A device inventory management method based on IDC device data collection

Abstract

The present invention discloses an equipment inventory management method based on IDC equipment data collection, relates to the IDC field, and solves the problem of poor inventory effect in the equipment inventory management method. The method comprises the following steps: step S1: performing equipment status monitoring on each data device in an equipment operation monitoring cycle, and respectively obtaining equipment operation data corresponding to each data device to obtain equipment status monitoring data; step S2: performing equipment safety monitoring on each data device in an equipment operation monitoring cycle according to the equipment status monitoring data, and respectively obtaining an equipment cycle safety index value corresponding to each data device to obtain equipment safety monitoring data; step S3: performing equipment abnormality inventory on each data device according to the equipment safety monitoring data and the equipment status monitoring data, and performing real-time abnormality warning on the data device according to the inventory results. The present invention improves the comprehensiveness and accuracy of equipment judgment results.

Description

Technical Field

[0001] The present invention belongs to the field of IDC and relates to data analysis technology, in particular to an equipment inventory management method based on IDC equipment data collection. Background Art

[0002] The existing equipment inventory management methods based on IDC equipment data collection have the following specific defects when conducting equipment inventory: 1. Existing equipment inventory management methods can often only collect and inventory equipment data within a fixed period of time, resulting in a certain time interval in the inventory process, which leads to a lack of continuity in the inventory results; 2. Existing equipment inventory management methods often only rely on the equipment's own early warning system to conduct abnormal situation inventory, and cannot provide data support for redundant operations of the inventory method, resulting in a lack of comprehensiveness and security in the judgment management process.

[0003] To this end, we propose an equipment inventory management method based on IDC equipment data collection. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide an equipment inventory management method based on IDC equipment data collection. The present invention is based on marking multiple data devices in the data center, monitoring the equipment status of each data device within the equipment operation monitoring cycle, and obtaining the equipment operation data corresponding to each data device by analyzing the monitoring results to obtain equipment status monitoring data. According to the equipment status monitoring data, equipment safety monitoring is performed on each data device in the equipment operation monitoring cycle, and the equipment cycle safety index value corresponding to each data device is obtained to obtain equipment safety monitoring data. Equipment abnormality inventory is performed on each data device according to the equipment safety monitoring data and the equipment status monitoring data, and real-time abnormality warning is issued to the data device based on the inventory results.

[0005] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solutions: an equipment inventory management method based on IDC equipment data collection, comprising the following specific steps: step S1: marking multiple data devices in the data center, performing equipment status monitoring on each data device within the equipment operation monitoring cycle, and obtaining the equipment operation data corresponding to each data device by analyzing the monitoring results to obtain equipment status monitoring data; step S2: performing equipment safety monitoring on each data device in the equipment operation monitoring cycle according to the equipment status monitoring data, and obtaining the equipment cycle safety index value corresponding to each data device to obtain equipment safety monitoring data; step S3: performing equipment abnormality inventory on each data device according to the equipment safety monitoring data and the equipment status monitoring data, and performing real-time abnormality warning on the data device according to the inventory results.

[0006] Furthermore, the step S1 further includes the following specific steps: step S11: acquiring all data devices currently in operation in the data center to obtain multiple data devices, and randomly selecting a data device from the multiple data devices acquired as a sample data device; step S12: in the process of monitoring the equipment operation status of multiple data devices, marking the time point corresponding to the current moment as the first status monitoring time point, marking the time point corresponding to a characteristic monitoring duration between the first status monitoring time point as the second status monitoring time point, and naming the period between the first status monitoring time point and the second status monitoring time point as the equipment operation monitoring cycle; step S13: monitoring the equipment CPU operation status of the sample data device to obtain to the CPU operating indicator value corresponding to the sample data device; step S14: monitor the device memory operating status of the sample data device to obtain the memory operating indicator value corresponding to the sample data device; step S15: monitor the device network operating status of the sample data device to obtain the network operating indicator value corresponding to the sample data device; step S16: define the CPU operating indicator value, memory operating indicator value and network operating indicator value corresponding to the sample data device as the device operating data corresponding to the sample data device; step S17: obtain the device operating data corresponding to each data device respectively to obtain multiple device operating data; step S18: define the device operation monitoring cycle and multiple device operation data as device status monitoring data.

[0007] Furthermore, the step S13 further includes the following specific steps: step S131: randomly selecting a number of CPU monitoring time points within the device operation monitoring cycle, and the time intervals between each two consecutive CPU monitoring time points are equal, to obtain a plurality of CPU monitoring time points; step S132: obtaining the device CPU utilization corresponding to each CPU monitoring time point of the sample data device, to obtain a plurality of device CPU utilizations; step S133: averaging the obtained plurality of device CPU utilizations to obtain the average CPU utilization of the device cycle; step S134: performing variance calculation on the obtained plurality of device CPU utilizations to obtain the variance of the device cycle CPU utilization; step S135: obtaining the sample data; According to the benchmark CPU utilization rate of the device under the corresponding working conditions at each CPU monitoring time point, multiple benchmark CPU utilization rates are obtained; step S136: the average of the obtained multiple benchmark CPU utilization rates is calculated to obtain the device cycle benchmark CPU utilization rate; step S137: the variance of the obtained multiple benchmark CPU utilization rates is calculated to obtain the device cycle benchmark CPU utilization variance; step S138: the device cycle average CPU utilization rate, the device cycle CPU utilization variance, the device cycle benchmark CPU utilization rate, and the device cycle benchmark CPU utilization variance are calculated to obtain the CPU operation index value corresponding to the sample data device; the CPU operation index value corresponding to the sample data device is calculated using the following formula: ; Among them, Cpz is the CPU operation indicator value corresponding to the sample data device, Cpl is the average CPU utilization of the device cycle, Cpj is the benchmark CPU utilization of the device cycle, Cfc is the variance of the CPU utilization of the device cycle, and Cfj is the variance of the benchmark CPU utilization of the device cycle.

[0008] Furthermore, the step S14 further includes the following specific steps: step S141: randomly selecting a number of memory monitoring time points within the device operation monitoring cycle, and the time intervals between each two consecutive memory monitoring time points are equal, to obtain a plurality of memory monitoring time points; step S142: obtaining the device memory utilization corresponding to each memory monitoring time point of the sample data device, to obtain a plurality of device memory utilizations; step S143: averaging the obtained plurality of device memory utilizations to obtain the average memory utilization of the device cycle; step S144: performing variance calculation on the obtained plurality of device memory utilizations to obtain the variance of the device cycle memory utilization; step S145: obtaining the sample data device; The data device obtains a plurality of benchmark memory utilizations at each memory monitoring time point under corresponding working conditions; step S146: averages the obtained plurality of benchmark memory utilizations to obtain a device cycle benchmark memory utilization; step S147: calculates the variance of the obtained plurality of benchmark memory utilizations to obtain a device cycle benchmark memory utilization variance; step S148: calculates the device cycle average memory utilization, the device cycle memory utilization variance, the device cycle benchmark memory utilization, and the device cycle benchmark memory utilization variance to obtain a memory operation index value corresponding to the sample data device; the memory operation index value corresponding to the sample data device is calculated using the following formula: ; Among them, Npz is the memory operation indicator value corresponding to the sample data device, Npl is the average memory utilization of the device cycle, Npj is the benchmark memory utilization of the device cycle, Nfc is the variance of the memory utilization of the device cycle, and Nfj is the variance of the benchmark memory utilization of the device cycle.

[0009] Furthermore, the step S15 further includes the following specific steps: step S151: randomly selecting a number of network monitoring time points within the device operation monitoring cycle, and the time intervals between each two consecutive network monitoring time points are equal, to obtain a plurality of network monitoring time points; step S152: obtaining the device bandwidth utilization corresponding to each network monitoring time point of the sample data device, to obtain a plurality of device bandwidth utilizations; step S153: averaging the obtained plurality of device bandwidth utilizations to obtain the average bandwidth utilization of the device cycle; step S154: performing variance calculation on the obtained plurality of device bandwidth utilizations to obtain the variance of the device cycle bandwidth utilization; step S155: obtaining the sample data device; The data device obtains a plurality of benchmark bandwidth utilizations at each network monitoring time point under corresponding working conditions; Step S156: Calculate the average of the obtained multiple benchmark bandwidth utilizations to obtain the device period benchmark bandwidth utilization; Step S157: Calculate the variance of the obtained multiple benchmark bandwidth utilizations to obtain the device period benchmark network utilization variance; Step S158: Calculate the device period average bandwidth utilization, the device period bandwidth utilization variance, the device period benchmark bandwidth utilization, and the device period benchmark network utilization variance to obtain the network operation index value corresponding to the sample data device; Calculate the network operation index value corresponding to the sample data device using the following formula: ; Among them, Wpz is the network operation indicator value corresponding to the sample data device, Wpl is the average bandwidth utilization of the device cycle, Wpj is the benchmark bandwidth utilization of the device cycle, Wfc is the variance of the bandwidth utilization of the device cycle, and Wfj is the variance of the benchmark network utilization of the device cycle.

[0010] Furthermore, the step S2 also includes the following specific steps: step S21: obtaining equipment status monitoring data, and obtaining an equipment operation monitoring cycle based on the equipment status monitoring data; step S22: obtaining multiple data devices in the data center, and randomly selecting a data device from the multiple data devices as a feature data device; step S23: performing operation safety monitoring on the feature data device in the equipment operation monitoring cycle, and obtaining an equipment cycle safety index value corresponding to the feature data device; step S24: obtaining the equipment cycle safety index value corresponding to each data device respectively, and obtaining multiple equipment cycle safety index values; step S25: defining multiple equipment cycle safety index values as equipment safety monitoring data.

[0011] Furthermore, the step S23 also includes the following specific steps: step S231: in the process of security monitoring of the characteristic data device, several different types of equipment warnings are set respectively, and the multiple different types of equipment warnings are named as the first type safety warning to the ath type safety warning; step S232: obtaining the number of warnings from the first type safety warning to the ath type safety warning during the equipment operation monitoring period of the characteristic data device, and obtaining the number of first type warnings to the ath type warning number; step S233: obtaining the average duration of the first type warning and the frequency of the first type warning to the ath type warning frequency; step S234: obtaining the average warning duration corresponding to the second type safety warning to the ath type safety warning respectively, and obtaining the average duration of the second type warning to the average duration of the ath type warning; step S235: calculating the average duration of the first type warning to the average duration of the ath type warning and the frequency of the first type warning to the ath type warning frequency to obtain the equipment cycle safety index value corresponding to the characteristic data device; the equipment cycle safety index value corresponding to the characteristic data device is calculated, and the specific formula is as follows: ; Among them, Azz is the equipment cycle safety index value corresponding to the characteristic data equipment, Scp1 to Scpa are the average duration of the first type of warning to the average duration of the a-th type of warning, and Pcj1 to Pcja are the frequency of the first type of warning to the frequency of the a-th type of warning.

[0012] Furthermore, the step S233 also includes the following specific steps: step S2331: obtaining the duration of the equipment operation monitoring cycle to obtain the monitoring cycle duration value, obtaining the ratio of the number of first type warnings to the number of type a warnings to the monitoring cycle duration value, and obtaining the first type warning frequency to the type a warning frequency; step S2332: randomly selecting several first type safety warnings within the equipment operation monitoring cycle to obtain multiple first type safety warnings, respectively obtaining the safety warning duration corresponding to each first type safety warning, obtaining multiple safety warning durations, and averaging the obtained safety warning durations to obtain the average first type warning duration.

[0013] Furthermore, the step S3 further includes the following specific steps: step S31: obtaining device status monitoring data, and obtaining the CPU operation index value, memory operation index value, and network operation index value corresponding to each data device according to the device status monitoring data; step S32: performing a device operation status inventory according to the CPU operation index value, memory operation index value, and network operation index value corresponding to each data device, and issuing a device operation status warning according to the inventory result; step S33: obtaining device safety monitoring data, and obtaining the device cycle safety index value corresponding to each data device according to the device safety monitoring data; step S3 4: Obtain the equipment cycle safety index threshold, compare the equipment cycle safety index value corresponding to each data device with the equipment cycle safety index threshold respectively, conduct an operation safety inventory of each data device and issue a safety warning based on the numerical comparison results; specifically as follows: Step S341: When the equipment cycle safety index value is greater than or equal to the equipment cycle safety index threshold, the corresponding data device is inventoried as having an abnormal safety status, and an abnormal safety status warning is issued; Step S342: When the equipment cycle safety index value is less than the equipment cycle safety index threshold, the corresponding data device is inventoried as having a normal safety status, and the operation safety inventory is continued for the normal safety status.

[0014] Furthermore, the step S32 further includes the following specific steps: step S321: taking the CPU operation index value, memory operation index value, and network operation index value corresponding to the same data device; step S322: calculating the CPU operation index value, memory operation index value, and network operation index value to obtain a comprehensive monitoring coefficient of device operation; the comprehensive monitoring coefficient of device operation is calculated, and the specific formula is as follows: ; Wherein, Zjx is the comprehensive monitoring coefficient of equipment operation, Cpz is the CPU operation index value, Wpz is the network operation index value, and Npz is the memory operation index value; Step S323: respectively obtain the CPU operation index threshold, the memory operation index threshold, and the network operation index threshold; Step S324: calculate the CPU operation index threshold, the memory operation index threshold, and the network operation index threshold to obtain the equipment operation comprehensive monitoring coefficient threshold; The specific formula for calculating the equipment operation comprehensive monitoring coefficient threshold is as follows: ; Wherein, Zjxy is the threshold value of the comprehensive monitoring coefficient of equipment operation, Cpzy is the threshold value of the CPU operation index, Wpzy is the threshold value of the network operation index, and Npzy is the threshold value of the memory operation index; Step S325: When the comprehensive monitoring coefficient of equipment operation is greater than or equal to the threshold value of the comprehensive monitoring coefficient of equipment operation, the corresponding data equipment will be counted as an abnormal operation status device, and an abnormal operation status warning will be issued; Step S326: When the comprehensive monitoring coefficient of equipment operation is less than the threshold value of the comprehensive monitoring coefficient of equipment operation, the corresponding data equipment will be counted as a normal operation status device, and the operation status inventory of the normal operation status device will continue to be performed.

[0015] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. The present invention realizes real-time acquisition of equipment safety monitoring data and equipment status monitoring data by dynamically updating the equipment operation monitoring cycle, which can improve the lack of continuity in the inventory process and make the abnormal warning of data equipment more accurate; 2. In addition to obtaining equipment safety monitoring data through existing warnings, the present invention performs real-time CPU monitoring, memory monitoring and network monitoring on data equipment respectively, provides data support for redundant operations in the inventory process, and improves the comprehensiveness of the inventory results. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] To facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0017] Figure 1 It is a diagram of the implementation steps of the present invention.

[0018] Figure 2 This is a block diagram of the overall system of the present invention.

[0019] Figure 3 This is a schematic diagram of the IDC computer room of the present invention. DETAILED DESCRIPTION

[0020] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. 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.

[0021] Example 1

[0022] See also Figure 1The present invention provides a technical solution: an equipment inventory management method based on IDC equipment data collection, comprising the following specific steps: Step S1: marking multiple data devices in a data center, performing equipment status monitoring on each data device during an equipment operation monitoring cycle, and obtaining equipment operation data corresponding to each data device by analyzing the monitoring results to obtain equipment status monitoring data; Step S1 further comprises the following specific steps: Step S11: obtaining all data devices currently in operation in the data center to obtain multiple data devices, and randomly selecting a data device from the multiple data devices obtained as a sample data device ; Step S12: In the process of monitoring the operation status of multiple data devices, the time point corresponding to the current moment is marked as the first state monitoring time point, the time point corresponding to a characteristic monitoring duration between the first state monitoring time point is marked as the second state monitoring time point, and the period between the first state monitoring time point and the second state monitoring time point is named the device operation monitoring cycle; Step S13: Monitor the CPU operation status of the sample data device to obtain the CPU operation indicator value corresponding to the sample data device; Step S13 also includes the following specific steps: Step S131: Randomly select a number of C PU monitoring time point, and the time interval between each two consecutive CPU monitoring time points is equal, to obtain multiple CPU monitoring time points; step S132: obtain the device CPU utilization corresponding to each CPU monitoring time point of the sample data device, to obtain multiple device CPU utilizations, step S133: average the obtained multiple device CPU utilizations to obtain the device cycle average CPU utilization; step S134: calculate the variance of the obtained multiple device CPU utilizations to obtain the device cycle CPU utilization variance; step S135: obtain the benchmark CPU utilization of the sample data device under the corresponding working conditions at each CPU monitoring time point U utilization, to obtain multiple benchmark CPU utilizations; step S136: average the obtained multiple benchmark CPU utilizations to obtain the device cycle benchmark CPU utilization; step S137: calculate the variance of the obtained multiple benchmark CPU utilizations to obtain the device cycle benchmark CPU utilization variance; step S138: calculate the device cycle average CPU utilization, the device cycle CPU utilization variance, the device cycle benchmark CPU utilization, and the device cycle benchmark CPU utilization variance to obtain the CPU operation index value corresponding to the sample data device; the CPU operation index value corresponding to the sample data device is calculated using the following formula: ; Wherein, Cpz is the CPU operation index value corresponding to the sample data device, Cpl is the average CPU utilization of the device cycle, Cpj is the benchmark CPU utilization of the device cycle, Cfc is the CPU utilization variance of the device cycle, and Cfj is the benchmark CPU utilization variance of the device cycle; Step S14: Monitor the device memory operation status of the sample data device to obtain the memory operation index value corresponding to the sample data device; Step S14 also includes the following specific steps: Step S141: Within the device operation monitoring cycle, randomly select several memory monitoring time points, and the time intervals between each two consecutive memory monitoring time points are equal to obtain multiple memory monitoring time points; Step S142: Obtain the device memory utilization corresponding to the sample data device at each memory monitoring time point to obtain multiple device memory utilizations, and Step S143: Average the obtained multiple device memory utilizations Calculate to obtain the average memory utilization of the device cycle; step S144: perform variance calculation on the obtained multiple device memory utilizations to obtain the variance of the device cycle memory utilization; step S145: obtain the benchmark memory utilization of the sample data device under the corresponding working conditions at each memory monitoring time point to obtain multiple benchmark memory utilizations; step S146: average the obtained multiple benchmark memory utilizations to obtain the device cycle benchmark memory utilization; step S147: perform variance calculation on the obtained multiple benchmark memory utilizations to obtain the device cycle benchmark memory utilization variance; step S148: calculate the device cycle average memory utilization, the device cycle memory utilization variance, the device cycle benchmark memory utilization and the device cycle benchmark memory utilization variance to obtain the memory operation index value corresponding to the sample data device; the memory operation index value corresponding to the sample data device is calculated, and the specific formula is as follows: ; Wherein, Npz is the memory operation index value corresponding to the sample data device, Npl is the average memory utilization of the device cycle, Npj is the benchmark memory utilization of the device cycle, Nfc is the variance of the memory utilization of the device cycle, and Nfj is the benchmark memory utilization variance of the device cycle; Step S15: Monitor the device network operation status of the sample data device to obtain the network operation index value corresponding to the sample data device; Step S15 also includes the following specific steps: Step S151: Within the device operation monitoring cycle, randomly select several network monitoring time points, and the time intervals between each two consecutive network monitoring time points are equal, to obtain multiple network monitoring time points; Step S152: Obtain the device bandwidth utilization corresponding to the sample data device at each network monitoring time point to obtain multiple device bandwidth utilizations, and Step S153: Calculate the average of the obtained multiple device bandwidth utilizations , obtaining the average bandwidth utilization of the device cycle; step S154: performing variance calculation on the obtained multiple device bandwidth utilizations to obtain the variance of the device cycle bandwidth utilization; step S155: obtaining the benchmark bandwidth utilization of the sample data device under the corresponding working conditions at each network monitoring time point to obtain multiple benchmark bandwidth utilizations; step S156: performing average calculation on the obtained multiple benchmark bandwidth utilizations to obtain the device cycle benchmark bandwidth utilization; step S157: performing variance calculation on the obtained multiple benchmark bandwidth utilizations to obtain the device cycle benchmark network utilization variance; step S158: calculating the network operation index value corresponding to the sample data device by the device cycle average bandwidth utilization, the device cycle bandwidth utilization variance, the device cycle benchmark bandwidth utilization and the device cycle benchmark network utilization variance; the network operation index value corresponding to the sample data device is calculated, and the specific formula is as follows: ; Wherein, Wpz is the network operation index value corresponding to the sample data device, Wpl is the average bandwidth utilization of the device cycle, Wpj is the benchmark bandwidth utilization of the device cycle, Wfc is the variance of the bandwidth utilization of the device cycle, and Wfj is the benchmark network utilization variance of the device cycle; Step S16: define the CPU operation index value, memory operation index value and network operation index value corresponding to the sample data device as the device operation data corresponding to the sample data device; Step S17: obtain the device operation data corresponding to each data device respectively to obtain multiple device operation data; Step S18: define the device operation monitoring cycle and multiple device operation data as device status monitoring data.

[0023] Step S2: Perform equipment safety monitoring on each data device that is within the equipment operation monitoring cycle according to the equipment status monitoring data, and obtain the equipment cycle safety index value corresponding to each data device to obtain equipment safety monitoring data; Step S2 also includes the following specific steps: Step S21: Obtain equipment status monitoring data, and obtain the equipment operation monitoring cycle according to the equipment status monitoring data; Step S22: Obtain multiple data devices in the data center, and randomly select a data device from the multiple data devices as a feature data device; Step S23: Perform operation safety monitoring on the feature data device that is within the equipment operation monitoring cycle to obtain the equipment cycle safety index value corresponding to the feature data device; Step S23 also includes the following specific steps: Step S231: In the process of safety monitoring the feature data device, set several different types of equipment warnings respectively, and name the multiple different types of equipment warnings as the first type safety warning to the ath type safety warning; Step S232: Obtain the number of warnings of the first type safety warning to the ath type safety warning respectively occurring in the feature data device within the equipment operation monitoring cycle, and obtain the number of first type warnings to the ath type warning number; Step S233: Obtain the first The average duration of type warning and the frequency of the first type warning to the ath type warning frequency; the step S233 also includes the following specific steps: step S2331: obtain the duration of the equipment operation monitoring period, obtain the monitoring period duration value, obtain the ratio of the first type warning times to the ath type warning times to the monitoring period duration value, and obtain the first type warning frequency to the ath type warning frequency; step S2332: randomly select several first type safety warnings within the equipment operation monitoring period, obtain multiple first type safety warnings, obtain the safety warning duration corresponding to each first type safety warning, and obtain to multiple security warning durations, and average the obtained security warning durations to obtain the average duration of the first type of warning; step S234: respectively obtain the average warning durations corresponding to the second type of security warning to the a-th type of security warning, and obtain the average duration of the second type of warning to the a-th type of warning; step S235: calculate the average duration of the first type of warning to the average duration of the a-th type of warning and the frequency of the first type of warning to the a-th type of warning to obtain the equipment cycle safety index value corresponding to the characteristic data device; calculate the equipment cycle safety index value corresponding to the characteristic data device, the specific formula is as follows: ; Wherein, Azz is the equipment cycle safety index value corresponding to the characteristic data device, Scp1 to Scpa are respectively the average duration of the first type of warning to the average duration of the a-th type of warning, and Pcj1 to Pcja are respectively the frequency of the first type of warning to the a-th type of warning; Step S24: Obtain the equipment cycle safety index value corresponding to each data device respectively to obtain multiple equipment cycle safety index values; Step S25: Define the multiple equipment cycle safety index values as equipment safety monitoring data.

[0024] Step S3: perform an inventory of device abnormalities for each data device based on the device safety monitoring data and the device status monitoring data, and issue a real-time abnormality warning for the data device based on the inventory results; the step S3 also includes the following specific steps: step S31: obtain device status monitoring data, and obtain the CPU operation index value, memory operation index value and network operation index value corresponding to each data device based on the device status monitoring data; step S32: perform a device operation status inventory based on the CPU operation index value, memory operation index value and network operation index value corresponding to each data device, and issue a device operation status warning based on the inventory results; the step S32 also includes the following specific steps: step S321: calculate the CPU operation index value, memory operation index value and network operation index value corresponding to the same data device; step S322: calculate the CPU operation index value, memory operation index value and network operation index value to obtain the device operation comprehensive monitoring coefficient; the device operation comprehensive monitoring coefficient is calculated, and the specific formula is as follows: ; Wherein, Zjx is the comprehensive monitoring coefficient of equipment operation, Cpz is the CPU operation index value, Wpz is the network operation index value, and Npz is the memory operation index value; Step S323: respectively obtain the CPU operation index threshold, the memory operation index threshold, and the network operation index threshold; Step S324: calculate the CPU operation index threshold, the memory operation index threshold, and the network operation index threshold to obtain the equipment operation comprehensive monitoring coefficient threshold; The specific formula for calculating the equipment operation comprehensive monitoring coefficient threshold is as follows: ; Wherein, Zjxy is the threshold value of the comprehensive monitoring coefficient of equipment operation, Cpzy is the threshold value of the CPU operation index, Wpz is the threshold value of the network operation index, and Npzy is the threshold value of the memory operation index; Step S325: When the comprehensive monitoring coefficient of equipment operation is greater than or equal to the threshold value of the comprehensive monitoring coefficient of equipment operation, the corresponding data equipment is counted as an abnormal operation state equipment, and an abnormal operation state warning is issued; Step S326: When the comprehensive monitoring coefficient of equipment operation is less than the threshold value of the comprehensive monitoring coefficient of equipment operation, the corresponding data equipment is counted as a normal operation state equipment, and the operation state inventory of the normal operation state equipment is continued; Step S33: Obtain equipment safety monitoring data, and obtain the equipment cycle safety index value corresponding to each data equipment according to the equipment safety monitoring data; Step S34: Obtain the equipment cycle safety index threshold, and compare the equipment cycle safety index value corresponding to each data equipment with the equipment safety monitoring data; The equipment cycle safety index threshold is compared, and an operation safety inventory of each data device is performed and a safety warning is issued based on the numerical comparison result; specifically as follows: Step S341: When the equipment cycle safety index value is greater than or equal to the equipment cycle safety index threshold, the corresponding data device is inventoried as an abnormal safety state, and an abnormal safety state warning is issued; Step S342: When the equipment cycle safety index value is less than the equipment cycle safety index threshold, the corresponding data device is inventoried as a normal safety state, and the operation safety inventory is continued for the normal safety state; in this application, if a corresponding calculation formula appears, the above calculation formula is dimensionless and its numerical calculation is taken. The weight coefficient, proportional coefficient and other coefficients in the formula are set to a result value obtained by quantifying each parameter. Regarding the size of the weight coefficient and the proportional coefficient, as long as it does not affect the proportional relationship between the parameter and the result value, it is fine.

[0025] Example 2

[0026] See also Figure 2 Based on another concept of the same invention, an equipment inventory management system based on IDC equipment data collection is proposed, which is applied to an equipment inventory management method based on IDC equipment data collection. The equipment inventory management system includes a device status module, a security data module, a device inventory module and a server. The device status module, the security data module and the device inventory module are respectively connected to the server, and the server controls the device status module, the security data module and the device judgment module respectively; the device status module marks multiple data devices in the data center, and monitors the device status of each data device during the device operation monitoring cycle, and obtains the device operation data corresponding to each data device by analyzing the monitoring results to obtain device status monitoring data; specifically as follows: all data devices currently in operation in the IDC are obtained to obtain multiple data devices, and a data device is randomly selected from the multiple data devices obtained as a sample data device.

[0027] It should be noted that in this application, the IDC mentioned here refers to the Internet Data Center, which is a facility that provides hosting and management of information technology (IT) equipment and services. Figure 3 The IDC computer room is mainly composed of physical equipment such as network servers, power equipment, and UPS power supplies; in this application, the data equipment involved here is specifically a network server; in the process of monitoring the equipment operation status of multiple data devices, the time point corresponding to the current moment is marked as the first status monitoring time point, and the time point corresponding to a characteristic monitoring duration between the first status monitoring time point is marked as the second status monitoring time point, and the time period between the first status monitoring time point and the second status monitoring time point is named the equipment operation monitoring cycle.

[0028] It should be noted here that: in this application, the characteristic monitoring time involved here is specifically 24 hours; in this application, as the time value corresponding to the current moment changes, the first state monitoring time point and the second state monitoring time point also change accordingly, thereby realizing the dynamic update of the equipment operation monitoring cycle; the sample data device is subjected to equipment CPU operation status monitoring to obtain the CPU operation index value corresponding to the sample data device; specifically as follows: within the equipment operation monitoring cycle, a number of CPU monitoring time points are randomly selected, and the time intervals between each two consecutive CPU monitoring time points are equal, to obtain multiple CPU monitoring time points; obtain the sample data device at each CPU The CPU utilization rates of the devices corresponding to the monitoring time points are respectively obtained to obtain multiple device CPU utilization rates, and the average of the obtained multiple device CPU utilization rates is calculated to obtain the average CPU utilization rate of the device cycle; the variance of the obtained multiple device CPU utilization rates is calculated to obtain the variance of the device cycle CPU utilization rate; the benchmark CPU utilization rate of the sample data device under the corresponding working conditions at each CPU monitoring time point is obtained to obtain multiple benchmark CPU utilization rates; the average of the obtained multiple benchmark CPU utilization rates is calculated to obtain the benchmark CPU utilization rate of the device cycle; the variance of the obtained multiple benchmark CPU utilization rates is calculated to obtain the benchmark CPU utilization variance of the device cycle.

[0029] In this application, specific working indicators corresponding to working conditions include but are not limited to memory usage, number of I / O operations, processes run by the CPU, and the amount of data corresponding to each running process; the device cycle average CPU utilization, device cycle CPU utilization variance, device cycle benchmark CPU utilization, and device cycle benchmark CPU utilization variance are calculated to obtain the CPU operating indicator value corresponding to the sample data device; the CPU operating indicator value corresponding to the sample data device is calculated using the following specific formula: ; Among them, Cpz is the CPU operation index value corresponding to the sample data device, Cpl is the average CPU utilization of the device cycle, Cpj is the benchmark CPU utilization of the device cycle, Cfc is the variance of the CPU utilization of the device cycle, and Cfj is the benchmark CPU utilization variance of the device cycle; the device memory operation status of the sample data device is monitored to obtain the memory operation index value corresponding to the sample data device; specifically as follows: within the device operation monitoring cycle, several memory monitoring time points are randomly selected, and the time intervals between each two consecutive memory monitoring time points are equal, to obtain multiple memory monitoring time points; obtain the sample data device at each memory monitoring time point The device memory utilization corresponding to each monitoring time point is stored to obtain multiple device memory utilization rates, and the average of the multiple device memory utilization rates is calculated to obtain the average memory utilization rate of the device cycle; the variance of the multiple device memory utilization rates is calculated to obtain the variance of the device cycle memory utilization rate; the benchmark memory utilization rate of the sample data device under the corresponding working conditions at each memory monitoring time point is obtained to obtain multiple benchmark memory utilization rates; the average of the multiple benchmark memory utilization rates is calculated to obtain the benchmark memory utilization rate of the device cycle; the variance of the multiple benchmark memory utilization rates is calculated to obtain the benchmark memory utilization variance of the device cycle.

[0030] It should be noted here that: in this application, the specific working indicators corresponding to the working conditions involved here include but are not limited to CPU usage, number of I / O operations, processes run by the CPU, and the amount of data corresponding to each running process; the device cycle average memory utilization, device cycle memory utilization variance, device cycle benchmark memory utilization, and device cycle benchmark memory utilization variance are calculated to obtain the memory operation indicator value corresponding to the sample data device; the memory operation indicator value corresponding to the sample data device is calculated using the following specific formula: ; Among them, Npz is the memory operation index value corresponding to the sample data device, Npl is the average memory utilization of the device cycle, Npj is the benchmark memory utilization of the device cycle, Nfc is the variance of the memory utilization of the device cycle, and Nfj is the benchmark memory utilization variance of the device cycle; the network operation status of the sample data device is monitored to obtain the network operation index value corresponding to the sample data device; specifically as follows: within the device operation monitoring cycle, a number of network monitoring time points are randomly selected, and the time intervals between each two consecutive network monitoring time points are equal, to obtain multiple network monitoring time points; obtain the sample data device at each network monitoring time point The device bandwidth utilization corresponding to each time point is measured to obtain multiple device bandwidth utilizations, and the average of the multiple device bandwidth utilizations is calculated to obtain the average bandwidth utilization of the device cycle; the variance of the multiple device bandwidth utilizations is calculated to obtain the variance of the device cycle bandwidth utilization; the benchmark bandwidth utilization of the sample data device under the corresponding working conditions at each network monitoring time point is obtained to obtain multiple benchmark bandwidth utilizations; the average of the multiple benchmark bandwidth utilizations is calculated to obtain the benchmark bandwidth utilization of the device cycle; the variance of the multiple benchmark bandwidth utilizations is calculated to obtain the variance of the device cycle benchmark network utilization.

[0031] It should be noted here that: in this application, the specific working indicators corresponding to the working conditions involved here include but are not limited to CPU usage, number of I / O operations, processes run by the CPU, and the amount of data corresponding to each running process; the network operation indicator value corresponding to the sample data device is obtained by calculating the average bandwidth utilization rate of the device cycle, the variance of the bandwidth utilization rate of the device cycle, the benchmark bandwidth utilization rate of the device cycle, and the variance of the benchmark network utilization rate of the device cycle; the network operation indicator value corresponding to the sample data device is calculated, and the specific formula is as follows: ; Wherein, Wpz is the network operation index value corresponding to the sample data device, Wpl is the average bandwidth utilization of the device cycle, Wpj is the benchmark bandwidth utilization of the device cycle, Wfc is the variance of the bandwidth utilization of the device cycle, and Wfj is the benchmark network utilization variance of the device cycle; the CPU operation index value, memory operation index value and network operation index value corresponding to the sample data device are defined as the device operation data corresponding to the sample data device; the acquisition process of the device operation data corresponding to the sample data device is repeated, and the device operation data corresponding to each data device is acquired respectively to obtain multiple device operation data; the device operation monitoring cycle and multiple device operation data are defined as device status monitoring data; the device status module acquires the device status monitoring data and transmits it to the security data module and the device inventory module; security The full data module performs equipment safety monitoring on each data device that is within the equipment operation monitoring cycle based on the equipment status monitoring data, and obtains the equipment cycle safety index value corresponding to each data device to obtain equipment safety monitoring data; obtains equipment status monitoring data, and obtains the equipment operation monitoring cycle based on the equipment status monitoring data; obtains multiple data devices in the data center, and randomly selects a data device from the multiple data devices as a feature data device; performs operation safety monitoring on the feature data device that is in the equipment operation monitoring cycle, and obtains the equipment cycle safety index value corresponding to the feature data device; specifically as follows: in the process of safety monitoring the feature data device, several different types of equipment warnings are set respectively, and the different types of equipment warnings are named as the first type safety warning to the ath type safety warning.

[0032] In this application, a mentioned here is the type quantity value corresponding to the device warning, and a is an integer greater than 0.

[0033] In the present application, the first type of security warning involved here can be an intrusion detection warning, the second type of security warning can be a network anomaly warning, the third type of security warning can be an access control warning, and the fourth type of security warning can be a physical security warning; obtain the number of warnings from the first type of security warning to the ath type of security warning during the equipment operation monitoring period of the characteristic data device, and obtain the number of first type warnings to the ath type of warning number; obtain the duration of the equipment operation monitoring period to obtain the monitoring period duration value, obtain the ratio of the first type warning number to the ath type warning number to the monitoring period duration value, and obtain the first type warning frequency to the ath type warning frequency; randomly select several first type security warnings during the equipment operation monitoring period. For a full warning, multiple first-type security warnings are obtained, and the security warning duration corresponding to each first-type security warning is obtained respectively to obtain multiple security warning durations, and the obtained security warning durations are averaged to obtain the average duration of the first-type warning; the average duration of the first-type warning is repeated to obtain the average warning duration corresponding to the second-type security warning to the a-th type security warning, and the average duration of the second-type warning to the a-th type warning is obtained; the average duration of the first-type warning to the average duration of the a-th type warning and the frequency of the first-type warning to the a-th type warning are calculated to obtain the equipment cycle safety index value corresponding to the characteristic data device; the equipment cycle safety index value corresponding to the characteristic data device is calculated, and the specific formula is as follows: ; Wherein, Azz is the equipment cycle safety index value corresponding to the characteristic data device, Scp1 to Scpa are respectively the average duration of the first type of warning to the average duration of the a-th type of warning, and Pcj1 to Pcja are respectively the frequency of the first type of warning to the a-th type of warning; repeat the process of obtaining the equipment cycle safety index value corresponding to the characteristic data device, obtain the equipment cycle safety index value corresponding to each data device respectively, and obtain multiple equipment cycle safety index values; define the multiple equipment cycle safety index values as equipment safety monitoring data; the security data module obtains the equipment safety monitoring data and transmits it to the equipment inventory module; the equipment inventory module performs equipment abnormality inventory for each data device according to the equipment safety monitoring data and the equipment status monitoring data, and The point results are used to provide real-time abnormal warnings for data devices; obtain device status monitoring data, and obtain the CPU operating indicator values, memory operating indicator values, and network operating indicator values corresponding to each data device based on the device status monitoring data; conduct device operation status inventory based on the CPU operating indicator values, memory operating indicator values, and network operating indicator values corresponding to each data device, and issue device operation status warnings based on the inventory results; specifically as follows: the CPU operating indicator values, memory operating indicator values, and network operating indicator values corresponding to the same data device are calculated; the CPU operating indicator values, memory operating indicator values, and network operating indicator values are calculated to obtain the device operation comprehensive monitoring coefficient; the device operation comprehensive monitoring coefficient is calculated using the following formula: ; Among them, Zjx is the comprehensive monitoring coefficient of device operation, Cpz is the CPU operation index value, Wpz is the network operation index value, and Npz is the memory operation index value; obtain the CPU operation index threshold, memory operation index threshold, and network operation index threshold respectively.

[0034] It should be noted that the CPU operating indicator threshold, memory operating indicator threshold, and network operating indicator threshold mentioned here are the maximum CPU operating indicator values, maximum memory operating indicator values, and maximum network operating indicator values for a device in normal operation, respectively. The CPU operating indicator threshold, memory operating indicator threshold, and network operating indicator threshold are calculated to obtain the device operation comprehensive monitoring coefficient threshold. The specific formula for calculating the device operation comprehensive monitoring coefficient threshold is as follows: ; Among them, Zjxy is the threshold of the comprehensive monitoring coefficient of equipment operation, Cpzy is the threshold of the CPU operation index, Wpzy is the threshold of the network operation index, and Npzy is the threshold of the memory operation index; when the comprehensive monitoring coefficient of equipment operation is greater than or equal to the threshold of the comprehensive monitoring coefficient of equipment operation, the corresponding data equipment will be counted as an abnormal operation status equipment, and an abnormal operation status warning will be issued; when the comprehensive monitoring coefficient of equipment operation is less than the threshold of the comprehensive monitoring coefficient of equipment operation, the corresponding data equipment will be counted as a normal operation status equipment, and the operation status inventory of the normal operation status equipment will continue to be carried out; obtain equipment safety monitoring data, and obtain the equipment cycle safety index value corresponding to each data equipment according to the equipment safety monitoring data; obtain the equipment cycle safety index threshold, and compare the equipment cycle safety index value corresponding to each data equipment with the equipment cycle safety index threshold respectively, and conduct an operation safety inventory of each data equipment according to the numerical comparison results and issue a safety warning.

[0035] It should be noted here that the equipment cycle safety index threshold involved here is the maximum equipment cycle safety index value corresponding to the normal safety status; specifically, when the equipment cycle safety index value is greater than or equal to the equipment cycle safety index threshold, the corresponding data device will be counted as having an abnormal safety status, and an abnormal safety status warning will be issued; when the equipment cycle safety index value is less than the equipment cycle safety index threshold, the corresponding data device will be counted as having a normal safety status, and the safety inventory will continue to be performed for the normal safety status.

[0036] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A device inventory management method based on IDC device data collection, characterized in that: The following specific steps are included: Step S1: Mark multiple data devices in the data center, monitor the device status of each data device during the device operation monitoring cycle, and obtain the device operation data corresponding to each data device by analyzing the monitoring results to obtain device status monitoring data; Step S2: performing equipment safety monitoring on each data device in the equipment operation monitoring cycle according to the equipment status monitoring data, and obtaining the equipment cycle safety index value corresponding to each data device to obtain equipment safety monitoring data; Calculate the equipment cycle safety index value corresponding to the characteristic data equipment. The specific formula is as follows: ； Among them, Azz is the equipment cycle safety index value corresponding to the characteristic data equipment, Scp1 to Scpa are the average duration of the first type of warning to the average duration of the a-th type of warning, and Pcj1 to Pcja are the frequency of the first type of warning to the frequency of the a-th type of warning; Step S3: perform an inventory of device abnormalities for each data device based on the device safety monitoring data and the device status monitoring data, and issue a real-time abnormality warning for the data device based on the inventory results; The step S1 further includes the following specific steps: Step S11: Acquire all data devices in a running state to obtain multiple data devices, and randomly select one data device from the acquired multiple data devices as a sample data device; Step S12: During the process of monitoring the operation status of multiple data devices, the time point corresponding to the current moment is marked as a first status monitoring time point, the time point corresponding to a characteristic monitoring duration between the first status monitoring time point is marked as a second status monitoring time point, and the period between the first status monitoring time point and the second status monitoring time point is named the device operation monitoring period; Step S13: Monitor the CPU operating status of the sample data device to obtain the CPU operating indicator value corresponding to the sample data device; Step S14: monitoring the memory operation status of the sample data device to obtain a memory operation indicator value corresponding to the sample data device; Step S15: Monitor the network operation status of the sample data device to obtain the network operation indicator value corresponding to the sample data device; Step S16: defining the CPU operation index value, memory operation index value, and network operation index value corresponding to the sample data device as the device operation data corresponding to the sample data device; Step S17: acquiring the device operation data corresponding to each data device respectively to obtain a plurality of device operation data; Step S18: defining the equipment operation monitoring cycle and a plurality of equipment operation data as equipment status monitoring data.

2. The device inventory management method based on IDC device data collection according to claim 1, characterized in that: The step S13 further includes the following specific steps: Step S131: randomly selecting a number of CPU monitoring time points within the device operation monitoring cycle, with the time intervals between each two consecutive CPU monitoring time points being equal, to obtain a plurality of CPU monitoring time points; Step S132: Obtain the device CPU utilization corresponding to each CPU monitoring time point of the sample data device to obtain multiple device CPU utilizations. Step S133: Calculate the average of the CPU utilizations of the multiple devices to obtain the average CPU utilization of the device period; Step S134: performing variance calculation on the CPU utilization rates of the multiple devices obtained to obtain the variance of the CPU utilization rates of the devices in a period; Step S135: Obtaining a benchmark CPU utilization rate of the sample data device under corresponding working conditions at each CPU monitoring time point to obtain multiple benchmark CPU utilization rates; Step S136: Calculate the average of the obtained multiple benchmark CPU utilizations to obtain the device period benchmark CPU utilization; Step S137: performing variance calculation on the obtained multiple benchmark CPU utilizations to obtain the device cycle benchmark CPU utilization variance; Step S138: Calculate the average CPU utilization rate of the device cycle, the variance of the CPU utilization rate of the device cycle, the benchmark CPU utilization rate of the device cycle, and the variance of the benchmark CPU utilization rate of the device cycle to obtain the CPU operation index value corresponding to the sample data device; Calculate the CPU operating indicator value corresponding to the sample data device. The specific formula is as follows: ； Among them, Cpz is the CPU operation indicator value corresponding to the sample data device, Cpl is the average CPU utilization of the device cycle, Cpj is the benchmark CPU utilization of the device cycle, Cfc is the variance of the CPU utilization of the device cycle, and Cfj is the variance of the benchmark CPU utilization of the device cycle.

3. The device inventory management method based on IDC device data collection according to claim 1, characterized in that: The step S14 further includes the following specific steps: Step S141: randomly selecting a number of memory monitoring time points within a device operation monitoring cycle, with the time intervals between each two consecutive memory monitoring time points being equal, to obtain a plurality of memory monitoring time points; Step S142: Obtain the device memory utilization rate corresponding to each memory monitoring time point of the sample data device to obtain multiple device memory utilization rates. Step S143: Calculate the average of the memory utilization rates of the multiple devices to obtain the average memory utilization rate of the device cycle; Step S144: performing variance calculation on the obtained multiple device memory utilizations to obtain the variance of the device cycle memory utilization; Step S145: Obtaining a benchmark memory utilization rate of the sample data device under corresponding working conditions at each memory monitoring time point to obtain multiple benchmark memory utilization rates; Step S146: Calculate the average of the obtained multiple benchmark memory utilizations to obtain the device cycle benchmark memory utilization; Step S147: performing variance calculation on the obtained multiple benchmark memory utilizations to obtain the device cycle benchmark memory utilization variance; Step S148: Calculating the average memory utilization rate of the device cycle, the variance of the memory utilization rate of the device cycle, the benchmark memory utilization rate of the device cycle, and the variance of the benchmark memory utilization rate of the device cycle to obtain the memory operation indicator value corresponding to the sample data device; Calculate the memory operation indicator value corresponding to the sample data device. The specific formula is as follows: ； Among them, Npz is the memory operation indicator value corresponding to the sample data device, Npl is the average memory utilization of the device cycle, Npj is the benchmark memory utilization of the device cycle, Nfc is the variance of the memory utilization of the device cycle, and Nfj is the variance of the benchmark memory utilization of the device cycle.

4. The device inventory management method based on IDC device data collection according to claim 1, characterized in that: The step S15 further includes the following specific steps: Step S151: randomly selecting a number of network monitoring time points within the device operation monitoring cycle, with the time intervals between each two consecutive network monitoring time points being equal, to obtain a plurality of network monitoring time points; Step S152: Obtain the device bandwidth utilization rate corresponding to each network monitoring time point of the sample data device, and obtain multiple device bandwidth utilization rates. Step S153: Calculate the average of the bandwidth utilization rates of the multiple devices to obtain the average bandwidth utilization rate of the device period; Step S154: performing variance calculation on the obtained bandwidth utilization rates of the multiple devices to obtain the variance of the device periodic bandwidth utilization rate; Step S155: Obtain the benchmark bandwidth utilization of the sample data device under the corresponding working conditions at each network monitoring time point to obtain multiple benchmark bandwidth utilizations; Step S156: Calculate the average of the obtained multiple benchmark bandwidth utilizations to obtain the device period benchmark bandwidth utilization; Step S157: performing variance calculation on the obtained multiple benchmark bandwidth utilizations to obtain the device cycle benchmark network utilization variance; Step S158: Calculate the device cycle average bandwidth utilization, device cycle bandwidth utilization variance, device cycle benchmark bandwidth utilization, and device cycle benchmark network utilization variance to obtain the network operation indicator value corresponding to the sample data device; Calculate the network operation indicator values corresponding to the sample data devices. The specific formula is as follows: ； Among them, Wpz is the network operation indicator value corresponding to the sample data device, Wpl is the average bandwidth utilization of the device cycle, Wpj is the benchmark bandwidth utilization of the device cycle, Wfc is the variance of the bandwidth utilization of the device cycle, and Wfj is the variance of the benchmark network utilization of the device cycle.

5. The device inventory management method based on IDC device data collection according to claim 1, characterized in that: The step S2 further includes the following specific steps: Step S21: Acquire device status monitoring data, and acquire a device operation monitoring period according to the device status monitoring data; Step S22: Acquire multiple data devices in the data center, and randomly select one data device from the multiple data devices as a feature data device; Step S23: Performing operation safety monitoring on the characteristic data device in the equipment operation monitoring cycle to obtain the equipment cycle safety index value corresponding to the characteristic data device; Step S24: acquiring the device cycle security index value corresponding to each data device respectively to obtain multiple device cycle security index values; Step S25: defining multiple equipment periodic safety indicator values as equipment safety monitoring data.

6. The device inventory management method based on IDC device data collection according to claim 5, characterized in that: The step S23 further includes the following specific steps: Step S231: During the security monitoring of the characteristic data device, several different types of device warnings are set, and the different types of device warnings are named as the first type of security warning to the ath type of security warning; Step S232: Obtain the number of warnings from the first type of safety warning to the ath type of safety warning during the equipment operation monitoring period of the characteristic data device, and obtain the number of warnings from the first type of warning to the ath type of warning; Step S233: Obtain the average duration of the first type of warning and the frequency of the first type of warning to the ath type of warning; Step S234: Obtain the average warning durations corresponding to the second type of security warning to the ath type of security warning, and obtain the average duration of the second type of warning to the ath type of warning; Step S235: Calculate the average duration of the first type of warning to the average duration of the ath type of warning and the frequency of the first type of warning to the ath type of warning to obtain the equipment periodic safety index value corresponding to the characteristic data equipment.

7. The device inventory management method based on IDC device data collection according to claim 6 is characterized in that: The step S233 further includes the following specific steps: Step S2331: Obtain the duration of the equipment operation monitoring cycle to obtain the monitoring cycle duration value, obtain the ratio of the first type warning number to the a-th type warning number to the monitoring cycle duration value, and obtain the first type warning frequency to the a-th type warning frequency; Step S2332: Randomly select several first-type safety warnings within the equipment operation monitoring cycle to obtain multiple first-type safety warnings, obtain the safety warning duration corresponding to each first-type safety warning respectively, obtain multiple safety warning durations, and calculate the average of the obtained safety warning durations to obtain the average duration of the first-type warning.

8. The device inventory management method based on IDC device data collection according to claim 1, characterized in that: The step S3 further includes the following specific steps: Step S31: Obtain device status monitoring data, and obtain the CPU operation index value, memory operation index value, and network operation index value corresponding to each data device according to the device status monitoring data; Step S32: performing a device operation status inventory based on the CPU operation index value, memory operation index value, and network operation index value corresponding to each data device, and issuing a device operation status warning based on the inventory results; Step S33: Acquire equipment safety monitoring data, and obtain the equipment periodic safety index value corresponding to each data device according to the equipment safety monitoring data; Step S34: Obtain the device cycle safety index threshold, compare the device cycle safety index value corresponding to each data device with the device cycle safety index threshold, perform an operation safety inventory of each data device based on the value comparison result, and issue a safety warning; The details are as follows: Step S341: When the device cycle safety index value is greater than or equal to the device cycle safety index threshold, the corresponding data device is counted as having a security status abnormality and a security status abnormality warning is issued; Step S342: When the equipment cycle safety index value is less than the equipment cycle safety index threshold, the corresponding data equipment is checked as being in a normal safety state, and the operation safety inventory is continued for the normal safety state.

9. The device inventory management method based on IDC device data collection according to claim 8, characterized in that: The step S32 further includes the following specific steps: Step S321: The CPU operation index value, memory operation index value and network operation index value corresponding to the same data device are obtained; Step S322: Calculate the CPU operation index value, the memory operation index value, and the network operation index value to obtain a comprehensive monitoring coefficient for device operation; Calculate the comprehensive monitoring coefficient of equipment operation. The specific formula is as follows: ； Where Zjx is the comprehensive monitoring coefficient of device operation, Cpz is the CPU operation index value, Wpz is the network operation index value, and Npz is the memory operation index value; Step S323: respectively obtaining a CPU operation index threshold, a memory operation index threshold, and a network operation index threshold; Step S324: Calculating the CPU operation index threshold, the memory operation index threshold, and the network operation index threshold to obtain a device operation comprehensive monitoring coefficient threshold; Step S325: When the equipment operation comprehensive monitoring coefficient is greater than or equal to the equipment operation comprehensive monitoring coefficient threshold, the corresponding data device is counted as an abnormal operation status device and an abnormal operation status warning is issued; Step S326: When the equipment operation comprehensive monitoring coefficient is less than the equipment operation comprehensive monitoring coefficient threshold, the corresponding data equipment is counted as a normal operation equipment, and the operation status inventory of the normal operation equipment is continued.

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