An alarm system for server equipment

Abstract

The present invention relates to the field of server equipment monitoring technology, specifically disclosing an alarm system for server equipment. The system comprises an operating status monitoring module, a data analysis module, an alarm triggering module, and an intelligent optimization module. This system uses thermal sensors and flow sensors to collect operating parameters in real time. The intelligent optimization module adjusts the thermal sensor sensitivity based on the rate of change in alarm trigger frequency, adjusts the data sampling period based on the distribution of alarm event time intervals, and dynamically adjusts the alarm priority based on the fluctuation amplitude of network traffic and the instantaneous rate of change of server load. This system can reduce false alarms, improve alarm real-time performance, and enhance system reliability, making it suitable for complex server operating environments.

Description

Technical Field

[0001] The present invention belongs to the technical field of server security monitoring, and in particular relates to an alarm system for server equipment. Background Art

[0002] With the widespread use of server equipment in modern data centers and cloud computing environments, real-time monitoring of server operating status and fault alarms have become critical to ensuring system stability and efficient operation and maintenance. However, existing server alarm systems still have significant deficiencies in adaptability to complex operating conditions, intelligent alarm mechanisms, and system integration, hindering both operational efficiency and system reliability.

[0003] For example, the patented technology with publication number CN113765687B uses a monitoring platform to detect the server's operating mode and, based on this information, determines whether the target server is in maintenance. It then sends an alarm notification when the alarm conditions are met. However, this solution primarily relies on determining the server's operating mode and fails to fully consider the dynamic changes in servers under various complex operating scenarios, potentially leading to false or missed alarms. For example, if a server is in maintenance mode but a critical heat dissipation component experiences an anomaly, the alarm may be delayed due to the mode priority setting, risking hardware damage. Furthermore, this solution's fixed alarm conditions lack adaptive adjustment capabilities, making it difficult to meet the personalized needs of different server devices. Another example is the patented technology with publication number CN112037820B, which collects audio streams and analyzes the target emotion type within them to send an alarm message to the server, thus achieving reliable alarms in low or no light conditions. However, this solution primarily focuses on emotion analysis in audio streams and fails to fully incorporate the actual operating parameters of the server device (such as temperature, load, and network traffic), potentially resulting in insufficient accuracy and timeliness of the alarm messages. For example, ambient noise could be misinterpreted as "emergency" and trigger a false alarm, while actual server temperature anomalies remain undetected. Furthermore, this solution relies on complex algorithms to process audio data, potentially increasing the system's computational burden and slowing down alarm response. Neither of the two existing technologies mentioned above achieves multi-source data fusion, lacks intelligent approaches for multi-parameter collaborative decision-making, and relies on fixed thresholds or pattern rules, making it impossible to dynamically optimize alarm strategies based on historical fault data or environmental changes.

[0004] The above problems show that the existing server alarm system still has certain deficiencies in terms of dynamic adaptability, multi-dimensional data fusion, and intelligent alarm mechanism. In particular, when faced with a diverse and complex operating environment, it is difficult for existing technologies to achieve efficient and accurate alarm functions. Therefore, there is an urgent need for a modular alarm system that can comprehensively analyze the various operating parameters of the server, optimize the adaptive ability of the alarm mechanism, and improve the accuracy and real-time performance of the alarm information, thereby meeting the needs of modern data centers for efficient and intelligent alarm systems. The present invention aims to solve the above technical problems and provide an innovative solution. Summary of the Invention

[0005] The present invention provides an alarm system for server equipment, which is used to overcome the problem in the prior art that the traditional alarm system has insufficient dynamic adaptability and multi-dimensional data fusion capabilities due to the frequent and complex fluctuations in the operating parameters of the server equipment.

[0006] To achieve the above object, the present invention provides an alarm system for a server device, comprising:

[0007] An operating status monitoring module for collecting operating parameters of a server device in real time through a sensor group, the sensor group including a thermal sensor disposed within the server for detecting temperature changes and a flow sensor connected to the server for detecting fluctuations in network flow; wherein the server includes at least one server device;

[0008] A data analysis module, connected to the operation status monitoring module, for performing feature extraction and anomaly analysis on the collected operation parameters;

[0009] An alarm trigger module, connected to the operation status monitoring module and the data analysis module respectively, for issuing an alarm signal when an operation parameter exceeds a set threshold value, the alarm trigger module including an alarm unit for generating an audible and visual alarm signal and a log storage unit for recording alarm events;

[0010] An intelligent optimization module is respectively connected to the operation status monitoring module, the data analysis module and the alarm trigger module, and is used to adjust the sensitivity of the thermal sensor according to the change rate of the alarm trigger frequency or adjust the data sampling period of the alarm system according to the time interval distribution of the alarm events in the log storage unit, and determine the priority of the alarm trigger according to the network traffic fluctuation amplitude and the instantaneous change rate of the server load; wherein the network traffic fluctuation amplitude is: the difference between the maximum and minimum values ​​in the network traffic time series collected by the traffic sensor within a preset time period; when the network traffic fluctuation amplitude exceeds the preset fluctuation amplitude threshold, it is determined whether the data processing capacity of the alarm system meets the requirements based on the instantaneous change rate of the server load. If the instantaneous change rate of the server load is greater than the preset change rate threshold, it is determined that the data processing capacity of the alarm system does not meet the requirements, and the priority of the alarm trigger is increased, wherein the increase in the priority of the alarm trigger is determined by the difference between the instantaneous change rate of the server load and the preset change rate threshold.

[0011] In an optional manner, the intelligent optimization module is used to determine whether the dynamic adaptability of the alarm system meets the requirements based on the change rate of the alarm trigger frequency. If the change rate of the alarm trigger frequency is greater than the preset first change rate, it is determined that the dynamic adaptability of the alarm system does not meet the requirements.

[0012] In an optional manner, the intelligent optimization module is used to preliminarily determine that the operating efficiency of the alarm system does not meet the requirements when the change rate of the alarm trigger frequency is greater than the preset first change rate and less than or equal to the preset second change rate, and determine whether the operating efficiency of the alarm system meets the requirements based on the time interval distribution of the alarm events in the log storage unit.

[0013] In an optional manner, the intelligent optimization module is used to reduce the sensitivity of the thermistor when the rate of change of the alarm trigger frequency is greater than the preset second rate of change; wherein the extent of the reduction in sensitivity of the thermistor is determined by the difference between the rate of change of the alarm trigger frequency and the preset second rate of change.

[0014] In an optional manner, the intelligent optimization module is used to determine whether the operating efficiency of the alarm system meets the requirements based on the time interval distribution of the alarm events in the log storage unit. If the standard deviation of the time interval distribution of the alarm events is greater than a preset first standard deviation, it is determined that the operating efficiency of the alarm system does not meet the requirements.

[0015] In an optional manner, the intelligent optimization module is used to preliminarily determine that the data processing capacity of the alarm system does not meet the requirements when the standard deviation of the time interval distribution of the alarm event is greater than a preset second standard deviation, and to determine whether the data processing capacity of the alarm system meets the requirements based on the instantaneous change rate of the server load.

[0016] In an optional manner, the intelligent optimization module is used to shorten the data sampling period of the alarm system when the standard deviation of the time interval distribution of the alarm event is greater than the preset first standard deviation and less than or equal to the preset second standard deviation.

[0017] In an optional manner, the extent of shortening the data sampling period of the alarm system is determined by the difference between the standard deviation of the time interval distribution of the alarm events and a preset first standard deviation.

[0018] In an optional manner, the intelligent optimization module synchronously monitors the CPU occupancy of the server device when shortening the data sampling period. When the CPU occupancy exceeds a preset load threshold, a reduction coefficient is used to dynamically attenuate the shortening amplitude, wherein the reduction coefficient is negatively correlated with the percentage by which the CPU occupancy exceeds the preset load threshold.

[0019] In an optional manner, the alarm trigger module also includes a multi-level notification unit. When the priority of the alarm trigger is increased, the multi-level notification unit activates the corresponding alarm channel combination according to the priority level, including simultaneously triggering the linkage response of SMS notification, email notification and visual panel warning sign.

[0020] Compared with the prior art, the alarm system for server equipment of the present invention has the following beneficial effects:

[0021] The alarm system of the present invention is equipped with an operation status monitoring module, a data analysis module, an alarm trigger module and an intelligent optimization module, and adjusts the sensitivity of the thermal sensor according to the change rate of the alarm trigger frequency. Since the ambient temperature fluctuates greatly during the operation of the server, it may cause the thermal sensor to misjudge. By reducing the sensitivity of the thermal sensor, false alarms caused by slight changes in the ambient temperature are reduced, and the accuracy of the alarm is improved; the data sampling period of the alarm system is adjusted according to the time interval distribution of the alarm events in the log storage unit. Since the data sampling frequency may be too high or too low due to the complexity of the server operation scenario, by shortening the data sampling period, abnormal events can be captured more timely, and the real-time nature of the alarm is improved; the priority of the alarm trigger is adjusted according to the instantaneous change rate of the server load. Since the server load fluctuation may affect the processing speed of the alarm information, by increasing the priority of the alarm trigger, it is ensured that key alarm information can be processed first, thereby enhancing the reliability and intelligence level of the system.

[0022] In addition, the present invention uses the following formula to calculate the alarm trigger priority: ;in, Indicates the adjusted alarm trigger priority, Indicates the initial priority, represents the priority adjustment coefficient, Indicates the instantaneous rate of change of server load, Represents the preset load change rate threshold. This formula dynamically adjusts alarm priorities by introducing the ratio of the load change rate to the threshold, ensuring that critical alarm information is processed first in high load fluctuations while avoiding unnecessary resource waste.

[0023] Other advantages, objects and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be learned from the practice of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0025] Figure 1 This is a structural diagram of an alarm system for server equipment according to the present invention;

[0026] Figure 2 It is a structural diagram of the operation status monitoring module;

[0027] Figure 3 It is the overall logic diagram;

[0028] Figure 4 Schematic diagram of the parameter relationship of the alarm trigger priority calculation formula. DETAILED DESCRIPTION

[0029] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0030] Figure 1 FIG. 1 shows a schematic structural diagram of an embodiment of an alarm system for server equipment provided by the present invention. Figure 1 As shown, the alarm system includes: an operation status monitoring module 110 , a data analysis module 120 , an alarm triggering module 130 and an intelligent optimization module 140 .

[0031] An operating status monitoring module 110 is configured to collect operating parameters of a server device in real time through a sensor group, wherein the sensor group includes a thermal sensor disposed within the server to detect temperature changes and a flow sensor connected to the server to detect fluctuations in network flow; wherein the server includes at least one server device;

[0032] The data analysis module 120 is connected to the operation status monitoring module 110 and is used to perform feature extraction and abnormality analysis on the collected operation parameters;

[0033] An alarm trigger module 130 is connected to the operating status monitoring module 110 and the data analysis module 120, respectively, and is used to issue an alarm signal when the operating parameters exceed the set threshold value. The alarm trigger module 130 includes an alarm unit for generating an audible and visual alarm signal and a log storage unit for recording alarm events;

[0034] The intelligent optimization module 140 is connected to the operation status monitoring module 110, the data analysis module 120, and the alarm triggering module 130, respectively, and is used to adjust the sensitivity of the thermal sensor according to the rate of change of the alarm trigger frequency, or adjust the data sampling period of the alarm system according to the time interval distribution of the alarm events in the log storage unit, and determine the priority of the alarm trigger according to the network traffic fluctuation amplitude and the instantaneous change rate of the server load. The network traffic fluctuation amplitude is: the difference between the maximum and minimum values ​​in the network traffic time series collected by the traffic sensor within a preset time period; when the network traffic fluctuation amplitude exceeds a preset fluctuation amplitude threshold, the instantaneous change rate of the server load is used to determine whether the data processing capacity of the alarm system meets the requirements. If the instantaneous change rate of the server load is greater than the preset change rate threshold, the data processing capacity of the alarm system is determined to be unsatisfactory and the priority of the alarm trigger is increased. The increase in the alarm trigger priority is determined by the difference between the instantaneous change rate of the server load and the preset change rate threshold.

[0035] Specifically, when the rate of change in the alarm trigger frequency (e.g., the rate of increase in the number of alarms per unit time) exceeds a preset threshold, the system reduces the sensitivity of the thermal sensor. For example, if the preset rate of change is 0.1 times per minute, but the actual rate reaches 0.15 times per minute, the sensitivity is reduced to reduce false alarms caused by small temperature fluctuations. If the standard deviation of the time interval distribution of alarm events is large (e.g., the interval time fluctuates significantly), the sampling period is shortened to improve real-time performance. For example, when the standard deviation increases from 0.5 seconds to 0.8 seconds, the sampling period is shortened from 1 second to 0.8 seconds. When the instantaneous rate of change in server load exceeds a threshold (e.g., 10%), the alarm priority is increased. For example, if the load suddenly soars from 30% to 50%, a high-priority alarm (e.g., SMS, email, and audio / visual interaction) is triggered.

[0036] In an alternative manner, the intelligent optimization module 140 is configured to determine whether the dynamic adaptability of the alarm system meets the requirements according to the change rate of the alarm trigger frequency. If the change rate of the alarm trigger frequency is greater than a preset first change rate, it is determined that the dynamic adaptability of the alarm system does not meet the requirements.

[0037] Specifically, the preset first change rate is the upper limit of the allowable alarm frequency fluctuation of the system. The first change rate is default set to 0.05 times / minute. If the actual change rate reaches 0.07 times / minute, it is determined that the dynamic adaptability is insufficient. For example, due to the aging of the cooling fan of a certain server, the temperature fluctuates frequently, triggering multiple alarms. At this time, it is recognized that the alarm frequency rises abnormally, triggering an optimization mechanism (such as reducing the sensitivity or adjusting the sampling period).

[0038] In an alternative manner, when the change rate of the alarm trigger frequency is greater than the preset first change rate and less than or equal to the preset second change rate, the intelligent optimization module 140 is configured to preliminarily determine that the operation efficiency of the alarm system does not meet the requirements, and determine whether the operation efficiency of the alarm system meets the requirements according to the time interval distribution of the alarm events in the log storage unit.

[0039] Specifically, when the change rate of the alarm trigger frequency is within a preset range (such as 0.05 < R ≤ 0.1 times / minute), a secondary verification needs to be carried out in combination with the standard deviation of the time interval distribution. Assuming that the standard deviation of the interval time of the alarm events is 0.6 seconds (the preset first standard deviation is 0.5 seconds), it is confirmed that the operation efficiency does not meet the requirements, and the data sampling period adjustment is started.

[0040] In an alternative manner, when the change rate of the alarm trigger frequency is greater than the preset second change rate, the intelligent optimization module 140 is configured to reduce the sensitivity of the thermal sensor, where the reduction amplitude of the sensitivity of the thermal sensor is determined by the difference between the change rate of the alarm trigger frequency and the preset second change rate.

[0041] Specifically, [[ID= (a)]] , represents the reduction amplitude of the sensitivity, is the sensitivity adjustment coefficient, is the change rate of the alarm trigger frequency, is the preset second change rate. For example, if < (b) = 0.1 times / minute, the actual = 0.​​​​​​In an optional manner, the intelligent optimization module 140 is used to determine whether the operating efficiency of the alarm system meets the requirements based on the time interval distribution of the alarm events in the log storage unit. If the standard deviation of the time interval distribution of the alarm events is greater than the preset first standard deviation, it is determined that the operating efficiency of the alarm system does not meet the requirements.

[0043] Specifically, the larger the distribution of alarm event intervals, the more severe the alarm interval fluctuations. For example, if the preset first standard deviation is 0.5 seconds and the actual standard deviation is 0.7 seconds, it means that the alarm interval is unstable (such as frequent short-interval alarms), and the sampling period needs to be shortened to improve response speed.

[0044] In an optional manner, the intelligent optimization module 140 is used to preliminarily determine that the data processing capacity of the alarm system does not meet the requirements when the standard deviation of the time interval distribution of the alarm event is greater than a preset second standard deviation, and to determine whether the data processing capacity of the alarm system meets the requirements based on the instantaneous change rate of the server load.

[0045] Specifically, when the standard deviation exceeds the threshold, the server load's instantaneous rate of change is checked for abnormalities. For example, if the load change rate suddenly increases to 15% (the threshold is 10%), it indicates insufficient data processing capacity and requires a higher alarm priority.

[0046] In an optional manner, the intelligent optimization module 140 is used to shorten the data sampling period of the alarm system when the standard deviation of the time interval distribution of the alarm event is greater than the preset first standard deviation and less than or equal to the preset second standard deviation, wherein the shortening extent of the data sampling period of the alarm system is determined by the difference between the standard deviation of the time interval distribution of the alarm event and the preset first standard deviation.

[0047] Specifically, , Indicates the shortening of the data sampling period. is the sampling period adjustment coefficient, is the standard deviation of the distribution of alarm event time intervals, is the preset first standard deviation. = 0.7 seconds, = 0.5 seconds, =0.2, then =0.2×(0.7-0.5)=0.04 seconds, and the sampling period is shortened from 1 second to 0.96 seconds.

[0048] In an optional manner, the intelligent optimization module 140 is used to determine whether the data processing capacity of the alarm system meets the requirements based on the instantaneous change rate of the server load. If the instantaneous change rate of the server load is greater than the preset change rate threshold, it is determined that the data processing capacity of the alarm system does not meet the requirements, and the priority of the alarm trigger is increased, wherein the increase in the priority of the alarm trigger is determined by the difference between the instantaneous change rate of the server load and the preset change rate threshold.

[0049] Specifically, Calculate; where, Indicates the adjusted alarm trigger priority, Indicates the initial priority, represents the priority adjustment coefficient, Indicates the instantaneous rate of change of server load, Indicates the preset load change rate threshold. =15%, =10%, =0.5, then = +0.5×(1.5-1)= +0.25, triggering a higher level alarm channel (such as starting SMS and phone notifications at the same time).

[0050] In an optional manner, the intelligent optimization module 140 synchronously monitors the CPU occupancy of the server device when shortening the data sampling period. When the CPU occupancy exceeds a preset load threshold, a reduction coefficient is used to dynamically attenuate the shortening amplitude, wherein the reduction coefficient is negatively correlated with the percentage by which the CPU occupancy exceeds the preset load threshold.

[0051] Specifically, when the CPU usage reaches 80% (the preset load threshold is 70%), the reduction factor is negatively correlated with the percentage exceeding the limit. For example, when the limit is exceeded by 10%, the reduction factor is 0.8, and the actual reduction in ΔT is adjusted from 0.04 seconds to 0.04 × 0.8 = 0.032 seconds, thus avoiding excessive CPU load.

[0052] In an optional manner, the alarm trigger module 130 also includes a multi-level notification unit. When the priority of the alarm trigger is increased, the multi-level notification unit activates the corresponding alarm channel combination according to the priority level, including simultaneously triggering the linkage response of SMS notification, email notification and visual panel warning sign.

[0053] The hierarchical alarm strategy is as follows: ① Low priority: Only triggers a visual panel alert. ② Medium priority: Email notification + panel alert. ③ High priority: SMS + email + audio and video linkage + phone notification.

[0054] In this embodiment, it is necessary to specifically explain that:

[0055] The operating status monitoring module 110 is the core data acquisition part of the entire alarm system. Its structure is as follows: Figure 2 As shown. The sensor group in the operation status monitoring module 110 includes a thermistor and a flow sensor, which are used to detect temperature changes inside the server and fluctuations in network traffic, respectively. The thermistor is arranged inside the server near the main heat-generating components, such as near the CPU and GPU, to ensure that temperature changes in key areas can be captured in real time. The flow sensor is connected to the server network interface to monitor fluctuations in data traffic in and out of the server. In actual applications, the thermistor uses a high-precision NTC thermistor, and its sensitivity can be dynamically adjusted according to environmental requirements; the flow sensor uses a hardware module that supports the Gigabit Ethernet protocol, which can adapt to different network bandwidth requirements. The operation status monitoring module 110 obtains temperature and flow data through periodic sampling, and transmits this data to the data analysis module 120 for further processing.

[0056] The data analysis module 120 receives raw data from the operating status monitoring module 110 and performs feature extraction and anomaly analysis. In specific implementations, the data analysis module 120 first preprocesses the collected temperature and flow rate data, including filtering, denoising, and normalization, to eliminate the impact of external interference on data quality. Subsequently, the data analysis module 120 uses machine learning algorithms to extract features from the data and identify potential abnormal patterns. For example, if the internal temperature of a server fluctuates significantly (a temperature change exceeding ±3°C) within a short period (the default setting is 10 seconds), the data analysis module 120 will flag this as a potential abnormal event. Similarly, if the fluctuation in network flow exceeds a preset threshold, it will also be recorded as an abnormal event. The data analysis module 120 also compares and analyzes historical data to determine whether the current operating parameters deviate from the normal range. If an anomaly is detected, the relevant information is transmitted to the alarm triggering module 130. The preset (fluctuation) thresholds can be set according to actual circumstances and are not limited here. For example, the fluctuation threshold for the thermal sensor can be set to 5°C, and the fluctuation threshold for the flow sensor can be set to 1 Gbps.

[0057] The alarm trigger module 130 generates an alarm signal based on the abnormality information provided by the data analysis module 120 and records the relevant alarm events. The alarm trigger module 130 comprises an alarm unit and a log storage unit. The alarm unit is responsible for generating audible and visual alarm signals, with the audible alarm signal being emitted by a buzzer and the visual alarm signal being generated by a flashing LED. In practical applications, the alarm unit's design fully considers human-computer interaction requirements. For example, the buzzer volume is adjustable, and the LED light color can be switched to red or yellow depending on the alarm level. The log storage unit is used to record the timestamp, abnormality type, and corresponding parameter values ​​of each alarm event, facilitating subsequent troubleshooting and system optimization. The alarm trigger module 130 operates as follows: Upon receiving abnormality information from the data analysis module 120, the alarm trigger module 130 first determines whether the current operating parameters exceed the set threshold (for example, the set threshold for a thermal sensor can be set to 60°C, or the set threshold for a flow sensor can be set to 2Gbps). If so, the alarm unit immediately activates the audible and visual alarm signals and writes the alarm event to the log storage unit.

[0058] The intelligent optimization module 140 is one of the key innovations of the present invention. By comprehensively analyzing the change rate of the alarm trigger frequency, the distribution of the time interval of the alarm events, and the instantaneous change rate of the server load, the intelligent optimization module 140 dynamically adjusts the various parameters of the alarm system, thereby improving the reliability and intelligence level of the system. Figure 3 As shown, in the specific implementation process, the intelligent optimization module 140 first calculates the rate of change of the alarm trigger frequency and compares it with the preset first rate of change and second rate of change (the first rate of change is less than the second rate of change). If the rate of change of the alarm trigger frequency is greater than the first rate of change but less than or equal to the second rate of change, it is preliminarily determined that the operating efficiency of the alarm system does not meet the requirements, and further analyzes the time interval distribution of the alarm events in the log storage unit. If the rate of change of the alarm trigger frequency is greater than the second rate of change, the sensitivity of the thermal sensor is reduced, and the reduction is determined by the formula Calculate, where Indicates the sensitivity reduction. is the sensitivity adjustment coefficient, is the rate of change of the alarm trigger frequency, For example, assuming the statistically obtained alarm trigger frequency change rate is 0.12 times / minute, the first change rate Set to 0.05 times / minute, second rate of change Set to 0.1 times / minute, The default value is 0.5. , calculated according to the formula , the sensitivity of the thermal sensor decreases by 1%. Through the above method, false alarms caused by slight changes in ambient temperature can be effectively reduced, and the accuracy of the alarm can be improved.

[0059] For the analysis of the time interval distribution of alarm events, the intelligent optimization module 140 calculates its standard deviation and compares it with the preset first standard deviation and second standard deviation. If the standard deviation is greater than the first standard deviation but less than or equal to the second standard deviation, the data sampling period of the alarm system is shortened, and the shortening amplitude is calculated by the formula Calculate, where Indicates the shortening of the data sampling period. is the sampling period adjustment coefficient, is the standard deviation of the distribution of alarm event time intervals, For example, assuming the standard deviation of the alarm event time interval distribution obtained by statistics is 0.89s, first standard deviation Set to 0.5s, second standard deviation Set to 0.5s, The default value is 0.2. ,but , the data sampling period is shortened by 0.078 seconds. By shortening the data sampling period, abnormal events can be captured more promptly, improving the real-time nature of alarms. If the standard deviation is greater than the second standard deviation, further analysis is performed on the instantaneous rate of change of the server load.

[0060] Analysis of the instantaneous change rate of server load is another important function of the intelligent optimization module 140. When the instantaneous change rate of server load exceeds the preset change rate threshold, the intelligent optimization module 140 will increase the priority of the alarm trigger, and the increase is calculated by the formula Calculate; where, Indicates the adjusted alarm trigger priority, Indicates the initial priority, represents the priority adjustment coefficient, Indicates the instantaneous rate of change of server load (e.g. 15%), Represents the preset load change rate threshold (e.g. 10%). The physical meaning of this formula is that when the server load fluctuates greatly, the alarm priority is dynamically adjusted to ensure that key alarm information can be processed first while avoiding unnecessary resource waste. Figure 4 As shown in the figure, the relationship between the variables in the formula clearly demonstrates the nonlinear mapping between load change rate and alarm priority. It should be noted that different alarm methods are used for different priorities. For example, high-priority alarms are notified through multiple channels, and operation and maintenance personnel respond within 5 seconds to avoid downtime risks.

[0061] In actual application scenarios, the alarm system of the present invention can be deployed in a server cluster in a data center to monitor the operating status of multiple servers. For example, in a large cloud computing center, the number of server devices is huge and the operating environment is complex. It is difficult for traditional alarm systems to meet the needs of dynamic adaptability and multi-dimensional data fusion. By deploying the alarm system of the present invention, the operating status monitoring module 110 can collect the temperature and flow data of each server in real time, and the data analysis module 120 conducts in-depth mining of these data to identify potential anomalies. When the temperature of a server suddenly rises or the network traffic fluctuates abnormally, the alarm trigger module 130 will immediately send out an audible and visual alarm signal and record the relevant information to the log storage unit. At the same time, the intelligent optimization module 140 dynamically adjusts the sensitivity of the thermal sensor, the data sampling period and the alarm trigger priority according to the change rate of the alarm trigger frequency, the distribution of the time interval of the alarm event and the instantaneous change rate of the server load, thereby ensuring that the alarm system can maintain efficient operation in a complex environment.

[0062] In addition, the alarm system of the present invention also has good scalability and compatibility. For example, the operating status monitoring module 110 can add other types of sensors, such as humidity sensors or voltage sensors, according to actual needs, to achieve monitoring of more operating parameters. The data analysis module 120 supports the integration of multiple machine learning algorithms and can select the optimal feature extraction and anomaly analysis methods according to different application scenarios. The sound and light alarm signal design of the alarm trigger module 130 is flexible, and different alarm modes can be customized according to user needs. The parameter adjustment strategy of the intelligent optimization module 140 can also be optimized according to specific application scenarios. For example, the sensitivity adjustment conditions can be appropriately relaxed in low-load scenarios, while in high-load scenarios, more attention is paid to the dynamic adjustment of alarm priorities.

[0063] The alarm system of this embodiment achieves comprehensive monitoring and intelligent management of the operating status of server equipment through the collaborative work of the operating status monitoring module 110, the data analysis module 120, the alarm trigger module 130, and the intelligent optimization module 140. Its core advantage lies in its ability to dynamically adjust the sensitivity of the thermal sensor, the data sampling period, and the alarm trigger priority based on the rate of change of the alarm trigger frequency, the distribution of the time intervals between alarm events, and the instantaneous rate of change of the server load, thereby significantly improving the accuracy, real-time nature, and reliability of the alarm. In actual application, the system can not only effectively reduce false alarms and missed alarms, but also ensure that key alarm information is processed first, providing a strong guarantee for the safe and stable operation of server equipment.

[0064] The above description is merely an illustration of preferred embodiments of the present invention and the underlying technical principles. Those skilled in the art should understand that the scope of the present invention is not limited to technical solutions formed by specific combinations of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the aforementioned concepts. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with similar functions disclosed in this invention.

[0065] It should be noted that the terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects and to define a specific order or precedence. Where appropriate, the order used for similar objects may be interchanged, such that the embodiments of the present application described herein can be implemented in an order other than the order shown or described.

[0066] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. An alarm system for server equipment, characterized in that: include: An operating status monitoring module for collecting operating parameters of a server device in real time through a sensor group, the sensor group including a thermal sensor disposed within the server for detecting temperature changes and a flow sensor connected to the server for detecting fluctuations in network flow; wherein the server includes at least one server device; A data analysis module, connected to the operation status monitoring module, for performing feature extraction and anomaly analysis on the collected operation parameters; An alarm trigger module, connected to the operation status monitoring module and the data analysis module respectively, for issuing an alarm signal when an operation parameter exceeds a set threshold value, the alarm trigger module including an alarm unit for generating an audible and visual alarm signal and a log storage unit for recording alarm events; An intelligent optimization module is respectively connected to the operation status monitoring module, the data analysis module and the alarm trigger module, and is used to adjust the sensitivity of the thermal sensor according to the change rate of the alarm trigger frequency or adjust the data sampling period of the alarm system according to the time interval distribution of the alarm events in the log storage unit, and determine the priority of the alarm trigger according to the network traffic fluctuation amplitude and the instantaneous change rate of the server load; wherein the network traffic fluctuation amplitude is: the difference between the maximum and minimum values ​​in the network traffic time series collected by the traffic sensor within a preset time period; when the network traffic fluctuation amplitude exceeds the preset fluctuation amplitude threshold, whether the data processing capacity of the alarm system meets the requirements is determined according to the instantaneous change rate of the server load; if the instantaneous change rate of the server load is greater than the preset change rate threshold, it is determined that the data processing capacity of the alarm system does not meet the requirements, and the priority of the alarm trigger is increased, wherein the increase in the priority of the alarm trigger is determined by the difference between the instantaneous change rate of the server load and the preset change rate threshold; The intelligent optimization module is used to determine whether the operating efficiency of the alarm system meets the requirements based on the time interval distribution of the alarm events in the log storage unit, and if the standard deviation of the time interval distribution of the alarm events is greater than a preset first standard deviation, it is determined that the operating efficiency of the alarm system does not meet the requirements; The intelligent optimization module is used to preliminarily determine that the data processing capacity of the alarm system does not meet the requirements when the standard deviation of the time interval distribution of the alarm event is greater than a preset second standard deviation, and to determine whether the data processing capacity of the alarm system meets the requirements based on the instantaneous change rate of the server load; The intelligent optimization module is used to shorten the data sampling period of the alarm system when the standard deviation of the time interval distribution of the alarm event is greater than the preset first standard deviation and less than or equal to the preset second standard deviation; wherein, the shortening extent of the data sampling period of the alarm system is determined by the difference between the standard deviation of the time interval distribution of the alarm event and the preset first standard deviation; the intelligent optimization module synchronously monitors the CPU occupancy rate of the server device when shortening the data sampling period, and when the CPU occupancy rate exceeds the preset load threshold, the shortening extent is dynamically attenuated using a reduction coefficient, wherein the reduction coefficient is negatively correlated with the percentage by which the CPU occupancy rate exceeds the preset load threshold.

2. The alarm system for server equipment according to claim 1, characterized in that: The intelligent optimization module is used to determine whether the dynamic adaptability of the alarm system meets the requirements based on the change rate of the alarm trigger frequency. If the change rate of the alarm trigger frequency is greater than the preset first change rate, it is determined that the dynamic adaptability of the alarm system does not meet the requirements.

3. The alarm system for server equipment according to claim 2, characterized in that: The intelligent optimization module is used to preliminarily determine that the operating efficiency of the alarm system does not meet the requirements when the change rate of the alarm trigger frequency is greater than the preset first change rate and less than or equal to the preset second change rate, and to determine whether the operating efficiency of the alarm system meets the requirements based on the time interval distribution of the alarm events in the log storage unit.

4. The alarm system for server equipment according to claim 3, characterized in that: The intelligent optimization module is used to reduce the sensitivity of the thermal sensor when the change rate of the alarm trigger frequency is greater than the preset second change rate, wherein the reduction extent of the sensitivity of the thermal sensor is determined by the difference between the change rate of the alarm trigger frequency and the preset second change rate.

5. The alarm system for server equipment according to claim 1, characterized in that: The alarm trigger module also includes a multi-level notification unit. When the priority of the alarm trigger is increased, the multi-level notification unit activates the corresponding alarm channel combination according to the priority level, including simultaneously triggering the linkage response of SMS notification, email notification and visual panel warning sign.

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