Equipment inventory management method based on IDC equipment data acquisition

Abstract

The invention discloses an equipment inventory management method based on IDC equipment data acquisition, relates to the field of IDC, solves the problem of poor inventory effect of the equipment inventory management method, and comprises the following steps: S1, carrying out equipment state monitoring on each piece of data equipment in an equipment operation monitoring period, s2, performing equipment safety monitoring on each piece of data equipment in an equipment operation monitoring period according to the equipment state monitoring data, respectively acquiring an equipment period safety index value corresponding to each piece of data equipment, and respectively acquiring equipment operation data corresponding to each piece of data equipment to obtain equipment state monitoring data, and S2, performing equipment safety monitoring on each piece of data equipment in the equipment operation monitoring period according to the equipment state monitoring data, and respectively acquiring an equipment period safety index value corresponding to each piece of data equipment; s3, according to the equipment safety monitoring data and the equipment state monitoring data, equipment abnormity checking is carried out on each piece of data equipment, and real-time abnormity early warning is carried out on the data equipment according to the checking result, and the comprehensiveness and the accuracy of the equipment judgment result are improved.

Description

Technical Field

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

[0002] The existing equipment inventory management method based on IDC equipment data collection has the following specific defects when conducting equipment inventory: 1. The existing equipment inventory management method can often only collect and inventory equipment data within a fixed period of time, so that there is a certain time interval in the inventory process, which leads to a lack of continuity in the inventory results; 2. The existing equipment inventory management method often only relies on the equipment's own early warning system to conduct abnormal situation inventory, and cannot provide data support for the redundant operations of the inventory method, which leads to the 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 deficiencies in the prior art, 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 a 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. According to the equipment safety monitoring data and the equipment status monitoring data, equipment abnormality inventory is performed on each data device, and real-time abnormality warning is issued to the data device according to the inventory results.

[0005] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: 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, and 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, and obtaining 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, and obtaining 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 also 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 points 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, and 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 also includes the following specific steps: step S131: randomly selecting a number of CPU monitoring time points within the equipment operation monitoring cycle, and the time interval between each two consecutive CPU monitoring time points is equal, so as to obtain a plurality of CPU monitoring time points; step S132: obtaining the device CPU utilization rate corresponding to each CPU monitoring time point of the sample data device, so as to obtain a plurality of device CPU utilization rates; step S133: averaging the obtained plurality of device CPU utilization rates, so as to obtain the average CPU utilization rate of the equipment cycle; step S134: performing variance calculation on the obtained plurality of device CPU utilization rates, so as to obtain the variance of the CPU utilization rate of the equipment cycle; step S135: obtaining the sample data; According to the benchmark CPU utilization of the device under the corresponding working conditions at each CPU monitoring time point, multiple benchmark CPU utilizations are obtained; step S136: average the obtained multiple benchmark CPU utilizations to obtain the device period benchmark CPU utilization; step S137: variance calculation is performed on the obtained multiple benchmark CPU utilizations to obtain the device period benchmark CPU utilization variance; step S138: the device period average CPU utilization, the device period CPU utilization variance, the device period benchmark CPU utilization and the device period 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, and the specific formula is as follows: ; 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 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, so as to obtain a plurality of memory monitoring time points; step S142: obtaining the device memory utilization rate corresponding to each memory monitoring time point of the sample data device, so as to obtain a plurality of device memory utilization rates; step S143: averaging the obtained plurality of device memory utilization rates, so as to obtain the average memory utilization rate of the device cycle; step S144: performing variance calculation on the obtained plurality of device memory utilization rates, so as to obtain the variance of the device cycle memory utilization rate; step S145: obtaining the sample data device; The benchmark memory utilization rate of the data device under the corresponding working conditions at each memory monitoring time point is obtained to obtain multiple benchmark memory utilization rates; step S146: average the obtained multiple benchmark memory utilization rates to obtain the device cycle benchmark memory utilization rate; step S147: calculate the variance of the obtained multiple benchmark memory utilization rates to obtain the device cycle benchmark memory utilization variance; step S148: calculate the device cycle average memory utilization rate, the device cycle memory utilization rate variance, the device cycle benchmark memory utilization rate and the device cycle benchmark memory utilization variance to obtain the memory operation index value corresponding to the sample data device; calculate the memory operation index value corresponding to the sample data device, the specific formula is as follows: ; 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 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 equipment operation monitoring cycle, and the time intervals between each two consecutive network monitoring time points are equal, so as to obtain a plurality of network monitoring time points; step S152: obtaining the device bandwidth utilization rate corresponding to each network monitoring time point of the sample data device, so as to obtain a plurality of device bandwidth utilization rates; step S153: averaging the obtained plurality of device bandwidth utilization rates, so as to obtain the average bandwidth utilization rate of the equipment cycle; step S154: performing variance calculation on the obtained plurality of device bandwidth utilization rates, so as to obtain the variance of the bandwidth utilization rate of the equipment cycle; step S155: obtaining the sample data device; The data device obtains multiple benchmark bandwidth utilizations at each network monitoring time point under the corresponding working conditions; step S156: average 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, the specific formula is as follows: ; Among them, 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.

[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 according to 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] Further, the step S23 also includes the following specific steps: step S231: in the process of security monitoring of the characteristic data equipment, several different types of equipment warnings are set respectively, and the multiple different types of equipment warnings are named as the first type of security warning to the ath type of security warning; step S232: obtaining 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 equipment, and obtaining the number of first type of warnings to the ath type of warning number; step S233: obtaining the average duration of the first type of warning and the frequency of the first type of warning to the ath type of warning frequency; step S234: obtaining the average warning duration corresponding to the second type of security warning to the ath type of security warning respectively, and obtaining the average duration of the second type of warning to the ath type of warning average duration; step S235: calculating 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 frequency to obtain the equipment periodic safety index value corresponding to the characteristic data equipment; the equipment periodic safety index value corresponding to the characteristic data equipment is calculated, and the specific formula is as follows: ; Among them, Azz is the equipment periodic 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: acquiring the duration of the equipment operation monitoring cycle to obtain a monitoring cycle duration value, acquiring the ratio of the number of first type warnings to the number of a-th type warnings to the monitoring cycle duration value, and obtaining the first type warning frequency to the a-th type warning frequency; step S2332: randomly selecting a number of first type safety warnings within the equipment operation monitoring cycle to obtain multiple first type safety warnings, respectively acquiring the safety warning duration corresponding to each first type safety warning to obtain 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 an inventory of the device operation status 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, and perform an operation safety inventory of each data device and issue a safety warning 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, count the corresponding data device as having an abnormal safety status, and issue an abnormal safety status warning; Step S342: When the equipment cycle safety index value is less than the equipment cycle safety index threshold, count the corresponding data device as having a normal safety status, and continue to perform an operation safety inventory 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 the equipment operation comprehensive monitoring coefficient; calculating the equipment operation comprehensive monitoring coefficient, 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; Calculate the equipment operation comprehensive monitoring coefficient threshold, the specific formula is as follows: ; Wherein, 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; Step S325: 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 is counted as an abnormal operation status equipment, and an abnormal operation status warning is issued; Step S326: 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 is counted as a normal operation status equipment, and the operation status inventory of the normal operation status equipment continues.

[0015] In summary, due to the adoption of the above-mentioned technical scheme, 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 early warning, the present invention performs real-time CPU monitoring, memory monitoring and network monitoring on data equipment respectively, which provides data support for redundant operations in the inventory process and improves the comprehensiveness of the inventory results. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order 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 It is the overall system block diagram of the present invention.

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

[0020] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Embodiment 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 within an equipment operation monitoring cycle, and acquiring equipment operation data corresponding to each data device by analyzing the monitoring results, to obtain equipment status monitoring data; said step S1 also 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 acquired multiple data devices 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 as the equipment operation monitoring cycle; Step S13: Monitor the CPU operation status of the sample data device to obtain the CPU operation index value corresponding to the sample data device; The step S13 also includes the following specific steps: Step S131: In the equipment operation monitoring cycle, 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 CP of the sample data device under the corresponding working conditions at each CPU monitoring time point U utilization, obtain multiple benchmark CPU utilizations; step S136: average the obtained multiple benchmark CPU utilizations to obtain the equipment period benchmark CPU utilization; step S137: calculate the variance of the obtained multiple benchmark CPU utilizations to obtain the equipment period benchmark CPU utilization variance; step S138: calculate the equipment period average CPU utilization, the equipment period CPU utilization variance, the equipment period benchmark CPU utilization and the equipment period benchmark CPU utilization variance to obtain the CPU operation index value corresponding to the sample data device; calculate the CPU operation index value corresponding to the sample data device, the specific formula is as follows: ; 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 variance of the CPU utilization 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; The step S14 also includes the following specific steps: Step S141: Within the device operation monitoring cycle, randomly select a number of memory monitoring time points, and the time intervals between each two consecutive memory monitoring time points are equal, so as 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, and obtain multiple device memory utilizations; 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; calculate the memory operation index value corresponding to the sample data device, 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; The step S15 also includes the following specific steps: Step S151: Within the device operation monitoring cycle, randomly select a number of network monitoring time points, and the time intervals between each two consecutive network monitoring time points are equal, so as 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, and obtain multiple device bandwidth utilizations, and step S153: Calculate the average of the obtained multiple device bandwidth utilizations , and obtain the average bandwidth utilization of the equipment cycle; step S154: calculate the variance of the obtained multiple equipment bandwidth utilizations to obtain the variance of the equipment cycle bandwidth utilization; step S155: obtain the benchmark bandwidth utilization of the sample data equipment 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 equipment cycle benchmark bandwidth utilization; step S157: calculate the variance of the obtained multiple benchmark bandwidth utilizations to obtain the equipment cycle benchmark network utilization variance; step S158: calculate the network operation index value corresponding to the sample data equipment by calculating the average bandwidth utilization of the equipment cycle, the variance of the equipment cycle bandwidth utilization, the equipment cycle benchmark bandwidth utilization and the equipment cycle benchmark network utilization variance; calculate the network operation index value corresponding to the sample data equipment, 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: respectively obtain the device operation data corresponding to each data device 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 in 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 respectively, and obtain the equipment safety monitoring data; the 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 the characteristic data device; step S23: perform operation safety monitoring on the characteristic data device in the equipment operation monitoring cycle, and obtain the equipment cycle safety index value corresponding to the characteristic data device; the step S23 also includes the following specific steps: step S231: in the process of safety monitoring the characteristic data device, set several different types of equipment warnings respectively, and name the various 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 characteristic data device in the equipment operation monitoring cycle, and obtain the number of warnings of the first type to the ath type; 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 cycle, obtain the monitoring cycle duration value, obtain the ratio of the first type warning times to the ath type warning times to the monitoring cycle duration value, and obtain the first type warning frequency to the ath type warning frequency; step S2332: randomly select a number of first type safety warnings within the equipment operation monitoring cycle, obtain multiple first type safety warnings, and 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, and obtain multiple equipment cycle safety index values; Step S25: Define multiple equipment cycle safety index values ​​as equipment safety monitoring data.

[0024] Step S3: perform an inventory of device abnormalities for each data device according to the device safety monitoring data and the device status monitoring data, and issue a real-time abnormal warning for the data device according to the inventory results; the step S3 also includes the following specific steps: step S31: obtain the 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: perform an inventory of the device operation status according to 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 according to 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; Calculate the equipment operation comprehensive monitoring coefficient threshold, the specific formula is as follows: ; Wherein, Zjxy is the threshold of the comprehensive monitoring coefficient of equipment operation, Cpzy is the threshold of the CPU operation index, Wpz is the threshold of the network operation index, and Npzy is the threshold of the memory operation index; Step S325: 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 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 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 periodic safety index value corresponding to each data equipment according to the equipment safety monitoring data; Step S34: Obtain the equipment periodic safety index threshold, and compare the equipment periodic safety index value corresponding to each data equipment with the equipment safety monitoring data; Step S35: Obtain the equipment periodic safety index threshold, and compare the equipment periodic safety index value corresponding to each data equipment with the equipment safety monitoring data; Step S36: Obtain the equipment periodic safety index threshold, and compare the equipment periodic safety index value corresponding to each data equipment with the equipment safety monitoring data; Step S37: Obtain the equipment periodic safety index threshold, and compare the equipment periodic safety index value corresponding to each data equipment with the equipment safety monitoring data; Step S38: Obtain the equipment periodic safety index threshold, and compare the equipment periodic safety index value corresponding to each data equipment with the equipment safety monitoring data; Step S39: Obtain the equipment periodic safety index threshold, and compare the equipment periodic safety index value corresponding to each data equipment with the equipment safety monitoring data; Step S40: Obtain the equipment periodic safety index threshold, and compare the equipment periodic safety index value corresponding to each data equipment with the equipment safety monitoring data; Step S41: Obtain the equipment periodic safety index threshold, and compare the equipment periodic safety index value corresponding to each data equipment with the equipment safety monitoring data; Step S42: Obtain the equipment periodic safety index threshold, and compare the equipment periodic The equipment cycle safety index threshold is compared, and an operation safety inventory is performed on each data device according to the numerical comparison result, and a safety warning is issued; 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 counted 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 counted as having a normal safety status, and the operation safety inventory is continued for the normal safety status; in the present application, if a corresponding calculation formula appears, the above calculation formula is dimensionless and its numerical calculation is taken, and the weight coefficient, proportional coefficient and other coefficients in the formula are set to a result value obtained by quantifying each parameter, and the size of the weight coefficient and the proportional coefficient can be set as long as it does not affect the proportional relationship between the parameter and the result value.

[0025] Embodiment 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 an equipment status module, a security data module, an equipment inventory module and a server. The equipment status module, the security data module and the equipment inventory module are respectively connected to the server, and the server controls the equipment status module, the security data module and the equipment judgment module respectively; the equipment status module marks multiple data devices in the data center, and performs equipment status monitoring on each data device during the equipment operation monitoring cycle, and obtains the equipment operation data corresponding to each data device by analyzing the monitoring results to obtain equipment 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 referred to here is specifically an Internet data center, which refers to 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 the present 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 points 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 as the equipment operation monitoring cycle.

[0028] It should be noted here that: in the present application, the characteristic monitoring duration involved here is specifically 24 hours; in the present application, as the time value corresponding to the current moment changes, the first status monitoring time point and the second status monitoring time point also change accordingly, thereby realizing the dynamic update of the equipment operation monitoring cycle; the equipment CPU operation status of the sample data equipment is monitored to obtain the CPU operation indicator value corresponding to the sample data equipment; specifically as follows: within the equipment operation monitoring cycle, a number of CPU monitoring time points are randomly selected, and the time interval between each two consecutive CPU monitoring time points is equal, so as 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 obtained to obtain multiple device CPU utilization rates, and the average of the multiple device CPU utilization rates is calculated to obtain the average CPU utilization rate of the device period; the variance of the multiple device CPU utilization rates is calculated to obtain the variance of the device period 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 multiple benchmark CPU utilization rates is calculated to obtain the benchmark CPU utilization rate of the device period; the variance of the multiple benchmark CPU utilization rates is calculated to obtain the variance of the benchmark CPU utilization rate of the device period.

[0029] In this application, the specific working indicators corresponding to the working conditions involved here 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 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 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, and the specific formula is as follows: ; 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 variance of the CPU utilization of the device cycle, and Cfj is the benchmark CPU utilization variance of the device cycle; monitor the device memory operation status of the sample data device to obtain the memory operation index value corresponding to the sample data device; specifically as follows: within the device operation monitoring cycle, randomly select several memory monitoring time points, and the time interval between each two consecutive memory monitoring time points is equal, to obtain multiple memory monitoring time points; obtain the sample data device in each memory The device memory utilization corresponding to each monitoring time point is obtained to obtain multiple device memory utilizations, and the average of the obtained multiple device memory utilizations is calculated to obtain the average memory utilization of the device cycle; the variance of the obtained multiple device memory utilizations is calculated to obtain the variance of the device cycle memory utilization; the benchmark memory utilization of the sample data device under the corresponding working conditions at each memory monitoring time point is obtained to obtain multiple benchmark memory utilizations; the average of the obtained multiple benchmark memory utilizations is calculated to obtain the device cycle benchmark memory utilization; the variance of the obtained multiple benchmark memory utilizations is calculated to obtain the variance of the device cycle benchmark memory utilization.

[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, I / O operation number, processes run by the CPU, and the amount of data corresponding to each running process; 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 of the device cycle 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, 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; 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 interval between each two consecutive network monitoring time points is equal, and multiple network monitoring time points are obtained; the sample data device is obtained 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, I / O operation number, the process run by the CPU and the amount of data corresponding to each running process; the average bandwidth utilization of the device cycle, the variance of the bandwidth utilization of the device cycle, the benchmark bandwidth utilization of the device cycle and the variance of the benchmark network utilization of the device cycle are calculated to obtain the network operation indicator value corresponding to the sample data device; 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 process of obtaining the device operation data corresponding to the sample data device is repeated, and the device operation data corresponding to each data device is obtained 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 obtains the device status monitoring data and transmits it to the security data module and the device inventory module; the security The full data module performs equipment safety monitoring on each data device in the equipment operation monitoring cycle according to the equipment status monitoring data, and obtains the equipment cycle safety index value corresponding to each data device to obtain the equipment safety monitoring data; obtains the equipment status monitoring data, and obtains the equipment operation monitoring cycle according to 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 the feature data device; performs operation safety monitoring on the feature data device 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 of 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 the present application, a referred to 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, and obtain the number of first type warnings to the ath type of warning; obtain the duration of the equipment operation monitoring period to obtain the monitoring period duration value, obtain the ratio of the first type of warning number to the ath type of warning number to the monitoring period duration value, and obtain the first type of warning frequency to the ath type of warning frequency; randomly select a number of first type of security warnings during the equipment operation monitoring period. Full warning, get multiple first-type security warnings, get the security warning duration corresponding to each first-type security warning respectively, get multiple security warning durations, and average the obtained security warning durations to get the average duration of the first-type warning; repeat the average duration of the first-type warning, get the average warning duration corresponding to the second-type security warning to the a-th type security warning respectively, and get the average duration of the second-type warning to the a-th type warning; calculate the equipment cycle safety index value corresponding to the characteristic data equipment from 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; calculate the equipment cycle safety index value corresponding to the characteristic data equipment, 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 safety 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 Point results to conduct real-time abnormal warning for data devices; 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; conduct 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 issue device operation status warning according to the inventory results; specifically as follows: the CPU operation index value, memory operation index value and network operation index value corresponding to the same data device; the CPU operation index value, memory operation index value and network operation index value are calculated 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; obtain the CPU operation index threshold, memory operation index threshold, and network operation index threshold respectively.

[0034] It should be noted here that: the CPU operation index threshold, memory operation index threshold and network operation index threshold involved here are respectively the maximum CPU operation index value, maximum memory operation index value and maximum network operation index value corresponding to the device in normal operation state; the CPU operation index threshold, memory operation index threshold and network operation index threshold are calculated to obtain the device operation comprehensive monitoring coefficient threshold; the device operation comprehensive monitoring coefficient threshold is calculated, and the specific formula is as follows: ; Wherein, 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 is counted as an abnormal operation status equipment, and an abnormal operation status warning is 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 is counted as a normal operation status equipment, and the operation status inventory of the normal operation status equipment continues; obtain the equipment safety monitoring data, and obtain the equipment periodic safety index value corresponding to each data device according to the equipment safety monitoring data; obtain the equipment periodic safety index threshold, and compare the equipment periodic safety index value corresponding to each data device with the equipment periodic safety index threshold respectively, and conduct an operation safety inventory of each data device and issue a safety warning according to the numerical comparison result.

[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 equipment is counted as having an abnormal safety status, and an abnormal safety status warning is issued; when the equipment cycle safety index value is less than the equipment cycle safety index threshold, the corresponding data equipment is counted as having a normal safety status, and the safety inventory is continued for the normal safety status.

[0036] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. 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 specific steps include: Step S1: Mark multiple data devices in the data center, monitor the device status of each data device within 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: Perform equipment safety monitoring on each data device in 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; 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 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 frequency of the a-th type of warning; Step S3: perform equipment abnormality inventory for each data device based on the equipment safety monitoring data and equipment status monitoring data, and issue real-time abnormality warning to the data device based on the inventory results.

2. According to claim 1, a device inventory management method based on IDC device data collection is characterized in that: The step S1 further includes the following specific steps: Step S11: acquiring all data devices in operation to obtain a plurality of data devices, and randomly selecting a data device from the plurality of acquired data devices 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 points 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 as the equipment operation monitoring cycle; Step S13: monitor the CPU operation status of the sample data device to obtain the CPU operation index value corresponding to the sample data device; Step S14: monitoring the memory operation status of the sample data device to obtain the memory operation index value corresponding to the sample data device; Step S15: Monitor the network operation status of the sample data device to obtain the network operation index 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: define the equipment operation monitoring cycle and a plurality of equipment operation data as equipment status monitoring data.

3. The device inventory management method based on IDC device data collection according to claim 2 is 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, and the time intervals between two consecutive CPU monitoring time points are equal, to obtain a plurality of CPU monitoring time points; Step S132: Obtain the device CPU utilization rate corresponding to each CPU monitoring time point of the sample data device to obtain multiple device CPU utilization rates. Step S133: Calculate the average of the CPU utilizations of the multiple devices to obtain the periodic average CPU utilization of the device; Step S134: performing variance calculation on the CPU utilizations of the multiple devices obtained to obtain the variance of the CPU utilizations of the device period; Step S135: Obtain the benchmark CPU utilization of the sample data device under the corresponding working conditions at each CPU monitoring time point to obtain multiple benchmark CPU utilizations; 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 equipment period benchmark CPU utilization variance; Step S138: Calculate the CPU operation index value corresponding to the sample data device by using the average CPU utilization rate of the device period, the variance of the CPU utilization rate of the device period, the benchmark CPU utilization rate of the device period, and the variance of the benchmark CPU utilization rate of the device period; Calculate the CPU operating index value corresponding to the sample data device. The specific formula is as follows: ； 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 variance of the benchmark CPU utilization of the device cycle.

4. The device inventory management method based on IDC device data collection according to claim 2 is 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, and the time intervals between two consecutive memory monitoring time points are 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 memory utilization rates of the multiple devices to obtain the variance of the device cycle memory utilization rate; Step S145: obtaining a benchmark memory utilization rate of the sample data device under a corresponding working condition at each memory monitoring time point, and obtaining a plurality of 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: Calculate 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 benchmark memory utilization variance of the device cycle to obtain the memory operation index value corresponding to the sample data device; Calculate the memory operation index value corresponding to the sample data device. The specific formula is as follows: ； 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 variance of the benchmark memory utilization of the device cycle.

5. The device inventory management method based on IDC device data collection according to claim 2 is 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 equipment operation monitoring cycle, and the time intervals between every two consecutive network monitoring time points are 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 bandwidth utilization rates of the multiple devices obtained 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 equipment period benchmark network utilization variance; Step S158: Calculate 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 benchmark network utilization variance of the device cycle to obtain the network operation index value corresponding to the sample data device; Calculate the network operation index values ​​corresponding to the sample data devices. The specific formula is as follows: ； Among them, 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.

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

7. The device inventory management method based on IDC device data collection according to claim 6 is characterized in that: The step S23 further includes the following specific steps: Step S231: in the process of security monitoring of the characteristic data device, several different types of device warnings are set respectively, and the multiple different types of device warnings are named as the first type of security warning to the ath type of security warning respectively; 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: respectively obtaining the average warning durations corresponding to the second type of security warning to the ath type of security warning, and obtaining the average duration of the second type of warning to the ath type of warning; Step S235: Calculate the equipment periodic safety index value corresponding to the characteristic data equipment by combining 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.

8. The device inventory management method based on IDC device data collection according to claim 7 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 value of the monitoring cycle duration, obtain the ratio of the first type warning number to the a-th type warning number to the value of the monitoring cycle duration, and obtain the first type warning frequency to the a-th type warning frequency; Step S2332: randomly select a number of 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 average the obtained safety warning durations to obtain the average duration of the first-type warning.

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

10. The device inventory management method based on IDC device data collection according to claim 9 is 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 of device operation; The comprehensive monitoring coefficient of equipment operation is calculated, and the specific formula is as follows: ； Among them, 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 obtaining a CPU operation index threshold, a memory operation index threshold, and a 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 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 equipment is counted as an abnormal operation status equipment, 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 status equipment, and the operation status inventory of the normal operation status equipment continues.

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