Antibody sequence optimization data processing system

Abstract

This invention relates to the field of electronic digital data processing technology and discloses a data processing system for antibody sequence optimization, comprising: a latency sensing module for acquiring network transmission latency data between computing nodes and a data aggregation center; a logical alignment module for setting virtual aggregation times and assigning logical weight positions; a phase mapping module for calculating the transmission phase offset of computing nodes; and a gating execution module for enabling the hardware-level clock gating function of the interface controller and writing antibody sequence fragment data packets into the logical data channel at the trigger time. This invention establishes a transmission phase compensation mechanism, enabling deterministic serial interleaving of multi-source data packets at the data aggregation center, eliminating communication latency between nodes, achieving synchronous alignment of computing resources with the data evolution state, and improving the overall throughput efficiency of industrial cloud platforms in processing high-dimensional interactive data streams.

Description

Technical Field

[0001] This invention relates to the field of electronic digital data processing technology, and in particular to a data processing system for antibody sequence optimization. Background Technology

[0002] In current large-scale industrial cloud computing environments, distributed parallel computing architectures are widely used to handle high-dimensional data optimization tasks. Existing cloud computing scheduling mechanisms allocate task flows based on physical resource indicators such as processor utilization, remaining memory, and storage bandwidth of computing nodes. This allocation method based on physical resource utilization aims to achieve cluster load balancing. In the process of data flow processing involving high-frequency interactions, the spatial correlation between data units creates technical contradictions. Taking protein sequence modeling as an example, residue data blocks at different positions exhibit interactive correlation characteristics. This logical interaction topology is mismatched with the linear storage and network topology at the physical level. When the scheduling engine allocates logically closely related computing units to physically isolated computing nodes, network handshakes and synchronization waits between nodes result in computational fragmentation. Data packets are randomly transmitted in the physical network topology, causing the scalability of distributed computing to decrease as the number of nodes increases.

[0003] Conventional approaches attempt to alleviate the aforementioned contradictions by increasing physical link bandwidth or the number of parallel units. However, general network protocol stacks cannot recognize the urgency of interactions within the data stream. Large-scale data is concurrently injected into the network within a short time window, causing transient congestion in core switches. Due to the lack of orchestration control over the data stream injection phase, queuing delays and head-of-line blocking occur at the physical layer, thereby compromising the isochronism of parallel tasks. Specific analysis reveals the following shortcomings in existing technologies: 1. Resource allocation logic is disconnected from data interaction characteristics, resulting in excessively high network synchronization overhead during distributed computing; 2. Lack of awareness of transient response characteristics of physical links makes it difficult to avoid background traffic interference in multi-tenant environments; 3. The scheduling mechanism is in a passive response state and cannot handle interaction hotspots during iteration. The dynamic offset, due to the physical resource mapping deviation leading to the degradation of computational efficiency, has limitations in the logic of existing scheduling control algorithms. Software optimization is difficult to adapt to the timing requirements of the underlying hardware. For example, Chinese invention patent application with publication number CN111309461A discloses a workflow optimization scheduling method based on an immune mechanism, which introduces an artificial immune mechanism to improve the convergence speed of task allocation. However, it essentially treats the task to be processed as a whole logical unit, ignores the micro-injection phase requirements generated by the three-dimensional coordinates and charge attributes of the residue data block space in the antibody sequence optimization scenario, deviates from the transient response characteristics of the physical link, and relies on the heuristic algorithm resource matching mode. When facing Gaussian random jitter and background traffic crosstalk in the industrial cloud environment, it is difficult to achieve nanosecond-level time slot control, causing the core switch to generate micro-burst queuing and head-of-line blocking due to the lack of phase orchestration.

[0004] Therefore, how to extract the interaction affinity features of the data to be processed to reconstruct the physical resource mapping relationship, and realize deterministic phase injection of the data stream at the micro-temporal level, has become the technical problem to be solved by this invention. Summary of the Invention

[0005] This invention provides a data processing system for antibody sequence optimization, comprising: The latency awareness module is used to acquire one-way network transmission latency data between computing nodes performing antibody sequence optimization tasks in the industrial cloud computing environment and the data aggregation center, and to construct a node latency matrix containing the latency vectors of each computing node based on the one-way network transmission latency data. The logical alignment module is used to set a global virtual convergence time for a group of parallel data processing units belonging to the same antibody sequence optimization subtask. The global virtual convergence time is set as a hardware clock reference point after the end of the current data processing cycle, and a unique logical weight position is assigned to each parallel data processing unit according to the topological correlation between antibody amino acid residue sequence fragments. The phase mapping module is used to calculate the corresponding transmission phase offset for each computing node based on the corresponding delay vector and logical weight position in the node delay matrix. The transmission phase offset is used to characterize the manual waiting time of the computing node relative to the hardware clock reference point. The gating execution module is used to enable the hardware layer clock gating function of the interface controller corresponding to the computing node, and lock the calculated antibody sequence fragment data packets in the transmission buffer until the system clock reaches the trigger time determined by the transmission phase offset. Then, the antibody sequence fragment data packets are written into the logical data channel so that the multi-source antibody sequence fragment data packets output by different computing nodes can achieve deterministic serial interleaving at the data aggregation center.

[0006] Preferably, the system further includes a feature extraction unit; the feature extraction unit is used to extract the cross-correlation entropy value between data blocks in the antibody sequence fragment data packet, and divide multiple virtual sub-channels in the logical data channel according to the cross-correlation entropy value; wherein, the phase mapping module is also used to assign bus priority weights to different virtual sub-channels according to the magnitude of the cross-correlation entropy value, and when transient congestion occurs in the logical data channel, to perform peak-shifting control on the background traffic according to the bus priority weight, and to prioritize the transmission of antibody sequence fragment data packets with high cross-correlation entropy values ​​in the current bus cycle.

[0007] Preferably, the phase mapping module performs the following mathematical logic operations when calculating the transmission phase offset: ,in, To transmit the phase offset; This is the moment of global virtual convergence; This represents the latency data transmitted over a one-way network; N is the logical weight position. This is the preset microscopic protection interval.

[0008] Preferably, the system also includes a cache management module; the cache management module is used to extract the correlation residuals generated by the computing node during data processing, and establish a three-level cache access priority within the computing node based on the correlation residuals; wherein, the cache management module locks antibody sequence fragment data packets that meet the preset correlation threshold in the on-chip cache, and pre-stores secondary affinity components with high residual characteristics in the video memory, so as to reduce the instruction pipeline stall rate during the computing process.

[0009] Preferably, the system also includes a fault-tolerant migration module; the fault-tolerant migration module is used to pre-mark backup computing nodes in the computing cluster when the correlation degree residual that has not reached the mapping threshold is extracted; wherein, the fault-tolerant migration module constructs a logical mapping index based on the correlation degree residual, and when a computing node experiences a shutdown failure, it migrates the current task state to the backup computing node through the logical mapping index, and uses the sending phase offset to perform output alignment.

[0010] Preferably, the latency sensing module obtains one-way network transmission latency data based on the physical clock stamp of the network card hardware layer, and the acquisition accuracy is set to 1ns.

[0011] Preferably, the gating execution module sends a time-triggered command to the interface controller, forcing computing nodes whose unidirectional network transmission latency is lower than a preset threshold to introduce a logical waiting time, in order to offset the differences in network paths between different computing nodes.

[0012] Preferably, the logical data channel is established on the logical topology relationship between the computing node and the graphics processor cluster, and the bandwidth of the virtual subchannel is reconstructed in real time according to the dynamic changes of the interaction association entropy value.

[0013] Preferably, when calculating the correlation residual, the cache management module uses the feature encoding of antibody amino acid residues as input and converts the feature encoding into a weight vector representing the data access frequency through mapping logic.

[0014] Preferably, the system is deployed in an industrial cloud computing environment, and the data aggregation center is set as a switching matrix with asynchronous pre-occupancy function. The switching matrix performs time slot arbitration on antibody sequence fragment data packets based on the transmission phase offset.

[0015] The beneficial effects of this invention are: 1. In the data processing for antibody sequence optimization, a reverse phase injection mechanism established by a phase orchestration controller reconstructs the injection timing of the data stream in the logical dimension based on the physical propagation delay mapping results. This enables residue data blocks from different physical nodes to form a non-overlapping serial sequence when they arrive at the convergence exchange node. This mechanism changes the uncontrolled concurrent mode of each computing node injecting data into the network after computation in the existing technology, eliminates the micro-burst queuing phenomenon at the convergence exchange port, keeps the buffer occupancy rate of the exchange at an extremely low level, thereby eliminating random jitter caused by head-of-line blocking and ensuring the isochronism of parallel computing tasks at the microsecond scale.

[0016] 2. By using the gradient prediction unit to extract the changing gradient of the interaction correlation factor within adjacent iteration cycles, the affinity upward trend can be identified based on the rate of change of residue interaction strength. Based on this trend, the resource scheduling engine can pre-complete the asynchronous pre-occupation of physical links before the end of the current iteration task, thereby achieving the advance alignment of computing resources with the data evolution state. This approach avoids the scheduling delay caused by data evolution ahead of resource reconstruction in existing technologies, eliminates resource allocation pauses during iteration cycle switching, and ensures that large-scale concurrent computing is always within the optimal bandwidth coverage range.

[0017] 3. By collecting the information interaction entropy value of residue data blocks through the entropy sampling unit, and dividing virtual sub-channels in the directional data transmission tunnel according to the entropy value, the system achieves fine-grained phase management of physical link occupancy rights. When transient congestion occurs in the underlying physical link, the system performs peak-shifting control on background traffic according to priority permissions, giving priority to ensuring that data streams with high information interaction entropy values ​​can pass through unimpeded within the bus cycle. This logical phase adjustment mechanism builds a clean logical transmission environment for high affinity computing tasks without increasing physical bandwidth, and solves the problem of random interference of multi-tenant traffic to high-precision scientific computing tasks in the public cloud shared environment. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein: Figure 1 This is a data processing system architecture diagram for antibody sequence optimization according to the present invention; Figure 2 This is a timing diagram of phase offset and serial interleaving for multi-source data packet transmission in this invention. Detailed Implementation

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0020] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, an embodiment or embodiment referred to herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. An embodiment appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0022] This invention is described in detail with reference to the schematic diagrams. When describing the embodiments of this invention, for ease of explanation, the cross-sectional views of the device structure will be partially enlarged without adhering to the general scale. Moreover, the schematic diagrams are only examples and should not limit the scope of protection of this invention. In addition, in actual manufacturing, the three-dimensional spatial dimensions of length, width and depth should be included.

[0023] Furthermore, in the description of this invention, it should be noted that the terms such as "upper," "lower," "inner," and "outer" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or component referred to has a specific orientation, or is constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0024] Unless otherwise explicitly specified and limited, the terms installation, connection, and linking in this invention should be interpreted broadly. For example, they can refer to fixed connection, detachable connection, or integrated connection; similarly, they can refer to mechanical connection, electrical connection, or direct connection, or indirect connection through an intermediate medium, or internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0025] A data processing system for antibody sequence optimization, comprising: The latency awareness module is used to acquire one-way network transmission latency data between computing nodes performing antibody sequence optimization tasks in the industrial cloud computing environment and the data aggregation center, and to construct a node latency matrix containing the latency vectors of each computing node based on the one-way network transmission latency data. The logical alignment module is used to set a global virtual convergence time for a group of parallel data processing units belonging to the same antibody sequence optimization subtask. The global virtual convergence time is set as a hardware clock reference point after the end of the current data processing cycle, and a unique logical weight position is assigned to each parallel data processing unit according to the topological correlation between antibody amino acid residue sequence fragments. The phase mapping module is used to calculate the corresponding transmission phase offset for each computing node based on the corresponding delay vector and logical weight position in the node delay matrix. The transmission phase offset is used to characterize the manual waiting time of the computing node relative to the hardware clock reference point. The gating execution module is used to enable the hardware layer clock gating function of the interface controller corresponding to the computing node, and lock the calculated antibody sequence fragment data packets in the transmission buffer until the system clock reaches the trigger time determined by the transmission phase offset. Then, the antibody sequence fragment data packets are written into the logical data channel so that the multi-source antibody sequence fragment data packets output by different computing nodes can achieve deterministic serial interleaving at the data aggregation center.

[0026] Preferably, the system further includes a feature extraction unit; the feature extraction unit is used to extract the cross-correlation entropy value between data blocks in the antibody sequence fragment data packet, and divide multiple virtual sub-channels in the logical data channel according to the cross-correlation entropy value; wherein, the phase mapping module is also used to assign bus priority weights to different virtual sub-channels according to the magnitude of the cross-correlation entropy value, and when transient congestion occurs in the logical data channel, to perform peak-shifting control on the background traffic according to the bus priority weight, and to prioritize the transmission of antibody sequence fragment data packets with high cross-correlation entropy values ​​in the current bus cycle.

[0027] Preferably, the phase mapping module performs the following mathematical logic operations when calculating the transmission phase offset: ,in, To transmit the phase offset; This is the moment of global virtual convergence; This represents the latency data transmitted over a one-way network; N is the logical weight position. This is the preset microscopic protection interval.

[0028] Preferably, the system also includes a cache management module; the cache management module is used to extract the correlation residuals generated by the computing node during data processing, and establish a three-level cache access priority within the computing node based on the correlation residuals; wherein, the cache management module locks antibody sequence fragment data packets that meet the preset correlation threshold in the on-chip cache, and pre-stores secondary affinity components with high residual characteristics in the video memory, so as to reduce the instruction pipeline stall rate during the computing process.

[0029] Preferably, the system also includes a fault-tolerant migration module; the fault-tolerant migration module is used to pre-mark backup computing nodes in the computing cluster when the correlation degree residual that has not reached the mapping threshold is extracted; wherein, the fault-tolerant migration module constructs a logical mapping index based on the correlation degree residual, and when a computing node experiences a shutdown failure, it migrates the current task state to the backup computing node through the logical mapping index, and uses the sending phase offset to perform output alignment.

[0030] Preferably, the latency sensing module obtains one-way network transmission latency data based on the physical clock stamp of the network card hardware layer, and the acquisition accuracy is set to 1ns.

[0031] Preferably, the gating execution module sends a time-triggered command to the interface controller, forcing computing nodes whose unidirectional network transmission latency is lower than a preset threshold to introduce a logical waiting time, in order to offset the differences in network paths between different computing nodes.

[0032] Preferably, the logical data channel is established on the logical topology relationship between the computing node and the graphics processor cluster, and the bandwidth of the virtual subchannel is reconstructed in real time according to the dynamic changes of the interaction association entropy value.

[0033] Preferably, when calculating the correlation residual, the cache management module uses the feature encoding of antibody amino acid residues as input and converts the feature encoding into a weight vector representing the data access frequency through mapping logic.

[0034] Preferably, the system is deployed in an industrial cloud computing environment, and the data aggregation center is set as a switching matrix with asynchronous pre-occupancy function. The switching matrix performs time slot arbitration on antibody sequence fragment data packets based on the transmission phase offset.

[0035] Example 1: In an industrial cloud computing environment containing 5000 graphics processing cores, when performing a full-atom dynamics simulation of antibody sequences, computing nodes located in different physical racks, after completing the processing of residue interaction data blocks, simultaneously inject multi-source data packets into the inbound port of the core aggregation switch within a microsecond-level time window in a concurrent mode. This concurrent mechanism causes micro-burst queuing backlog at the switch crossbar when processing high-frequency interactive data streams, resulting in a synchronous waiting state caused by head-of-queue blockage on the computing nodes. The antibody sequence optimized data processing system does not increase the physical link bandwidth, but intervenes by using a data injection phase orchestration mechanism; the latency sensing module measures the latency of each participating antibody sequence with an acquisition accuracy of 1ns based on the physical clock stamp of the network card hardware layer. The sequence optimization subtask transmits latency data via a one-way network between the computing nodes and the data aggregation center. Based on this latency data, a node latency matrix containing the latency vectors of each computing node is constructed. The logical alignment module assigns a unique logical weight position to each parallel data processing unit based on the topological correlation between antibody amino acid residue sequence fragments. A hardware clock reference point at the end of the current data processing cycle is set as the global virtual aggregation time. The phase mapping module calculates the corresponding transmission phase offset for each computing node based on the latency vector and logical weight position in the node latency matrix. This transmission phase offset characterizes the manual waiting time of the computing node relative to the hardware clock reference point. The specific mathematical logic operations are as follows. ,in, To transmit the phase offset, For the global virtual convergence moment, For one-way network transmission latency data, N is a dimensionless logical weight position. This is the preset microscopic protection interval.

[0036] The gating execution module enables the hardware-level clock gating function of the interface controller corresponding to the compute node. The specific implementation path is as follows: Utilizing the physical function pass-through driver of single-root input / output virtualization technology, the instruction space of the compute node's virtual machine is directly mapped to the physical register base address of the interface controller. The gating execution module, through memory-mapped input / output, directly writes an instruction code of 0xFF to the control register with an address offset of 0x4010. This bypasses the kernel interrupt handling chain and virtualization protocol stack scheduling overhead of the cloud environment host machine, achieving microsecond-level direct trigger control of the underlying physical network card hardware clock drive circuit. This locks the computed antibody sequence fragment data packet in the transmission buffer until the system clock reaches the trigger time determined by the transmission phase offset, at which point the antibody sequence... The sequence fragment data packets are written into the logical data channel; the physical boundary parameters provided by the latency sensing module and the sequence correlation features output by the logical alignment module constitute the computational input of the phase mapping module, so as to transform the multi-source concurrent traffic into a data stream with deterministic timing on the micro time axis. The phase offset setting drives the gating execution action at the physical hardware level. The multi-source antibody sequence fragment data packets output by different computing nodes achieve deterministic serial interleaving at the data aggregation center. The exchange matrix with asynchronous pre-occupancy function performs time slot arbitration on the antibody sequence fragment data packets according to the sending phase offset. Its buffer occupancy rate maintains the lower limit of the baseline zero value during data interleaving. The computing power resources across nodes complete the synchronization alignment of the data evolution state on the microsecond scale, improving the overall throughput efficiency of the industrial cloud platform in processing interactive data streams.

[0037] Example 2: When evaluating the effectiveness of the data injection phase orchestration mechanism in eliminating transient congestion, an industrial cloud computing simulation platform based on discrete event simulation logic is constructed. This platform solves the standard queuing theory control equations to simulate the underlying hardware state of the core aggregation switch, and actively injects a 20% Gaussian random jitter amount into the background traffic to simulate clock drift and background crosstalk in the physical link; mathematical logic formulas are extracted for calculating the transmission phase offset. ,in, To transmit the phase offset, For the global virtual convergence moment, For one-way network transmission of latency data, For dimensionless logical weight positions, Preset microscopic protection interval; Preset microscopic protection interval The physical anti-collision boundary of adjacent data packets and the bus idle overhead of the logical data channel are determined based on the physical signal transmission rules. When the clock drift variance of the underlying link increases, a micro-protection interval is preset. The value tends towards the upper limit of the preset range to prevent data sequence aliasing, and is set for the interface controller operating condition of 100Gbps line rate and carrying 1500Byte standard load data. It is 120ns.

[0038] The test benchmarks for both the comparative sample group and the sample group of this invention are set at 500 computing nodes completing the calculation of antibody sequence fragment data blocks and triggering data injection requests at the same physical moment, with a global virtual convergence time. With a uniform timeout of 10000 ns, in the comparative sample group using a conventional general network protocol stack, the concurrent input of 500 computing nodes formed a raw transient data stream with an arrival rate of 50 Tbps at the core switch port. The switching matrix buffer occupancy rate climbed to 98.5% within 2 μs, resulting in a communication packet loss rate of 14.2%. In the sample group of this invention using an antibody sequence-optimized data processing system, the latency data for unidirectional network transmission was... For a specific computing node with a 450ns time complexity and a logical weight N of 5, the phase mapping module calculates its transmitted phase offset based on the aforementioned formula. The transmit phase offset is 8950ns. This offset drives the gating module to lock the transmit buffer until the trigger time arrives. A problem intensity gradient comparison system is set to verify the preset micro-protection interval. The boundary law, measured data shows that when When the value is set to the lower limit of 50ns, background clock drift causes envelope overlap, resulting in buffer occupancy fluctuating at 45.3%. When the set 120ns is reached, the buffer occupancy rate exhibits a non-linear decreasing trend and stabilizes at 4.1%. After exceeding the 180ns overload threshold, the buffer occupancy rate remained constant and the overall sequence transmission cycle increased exponentially from the baseline 2.5ms to 8.7ms, confirming that 120ns is the operating window that balances anti-collision and high throughput. Actual timing data shows that by transforming the spatial dimension of bus contention into the temporal dimension of phase offset waiting mechanism, the switch buffer occupancy rate under extreme concurrency conditions is compressed. By relying on the mathematical mapping of node delay matrix and logical weight position, the physical arrival delay of computing power output is eliminated, enabling massive antibody sequence fragment data to form deterministic serial interleaving at the data aggregation center, achieving synchronous alignment between the computing power resources and data evolution status of the industrial cloud platform.

[0039] Example 3: When performing high-frequency antibody sequence dynamics simulation in an industrial cloud computing environment, the logical data channel of the aggregation switch is prone to local bandwidth congestion due to concurrent input. The feature extraction unit reads the antibody sequence fragment data packets residing in the transmission buffer and extracts the interaction correlation entropy value between data blocks. This extraction process is obtained by analyzing the spatial three-dimensional coordinates and charge attribute parameters of amino acid residues in the data block, calculating the root mean square error of the Coulomb interaction energy between residue pairs and normalizing it. The interaction correlation entropy value is used to quantify the probability of physical conformational mutation of the local sequence within the current simulation step. Based on the numerical gradient of the interaction correlation entropy value, the feature extraction unit divides the logical data channel into multiple virtual sub-channels, including a key interaction sub-channel for carrying mutation features and a regular maintenance sub-channel for carrying steady-state data.

[0040] The phase mapping module assigns bus priority weights to different virtual subchannels based on the magnitude of the interaction correlation entropy value, thus establishing the data transmission order. The specific mathematical logic operations are as follows. ,in, The bus priority weight is a dimensionless parameter. The interaction correlation entropy value is in bits, and μ is the preset priority conversion coefficient, which is the reciprocal of the bit value. The baseline weighting constant is set by the system based on the median of the entropy distribution obtained in 1000 standard antibody folding simulation experiments. The priority conversion coefficient μ is set to 0.5. When transient congestion occurs in the logical data channel, i.e., the switch port buffer occupancy rate is greater than the warning threshold of 85% for 50ms, the phase mapping module performs peak-shifting control on the background traffic according to the bus priority weight. This module sets the virtual subchannels with a bus priority weight value greater than 2.5 to physical pass-through state, giving priority to ensuring that antibody sequence fragment data packets with high interaction correlation entropy values ​​are transmitted within the current bus cycle. It intervenes in the virtual subchannels with a bus priority weight value less than or equal to 2.5, forcing the corresponding low-weight background traffic to add a 200μs artificial waiting period in the sending buffer. The quantification entropy value index derived from molecular physical characteristics drives the reallocation of bus resources in the logical data channel, enabling the computational data carrying spatial conformational changes to penetrate network congestion obstacles and maintain the global temporal consistency of the parallel computing nodes of the industrial cloud platform when processing macromolecular interaction models.

[0041] Example 4: Before deploying a new batch of antibody sequence optimization tasks in an industrial cloud computing environment for the first time, in order to establish a physical benchmark for network transmission latency data, the system initiates a pre-deployment calibration procedure for the underlying hardware link. The latency awareness module sends a continuously preset number of standard diagnostic probes between each computing node in the physical rack and the data aggregation center. The standard diagnostic probes carry a fixed payload length of 1500 bytes and a physical transmission clock stamp of the network card hardware layer. When the data aggregation center receives the standard diagnostic probes, it records the corresponding physical arrival clock stamp and feeds it back to the original computing node. The latency awareness module extracts the set of time differences between the transmission and arrival clock stamps, removes extreme outliers that deviate from the distribution mean by a specific standard deviation, and calculates the remaining valid values. The arithmetic mean of the differences is used as the initial one-way network transmission delay data and filled into the corresponding vector position of the node delay matrix. Before the actual injection of the antibody data stream, the procedure completes the offline calibration of the physical propagation delay characteristics of all network nodes, eliminating the delay measurement basis error caused by differences in optical module devices and the initial fiber optic link length tolerance. Before task deployment, the underlying link physical reference is calibrated. A hardware synchronization method based on the IEEE 1588v2 precise time protocol is adopted, utilizing the internal hardware timestamp unit of each computing node's network card to phase lock with the global reference clock of the data aggregation center. A 1500-byte standard probe packet is selected to test 1000 round-trip delays. The arithmetic mean of the round-trip delay is calculated and the inherent processing delay of the network card is subtracted to obtain the one-way network transmission delay data. Using a heartbeat synchronization signal with a sliding time window of 100,000 hardware clock cycles, periodic monitoring is performed. In case of fluctuations, when the absolute value of the difference between the measured transient delay and the recorded value of the node delay matrix exceeds the 2ns hardware tolerance threshold, the matrix data is automatically overwritten and updated to eliminate the impact of physical clock drift caused by device thermal power fluctuations on the transmission phase offset. Interference with calculation accuracy.

[0042] To address the physical clock drift caused by fluctuations in equipment thermal power consumption during the multi-day antibody all-atom dynamics simulation, the system utilizes the routine maintenance subchannel carrying steady-state data within the logic data channel to perform baseline dynamic reconstruction. The latency sensing module, using a set sliding time window of 100,000 hardware clock cycles, periodically and repeatedly sends a heartbeat synchronization signal carrying the latest clock stamp during idle time slots within the routine maintenance subchannel. The transient network transmission latency is then measured using this heartbeat synchronization signal. The unidirectional network transmission delay data currently recorded in the node delay matrix Satisfy absolute difference logic hour, To achieve the set 2ns hardware tolerance threshold, the latency sensing module uses transient network transmission latency. The corresponding data in the node delay matrix is ​​overwritten, and the phase mapping module synchronously extracts the updated node delay matrix to recalculate the transmission phase offset. The parameter adaptive refresh action triggered by the physical layer error accumulation maintains the matching between the transmission phase offset and the dynamic impedance state of the physical link, stabilizing the timing alignment accuracy of the global virtual convergence time under long-cycle operation.

[0043] Example 5: In a high-density concurrent computing power scheduling scenario within an industrial cloud computing environment, the logical alignment module initiates a weight allocation procedure based on a spatial distance graph matrix. This establishes a quantitative index for the topological correlation of antibody amino acid residue sequence fragments. The module reads the three-dimensional coordinate data of residue atoms in the sequence fragments currently carried by each parallel data processing unit, calculates the reciprocal of the Euclidean distance between the physical centroids of different sequence fragments, and constructs an adjacency matrix characterizing the spatial interaction strength. It extracts the eigenvectors of the adjacency matrix and calculates the topological centrality score corresponding to each sequence fragment. The logical alignment module maps the descending order of the topological centrality scores to a continuously increasing unique positive integer in the node delay matrix, setting this as the logical weight position. The computing node carrying the sequence fragment with the highest interaction density obtains the lowest logical weight position, which is then converted into the highest value in the mapping calculation of the sent phase offset. The short manual waiting time parameter is mapped as follows: The system calls the quicksort logic to sort the topological centrality scores of the 500 parallel processing units in the current computing cluster from largest to smallest. The sorted rank corresponds to the logical weight position, so that the processing unit ranked 1st gets a logical weight position of 1, the processing unit ranked 500th gets a logical weight position of 500, and so on. If there are identical scores, a secondary sort is performed based on the value of the physical media access control address of the computing node. This ensures that in the entire antibody sequence optimization subtask, each logical weight position is a unique positive integer and exhibits a monotonically continuous distribution. The feature extraction unit reads the amino acid residue charge attribute parameters in the antibody sequence fragment data packet payload, calculates the mean square error of the Coulomb interaction energy between residue pairs, and normalizes it to obtain the interaction correlation entropy value that quantifies the probability of local sequence conformational mutation. According to the formula Determine bus priority weights Where μ is set to 0.5, Set to 1.0, it provides a closed-loop quantitative basis for time slot arbitration during transient congestion of logical data channels.

[0044] The system operates a dynamic calibration control loop for the micro-protection interval within the phase mapping module. This loop addresses the overlapping boundaries in the timing of the transmitted phase offset caused by temperature drift of the photoelectric conversion devices in the underlying physical link. It utilizes a hardware counter with a fixed sampling window of 10,000 Ethernet data frames to collect data on the time deviation between the actual arrival time of the data packets at the data aggregation center and the expected aggregation time. The discrete standard deviation of the time deviation within the current window is calculated and extracted as a clock jitter variable, which is then used in accordance with the control formula. Real-time reconstructed parameters, among which, For the updated micro-protection interval, γ is the inherent physical bus recovery time base of the network interface controller, and γ is a dimensionless system margin response coefficient used to control the compensation slope for jitter. For the collected clock jitter variable, when the clock jitter variable shows an upward trend due to the accumulation of thermal noise in the physical layer, the control loop forcibly widens the time gap between adjacent antibody sequence fragment data packets in the logical data channel based on the above linear compensation logic, quantizes the closed-loop feedback drive parameters for adaptive reconstruction, and eliminates the probability of interference at the beginning and end of the data flow at the cross switch of the core switch.

[0045] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the protection scope of the present invention.

Claims

1. A data processing system for antibody sequence optimization, characterized in that, include: The latency awareness module is used to acquire one-way network transmission latency data between computing nodes performing antibody sequence optimization tasks in the industrial cloud computing environment and the data aggregation center, and to construct a node latency matrix containing the latency vectors of each computing node based on the one-way network transmission latency data. The logical alignment module is used to set a global virtual convergence time for a group of parallel data processing units belonging to the same antibody sequence optimization subtask. The global virtual convergence time is set as a hardware clock reference point after the end of the current data processing cycle, and a unique logical weight position is assigned to each parallel data processing unit according to the topological correlation between antibody amino acid residue sequence fragments. The phase mapping module is used to calculate the corresponding transmission phase offset for each computing node based on the corresponding delay vector and logical weight position in the node delay matrix. The transmission phase offset is used to characterize the manual waiting time of the computing node relative to the hardware clock reference point. The gating execution module is used to enable the hardware layer clock gating function of the interface controller corresponding to the computing node, and lock the calculated antibody sequence fragment data packets in the transmission buffer until the system clock reaches the trigger time determined by the transmission phase offset. Then, the antibody sequence fragment data packets are written into the logical data channel so that the multi-source antibody sequence fragment data packets output by different computing nodes can achieve deterministic serial interleaving at the data aggregation center.

2. The data processing system for antibody sequence optimization according to claim 1, characterized in that, The system also includes a feature extraction unit; The feature extraction unit is used to extract the cross-correlation entropy value between data blocks in the antibody sequence fragment data packet, and divide the logical data channel into multiple virtual sub-channels based on the cross-correlation entropy value. The phase mapping module is also used to assign bus priority weights to different virtual sub-channels based on the magnitude of the cross-correlation entropy value, and to perform peak-shifting control on the background traffic based on the bus priority weight when transient congestion occurs in the logical data channel, so as to prioritize the transmission of antibody sequence fragment data packets with high cross-correlation entropy values ​​in the current bus cycle.

3. The data processing system for antibody sequence optimization according to claim 1, characterized in that, When calculating the transmit phase offset, the phase mapping module performs the following mathematical logic operations: ,in, To transmit the phase offset; This is the moment of global virtual convergence; This represents the latency data transmitted over a one-way network; N is the logical weight position. This is the preset microscopic protection interval.

4. The data processing system for antibody sequence optimization according to claim 1, characterized in that, The system also includes a cache management module; The cache management module is used to extract the correlation residuals generated by the computing node during data processing and establish a three-level cache access priority within the computing node based on the correlation residuals. Specifically, the cache management module locks antibody sequence fragment data packets that meet the preset correlation threshold in the on-chip cache and pre-stores secondary affinity components with high residual characteristics in the video memory to reduce the instruction pipeline stall rate during the computing process.

5. The data processing system for antibody sequence optimization according to claim 4, characterized in that, The system also includes a fault-tolerant migration module; The fault-tolerant migration module is used to pre-mark backup computing nodes in the computing cluster when the correlation residual is extracted and has not reached the mapping threshold. The fault-tolerant migration module constructs a logical mapping index based on the correlation residual. When a computing node fails to run, the current task state is migrated to the backup computing node through the logical mapping index, and the output is aligned by sending the phase offset.

6. The data processing system for antibody sequence optimization according to claim 1, characterized in that, The latency sensing module obtains one-way network transmission latency data based on the physical clock stamp of the network card hardware layer, and the acquisition accuracy is set to 1ns.

7. The data processing system for antibody sequence optimization according to claim 1, characterized in that, The gating execution module sends a time-triggered command to the interface controller, forcing computing nodes whose one-way network transmission latency is lower than a preset threshold to introduce a logical waiting time in order to offset the differences in network paths between different computing nodes.

8. The data processing system for antibody sequence optimization according to claim 2, characterized in that, The logical data channel is established on the logical topology relationship between the computing node and the graphics processor cluster, and the bandwidth of the virtual subchannel is reconstructed in real time according to the dynamic changes of the interaction association entropy value.

9. The data processing system for antibody sequence optimization according to claim 4, characterized in that, When calculating the correlation residual, the cache management module uses the feature encoding of antibody amino acid residues as input and converts the feature encoding into a weight vector representing the data access frequency through mapping logic.

10. The data processing system for antibody sequence optimization according to claim 1, characterized in that, The system is deployed in an industrial cloud computing environment, and the data aggregation center is set up as a switching matrix with asynchronous pre-occupancy function. The switching matrix performs time slot arbitration on antibody sequence fragment data packets based on the transmission phase offset.

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