channel carrying capacity state vector calculated from each candidate receiving path. For the sliding window response count increment, The output queue has discrete consumption rate. The system uses a pre-defined normalized dimensionless reference queue bit depth constant with a fixed value of 64. The index of the candidate receiving path; the slash symbol represents the division operator, and the asterisk represents the multiplication operator; the local channel carrying capacity state vector. When the modulus is greater than the set upper limit, the intrinsic channel prediction adaptive fragmentation control module uses the formula... Determine the output and calculate the target logic cascade size for data fragmentation. The measured value is 819 bytes, of which, The target logical concatenation size for data fragmentation, in bytes. The system defaults to a base fragmentation step size constant, which is fixed at 512 bytes. The index is the candidate receiving path. The plus sign is the addition operator and the asterisk is the multiplication operator. However, the partial missing control group under the same mobile conditions has a data slice size that remains constant at 1024 bytes due to the lack of carrying capacity estimation, resulting in continuous retransmission blocking.

[0048] When calibrating the boundary states of the discrete event determination unit, adjust the throughput threshold constant. The value is used to test the variation boundary of the control response, when the throughput threshold constant is constant. When the lower limit is set to 4, the system generates a pre-set path selection suppression state in the buffer of each candidate receiving path, resulting in a non-linear decrease in the data throughput measured at the data receiving end. When the throughput threshold constant is... When the maximum value is set to 32, the pushed queue depth exceeds the buffer limit of the network interface controller, causing data overflow in the transmit hardware buffer. This results in a packet loss rate increasing to the measured value of 14.6%, thus lowering the throughput threshold constant. When set to the normal median value of 12, the fragmented and reassembled data reproduces a stable throughput state; when the channel simulator generates a sudden strong frequency sweep interference that causes the physical channel to be interrupted, the locally extracted sliding window response count increment... When the output is zero for two consecutive discrete time window periods, the robust cutoff control branch inside the endogenous channel prediction adaptive fragmentation control module cuts off the conventional formula calculation, and the data fragmentation target logic cascade size is determined. By forcibly using a fixed boundary value of 128 bytes, the multi-path routing distribution module segments the generated variable-length stream data set and distributes it blindly to candidate receiving paths on an even basis, keeping the packet loss rate under disconnection transients within the measured value of 3.1%. In contrast, the control group, due to maintaining the state of broadcasting routing metadata, caused the routing view to diverge synchronously in the line, resulting in an overload failure of the routing calculation engine.

[0049] The system flow parameters summarized under different speed levels show that, at a low speed level with a relative movement speed of 10 m / s, the routing metadata synchronization bandwidth usage of the sample group of this invention is 0%, the average end-to-end delivery latency is 6.2 ms, and the variable-length data fragmentation loss rate is 0.01%; at a medium speed level with a relative movement speed of 25 m / s, the routing metadata synchronization bandwidth usage of the sample group of this invention remains at 0%, the average end-to-end delivery latency is 12.8 ms, and the variable-length data fragmentation loss rate is 0.12%; at a high speed level with a relative movement speed of 40 m / s, the routing metadata synchronization bandwidth usage of the sample group of this invention is 0%. The bandwidth usage remained at 0%, the end-to-end average delivery latency remained at the measured value of 22.4ms, and the variable-length data fragment loss rate remained at 0.41% at the data point level. In contrast, the corresponding control group at the high-speed level had a routing metadata synchronization bandwidth overhead of 42.1% at the data point level, an end-to-end average delivery latency of 185.3ms, and a variable-length data fragment loss rate of 18.42%. The end-to-end average delivery latency of the partially missing control group became 98.7ms, and the variable-length data fragment loss rate became 11.23%. This indicates that the sliding window response count increment extracted by the local network data flow intrinsic parameter extraction module... With output queue discrete consumption rate In combination, the target logical cascade size of data shards can be adjusted by the feedforward of the distributed sharded cache topology reorganization module. This offsets the fluctuations caused by the dynamic network topology evolution at the initial endpoint position, avoids closed-loop deadlock and data fragmentation scheduling divergence caused by timing lag adjustment, and maintains the physical transmission continuity and receiver reassembly integrity of the digital information transmission link in a highly dynamic topology disturbance environment.

[0050] Example 3: This example combines Figures 1 to 2 This describes a dynamic network multi-node distributed data sharding scheduling optimization system, such as... Figure 1 As shown, the system receives raw multi-source stream data input to the distributed fragmentation buffer topology reassembly module. The module adjusts the memory pointer movement step size according to the size or instructions, segments the data into fragment sets, and pushes them into the target transmission buffer. The distributed fragmentation buffer topology reassembly module delivers the data to the target transmission buffer interaction node by pushing the variable-length data fragment sets along a physical path. When the target transmission buffer exceeds a set congestion threshold, the reverse congestion suppression channel module is triggered. When the queue depth of the interaction node exceeds a set congestion threshold, a sliding window marker is set in the reverse data packet header. The endogenous channel prediction adaptive fragmentation control module parses and captures the marker bit and allocates the traffic. The weight is reduced by 50%. The local network data stream intrinsic parameter extraction module continuously monitors the status of the transmission register and the local network interface controller, and extracts the response count increment and consumption rate. It extracts the sliding window response count increment and output queue discrete consumption rate for each candidate receiving path and inputs them to the intrinsic channel prediction adaptive fragmentation control module. The intrinsic channel prediction adaptive fragmentation control module calculates the local channel carrying capacity state vector and the target logic concatenation size. When the value is continuously zero, the calculation is turned off and the multi-path equal-sharing blind transmission flow instruction is output. The module also sends the data fragmentation target logic concatenation size or multi-path equal-sharing blind transmission flow instruction to the distributed fragmentation cache topology reorganization module.

[0051] like Figure 2 As shown, the output of the local network data stream intrinsic parameter extraction module is connected to the input of the endogenous channel prediction adaptive fragmentation control module, the output of the endogenous channel prediction adaptive fragmentation control module is connected to the input of the distributed fragmentation cache topology reassembly module, the output of the distributed fragmentation cache topology reassembly module is connected to the input of the reverse congestion suppression channel module, and the output of the reverse congestion suppression channel module is fed back to the right input of the endogenous channel prediction adaptive fragmentation control module.

[0052] Example 4: In a dynamic networked multi-node distributed data sharding scheduling optimization system, a dimensionless reference queue bit depth constant is used. The parameter range is 32 to 128, and the local throughput threshold constant is... The parameter range is 8 to 24; to determine the parameter values, the impact of different parameters on the network data transmission status was tested during each set discrete time window period of 5ms. At the end, if the dimensionless reference queue depth constant is used... Setting the lower limit to 32, the local channel carrying capacity state vector calculated by the nonlinear mapping matrix is... Oscillations caused by multipath fading noise disturbances affect the calculated target logic cascade size for data fragmentation. Frequent discontinuous changes reduce the throughput efficiency of the transmit hardware buffer; if the dimensionless reference queue bit depth constant is used... Setting the upper limit to 128, the local channel carrying capacity state vector The sensitivity to tracking changes in the transmit register state decreases, causing the fragmentation boundary adjustment speed to be slower than the topology reconstruction frequency, resulting in local data backlogs on the data transmission line; when the dimensionless reference queue bit depth constant is used... When the median value is set to 64, the local channel carrying capacity state vector By tracking the sampled changes in the transmit register state of the network interface controller, 64 was determined to be a dimensionless reference queue bit depth constant. The selected fixed value.

[0053] Determining the local throughput threshold constant When determining the value, the network interface controller continuously collects the current mount queue length values ​​for different candidate receive paths. With local channel carrying capacity state vector The modulus; if the throughput threshold constant is... Setting the lower limit to 8 tightens the constraint boundary of the discrete event determination unit's output data distribution instruction, causing the variable-length data fragments to remain in the memory buffer after being segmented, thus prolonging the data transmission latency; if the throughput threshold constant is set to 8... Setting the upper limit to 24 relaxes the distribution restriction, causing the data rate pushed into the transmit hardware buffer to exceed the actual channel clearing rate of the candidate receive path, resulting in overflow and packet loss in the target transmit buffer; when the throughput threshold constant is set... When set to a fixed value of 12, the inequality constraints A balance was struck between the buffer capacity of the transmit hardware buffer and the data transmission time, reducing buffer congestion and keeping the average data block breakage loss rate below 0.5%. Therefore, 12 was determined as the local throughput threshold constant. The selected fixed value; when calculating the state vector, the nonlinear mapping matrix reads the sliding window response count increment. With output queue discrete consumption rate As an independent input, the sliding window response count increment is used in the internal logic level. With output queue discrete consumption rate Perform multiplicative step composite operations, and use the normalized dimensionless reference queue bit depth constant 64 as the denominator to run division convergence calculations. Filter out pulse spikes caused by physical obstruction of the wireless link to complete the local channel carrying capacity state vector. The calculation output of the modulus value, in the specific matrix mapping process, the system achieves dimensionality reduction projection by configuring a fixed two-row, two-column constant matrix in the data register of the endogenous channel prediction adaptive fragmentation control module. The first row, first column and the first row, second column of this constant matrix are pre-written with fixed constants of 0.85 and 0.15, and the first row, first column and the second row, second column are pre-written with fixed constants of 0.50 and 0.50. The specific execution sequence of de-tagging weighted multiplication and addition hardware processing actions is as follows: the arithmetic logic unit of the module constructs a one-dimensional input data array containing two elements from the real-time collected and input sliding window response count increment and the output queue discrete consumption rate. Then, it performs a multiplication operation on the first component of the array with 0.85 in the first row of the matrix, performs a multiplication operation on the second component with 0.15 in the first row, and adds the two product results. Simultaneously, it adds the first component with the... The matrix is ​​multiplied by 0.50 in the second row, and the second component is multiplied by 0.50 in the second row. The product of these two components is then summed to generate two intermediate weighted numerical components online. Subsequently, the main control operator controls the hardware circuit to perform a trace transformation by adding the main diagonal elements of these two intermediate weighted numerical components, thereby completely reducing the two-dimensional time-varying features to a single composite scalar value. The internally integrated exponentially weighted moving average filter is then invoked to perform real-time moving average filtering and updating of the scalar value with a discrete sliding smoothing factor of 0.125. This smooths out burst pulse spikes caused by wireless multipath fading. Finally, the filtered composite scalar value is written into the output buffer as the scalar magnitude of the local channel carrying capacity state vector, thereby completing the feedforward control flow update in a closed loop in the digital space and improving the smoothness of the output result of the local channel carrying capacity state vector.

[0054] When the distributed data storage and digital information transmission system is deployed in a multi-node cluster, the global distribution task is distributed to each computing routing node at the multi-stream data aggregation node according to a quantitative polling strategy that combines the shortest topology hop count and dynamic bandwidth margin, maintaining the distributed resource balance within the large-scale cluster. Before processing multi-source and multi-stream information streams, the system obtains the user's authorization protocol status and runs a de-identification hash mapping step within the local computing routing node, converting the source network address and user identifier into a fixed hash hash feature, severing the reverse tracing association between data and natural persons. In the multi-path routing distribution module, only the de-identified unsigned feature stream is transmitted, embedding privacy protection logic into the feedforward slicing control flow of the local endpoint.

[0055] Example 5: When the system faces a heterogeneous network interface controller hardware environment deployment, the local network data stream intrinsic parameter extraction module initiates the initial calibration timing before multi-source stream data slice distribution. By reading the number of hardware descriptors and the maximum transmission unit bit depth of the heterogeneous network interface controller, the basic slice step size constant is determined. The data is locked at 512 bytes. Simultaneously, in an offline static network topology state, 64 probe packets are sent in parallel to the reference receiving endpoint, and the state of the transmit register of the heterogeneous network interface controller is monitored. The physical dissipation delay of converting the transmit register's self-full flag state to the idle descriptor state is used as the hardware clearing reference. The average transmit queue depth measurement value within 10 consecutive time windows is established as the dimensionless reference queue bit depth constant. The initial calibration reference value, thereby determining the discrete time window period. Increment of sliding window response count With output queue discrete consumption rate Obtain a physical adaptive reference that is compatible with the current hardware communication link.

[0056] Under dynamic throughput conditions with high-frequency convergence of multi-source service data, the hardware transmit buffer of the heterogeneous network interface controller experiences queuing pressure due to sudden traffic surges. The discrete event determination unit retrieves the initial calibration benchmark value established through the aforementioned calibration timing and the dimensionless current mounted queue length value collected in real time. With local channel carrying capacity state vector Modulus input inequality constraints In the process, when the hardware clearing delay increases from 125.6ms to 358.2ms and packet loss interference is introduced by the channel simulator, the feedforward prediction arbitration module issues an instruction based on the distribution operator that satisfies the inequality constraint, controlling the distributed fragmentation buffer topology reassembly module to push the set of split variable-length data fragments into the target transmission buffer. This solves the transmission bandwidth occupation caused by topology distortion without broadcasting routing metadata, keeping the end-to-end average delivery delay under high dynamic conditions within 25ms, and maintaining the data fragment reassembly integrity at the receiving end above 99.5%.

[0057] The above description is only a few preferred embodiments of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, technical solutions formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention.

Claims

1. A dynamic networked multi-node distributed data sharding scheduling optimization system, characterized in that, The system includes: The local network data stream intrinsic parameter extraction module is used to continuously monitor the transmit register status of the local network interface controller and extract the sliding window response count increment and output queue discrete consumption rate for each candidate receive path within a set discrete time window. The intrinsic channel prediction adaptive fragmentation control module is connected to the local network data stream intrinsic parameter extraction module. It is used to calculate the local channel carrying capacity state vector and the data fragmentation target logic concatenation size of each candidate receiving path based on the sliding window response count increment and the output queue discrete consumption rate. The intrinsic channel prediction adaptive fragmentation control module is also used to shut down the conventional calculation of the data fragmentation target logic concatenation size and output a multi-path evenly distributed blind transmission flow instruction when the local channel carrying capacity state vector corresponding to a specified receiving path in the candidate receiving path continuously outputs zero values. The distributed fragmented cache topology reorganization module is connected to the endogenous channel prediction adaptive fragmentation control module and receives the input raw multi-source stream data. It is used to adjust the memory pointer movement step size according to the data fragment target logical cascade size or the multi-path evenly distributed blind transmission flow instruction, and to divide the raw multi-source stream data into a variable-length data fragment set and push it into the target transmission buffer.

2. The dynamic networking multi-node distributed data sharding scheduling optimization system according to claim 1, characterized in that, The system also includes a reverse congestion suppression channel module connected to the distributed fragmentation cache topology reassembly module. When the queue depth of the intersection node corresponding to the specified receiving path exceeds the set congestion threshold, the reverse congestion suppression channel module sets a fixed sliding window marker in the reverse data packet header so that the endogenous channel prediction adaptive fragmentation control module can reduce the traffic allocation weight of the corresponding specified receiving path by 50% when parsing and capturing the sliding window marker.

3. The dynamic networking multi-node distributed data sharding scheduling optimization system according to claim 1, characterized in that, The intrinsic channel prediction adaptive fragmentation control module includes a discrete event determination unit. The discrete event determination unit is used to output a data distribution instruction when a specified receiving path in each candidate receiving path satisfies the condition that the ratio of its queue length value to the magnitude of its corresponding local channel carrying capacity state vector is less than or equal to a set throughput threshold constant. Both the queue length value and the magnitude of the local channel carrying capacity state vector are normalized dimensionless values. The intrinsic channel prediction adaptive fragmentation control module controls the distributed fragmentation buffer topology reassembly module to push the variable-length data fragment set into the target transmission buffer according to the data distribution instruction.

4. The dynamic networking multi-node distributed data sharding scheduling optimization system according to claim 1, characterized in that, The intrinsic channel prediction adaptive fragmentation control module includes a dynamic redundancy fault-tolerant coding module. The dynamic redundancy fault-tolerant coding module is used to perform a horizontal XOR operation on the regular data fragments in the variable-length data fragment set to generate redundant companion check fragments when the differential rate of change of the local channel carrying capacity state vector exceeds the set slope threshold for three consecutive sampling periods.

5. The dynamic networking multi-node distributed data sharding scheduling optimization system according to claim 1, characterized in that, The local network data stream intrinsic parameter extraction module includes the following steps during operation: monitoring the status of the local network interface controller's transmit register and reading the status of the full register flag and the number of idle descriptors; at the end of each set discrete time window, obtaining the sliding window response count increment by performing a differential operation on the number of response data packets at the current time and the end time of the previous discrete time window.

6. The dynamic networking multi-node distributed data sharding scheduling optimization system according to claim 1, characterized in that, The local network data stream intrinsic parameter extraction module includes a queue variability analysis module. The queue variability analysis module is used to collect discrete samples of the length of the transmission hardware buffer queue in real time and calculate the quadratic difference value between adjacent discrete samples to generate the output queue discrete consumption variability.

7. The dynamic networking multi-node distributed data sharding scheduling optimization system according to claim 1, characterized in that, The intrinsic channel prediction adaptive fragmentation control module includes the following steps during operation: inputting the sliding window response count increment and the output queue discrete consumption rate into a nonlinear mapping matrix and stripping the channel time-varying noise to generate a local channel carrying capacity state vector characterizing the transmission capability of each candidate receiving path; when the local channel carrying capacity state vector is greater than a set saturation threshold, determining that the data fragmentation target logic concatenation size is proportional to the magnitude of the local channel carrying capacity state vector.

8. The dynamic networking multi-node distributed data sharding scheduling optimization system according to claim 1, characterized in that, The endogenous channel prediction adaptive fragmentation control module also includes an anti-divergence robust control module. The anti-divergence robust control module is used to shut down the conventional formula calculation path for the data fragmentation target logical cascade size when the local channel carrying capacity state vector outputs zero values ​​for four consecutive sampling periods, and send a multi-path evenly distributed blind transmission flow instruction to the distributed fragmentation cache topology reorganization module.

9. The dynamic networking multi-node distributed data sharding scheduling optimization system according to claim 1, characterized in that, The distributed sharded cache topology reorganization module includes the following steps during runtime: receiving the target logical concatenation size of the data shards and synchronously modifying the movement step of the memory pointer to a byte length consistent with the target logical concatenation size of the data shards; segmenting and truncating the original multi-source stream data cached in memory according to the modified movement step to generate a variable-length data shard set.

10. A dynamic networking multi-node distributed data sharding scheduling optimization system according to claim 1, characterized in that, The distributed fragmentation cache topology reassembly module also includes a multi-path routing distribution module. The multi-path routing distribution module is connected to the intrinsic channel prediction adaptive fragmentation control module and is used to receive multi-path equal-sharing blind transmission instructions. It divides the variable-length data fragment set generated by the distributed fragmentation cache topology reassembly module into equal shares according to the number of each candidate receiving path. Without relying on the feedback response from the receiving end, it pushes the divided equal-share data fragments into the hardware transmission buffer corresponding to each candidate receiving path in parallel, so that the fragment size is stabilized within the set minimum boundary range for circulation.

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