Bidding Process Optimization Method Based on Cross-Analysis of Dynamic Duration and Real-Time Quotes

Abstract

This invention specifically relates to an optimization method for the bidding process based on cross-analysis of dynamic duration and real-time bids, belonging to the field of electronic bidding technology. It includes: mapping physical memory as a flow field mesh to mount virtual fluid particle structures; marking these structures as continuous or dispersed phases using hardware masks based on their attributes; and calculating the memory byte occupancy rate of each phase. In this invention, bidding data is mapped as virtual fluid particle structures containing equivalent mass and initial transient velocity, and physical memory is mapped as a flow field mesh, achieving deep coupling between bidding data flow and hardware resources. The invention uses the fluid dynamics continuity equation to simulate the dynamic change process of memory phases, extracts system operating state parameters through hardware probes to generate interphase drag coefficients, identifies and reclaims dispersed phase memory resources, and alleviates memory fragmentation problems in high-concurrency bidding scenarios to a certain extent.

Description

Technical Field

[0001] This invention relates to the field of electronic bidding technology, and in particular to a bidding process optimization method based on cross-analysis of dynamic duration and real-time bids. Background Technology

[0002] With the rapid development of the digital economy, real-time bidding technology has been widely applied in various fields such as commodity trading, electricity spot markets, digital advertising, and government procurement. In dynamic duration bidding models, the real-time nature and stability of the bidding process have a significant impact on transaction results and market efficiency. Especially in trading scenarios with high concurrency and low latency requirements, improved system responsiveness can provide a more sufficient time window for bidding decisions, helping to increase the success rate of transactions.

[0003] Existing methods for optimizing the bidding process mainly explore technologies from two dimensions:

[0004] Firstly, at the level of bidding strategy algorithms, a price prediction model is established by analyzing historical transaction data to optimize the timing of bidding and the increment of bids; Secondly, optimizations are made at the network transport layer by reducing transmission latency through data compression, path selection, and other methods.

[0005] These technologies have achieved certain results in their respective application scenarios, providing valuable technical accumulation for improving the overall performance of bidding systems.

[0006] As the market transaction volume continues to expand and the number of participants increases, the data concurrency and processing complexity faced by the bidding system have significantly increased.

[0007] There is still room for further optimization in areas such as multi-source data time synchronization, dynamic scheduling of memory resources, precise control of system back pressure, and coordination between bidding strategies and hardware status. Summary of the Invention

[0008] The purpose of this invention is to propose an optimization method for the bidding process based on cross-analysis of dynamic duration and real-time price quotes in order to solve the above-mentioned problems.

[0009] To achieve the above objectives, the present invention adopts the following technical solution: Bidding process optimization methods based on cross-analysis of dynamic duration and real-time quotes include: Extract the quotation data from the data frame, combine it with the basic transmission time and delay jitter variance obtained from the mapping table to calculate the alignment time, and map the quotation data into a virtual fluid particle structure containing equivalent mass and initial transient velocity; Physical memory is mapped to a flow field mesh to mount virtual fluid particle structures. Based on their properties, they are marked as continuous or dispersed phases using a hardware mask, and the memory byte occupancy rate of each phase is calculated. Extract the page fault frequency and lock contention cycle and combine them into an interphase drag coefficient. Calculate the difference in instruction throughput between the continuous phase and the dispersed phase. Multiply the difference by the interphase drag coefficient to obtain the slip velocity. The sliding velocity is compared with the hardware separation threshold, and the dispersed phase that meets the settling state is transferred to the recycling memory pool. The remaining continuous phase is then subjected to compact reconstruction. Based on the memory byte occupancy rate of the continuous phase, the instruction throughput rate, and the dynamic countdown time obtained by the hardware timer, the next round of bidding step size is calculated. The back pressure is generated by reducing the active limit value of the send descriptor with the dynamic countdown time, and the bidding step size is written to the doorbell register as an instruction.

[0010] Preferably, the step of calculating the alignment time by combining the basic transmission time obtained from the mapping table with the delay jitter variance has the following characteristics, applicable only to the initial reception step of multi-source bidding data streams: The system uses the underlying arithmetic logic unit to extract round-trip time data from ten consecutive communications, and iteratively calculates the basic transmission time and delay jitter variance of the preset network path, which is then written into the cache mapped by the static random access memory. The absolute alignment time of the quotation data is calculated according to the following underlying alignment formula: ; in, For absolute alignment time, This is the parsed local transmission timestamp from the terminal. The base transfer time is obtained from the mapping table. To delay jitter variance, This is a confidence adjustment factor that is dynamically adjusted based on the physical depth of the network interface card's hardware receive queue.

[0011] Preferably, mapping the quotation data into a virtual fluid particle structure containing equivalent mass and initial transient velocity specifically includes: When constructing a circular buffer, the read pointer and write pointer are forced to be distributed in separate, isolated cache lines, and placeholder data blocks are filled between them to prevent false sharing of caches in multi-core processors; The most significant bit is extracted based on the difference between the quoted amount and the benchmark price and used as the physical index address of the hardware table. The equivalent mass is then directly read using the underlying leading zero counting fast shift instruction and table lookup instruction. Extract the absolute hardware time interval between consecutively sent requests and send it to the hardware performance counter. Allocate the concurrent priority of the operating system kernel thread according to the clock cycle length of the hardware performance counter to define the initial transient speed. The absolute alignment time, the equivalent quality, and the concurrency priority are encapsulated together with the physical memory absolute address pointer in a reserved field of a composite structure whose size is limited to the width of a single cache line, and a memory barrier synchronization instruction is executed to ensure that write operations on the bus are visible.

[0012] Preferably, the process of mapping physical memory to a flow field mesh to mount virtual fluid particle structures is as follows: The system requests the operating system kernel to lock a contiguous physical giant page heap space to reduce the translation backstop miss rate. The first dimension of the logical address is defined as a dynamic remaining duration mapping segment, the second dimension of the logical address is defined as a quote deviation interval segment, and the third dimension of the logical address is defined as a terminal reputation mapping segment. Using the logical addressing coordinate system of the obtained three-dimensional mesh as ,but: Physical target address = heap space base address + ( Maximum span of axis Maximum span of axis + Maximum span of axis + ) Number of bytes occupied by a single control unit; Perform a three-dimensional base address superposition offset mapping to map out mutually isolated fixed-size memory blocks that form control volumes. Each control volume corresponds to a doubly linked list head node pointer that mounts a virtual fluid particle structure.

[0013] Preferably, the calculation of the memory byte occupancy rate for each phase state is performed in the following manner: The main control bidding thread is prohibited from participating in prediction calculations. The physical calculation coroutine is hidden by binding the thread affinity of the operating system. The control volume state variables are reconstructed into a compressed row storage format of sparse matrix. Hiding the physical computation coroutine calls to the Single Instruction Multiple Data Extended Instruction Set and wide bit vector registers, the numerical prediction results of each phase state within the discrete time step are calculated according to the following continuity equation: ; in, Represents whether the memory phase type is continuous or dispersed. For continuous-time variables, the processor strictly follows the discrete time step. Perform fixed-width time slice calculations; This represents the memory byte occupancy rate for the corresponding phase. To set the virtual density constant for the corresponding phase state; The physical flow velocity vector is mapped to the throughput rate extracted by the hardware pointer that pops the corresponding phase data packet from the doubly linked list. As a divergence operator, the direct memory access controller periodically captures the actual absolute changes in the length of the physical linked list nodes between adjacent grid control blocks. This is a quality source item.

[0014] Preferably, the extraction of page fault frequency and lock contention cycle is combined into an interphase drag coefficient, the instruction throughput rate difference between the continuous phase and the dispersed phase is calculated, and the difference is multiplied by the interphase drag coefficient to obtain the slip velocity, specifically including: The background probe thread monitors the frequency of hardware page faults caused by the translation back buffer miss due to out-of-bounds addressing of scattered phases, and uses the kernel probe mechanism to monitor the number of spinlock idle clock cycles when the main working thread accesses the doubly linked list. The two are weighted and merged by hardware displacement instructions to generate a hardware congestion index and written into the global shared memory segment as the interphase drag coefficient. At the end of the discrete hardware clock cycle, the arithmetic logic unit executes basic subtraction assembly instructions to calculate the absolute difference between the number of continuous phases and discrete phases completed in the processor's general-purpose registers. The absolute difference and the interphase drag coefficient are then used by the floating-point arithmetic unit to execute a single multiplication instruction to obtain the slip velocity, and the result is forcibly overwritten into the extended field of the corresponding virtual fluid particle structure.

[0015] Preferably, the process of transferring the dispersed phase that meets the settling condition to the recycling memory pool is as follows: When the main worker thread traverses the doubly linked list, it calls a comparison instruction to compare the sliding speed with the static hardware separation threshold locked in the first-level high-speed data cache, triggering a hardware soft interrupt in the processor flag register; it uses the locked system bus prefix mechanism to compare and exchange hardware instruction primitives, atomically modifying the predecessor and successor address pointers of the exception node within the uninterruptible hardware execution cycle, removing it from the high-speed main memory pool and adding it to the remote abandoned sleep queue linked list; The lowest priority garbage collection auxiliary thread is deployed to perform read-only idle polling on the remote abandoned sleep queue list. When the difference between the hardware timestamp counter caused by the thread's sleep and wake-up context switching exceeds the safe dead zone threshold, the system's underlying memory is called to release the physical mapping trap instruction and return the physical page frame to the operating system kernel.

[0016] Preferably, the dense reconstruction of the remaining continuous phase specifically includes: Deploy a memory reorganization monitoring probe thread to scan the physical distribution span of the continuously phase-marked memory grid. When it detects that a live valid bid structure spans too many physical memory pages, causing the hardware prefetcher to fail and the cache consistency maintenance cost across physical page frames to increase, a reorganization signal is issued. During the nanosecond-level idle period window when the processor does not process external network interrupt pulses, call the dedicated assembly instructions for vector parallel loading and vector parallel storage in the processor's Single Instruction Multiple Data Extended Instruction Set. The scattered continuous phase structure is moved to a continuous and compact new giant page physical space with high-density address aggregation using bus burst transfer rate, and the hexadecimal offset of the associated predecessor and successor memory pointers is corrected simultaneously.

[0017] Preferably, the step of calculating the next round of bidding step size based on the memory byte occupancy rate of the continuous phase, the instruction throughput rate, and the dynamic countdown duration obtained by the hardware timer specifically includes: For continuous phase data streams, parameters are extracted through the underlying performance monitoring unit. In order to avoid the clock cycle loss caused by the floating-point exponent Taylor series expansion algorithm, the discrete result of the decay exponent function is pre-compiled into a one-dimensional hardware lookup table. The high-order effective field of the dynamic countdown duration is used as the offset address index to quickly read the approximate constant value of the weight. The next bidding step size is calculated by executing multiply-accumulate machine instructions according to the following hardware state transition equation. : ; in, For the next round of bidding steps, This is the basic statutory minimum markup step constant for hard-coded locking in the read-only segment of the high-speed Level 1 data cache. The coefficients are the scaling transformation constants. For continuous phase real-time memory byte occupancy, For the virtual density constant of the continuous phase, For continuous phase command throughput rate vector, The time decay sensitivity coefficient, The current dynamic remaining countdown timer for the system is obtained from the pin of the advanced programmable interrupt controller. This is a physical constant factor.

[0018] Preferably, the step of generating backpressure by reducing the active threshold value of the sending descriptor according to the dynamic countdown duration and issuing an instruction to write the bidding step size to the doorbell register specifically involves: The high-speed control thread of the core sends a memory-mapped write instruction to the hardware configuration register space of the network card device, overwriting the active valid tail pointer and maximum limit value of the transmit descriptor that the direct memory access controller allows to poll. The state update packets that are forcibly intercepted and do not have the highest non-maskable interrupt priority are directly overwritten and eliminated in the second-level unified cache, generating back pressure. In the fixed-length data packet template of the bypass network pre-allocated memory pool in the data plane development kit, the bidding step size is written to the payload area using advanced wide bit vectorized memory bus instructions. The physical starting memory base address of the data packet template is filled into the transmit descriptor pointer field. The memory barrier synchronization instruction is executed to ensure that the bus buffer is flushed into the physical memory silicon space. An incrementing cursor value is written to the transmit doorbell register to drive the external component interconnect standard protocol physical bus to extract data frames and perform photoelectric conversion and transmission.

[0019] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. This invention achieves deep coupling between bidding data flow and hardware resources by mapping bidding data into a virtual fluid particle structure containing equivalent mass and initial transient velocity, and mapping physical memory into a flow field mesh; it uses the fluid dynamics continuity equation to simulate the dynamic change process of memory phase, extracts system operating state parameters through hardware probes to generate interphase drag coefficients, identifies and reclaims dispersed phase memory resources, and alleviates the memory fragmentation problem in high-concurrency bidding scenarios to a certain extent.

[0020] 2. This invention achieves backpressure control by using a bidding step size calculation method based on continuous phase memory state, instruction throughput rate and dynamic countdown duration, combined with dynamic adjustment of the active limit value of the sending descriptor. This enables the bidding strategy to achieve synergistic optimization with the system hardware state and remaining bidding duration, which helps to reduce the frequency of invalid bids, improve the decision-making efficiency and execution success rate of the bidding process, and better adapt to complex and ever-changing high-concurrency transaction scenarios. Attached Figure Description

[0021] Further details, features, and advantages of this application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which: Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0022] Several embodiments of this application will now be described in more detail with reference to the accompanying drawings to enable those skilled in the art to implement this application. This application may be embodied in many different forms and for various purposes and should not be limited to the embodiments set forth herein. These embodiments are provided to make this application thorough and complete, and to fully convey the scope of this application to those skilled in the art. The embodiments described do not limit this application.

[0023] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It will be further understood that terms such as those defined in commonly used dictionaries shall be interpreted as having a meaning consistent with their meaning in the relevant field and / or the context of this specification, and shall not be interpreted in an idealized or overly formal sense unless expressly defined herein.

[0024] Example 1 Its specific implementation method is combined with the appendix Figure 1 Please provide a detailed explanation.

[0025] Appendix Figure 1 The flowchart of the bidding process optimization method based on cross-analysis of dynamic duration and real-time price provided in the embodiments of the present invention shows the complete steps from the initial reception of multi-source bidding data stream to the updating and distribution of pure stream parameters.

[0026] In this embodiment, it includes: Step 1, Initial reception of multi-source bidding data stream: Extract the bidding data from the data frame, calculate the alignment time by combining the basic transmission time and delay jitter variance obtained from the mapping table, and map the bidding data into a virtual fluid particle structure containing equivalent mass and initial transient velocity; This step addresses the underlying physical processing latency issue when massive amounts of terminal bidding data arrive at the server's network interface card in high-concurrency bidding scenarios. It directly converts the Ethernet data packets at the business layer into memory structures that can be directly recognized by the subsequent underlying fluid dynamics scheduling engine.

[0027] Specifically, it includes the following processes: To address the issues of frequent context switching between kernel mode and user mode, multiple memory copies, and excessive system interrupt overhead when the conventional operating system's Transmission Control Protocol / Internet Protocol (TCP / IP) stack processes bursty, high-frequency bidding data, the system employs a pure user-mode data plane kernel bypass technique at the network receiving end.

[0028] When the optoelectronic network signal from the multi-source bidding terminal is converted into an Ethernet data frame and arrives at the physical network interface card (NIC), the underlying physical hardware driver no longer sends hardware interrupt requests to the operating system kernel.

[0029] Instead, the system utilizes a high-speed direct memory access (DMA) controller on the peripheral component interconnect standard bus to directly transfer the raw binary data stream to a massive circular buffer array pre-allocated in the user-mode physical memory space.

[0030] To support this architecture, the system requests a large number of contiguous physical page frames from the operating system during the initialization phase to prevent these memory frames from being swapped to disk virtual memory.

[0031] In the construction of this circular buffer array, the system adopts a strict cache line boundary alignment technique for memory layout design.

[0032] Modern CPU cache coherence protocols (such as MESI) use a 64-byte or 128-byte cache line as the smallest unit of data synchronization. To prevent severe cache false sharing caused by concurrent reads of the head and tail pointers of a multi-core processor, the read pointer and write pointer are forcibly distributed in completely different, physically isolated cache lines, with a large number of meaningless placeholder data blocks filling the space between them.

[0033] Each node of the circular buffer is defined as a basic data receiving physical slot used to temporarily store the raw Ethernet binary bit stream that has not been parsed by advanced services.

[0034] During this process, the system does not perform any high-level application layer business protocol unpacking and verification. It only relies on the cyclic redundancy check (CRC) hardware calculation result of the physical network layer to determine the landing point of the Ethernet data frame in memory. Thus, the physical reception delay of the bidding instruction is compressed to the nanosecond level as soon as the hardware signal arrives.

[0035] A background polling worker thread deployed on a specific Non-Unified Memory Access (NUMA) node polls the status register of the aforementioned circular buffer at a high frequency. Once a valid data frame is detected falling into the slot, the processor directly performs efficient bitwise operations to unpack the raw binary bitstream.

[0036] The worker thread uses hard-coded byte offsets to skip the standard Ethernet Media Access Control (MAC) header and Internet Protocol (IP) header, and directly extracts the bidding terminal network address, terminal local hardware timestamp, target object identifier, and real-time bid amount from the application layer payload.

[0037] Due to byte order issues in network transmission, the processor calls the built-in byte-switching machine instruction to convert the bit stream from network byte order to local host byte order.

[0038] Because bidding terminals in different regions pass through different physical routers and switch nodes, there are significant differences in network latency, which causes a mismatch between the physical order in which data packets arrive at the server motherboard and the actual order in which bids are placed.

[0039] The system executes timing analysis and network jitter compensation logic. Assume the local nanosecond time (in milliseconds) at which the server-side hardware clock receives the data packet is... The parsed local sending timestamp of the terminal is .

[0040] The system pre-maintains a terminal historical network latency mapping table based on a high-strength hash algorithm in a cache mapped from static random access memory (SRAM). By extracting the round-trip time (RTT) data from the past ten consecutive communications, the underlying arithmetic logic unit (ALU) iteratively calculates the basic transmission time for that specific network path. and delay jitter variance .

[0041] If a hash address collision occurs, the system uses the underlying physical memory chain addressing method to perform lock-free linked list traversal and addressing.

[0042] The absolute alignment time of the quote data packet is calculated using the following formula. : ; in, This is a confidence adjustment factor that is dynamically adjusted based on the current physical depth of the network card's hardware receive queue. It is usually assigned a value by the system bus monitoring module, and its value range is set between specific security thresholds.

[0043] This formula allows the system to align all price quotes from heterogeneous physical sources to the server's absolute clock cycle frequency axis, thus addressing timing discrepancies caused by network node queuing jitter.

[0044] This step transforms the bidding parameters at the abstract business level into physical addressing attributes of the underlying memory. The system instantiates a compact composite structure in the physical giant page frame, named "virtual fluid particle".

[0045] To maximize the utilization of the processor's single cache fetch, the total size of this structure is kept within the size of a single cache line.

[0046] Each time-aligned quote data packet is encapsulated as a virtual fluid particle and assigned the following underlying physical properties: First, the arithmetic-logical transformation of particle equivalent mass: The system extracts the current bid amount from the data packet and compares it with the current system benchmark price of the bidding item. The system directly invokes the leading zero-count fast shift instruction and table lookup instruction from the CPU instruction set. The system pre-compiles all possible price deviation ranges into a static constant-mapped bitmap residing in high-level static random access memory. The processor extracts the most significant bit (MSB) of the calculated price difference as the physical index address of the hardware table and directly reads the corresponding equivalent quality value from this table. Data packets with larger bid increases are assigned a greater equivalent quality weight in subsequent memory addressing. Second, the non-formulaic hardware-level timer assignment of the initial transient velocity of the particle: The system extracts specific low-level parameters, namely the absolute hardware time interval between two consecutive requests sent by a specific bidding terminal. And it is directly sent to the processor's hardware performance counter for determination; The system according to The clock cycle length directly determines whether the structure is assigned to a high-priority underlying kernel thread (corresponding to an extremely high initial flow rate from physical memory) or a low-priority polling thread (corresponding to an extremely low initial flow rate from physical memory) in the next-level multiphase flow field addressing network. Terminal data packets with high-frequency abnormal bidding will be forcibly mounted to the restricted low-speed polling queue memory pointer by the operating system.

[0047] The absolute alignment time after the above processing The particle physical equivalent mass obtained from the lookup table, the concurrent thread priority characteristics allocated by the hardware counter, and the physical memory absolute address pointer of the original business data packet are written together into the reserved field of the virtual fluid particle structure. At the same time, the memory barrier instruction is used to ensure that all write operations are visible on the bus, thus completing the initialization and encapsulation of the underlying physical characteristics.

[0048] Step 2, virtual Eulerian multiphase flow field construction: map physical memory as a flow field mesh to mount virtual fluid particle structures, and mark them as continuous or dispersed phases using hardware masks based on their properties, and calculate the memory byte occupancy of each phase using the continuity equation; After receiving the underlying hardware data and encapsulating the structure into particles, this step will use the computer's main memory array to construct a virtual fluid dynamics space.

[0049] This step transforms the physical Euler multiphase flow theory into a three-dimensional matrix hash shift operation and physical interconnect network state tracking mechanism in the computer memory heap space.

[0050] The core computer science concept of trading storage space for processor execution cycles enables the natural damping and hierarchical extraction of dirty and clean data at the physical memory level.

[0051] The specific operation process is as follows: The system requests a huge contiguous physical heap space from the operating system kernel, consisting of giant pages of two megabytes or one gigabyte in size.

[0052] Using massive pages can reduce the miss rate of the Translation Lookaside Buffer (TLB), thereby speeding up the translation process from virtual to physical addresses.

[0053] The system logically maps this contiguous memory block as a three-dimensional virtual flow field mesh. Let the logical addressing coordinate system of this three-dimensional mesh be... ; The system will use the first dimension of the logical address. The axis is defined as a "dynamic remaining time mapping segment", and its base address offset represents different time slices of the bidding countdown (for example, a fixed memory address step value corresponds to every ten milliseconds). The system will use the second dimension of the logical address The axis is defined as a "quote deviation interval segment", representing the range of difference between the quoted price and the benchmark price that are mapped to completely different memory page segments; The system will use the third dimension of the logical address An axis is defined as a "terminal reputation mapping segment" and is used to store data packets in the corresponding high and low priority storage areas based on the historical scores of a specific terminal network address.

[0054] The entire contiguous giant page memory region is divided into a massive number of tiny, fixed-size memory blocks, each of which is called a "control volume".

[0055] Each control volume corresponds to an independent doubly linked list head node pointer in the system memory pool, used to mount virtual fluid particle structures falling into that logical address space. When adding data, the processor uses the basic address addition formula: Physical target address = heap space base address + ( Maximum span of axis Maximum span of axis + Maximum span of axis + ) Number of bytes occupied by a single control unit; By using this high-dimensional memory base address superimposed offset calculation mapping, the system transforms all the business logic that originally required massive application layer traversal and comparison into a constant-time physical address addressing operation that can be executed by the underlying central processing unit in a single instruction cycle.

[0056] In the Euler-Euler model of macroscopic fluid mechanics, different states of matter are interpenetrating and exhibit a continuous distribution.

[0057] To implement this physical model at the computer's underlying level, the system divides all virtual fluid particles flowing into the physical memory grid into two basic underlying phases: Continuous phase and dispersed phase; Continuous phases represent low-frequency, high-value data streams that conform to normal bidding business logic, and they flow freely on the memory bus. Dispersed phases represent abnormal data streams from malicious order-brushing and high-frequency machine script concurrent attacks, which will face significant addressing resistance.

[0058] To address the physical determination and differentiation of phase states, the system introduces a low-level processor-level wide bit vector register and bitmap mask mechanism.

[0059] When a large number of virtual fluid particle structures are pushed into the memory grid, the processor's Single Instruction Multiple Data (SIMD) vector processing unit (such as an AVX-512 register with a width of 512 bits) loads the attribute fields of multiple particles at once.

[0060] The vector processing units compare the physical time interval parameters of these particles in parallel. The price deviation is also compared with the most significant bit, and a bitwise AND or OR operation is performed with the fixed threshold bit that is preset in the hardware mask register.

[0061] If the time interval parameter of a specific particle is lower than a specific hardware instruction cycle threshold, and its deviation points to an abnormal range, the hardware vector unit will directly modify the status bit register in the physical memory address of the structure within a tiny instruction cycle, forcibly placing the hardware flag bit representing the dispersed phase (for example, reversing and setting the seventh bit in the address to one).

[0062] This low-level phase classification, which relies entirely on hardware parallel bit operations, efficiently demonstrates a physical process similar to the pre-separation of substances of different densities in fluid mechanics.

[0063] Within any allocated physical memory control volume, data packet nodes marked as continuous phases and data packet nodes marked as dispersed phases jointly occupy the pre-set upper limit of available physical memory capacity in that space.

[0064] The system utilizes the classic physical concept of "volume fraction" from fluid mechanics to characterize the proportion and congestion level of two phases of data within a memory grid at a given hardware clock scale. In the underlying computer system architecture, the true technical mapping of volume fraction is explicitly defined as: The ratio of the actual number of bytes of physical memory currently occupied by a queue of a specific phase structure to the maximum number of bytes allowed to be allocated in that memory grid. The system maintains the absolute conservation law of memory space capacity in the underlying memory manager logic.

[0065] The fluid dynamics continuity equation based on mass conservation is used to guide the background thread scheduler in real time to allocate concurrent computing power to the memory grid; Partial differential continuity equation: ; in, Represents different memory phase types (i.e., continuous phase or dispersed phase labels); This refers to a continuous-time variable in the physical flow field. To adapt to the characteristics of electronic digital data processing in computers, when discretized at the underlying processor hardware level, the system calculates it according to discrete time steps. The fixed-width time slice operation is performed, and the step size parameter is directly controlled by the physical interrupt frequency of the hardware clock crystal generator of the server system motherboard. The defined and real-time calculated underlying memory byte occupancy rate (i.e., mapped to the volume fraction in the physical flow field). The virtual density constant for this specific phase state data initially set for the system (used to evaluate the physical processing complexity weights of different data types). As a physical flow velocity vector, it is rigorously mapped at the system level to the "hardware pointer extraction throughput rate" of the data packet of this phase in the current independent doubly linked list grid, which is obtained by the core worker thread pointer and successfully popped from the queue; As a spatial divergence operator, in the underlying memory data structure system, it is defined as the algebraic sum of the net inflow and net outflow bytes between the current memory control volume grid and its six adjacent logical memory grids when data pointers are transferred due to business degradation or upgrade; For physical quality source items, their computer mapping represents the instantaneous increase in bytes caused by the network interface card's direct storage access (DMA) port forcibly appending new data packets to the physical memory space of that particular grid. The construction of the above continuous partial differential equations aims to monitor the physical congestion of the three-dimensional memory grid at the microscopic level in a highly real-time manner.

[0066] However, general-purpose central processing unit architectures cannot directly parse and process partial differential continuity algebraic equations; The system designs a sparse matrix storage and solution structure based on underlying hardware acceleration. The system directly reconstructs the various physical state variables within each memory-controlled volume grid into a compressed row storage (CSR) format of a sparse matrix in memory.

[0067] This structure contains a numerical array, a column index array, and a row offset pointer array. For divergence operator operations representing physical inflows and outflows, the system never calls the upwind difference calculation model for prediction. Instead, it directly accesses the controller using memory, periodically captures the actual absolute changes in the length of physical linked list nodes between adjacent grid control blocks, and writes them into the matrix array.

[0068] When predicting byte occupancy within a finite discrete time step in the future, the system's software architecture prohibits the main control bidding thread group from participating in any computational work.

[0069] Instead, the system utilizes the thread affinity binding technology provided by the underlying operating system to wake up a hidden physical computing coroutine that exclusively occupies a specific processor core.

[0070] This physical coroutine deeply invokes the server motherboard processor's Single Instruction Multiple Data Extended Instruction Set. It utilizes the processor's widest vector register to directly execute a large number of floating-point multiply-accumulate (FMA) machine instructions concurrently on the compressed matrix row data and throughput vector.

[0071] This underlying hardware-intervention design allows the solution of continuous partial differential equations to be freed from the conventional stacking of software mathematical code, and completely degenerated into the pure consumption of high-speed bit mode transfer between specific hardware registers, hardware cache hits, and physical clock cycles.

[0072] Step 3, Calculation of interphase drag slip speed: Extract the page fault interrupt frequency and lock contention cycle by probe and combine them into interphase drag coefficient. Calculate the difference in instruction throughput rate between the continuous phase and the dispersed phase. Multiply the difference by the interphase drag coefficient to obtain the slip speed. After the virtual Euler multiphase flow field is constructed and the actual byte occupancy rate of each underlying memory grid is predicted by the system, this step provides a basis for judgment for subsequent memory isolation actions by quantifying the degree of physical interference of abnormal probing data packets to the system's processing of normal core quotation data.

[0073] In modern server motherboard bus communication mechanisms and unified memory architecture (UMA) or non-unified memory access architecture (NUMA), the process of network data packets moving and being processed in memory is equivalently mapped by system architects as the accelerated motion of fluid physics particles moving through a pipe under pressure.

[0074] When continuous phase structures and dispersed phase structures coexist within the same giant page memory control volume, they will inevitably compete for the same instruction pipeline, limited high-level data cache, and execution port of the arithmetic logic unit of the central processing unit.

[0075] The system utilizes the clock generator and interrupt mechanism in the computer hardware architecture to directly construct the physical power source of the flow field. The system is set at the kernel level that the shorter the remaining real time of the bidding countdown, the higher the frequency of interrupt pulses sent by the system motherboard hardware timer to the Advanced Programmable Interrupt Controller (APIC).

[0076] This low-level, high-frequency physical interrupt signal directly constitutes the enormous physical "pressure" that drives the data packet structure to rapidly iterate forward in the memory linked list.

[0077] Meanwhile, the system defines the following in its underlying physical logic: The "actual instruction throughput rate" of the two phase data packets in the processor's internal pipeline is their respective flow field physical velocity vector.

[0078] Because of its regular data structure and the fact that the bid range meets the expected business logic, when a continuous phase is extracted in the arithmetic logic unit, its corresponding assembly instruction branch prediction hit rate is extremely high, the instruction execution is extremely smooth, and the instruction throughput is extremely high.

[0079] Because scattered phases contain a large number of abnormal extreme values ​​or illegal string probes, they are very likely to cause the processor's hardware branch prediction unit to fail to predict when they are sent to the register for decoding and calculation, which in turn triggers expensive processor instruction pipeline flushing operations.

[0080] This hardware-level pipeline physical pause and overload constitutes, at the underlying logic, the initial hydrodynamic resistance generated by the dispersed relative to the normal continuous phase data packet propulsion.

[0081] When a massive influx of high-frequency, probing anomaly quotes (dispersed phases) into the server's memory system, they not only consume valuable network interface bandwidth but also cause severe CPU cache thrashing.

[0082] The system no longer uses algebraic calculation formulas based on the equivalent diameter of data packet bytes and the calculus correction factor of physical porosity to derive the abstract interphase drag coefficient.

[0083] Instead, the system directly hard-mounts the logic for generating this coefficient onto the hardware performance counter monitoring array of the operating system kernel.

[0084] In order to obtain the actual congestion resistance of the physical environment, the system allocates a background hardware probe thread with the lowest priority in the operating system to periodically send read machine instructions to the processor's specific mode register.

[0085] The probe thread monitors and reads the frequency of hardware page faults in the operating system; When a large number of malicious concurrent scattered phase data packets attempt to access discontinuous underlying memory space across physical page frames, it immediately triggers a hardware miss event in the Translation Lookaside Buffer (TLB) of the Memory Management Unit (MMU), resulting in a large number of page fault exception traps. This underlying hardware page fault storm is physically equivalent to a sharp increase in viscosity in a virtual multiphase fluid environment.

[0086] The probe thread delves into the operating system kernel mode and uses a dynamic probe point mechanism (such as the Kprobes mechanism in the Linux kernel) to monitor in real time all concurrent bidding worker threads and the number of spinlock cycles or mutex lock sleep contention waiting clock cycles that occur when accessing the head pointer of the doubly linked list of the giant page control volume.

[0087] The system extracts the collected low-level hardware page fault frequency and the total number of clock cycles consumed by spinlock idling into a specific register. It then performs a simulated weighted merging operation using basic hardware shift instructions to generate a dimensionless overall low-level hardware congestion index. This hardware congestion index is then directly written as the current flow field "interphase drag coefficient" into a globally shared memory segment accessible to all core threads.

[0088] In a physical model, glide velocity characterizes the relative velocity between swarms of particles in different phases. In the underlying computer architecture of this system, glide velocity represents the extent to which dirty data containing malicious probing characteristics lags significantly behind normal business data in terms of processor physical execution progress. After obtaining real-time hardware congestion metrics, the system needs to calculate the current glide velocity.

[0089] The system explicitly defines at the underlying architecture layer that the value of the sliding speed is equivalent to the arithmetic difference on the hardware performance counter between the "actual instruction throughput completed" of the processor core processing the continuous phase structure sequence and the "actual instruction throughput completed" of the processor core processing the dispersed phase structure sequence within the current extremely short discrete time slice.

[0090] To ensure the system operates at the extremely low latency required for high-frequency trading, the system refuses to use any complex matrix time integral derivation or Taylor series expansion algorithms to solve for speed.

[0091] At the end of each discrete hardware clock cycle, the dedicated worker thread uses non-privileged instructions to read the absolute number of memory control volume nodes that have been successfully popped from the two phase queues into two independent 64-bit wide general-purpose registers of the central processing unit.

[0092] The arithmetic logic unit, controlled by a single, extremely simple basic assembly-level subtraction instruction (SUB instruction), directly yields the absolute difference in throughput between the two components at the processor's underlying level.

[0093] The processor loads the hardware congestion index (i.e., the flow field interphase drag coefficient) obtained and cached in a specific register in the previous step into the floating-point unit, and performs a basic hardware-level single multiplication instruction with the above physical throughput difference.

[0094] The final product output by the arithmetic unit is the "slip velocity" in a physical sense. This value is not processed by any complex smoothing filtering function, but is directly written into a specific reserved extended field in the memory address space of the corresponding virtual fluid particle structure through a write operation instruction. Thus, the physical characteristics of the degree of mutual interference between phases are quantified at a low level under the strict condition of consuming very few CPU instruction cycles.

[0095] Step 4, Abnormal data phase separation and filtering: The sliding velocity is compared with the hardware separation threshold. The dispersed phase that meets the settling state is transferred to the recycling memory pool using the comparison and exchange instructions. The remaining continuous phase is then reconstructed into a dense structure. After successfully translating the macroscopic physical concept of "slip velocity" from fluid mechanics into the underlying hardware register difference calculation and clock cycle consumption result, this step performs substantial system-level data physical diversion and memory isolation operations.

[0096] The system utilizes the physical macroscopic phenomena of centrifugal sedimentation and Stokes sedimentation in fluid mechanics to automatically remove abnormal data packets that seriously interfere with the bidding process from the main processing hardware pipeline without adding extra large business logic judgment branch overhead, relying on pure physical memory pointer lock-free redirection.

[0097] The system extracts the pre-encapsulated hardware time interval parameters (representing the high-frequency request attributes of the data source) and price deviation feature bits from the memory structure, and directly uses the arithmetic logic unit to construct a pure hardware-level comprehensive comparison and judgment condition.

[0098] The system pre-programs and locks a constant static hardware separation physical threshold in the processor's valuable L1 high-speed data cache segment.

[0099] When the main bidding worker thread traverses the giant doubly linked list of pages due to business needs, the processor reads the "slip speed" field value of the currently traversed physical node and immediately calls the most basic comparison instruction (CMP instruction) to directly perform a binary-level ultra-fast comparison with the static threshold in the L1 cache.

[0100] Once the specific position in the processor flags register is set to valid by the comparison result (indicating that the scattered phase data packet has caused serious encroachment on the processor memory bus bandwidth and cache lines, reaching the "critical settling separation state" in fluid dynamics at the physical level), the system hardware will directly throw a lightweight soft interrupt signal, thereby instantly triggering the subsequent physical memory pointer stripping action.

[0101] Once an abnormal data packet structure is marked by a soft interrupt signal as meeting the critical settling state, the system must immediately remove it from the physical memory of the high-speed circulating continuous phase flow field.

[0102] To avoid exhausting the bandwidth of the external system bus of the central processing unit due to direct copying operations of large memory blocks, the system adopts a low-level lock-free concurrency control architecture based on zero copy to achieve the natural deposition and stratification of phase states on the physical medium.

[0103] The system utilizes the physical space characteristics of a non-uniform memory access architecture on the server motherboard's physical memory modules to forcibly divide two memory node regions at different distances from the physical processor core: Located near the high-speed processing core's fast access mapping channel, the main memory pool serves as the "physical main channel" for data flow; And the abandoned and reclaimed memory pool located deep in the address space of ordinary dynamic random access memory with extremely long access latency, serving as a "sedimentation bottom shell" for abandoned data.

[0104] For all virtual fluid particle structures marked as being in a critical settling state, the core memory control thread, when executing actions, utilizes the underlying system bus prefix locking mechanism provided by modern multi-core processors to compare and exchange hardware instruction primitives.

[0105] This primitive can atomically modify the physical predecessor and successor address pointers of the original linked list node containing the isolated structure within a hardware execution cycle that cannot be forcibly interrupted by other hardware interrupts.

[0106] Through this atomic hardware instruction, the system forcibly removes the pointer of the abnormal node from the memory control volume linked list representing the "physical mainstream" and instantly links its address pointer into the abandoned hibernation queue linked list representing the "precipitate bottom shell".

[0107] Throughout the entire rapid settling and isolation process, the data block containing massive payload bytes such as malicious bid values, timestamp messages, and digital signatures did not undergo any relocation in its actual charge storage location on the physical memory silicon wafer. It was merely that the underlying logical path of the data block, which was addressed and mapped by the main thread, was cut off and reconstructed.

[0108] This zero-copy, pure pointer-level stripping operation squeezes the system-level latency jitter caused by dirty data filtering to the limit allowed by the physical architecture.

[0109] The scattered abnormal data streams that were forcibly isolated to the remote sedimentation bottom shell memory area no longer participate in the bidding matching business judgment on the main processing core pipeline, but their structure still occupies valuable physical page frame resources.

[0110] If it is not destroyed, the system will inevitably run out of memory and crash as concurrent attacks continue.

[0111] The system directly transforms the macroscopic phenomenon of energy decay and dissipation in fluid mechanics into the time slice allocation and clock consumption logic of the computer thread scheduler: The system allocates a garbage collection auxiliary thread with the lowest execution priority at the operating system level on the remote memory node, which is specifically responsible for taking over the abandoned linked list of the sedimentation bottom shell.

[0112] When the garbage collection thread gets a meager processor execution time slice, it simply performs read-only idle polling on the discarded list without any actual data processing meaning (equivalent to using the processor's no-operation assembly instructions to perform a hardware-level simulation of physical energy dissipation).

[0113] The system monitors the difference between the processor's hardware timestamp counter and the value before and after the recycling thread reads a specific batch of isolated discarded data within the kernel. Due to the thread's prolonged sleep and wake-up context switching caused by frequent deprivation of its running rights by the operating system's preemptive scheduler, when the total number of stagnant clock cycles consumed by this batch of accumulated data exceeds the aging safety dead zone threshold set by the physical memory management, the system directly determines in physical terms that the underlying kinetic energy of this batch of abnormal data in the virtual environment has been completely dissipated and exhausted.

[0114] The background management thread does not perform any arithmetic or algebraic addition operations. Instead, it directly calls the operating system's lowest-level memory unmapping system trap instruction (such as the munmap system call mechanism that conforms to the portable operating system interface standard) or sends a hardware-level release and destruction signal to the underlying high-speed physical memory allocator module.

[0115] The system kernel's virtual memory management module will directly and forcibly take over these remote physical page frames, forcibly erase and refresh the page table directory mapping relationship, and return the occupied underlying memory silicon space to the operating system kernel's unified allocation pool.

[0116] This move directly eliminates the risk of malicious concurrent data packet remnants from the underlying physical architecture, and strongly prevents hardware crashes caused by memory overflow.

[0117] After the memory addressing pointers of a large number of scattered abnormal data nodes are forcibly removed and destroyed, a large number of "holes" will inevitably be generated in the continuity of the physical address space in the main memory grid logical flow field.

[0118] This phenomenon is known as cavitation in fluid mechanics. In the underlying hardware addressing architecture of a computer, this means that the distribution of surviving data packet list nodes on the physical memory address silicon chip becomes extremely sparse and discrete, violating the principle of spatial locality when the processor executes a program. This can lead to the failure of the processor's hardware prefetch instruction set and a precipitous drop in the hit rate of each level of high-level cache.

[0119] To ensure that the high-speed physical operation of the processor pipeline can receive sufficient data supply during the final countdown of the bidding system, the system activates the underlying hardware memory density mechanism.

[0120] The memory remodeling monitoring probe thread deployed in the system background periodically scans the actual physical distribution span of the memory grid in the continuous phase-marked state.

[0121] When the probe detects that a currently surviving high-value, valid bidding structure has crossed an excessive number of irrelevant physical memory pages in order to maintain logical connectivity, causing a sharp increase in the physical cost of maintaining cache consistency across physical page frames, the monitoring system will forcibly activate the underlying hardware-level memory reorganization and copy mechanism within a very short nanosecond-level idle period window during which the central processing unit briefly does not process external network interrupt pulses.

[0122] In order to achieve rapid refactoring without blocking the main thread, the system forcibly calls dedicated assembly instructions for vector parallel load and vector parallel store in the processor’s internal single instruction multiple data (SIMD) extended instruction set, which supports 256-bit or 512-bit ultra-wide instruction sets.

[0123] Leveraging the powerful vectorized parallel bus throughput copy hardware mechanism described above, the system performs high-density address aggregation and relocation of high-value data packet structures scattered across different physical page frames to a newly requested, absolutely contiguous, and compact giant page physical space at an extremely high bus burst transfer rate. Simultaneously, the underlying control module corrects the hexadecimal offsets of the predecessor and successor memory pointers for all affected structures during the relocation.

[0124] Through this hardware-level reconstruction intervention technology that prevents physical scattering of cavitation in the flow field, the system can powerfully restore the underlying physical memory layout of core high-value pricing data to an absolutely compact, high-density, continuous linear physical array state within a short clock cycle.

[0125] This underlying operation enables the arithmetic logic unit of the central processing unit to read the data stream into the execution pipeline at full speed with excellent L1 data cache and L2 unified cache hardware hit rate when performing final matching and extraction of bidding parameters and decisive sorting comparison. At the underlying silicon physical hardware architecture level, it eliminates all performance obstacles caused by memory fragmentation.

[0126] Step 5, Pure Stream Parameter Update and Distribution: Based on the memory byte occupancy rate of the continuous phase, the instruction throughput rate, and the dynamic countdown duration obtained by the hardware timer, calculate the next round of bidding step size, forcibly reduce the active limit value of the sending descriptor according to the dynamic countdown duration to generate back pressure, and write the bidding step size to the doorbell register as an instruction to distribute. After the abnormal, tentative, high-frequency concurrent data (i.e., the structure nodes marked as dispersed phases by the hardware bitmap mask) is forcibly physically deposited by the operating system-level memory unmapping mechanism and physically stripped and destroyed from the high-speed main memory flow field, only the core high-value normal quotation data flow (i.e., continuous phase data flow) with high "memory byte occupancy rate" and high "instruction processing throughput momentum" is retained in the server's physical main memory giant page grid.

[0127] This step utilizes the ultra-fast processing state parameters of these pure continuous phases at the underlying level, combined with the dynamic remaining countdown timer provided by the server motherboard hardware timer, to adaptively generate the control parameters for the next round of bidding strategy within the hardware computing unit.

[0128] The dynamic adjustment mechanism of the bidding increment step is equivalent to the energy conservation deduction process of "fluid kinetic energy being converted into internal pressure potential energy" in macroscopic fluid mechanics, but its execution logic is completely pushed down to the hardware floating-point unit (FPU) of the central processing unit for single instruction cycle implementation.

[0129] The system targets a clean, continuous phase data stream in the main memory physical grid by directly reading the hardware clock cycle counter in the system performance monitoring unit (PMU). Extract its current average instruction throughput vector (Physically mapped to flow rate); Extract the current physical memory actual byte usage calculated in the previous steps. (Physically mapped to volume fraction); Retrieve the system's hard-coded virtual density constant ; And the current dynamic remaining countdown timer of the system, which is directly obtained from the motherboard's Advanced Programmable Interrupt Controller (APIC) pins. ; Calculate the physical step size parameter for the next round of recommendation bidding in real time without consuming main thread resources. : ; in, The basic statutory minimum increment step constant is set for the system. To avoid bus latency caused by reading the constant from main memory, this constant is hard-coded and locked by the operating system in the read-only and execute-only segment of the processor's high-speed L1 data cache during the system startup initialization phase, ensuring that the arithmetic unit can fetch it with zero wait time. This characterizes the macroscopic "computational kinetics" of the continuous-phase data stream within the underlying processing pipeline. This macroscopic "computational kinetics" is determined by the real-time byte occupancy rate of the continuous phase. Density constant and instruction throughput vector The square of the modulus determines the seamless inheritance of the characteristics of each underlying parameter in the preceding control steps. When the bidding becomes fierce, the number of clean data packets entering per unit time and allowed by the hardware bitmap mask surges, the processor's instruction throughput increases geometrically, and the physical memory usage soars. This momentum indicator will be rapidly amplified by the processor's hardware multiplier. This is a dimensionless scaling conversion constant coefficient that converts the underlying throughput momentum into the front-end price physical amplitude. The nonlinear activation decay function is constructed based on the remaining duration. This is the time decay sensitivity coefficient; To avoid using complex Taylor series expansion algorithms that consume a lot of clock cycles to calculate floating-point exponents, which would cause instruction pipeline stalls, the system pre-compiles all possible discrete results of the exponential function into a dense one-dimensional hardware lookup table in memory space.

[0130] The remaining time obtained by the processor instruction decoder using the current hardware clock. The most significant bits are used directly as the offset address index in physical memory.

[0131] By using a single high-speed base-address-indexed addressing assembly instruction, the corresponding approximate constant weight value can be directly retrieved from the memory lookup table.

[0132] The processor's arithmetic logic unit compiles the core state equations into underlying single-instruction multiple-data (SIMD) floating-point multiply-accumulate (FMA) machine instructions.

[0133] As each batch of data within the memory control volume completes its physical processing, the vector register performs microsecond-level real-time seamless recalculation. This process naturally transforms the underlying hardware pipeline's heat generation into a price access threshold exposed to the external network, automatically blocking low-value, minute-price-increase request instructions from the underlying hardware source.

[0134] To address the issue that high-concurrency feedback state outflows can easily lead to the paralysis of LAN switches and severe congestion of server motherboard network card transmission ports, the system transforms the macroscopic physical phenomenon of "flow field cross-section contraction" into a hard circuit-level constraint on the available physical node length of the underlying transmission descriptor ring of the Ethernet physical network interface card (NIC).

[0135] In the standard driver physical architecture of modern 10 Gigabit Ethernet cards, the capacity of the network card's outgoing data transmission buffer is not determined by the software queue, but is composed of a fixed number of hardware transmit descriptors in a circular array pre-allocated in main memory.

[0136] Each descriptor points to a physical memory address of data to be sent, as the auction countdown timer continues. As the system approaches zero, it will absolutely no longer push requests for descriptors containing state update packets to the network hardware layer without limit.

[0137] The system assigns an ultra-high-speed control thread that is bound to a specific CPU physical core using affinity technology, and polls the interrupt countdown register of the timer in real time at a high frequency.

[0138] As time rapidly decreases, this particular control thread bypasses the operating system and sends memory-mapped write machine instructions directly to the hardware configuration register space of the Ethernet card device.

[0139] That is, the control thread forcibly overwrites and reduces the active valid tail pointer and maximum limit value of the transmit descriptor ring that the network card hardware direct memory access controller allows to poll and read, based on the urgency of the countdown.

[0140] This creates a hardware-level backpressure blocking effect on the underlying physical communication link: The actual data outgoing physical channel of the network card (i.e. the physical cross section that is equivalently mapped) is forcibly narrowed. When the available descriptor nodes are exhausted rapidly, the update packet sent in the normal state of the ordinary polling priority queue will not be able to obtain the direct memory access mapping descriptor entry at the physical network card hardware level. As a result, it will be forcibly overwritten or discarded by the new data in the processor's L2 unified cache.

[0141] At this point, only the decisive bid update data packets, whose data structures carry extremely high "computational power" and "real-time byte utilization" in the front-end processing pipeline, will be given the highest non-maskable interrupt (NMI) level processing priority by the system, thereby forcibly preempting the few remaining active sending nodes in the network card descriptor ring. Through this pure hardware control instruction based on the dynamic forced contraction of the network card's underlying communication descriptor ring boundary, the physical motherboard bus and external fiber optic switches are completely protected from the surge impact of the data broadcast storm, ensuring the absolutely unimpeded physical transmission of the highest priority control instruction at the moment of bidding.

[0142] After obtaining the latest dynamic price increment step generated by the hardware floating-point arithmetic unit After filtering the decisive feedback content through the underlying hardware backpressure descriptor control mechanism, the system executes the final data distribution to all remote bidding terminals across the network.

[0143] To eliminate the millisecond-level long-tail latency caused by the dynamic memory allocation and multi-layer network packet header encapsulation of the TCP / IP protocol stack in the operating system kernel space, the server sending end fully takes over the physical layer and adopts a pure user-space bypass network architecture supported by the Data Plane Development Kit (DPDK).

[0144] During the initialization phase of system startup, the core sending thread has already pre-built a massive amount of fixed-length underlying data packet memory templates in a continuous physical giant page pre-allocated memory pool.

[0145] These templates residing in physical main memory have been hard-coded and filled with static Ethernet Media Access Control (MAC) header source and destination addresses, Internet Protocol (IP) header, and connectionless User Datagram Protocol (UDP) header fields.

[0146] In the split second when the decisive parameters need to be issued, the processor only needs to use advanced wide-bit vectorized memory bus instructions to retrieve the latest step size parameters calculated from the aforementioned registers. And the status word of the target item, including whether the bid was successful or not, is directly overwritten and written to the payload area reserved at the end of the physical memory template within a single tiny clock cycle.

[0147] The worker thread uses the scarce active physical send descriptors locked in the preceding steps to fill the physical starting memory base address of the completed data packet template into the 64-bit pointer field of the network card send descriptor structure.

[0148] To prevent data inconsistency caused by instruction reordering in modern out-of-order processors, the processor forcibly executes a memory barrier synchronization instruction (such as the sfence or mfence instruction in the x86 architecture) to ensure that all payload data bytes are completely flushed from the bus cache into the specified physical memory silicon space.

[0149] The worker thread directly writes a new hexadecimal tail cursor increment value to a specific "transmit doorbell register" of the Ethernet controller through the underlying memory-mapped input / output (MMIO) mechanism.

[0150] This is essentially a physical execution pulse trigger signal sent directly from the motherboard's central processing unit to the hardware network card's communication chip. After receiving the doorbell register's numerical voltage change update, the 10 Gigabit physical network card's internal hardware bus controller immediately initiates a direct memory access read bus transaction to the motherboard's memory controller via the external component interconnect standard protocol.

[0151] The network card hardware automatically extracts the packaged decisive data frame from the user-space giant page memory pool into the network card hardware's built-in first-in-first-out silicon queue, and immediately drives the serializer module to convert it from a digital electrical signal into a high-speed optical signal and send it to the physical backbone fiber link layer.

[0152] The entire process of encapsulating bidding instructions and sending them over the network occurs between the operating system's pure user-mode heap space and the underlying physical network card. There is no context switch, system call trapping, interruption, or sleep waiting. This achieves synchronization between hardware physical instruction generation and network-wide broadcast, addressing the uncertainty of bidding network latency.

[0153] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0154] It should be noted that, in this document, the use of relational terms such as "first" and "second" is merely for distinguishing one entity or operation from another, and does not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0155] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0156] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0157] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A bidding process optimization method based on cross-analysis of dynamic duration and real-time quotes, characterized in that, include: Extract the quotation data from the data frame, combine the basic transmission time and delay jitter variance obtained from the mapping table to calculate the alignment time, and map the quotation data into a virtual fluid particle structure containing equivalent mass and initial transient velocity; Physical memory is mapped to a flow field mesh to mount virtual fluid particle structures. Based on their properties, they are marked as continuous or dispersed phases using a hardware mask, and the memory byte occupancy rate of each phase is calculated. Extract the page fault frequency and lock contention cycle and combine them into an interphase drag coefficient. Calculate the difference in instruction throughput between the continuous phase and the dispersed phase. Multiply the difference by the interphase drag coefficient to obtain the slip velocity. The sliding velocity is compared with the hardware separation threshold, and the dispersed phase that meets the settling state is transferred to the recycling memory pool. The remaining continuous phase is then subjected to compact reconstruction. Based on the memory byte occupancy rate of the continuous phase, the instruction throughput rate, and the dynamic countdown time obtained by the hardware timer, the next round of bidding step size is calculated. The back pressure is generated by reducing the active limit value of the send descriptor with the dynamic countdown time, and the bidding step size is written to the doorbell register as an instruction.

2. The bidding process optimization method based on cross-analysis of dynamic duration and real-time price as described in claim 1, characterized in that, The alignment time is calculated by combining the base transmission time obtained from the mapping table with the delay jitter variance, and has the following characteristics specific to the initial reception step of multi-source bidding data streams: The system uses the underlying arithmetic logic unit to extract round-trip time data from ten consecutive communications, and iteratively calculates the basic transmission time and delay jitter variance of the preset network path, which is then written into the cache mapped by the static random access memory. The absolute alignment time of the quotation data is calculated according to the following underlying alignment formula: ； in, For absolute alignment time, This is the parsed local transmission timestamp from the terminal. The base transfer time is obtained from the mapping table. To delay jitter variance, This is a confidence adjustment factor that is dynamically adjusted based on the physical depth of the network interface card's hardware receive queue.

3. The bidding process optimization method based on cross-analysis of dynamic duration and real-time price as described in claim 2, characterized in that, The quotation data is mapped to a virtual fluid particle structure containing equivalent mass and initial transient velocity, specifically including: When constructing a circular buffer, the read pointer and write pointer are forced to be distributed in separate, isolated cache lines, and placeholder data blocks are filled between them to prevent false sharing of caches in multi-core processors; The most significant bit is extracted based on the difference between the quoted amount and the benchmark price and used as the physical index address of the hardware table. The equivalent mass is then directly read using the underlying leading zero counting fast shift instruction and table lookup instruction. Extract the absolute hardware time interval between consecutively sent requests and send it to the hardware performance counter. Allocate the concurrent priority of the operating system kernel thread according to the clock cycle length of the hardware performance counter to define the initial transient speed. The absolute alignment time, equivalent quality, and concurrency priority are encapsulated together with the physical memory absolute address pointer in a reserved field of a composite structure whose size is limited to the width of a single cache line, and memory barrier synchronization instructions are executed to ensure that write operations on the bus are visible.

4. The bidding process optimization method based on cross-analysis of dynamic duration and real-time bids as described in claim 1, characterized in that, Mapping physical memory to a flow field mesh to mount virtual fluid particle structures is the process as follows: The system requests the operating system kernel to lock a contiguous physical giant page heap space to reduce the translation backstop miss rate. The first dimension of the logical address is defined as a dynamic remaining duration mapping segment, the second dimension of the logical address is defined as a quote deviation interval segment, and the third dimension of the logical address is defined as a terminal reputation mapping segment. Using the logical addressing coordinate system of the obtained three-dimensional mesh as ,but: Physical target address = heap space base address + ( Maximum span of axis Maximum span of axis + Maximum span of axis + ) Number of bytes occupied by a single control unit; Perform a three-dimensional base address superposition offset mapping to map out mutually isolated fixed-size memory blocks that form control volumes. Each control volume corresponds to a doubly linked list head node pointer that mounts a virtual fluid particle structure.

5. The bidding process optimization method based on cross-analysis of dynamic duration and real-time price as described in claim 1, characterized in that, The memory byte usage rate for each phase is calculated as follows: The main control bidding thread is prohibited from participating in prediction calculations. The physical calculation coroutine is hidden by binding the thread affinity of the operating system. The control volume state variables are reconstructed into a compressed row storage format of sparse matrix. Hiding the physical computation coroutine calls to the Single Instruction Multiple Data Extended Instruction Set and wide bit vector registers, the numerical prediction results of each phase state within the discrete time step are calculated according to the following continuity equation: ； in, Represents whether the memory phase type is continuous or dispersed. For continuous-time variables, the processor strictly follows the discrete time step. Perform fixed-width time slice calculations; This represents the memory byte occupancy rate for the corresponding phase. To set the virtual density constant for the corresponding phase state; The physical flow velocity vector is mapped to the throughput rate extracted by the hardware pointer that pops the corresponding phase data packet from the doubly linked list. As a divergence operator, the direct memory access controller periodically captures the actual absolute changes in the length of the physical linked list nodes between adjacent grid control blocks. This is a quality source item.

6. The bidding process optimization method based on cross-analysis of dynamic duration and real-time price as described in claim 1, characterized in that, The page fault frequency and lock contention cycle are extracted and combined into an interphase drag coefficient. The instruction throughput rate difference between the continuous phase and the distributed phase is calculated. The difference is multiplied by the interphase drag coefficient to obtain the slip velocity, specifically including: The background probe thread monitors the frequency of hardware page faults caused by the translation back buffer miss due to out-of-bounds addressing of scattered phases, and uses the kernel probe mechanism to monitor the number of spinlock idle clock cycles when the main working thread accesses the doubly linked list. The two are weighted and merged by hardware displacement instructions to generate a hardware congestion index and written into the global shared memory segment as the interphase drag coefficient. At the end of the discrete hardware clock cycle, the arithmetic logic unit executes basic subtraction assembly instructions to calculate the absolute difference between the number of continuous phases and discrete phases completed in the processor's general-purpose registers. The absolute difference and the interphase drag coefficient are then multiplied by the floating-point arithmetic unit to obtain the slip velocity, which is then forcibly overwritten into the extended field of the corresponding virtual fluid particle structure.

7. The bidding process optimization method based on cross-analysis of dynamic duration and real-time price as described in claim 1, characterized in that, The process of transferring the dispersed phase that meets the settling condition to the recycling pool is as follows: When the main worker thread traverses the doubly linked list, it calls a comparison instruction to compare the sliding speed with the static hardware separation threshold locked in the first-level high-speed data cache, triggering a hardware soft interrupt in the processor flag register; it uses the locked system bus prefix mechanism to compare and exchange hardware instruction primitives, atomically modifying the predecessor and successor address pointers of the exception node within the uninterruptible hardware execution cycle, removing it from the high-speed main memory pool and adding it to the remote abandoned sleep queue linked list; The lowest priority garbage collection auxiliary thread is deployed to perform read-only idle polling on the remote abandoned sleep queue list. When the difference between the hardware timestamp counter caused by the thread's sleep and wake-up context switching exceeds the safe dead zone threshold, the system's underlying memory is called to release the physical mapping trap instruction and return the physical page frame to the operating system kernel.

8. The bidding process optimization method based on cross-analysis of dynamic duration and real-time price as described in claim 1, characterized in that, Perform dense reconstruction on the remaining continuous phase, specifically including: Deploy a memory reorganization monitoring probe thread to scan the physical distribution span of the continuously phase-marked memory grid. When it detects that a live valid bidding structure spans too many physical memory pages, causing the hardware prefetcher to fail and the cache consistency maintenance cost across physical page frames to increase, a reorganization signal is issued. During the nanosecond-level idle period window when the processor does not process external network interrupt pulses, call the dedicated assembly instructions for vector parallel loading and vector parallel storage in the processor's Single Instruction Multiple Data Extended Instruction Set. The scattered continuous phase structure is moved to a continuous and compact new giant page physical space with high-density address aggregation using bus burst transfer rate, and the hexadecimal offset of the associated predecessor and successor memory pointers is corrected simultaneously.

9. The bidding process optimization method based on cross-analysis of dynamic duration and real-time price as described in claim 1, characterized in that, Based on the memory byte occupancy rate of the consecutive phases, the instruction throughput rate, and the dynamic countdown duration obtained from the hardware timer, the next round of bidding step size is calculated, specifically including: For continuous phase data streams, parameters are extracted through the underlying performance monitoring unit. In order to avoid the clock cycle loss caused by the floating-point exponent Taylor series expansion algorithm, the discrete result of the decay exponent function is pre-compiled into a one-dimensional hardware lookup table. The high-order effective field of the dynamic countdown duration is used as the offset address index to quickly read the approximate constant value of the weight. The next bidding step size is calculated by executing multiply-accumulate machine instructions according to the following hardware state transition equation. : ； in, For the next round of bidding steps, This is the basic statutory minimum markup step constant for hard-coded locking in the read-only segment of the high-speed Level 1 data cache. The coefficients are the scaling transformation constants. For continuous phase real-time memory byte occupancy, For the virtual density constant of the continuous phase, For continuous phase command throughput rate vector, The time decay sensitivity coefficient, The current dynamic remaining countdown timer for the system is obtained from the pin of the advanced programmable interrupt controller. This is a physical constant factor.

10. The bidding process optimization method based on cross-analysis of dynamic duration and real-time bids according to claim 1, characterized in that, The back pressure is generated by reducing the active limit value of the send descriptor according to the dynamic countdown duration, and the instruction to write the bidding step size to the doorbell register is issued. Specifically: The high-speed control thread of the core sends a memory-mapped write instruction to the hardware configuration register space of the network card device, overwriting the active valid tail pointer and maximum limit value of the transmit descriptor that the direct memory access controller allows to poll. The state update packets that are forcibly intercepted and do not have the highest non-maskable interrupt priority are directly overwritten and eliminated in the second-level unified cache, generating back pressure. In the fixed-length data packet template of the bypass network pre-allocated memory pool in the data plane development kit, the bidding step size is written to the payload area using advanced wide bit vectorized memory bus instructions. The physical starting memory base address of the data packet template is filled into the transmit descriptor pointer field. The memory barrier synchronization instruction is executed to ensure that the bus buffer is flushed into the physical memory silicon space. An incrementing cursor value is written to the transmit doorbell register to drive the external component interconnect standard protocol physical bus to extract data frames and perform photoelectric conversion and transmission.

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