System operation method and apparatus

Abstract

The application discloses a system operation method and device, and relates to the technical field of computers, which comprises the following steps: performing sequence smoothing processing on hardware operation parameters to obtain a deviation characteristic quantity; updating operation parameters of the target system based on the deviation characteristic quantity; and controlling the operation of the target system based on the updated operation parameters. The hardware layer operation state in the system operation process is captured in real time, then the sequence smoothing processing is performed on the hardware operation parameters to obtain the deviation characteristic quantity for indicating the deviation degree of the current load of the target system relative to the reference load, the interference caused by the fluctuation of the hardware parameters is effectively filtered, and the system load state is accurately perceived; the memory resource exhaustion caused by the instantiation of a large number of alarm objects is avoided by dynamically adjusting the operation parameters, the memory overflow risk is fundamentally avoided, and the stable operation of the system in an extreme concurrent scene is ensured.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and more specifically to a system operation method and apparatus. Background Technology

[0002] In extreme scenarios involving large-scale physical failures of underlying IT infrastructure such as core switches and power supply modules in data centers, monitoring systems can experience a surge of hundreds of thousands or even millions of discrete alarm messages per second. At this point, the system often operates in an isolated state—without topological connections, original logs, or external dependencies—requiring efficient rate limiting and noise reduction for handling this massive volume of alarms. To adapt to this extreme scenario, existing technologies have proposed corresponding solutions. The mainstream approach employs memory-resident queues based on sliding window or leaky bucket algorithms for rate limiting and noise reduction, or introduces cardinality estimation algorithms to construct stateless feature deduplication schemes to cope with the processing needs of massive concurrent alarm messages.

[0003] Current mainstream technologies all reveal fundamental technical bottlenecks when faced with extreme concurrent alarm traffic, with the core issue being memory overflow and the resulting risk of system paralysis. Specifically, traditional microservice-based operation and maintenance monitoring systems rely on memory-resident queues for rate limiting and noise reduction. This approach can lead to catastrophic systemic risks of memory overflow (OOM) when encountering extreme concurrent alarm traffic: When massive alarm messages flood in, the system needs to frequently instantiate alarm objects in the Java Virtual Machine (JVM) heap memory. Because the production rate of alarm objects is much higher than the consumption rate, a large number of short-lifetime objects violate the generational memory assumption and enter the old generation prematurely, thus frequently triggering global garbage collection (GC). During global GC, the CPU needs to frequently traverse the object reference graph, and massive memory read and write instructions will completely occupy the physical bandwidth of the memory controller, causing system-level operational pauses. Ultimately, the exhaustion of memory resources leads to cascading paralysis of the monitoring nodes themselves, creating a blind spot for underlying IT operations. Summary of the Invention

[0004] The main objective of this invention is to provide a system operation method and apparatus. Addressing the memory overflow and system crash problems of existing technologies, this invention first acquires the hardware operating parameters of the target system, capturing the hardware-level operating status in real time during system operation, providing accurate and reliable basic data support for subsequent load assessment. Then, the hardware operating parameters are sequentially smoothed to obtain a deviation representation quantity indicating the degree of deviation of the target system's current load from a baseline load, effectively filtering interference caused by hardware parameter fluctuations and accurately perceiving the system load status. Next, the operating parameters of the target system are updated based on the deviation representation quantity, linking the operating parameters with the current system load status, breaking the limitation of traditional fixed operating parameters. Finally, the target system operation is controlled based on the updated operating parameters. By dynamically adjusting the operating parameters, memory resource exhaustion caused by the instantiation of massive alarm objects is avoided, fundamentally mitigating the risk of memory overflow, preventing cascading failures of monitoring nodes, and ensuring stable system operation under extreme concurrency scenarios.

[0005] To achieve the above objectives, the embodiments of this application provide the following technical solutions: According to a first aspect of the embodiments of this application, a system operation method is provided, the method comprising: Obtain the hardware operating parameters of the target system; The hardware operating parameters are subjected to sequential smoothing to obtain a deviation characterization quantity, which is used to indicate the degree of deviation of the current load of the target system from the reference load. Based on the deviation characterization, update the operating parameters of the target system; The target system is controlled to operate based on the updated operating parameters.

[0006] Optionally, updating the operating parameters of the target system based on the deviation characterization includes: The deviation characterization quantity is input into a preset load determination network to obtain the current load level of the target system; Based on the current load level, generate corresponding operating parameter adjustment instructions; Based on the adjustment instructions, the values ​​of the core operating parameters corresponding to the target system are determined, and the operating parameters are updated.

[0007] Optionally, the load determination network includes a proportional calculation unit, an integral calculation unit, and a differential calculation unit; the step of inputting the deviation characterization quantity into the load determination network to obtain the current load level includes: The instantaneous deviation between the deviation characterization quantity and the preset load threshold is calculated using the proportional calculation unit. The instantaneous deviation value is accumulated over time using the integral calculation unit to obtain the cumulative deviation value; The rate of change of the deviation characterization quantity is calculated using the differential calculation unit, and the slope of the deviation change is obtained. The current load level is determined based on the instantaneous deviation value, cumulative deviation value, and deviation change slope.

[0008] Optionally, generating corresponding operating parameter adjustment instructions based on the current load level specifically includes: When the current load level is a first preset level, a first type of control parameter adjustment instruction is generated, and the adjustment instruction is used to update the first type of control parameter to a first target value; When the current load level is the second preset level, a second type of control parameter adjustment instruction is generated, which is used to update the second type of control parameter to the second target value; When the current load level is the third preset level, a concurrency control parameter adjustment instruction is generated, which is used to update the concurrency control parameters to the third target value; When the current load level is the fourth preset level, a flow control instruction is generated. The flow control instruction is used to generate a flow control frame at the physical network link layer and send the flow control frame to the upstream data source.

[0009] Optionally, the step of modifying the values ​​of the corresponding core operating parameters of the target system based on the adjustment instruction to complete the update of the operating parameters includes: After updating the first type of control parameters based on the first type of control parameter adjustment instruction, increase the displacement of the bitwise right shift operation performed on the data statistics value in the first data processing stage; After updating the second type of control parameters based on the second type of control parameter adjustment instruction, the number of hash mapping operations performed in the second data processing stage to determine whether a single data feature is repeated is reduced; After updating the concurrency control parameters based on the concurrency control parameter adjustment instruction, the maximum number of times a retry operation is performed when there is a resource access conflict is set to zero.

[0010] Optionally, the step of performing sequence smoothing on the hardware operating parameters to obtain a deviation characterization quantity includes: Construct a time series of hardware operating parameters according to the time dimension; The time series of the hardware operating parameters is processed by multi-order exponential moving average to obtain smoothed series with at least two different periods; The difference between each smoothed sequence is calculated to obtain the deviation characterization.

[0011] Optionally, the step of performing multi-order exponential moving average processing on the time series of the hardware operating parameters to obtain a smoothed series with at least two different periods includes: The time series of the hardware operating parameters is processed by the first exponential moving average to obtain a short-period smoothed sequence, which is used to capture sudden fluctuations in the target system load. The short-period smoothed sequence is processed by a quadratic exponential moving average to obtain a long-period smoothed sequence, which is used to anchor the benchmark load level of the target system.

[0012] Optionally, the method further includes: Continuously collect the hardware operating parameters of the target system and obtain real-time deviation characterization quantities; When the real-time deviation characterization quantity falls below the preset threshold of the corresponding load level, the system safety margin is accumulated through the integral calculation unit of the load determination network. The system safety margin is used to filter secondary load fluctuations. When the system's safe operating margin reaches the preset standard, an operating parameter recovery command is generated in reverse order from the current load level to the normal load level. Based on the aforementioned operating parameter recovery instructions, the core operating parameters of the target system are gradually reset to the baseline values; After the parameters are restored, the target system returns to its baseline operating state and continues to monitor load changes.

[0013] Optionally, obtaining the hardware operating parameters of the target system includes: By reading instructions in user mode without privilege, and according to preset time slices, the number of processor instructions retired and the amount of data transfer transactions on the main memory bus in each time slice of the target system are continuously collected. The number of processor instructions retired and the amount of data transfer transactions on the main memory bus collected in each time slice are arranged in chronological order to form a parameter sequence; The parameter sequence is integrated into the hardware operating parameters of the target system.

[0014] According to a second aspect of the embodiments of this application, a system operating apparatus is provided, the apparatus comprising: The parameter acquisition module is used to acquire the hardware operating parameters of the target system; The deviation characterization calculation module is used to perform sequential smoothing processing on the hardware operating parameters to obtain the deviation characterization, which is used to indicate the degree of deviation of the current load of the target system from the reference load. The parameter update module is used to update the operating parameters of the target system based on the deviation characterization quantity; The system operation control module is used to control the operation of the target system based on the updated operating parameters.

[0015] In summary, this application provides a system operation method and apparatus. The method involves: acquiring hardware operating parameters of a target system; performing sequential smoothing on the hardware operating parameters to obtain a deviation characterization, which indicates the degree of deviation of the current load of the target system from a reference load; updating the operating parameters of the target system based on the deviation characterization; and controlling the operation of the target system based on the updated operating parameters. First, the hardware operating parameters of the target system are acquired, and the hardware-level operating status during system operation is captured in real time, providing real and reliable basic data support for subsequent load judgment. Then, the hardware operating parameters are sequentially smoothed to obtain a deviation characterization quantity that indicates the degree of deviation of the target system's current load from the benchmark load, effectively filtering the interference caused by hardware parameter fluctuations and accurately sensing the system load status. Next, the operating parameters of the target system are updated based on the deviation characterization quantity, so that the operating parameters are linked with the current system load status, breaking the limitation of traditional fixed operating parameters. Finally, the operation of the target system is controlled based on the updated operating parameters. By dynamically adjusting the operating parameters, memory resource exhaustion caused by the instantiation of massive alarm objects is avoided, fundamentally avoiding the risk of memory overflow, preventing cascading paralysis of monitoring nodes, and ensuring stable operation of the system under extreme concurrency scenarios. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art 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 the structures shown in these drawings without creative effort.

[0017] The structures, proportions, sizes, etc. illustrated in this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed herein, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0018] Figure 1 A flowchart of a system operation method provided in this application embodiment; Figure 2 A schematic diagram of the system operation device provided in the embodiments of this application; Figure 3 This paper shows a structural diagram of an electronic device provided in an embodiment of this application; Figure 4 A diagram of a computer-readable storage medium provided in an embodiment of this application is shown.

[0019] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0021] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0022] Furthermore, in this invention, descriptions involving "first," "second," etc., are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0023] In this invention, unless otherwise explicitly specified and limited, the terms "connection," "fixed," etc., should be interpreted broadly. For example, "fixed" can mean a fixed connection, a detachable connection, or an integral part; it can mean a mechanical connection or an electrical connection; it can mean a direct connection or an indirect connection through an intermediate medium; it can mean the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0024] Furthermore, the technical solutions of the various embodiments of the present invention can be combined with each other, but only if they are feasible for those skilled in the art. If the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0025] This solution addresses the systemic vulnerabilities of traditional monitoring architectures under high pressure, specifically targeting scenarios with massive concurrent alarms caused by extreme failures in underlying IT infrastructure and the absence of external dependencies. First, it overcomes the system's survival bottleneck under extreme concurrency pressure by achieving deterministic memory overflow protection. This eliminates reliance on runtime dynamic memory and, through a processing paradigm of constant memory boundaries and static data structures, achieves absolute immunity to memory overflow risks at the physical level. This ensures stable system operation under the most stringent concurrency pressure, preventing business blind spots caused by system crashes. Second, it constructs a proactive adaptive control mechanism based on microarchitecture state awareness. By real-time collection of underlying hardware operating parameters such as processor instruction retirement rate and memory bus transaction volume, it performs sequence smoothing and trend analysis to generate deviation representations. This enables nanosecond-level perception and prediction of system computing load "drift" trends, allowing proactive and smooth adjustments to internal processing logic before resources are truly exhausted, shifting from passive threshold response to proactive trend intervention. When computing power approaches its limit, a flexible degradation strategy of "trading accuracy for survival" is adopted. Based on the load status, the core accuracy parameters of the data processing algorithm are dynamically updated, and the processing accuracy is actively and controllably reduced. This sacrifices the accuracy of some non-critical calculations in exchange for the overall throughput and stability of the system, achieving the optimal flexible allocation of computing resources. At the same time, a complete dynamic feedback control closed loop is constructed to achieve system-level autonomous recovery and steady-state regression without external intervention. Through continuous monitoring and deviation analysis, the load decline is automatically identified, and the operating parameters are gradually restored to the optimal state based on the damping algorithm. This achieves unattended closed-loop management of the entire process from storm triggering and adaptive defense to storm passage and autonomous recovery, improving the overall resilience of the system and fundamentally solving the problems of memory crashes, response delays, rigid control, and reliance on manual intervention in traditional monitoring architectures.

[0026] Figure 1 This application illustrates a system operation method provided by an embodiment of the present application, the method comprising: Step 101: Obtain the hardware operating parameters of the target system; Step 102: Perform sequential smoothing on the hardware operating parameters to obtain a deviation characterization quantity, which is used to indicate the degree of deviation of the current load of the target system from the reference load; Step 103: Update the operating parameters of the target system based on the deviation characterization quantity; Step 104: Control the operation of the target system based on the updated operating parameters.

[0027] In practical applications, the load status of a target system fluctuates dynamically with changes in business volume. Fixed operating parameters cannot adapt to these dynamic load changes, easily leading to system lag or paralysis under high load, or resource waste under low load. Therefore, there is an urgent need for a system operation method that can dynamically adjust operating parameters according to load status. This application's embodiments achieve accurate perception of the target system's load status and dynamic adaptation of operating parameters, solving the problems of poor load adaptability and insufficient operational stability caused by fixed operating parameters in traditional systems, ensuring stable and efficient system operation under different load conditions.

[0028] Step 101 acquires the hardware operating parameters of the target system, enabling low-level data collection of the system's operating status. These parameters, including processor and memory bus parameters, directly reflect the system's real-time load, providing reliable foundational data for subsequent load assessment and preventing biases caused by missing or inaccurate data. Step 102 performs sequence smoothing on the hardware operating parameters, effectively filtering out random fluctuations during acquisition. This ensures the obtained deviation metrics clearly and accurately indicate the degree of deviation of the target system's current load from the baseline load, facilitating precise parameter adjustments based on this deviation.

[0029] Further, step 103 updates the operating parameters of the target system based on the deviation characterization quantity, enabling the operating parameters to be dynamically adjusted according to the degree of load deviation, breaking the limitation of fixed operating parameters in the traditional way, and achieving precise matching between operating parameters and load status; step 104 controls the operation of the target system based on the updated operating parameters, ensuring that the system can reduce load pressure by adjusting operating parameters when the load is high, and can make reasonable use of resources when the load is low, thereby improving the stability, reliability and resource utilization of the system operation, and achieving the technical goal of the system dynamically adapting to load changes and operating stably and efficiently.

[0030] In one possible implementation, in step 101, acquiring the hardware operating parameters of the target system includes: continuously collecting the number of processor instruction retirements and the amount of main memory bus data transfer transactions in each time slice of the target system by reading instructions in user mode without privilege, according to a preset time slice; arranging the number of processor instruction retirements and the amount of main memory bus data transfer transactions collected in each time slice in chronological order to form a parameter sequence; and integrating the parameter sequence into the hardware operating parameters of the target system.

[0031] Traditional hardware parameter acquisition methods may pose system security risks due to excessive privileges, or suffer from problems such as unreasonable acquisition frequency and disorganized data, affecting the accuracy of subsequent load analysis. Therefore, a standardized and efficient acquisition method is needed. In step 101 of this application embodiment, the acquisition of hardware operating parameters of the target system is further refined to ensure the accuracy, real-time performance, and reliability of hardware operating parameter acquisition, providing high-quality basic data support for subsequent load status judgment and parameter adjustment.

[0032] In practical implementation, collecting hardware operating parameters through user-mode non-privileged read commands effectively avoids system security risks caused by using privileged commands, while ensuring that the collection operation does not interfere with the normal operation of the target system. Continuous collection according to preset time slices enables real-time monitoring of the target system's hardware operating status. The preset time slices can be reasonably set according to the actual operating needs of the system and the hardware response characteristics, ensuring both the real-time nature of the collected data and avoiding excessive system resource consumption due to excessive collection frequency.

[0033] The specific parameters collected are the number of processor instruction retirements and the number of main memory bus data transfer transactions. These two parameters directly reflect the processor's operating load and memory data transfer pressure of the target system, and are core indicators for judging the system's load status. Arranging the parameters collected in each time slice in chronological order to form a parameter sequence clearly shows the trend of parameter changes over time, facilitating subsequent sequence smoothing. Integrating the parameter sequence into the hardware operating parameters of the target system enables a comprehensive summary of the system's hardware operating status, providing complete and coherent input data for the sequence smoothing process in step 102, ensuring the accuracy of subsequent deviation characterization calculations.

[0034] In one possible implementation, step 102, the step of performing sequence smoothing on the hardware operating parameters to obtain a deviation characterization quantity, includes: constructing a hardware operating parameter time series according to the time dimension; performing multi-order exponential moving average processing on the hardware operating parameter time series to obtain at least two smoothed sequences with different periods; and calculating the difference between each of the smoothed sequences to obtain the deviation characterization quantity.

[0035] In one possible implementation, the step of performing multi-order exponential moving average processing on the hardware operating parameter time series to obtain at least two smoothed sequences with different periods includes: performing an initial exponential moving average processing on the hardware operating parameter time series to obtain a short-period smoothed sequence, the short-period smoothed sequence being used to capture sudden fluctuations in the target system load; and performing a second exponential moving average processing on the short-period smoothed sequence to obtain a long-period smoothed sequence, the long-period smoothed sequence being used to anchor the baseline load level of the target system.

[0036] Hardware operating parameters are susceptible to fluctuations due to external interference during the acquisition process. Directly using these parameters for load assessment can lead to inaccurate judgments of load deviation, thus affecting the rationality of parameter adjustments. Therefore, it is necessary to optimize the acquired parameters through sequence smoothing. This application embodiment details the sequence smoothing process and multi-order exponential moving average processing method for hardware operating parameters in step 102. This filters out random fluctuations generated during hardware operating parameter acquisition, accurately extracts the changing trend of the target system load, and thus obtains a deviation representation that accurately reflects the degree of deviation of the current load from the baseline load.

[0037] In the specific implementation process, the hardware operating parameters are first constructed into a time series according to the time dimension, so that the collected discrete parameters form a coherent time change sequence, clearly showing the change pattern of hardware operating parameters over time, laying the foundation for subsequent smoothing processing. Subsequently, the hardware operating parameter time series is processed by multiple exponential moving averages to obtain smoothed sequences with at least two different periods. Through multi-stage processing, short-term fluctuations of the load can be better separated from the long-term benchmark level, improving the accuracy of the deviation representation.

[0038] The specific method of multi-order exponential moving average processing is as follows: First, the time series of hardware operating parameters is processed by an initial exponential moving average to obtain a short-period smoothed sequence. This sequence can quickly capture sudden fluctuations in the target system load and reflect short-term load changes in a timely manner. Then, a second exponential moving average is processed on the short-period smoothed sequence to obtain a long-period smoothed sequence. This sequence can effectively filter the interference of short-term fluctuations, anchor the benchmark load level of the target system, and clarify the load benchmark when the system is operating normally. Finally, by calculating the difference between each smoothed sequence, a deviation characterization quantity is obtained. This difference can intuitively reflect the degree of deviation of the current load from the benchmark load, providing an accurate judgment basis for updating the operating parameters in step 103, and ensuring that subsequent adjustments to the operating parameters can fit the actual load state of the system.

[0039] In one possible implementation, in step 103, updating the operating parameters of the target system based on the deviation characterization includes: inputting the deviation characterization into a preset load determination network to obtain the current load level of the target system; generating a corresponding operating parameter adjustment instruction based on the current load level; determining the values ​​of the corresponding core operating parameters of the target system based on the adjustment instruction, and completing the update of the operating parameters.

[0040] This embodiment refines the process of updating the target system operating parameters based on the deviation characterization in step 103, achieving precise and dynamic adjustment of the operating parameters. This ensures that the operating parameters accurately match the current load state of the target system, guaranteeing stable operation of the system under different load levels. The deviation characterization accurately reflects the degree of deviation of the system's current load from the baseline load. Based on this, through standardized load level determination and targeted parameter adjustment command generation, the blindness of parameter adjustment can be avoided, improving the rationality and effectiveness of parameter updates, thereby providing reliable support for the system operation control in step 104.

[0041] In one possible implementation, the load determination network includes a proportional calculation unit, an integral calculation unit, and a differential calculation unit; the step of inputting the deviation characterization quantity into the load determination network to obtain the current load level includes: calculating the instantaneous deviation value between the deviation characterization quantity and a preset load threshold using the proportional calculation unit; accumulating the instantaneous deviation value over time using the integral calculation unit to obtain a cumulative deviation value; calculating the rate of change of the deviation characterization quantity using the differential calculation unit to obtain the deviation change slope; and determining the current load level based on the instantaneous deviation value, the cumulative deviation value, and the deviation change slope.

[0042] In one possible implementation, generating corresponding operating parameter adjustment instructions based on the current load level specifically includes: when the current load level is a first preset level, generating a first type of control parameter adjustment instruction, the adjustment instruction being used to update the first type of control parameter to a first target value; when the current load level is a second preset level, generating a second type of control parameter adjustment instruction, the adjustment instruction being used to update the second type of control parameter to a second target value; when the current load level is a third preset level, generating a concurrency control parameter adjustment instruction, the adjustment instruction being used to update the concurrency control parameter to a third target value; and when the current load level is a fourth preset level, generating a flow control instruction, the flow control instruction being used to generate a flow control frame at the physical network link layer and send the flow control frame to the upstream data source.

[0043] In one possible implementation, the step of modifying the values ​​of the corresponding core operating parameters of the target system based on the adjustment instructions to complete the update of the operating parameters includes: after updating the first type of control parameters based on the first type of control parameter adjustment instructions, increasing the displacement of the bitwise right shift operation performed on the data statistics values ​​in the first data processing stage; after updating the second type of control parameters based on the second type of control parameter adjustment instructions, reducing the number of hash mapping operations performed in the second data processing stage to determine whether a single data feature is repeated; and after updating the concurrency control parameters based on the concurrency control parameter adjustment instructions, setting the upper limit of the number of retry operations performed when there is a resource access conflict to zero.

[0044] In the specific implementation process, the deviation characterization quantity is first input into a preset load determination network, which accurately determines the current load level of the target system. The load determination network includes a proportional calculation unit, an integral calculation unit, and a differential calculation unit, which work together to determine the load level: the proportional calculation unit calculates the instantaneous deviation between the deviation characterization quantity and the preset load threshold, directly reflecting the immediate deviation of the current load; the integral calculation unit accumulates the instantaneous deviation value over time to obtain the cumulative deviation value, reflecting the persistence of the load deviation; and the differential calculation unit calculates the rate of change of the deviation characterization quantity to obtain the slope of the deviation change, predicting the trend of load changes. By combining the instantaneous deviation value, the cumulative deviation value, and the slope of the deviation change, the current load level of the target system can be comprehensively and accurately determined, providing a clear basis for the generation of subsequent adjustment instructions.

[0045] Based on the determined current load level, corresponding operating parameter adjustment instructions are generated. Different load levels correspond to different adjustment instructions to ensure the targeted nature of the adjustments. When the current load level is the first preset level, a first type of control parameter adjustment instruction is generated to update the first type of control parameter to the first target value; when the current load level is the second preset level, a second type of control parameter adjustment instruction is generated to update the second type of control parameter to the second target value; when the current load level is the third preset level, a concurrency control parameter adjustment instruction is generated to update the concurrency control parameter to the third target value; when the current load level is the fourth preset level, a flow control instruction is generated to generate a flow control frame at the physical network link layer and send the flow control frame to the upstream data source.

[0046] Finally, based on the generated adjustment instructions, the values ​​of the corresponding core operating parameters of the target system are modified to complete the update of the operating parameters. After updating the first type of control parameters based on the first type of control parameter adjustment instructions, the bitwise right shift operation performed on the data statistics values ​​in the first data processing stage is increased; after updating the second type of control parameters based on the second type of control parameter adjustment instructions, the number of hash mapping operations performed in the second data processing stage to determine whether a single data feature is duplicated is reduced; after updating the concurrency control parameters based on the concurrency control parameter adjustment instructions, the upper limit of the number of retry operations when resource access conflicts occur is set to zero. Through the above targeted parameter adjustments, the operating parameters of the target system can be adapted to the current load level, ensuring the stability and rationality of system operation.

[0047] In one possible implementation, the method further includes: continuously collecting hardware operating parameters of the target system and obtaining real-time deviation indicators; when the real-time deviation indicators fall below a preset threshold corresponding to the load level, accumulating a system safety margin through the integral calculation unit of the load determination network, wherein the system safety margin is used to filter secondary load fluctuations; when the system safety margin reaches a preset standard, generating an operating parameter recovery instruction in reverse order from the current load level to the normal load level; based on the operating parameter recovery instruction, gradually resetting the core operating parameters of the target system to baseline values; after the parameter recovery is completed, the target system returns to the baseline operating state and continuously monitors load changes.

[0048] In actual operation, the load state of the target system is not constant. When extreme load pressure subsides, directly resetting the core operating parameters to baseline values ​​may lead to system instability due to secondary load fluctuations. Therefore, a standardized self-healing recovery process is needed to achieve gradual parameter recovery and a smooth system transition. This application provides a self-healing recovery mechanism for system operating parameters, enabling the target system to autonomously return to steady state after load pressure subsides. This avoids system fluctuations caused by blindly restoring operating parameters due to load decline, ensuring the continuity and stability of system operation.

[0049] The self-healing recovery process is as follows: First, the hardware operating parameters of the target system are continuously collected, and real-time deviation metrics are obtained based on these parameters to ensure real-time monitoring of system load changes and timely detection of load decline signals. When the real-time deviation metrics fall below the preset threshold for the corresponding load level, it indicates that the system load pressure has been alleviated. At this point, the system's safe operating margin is accumulated through the integral calculation unit of the load judgment network. This safe operating margin effectively filters secondary load fluctuations, preventing short-term load fluctuations from being misjudged as a continuous load decline, and ensuring the accuracy of parameter recovery timing.

[0050] When the system's safe operating margin reaches the preset standard, it indicates that the system is in a stable low-load state. At this point, operating parameter recovery instructions are generated in reverse order from the current load level to the normal load level to ensure that the parameter recovery process is carried out in an orderly manner and to avoid system instability caused by a chaotic recovery order. Subsequently, based on the generated operating parameter recovery instructions, the core operating parameters of the target system are gradually reset to the baseline values. Through gradual recovery, the system's operating state transitions smoothly, avoiding operational shocks caused by sudden parameter changes.

[0051] Once all core operating parameters are restored to their baseline values, the target system returns to its baseline operating state. At the same time, it continues to monitor hardware operating parameters and real-time deviation indicators to promptly capture subsequent load changes, further enhancing the operational resilience and stability of the target system and ensuring long-term stable operation.

[0052] In this embodiment, the target system is configured with multiple decoupled data processing modules. The target system is controlled to run based on the updated operating parameters. Specifically, the updated operating parameters are sent to each data processing module of the target system. Each data processing module adjusts its own data processing logic according to the received updated operating parameters. Finally, the target system executes the entire process of data acquisition, feature processing and load control based on the adjusted processing logic of each data processing module.

[0053] The multiple decoupled data processing modules include a data statistics module, a feature processing module, and a load control module. Each module adjusts its processing logic according to the updated operating parameters as follows: the data statistics module adjusts the aging and cleaning rhythm of statistical data according to the updated data decay parameters; the feature processing module adjusts the feature mapping and deduplication calculation process according to the updated feature calculation parameters; and the load control module adjusts the concurrent execution rules of hardware atomic operations according to the updated concurrent retry parameters. The adjusted processing logic of each module cooperates with each other to jointly support the entire process operation of the target system.

[0054] Each data processing module corresponds to an independent worker thread. To improve system efficiency, this method also includes thread and data interaction optimization steps: the worker threads corresponding to each data processing module are bound to different physical cores of the target system's processor to avoid cache invalidation caused by cross-core migration of threads; data processing modules interact with each other through a lock-free data transmission channel, which only transmits the memory index cursor of the data. The receiver uses pointer arithmetic offset operations to directly locate the physical memory address of the data based on the memory index cursor, realizing zero-copy data transmission between modules and reducing resource consumption during data transmission.

[0055] Before acquiring the hardware operating parameters of the target system, a data ingestion step is also included. The specific process is as follows: a bypass listening program is deployed at the bottom layer of the target system's network protocol stack to intercept raw data frames; the overhead of transport and network layer protocol parsing of the raw data frames is stripped away, and data feature planes are directly extracted; the validity of the data feature planes is determined, and valid data is pushed to the target system's processing module, while redundant data is discarded directly at the data ingestion layer; the valid data pushed to the processing module serves as the basic data source for the target system to generate hardware operating parameters. Specifically, when pushing valid data to the target system's processing module, the valid data is encapsulated into a fixed-length memory index cursor and pushed to the processing module through a zero-copy mechanism; after receiving the memory index cursor, the processing module directly locates the physical memory address of the valid data through pointer arithmetic offset operations, achieving copyless data reading and subsequent processing. In addition, when the target system is running based on the updated operating parameters, if the current load level of the target system is at the disaster load level, dual emergency measures must be taken: send a physical layer pause command to the upstream data transmission device to forcibly suppress the transmission rate of upstream data; increase the data circuit breaker ratio of the target system, retain only the data transmission and processing of the preset privilege level, and suspend the data processing operations of the non-privileged level. These measures will reduce the processing pressure of the target system and prevent the system from being paralyzed due to overload.

[0056] In summary, this application's embodiments construct a pure data flow micro-feature mapping engine that is completely independent of external business logic topology, while stripping away all external dependencies. This engine aims to overcome the limitations of missing topology and log fragmentation, enabling lossless extraction and frequency compression of discrete alarm concurrency features exhibiting high-frequency pulse bursts within microsecond-level time slices, down to nanosecond-level instruction cycles. It adapts to isolated operation scenarios in underlying IT operations where there is no topology, no logs, and no external storage, ensuring the timeliness and completeness of alarm feature extraction under extreme fault conditions, and providing an accurate foundation for subsequent frequency statistics and noise reduction processing.

[0057] To eliminate the risk of OutOfMemoryError (OOM), this application establishes a baseline underlying data structure with both time and space complexity reaching the theoretical limit of O(1). Through static pre-allocation and physical page locking, it ensures that physical memory overhead consistently converges to the absolute threshold set during initialization, eliminating uncontrollable memory issues caused by dynamic heap allocation during runtime. This design aims to directly block uncontrolled object heap allocation behavior caused by sudden traffic surges at the physical mapping level, thereby cutting off the system-level triggering path that leads to memory overflow failures, ensuring system memory stability under extreme concurrency storms, and achieving system-level immunity to OOM errors. By introducing lock-free flow, CAS instructions, forced cache line isolation, and FMA instructions, a low-latency, high-throughput pipeline highly compatible with modern CPU microarchitecture is constructed, eliminating latency losses caused by cross-core pseudo-sharing and thread context switching. Ultimately, this provides a technical path for the underlying monitoring system under extreme concurrency storms, without relying on external intervention, possessing adaptive flexible degradation and microsecond-level steady-state self-healing capabilities, ensuring stable operation and rapid recovery even under computational power limits.

[0058] The method provided in the embodiments of this application will be described in detail below with reference to specific implementation methods.

[0059] This application proposes a lock-free circular buffer unidirectional data pipeline architecture based on three deeply decoupled modules (S1, S2, and S3). Through the functional division and physical isolation of the three modules, a complete closed loop of "ingestion-denoising-control" is formed. By isolating data ingestion, feature denoising, and backpressure control at the microarchitecture level pipeline level, the physical core affinity of the execution logic of each module is ensured, cross-module computational power interference is avoided, and overall processing efficiency is improved. Simultaneously, the system establishes a core protection boundary based on a problem- and solution-oriented software-hardware collaborative dynamic feedback control law, achieving closed-loop adaptive throttling of underlying computational power overhead, ensuring the controllability and stability of the system under extreme concurrency.

[0060] Module S1: Frequency Statistical Algorithm for Sketch-Based and Pure User-Mode Polling Displacement Aging For the feature mapping of 190,000 baseline alarms, module S1 constructs a conservative update two-dimensional counter array with a space complexity of O(1), which can achieve accurate frequency statistics of massive alarm features within a limited memory space, meeting the core requirements of static memory management. In terms of physical memory allocation, the array is precisely locked to an 80KB static contiguous memory region. Through operating system-level physical page locking, the absolute determinism of memory consumption is ensured, avoiding the risk of OOM from the root. In order to achieve efficient sliding window decay and avoid system call overhead, this application embodiment deploys a bit-by-bit right shift global decay mechanism triggered by active polling based on pure user-mode RDTSCP timestamp reading instructions, which can complete frequency aging without entering kernel mode. Through a specific daemon thread, the processor timestamp difference is obtained using non-privileged instructions, and the single-cycle hardware barrel shifter is sent out according to the microsecond-level clock interval to smoothly achieve high-speed frequency compression and aging cleanup, ensuring accurate identification of high-frequency alarm features and effective filtering of low-frequency noise.

[0061] Module S2: Lock-free operation and cache line isolation noise reduction based on block filter To address global memory contention and bus storm issues, module S2 employs a block-based Bloom filter for discrete feature physical surface noise reduction. This enables rapid deduplication and noise reduction of alarm features, improving alarm processing accuracy. It also anchors a strict 1.71MB off-heap lock mapping to physical memory capacity, working in conjunction with the 80KB static memory of module S1 to build a system-level memory immunity system. The core of this module lies in its strict implementation of a 64-byte cache line alignment localized physical addressing strategy, ensuring perfect adaptation of each data block to the processor's L1 cache line and reducing cache miss probability. During concurrent updates of specific bit surfaces by multiple threads, the target bit field is forcibly constrained within a single data cache line, preventing multi-core pseudo-sharing effects at the root of physical addressing and avoiding bus saturation storms. Meanwhile, in the feature hashing stage, hardware-level FMA instructions are combined to accelerate the parallel scaling process of the k=5 base hash function, compressing the hash calculation latency to a constant clock cycle. It also relies on pure low-level CAS operation instructions to provide atomic state machine transition guarantee, realizing absolutely lock-free concurrent writing, avoiding thread suspension and context switching, and adapting to the needs of 500,000 concurrent alarm processing.

[0062] Module S3: Inverse backpressure mechanism based on double-order extreme value pursuit and dynamic feedback control law Module S3, as the core control hub of the system's closed loop, undertakes the core functions of computing power status perception and dynamic backpressure regulation. Internally, it runs a Dual-EMA algorithm engine to capture abnormal drifts in the overall system throughput variance in real time, accurately perceive changes in computing power load, and construct a decision matrix for a multi-level discrete water level state machine to accurately determine the system's operating status. When facing the critical point of physical hardware resource exhaustion, the state machine engine activates a dynamic feedback control law of software and hardware collaboration based on the instantaneous computing power pressure offset variable: it dynamically modifies the step size constant of the shift register in module S1 to accelerate the zeroing decay of old records, reducing memory usage and computing power consumption; and it directly cuts off the upper bound of the hash execution loop iteration in module S2, forcibly truncating the hash iteration upper bound k from the optimal 5 iterations to a strict 2 iterations, thereby releasing the random read bandwidth of the underlying storage controller and achieving dynamic balance of computing power. Under disaster-level conditions, the CAS spin limit of the underlying flow is strictly reduced to 0 times, directly implementing physical backpressure and lossy degradation to achieve proactive transfer of computing power and self-protection, ensuring that the system does not crash or collapse under extreme storms.

[0063] By abandoning all dynamic object creation paradigms at the high-level language level and relying on memory pre-allocation and the operating system's physical page locking mechanism, static byte-level mapping with zero dynamic heap allocation at runtime is achieved, fundamentally eliminating the overflow risks caused by memory fragmentation and dynamic expansion. When dealing with the deduplication of 190,000 baseline alarm features and extreme concurrent burst traffic, the conservative update array of the S1 module consumes a fixed 80KB of physical memory, and the block bitmap array of the S2 module consumes a fixed 1.71MB of physical memory. The total memory consumption of the two core modules is strictly less than 2MB, and the space occupation is controllable and deterministic. The system's core state machine can easily handle instantaneous throughput of up to one million requests within a total physical memory tolerance of strictly less than 2MB, completely eliminating the risk of memory exhaustion and shutdown, and ensuring that the system can still run stably in an isolated environment without external storage and log support.

[0064] Compared to traditional protection architectures based on concurrent hash tables and mutexes, this system achieves orders-of-magnitude efficiency improvements in processing latency and concurrent throughput. This is primarily due to its deep adaptation to and extreme optimization of modern processor microarchitecture characteristics. Through a lock-free ring design throughout the entire process and strict physical memory visibility barrier instructions, the system substantially eliminates the high latency costs associated with cross-core consistency probing and thread context switching at the microarchitecture level, compressing single alarm processing latency to the nanosecond level. The locality of addressing mode, highly consistent with the processor cache hierarchy, including contiguous memory allocation in the S1 module and 64-byte cache line alignment in the S2 module, ensures that hot data maintains a very high resident rate in the L1 and L2 caches, reducing the number of main memory accesses. This keeps the noise reduction judgment clock cycle consumption for single alarm processing stably suppressed to a constant level at the underlying level, achieving an ultra-fast processing capability almost equivalent to the direct packet loss throughput limit of the network protocol stack, capable of easily handling the impact of a 500,000 concurrent power outage storm.

[0065] Faced with unpredictable traffic surges, this system eliminates its reliance on external maintenance commands. Through the dynamic feedback control law of the S3 module, it achieves closed-loop autonomy and steady-state self-healing, a core advantage that distinguishes this embodiment from traditional alarm systems. Utilizing the dynamic feedback control law, a hash precision reduction strategy is proactively executed at extreme pressure critical points. This achieves a reasonable trade-off between "precision for security" by sacrificing a small increase in false positive rate for a significant reduction in processor read bandwidth and dynamic balance against overflow. This mechanism ensures the system possesses closed-loop autonomy and steady-state self-healing capabilities: immediate physical blocking upon storm onset, resolute retry backoff under the limits of underlying computing power, and second-level perception and smooth recovery of all parameters to baseline performance after the storm subsides. Self-protection and recovery under extreme scenarios can be achieved without manual intervention, improving system reliability and operational efficiency.

[0066] First aspect: Detailed explanation of the underlying topology of the system architecture and topology-free alarm data flow operation. 1. Mechanism for reorganizing the underlying contiguous memory block structure and strict byte-level alignment of topology-free discrete alarm entities In extreme high-pressure scenarios involving massive concurrent alarm storms, the dynamic instantiation of alarm objects in heap memory by traditional object-oriented programming languages ​​leads to catastrophic memory fragmentation and uncontrollable garbage collection pauses, making it unsuitable for isolated operating environments without topology or external storage. To completely eliminate this physical bottleneck, this system abandons all runtime object allocation mechanisms of high-level languages ​​at the lowest level, directly defining strictly byte-aligned discrete alarm entity structures in purely contiguous physical memory blocks. This achieves efficient storage and fast access to alarm data while avoiding memory fragmentation, laying the hardware foundation for lock-free concurrent processing.

[0067] Based on the underlying C language standard structure definition, the memory layout of a single alarm entity is strictly limited to 32 bytes. This size design is fully compatible with the cache architecture of modern processors, ensuring that a single entity can be efficiently loaded into the cache and reducing latency caused by cache misses. Its structure definition is as follows: structAlertEntity{ uint64_thash_identifier; uint64_ttimestamp_cursor; uint32_tstate_flags; uint8_treserved_padding

[12] ; }; In the above structure layout, the hash_identifier field occupies 8 bytes and is used to store the alarm feature hash value of unsigned long integer, serving as a globally unique identifier and providing the core basis for frequency statistics of the S1 module and noise reduction and deduplication of the S2 module. The timestamp_cursor field also occupies 8 bytes of unsigned long integer and is used to record the processor's invariant timestamp obtained in pure user space, serving as the absolute time reference for frequency decay and aging mechanisms, ensuring that the sliding window aging of the S1 module can be accurately triggered. The state_flags field occupies 4 bytes of unsigned integer and is used to identify the priority level and dynamic degradation routing mask of the current alarm entity, providing a status reference for the backpressure control of the S3 module and realizing differentiated processing of alarms with different priorities.

[0068] Without a trailing padding field, the actual effective payload of this structure is 20 bytes. 20 bytes is not a power of 2. In contiguous memory array allocations, if the entity size is 20 bytes, the 0th entity starts at offset 0, the 1st entity starts at offset 20, the 2nd entity starts at offset 40, and the 3rd entity starts at offset 60. Modern CPUs, such as the Skylake microarchitecture, have a strictly 64-byte L1 cache line size. When the CPU attempts to read the 3rd entity starting at offset 60, the first 4 bytes of that entity are at the end of the current 64-byte cache line, while the remaining 16 bytes overflow and span to the next contiguous 64-byte cache line.

[0069] This cross-boundary phenomenon in physical addressing is called cache line splitting. At the microarchitecture level, cache line splitting forces the memory controller to consume two independent load ports within the same clock cycle, initiating two independent memory read transactions to the cache coherency bus, and then concatenating the data from the two cache lines within the pipeline. This double memory addressing instruction overhead forces the L1 cache hit latency, which originally only requires 4 to 5 clock cycles, to degrade to more than 11 clock cycles, severely compresses the out-of-order execution engine's storage buffer queue, significantly reduces system processing efficiency, and makes it unsuitable for high-frequency concurrency scenarios.

[0070] To absolutely avoid such hardware-level penalties, the system forcibly pads the `state_flags` field with a 12-byte `reserved_padding` field. This padding operation precisely expands the size of a single alarm entity to 32 bytes. Under this strict alignment mechanism, two consecutive 32-byte alarm entities can be precisely aligned to a single 64-byte data cache line boundary. This byte-level alignment strategy based on hardware microarchitecture characteristics ensures from the root of physical addressing that any single entity's read and write operations trigger only one atomic memory bus transaction, achieving a physical proof of consistent reads under extreme concurrency, and laying the hardware foundation for subsequent lock-free concurrent processing. Its physical distribution characteristics are as follows: Entity 0: Memory offset range from 000 to 031 bytes, located in the 64-byte cache line CacheLine0, with absolutely no cache line crossing, and the instruction fetch cycle is expected to be 4 to 5 clock cycles.

[0071] Entity 1: Memory offset range from 032 to 063 bytes, located in the 64-byte cache line CacheLine0, with absolutely no cache line crossing, and the instruction fetch cycle is expected to be 4 to 5 clock cycles.

[0072] Entity 2: Memory offset range from 064 to 095 bytes, located in the 64-byte cache line CacheLine1, with absolutely no cache line crossing, and the instruction fetch cycle is expected to be 4 to 5 clock cycles.

[0073] Entity 3: Memory offset range from 096 to 127 bytes, located in the 64-byte cache line CacheLine1, with absolutely no cache line crossing, and the instruction fetch cycle is expected to be 4 to 5 clock cycles.

[0074] 2. Zero-fragmentation memory-mapped file virtual address space pre-allocation technology for expansion potential of millions of records. Faced with a potential surge of millions of discrete alarms, relying on kernel-level system calls for incremental heap memory expansion would trigger frequent page faults and kernel-mode context switches, severely impacting system processing efficiency. Therefore, this system bypasses the traditional kernel-mode file input / output trapping mechanism by using the mmap system call to construct the underlying virtual address space, achieving efficient memory pre-allocation and on-demand usage, thus meeting the core requirements of static memory management and high-concurrency processing.

[0075] During system initialization, the engine directly requests a contiguous virtual address space from the operating system. The target capacity of this space is strictly aligned to 2^20 using binary carry, which is a baseline size to accommodate 1,048,576 32-byte entities. The calculation process is 1,048,576 multiplied by 32 bytes, totaling 33,554,432 bytes, which is an absolutely precise 32MB physical memory limit, ensuring deterministic memory consumption and eliminating the risk of memory overflow.

[0076] When the mmap function is called, the system forcibly specifies the MAP_PRIVATE and MAP_ANONYMOUS flags. The underlying mechanism of this flag combination is that after receiving the allocation instruction, the operating system kernel does not immediately deduct 32MB of physical page frames from main memory. Instead, it only establishes a mapping relationship between the virtual memory regions in the current process's page table. All virtual page table entries that are not actually written to are uniformly pointed to the global physical zero page maintained by the operating system kernel through the hardware memory management unit, achieving zero physical memory overhead during the initialization phase and improving system startup efficiency.

[0077] When an external storm triggers and the data stream begins to perform a full write to the array index locked at the base address, the hardware memory management unit detects the write attempt to the read-only zero page, thus triggering a lightweight copy-on-write page fault. Only then does the kernel actually mount the physical memory page to the corresponding address in 4KB page frames and execute a hardware-level full write mechanism for the inactive tail interval zero-value bitmap. This zero-fragmentation pre-allocation and on-demand zero-fill paging mechanism ensures that while maintaining a massive 32MB throughput potential, the system's static startup physical overhead approaches zero. Even under peak pressure, the memory of 1,048,576 entities is smoothly activated at contiguous addresses, completely cutting off the kernel lock contention and global memory allocator deadlock path caused by high-frequency memory requests during peak periods, thus ensuring system stability under extreme concurrency.

[0078] 3. Decoupling of the S1-S2-S3 three-tier module processing and strong binding isolation of the CPU physical core for handling concurrent load balancing. In multi-core symmetric multiprocessing architectures, frequent scheduling of multiple threads across physical cores can lead to extremely high performance overhead. When balancing the load, the operating system's Completely Fair Scheduler (CFS) migrates worker threads from busy cores to idle cores. This migration causes hot data residing in the original core's L1 and L2 caches to become invalid instantly. The target core must then reload the context data via the slower L3 shared cache or even the main memory bus, resulting in memory access pauses of hundreds of clock cycles, which is unsuitable for handling high-frequency concurrency scenarios.

[0079] To eliminate this scheduler-level interference, this system deeply decouples the overall data pipeline into three modules: S1 (data ingestion and frequency statistics), S2 (feature denoising), and S3 (backpressure control). This ensures that each module is functionally independent and physically isolated, avoiding cross-module computational interference. During the system boot phase, each of the three modules generates its own independent worker thread, ensuring that they can run in parallel and efficiently.

[0080] The system then implements an absolutely strict core-binding strategy using operating system-level affinity system calls. The worker threads of module S1 are forcibly bound to physical core 0, module S2 to physical core 1, and module S3 to physical core 2. By modifying the CPU mask bitmap in the kernel task structure, the system directly deprives these three core threads of the right to be migrated to other physical cores at the scheduler level, ensuring that each module's threads always run on their dedicated core and maintaining the resident rate of hot data in the cache.

[0081] Building upon physical isolation, the data relay between modules abandons any form of blocking queue, instead constructing a lock-free circular buffer based on a unidirectional producer-consumer model to avoid thread blocking and context switching. This topology ensures that S1, as the producer, is only responsible for unidirectional writing to the tail cursor of the circular queue, and S2, as the consumer, is only responsible for unidirectional reading from the head cursor. This unidirectional data flow under memory barrier isolation, combined with the absolute strong binding of physical cores, ensures that the instruction stream of each module always cyclically hits in its own dedicated L1 instruction cache. Hotspot alarm data maintains a very high resident rate in the L1 data cache, squeezing the concurrent computing power of the central processing unit to its theoretical limit and improving the overall processing throughput and efficiency of the system.

[0082] 4. A transparent proxy data ingestion channel for low-level IT operations and maintenance bypass monitoring and zero-service intrusion into the kernel mode. Traditional monitoring systems typically rely on embedding data points in application-layer code or reading disk log files for data ingestion. In disaster scenarios involving widespread power outages at the underlying hardware level or abnormal restarts of core switches, business processes are often already blocked or crashed, and the disk input / output subsystem may also become deadlocked due to storage LAN interruptions, making it impossible to effectively ingest alarm data, leading to system failure and making it unsuitable for isolated operation scenarios.

[0083] To overcome this isolated, unregulated operation, this system implements a transparent proxy mechanism on the data ingestion channel that allows for zero intrusion into kernel-mode services. This ensures normal ingestion of alarm data even in disaster scenarios without requiring modification of business code or relying on business processes. By mounting an extended BPF program, the system directly achieves ultra-fast interception of Ethernet frames at the data link layer within a topology-free execution framework at the lowest level of the host machine's network protocol stack, bypassing the complex protocol parsing of the transport and network layers, significantly reducing ingestion latency.

[0084] When the network interface card (NIC) driver receives an optical signal and converts it into a raw packet in memory, the pre-compiled and kernel-verified low-level bytecode is executed before the traditional socket buffer is dynamically allocated by the kernel network subsystem. This low-level logic directly eliminates the complex protocol parsing overhead of the transport and network layers, directly locating the characteristic bit surface of the alarm packet through pointer arithmetic offset, thus improving packet parsing efficiency. If a specific discrete alarm characteristic is hit, the low-level program directly returns a pass instruction and delivers the packet to the user-space S1 module with zero copy through a dedicated circular buffer, avoiding the computational power loss caused by data copying. If it is determined to be absolute redundancy or low-priority background noise, a drop instruction is directly issued at the NIC driver layer for physical discarding, reducing the occupation of system resources by invalid data. This bypass listening mechanism compresses the intake delay of a single packet from the microsecond level to the nanosecond level, ensuring the initial wave of extremely fast penetration capability under a 500,000 concurrent power outage storm, and buying time for subsequent frequency statistics and noise reduction processing.

[0085] 5. Zero-copy communication protocol for 8-byte cursor memory between third-order pipelines When the S1, S2 and S3 three-level modules relay data through a lock-free circular buffer, if the 32-byte alarm entity structure is directly copied from the producer's memory space to the consumer's memory space, the massive number of memory transfer instructions will quickly saturate the physical bandwidth of the memory controller, resulting in increased data transmission latency and making it unsuitable for high-frequency concurrent scenarios.

[0086] To achieve true zero-copy memory, this system implements an extreme circular buffer payload compression strategy at the communication protocol level between modules. This minimizes data transmission overhead and improves cross-module data relay efficiency. The lock-free circular buffer does not store any alarm entity data; instead, it functions as a pure index cursor transmission channel. The data payload transmitted across modules is only a 64-bit (8-byte) memory base address cursor index value, significantly reducing data transmission volume and improving transmission efficiency.

[0087] When module S1 writes a new 32-byte entity into a 32MB static array pre-allocated by memory mapping, it atomically pushes the entity's relative index value in the array as an 8-byte unsigned long integer into the circular buffer, ensuring the atomicity and safety of data transfer. After module S2 pops this 8-byte index value from the buffer, it uses a purely low-level in-place read-only pointer arithmetic offset mechanism to directly add the index value multiplied by the 32-byte offset to the physical base address of the static memory region, instantly locating the absolute physical memory address of the target entity without needing to copy data.

[0088] Each consumer thread performs bitwise comparisons and state machine modifications entirely based on the calculated physical address, without any substantial data block movement operations. This communication protocol, which only transmits 8-byte integer cursors, not only reduces the system bus bandwidth overhead for cross-module message passing by three-quarters, but also allows the cursor itself to be packed extremely compactly in the L1 data cache. Since an 8-byte pointer is exactly the basic word length of a 64-bit architecture, a single CAS operation can complete the cursor update, perfectly adapting to the extremely fast response capability of modern superscalar processors for transferring values ​​between registers, further improving the system's concurrent processing efficiency.

[0089] Secondly, module S1: an underlying architecture based on conservative update sketches and pure user-space clock polling offset aging. 1. Collision-free conservative update filtering judgment for Count-MinSketch two-dimensional counter matrix For the feature mapping of 190,000 baseline alarms, module S1 completely abandons the traditional linked list hash table that relies on a large number of heap memory nodes and complex red-black tree rotation and rebalancing operations. Such structures involve dynamic expansion and tree rebalancing operations, which cannot adapt to extreme concurrency scenarios and are prone to memory overflow. Instead, S1 constructs a two-dimensional counter matrix with a theoretical limit of O(1) space complexity. It does not require dynamic expansion and can accommodate the frequency statistics of massive alarm features in a fixed memory space, which meets the core requirements of the system's static memory management.

[0090] Under extreme concurrent surges, traditional counter matrices, upon receiving an update instruction for the same feature mapping slot, indiscriminately increment all slot counters calculated using multiple independent hash functions for that feature. This unconditional incrementing mechanism is highly susceptible to the impact of hash collisions with other high-frequency noise features in high-frequency pulse data streams with Poisson or Zippo distributions. This can lead to the unwarranted inflation of low-frequency feature counters, resulting in severe localized false high counts and affecting the accuracy of high-frequency alarm identification.

[0091] To suppress this artificially inflated frequency at its mathematical root, this module strictly implements a collision-free conservative update extraction algorithm to ensure the accuracy of frequency statistics and provide a reliable frequency basis for the subsequent noise reduction and deduplication in the S2 module. Its underlying logic is as follows: When a new discrete alarm feature enters the S1 module, the system first uses multiple independent hash functions to calculate the corresponding multiple mapping slot indices in parallel, and then initiates concurrent read instructions to main memory to quickly obtain the current count value of each slot. Subsequently, the CPU's arithmetic logic unit quickly performs a local absolute minimum extraction operation on the current count values ​​returned by these slots to lock in the most likely true frequency value of the current feature.

[0092] After extracting the minimum value, the conditional increment command issuance mechanism plays a crucial role. The system no longer blindly increments all hit slots; instead, it uses comparison commands to determine which slots have an increment command issued only to those specific slots whose current count is strictly equal to the local minimum. Slots with counts already higher than the minimum are simply backed off without modification. This conservative update logic mathematically establishes a natural immunity to high-frequency impulse noise, as any redundant counts generated by external hash collisions are bypassed, ensuring high frequency accuracy when accommodating a massive number of alarm features within a limited physical space. By significantly reducing the average error probability, the system uses this logic to ensure that even under conditions of extremely high baseline noise, truly important high-frequency alarms still possess clear identifiability, providing accurate frequency support for the S2 module's secondary noise reduction.

[0093] 2. Continuous flattening allocation of matrices in physical memory and constant-level pointer arithmetic offset addressing In high-level languages, the conventional implementation of two-dimensional arrays often uses pointer arrays, where the first dimension stores a memory pointer to the starting address of the second-dimensional array. This non-contiguous discrete heap allocation mode requires two indirect memory jumps during addressing, which easily triggers cache misses and causes processor pipeline stalls, significantly reducing access efficiency and making it unsuitable for the fast frequency statistics requirements in high-frequency concurrency scenarios.

[0094] To ensure a high hit rate for continuous instruction prefetching and improve memory addressing efficiency, this module implements a brute-force, continuous, flattened allocation of the two-dimensional counter matrix in physical memory. By adopting a static memory contiguous allocation method, the entire matrix is ​​flattened into an absolutely contiguous one-dimensional memory space, ensuring that the counters are closely adjacent in physical address, thus providing a basis for optimizing the hardware prefetcher.

[0095] When locating feature slots, two-dimensional coordinates are directly converted into one-dimensional physical offsets using a physical one-dimensionalization formula, eliminating the need for complex pointer jumps and enabling fast addressing. The derivation formula is: the target physical address equals the base address plus the product of the calculated row index i multiplied by the total matrix width w plus the column index j, and finally multiplied by 4 bytes. That is, the physical offset equals (i*w+j)*4 bytes. This formula is concise and efficient, and can be calculated using a single FMA hardware instruction.

[0096] This pure constant-level pointer arithmetic offset addressing compresses multidimensional lookups into a single, single-clock-cycle FMA hardware instruction, significantly reducing addressing latency. Since all counters are physically adjacent, when the processor accesses a counter, its built-in hardware prefetcher, based on the principle of spatial locality, preloads subsequent consecutive byte blocks from main memory into the L2 and L1 caches, completely eliminating the high latency caused by random memory accesses. This hardware microarchitecture optimization of memory addressing reduces processing latency and improves the frequency statistics efficiency of the S1 module.

[0097] 3. Rigorous mathematical derivation of the absolute boundary of 80KB static physical memory and operating system-level forced locking To achieve absolute immunity to OOM errors, the physical memory consumption of the S1 module is no longer a dynamic variable at runtime, but a static physical boundary that is fixed at compile time through rigorous mathematical derivation. It adopts a static memory management and memory capacity fixed locking strategy to completely eliminate the risk of memory overflow, which is consistent with the overall memory immunity design of the system.

[0098] The extreme value operating condition mathematical baseline is imported as follows: The system sets the upper limit of the tolerable error rate epsilon value for the feature statistics algorithm to be 0.001, and the confidence interval deviation delta value to be 0.01, ensuring that the accuracy of frequency statistics meets business requirements. According to the classic theoretical calculation formula of the algorithm, the theoretical width w of the matrix is ​​equal to the base of the natural logarithm e divided by the error rate epsilon, i.e., w = e / 0.001. The base of the natural logarithm e is approximately 2.718, and the calculated theoretical width w is approximately 2718. The theoretical depth d of the matrix is ​​equal to 1 of the natural logarithm divided by the confidence interval deviation delta, i.e., d = ln(1 / 0.01). The calculated theoretical depth d is approximately 4.6.

[0099] To adapt to the word length alignment mechanism of the underlying hardware and avoid expensive division instructions, thereby reducing computational power consumption, the above theoretical values ​​need to be corrected at the hardware level. The depth d is forced to be rounded up to 5 to ensure the independence of the hash function and improve the uniformity of the hash mapping; the width w must support ultra-fast modulo operation based on bitwise AND operation, so it is forced to be aligned up to the nearest power of 2, that is, from 2718 to 4096, to ensure efficient execution of the modulo operation.

[0100] Based on the corrected dimensions, this two-dimensional matrix is ​​actually flattened into a one-dimensional static array containing 20480 slots. In the plain text pseudocode structure definition, its declaration is as follows: structConservativeSketchMatrix{ uint32_tcounters

[20480] ; }; This structure contains a total of 20480 unsigned 32-bit integer counters. Since each uint32_t occupies 4 physical bytes, the total memory consumption is precisely locked at 20480 multiplied by 4 bytes, which is an exact 81920 bytes. Dividing 81920 bytes by 1024 yields an absolute 80KB of contiguous physical memory occupied by the matrix, achieving the design goal of fixing the memory consumption of the S1 module to 80KB. This aligns with the overall system memory control strategy, ensuring absolute determinism in memory consumption.

[0101] At the operating system level, after acquiring this 80KB contiguous memory using the system-level allocator, the system immediately calls the `mlock` system call function to perform hard isolation of this 80KB region in main memory to prevent page faults. The `mlock` mechanism forcibly instructs the operating system kernel page table to permanently prohibit the physical address space from being swapped out to the disk's swap partition. This completely eliminates the possibility of hard page faults when the system is extremely busy, establishing a static byte baseline locking strategy that is absolutely immune to OOM (Out of Memory) errors from the root, effectively avoiding memory overflows, ensuring stable system operation, and adapting to extreme concurrency scenarios in isolated environments.

[0102] 4. Non-cryptographic rapid generation of hash function seeds with d=5 and a general-purpose register resident allocation strategy For mapping requirements with a depth d equal to 5, the system must perform five completely independent hash operations on the same alarm feature to ensure the uniformity of the hash mapping, reduce hash collisions, and improve the accuracy of frequency statistics. If a secure cryptographic hash algorithm is called on a critical data path, its complex bit block avalanche calculation and multi-round iterative logic will generate catastrophic nanosecond-level overhead, making it unsuitable for the processing needs of high-frequency concurrency scenarios.

[0103] Therefore, this module employs an extremely lightweight, non-cryptographic, high-speed hash generation algorithm. Based on a single underlying hash variant and five preset prime number seeds, it rapidly derives five sets of coordinates. A multi-hash function generation strategy is used to significantly improve hash calculation efficiency while ensuring hash uniformity. To further optimize computing power, the system implements a resident allocation strategy for general-purpose registers, minimizing memory access overhead. At the compiler's inline assembly level, the intermediate states and shift constants of these five hash operations are forcibly bound to the CPU's general-purpose registers. This resident allocation avoids frequent overflows of local variables into the thread stack memory during hash operations, enabling rapid data flipping purely within the register file. This forces the five-fold hash calculation latency for a single alarm to be compressed within a constant number of clock cycles, effectively improving hash calculation efficiency, reducing processing latency, and providing computing power support for the high-frequency statistics of the S1 module.

[0104] 5. The pure user-mode passive timestamp observation mechanism completely eliminates the need for traditional high-overhead hardware clock interrupts. Traditional time-window aging mechanisms typically rely on the timer interface provided by the operating system. At its core, this mechanism is a classic example of the negative consequences of correcting clock interrupt fallacies: it depends on a hardware clock chip periodically sending interrupt signals to the CPU, forcing the processor to suspend the currently executing thread, save the context register state, enter kernel mode and execute complex interrupt handling routines, and then restore the context to return to user mode. This series of system-level timer soft interrupt traps not only consumes thousands of clock cycles but also violates the locality of the L1 instruction cache. When handling hundreds of thousands of concurrent alarms per second, it directly leads to severe computational exhaustion and cannot meet the aging requirements of high-frequency concurrency scenarios.

[0105] To completely eliminate the overhead of traditional high-overhead hardware clock interrupts and reduce clock acquisition overhead, this application embodiment constructs an independent time wheel daemon thread. This thread maintains smooth operation of user space using a purely user-mode passive timestamp observation mechanism. Employing a purely user-mode timestamp acquisition mechanism, it avoids entering kernel mode throughout the process, thus preventing computational losses caused by context switching. This daemon thread does not rely on system sleep or timer blocking; instead, it actively enters a microsecond-level high-speed spin state to ensure real-time timestamp acquisition.

[0106] When using spin-to-read timestamps, if only regular instructions are used, the out-of-order execution pipeline of modern superscalar processors may reorder the timestamp read instructions before other memory access instructions, resulting in severe timing discrepancies in the acquired time slices and affecting the accuracy of the aging mechanism. To address this challenge, the system employs a strict serialization barrier design. This embodiment directly uses the non-privileged instruction RDTSCP to obtain the difference between the processor's invariant timestamp counters. By implementing timestamp acquisition based on the RDTSCP instruction, the accuracy and timing consistency of the timestamps are ensured.

[0107] The RDTSCP instruction not only reads the number of clock cycles since the processor reset with extremely low constant-level latency, but also possesses microarchitecture-level serialization properties. It mandates that all instructions preceding RDTSCP in the instruction decoding queue must be executed before the front-end and their memory load results must be fully visible globally before a read operation can be performed. Since this clock cycle counter exists as a 64-bit register, after the RDTSCP instruction executes, the high 32 bits of the clock cycle register are loaded into the EDX register, and the low 32 bits are loaded into the EAX register. Then, through rapid bitwise operations, EDX is shifted left by 32 bits and logically ORed with EAX to form a complete 64-bit invariant timestamp. This process occurs entirely within pure user-space registers, with a single retrieval latency consistently around 24 clock cycles. This avoids any system calls, achieving a physical proof of zero context switching overhead, effectively reducing timestamp retrieval costs, improving system processing efficiency, and providing precise time support for the S1 module's microsecond-level aging mechanism. A core physical comparison of the two mechanisms is as follows: Traditional system clocks are triggered by hardware or software interrupts, involving a privilege level transition from user mode to kernel mode. This can lead to context switching overhead that severely damages the L1 instruction cache, with a single expected clock cycle consumption of more than 2,000 cycles.

[0108] Pure user-mode passive observation: triggered by RDTSCP non-privileged instructions, it maintains pure user-mode operation throughout, with the characteristic of absolute zero context loss, and the expected clock cycle consumption per cycle is stable at about 24 cycles.

[0109] 6. Microsecond-level single-cycle bit-shift aging mechanism to avoid pipeline stalls in the arithmetic logic unit divider. When implementing sliding window frequency decay, conventional algorithms often calculate the decay coefficient based on elapsed time slices and require each counter to be divided by this coefficient. However, in the underlying physical execution unit, while modern CPUs can achieve very fast execution of multiplication, complex arithmetic logic unit dividers require a dozen or even up to 90 clock cycles to execute a single floating-point division instruction or a long integer division instruction. More critically, division hardware units are often not fully pipelined; the continuous issuance of multiple division instructions will cause deep congestion at the execution port and pipeline pauses of hundreds of cycles, making it unsuitable for high-frequency aging requirements and affecting the system's concurrent processing efficiency.

[0110] To maximize processing performance, module S1 completely abandons floating-point and integer division instructions that often consume dozens of clock cycles. Instead, the system was designed from the outset to use constant-level bitwise right shift operations to replace all frequency halving attenuation operations. This bitwise right shift-based frequency attenuation mechanism reduces the clock cycle consumption of attenuation operations and improves aging efficiency.

[0111] When the user-mode daemon thread determines that the microsecond-level clock gap has reached the aging threshold, the system directly sends the pass-through circuit shift microcode of the single-cycle hardware barrel shifter to the memory address storing the count value. By performing a right shift operation (i.e., right shift sign >>), the binary bit plane of all unsigned 32-bit integer counters slides to the right as a whole, the least significant bit is physically discarded, and the most significant bit is filled with zero, achieving precise halving of frequency decay, which meets the core requirement of sliding window aging.

[0112] The barrel shifter is a pure combinational logic circuit that bypasses any complex iterative subtractor network in its underlying arithmetic logic unit. Instead, it directly uses a crossbar switch matrix to complete the precise halving of values ​​within just one absolute clock cycle. Compared to traditional division, this mechanism saves at least 14 to 24 clock cycles per aging instruction. When facing a full scan of an 80KB matrix, the total computational power saving reaches an astonishing millions of clock cycles, effectively improving frequency decay efficiency, reducing system computational power consumption, and ensuring that the S1 module can still efficiently complete aging cleanup in high-frequency concurrent scenarios. A comparison of the execution of the two underlying microcodes is as follows: Floating-point or integer division instructions (FDIV / DIV): rely on the underlying iterative subtractor network execution unit, have extremely poor hardware pipeline support and are prone to blocking the issue port, with a single instruction expected latency of up to 15 to 90 cycles, which can easily cause pipeline stalls in large-scale concurrent stress scenarios.

[0113] Bitwise Right Shift Instruction (SHR): Utilizing a barrel shifter cross-switch matrix, it possesses perfect single-cycle throughput characteristics, with an expected latency of only one cycle per instruction, achieving seamless execution and extremely smooth stress resistance under large-scale concurrency.

[0114] 7. Time-wheel fragmentation distribution mechanism's global execution peak smoothing strategy for 80KB matrix aging tasks. Although a single shift instruction only takes one clock cycle, the 80KB matrix contains as many as 20,480 unsigned 32-bit integer counters. If, within a single microsecond-level time slice, the daemon thread continuously issues more than 20,000 assembly instruction chains of memory read, bit-right shift, compare, and write-back operations, it will instantly create a huge traffic surge on the CPU's L1 cache and main memory bus. This sudden burst of memory bandwidth congestion will directly block the alarm feature matching read operation being performed by the S1 ingestion thread, forming a micro-level global execution spike and computational glitches, affecting the overall system processing efficiency.

[0115] To mitigate this hardware-level spike in interference and ensure that aging operations do not affect the core data ingestion process, the system implements a sophisticated time-wheel sharding mechanism. This time-wheel sharding aging task scheduling strategy evenly distributes aging tasks across multiple time slices, avoiding computational overload on a single time slice. At the logical addressing level, the system evenly divides the 80KB static array into 10 independent data blocks. Each data block precisely contains 2048 counters, corresponding to 8192 bytes of contiguous memory, ensuring that the size of each data block is adapted to cache access characteristics.

[0116] When polling, the daemon thread no longer ages the entire matrix within a single clock cycle tick. Instead, it performs staggered scheduling in microsecond-level clock intervals. In the first microsecond interval, the daemon thread uses only the instruction prefetch mechanism to continuously issue 2048 shift instruction microcodes to data block 0; in the second microsecond interval, it issues them to data block 1. This smooth release strategy, which divides the task into 10 time slices, ensures that the system cache update bandwidth is evenly distributed across the entire time axis, successfully smoothing out the peak of aging tasks at the macro level. This ensures that even under the extremely severe throughput impact of a 500,000 concurrent power outage storm, the single latency of the core ingestion thread can still be stably maintained at a constant physical baseline of tens of nanoseconds, effectively improving system concurrency stability and avoiding computational spikes.

[0117] Part Three, Module S2: Block Bloom Filter with Forced Cache Line Alignment and Lock-Free Concurrency Control Core In modern large-scale distributed systems and high-speed network traffic analysis, deduplication and noise reduction of massive discrete features remains one of the core computational bottlenecks. In a high-frequency noise reduction scenario with up to 190,000 baseline alarms, the core task of module S2 is to perform secondary deep deduplication on the alarm features output by module S1, taking into account the frequency statistics results of module S1, to achieve accurate deduplication and noise reduction of alarm features, providing clean and efficient feature data support for the backpressure control of module S3. Since the system operates in an extreme isolated environment without topology, logs, or external storage support, the noise reduction tool must rely solely on the CPU's internal cache and main memory system to achieve state recording and deterministic verification of massive feature dimensions without retrieving the original packet content or relying on any external input / output devices, thus meeting the overall isolated operation design requirements of the system. This stringent physical constraint requires the system architecture to move beyond pure algorithmic complexity analysis and delve into the microarchitecture level. By comprehensively considering data structures, memory layout, silicon physical characteristics, and instruction pipelines, a physical plane noise reduction foundation with extremely high throughput and nanosecond-level response capability is constructed to meet the processing requirements of 500,000 concurrent alarms. It also works in conjunction with the S1 and S3 modules to build a complete lock-free high-throughput pipeline.

[0118] 1. Selection of discrete feature physical plane denoising tools and the logical closed loop of its approximate member query architecture When evaluating high-frequency noise reduction tools in isolated environments, traditional exact member lookup data structures face insurmountable performance and space gaps. For example, while traditional hash tables or balanced tree structures can provide precise key-value lookups, they inevitably trigger dynamic resizing or tree rebalancing when faced with unpredictable massive concurrent ingestion. In single-threaded models, tail latency spikes caused by resizing are already intolerable; while in the concurrent environment of symmetric multiprocessing architectures, global lock contention or complex lock-free pointer migrations can lead to non-linear throughput collapse, and dynamic memory allocation is highly susceptible to page faults and OutOfMemoryError (OOM) systemic risks at the operating system level, contradicting the core design of static memory management and OOM immunity.

[0119] To achieve static immunity to memory overflow risks on the physical plane, and to meet the overall system requirements of zero dynamic memory allocation and absolute immunity to OOM, an approximate member query data structure has become an inevitable engineering choice. Among the many variants, although the cuckoo filter or quotient filter has specific advantages in terms of space efficiency or support for dynamic deletion, the cuckoo filter, under high load, will trigger the eviction and redirection of long chains during insertion operations, which seriously undermines the strict O(1) time complexity and nanosecond-level real-time guarantee, and cannot adapt to the high-speed processing requirements of high-frequency concurrent alarms.

[0120] Based on the aforementioned microscopic physical constraints, this system explicitly selects the standard Bloom filter as the core prototype of the underlying physical bit-plane noise reduction tool. The core logic of this selection lies in the fact that the Bloom filter has an absolutely strict O(1) time complexity, and its feature mapping and bit writing time are completely constant. It will not fluctuate with the increase of the scale of alarms, the increase of memory load, or the intensification of hash collisions, and can adapt to the real-time requirements of high-frequency concurrency scenarios. More importantly, the Bloom filter achieves absolute constant space complexity through pre-allocated bit array mapping. Compared with the heap memory acquisition action that must be performed when adding elements in traditional hash tables, the Bloom filter locks all physical memory bits at once during the initialization phase. This not only completely eliminates the runtime system call overhead, but also satisfies the physical-level static immunity requirement of isolated systems against OOM risk in a closed loop. In conjunction with the 80KB static memory allocation strategy of the S1 module, it constructs a system-level memory immunity system.

[0121] 2. Precise mathematical model derivation of the global theoretical bit array size and 1.71MB memory residency anchoring. To maintain an extremely low false alarm rate under high-concurrency stress scenarios, ensure that noise reduction accuracy meets business requirements, and ensure that data structures can perfectly reside in the high-speed cache of modern processors to avoid main memory access latency, thereby further reducing processing latency and improving throughput, the S2 module has performed rigorous probabilistic statistical deduction and spatial extreme value anchoring on the scale of the physical memory bit array, achieving absolute determinism of memory consumption, which meets the core requirements of the system's static memory management.

[0122] (1) Import of million-concurrent stress-resistant redundancy baseline and probability distribution parameters Before conducting mathematical deductions, it is essential to define the system's fault tolerance baseline to ensure that the noise reduction tool can adapt to extreme concurrency scenarios. A target capacity of n = 1,000,000 is set, representing the upper limit of discrete feature data the system is expected to process in a single noise reduction cycle, covering the processing needs of millions of concurrent alarms. Simultaneously, based on security and business accuracy requirements, a target false positive tolerance rate of p = 0.001 is set to ensure the accuracy of alarm features after noise reduction, providing a reliable basis for the subsequent backpressure control of the S3 module. In the probabilistic model of the Bloom filter, assuming a perfectly uniform distribution of hash functions, when n elements are inserted into a bit array of length m, and each element uses k independent hash functions, the probability that a specific bit will remain 0 after all insertions is approximately (1-1 / m)^(kn) equal to e^(-kn / m). Therefore, when querying a non-existent element, the false positive probability that all k corresponding bits happen to be set to 1 is p, which is approximately equal to (1-e^(-kn / m))^k. This model ensures that the noise reduction accuracy is controllable.

[0123] (2) Derivation of the theoretical bit array length and core formulas of calculus To minimize physical memory overhead m under the constraints of n and p, achieve efficient utilization of memory resources, and simultaneously meet the requirements of static memory locking, the optimal number of hash functions k = (m / n) * ln(2) can be obtained by taking the partial derivative of the false positive probability formula and setting it to zero. Substituting this optimal k value back into the probability formula and performing algebraic transformations, the core formula for deriving the total length m of the bit array can be obtained: m = -(n * ln(p)) / (ln(2))^2. This formula provides theoretical support for the precise setting of memory size.

[0124] Substituting the system's baseline parameters directly into the calculation model, we find that the natural logarithm ln(0.001) is approximately -6.9077, and the denominator (ln(2))^2 is approximately 0.48045. Division yields m = -(1000000*-6.9077) / 0.48045, with a theoretical calculation result of approximately 14,378,955 bits, providing a precise theoretical basis for subsequent memory alignment and space locking.

[0125] (3) Word length alignment and space locking of 64-bit central processing unit In modern 64-bit processor architectures, the data bus width between the memory controller and the arithmetic logic unit is typically 64 bits. If memory allocation is not strictly aligned to the 64-bit word length, the processor will be forced to initiate two read / write cycles across words or even cache lines when executing load or store microinstructions, leading to severe microcode pauses, significantly reducing processing efficiency, and making it unsuitable for high-frequency concurrency scenarios. Therefore, to adapt to the native word length processing efficiency of 64-bit processors and maximize memory access and instruction execution efficiency, the system must round up the total number of bits m to an absolute multiple of 64. The calculation shows that 14,378,955 / 64 is approximately equal to 224,671.17. After static alignment macro expansion by the compiler, this is rounded up to 224,672 unsigned long integer machine words, ensuring that the memory layout fully adapts to the processor hardware characteristics.

[0126] (4) Physical space extreme value anchoring and hit rate optimization Based on the word length alignment, the total physical memory consumption of the system is equal to 224672 * 8 bytes, or precisely 1797376 bytes. Converting this to standard capacity units: 1797376 / 1024 / 1024 is approximately 1.714MB. Therefore, during the compilation phase, the system statically anchors a 1.71MB off-heap locked mapping region through the operating system's locked memory allocation interface, serving as an insurmountable absolute memory boundary for the S2 module. This ensures absolute certainty in memory consumption, working in conjunction with the 80KB static memory of the S1 module to ensure that the total memory consumption of the system's core modules is strictly controllable, completely eliminating the risk of OutOfMemoryError (OOM).

[0127] This extreme setting of 1.71MB reflects profound microarchitectural considerations. Modern processors typically have L3 caches ranging from several megabytes to tens of megabytes. This means the entire 1.71MB noise-reducing bit array can reside completely and permanently in the L3 cache, and even a portion of the L2 cache, completely eliminating access to the slower main memory and significantly reducing memory access latency. Simultaneously, this 1.71MB contiguous memory region perfectly matches the 2MB big page mechanism of modern operating systems. Through big page allocation, the physical-to-virtual address mapping of this data structure only requires a single entry, thereby reducing the page table traversal penalty to absolute zero when performing hundreds of millions of random hash lookups, further improving hash addressing efficiency and adapting to the processing needs of high-frequency concurrency scenarios.

[0128] The derived values ​​and microarchitectural significance of the performance benchmark metrics are as follows: the target capacity n is 1,000,000 features to meet the requirements of extremely high-frequency alarm concurrency ingestion; the theoretical fault tolerance p is 0.001 to ensure the core judgment accuracy of the noise reduction plane; the optimal number of hashes k is strictly established as 5 to balance the pipeline computational burden and dispersion; the absolute physical memory m is approximately 1.71MB to achieve L3 cache resident and single large page coverage, comprehensively ensuring the high-frequency, high-efficiency, and stable operation of the S2 module.

[0129] 3. Physical isolation reconstruction of block Bloom filters based on 64-byte cache line alignment Although 1.71MB of static memory perfectly matches the physical boundaries of the cache, satisfying the requirements of memory locking and cache residency, the standard Bloom filter exhibits a devastating hardware performance bottleneck in a multi-core, high-concurrency environment due to its microscopic random memory access patterns. This easily leads to cross-core pseudo-sharing and bus storms, making it unsuitable for handling 500,000 concurrent alarms. To overcome these architectural limitations, the system requires not only algorithmic restructuring but also mandatory physical isolation through silicon-level memory consistency protocols to ensure efficiency and stability during multi-core concurrent processing.

[0130] (1) Tracing the origins of cross-core consistency physical blocking and bus storm In a standard Bloom filter design, k hash mappings of a feature distribute the bits to be operated on completely randomly and evenly across a 1.71MB global physical space. Modern processors' memory subsystems do not operate on a byte-by-byte basis, but rather use cache lines as the smallest loading unit (typically 64 bytes or 512 bits). When performing a feature insertion serially, since the k targets are likely distributed across different cache lines, the processor must trigger k independent cache line loads. This is a severe waste of memory bandwidth and reduces memory access efficiency.

[0131] In a multi-core architecture, this flaw evolves into a fatal physical blockage. When a worker thread on physical core 0 attempts to modify a bit, it must broadcast an ownership request via the interconnect bus to acquire exclusive modification rights to that 64-byte cache line. Because the standard global Bloom filter randomly distributes bits, there's a very high probability that core 0 will modify position X, while core 1 simultaneously needs to modify the adjacent position Y, and X and Y happen to fall within the same 64-byte cache line boundary. This is known as the false sharing effect in microarchitecture. At this point, based on the MESI cache coherence protocol, core 0's modification will cause that cache line to be hardware-forced invalidated in core 1's cache. Subsequently, core 1 must pause execution, re-send a request to the bus, forcing core 0 to write the modified data back to the L3 cache or main memory, and then core 1 will fetch it again. This high-frequency cache line invalidation and bus arbitration probing, under high-frequency concurrent insertions of 500,000, will quickly saturate the processor interconnect bus bandwidth, evolving into a severe bus saturation storm, completely destroying the system's concurrent processing capabilities, contradicting the core design goals of high throughput and low latency.

[0132] (2) Compilation primitive intervention and underlying memory layout reshaping In order to eliminate the performance bottlenecks of false sharing and multiple cache misses at the root of microarchitectural addressing and ensure the efficiency of multi-core concurrent processing, the system completely abandons the global random mapping paradigm and instead adopts a microarchitectural refactoring strategy of block Bloom filter, which improves concurrency performance through physical isolation.

[0133] The first step of this strategy is to use advanced compiler guidance mechanisms to rigidly divide the 1.71MB global contiguous space into 28,084 independent data blocks. The system explicitly specifies that the size of each data block is strictly equal to 512 bits (i.e., 64 bytes), so that it perfectly matches the size of a single physical L1 cache line in the spatial dimension, ensuring that each data block can be fully loaded into a single L1 cache line and avoiding the latency caused by loading across cache lines.

[0134] To ensure that this logical split does not result in any misalignment during actual physical memory mapping, the system employs the C++ standard `alignas(64)` memory alignment primitive. The micro-definition of its core data structure is as follows: struct alignas(64) BloomBlock{ uint64_twords[8]; }; structFilterContainer{ BloomBlockblocks

[28084] ; }; Through the mandatory intervention of `alignas(64)`, the compiler and operating system linker make an absolute guarantee: the starting memory address of any Bloom Block in the structure array must be an integer multiple of 64. This physical isolation and reconstruction ensures from the source that any Bloom Block will never cross two different hardware cache lines, completely eliminating the risk of cross-cache line loading penalties and misaligned false sharing caused by data misalignment, and laying the hardware foundation for lock-free concurrent processing on multiple cores.

[0135] (3) Locality constraint of second-level addressing and O(1) cache miss guarantee After constructing a memory layout that fits perfectly with the physical cache lines, the system implements an extremely stringent second-level addressing locality of computation strategy to further optimize memory access efficiency and ensure low latency in high-frequency concurrency scenarios.

[0136] Traditional Bloom filters use k independent hash functions for global addressing. In a block Bloom filter, however, feature hashing is divided into two distinct levels: The first level of addressing calculates a global target block index based on the fingerprint of the alarm feature, precisely allocating the feature to a specific 64-byte Bloom block, thus achieving feature partitioning. Then, in the second level of addressing, all subsequent k=5 sets of hash operations and bit mappings are strictly constrained to be performed within this narrow 64-byte local space, ensuring that all operations are completed within a single cache line.

[0137] Because the entire process of probing and updating k bits is completely localized and confined to a single cache line, modern out-of-order execution processors only need to issue a cache line load request to the memory subsystem once. Once the 64 bytes of data arrive at the L1 cache, the arithmetic logic unit in the pipeline can complete the determination and writing of all 5 bits through register-level bitwise operations within a few clock cycles. This hardens the number of cache misses caused by processing an alarm feature from an uncontrollable k times to an absolute 1 time, successfully achieving O(1) complexity at the micro-level memory access, significantly reducing memory access latency, improving concurrent processing efficiency, and adapting to the processing needs of 500,000 concurrent alarms.

[0138] (4) Optimal trade-off statement in engineering practice and local false positive degradation As a rigorous industrial-grade system architecture, this system objectively acknowledges that the physical isolation reconstruction of the block Bloom filter breaks the core assumption of global uniform distribution of the standard Bloom filter in mathematical theory, which will lead to a slight deterioration in the false positive rate. However, this deterioration is a reasonable engineering trade-off for improving concurrent performance.

[0139] According to the probabilistic model, when massive alarm features are randomly mapped to 28,084 independent 64-byte blocks through the first-level hash, the number of features carried by each block is no longer the ideal average, but strictly follows the Poisson distribution. This statistically inevitable unevenness leads to some cache blocks being lightly loaded, while other local blocks inevitably become heavily loaded hotspots.

[0140] Within high-load data blocks, the limited 512 bits are filled and saturated faster than expected, causing a surge in the local false positive rate for these specific blocks. The calculation of the overall false positive rate of the block Bloom filter degenerates into an extremely complex binomial and Poisson integral summation model, i.e., FPP = summation(binomial coefficient * block allocation probability * intra-block false positive rate).

[0141] Mathematical calculations show that load imbalance in local data blocks leads to a slight degradation in the overall false positive rate compared to a globally uniform array. At a scale of 1.71MB, the theoretical false positive rate of 0.1% might rise to approximately 0.13%. However, in a system simulation with a peak of 500,000 concurrent requests, this negligible loss of mathematical precision completely eliminated cross-core false sharing, successfully resulting in a geometric increase in concurrent throughput and a significant reduction in tail latency. This trade-off based on microarchitectural understanding has been established as the most reasonable underlying engineering practice, ensuring core concurrent performance while keeping the false positive rate within an acceptable range for business needs, achieving an optimal balance between performance and accuracy.

[0142] 4. Optimizing concurrent computing power scheduling based on FMA instructions After resolving the memory bus storm issue, the computational bottleneck shifted to the processor instruction execution pipeline. The system needs to perform k=5 independent hash calculations on the same feature to determine the coordinates of 5 bits. If independent strong hash algorithms are directly called, the instruction sequence will quickly exhaust the issue ports of the execution unit, making it unsuitable for the computational demands of high-frequency concurrency scenarios. To completely eliminate the high hash scheduling overhead and maximize the utilization of the processor's underlying computing power, the system introduces a dual hash paradigm and underlying FMA hardware circuitry to achieve efficient execution of hash calculations.

[0143] (1) Instruction dimensionality reduction of the double hash space scaling algorithm To simplify computation and reduce the instruction overhead of hash operations, the system abandons the independent five hash operations, instead executing only one main hash function and truncating its output into two basic hash values, A and B. Then, using the linear derivation formula of the double hash algorithm: hi = A + i * B (where the constant i cycles through values ​​from 0 to 4), the system calculates all the hash coordinates required subsequently. This mechanism directly simplifies complex hash operations into a very basic sequence of integer multiplication and addition, significantly reducing the number of instructions executed, lowering computational power consumption, and improving hash calculation efficiency.

[0144] (2) The underlying hardware cascading microarchitecture mechanism of FMA instructions In a traditional CPU, executing the A+i*B operation requires scheduling at least two separate multiplication and addition instructions. During execution, the hardware must write the multiplication result back to a physical register, and then the adder unit reads the result from the register in the next execution cycle and completes the final addition. This data hazard across execution units and the register write-back delay often consumes 5 to 8 extremely valuable clock cycles, making it unsuitable for nanosecond-level processing requirements.

[0145] Modern processors fully integrate the floating-point operation and advanced vector extension unit (FMA) mechanism at the hardware level. In the silicon-level routing design of the FMA module, the processor completely overturns the traditional independent unit architecture, physically cascading the output of the multiplier circuit and the input of the adder circuit through hard-wired connections, realizing hardware-level coordination of multiplication and addition operations.

[0146] When the system issues FMA primitives, the key microarchitectural innovation lies in the hardware providing a physical bypass pass-through circuit. This circuit intercepts the raw, high-precision intermediate results from the multiplier tree output, which are not yet written to any registers, and feeds them directly to the input pins of the downstream adder with an extremely low latency of a single instruction cycle. This low-level silicon-level hardware cascading mechanism not only completely eliminates the cumbersome step of writing back the intermediate results but also seamlessly merges them into a single, indivisible microinstruction at the hardware decoding front end. The system utilizes this instruction stream to accelerate coordinate derivation, drastically reducing the total latency for generating all five sets of bit-addressable coordinates to a constant number of extremely low clock cycles. This significantly relieves the concurrent pressure on the out-of-order execution engine, improves hash calculation efficiency, and provides sufficient computing power to support the high-frequency noise reduction of the S2 module.

[0147] 5. Completely abandons the underlying CAS operation atomic state machine flow design of mutex locks. When 500,000 alarms simultaneously flood the processing node, using operating system-level mutex locks to protect cache lines would result in threads failing to acquire the lock being suspended, causing kernel context switches. This would completely destroy the system's nanosecond-level real-time performance, making it unsuitable for high-frequency concurrency scenarios. Therefore, the system completely abandons all forms of blocking locks and instead builds an optimistic concurrent lock-free state machine mechanism based on CAS primitives in the pure low-level hardware instruction set. This enables non-blocking concurrent writes, ensuring low latency and high throughput in high-frequency concurrency scenarios.

[0148] (1) Slow-motion hardware flow: Microscopic decomposition of CAS instructions When a worker thread attempts to atomically update the bitmask of a specific 64-bit machine word, the core of its instruction sequence lies in using CAS instructions with the LOCK hardware prefix to ensure the safety of concurrent writes through hardware-level atomic operations. The micro-operation flow at the microarchitecture level is as follows: First, loading the old value and generating the mask. The thread optimistically reads the current real-time value of the target machine word from the L1 data cache into a register using lock-free instructions. In a separate register, using a bitwise OR instruction, the old value is superimposed on the bitmask located by the target feature to generate the expected new target value, ensuring the accuracy of the bit update.

[0149] The second step involves locking the request and arbitrating ownership. The system issues a CAS machine code with the LOCK prefix. The processor hardware sends a probe frame to the cache bus. If the target cache line is not currently exclusively accessed by the core, the request forces other competing cores to invalidate their cache copies, while simultaneously locking the cache line to the core that issued the request. This cache locking phase establishes a microscopic hardware protection zone, ensuring the current core's exclusive access to the cache line and preventing concurrent conflicts.

[0150] The third step is atomic comparison verification and disk write. Within the fully controlled physical shielded window of the cache, the processor's comparison network hardwires the actual value in the memory slot with the copy of the old value from the first step. If they match perfectly, it proves that no other concurrent core has tampered with the memory word. The hardware arithmetic unit then atomically writes the new value back to the memory slot and releases the cache lock. Otherwise, if they don't match, it indicates a conflicting write. The instruction will abandon the disk write and immediately reload the latest value into the register, while the hardware clears the zero flag in the status register. The software logic can determine that this round of optimistic concurrent write has failed by detecting this status. It must be pointed out that it is precisely because the 64-byte alignment mentioned above ensures that all update operations are strictly confined to the same cache line that the processor's cache controller can use extremely fast cache locking technology for consistency arbitration, completely guaranteeing the smooth flow of the atomic state machine and achieving lock-free concurrency security and reliability.

[0151] (2) Microarchitectural intervention in the failure avoidance and PAUSE instruction cycle When optimistic operations fail due to high-frequency contention, the system thread enters a spin retry loop. If only a compact, purely infinite loop were written, the out-of-order execution engine would speculatively fire hundreds or thousands of useless instructions in a very short time. This would not only exhaust the power consumption of the execution unit but also create a severe retry storm on the bus, exacerbating bus congestion. Therefore, the system explicitly weaves low-level PAUSE backoff machine code into the spin retry loop to achieve ordered backoff after failure, avoiding bus storms and wasted computing power.

[0152] (3) The radical physical meaning and resource transfer of backoff delay in modern microarchitecture In early architectures, the PAUSE instruction was designed to have a very short execution time, typically only about 10 clock cycles. However, in modern microarchitectures designed for massive concurrency (such as Skylake and its derivatives), chip designs have forced the execution latency of the PAUSE instruction to increase by more than an order of magnitude, reaching approximately 140 clock cycles.

[0153] This 14-fold jump in instruction latency is a clever design for maximizing the resource utilization of synchronous multithreaded systems. Two logical hyperthreads within the same physical core fiercely compete for arithmetic execution ports. When logical thread A fails and enters a spin state, it temporarily contributes nothing to the computation. By suspending for approximately 140 cycles, the physical significance is that it forces the physical core to tilt and relinquish all valuable instruction issuance ports to another logical partner thread B, which is performing meaningful forward operations, thus achieving efficient utilization of computing resources. Simultaneously, the long-latency backoff mechanism acts as a natural hardware throttling valve. It significantly reduces the frequency of failed threads sending continuous read probes to the cache. This allows cache lines, which are frequently tampered with due to high-frequency conflicts, ample physical space to smoothly synchronize data to other cores through the consistency protocol. Bus saturation storms dissipate during this micro-wait, ensuring the stability and efficiency of multi-core concurrent processing.

[0154] 6. Memory barrier instructions intervene in the strong consistency and visibility guarantee of the processor's memory buffer. Modern processors implement complex out-of-order execution and memory buffering models. When the pipeline issues a store instruction for a single bit, the data is first buffered into the core's private memory buffer and queued before the pipeline continues to issue other instructions. This means that although the core itself sees the write as complete, this data modification is absolutely invisible to other cores in the system until the memory buffer is emptied into the L1 cache. This can lead to different cores reading inconsistent data, affecting the accuracy of noise reduction results and compromising the system's concurrent consistency.

[0155] To pull the microarchitecture back from probabilistic out-of-order illusions to a safe track of strong consistency and ensure data consistency during multi-core concurrent processing, this system forcibly weaves a hardware-level memory barrier mechanism into the critical read / write nodes of the lock-free state machine transition. Before the core feature bitmask update operation is completed and the judgment result is submitted to the subsequent pipeline, a storage memory barrier instruction is inserted. This barrier instruction acts like a microcode dam, forcing the processor to suspend all subsequent storage operations until all write operations accumulated in the current storage buffer are truly drained and solidified into the L1 data cache. This ensures that any feature bit modification operation before this dam is absolutely globally visible, completely preventing noise reduction to disk from occurring before the identifier return, ensuring the accuracy and reliability of the noise reduction results output by the S2 module, and providing consistent and accurate feature data support for the subsequent backpressure control of the S3 module.

[0156] Part Four, Module S3: Dual-EMA Extremum Tracking and Hardware-Software Co-operational Dynamic Feedback Control Law for Recurring Carry-on Dependencies 1. Analysis of the vulnerability of traditional resident queue one-way rate limiting mechanism to memory exhaustion under the concurrency storm of underlying IT operation and maintenance. In extreme failure scenarios involving the underlying infrastructure, such as physical power outages of core switches or widespread oscillations in aggregation layer links, monitoring systems will instantly face concurrent alarm data flows exceeding the baseline by tens or even hundreds of times. Existing conventional traffic shaping, rate limiting, and noise reduction techniques generally employ unidirectional buffering mechanisms based on application-layer memory-resident queues, such as memory object stacking designs based on token bucket or leaky bucket algorithms. While this traditional architecture can effectively smooth out peaks and valleys when dealing with stable traffic fluctuations, it exposes extremely critical vulnerabilities to memory exhaustion and irreversible system downtime risks when facing high-frequency pulse storms at the underlying level.

[0157] The specific micro-mechanism lies in the fact that when concurrent packets flood in at an extremely high frequency, the network interface controller writes the raw data frames at high speed to the socket buffer in the kernel space through direct memory access. Subsequently, the operating system kernel triggers soft interrupts and context switches at high frequency, copying massive amounts of data to user space. The runtime environment of high-level programming languages ​​is forced to instantiate massive amounts of alarm objects, timestamp cursors, and associated string sequences at high frequency in the young generation region of the JVM heap memory. Since the consumption rate of the backend noise reduction processing thread is strictly limited by the execution cycle of the CPU arithmetic logic unit and the random access latency of main memory, the object generation rate exponentially exceeds the object consumption rate at the moment of the storm. This rate inversion directly leads to the rapid accumulation of alarm objects with extremely short lifespans in the young generation Eden space, and a large number of elements in the resident queue cannot be dequeued and reclaimed in time.

[0158] As the Eden space threshold is rapidly breached, the JVM frequently triggers lightweight GC pauses. Conventionally, lightweight GC should quickly clean up short-lived objects. However, in the specific context of queue backlog, these alerted objects are held by long-term references, causing a large number of objects that should have been destroyed to be forced to cross the survivor space and be prematurely promoted to the old generation memory space, violating the generational assumption. The rapid filling of the old generation's physical contiguous space inevitably triggers a long system-level pause caused by global GC. During the clock cycle of the global pause, the CPU must suspend all worker threads and traverse a massive object reference graph with millions of nodes at high frequency. The massive random memory read / write instructions cause a catastrophic strain on the physical bandwidth of the memory controller, while simultaneously causing a precipitous drop in the processor's cache hit rate.

[0159] This hardware-level bus bandwidth saturation caused by upper-level nonlinear memory expansion leads to system failure within milliseconds. As heap memory is completely exhausted, the runtime environment requests more physical page frames from the operating system kernel. When both main memory and disk swap space are depleted, the operating system's memory management subsystem triggers the underlying OOMKiller forced reclamation mechanism. The kernel's memory exhaustion killer heuristic algorithm iterates through all processes, calculates penalty scores, and ultimately sends a SIGKILL signal to terminate the monitoring process consuming the most memory. This not only results in the complete loss of alarm data being processed but also causes the monitoring nodes, originally intended to protect system stability, to cascade and fail, creating a business blind spot. Therefore, at the lowest level of logical design, completely abandoning all resident buffer queues based on dynamic heap allocation and instead seeking static control laws with absolute memory upper limits becomes the necessary physical path to ensure system survival under extreme pressure scenarios.

[0160] 2. Clarification of the Essence of Dual-EMA Time Series Construction and FMA Computational Power Optimization To accurately capture throughput extremes and variance drift while eliminating dynamic memory allocation, module S3 deploys a Dual-EMA (double-order exponential moving average) algorithm engine in purely static, physically contiguous memory. While traditional single-order EMA algorithms can smooth system scalars, their inherent lag cannot meet the ultra-fast response requirements of microsecond-level power outage storms. Dual-EMA, by performing a second smoothing strip on the time series and utilizing the difference between the original and smoothed sequences for compensation, significantly eliminates phase delay, enabling the system to maintain nanosecond-level sensitivity to extreme value abrupt changes while preserving data smoothness.

[0161] This engine extracts microsecond-level time slice features in real time by constructing a dual-time series model consisting of short-cycle extreme value tracking and long-cycle macro-level water level anchoring. The short-cycle series is used to sensitively capture spikes and glitches in sudden traffic, while the long-cycle series is used to smoothly depict the macro-level profile of the system's baseline throughput. The core recursive physical equation of Dual-EMA is expanded into a plain text multiply-add form in the underlying code without any floating-point division overhead. Its basic recursive formula for the short-cycle series is expressed as: EMA(t) = x(t) * alpha + EMA(t-1) * (1-alpha). In this formula, EMA(t) represents the smoothed estimate of the current microsecond cycle, x(t) represents the actual number of instruction retirements captured by the hardware performance counter in the current cycle, alpha represents the decay damping constant between 0 and 1, and EMA(t-1) is the smoothed historical estimate of the previous clock cycle. To completely eliminate the inherent hysteresis of single-order EMA, the Dual-EMA engine uses the difference compensation between the original sequence and the smoothed sequence to perform phase lead correction. Its final dual-order extreme value output formula is strictly defined as: DEMA(t)=2*EMA(t)-EMA(EMA(t)).

[0162] However, when mapping this recursive formula to the underlying pipeline of modern superscalar CPUs, an absolute physical bottleneck known as loop-carry dependency must be confronted and thoroughly revealed. In the above formula, the calculation result of the current cycle t is mathematically and logically absolutely dependent on the physical output of the calculation result of the previous cycle t-1. This strong data dependency forms a rigid serialized execution chain in out-of-order execution engines that cannot be broken by branch prediction, register renaming, or instruction reordering mechanisms.

[0163] To accelerate such intensive floating-point operations, modern microarchitectures have introduced fused multiply-add (FMA) instructions. FMA physically merges multiplication and addition operations into a single microinstruction within the underlying execution unit, completing high-precision computations simultaneously in a single hardware issue, avoiding repeated reads and writes of intermediate results to the register file. However, it is crucial to clarify the true limits of the computational power gains from FMA's hardware-level optimization: FMA's essential role is to significantly alleviate the issue bandwidth pressure on the front-end instruction decoder and reduce the concurrent scheduling pressure on the back-end reservation station, but it absolutely cannot reduce the inherent physical latency of a single floating-point operation.

[0164] In microarchitectures such as Skylake or newer ones, the physical execution pipeline of FMA operations is extremely rigid, with each execution strictly limited by a physical latency of 4 to 5 absolute clock cycles. Due to the inherent dependencies carried by the loop, the next iteration of the FMA instruction must be hard-blocked in the reservation station, forcibly waiting for these 4 to 5 clock cycles until the previous FMA instruction writes the computation result back to the target physical register through the bypass network. Therefore, even with extremely aggressive loop unrolling by the compiler, the maximum throughput of this timing sequence within a single thread is still strictly limited to the physical limit of executing one iteration every 4 clock cycles. This rigid latency constitutes an insurmountable hardware bottleneck for the Dual-EMA algorithm under nanosecond-level computing power constraints.

[0165] 3. Multi-stage water level drift dynamic decision and PID algorithm damping compensation control for throughput extreme value variance Faced with the computational power constraints imposed by cyclic dependency, the system must abandon the brute-force attempt to absorb storms by infinitely increasing computational throughput. Instead, it must construct a feedback decision mechanism that can tolerate computational latency and possesses extremely high robustness. Module S3 abandons coarse-grained metrics at the operating system level, directly accessing the microarchitecture level. Through non-privileged user-space instruction reads, it extracts the number of instruction retirements and the amount of data transfer transactions on the main memory bus within microsecond-level clock cycles at high frequency. Under the normal operating mode of a large-scale concurrent cluster, the scalar system of these micro-performance counters strictly follows the expectation law of a Gaussian distribution. The difference between the short and long periods (i.e., the deviation variable) calculated by Dual-EMA precisely represents the absolute distance between the current system throughput and the baseline Gaussian expectation.

[0166] When an extreme power outage storm is triggered, alarm data streams surge in exponential pulses, causing frequent pauses in the processor pipeline due to data dependencies and cache misses, resulting in a sharp drop in instruction retirement rate. Simultaneously, a massive influx of new packets attempts to occupy memory, causing a surge in main memory bus transactions. The system's scalar architecture experiences severe Gaussian tail drift within microsecond slices. If only a single hard absolute threshold is used for state machine transition decisions, when flow fluctuations hover just around the threshold edge, it can easily trigger a ping-pong oscillation effect, causing the state machine to frequently flip between normal and high-risk levels. This uncontrolled ping-pong oscillation leads to frequent and repeated alterations of the system's underlying decay parameters and concurrency lock granularity, causing frequent CPU cache line failures and overloads, ultimately leading to a more catastrophic collapse of computing power.

[0167] To completely eliminate boundary oscillations, module S3 creatively introduces a damping compensation control layer of the PID algorithm into the deviation variable decision network. This control layer consists of three computational units working together: the proportional unit calculates the instantaneous error of the current deviation variable from the absolute extreme threshold in real time, providing direct correction driving force; the integral unit accumulates the error within historical microsecond slices to eliminate steady-state steady-state error caused by slow drift in system throughput; and the differential unit calculates the slope of the deviation variable change, senses the acceleration of sudden flow rises in advance, and applies reverse physical damping, effectively suppressing the occurrence of overshoot.

[0168] By intervening with a PID damping feedback network, the system smoothly reshapes the originally sharp step transition signal into a hysteresis loop curve. When an instantaneous pulse attempts to exceed the threshold, the differential damping absorbs the impact energy, delaying the transition. Only when the deviation of the throughput variance accumulates sufficiently over time to exceed the limit of the integral damping will the state machine robustly, smoothly, and irreversibly transition to the next physical water level. This combined hardware and software control law completely guarantees the absolute stability of multi-level water level decisions under extreme high pressure.

[0169] 4. A panoramic view of the dynamic modification matrix of the underlying parameters of the four-stage water level state machine based on deviation variable feedback. With the smoothing effect of the PID algorithm, module S3 constructs a four-level water level state machine consisting of normal water level, warning water level, high-risk water level, and disaster water level. When the smoothed deviation variable triggers an irreversible water level transition, the state machine engine directly sends low-level microcode to the working memory base address area of ​​modules S1 and S2, dynamically modifying the four core memory parameters of the control system. This dynamic feedback control law runs entirely within the closed loop of the system, requiring no external configuration center or manual intervention.

[0170] Normal performance is defined as a throughput deviation of strictly less than 50%. At this point, the system is in its optimal performance-driven mode. The smoothing constant alpha of the Dual-EMA is set to a high extreme sensitivity of 0.85 to ensure instantaneous capture of any slight jitter. The CAS spin limit of the underlying flow is fully released to the maximum limit of 1024 times. This parameter allows worker threads to perform long lock-free spin waits when faced with brief feature hash slot collisions. The step size of the 80KB sketch array shift register in the S1 module remains the default standard constant. The Bloom filter hash addressing loop in the S2 module maintains optimal accuracy, i.e., the number of independent hash functions k is strictly kept at 5 times, ensuring the false positive rate is suppressed to the theoretical lower limit.

[0171] The warning level is defined as the throughput deviation drifting to the 50% to 75% range. Triggering this level indicates the beginning of micro-level contention and queuing for system physical resources. At this point, the PID damper takes effect, and the state machine engine forcibly issues the first round of parameter modification instructions. The CAS spin limit is decisively halved and reduced to 512 spins. Its core purpose is to suppress the catastrophic eavesdropping storm induced by excessively long spins on the ring bus. Simultaneously, the system generates and sends a 0x01 weak backpressure signal flag to the front-end network ingestion channel. This flag will activate the bit-by-bit discard logic for edge packets, intercepting low-priority alarms with redundant topology features before they enter the ring buffer.

[0172] The high-risk level is defined as a throughput deviation deteriorating to between 75% and 90%. This level signifies that the computing power center is facing substantial exhaustion, and the system officially activates a lossy degradation defense mechanism. The CAS spin limit is forcibly truncated to 128 times, forcing threads encountering contention to quickly abandon retrying and discard alarm objects. A more critical destructive modification occurs at this stage: the state machine forcibly modifies the step size parameter of the displacement register of the 80KB static array in module S1. By instantly amplifying the displacement constant, it forcibly triggers the rapid collapse of 75% of the historical count values ​​within the matrix to zero, absolutely preventing physical overflow of unsigned integers during the storm. Simultaneously, the state machine implements a precision reduction strategy for module S2, forcibly truncating the upper bound k of hash iteration from the optimal 5 times to a strict 2 times. At the underlying physical execution level, this directly eliminates pseudo-random memory jump addressing, instantly releasing 60% of the main memory random read bandwidth, achieving an extreme balance under the extreme computing power situation.

[0173] The disaster threshold is defined as the absolute red line where throughput deviation exceeds 90%. This state signifies that an OOM (Out of Memory) failure or cache bus deadlock is on the verge of triggering. The state machine instantly triggers the highest-privilege hardware circuit breaker defense. All CAS (Compare-and-Swap) spin limits at the underlying level are forcibly cleared to 0. Once a memory slot is detected to be occupied by another core, the processor immediately executes a PAUSE (Pause) instruction to completely prohibit any concurrent retries. The circuit breaker rate for alarms is instantly increased to 99.99%, reserving only one ten-thousandth of the bandwidth for the highest-privilege heartbeat message pass-through. Simultaneously, the engine directly bypasses the operating system network stack and forcibly broadcasts an Ethernet MAC pause frame to the directly connected core switch at the physical link layer, using the hardware backpressure mechanism of the physical protocol to forcibly suppress the upstream transmission rate. The system will maintain this mode until the storm recedes, and then smoothly restore all tampered parameters using the PID reverse compensation mechanism.

[0174] Part Five: Slow-motion simulation of a panoramic sandbox model demonstrating extreme throughput and OOM (Out of Memory) defense against a 500,000 concurrent power outage storm. To physically verify the system's survivability under extreme failure scenarios, this section will conduct a microscopic analysis of the underlying processes when the system encounters a storm of 500,000 concurrent alarms caused by a large-scale power outage of the data center's core power supply module, using a strict absolute timeline as the scale. The simulation assumes complete paralysis of the external topology, and the observation perspective is reduced to the CPU out-of-order execution pipeline, cache line state machine, and operating system memory controller level.

[0175] 1. t=0ms to 15ms: Storm initial wave arrival and efficient intake by bypass network card At t=0ms, a physical power failure in the core switch cluster triggered a cascading port connectivity interruption, resulting in a massive influx of Ethernet alarm messages into the host network interface card (NIC). The extended BPF bytecode, pre-mounted in the kernel, was activated. This low-level interceptor directly reads the Ethernet frame header within the NIC driver's hard interrupt execution context, completely bypassing the parsing of the traditional network protocol stack. For valid discrete alarm entities, the program extracts the characteristic hash value and processor timestamp, encapsulates them into an 8-byte cursor payload, and pushes it into a lock-free circular buffer using a zero-copy mechanism. Within the initial 15ms, before the CPU front-end decoder is fully loaded, the ingestion threads of the S1 and S2 modules process the initial traffic with extremely high throughput.

[0176] 2. t=15ms to 30ms: Microarchitecture resource squeeze and 140-cycle pause backoff As the number of concurrent alarms climbed to hundreds of thousands per second at t=15ms, the computing resources of the physical cores began to be strained. Multiple worker threads within the S2 module frequently calculated the first-level hash and attempted to concurrently modify the same 64-byte cache line located in the 1.71MB block Bloom filter.

[0177] When thread A on physical core 0 acquires exclusive modification rights to the cache line, the CAS instruction issued by thread B on physical core 1 fails during hardware arithmetic logic unit comparison, entering a lock-free spin retry loop. This system explicitly weaves underlying PAUSE machine code into the backoff loop body after the CAS failure. In modern microarchitectures, hardware design increases the execution latency of PAUSE instructions to approximately 140 clock cycles. This 140-cycle physical suspension plays a crucial intervention role. It forces the physical core to cut off speculative issues in the pipeline, yielding the execution port to its hyper-threaded counterpart on the same core. Simultaneously, it provides ample time for the cross-core cache coherency bus, allowing thread A to synchronize the modified bitmask from its private storage buffer to the L1 data cache, successfully suppressing the bus saturation storm within micro-wait.

[0178] 3. t=30ms to 80ms: Computing power fatigue manifestation and Dual-EMA extreme value tracking intervention As the storm continued to pour in, frequent cache line failures caused a drop in the CPU instruction retirement rate. At this point, the S3 module used pure user-space instructions to frequently read the hardware performance counters and detect deviations in the actual throughput variance from the baseline.

[0179] The system's underlying Dual-EMA algorithm engine operates at high speed, executing the plain text multiplication-addition formula EMA(t)=x(t)*alpha+EMA(t-1)*(1-alpha) to construct short-period and long-period sequences. The short-period sequence quickly absorbs performance glitches, and the calculated deviation variable exceeds the 50% warning level. The PID damping compensation network intervenes in real time. After the integral and differential units absorb the initial numerical jitter, the state machine smoothly sends the first round of parameter modification microcode to the front end: reducing the CAS spin retry limit from the default 1024 times to 512 times, forcing some threads to exit the contention early to suppress physical queuing.

[0180] 4. t=80ms to 120ms: High-risk water level jump and lossy degradation from k=5 to k=2 As time approached t=80ms, the flood peak reached its extreme value, and the deviation variable exceeded the 75% red line, causing the S3 state machine to transition to a high-risk level. Faced with the extreme strain on the computing power center, the system activated a lossy degradation defense mechanism.

[0181] Under normal mode, the number of independent hash functions, k, in the S2 module's hash addressing loop is strictly maintained at 5. However, under high-risk conditions, the state machine directly cuts off the upper bound of the hash deduction loop iteration, forcibly truncating the number of hash mappings from k=5 to a strict k=2. The physical significance of this parameter mutation is that when processing a single alarm, the number of FMA fused multiply-accumulate instructions issued by the CPU is instantly halved; and three physical read transactions for the 1.71MB off-heap locked memory bit plane are forcibly exempted. According to the false positive rate formula, reducing the value of k will lead to a slight deterioration in the local false positive rate. However, at the critical point of 500,000 concurrency, this loss of mathematical precision directly releases up to 60% of the main memory random read bandwidth, forcibly giving up the instruction cycle to the basic network ingestion thread, achieving an effective balance under extreme computing power conditions.

[0182] 5. t=120ms to 200ms: Silent verification of absolute immunity to physical effects against OOM. In traditional resident queue architectures, when faced with high-frequency pulses of t=120ms, the object instantiation rate exceeds the consumption rate, and short-lifetime objects will accumulate rapidly in the young generation region, eventually triggering a system-level OOM.

[0183] In the system's sandbox, the load flowing through the three-stage pipeline is only an 8-byte cursor index. The two-dimensional counter array of module S1 is locked in a statically pre-allocated 80KB contiguous memory; the block Bloom filter of module S2 is fixedly anchored within the 1.71MB off-heap physical page lock space allocated during system initialization. During this extreme stress period, low-level performance monitoring confirmed the physical verification effect of OOM prevention: under a peak of 500,000 concurrent requests, the number of page faults triggered by the operating system was 0, and the global GC pause time of the JVM was 0ms. The system withstood the peak within a total physical memory tolerance of less than 2MB, achieving physical immunity to systemic risks from the root cause of static byte mapping.

[0184] 6. t=200ms to 500ms: Disaster water level triggering, CAS clearing, and MAC pause frame. The storm stalled for 200ms, and the deviation variable exceeded the 90% disaster recovery threshold, bringing the hardware-level cache deadlock to a critical point. The S3 state machine took over control and triggered a circuit breaker.

[0185] The state machine sends microcode instructions to the underlying layer: all CAS spin retry limits are forcibly cleared to zero. Any S2 worker thread attempting a bitmask update will not execute a PAUSE backoff if it detects that the target cache line is not in an exclusive state. The processor immediately abandons optimistic writes, discards the memory pointer being processed, and destroys the entity. The alarm ingestion circuit breaker rate is increased to 99.99%.

[0186] Simultaneously, the system bypasses the operating system's network stack and forcibly constructs an IEEE 802.3x standard Ethernet MAC pause frame at the physical link layer. The message's EtherType is set to 0x8808, the opcode to 0x0001, and the pause time parameter to 0xFFFF. When the upstream switch's physical layer port detects this 0x0001 opcode, its transmission scheduling circuit is forcibly frozen, using the underlying physical hardware backpressure to buy the host CPU some breathing room to survive.

[0187] 7. t=500ms to 2000ms: Storm passage and PID integral damping reshaping When the physical block of MAC pause frames takes effect, concurrent traffic declines after t=500ms. The time wheel daemon thread of module S1 performs right shift operations by polling at the microsecond level, and a large area of ​​the historical count values ​​inside the array return to zero. The Dual-EMA engine of module S3 detects a rebound in the instruction retirement rate, and the deviation variable curve converges to within 50%.

[0188] Thanks to the PID damping compensation network, the system did not hastily disarm. The integral unit forced the system to accumulate a safety margin under low load conditions, filtering out the secondary aftershocks of the storm. At t=1000ms, the safety integral reached the threshold, and the state machine sequentially executed the degradation parameters: the CAS spin limit was restored to 1024 times; the hash iteration upper bound of the S2 module was reset back to k=5; and physical packet loss was completely eliminated in the network ingestion channel. The system completed a smooth self-healing process with zero human intervention within 2000ms.

[0189] In summary, this application provides a system operation method that involves acquiring the hardware operating parameters of a target system; performing sequence smoothing on the hardware operating parameters to obtain a deviation characterization, which indicates the degree of deviation of the current load of the target system from the baseline load; updating the operating parameters of the target system based on the deviation characterization; and controlling the operation of the target system based on the updated operating parameters. First, the hardware operating parameters of the target system are acquired to capture the hardware-level operating status during system operation in real time, providing accurate and reliable basic data support for subsequent load judgment. Then, the hardware operating parameters are performed sequence smoothing to obtain a deviation characterization indicating the degree of deviation of the current load of the target system from the baseline load, effectively filtering interference caused by hardware parameter fluctuations and accurately perceiving the system load status. Next, the operating parameters of the target system are updated based on the deviation characterization, linking the operating parameters with the current system load status, breaking the limitations of traditional fixed operating parameters. Finally, the target system is controlled based on the updated operating parameters. By dynamically adjusting the operating parameters, memory resource exhaustion caused by the instantiation of massive alarm objects is avoided, fundamentally mitigating the risk of memory overflow, preventing cascading paralysis of monitoring nodes, and ensuring stable system operation under extreme concurrency scenarios.

[0190] Based on the same technical concept, embodiments of this application also provide a system operating device, such as... Figure 2 As shown, the device includes: The parameter acquisition module 201 is used to acquire the hardware operating parameters of the target system. The deviation characterization calculation module 202 is used to perform sequential smoothing processing on the hardware operating parameters to obtain the deviation characterization, which is used to indicate the degree of deviation of the current load of the target system from the reference load. The parameter update module 203 is used to update the operating parameters of the target system based on the deviation characterization quantity; The system operation control module 204 is used to control the operation of the target system based on the updated operating parameters.

[0191] This application also provides an electronic device corresponding to the method provided in the foregoing embodiments. Please refer to... Figure 3 The diagram illustrates an electronic device provided by some embodiments of this application. The electronic device 30 may include: a processor 300, a memory 301, a bus 302, and a communication interface 303, wherein the processor 300, the communication interface 303, and the memory 301 are connected via the bus 302; the memory 301 stores a computer program that can run on the processor 300, and when the processor 300 runs the computer program, it executes the method provided by any of the foregoing embodiments of this application.

[0192] The memory 301 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one physical port (which can be wired or wireless), such as the Internet, wide area network, local area network, or metropolitan area network.

[0193] Bus 302 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory 301 is used to store programs. After receiving an execution instruction, the processor 300 executes the program. The method disclosed in any of the foregoing embodiments of this application can be applied to the processor 300, or implemented by the processor 300.

[0194] The processor 300 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 300 or by instructions in software form. The processor 300 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules may reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 301. The processor 300 reads the information in memory 301 and, in conjunction with its hardware, completes the steps of the above method.

[0195] The electronic devices and methods provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods they employ, operate, or implement.

[0196] This application also provides a computer-readable storage medium corresponding to the method provided in the foregoing embodiments. Please refer to... Figure 4 The computer-readable storage medium shown is an optical disc 40, on which a computer program (i.e., a program product) is stored, which, when run by a processor, performs the methods provided in any of the foregoing embodiments.

[0197] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. All equivalent structural transformations made under the concept of the present invention using the contents of the present invention specification and drawings, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A system operation method, characterized in that, The method includes: Obtain the hardware operating parameters of the target system; The hardware operating parameters are subjected to sequential smoothing to obtain a deviation characterization quantity, which is used to indicate the degree of deviation of the current load of the target system from the reference load. Based on the deviation characterization, update the operating parameters of the target system; The target system is controlled to operate based on the updated operating parameters.

2. The method as described in claim 1, characterized in that, The step of updating the operating parameters of the target system based on the deviation characterization includes: The deviation characterization quantity is input into a preset load determination network to obtain the current load level of the target system; Based on the current load level, generate corresponding operating parameter adjustment instructions; Based on the adjustment instructions, the values ​​of the core operating parameters corresponding to the target system are determined, and the operating parameters are updated.

3. The method as described in claim 2, characterized in that, The load determination network includes a proportional calculation unit, an integral calculation unit, and a differential calculation unit; the step of inputting the deviation characterization quantity into the load determination network to obtain the current load level includes: The instantaneous deviation between the deviation characterization quantity and the preset load threshold is calculated using the proportional calculation unit. The instantaneous deviation value is accumulated over time using the integral calculation unit to obtain the cumulative deviation value; The rate of change of the deviation characterization quantity is calculated using the differential calculation unit, and the slope of the deviation change is obtained. The current load level is determined based on the instantaneous deviation value, cumulative deviation value, and deviation change slope.

4. The method as described in claim 3, characterized in that, The step of generating corresponding operating parameter adjustment instructions based on the current load level specifically includes: When the current load level is a first preset level, a first type of control parameter adjustment instruction is generated, and the adjustment instruction is used to update the first type of control parameter to a first target value; When the current load level is the second preset level, a second type of control parameter adjustment instruction is generated, which is used to update the second type of control parameter to the second target value; When the current load level is the third preset level, a concurrency control parameter adjustment instruction is generated, which is used to update the concurrency control parameters to the third target value; When the current load level is the fourth preset level, a flow control instruction is generated. The flow control instruction is used to generate a flow control frame at the physical network link layer and send the flow control frame to the upstream data source.

5. The method as described in claim 4, characterized in that, The step of modifying the values ​​of the corresponding core operating parameters of the target system based on the adjustment instruction to complete the update of the operating parameters includes: After updating the first type of control parameters based on the first type of control parameter adjustment instruction, increase the displacement of the bitwise right shift operation performed on the data statistics value in the first data processing stage; After updating the second type of control parameters based on the second type of control parameter adjustment instruction, the number of hash mapping operations performed in the second data processing stage to determine whether a single data feature is repeated is reduced; After updating the concurrency control parameters based on the concurrency control parameter adjustment instruction, the maximum number of times a retry operation is performed when there is a resource access conflict is set to zero.

6. The method as described in claim 1, characterized in that, The step of performing sequence smoothing on the hardware operating parameters to obtain a deviation characterization quantity includes: Construct a time series of hardware operating parameters according to the time dimension; The time series of the hardware operating parameters is processed by multi-order exponential moving average to obtain smoothed series with at least two different periods; The difference between each smoothed sequence is calculated to obtain the deviation characterization.

7. The method as described in claim 6, characterized in that, The step of performing multi-order exponential moving average processing on the time series of the hardware operating parameters to obtain smoothed sequences with at least two different periods includes: The time series of the hardware operating parameters is processed by the first exponential moving average to obtain a short-period smoothed sequence, which is used to capture sudden fluctuations in the target system load. The short-period smoothed sequence is processed by a quadratic exponential moving average to obtain a long-period smoothed sequence, which is used to anchor the benchmark load level of the target system.

8. The method as described in claim 1, characterized in that, The method further includes: Continuously collect the hardware operating parameters of the target system and obtain real-time deviation characterization quantities; When the real-time deviation characterization quantity falls below the preset threshold of the corresponding load level, the system safety margin is accumulated through the integral calculation unit of the load determination network. The system safety margin is used to filter secondary load fluctuations. When the system's safe operating margin reaches the preset standard, an operating parameter recovery command is generated in reverse order from the current load level to the normal load level. Based on the aforementioned operating parameter recovery instructions, the core operating parameters of the target system are gradually reset to the baseline values; After the parameters are restored, the target system returns to its baseline operating state and continues to monitor load changes.

9. The method as described in claim 1, characterized in that, The acquisition of the target system's hardware operating parameters includes: By reading instructions in user mode without privilege, and according to preset time slices, the number of processor instructions retired and the amount of data transfer transactions on the main memory bus in each time slice of the target system are continuously collected. The number of processor instructions retired and the amount of data transfer transactions on the main memory bus collected in each time slice are arranged in chronological order to form a parameter sequence; The parameter sequence is integrated into the hardware operating parameters of the target system.

10. A system operating device, characterized in that, The device includes: The parameter acquisition module is used to acquire the hardware operating parameters of the target system; The deviation characterization calculation module is used to perform sequential smoothing processing on the hardware operating parameters to obtain the deviation characterization, which is used to indicate the degree of deviation of the current load of the target system from the reference load. The parameter update module is used to update the operating parameters of the target system based on the deviation characterization quantity; The system operation control module is used to control the operation of the target system based on the updated operating parameters.

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