A cabinet security method and system based on the Internet of Things

Abstract

This invention relates to the field of Internet of Things (IoT) technology, specifically to an IoT-based cabinet security method and system. The method includes the following steps: collecting temperature, humidity, access control, and vibration records from the data center to form a cabinet environmental data sequence; analyzing data alignment and change characteristics to generate an environmental assessment result; linking electronic locks, audible and visual alarms, and temperature control equipment to generate a security response execution result; and adjusting alarm range settings based on abnormal fluctuations and triggering conditions to obtain alarm baseline adjustment indicators. This invention integrates multi-dimensional data such as temperature, humidity, vibration, and access control status to monitor cabinet environmental changes in real time and automatically identify anomalies. Based on comprehensive data analysis, it accurately assesses the environmental safety status and automatically triggers emergency measures such as locking and alarms, eliminating the lag of manual intervention. It can quickly respond to abnormal events, adjust temperature control equipment, ensure safe equipment operation, and improve the intelligence and automation level of cabinet security.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, and in particular to a cabinet security method and system based on IoT. Background Technology

[0002] The Internet of Things (IoT) technology involves connecting various physical devices and sensors via the internet to achieve information exchange and intelligent control between devices. It is widely used in smart homes, intelligent transportation, environmental monitoring, industrial automation, and other fields, promoting interconnectivity and automated decision-making among devices. Traditional IoT cabinet security methods refer to the technology of intelligent security management of cabinets using IoT technology. This primarily focuses on the physical security and protection of the cabinets, typically through the installation of sensors, cameras, alarm devices, and other equipment to monitor the cabinet's status, environmental changes, and external interference in real time.

[0003] Existing methods rely on single monitoring devices and focus on physical security monitoring, failing to effectively integrate multiple environmental factors. When multiple environmental factors interact, it is difficult to identify potential risks in real time. Information such as temperature, humidity, vibration, and access control status is not analyzed in a coordinated manner, resulting in insufficient ability to identify environmental changes. Abnormal events rely on manual intervention, resulting in slow response and a lack of automated intelligent processing, which increases security risks. In emergency situations, effective measures cannot be taken in a timely manner, affecting the security of cabinet equipment. There is a problem of delayed response, making it impossible to achieve accurate and rapid anomaly response. Summary of the Invention

[0004] To address the technical problems existing in the prior art, this invention provides a cabinet security method and system based on the Internet of Things (IoT). The technical solution is as follows: On the one hand, an IoT-based cabinet security method is provided, including the following steps: S1: Based on the data center server room, verify the sampling interval and monitoring polling rhythm of the cold aisle temperature sensor, filter out temperature records that exceed the range, determine the flip-off time of the access control signal, screen out stable segments of bottom vibration acceleration, verify the validity of humidity records, and obtain the rack environment data sequence. S2: Based on the cabinet environment data sequence, verify the timestamp alignment status, determine the availability of temperature records at the synchronization moment, calculate the direction of humidity sequence change, check the access control period that is kept open, identify the stable range of vibration data, and obtain the cabinet environment assessment result. S3: Based on the cabinet environment assessment results, analyze whether the temperature and humidity are within the operating range, assess the matching relationship between the access control status and the authorized time period, determine unauthorized opening situations, filter irrelevant vibration interference records, and obtain cabinet abnormal risk data; S4: Based on the cabinet abnormal risk data, check the current status of the electric lock and execute the locking control, verify the locking feedback information, trigger the audible and visual alarm, verify the alarm feedback status, adjust the operating mode of the temperature control equipment according to the temperature status, and obtain the security response execution result. S5: For the cabinet abnormal risk data, analyze the time series fluctuation characteristics, combine the security response execution results, count the trigger frequency, evaluate the relationship between trigger records and operation and maintenance handling time, optimize the alarm interval setting, and obtain alarm benchmark adjustment indicators.

[0005] On the other hand, the cabinet environment data sequence includes an environmental time index table, a sensor status identifier set, and a sampled valid record set; the cabinet environment assessment results include environmental operation level, status continuity identifier, and environmental correlation judgment item; the cabinet abnormal risk data includes an abnormality type identifier set, risk level classification item, and event correlation table; the security response execution results include control action confirmation identifier, equipment feedback status set, and response completion status identifier; and the alarm benchmark adjustment index includes alarm interval correction parameters, trigger sensitivity adjustment item, and alarm strategy update identifier.

[0006] On the other hand, the specific steps for obtaining the rack environment data sequence are as follows: S101: Based on the data center computer room, analyze the relationship between the sampling period and the monitoring period, calculate the time interval difference, determine the period matching status, filter the temperature recording range data, compare abnormal data and missing data, adjust the time index sorting and time granularity, and obtain the temperature time alignment index set. S102: Based on the temperature-time alignment index set, analyze the corresponding access control opening and closing signals, calculate the differences between adjacent states, determine the state reversal time, filter access control change time data, calculate the average amplitude of the vibration probe acceleration sliding window, compare the window fluctuation range, determine the stable section, and obtain the acceleration stable section set. S103: Based on the acceleration stable segment set, analyze the corresponding humidity sampling records, compare the upper and lower limits of the humidity range, identify the data within the range, adjust the effective humidity record timestamp, associate the access control change time data, and obtain the cabinet environment data sequence.

[0007] On the other hand, the specific steps for obtaining the cabinet environment assessment results are as follows: S201: Based on the cabinet environment data sequence, calculate the time interval difference of each sensor record, compare the time granularity benchmark to determine the alignment status, identify time misalignment records, adjust the data combination relationship at the same moment, determine the consistency between the temperature record range status and the sensor status identifier, and obtain the time consistency identifier set. S202: Based on the time consistency identifier set, extract the humidity record time series, analyze the humidity difference direction between adjacent moments, determine the continuous change segment, calculate the duration of the access control opening and closing state, identify the continuous segment, determine the continuity of the vibration record sampling interval, determine the acceleration fluctuation amplitude range, and obtain the environmental correlation judgment item. S203: Based on the environmental correlation judgment item, calculate the continuous duration of the temperature range state, divide the operation segment, integrate the humidity change segment, the access control continuous segment and the vibration stability segment, adjust the segment combination relationship, divide the environmental operation level, and obtain the cabinet environment assessment result.

[0008] On the other hand, the specific steps for obtaining the cabinet anomaly risk data are as follows: S301: Based on the cabinet environment assessment results, retrieve the temperature range corresponding to the environmental operation level, compare the temperature record with the upper and lower limits of the cabinet operation range, determine the duration of the out-of-bounds range, filter continuous out-of-bounds segments, analyze the humidity record with the cabinet environment requirement range, compare the humidity range coverage status, determine the duration of the deviation range, identify continuous deviation segments, and obtain the temperature and humidity deviation judgment set. S302: Based on the temperature and humidity deviation judgment set, calculate the start and end times of the access control opening section, compare the time overlap relationship between the opening section and the authorized opening time period, determine the unauthorized covered section, identify the uncovered opening segment, analyze the boundary time of the opening segment, associate the time of access control status change, and obtain the abnormal opening event set. S303: Based on the set of abnormal opening events, compare the vibration recording time distribution with the overlap of the event time window, filter vibration segments within the event time window, determine the validity of the event-related vibration segment, adjust the risk segment combination, classify the abnormal type identifier, and obtain the cabinet abnormal risk data.

[0009] On the other hand, the specific steps for obtaining the security response execution result are as follows: S401: Based on the cabinet abnormal risk data, detect the current status identifier of the electric lock, compare the correspondence between the locking status and the risk level classification item, execute the locking command sending operation, detect the consistency between the lock status feedback identifier and the command identifier, and obtain the locking action confirmation identifier. S402: Based on the locking action confirmation flag, detect the triggering condition of the audible and visual alarm, execute the alarm start command, collect the alarm start signal feedback data, compare the consistency between the feedback status and the start command status, and obtain the device feedback status set. S403: Based on the device feedback status set, detect the current temperature status segment of the cabinet, compare the relationship between the temperature segment and the safe temperature range, adjust the working mode of the temperature control device, and obtain the security response execution result.

[0010] On the other hand, the specific steps for obtaining the alarm baseline adjustment index are as follows: S501: Based on the cabinet abnormal risk data, extract the time series of abnormal type identifier set, calculate the risk level difference amplitude between adjacent times, identify continuous fluctuation segments, determine the direction of change of fluctuation segments, filter abnormal trend segments, and obtain the risk fluctuation trend set. S502: Based on the security response execution results, statistically analyze the time distribution of response completion status indicators, calculate the number of triggers per unit time, associate the time segment of the risk fluctuation trend set, compare the overlap relationship between the number of triggers and the abnormal trend, determine the cause of abnormal triggering, and obtain the trigger association analysis set; S503: Based on the trigger correlation analysis set, retrieve the time segment of the operation and maintenance record and the cabinet status segment, compare the overlap relationship between the abnormal trigger cause and the operation and maintenance time, adjust the upper and lower limits of the alarm interval, correct the trigger sensitivity parameters, and obtain the alarm benchmark adjustment index.

[0011] On the other hand, the cold aisle refers to the cold aisle area between server racks in the computer room, the range refers to the effective measurement range of the sensor, and the alignment status refers to whether the timestamps of the sensor data are consistent.

[0012] On the other hand, the direction of humidity sequence change refers to the trend of humidity data over a certain period of time, whether there is a pattern of increase or decrease, and the time series fluctuation characteristics refer to the change pattern of time series data over time.

[0013] On the other hand, an IoT-based cabinet security system is provided, which is applied to an IoT-based cabinet security method, including: The environmental data acquisition module is based in the data center server room. It checks the sampling interval and monitoring polling rhythm of the cold aisle temperature sensor, filters out temperature records that exceed the range, interprets the door access signal flip-off time point, screens stable segments of bottom vibration acceleration, verifies the validity of humidity records, and obtains the rack environmental data sequence. The data verification and evaluation module verifies the timestamp alignment status based on the cabinet environment data sequence, determines the availability of temperature records at the synchronization moment, calculates the direction of humidity sequence change, checks the period during which the access control remains open, identifies the stable range of vibration data, and obtains the cabinet environment evaluation result. Based on the cabinet environment assessment results, the abnormal risk identification module analyzes whether the temperature and humidity are within the operating range, assesses the matching relationship between the access control status and the authorized time period, judges unauthorized opening situations, filters irrelevant vibration interference records, and obtains cabinet abnormal risk data. Based on the cabinet's abnormal risk data, the response control execution module checks the current status of the electric lock and executes locking control, verifies the locking feedback information, triggers the audible and visual alarm, verifies the alarm feedback status, adjusts the operating mode of the temperature control equipment according to the temperature status, and obtains the security response execution result. The alarm optimization module analyzes the time series fluctuation characteristics of the cabinet abnormal risk data, combines the security response execution results, counts the trigger frequency, evaluates the relationship between trigger records and operation and maintenance handling time, optimizes the alarm interval setting, and obtains alarm benchmark adjustment indicators.

[0014] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: By integrating multi-dimensional data such as temperature, humidity, vibration, and access control status, the system monitors changes in the cabinet environment in real time and automatically identifies anomalies. Based on comprehensive data analysis, it accurately assesses the environmental safety status and automatically triggers emergency measures such as locking and alarms. This eliminates the lag of manual intervention, enables rapid response to abnormal events, adjusts temperature control equipment, and ensures safe equipment operation. By optimizing alarm ranges, it improves alarm accuracy and response speed, addresses the shortcomings of existing solutions in handling complex scenarios, and enhances the intelligence and automation level of cabinet security. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of the main steps of the present invention; Figure 2 This is a flowchart of steps S1 of the present invention; Figure 3 This is a flowchart of steps S2 of the present invention; Figure 4 This is a flowchart of steps S3 of the present invention; Figure 5 This is a flowchart of step S4 of the present invention; Figure 6 This is a flowchart of steps S5 of the present invention; Figure 7 This is a system block diagram of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0019] This invention provides a cabinet security method based on the Internet of Things, such as... Figure 1 As shown, it includes the following steps: S1: Based on the data center computer room, obtain the sampling period of the cold aisle temperature sensor between the rack rows, determine the matching of the sampling period and the monitoring period, filter the temperature records within the range, analyze the time of change of the rack door access control opening and closing signal, filter the stable acceleration records of the vibration probe at the bottom of the rack, determine the humidity records within the range of the humidity sensor, and obtain the rack environment data sequence. S2: Based on the cabinet environment data sequence, analyze the timestamp alignment, determine the valid status of temperature records at the same time, calculate the humidity record change trend, analyze the duration of access control opening and closing, identify the stable section of vibration records, and obtain the cabinet environment assessment results. S3: Based on the cabinet environment assessment results, check whether the temperature data exceeds the cabinet operating range, analyze the matching of humidity data with the cabinet environment requirements, assess the matching of access control status with authorized opening time periods, determine whether there are unauthorized opening events, and then filter the vibration data to eliminate irrelevant vibration effects and obtain cabinet abnormal risk data. S4: Based on the cabinet abnormal risk data, check the status of the cabinet electric lock, execute the locking operation, and verify whether the locking command is successfully executed. Trigger the audible and visual alarm according to the abnormal status, check the alarm's activation signal feedback data, determine whether the response is effective, and then adjust the working mode of the temperature control equipment according to the cabinet temperature status to match the safe temperature range of the cabinet, and obtain the security response execution result. S5: Analyze the time series fluctuations of cabinet anomaly risk data, identify abnormal trends within the fluctuation range, analyze the trigger frequency of security response execution results, assess the correlation between trigger frequency and operation, determine the cause of anomaly triggering, and then optimize alarm interval settings based on maintenance records and cabinet status to obtain alarm baseline adjustment indicators.

[0020] The cabinet environment data sequence includes an environmental time index table, a sensor status identifier set, and a sampled valid record set. The cabinet environment assessment results include environmental operation level, status continuity identifier, and environmental correlation judgment items. The cabinet anomaly risk data includes an anomaly type identifier set, risk level classification items, and an event correlation table. The security response execution results include control action confirmation identifiers, equipment feedback status sets, and response completion status identifiers. The alarm baseline adjustment indicators include alarm interval correction parameters, trigger sensitivity adjustment items, and alarm policy update identifiers.

[0021] In S1, the cold aisle between racks refers to the cold aisle area between racks in the computer room, which is usually a space specifically used for air conditioning cooling and air circulation to ensure that the equipment inside the racks maintains an appropriate temperature; the monitoring cycle refers to the time interval or period for data collection and monitoring of the rack environment; the measurement range refers to the effective measurement range of the equipment (such as temperature sensors, humidity sensors, etc.), and data within this range is considered valid; the opening and closing signal change moment refers to the time point when the opening and closing state of the rack access control system changes, such as the opening and closing of the rack door; stable acceleration recording refers to the acceleration data recorded by the vibration sensor, and the data should remain relatively stable within a certain period of time, usually to eliminate abnormal data caused by transient interference.

[0022] In S2, alignment refers to whether the timestamps of the sensor data are consistent, ensuring that all data are compared and analyzed at the same point in time; valid temperature recording refers to whether the output data of the temperature sensor is within the valid range and whether it can reflect the true cabinet temperature; humidity recording trend refers to the trend of humidity data over a certain period of time, whether there is a pattern of increase or decrease; stable vibration recording refers to the vibration data of the vibration sensor remaining stable during certain periods of time during data acquisition, without external interference.

[0023] In S3, the rack operating range refers to the temperature and humidity range allowed for the operation of the equipment inside the rack. When the parameters exceed this range, it may cause equipment failure or instability. Unauthorized opening events refer to the rack door being opened without authorization, usually unauthorized access to the equipment. Vibration data filtering refers to removing abnormal fluctuations or noise from vibration sensor data to ensure the accuracy of the analysis results. Vibration impact refers to the abnormal effects that vibration interference may have on equipment or sensor data, which need to be eliminated through data filtering and other methods.

[0024] In S4, status check refers to checking the current status of the equipment, such as checking whether the cabinet door is locked or whether the cabinet temperature is within the normal range; abnormal status refers to the status of the equipment or system not conforming to normal working standards, such as the cabinet door being opened without authorization or the temperature being too high; start signal feedback data refers to the data transmitted back from devices such as alarms, indicating whether the equipment has started successfully, such as whether the audible and visual alarm has issued an alarm signal.

[0025] In S5, time series fluctuation refers to the change pattern of time series data (such as temperature, humidity, vibration, etc.) over time. Fluctuations can reflect the dynamic behavior of the system. Abnormal trend refers to the change trend in time series data that does not conform to the normal pattern. It is usually a precursor to potential faults or abnormal states. Trigger frequency refers to the frequency of security system response or the number of trigger events, which is used to measure the frequency of event occurrence. Abnormal trigger cause refers to the root cause of the abnormal event that causes the security system response, such as excessive temperature or unauthorized access control. Maintenance records and cabinet status refer to the maintenance records of cabinet equipment by maintenance personnel and the current status information of the equipment, which are used to optimize alarm settings and system adjustments.

[0026] like Figure 2 As shown, the specific steps for obtaining the rack environment data sequence are as follows: S101: Based on the data center computer room, analyze the relationship between the sampling period and the monitoring period, calculate the time interval difference, determine the period matching status, filter the temperature recording range data, compare abnormal data and missing data, adjust the time index sorting and time granularity, and obtain the temperature time alignment index set. Based on the data center server room, the sampling period and monitoring period of the temperature sensor are obtained, the time interval difference between them is calculated, and it is determined whether the two periods match. For example, assuming the sampling period is 30 minutes and the monitoring period is 15 minutes, the difference is calculated to be -15 minutes, indicating that the monitoring period is shorter than the sampling period. In this case, the monitoring period can be adjusted or the sampling period can be changed to ensure data synchronization. Next, the temperature data is filtered to ensure that it is within the effective range. For example, the effective range of the temperature sensor is -10℃ to 80℃. If the temperature data exceeds this range, such as records of 90℃ or -15℃, they need to be removed. For the remaining effective data, it is aligned and sorted according to the timestamp. If there are any missing records, the missing time points need to be filled by interpolation methods to ensure the continuity and accuracy of the data. The adjusted data set is the temperature time alignment index set, ensuring the consistency of the data in the time dimension and providing a reliable foundation for subsequent analysis.

[0027] S102: Based on the temperature-time aligned index set, analyze the corresponding access control opening and closing signals, calculate the differences between adjacent states, determine the state reversal time, filter access control change time data, calculate the average amplitude of the vibration probe acceleration sliding window, compare the window fluctuation range, determine the stable section, and obtain the set of acceleration stable sections. To analyze the access control's opening and closing signals, firstly, the differences between access control states are calculated. For example, if the access control state changes from "closed" to "open," the difference is 1; if it changes from "open" to "closed," the difference is -1. In this way, the transition time of each access control state is identified. Assuming the transition times occur at 10:00, 14:30, and 17:45, these times are recorded as the access control change times. Next, the vibration sensor data is analyzed. To filter out transient interference, a sliding window method is used to process the acceleration data, assuming the window size is 1. At 0 seconds, the acceleration fluctuation range within the window is calculated. If the acceleration fluctuation exceeds 0.5 m / s² within a certain time period, the data segment is considered unstable and needs to be excluded. If the fluctuation is small, below 0.5 m / s², the data segment is considered stable and can be retained. In this way, stable vibration time segments are selected. Assuming that the stable segments are 10:00 to 10:30, 14:00 to 14:30, and 17:30 to 18:00, then these are the stable acceleration segment sets, providing a reliable reference for subsequent environmental data analysis.

[0028] S103: Based on the acceleration stable segment set, analyze the corresponding humidity sampling records, compare the upper and lower limits of the humidity range, identify the data within the range, adjust the effective humidity record timestamp, associate the access control change time data, and obtain the cabinet environment data sequence. The humidity records are compared to ensure they are within the sensor's effective range. Assuming the humidity sensor's range is 20% to 80% relative humidity, if the recorded humidity is 75%, 80%, or 65% at a certain time, the data is within the valid range and meets the requirements. If the recorded humidity is 90% or 10%, the data is considered invalid and needs to be discarded. For valid humidity data, the timestamp is adjusted to correlate it with the access control change time. Assuming the access control opening and closing times are 10:00 and 14:30, and the humidity records are at 10:05 and 14:35, the timestamp of the humidity records is adjusted to ensure synchronization with the data at the access control change time. Through this process, a complete cabinet environment data sequence is obtained, including temperature, humidity, and vibration data, all of which have been effectively filtered and time-aligned. Ultimately, the data can support subsequent environmental assessments and anomaly risk detection.

[0029] like Figure 3 As shown, the specific steps for obtaining the rack environment assessment results are as follows: S201: Based on the cabinet environment data sequence, calculate the time interval difference of each sensor record, compare the time granularity benchmark to determine the alignment status, identify time misalignment records, adjust the data combination relationship at the same moment, determine the consistency between the temperature record range status and the sensor status identifier, and obtain the time consistency identifier set. Calculate the time interval difference between each sensor record. Assuming two time records are 10:00 and 10:15, the calculated difference indicates a time interval of 15 minutes. If the difference is too large, such as greater than 30 minutes, the recording time is considered misaligned and needs adjustment. Next, compare with the time granularity reference to determine if time alignment is necessary. Assuming the sensor sampling period is 10 minutes, if the recorded time interval is 8 minutes or 12 minutes, the time points need to be adjusted to align with the reference period (10 minutes). Misaligned records need to be discarded or re-interpolated for alignment. By processing time alignment and interval difference, time misalignment records can be identified and adjusted. Then, the range status of the temperature record is checked to ensure that it is within the effective measurement range of the temperature sensor. Assuming that the temperature sensor's range is -10℃ to 80℃, if the temperature data is 85℃ or -12℃, the record needs to be judged as abnormal data and removed or corrected. Finally, combined with the sensor status identifier, the sensor status is ensured to be consistent with the data record. If there are records with inconsistent status, they need to be marked or adjusted. Finally, a time consistency identifier set is obtained to represent the consistency of data time alignment and sensor status.

[0030] S202: Based on the time consistency identifier set, extract the time series of humidity records, analyze the direction of humidity difference between adjacent moments, determine the continuous change segment, calculate the duration of access control opening and closing status, identify the continuous segment, determine the continuity of vibration record sampling interval, determine the range of acceleration fluctuation amplitude, and obtain environmental correlation judgment items. Extracting the time series of humidity records and analyzing the direction of humidity difference between adjacent moments, assuming the humidity record shows 75% at 10:00 and 77% at 10:15, the humidity difference is 2%, indicating an increase in humidity. A negative humidity difference indicates a decrease in humidity. This method can identify the continuity of humidity changes and determine whether the humidity maintains a continuous trend. For example, if the humidity continuously increases from 10:00 to 14:00, this segment can be considered a continuous humidity change segment. Next, the duration of the access control's open / closed state is calculated. Assuming the access control state is "open" between 10:00 and 10:30, and the access control duration is 30 minutes, if the access control's open / closed duration exceeds the specified limit... If a certain safety threshold is set (e.g., exceeding 30 minutes), the access control system can be considered abnormal. When analyzing vibration records, it is first necessary to ensure the continuity of the vibration record sampling interval. Assuming the sampling interval is once every 10 seconds, if there is a time interval of 20 seconds or longer in a certain period, it indicates that the sampling is discontinuous. Next, the amplitude range of acceleration fluctuation is calculated. Assuming the acceleration fluctuation range is from 0.5 m / s² to 1.5 m / s², it can be judged that the vibration amplitude of this section is within the normal range. If the fluctuation amplitude is greater than 1.5 m / s², it is considered that the vibration is too large. Through the analysis of multiple dimensions such as humidity change, access control status, and vibration stability, environmental correlation judgment items are obtained, which serve as the basis for cabinet environment assessment.

[0031] S203: Based on environmental correlation judgment items, calculate the continuous duration of temperature range status, divide the operating segments, integrate humidity change segments, access control continuous segments and vibration stability segments, adjust the segment combination relationship, divide the environmental operation level, and obtain the cabinet environment assessment results. First, the continuous duration of the temperature measurement range is calculated. Assuming the temperature range is between -10℃ and 80℃ for 6 consecutive hours, the environment is divided into operating segments. If the temperature, humidity, access control, and vibration data remain within the normal range for a certain period, this period can be considered the stable operating segment of the cabinet. Then, the humidity variation segment, the continuous access control segment, and the stable vibration segment are integrated to combine the segments. For example, if the temperature and humidity are stable between 10:00 and 12:00, the access control is continuously open, and the vibration is stable without abnormalities, this period can be integrated into the stable operating segment of the environment. By integrating and adjusting the segments, the operating level of the environment is determined. If the cabinet operates normally without any abnormal events during a certain period, this period is rated as high security level. If some data of the cabinet exceeds the normal range at other times, this period may be rated as low security level. Finally, the cabinet environment assessment result is obtained. Through this process, the overall safety status and operating level of the cabinet can be accurately assessed.

[0032] like Figure 4As shown, the specific steps for obtaining cabinet anomaly risk data are as follows: S301: Based on the cabinet environment assessment results, retrieve the temperature range corresponding to the environmental operation level, compare the temperature record with the upper and lower limits of the cabinet operation range, determine the duration of the out-of-bounds range, filter continuous out-of-bounds segments, analyze the humidity record with the range of cabinet environmental requirements, compare the humidity range coverage status, determine the duration of the deviation range, identify continuous deviation segments, and obtain the temperature and humidity deviation judgment set. First, the rack temperature records are retrieved and compared with the rack's operating range. Assuming the rack's temperature range is 20℃ to 30℃, a temperature record of 18℃ or 32℃ is considered out of range. Next, the duration of the out-of-range segment is calculated. For example, if the temperature remains above 32℃ for 5 minutes, this period is considered an out-of-range segment and recorded. Then, all consecutive out-of-range segments are filtered out. For instance, if the temperature exceeds 30℃ for 15 minutes, it can be considered a consecutive out-of-range segment. Next, humidity records are analyzed. Assuming the humidity range is 30% to 60%, a humidity record of 25% or 65% is considered out of range and needs to be compared with the rack's environmental requirements. By comparing, the degree of deviation between the humidity record and the environmental requirements is found, and the time period of humidity deviation is calculated. If the humidity remains above 65% for more than 10 minutes, this period is considered a humidity deviation segment, identifying the continuous deviation segment. Finally, all temperature and humidity deviation information is summarized and formed into a temperature and humidity deviation judgment set, providing a basis for subsequent anomaly assessment.

[0033] S302: Based on the temperature and humidity deviation judgment set, calculate the start and end times of the access control opening section, compare the time overlap relationship between the opening section and the authorized opening period, determine the unauthorized covered section, identify the uncovered opening segment, analyze the boundary time of the opening segment, associate the time of access control status change, and obtain the abnormal opening event set; Extract the start and end times of the access control open segment. Assuming the access control is open from 10:00 to 10:20, compare it with the authorized open time period of the server rack, assuming the authorized open time period is 10:05 to 10:15. The access control open segment overlaps with the authorized open time period, specifically from 10:05 to 10:15. Next, determine the unauthorized coverage segment. If the access control is open between 10:00 and 10:05 or between 10:15 and 10:20, then these two time periods are considered unauthorized coverage segments. The analysis focuses on the boundary times of access control opening segments. For example, if the access control opening time is between 10:00 and 10:30, then the boundary times are 10:00 and 10:30. The access control status changes at these two time points. Furthermore, the analysis correlates the timing of these status changes. For instance, if the access control status changes at 10:00, 10:15, and 10:30, the timing of these changes needs to be recorded. Through analysis, unauthorized opening segments are identified, and these events are collected into an abnormal opening event set and marked as potential illegal opening events.

[0034] S303: Based on the set of abnormal opening events, compare the time distribution of vibration records with the overlap of event time windows, filter vibration segments within the event time window, determine the validity of event-related vibration segments, adjust the risk segment combination, classify abnormal type identifiers, and obtain cabinet abnormal risk data. To filter vibration segments within an event time window, use the following formula: ; Calculate the event-related vibration difference index value to determine the validity of the event-related vibration segment; in, This represents the event-related vibration difference index value corresponding to the m-th abnormal activation event. This represents the vibration recording amplitude data at the k-th sampling moment that falls within the time window of the abnormal activation event. The vibration reference value represents the time window boundary mapping corresponding to the time window of the abnormal opening event at the k-th sampling time. This represents the number of vibration sampling points included within the time window of the abnormal activation event. This represents the event sequence number identifier in the abnormal start event set.

[0035] Event-related vibration difference index value This formula measures the difference between a recorded vibration and a reference vibration value within a specific time window, thereby determining whether a vibration event is associated with a specific anomalous event. It helps quantify and assess the changes in vibration data during an event, facilitating further analysis and assessment to determine if it constitutes an abnormal risk event. The formula calculates the vibration record... and reference vibration value The differences between them reflect the changes in vibration intensity at each time point. The difference between the vibration value and the reference vibration value is calculated. The overall intensity of the vibration change is obtained by squaring the difference and taking the square root. A larger difference leads to a higher exponent value, indicating a greater likelihood of vibration anomalies. The deviation correction term in the formula (by subtracting the cumulative deviation) is used to remove noise and accidental vibration changes, ensuring a more accurate and stable calculated vibration difference. The resulting exponent value... This reflects the strength of the correlation between the vibration record and the time window of the abnormal event. If this value is large, it indicates that the correlation between the vibration and the abnormal event is strong; conversely, if the value is small, it may indicate that the correlation between the vibration event and the target abnormal event is weak.

[0036] If the abnormal activation event occurs within the time window of 13:00 to 13:10, and the vibration data comes from the vibration sensor, the event-related vibration difference index value will be calculated. At this time, it is first necessary to obtain vibration records. And the corresponding time window boundary mapping vibration reference value If the original data of the vibration record and reference vibration value are as follows: Vibration Recording (Original values): 0.05 m / s², 0.04 m / s², 0.06 m / s², 0.03 m / s²; Reference vibration value (Original values): 0.02 m / s², 0.02 m / s², 0.03 m / s², 0.02 m / s²; Maximum value normalization (i.e., each data point divided by the maximum value of the data column) is used. The normalization steps are as follows: Vibration Recording (Original value) normalization: The maximum value is 0.06 m / s² (i.e. Normalized vibration records : , , , .

[0037] Reference vibration value (Original value) normalization: The maximum value is 0.03 m / s² (i.e. Normalized reference vibration value : , , , .

[0038] For each Calculate the difference between the normalized vibration record and the reference vibration value. : , , , .

[0039] Square the differences and sum the squares of all differences: ; Find the square root of the sum of squares of the above differences: ; Calculate the cumulative sum of the differences: ; Calculate the sum of the absolute values ​​of the reference vibration values: ; Calculate the event-related vibration difference index value : ; Based on a preset threshold range The values ​​are divided into the following ranges: This indicates a significant difference in vibration levels, with vibration changes exceeding the normal fluctuation range, which may trigger an abnormal alarm. In this case, the server rack may be in an abnormal state, requiring further safety response measures.

[0040] This indicates that the vibration difference is moderate and the vibration change is small, but close to the threshold. It is generally considered to be within the normal fluctuation range and does not require an emergency response. At this time, further observation or data recording can be performed, but no immediate alarm will be triggered.

[0041] This indicates that the vibration difference is very small, the vibration change is within the normal fluctuation range, the equipment is operating normally, and no alarm or safety response measures need to be triggered.

[0042] result In If the vibration is within the specified range, it indicates that the vibration difference is very small, the vibration change is within the normal fluctuation range, the equipment is operating normally, and there is no need to trigger any alarms or safety response measures.

[0043] like Figure 5 As shown, the specific steps for obtaining the security response execution result are as follows: S401: Based on the cabinet abnormal risk data, detect the current status indicator of the electric lock, compare the correspondence between the locking status and the risk level classification item, execute the locking command sending operation, detect the consistency between the lock status feedback indicator and the command indicator, and obtain the locking action confirmation indicator. The system detects the current status indicator of the electronic lock. Assuming the lock's status can be "locked" or "unlocked," and the current status is "unlocked," it compares the current status with the risk level classification. For example, when the risk level is high, the lock status should be "locked." If the current status is "unlocked," a locking command needs to be sent. If a high risk level is detected and the current status is "unlocked," a locking command is sent to the lock. Next, it checks if the lock's status feedback indicator matches the command indicator. If the locking command is successfully sent and the lock's feedback status is "locked," the locking operation is confirmed as successful, and a locking action confirmation indicator is generated. If the feedback status is "unlocked," the command needs to be resent until the command feedback matches. Finally, the obtained locking action confirmation indicator indicates that the electronic lock has been successfully locked, and the execution status of the operation can be confirmed based on the feedback result.

[0044] S402: Based on the locking action confirmation flag, detect the triggering condition of the audible and visual alarm, execute the alarm start command, collect the alarm start signal feedback data, compare the consistency between the feedback status and the start command status, and obtain the equipment feedback status set. Based on the locking action confirmation flag, the trigger conditions of the audible and visual alarm are detected. Assuming the trigger conditions are a change in the locked state or an abnormal temperature, after the electric lock is successfully locked, the trigger conditions of the audible and visual alarm will be checked. If they are met, the alarm start command will be executed. Assuming the alarm start command is issued immediately after the electric lock is locked, the feedback data of the alarm start signal will be collected. Assuming the alarm sends a signal and returns "start", the consistency between the alarm feedback status and the start command status will be compared. If the feedback is "start", the start command is considered to have been successfully executed, and the device feedback status is "started". If the feedback status is "not started", the start command needs to be checked again and reissued. Through this comparison and feedback mechanism, the device feedback status set is obtained, which includes various states of device response, ensuring that the alarm works normally under abnormal conditions.

[0045] S403: Based on the device feedback status set, detect the current temperature status segment of the cabinet, compare the temperature segment with the safe temperature range, adjust the working mode of the temperature control equipment, and obtain the security response execution result; The system detects the current temperature range of the server rack. Assuming the temperature record shows a rack temperature of 32℃, this temperature is compared with the safe temperature range (e.g., 20℃ to 30℃). If 32℃ exceeds the safe range, the temperature control equipment needs adjustment. The operating mode of the temperature control equipment is adjusted. If the current mode is "cooling," it is changed to "heating" mode, or adjusted to an appropriate operating temperature to reduce the rack temperature to the safe range. If the temperature has returned to the normal range (e.g., 25℃), the temperature control equipment is considered to have been successfully adjusted to the safe range, and the security response execution result is obtained. Through this operation, the effective cooperation between the temperature control equipment and the security equipment is ensured, and corresponding emergency response measures are executed in abnormal situations.

[0046] like Figure 6 As shown, the specific steps for obtaining the alarm baseline adjustment index are as follows: S501: Based on the cabinet anomaly risk data, extract the time series of anomaly type identifier set, calculate the risk level difference amplitude between adjacent times, identify continuous fluctuation segments, determine the direction of change of fluctuation segments, filter anomaly trend segments, and obtain the risk fluctuation trend set. Extract the time series of anomaly type identifiers. Assume the anomaly type identifiers are "excessively high temperature," "unauthorized access control opening," and "abnormal vibration," corresponding to time series of 10:00-10:05, 10:15-10:20, and 10:25-10:30. Next, calculate the risk level difference amplitude between adjacent time periods. Assume the risk level is "high" from 10:00 to 10:05 and "medium" from 10:05 to 10:15, with a difference amplitude of 1 (high to medium). By calculating the difference amplitude, continuous fluctuation segments can be identified. If the amplitude of continuous fluctuations... If the degree is greater than a certain threshold (e.g., the difference amplitude is greater than 0.5), then the time period is considered a fluctuation segment. Next, the direction of change of the fluctuation segment is determined. If the risk level drops from "high" to "medium", it is a fluctuation in the "downward" direction; if it rises from "low" to "high", it is a fluctuation in the "upward" direction. Through this process, abnormal trend segments can be screened out. For example, if the risk level drops from "high" to "medium" from 10:00 to 10:05, it is identified as an abnormal trend segment. Finally, by screening the fluctuation trends, a risk fluctuation trend set is obtained for further abnormal trend analysis.

[0047] S502: Based on the security response execution results, statistically analyze the time distribution of response completion status indicators, calculate the number of triggers per unit time, associate the risk fluctuation trend set time segment, compare the overlap relationship between the number of triggers and the abnormal trend, determine the cause of abnormal triggers, and obtain the trigger association analysis set; The time distribution of response completion status indicators was analyzed. Assuming the response status records were "completed" between 10:05 and 10:10, and between 10:20 and 10:25, the number of triggers per unit time was calculated. There was one trigger between 10:05 and 10:10, and also one trigger between 10:20 and 10:25. Therefore, the number of triggers per unit time was 0.2 times / minute (assuming a statistical time of 5 minutes). Next, the number of triggers was correlated with the time intervals of the risk fluctuation trend set. Assuming the risk fluctuation trend... The time intervals for the set are 10:00 to 10:05 and 10:10 to 10:15. The overlap between the number of triggers and the abnormal trend is compared. If the number of triggers occurs between 10:05 and 10:10, and the fluctuation of the abnormal trend is between 10:00 and 10:05, then the overlap between the two is strong. Through this comparison, the cause of the abnormal trigger can be determined. If the correlation between the number of triggers and the abnormal fluctuation trend is high, then the triggering of the abnormal event is considered to be related to the temperature and humidity fluctuations in the environment. Finally, the trigger correlation analysis set is obtained.

[0048] S503: Based on the trigger correlation analysis set, retrieve the time segment of the operation and maintenance record and the cabinet status segment, compare the overlap relationship between the abnormal trigger cause and the operation and maintenance time, adjust the upper and lower limit settings of the alarm interval, correct the trigger sensitivity parameters, and obtain the alarm baseline adjustment index. The system retrieves the operation and maintenance (O&M) record time range and the cabinet status range. Assuming the O&M record time is from 10:00 to 10:05 and the cabinet status range is from 10:05 to 10:10, it then compares the overlap between the anomaly triggering cause and the O&M operation time. If an anomaly trigger occurs within the O&M operation period, it indicates that the anomaly is related to the O&M operation. If the triggered anomaly time overlaps with the O&M record, the upper and lower limits of the alarm range will be adjusted. For example, the alarm upper and lower limits will be set to a temperature range of 30℃ to 35℃ instead of the original 30℃ to 32℃. Furthermore, the trigger sensitivity parameter will be corrected. Assuming the original sensitivity was set to 0.5℃ / minute, after optimization, the sensitivity will be corrected to 0.2℃ / minute. This allows for more accurate detection of anomalies with small temperature fluctuations, resulting in alarm baseline adjustment indicators. Through these optimization measures, the accuracy and flexibility of alarms can be improved.

[0049] like Figure 7 As shown, an IoT-based cabinet security system includes: The environmental data acquisition module is based in the data center server room. It checks the sampling interval and monitoring polling rhythm of the cold aisle temperature sensor, filters out temperature records that exceed the range, interprets the door access signal flip-off time point, screens stable segments of bottom vibration acceleration, verifies the validity of humidity records, and obtains the rack environmental data sequence. The data verification and evaluation module verifies the timestamp alignment status based on the cabinet environment data sequence, determines the availability of temperature records at the synchronization moment, calculates the direction of humidity sequence change, checks the period during which the access control is kept open, identifies the stable range of vibration data, and obtains the cabinet environment evaluation results. The abnormal risk identification module analyzes whether the temperature and humidity are within the operating range based on the cabinet environment assessment results, evaluates the matching relationship between access control status and authorized time period, judges unauthorized opening situations, filters irrelevant vibration interference records, and obtains cabinet abnormal risk data. Based on the cabinet's abnormal risk data, the response control execution module checks the current status of the electric lock and executes locking control, verifies the locking feedback information, triggers the audible and visual alarm, verifies the alarm feedback status, adjusts the operating mode of the temperature control equipment according to the temperature status, and obtains the security response execution result. The alarm optimization module analyzes the time series fluctuation characteristics of cabinet anomaly risk data, combines the security response execution results, counts the trigger frequency, evaluates the relationship between trigger records and operation and maintenance handling time, optimizes alarm interval settings, and obtains alarm baseline adjustment indicators.

[0050] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of protection of the described technical solutions.

Claims

1. A cabinet security method based on Internet of Things, characterized in that, The method includes: S1: Based on the data center server room, verify the sampling interval and monitoring polling rhythm of the cold aisle temperature sensor, filter out temperature records that exceed the range, determine the flip-off time of the access control signal, screen out stable segments of bottom vibration acceleration, verify the validity of humidity records, and obtain the rack environment data sequence. S2: Based on the cabinet environment data sequence, verify the timestamp alignment status, determine the availability of temperature records at the synchronization moment, calculate the direction of humidity sequence change, check the access control period that is kept open, identify the stable range of vibration data, and obtain the cabinet environment assessment result. S3: Based on the cabinet environment assessment results, analyze whether the temperature and humidity are within the operating range, assess the matching relationship between the access control status and the authorized time period, determine unauthorized opening situations, filter irrelevant vibration interference records, and obtain cabinet abnormal risk data; S4: Based on the cabinet abnormal risk data, check the current status of the electric lock and execute the locking control, verify the locking feedback information, trigger the audible and visual alarm, verify the alarm feedback status, adjust the operating mode of the temperature control equipment according to the temperature status, and obtain the security response execution result. S5: For the cabinet abnormal risk data, analyze the time series fluctuation characteristics, combine the security response execution results, count the trigger frequency, evaluate the relationship between trigger records and operation and maintenance handling time, optimize the alarm interval setting, and obtain alarm benchmark adjustment indicators. 2.The Internet of Things based cabinet security method according to claim 1, wherein, The cabinet environment data sequence includes an environmental time index table, a sensor status identifier set, and a sampled valid record set. The cabinet environment assessment results include environmental operation level, status continuity identifier, and environmental correlation judgment item. The cabinet abnormal risk data includes an abnormality type identifier set, risk level classification item, and event correlation table. The security response execution results include control action confirmation identifier, equipment feedback status set, and response completion status identifier. The alarm benchmark adjustment index includes alarm interval correction parameters, trigger sensitivity adjustment item, and alarm strategy update identifier. 3.The Internet of Things based security method for cabinets according to claim 1, wherein, The specific steps for obtaining the rack environment data sequence are as follows: S101: Based on the data center computer room, analyze the relationship between the sampling period and the monitoring period, calculate the time interval difference, determine the period matching status, filter the temperature recording range data, compare abnormal data and missing data, adjust the time index sorting and time granularity, and obtain the temperature time alignment index set. S102: Based on the temperature-time alignment index set, analyze the corresponding access control opening and closing signals, calculate the differences between adjacent states, determine the state reversal time, filter access control change time data, calculate the average amplitude of the vibration probe acceleration sliding window, compare the window fluctuation range, determine the stable section, and obtain the acceleration stable section set. S103: Based on the acceleration stable segment set, analyze the corresponding humidity sampling records, compare the upper and lower limits of the humidity range, identify the data within the range, adjust the effective humidity record timestamp, associate the access control change time data, and obtain the cabinet environment data sequence. 4.The Internet of Things based security method for cabinets according to claim 1, wherein, The specific steps for obtaining the cabinet environment assessment results are as follows: S201: Based on the cabinet environment data sequence, calculate the time interval difference of each sensor record, compare the time granularity benchmark to determine the alignment status, identify time misalignment records, adjust the data combination relationship at the same moment, determine the consistency between the temperature record range status and the sensor status identifier, and obtain the time consistency identifier set. S202: Based on the time consistency identifier set, extract the humidity record time series, analyze the humidity difference direction between adjacent moments, determine the continuous change segment, calculate the duration of the access control opening and closing state, identify the continuous segment, determine the continuity of the vibration record sampling interval, determine the acceleration fluctuation amplitude range, and obtain the environmental correlation judgment item. S203: Based on the environmental correlation judgment item, calculate the continuous duration of the temperature range state, divide the operation segment, integrate the humidity change segment, the access control continuous segment and the vibration stability segment, adjust the segment combination relationship, divide the environmental operation level, and obtain the cabinet environment assessment result.

5. The IoT-based cabinet security method according to claim 1, characterized in that, The specific steps for obtaining the cabinet anomaly risk data are as follows: S301: Based on the cabinet environment assessment results, retrieve the temperature range corresponding to the environmental operation level, compare the temperature record with the upper and lower limits of the cabinet operation range, determine the duration of the out-of-bounds range, filter continuous out-of-bounds segments, analyze the humidity record with the cabinet environment requirement range, compare the humidity range coverage status, determine the duration of the deviation range, identify continuous deviation segments, and obtain the temperature and humidity deviation judgment set. S302: Based on the temperature and humidity deviation judgment set, calculate the start and end times of the access control opening section, compare the time overlap relationship between the opening section and the authorized opening time period, determine the unauthorized covered section, identify the uncovered opening segment, analyze the boundary time of the opening segment, associate the time of access control status change, and obtain the abnormal opening event set. S303: Based on the set of abnormal opening events, compare the vibration recording time distribution with the overlap of the event time window, filter vibration segments within the event time window, determine the validity of the event-related vibration segment, adjust the risk segment combination, classify the abnormal type identifier, and obtain the cabinet abnormal risk data.

6. The IoT-based cabinet security method according to claim 1, characterized in that, The specific steps for obtaining the security response execution result are as follows: S401: Based on the cabinet abnormal risk data, detect the current status identifier of the electric lock, compare the correspondence between the locking status and the risk level classification item, execute the locking command sending operation, detect the consistency between the lock status feedback identifier and the command identifier, and obtain the locking action confirmation identifier. S402: Based on the locking action confirmation flag, detect the triggering condition of the audible and visual alarm, execute the alarm start command, collect the alarm start signal feedback data, compare the consistency between the feedback status and the start command status, and obtain the device feedback status set. S403: Based on the device feedback status set, detect the current temperature status segment of the cabinet, compare the relationship between the temperature segment and the safe temperature range, adjust the working mode of the temperature control device, and obtain the security response execution result.

7. The IoT-based cabinet security method according to claim 1, characterized in that, The specific steps for obtaining the alarm baseline adjustment index are as follows: S501: Based on the cabinet abnormal risk data, extract the time series of abnormal type identifier set, calculate the risk level difference amplitude between adjacent times, identify continuous fluctuation segments, determine the direction of change of fluctuation segments, filter abnormal trend segments, and obtain the risk fluctuation trend set. S502: Based on the security response execution results, statistically analyze the time distribution of response completion status indicators, calculate the number of triggers per unit time, associate the time segment of the risk fluctuation trend set, compare the overlap relationship between the number of triggers and the abnormal trend, determine the cause of abnormal triggering, and obtain the trigger association analysis set; S503: Based on the trigger correlation analysis set, retrieve the time segment of the operation and maintenance record and the cabinet status segment, compare the overlap relationship between the abnormal trigger cause and the operation and maintenance time, adjust the upper and lower limits of the alarm interval, correct the trigger sensitivity parameters, and obtain the alarm benchmark adjustment index.

8. The IoT-based cabinet security method according to claim 1, characterized in that, The cold aisle refers to the cold aisle area between server racks in the computer room; the range refers to the effective measurement range of the sensor; and the alignment status refers to whether the timestamps of the sensor data are consistent.

9. The IoT-based cabinet security method according to claim 1, characterized in that, The direction of humidity sequence change refers to the trend of humidity data over a certain period of time, whether there is an upward or downward pattern, and the time series fluctuation characteristics refer to the change pattern of time series data over time.

10. A cabinet security system based on the Internet of Things (IoT), the system being used to implement the cabinet security method based on the IoT as described in any one of claims 1-9, characterized in that, The system includes: The environmental data acquisition module is based in the data center server room. It checks the sampling interval and monitoring polling rhythm of the cold aisle temperature sensor, filters out temperature records that exceed the range, interprets the door access signal flip-off time point, screens stable segments of bottom vibration acceleration, verifies the validity of humidity records, and obtains the rack environmental data sequence. The data verification and evaluation module verifies the timestamp alignment status based on the cabinet environment data sequence, determines the availability of temperature records at the synchronization moment, calculates the direction of humidity sequence change, checks the period during which the access control remains open, identifies the stable range of vibration data, and obtains the cabinet environment evaluation result. Based on the cabinet environment assessment results, the abnormal risk identification module analyzes whether the temperature and humidity are within the operating range, assesses the matching relationship between the access control status and the authorized time period, judges unauthorized opening situations, filters irrelevant vibration interference records, and obtains cabinet abnormal risk data. Based on the cabinet's abnormal risk data, the response control execution module checks the current status of the electric lock and executes locking control, verifies the locking feedback information, triggers the audible and visual alarm, verifies the alarm feedback status, adjusts the operating mode of the temperature control equipment according to the temperature status, and obtains the security response execution result. The alarm optimization module analyzes the time series fluctuation characteristics of the cabinet abnormal risk data, combines the security response execution results, counts the trigger frequency, evaluates the relationship between trigger records and operation and maintenance handling time, optimizes the alarm interval setting, and obtains alarm benchmark adjustment indicators.

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