Smart city data center operation and maintenance management system based on big data

Abstract

The invention belongs to the technical field of smart city operation and maintenance management, and particularly discloses a smart city data center operation and maintenance management system based on big data. The system comprises an infrastructure monitoring module, an infrastructure analysis module, an equipment operation monitoring module, an equipment operation analysis module, a business application monitoring module, a business application analysis module, a smart city analysis module and a smart city management terminal. According to the invention, operation and maintenance management is carried out on the smart city data center, the maintenance timeliness of the infrastructure of the data center is guaranteed, the application attention of business application is further guaranteed, the frequency of equipment faults is reduced, and the operation and maintenance management of the smart city is carried out through three latitudes of infrastructure, equipment operation and business application. The multi-latitude monitoring analysis of the smart city data center is realized, so that the comprehensiveness and the fineness of the data monitoring analysis are guaranteed, and the timeliness of finding potential problems in equipment is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of smart city operation and maintenance management, and relates to a smart city data center operation and maintenance management system based on big data. Background Art

[0002] The smart city data center is the core infrastructure for centralized storage, management and processing of various types of urban data. It has functions such as data aggregation, storage and analysis. In order to ensure the security and integrity of the data and prevent data leakage, loss and damage, its servers need to be operated and managed.

[0003] For example, the Chinese invention patent with publication number CN118586728A discloses a smart city operation and maintenance management system and method, which belongs to the field of smart city operation and maintenance technology. In order to solve the problem that the existing system equipment operation and maintenance mode is mainly operated through information transmission between the device end, platform end and maintenance end, it is difficult for the device end to effectively arrange the frequency of inspections, and it is necessary to increase the types and frequencies of data collection. The present invention deploys IoT devices, sensors, cameras and other equipment, establishes wired and wireless networks, builds data centers and cloud platforms, realizes the connectivity and sharing of data across the entire network, and the point-by-point data collection and perception of IoT devices, sensors, and cameras. According to the needs of urban operation and maintenance management, the appropriate data collection frequency can be set, and the data asset storage setting with security assurance and maintenance can be used to realize data input, data asset strip processing and output, encrypt the data required for smart city operation and maintenance, ensure the security of smart operation and maintenance supervision data, and conduct regular security audits and assessments on it.

[0004] The above existing technologies have the following deficiencies: 1. The current operation and maintenance management mainly focuses on urban operation and maintenance management, that is, insufficient attention is paid to the operation and maintenance management of smart city data centers, and the timeliness of maintenance of data center infrastructure cannot be guaranteed. At the same time, the application attention of business applications cannot be guaranteed, thereby increasing the frequency of equipment failures, which in turn leads to reduced user satisfaction.

[0005] 2. When conducting smart city operation and maintenance management, only the security of equipment operation data is considered, and the data at the equipment infrastructure level, equipment operation level and business application level are not considered. This leads to a single dimension of data monitoring and analysis, resulting in insufficient comprehensiveness and precision of data monitoring and analysis, and at the same time, it is impossible to ensure the timely discovery of potential problems in the equipment. Summary of the invention

[0006] In view of this, in order to solve the problems raised in the above background technology, a smart city data center operation and maintenance management system based on big data is proposed.

[0007] The purpose of the present invention can be achieved through the following technical solutions: The present invention provides a smart city data center operation and maintenance management system based on big data, including: an infrastructure monitoring module for monitoring the infrastructure data of servers in the smart city data center.

[0008] The infrastructure analysis module is used to analyze the infrastructure qualification index of the servers in the smart city data center based on the infrastructure data of the servers in the smart city data center.

[0009] The equipment operation monitoring module is used to monitor the equipment operation data of the servers in the smart city data center.

[0010] The equipment operation analysis module is used to analyze the equipment operation qualification index of the servers in the smart city data center based on the equipment operation data of the servers in the smart city data center.

[0011] The business application monitoring module is used to monitor the business application data of the servers in the smart city data center.

[0012] The business application analysis module is used to analyze the business application qualification index of the servers in the smart city data center according to the business applications of the servers in the smart city data center.

[0013] The smart city analysis module is used to analyze the data management information of the servers in the smart city data center according to the infrastructure qualification index, equipment operation qualification index and business application qualification index of the servers in the smart city data center.

[0014] The smart city management terminal is used to perform corresponding management based on the data management information of the server in the smart city data center.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention breaks the current problem of insufficient attention to the operation and maintenance management of smart city data centers by performing operation and maintenance management on smart city data centers, ensuring the timeliness of maintenance of data center infrastructure while ensuring the application attention of business applications, thereby reducing the frequency of equipment failures and improving user satisfaction.

[0016] (2) The present invention performs smart city operation and maintenance management through three dimensions: infrastructure, equipment operation, and business application. It avoids the current shortcomings of not considering the infrastructure level, equipment operation level, and business application level of the equipment, and realizes multi-dimensional monitoring and analysis of the smart city data center, thereby ensuring the comprehensiveness and precision of data monitoring and analysis, and further ensuring the timeliness of the discovery of potential problems in the equipment, while providing targeted decision support for the operation and maintenance management of the smart city data center.

[0017] (3) The present invention analyzes the infrastructure qualification index of the server in the smart city data center according to the input voltage qualification index, the output voltage qualification index, the current qualification index and the power qualification index, thereby avoiding the one-sidedness of a single indicator, thereby more comprehensively and accurately evaluating the infrastructure status of the server, and at the same time highlighting the key infrastructure indicators by setting weights.

[0018] (4) The present invention improves the intuitiveness of the analysis of the changes in the server input voltage over time by constructing the input voltage change curve of the server and calculating the qualified index of the input voltage change trend of the server. At the same time, the analysis accuracy and effectiveness of the input voltage change trend are improved through the trend of the input voltage change curve, thereby improving the maintenance reliability of subsequent preventive maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0020] Figure 1 It is a schematic diagram of the connection of each module of the system of the present invention.

[0021] Figure 2 This is a connection diagram of the qualified index analysis steps of the equipment of the present invention.

[0022] Figure 3 It is a schematic diagram of the connection steps of the business application qualification index analysis step of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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.

[0024] See also Figure 1 As shown, the present invention provides a smart city data center operation and maintenance management system based on big data, the system including: an infrastructure monitoring module, an infrastructure analysis module, an equipment operation monitoring module, an equipment operation analysis module, a business application monitoring module, a business application analysis module, a smart city analysis module and a smart city management terminal.

[0025] In the above, the infrastructure analysis module is connected to the infrastructure monitoring module and the smart city analysis module respectively, the equipment operation analysis module is connected to the equipment operation monitoring module and the smart city analysis module respectively, the business application analysis module is connected to the business application monitoring module and the smart city analysis module respectively, and the smart city analysis module is also connected to the smart city management terminal.

[0026] The infrastructure monitoring module is used to monitor the infrastructure data of the servers in the smart city data center.

[0027] It should be added that the infrastructure data of the servers in the smart city data center includes: the input voltage, output voltage, current and power of the server at each monitoring time point.

[0028] It should be added that the input voltage of the server at each monitoring time point is monitored by a digital voltmeter installed in the server power supply or power distribution unit (PDU), the output voltage of the server at each monitoring time point is monitored by a voltage monitoring sensor, the current of the server at each monitoring time point is monitored by a current transformer installed on the power cord or power distribution unit of the server, and the power of the server at each monitoring time point is monitored by an intelligent power meter connected to the power line of the server.

[0029] The infrastructure analysis module is used to analyze the infrastructure qualification index of the servers in the smart city data center based on the infrastructure data of the servers in the smart city data center.

[0030] Exemplarily, the analysis of the infrastructure qualification index of the server in the smart city data center includes: F1, extracting the input voltage of the server at each monitoring time point from the infrastructure data of the smart city data center, and then calculating the input voltage qualification index β1 of the server.

[0031] Furthermore, the statistical server input voltage qualification index includes: F1-1, comparing the input voltage of the server at each monitoring time point with a set input voltage range.

[0032] F1-2. If the input voltage of the server at a certain monitoring time point is within the set input voltage range, the input voltage of the server at the monitoring time point is recorded as a qualified input voltage, and then the number of qualified voltages is counted, and the ratio of the qualified voltage to the number of input voltages is used as the input voltage qualified ratio of the server, recorded as K.

[0033] F1-3. With the monitoring time point as the horizontal axis and the input voltage as the horizontal axis, construct the input voltage change curve of the server, and then calculate the input voltage change trend qualification index φ of the server.

[0034] Furthermore, the statistical server input voltage change trend qualification index includes: F1-3-1, extracting the number of fluctuation points from the server input voltage change curve and the horizontal distance and vertical distance between each fluctuation point and its next fluctuation point.

[0035] It should be supplemented that the fluctuation points refer to peak points or valley points in the input voltage variation curve.

[0036] It should be added that the horizontal distance refers to the interval between two fluctuation points on the time axis, that is, the time difference between the monitoring time points corresponding to the two fluctuation points. For example, if the monitoring time corresponding to one fluctuation point is 10 am, and the monitoring time corresponding to the other fluctuation point is 10:30 am, then the horizontal distance between the two fluctuation points is 30 minutes. The vertical distance refers to the difference in voltage value between the two fluctuation points, that is, the absolute value of the difference between the input voltage values ​​corresponding to the two fluctuation points. For example, if the input voltage of one fluctuation point is 220V and the input voltage of the other fluctuation point is 215V, the vertical distance between the two fluctuation points is 5V.

[0037] F1-3-2. Compare the horizontal distance and vertical distance of each fluctuation point to the next fluctuation point with the horizontal distance interval and vertical distance interval of the reference set.

[0038] F1-3-3. The fluctuation point whose horizontal distance to the next fluctuation point is within the set reference horizontal distance interval is recorded as a qualified fluctuation point for horizontal distance, thereby obtaining the number of qualified fluctuation points for horizontal distance, and the number of qualified fluctuation points for vertical distance is obtained in the same way as the number of qualified fluctuation points for horizontal distance.

[0039] F1-3-4. The ratios of the number of qualified fluctuation points in the horizontal distance and the number of qualified fluctuation points in the vertical distance to the number of fluctuation points are respectively used as the horizontal distance qualified index and vertical distance qualified index of the server, and are recorded as l and l′ respectively.

[0040] F1-3-5, statistics of the server input voltage change trend qualified index φ,

[0041] F1-4, statistics of the server input voltage qualification index β1, K′ and φ′ are the input voltage qualified ratio and input voltage change trend qualified index of the set reference respectively.

[0042] It should be added that K′ and φ′ are both extracted from the technical specification table of operation and maintenance management of smart city data center.

[0043] The embodiment of the present invention improves the intuitiveness of the analysis of the changes in the server input voltage over time by constructing the input voltage change curve of the server and calculating the qualified index of the input voltage change trend of the server. At the same time, the analysis accuracy and effectiveness of the input voltage change trend are improved through the trend of the input voltage change curve, thereby improving the maintenance reliability of subsequent preventive maintenance.

[0044] F2. Extract the output voltage, current and power of the server at each monitoring time point from the infrastructure data of the smart city data center, and then calculate the output voltage qualification index, current qualification index and power qualification index of the server in the same way as β1, and record them as β2, β3 and β4 respectively.

[0045] F3. Calculate the infrastructure qualification index α1 of the server in the smart city data center, α1=β1*r1+β2*r2+β3*r3+β4*r4, r1, r2, r3 and r4 are the weights of the set input voltage qualification index, output voltage qualification index, current qualification index and power qualification index respectively, r1+r2+r3+r4=1, r4>r1>r2>r3.

[0046] It should be added that the power qualification index is crucial in the servers of smart city data centers. The power consumption of the server is directly related to the energy cost of the data center and the stability of the energy supply. Excessive or unqualified power consumption may cause power supply overload and affect the operation of the entire data center. For example, if the server power is unqualified, the power supply system of the data center may be overloaded, causing tripping and other faults. The input voltage is the front-end condition for the operation of the server. If the input voltage is unqualified, it may cause damage to the power conversion module, motherboard and other components inside the server, and even fail to start the server normally. For example, too high input voltage may break down components such as capacitors in the server power supply, and too low input voltage may prevent the server from starting hard disks and other devices normally, but its impact on the server is less than the power qualification index. Therefore, the weight of the power qualification index is greater than the weight of the input voltage qualification index. The output voltage qualification index mainly affects the internal components of the server. Normal operation, if the output voltage is unqualified, it will affect the working stability of key components such as the motherboard, CPU, and memory. However, it is a parameter after the server has received the input voltage and performed internal conversion. In the entire infrastructure qualification system, its impact range is more limited to the internal components of the server than the input voltage, so the weight is lower than the input voltage qualification index. Although the current qualification index is also important for the operation of the server, its importance is relatively low in the server infrastructure qualification index system. The current is generated under the drive of voltage, and the power management system inside the server usually performs certain adaptation and protection on the current. When considering the main aspects of infrastructure qualification, the impact of the current qualification index is relatively small, so set r4>r1>r2>r3. For the convenience of analysis, r1 can be specifically taken as 0.3, r2 can be specifically taken as 0.2, r3 can be specifically taken as 0.1, and r4 can be specifically taken as 0.4.

[0047] The embodiment of the present invention analyzes the infrastructure qualification index of the server in the smart city data center according to the input voltage qualification index, the output voltage qualification index, the current qualification index and the power qualification index, thereby avoiding the one-sidedness of a single indicator, thereby more comprehensively and accurately evaluating the infrastructure status of the server, and at the same time highlighting the key infrastructure indicators by setting weights.

[0048] The device operation monitoring module is used to monitor the device operation data of the server in the smart city data center.

[0049] It should be added that the equipment operation data of the servers in the smart city data center include: the CPU utilization rate, memory utilization rate, disk space utilization rate and network bandwidth utilization rate of the servers at each monitoring time point.

[0050] It should be added that the CPU utilization rate of the server at each monitoring time point is obtained by viewing the Task Manager, the memory utilization rate of the server at each monitoring time point is obtained by viewing the "Performance" tab in the Task Manager, the disk space utilization rate of the server at each monitoring time point is obtained by viewing the "Properties" of the disk drive, and the network bandwidth utilization rate of the server at each monitoring time point is obtained by viewing the "Network" tab in the Resource Monitor.

[0051] The device operation analysis module is used to analyze the device operation qualification index of the servers in the smart city data center based on the device operation data of the servers in the smart city data center.

[0052] See also Figure 2 As shown, exemplarily, the analysis of the equipment operation qualification index of the server in the smart city data center includes: G1, extracting the CPU usage rate of the server at each monitoring time point from the equipment operation data of the server in the smart city data center, and then calculating the CPU usage qualification index of the server.

[0053] Furthermore, the CPU usage qualification index of the statistical server includes: G1-1, comparing the CPU usage of the server at each monitoring time point with a set reference CPU usage interval.

[0054] G1-2. If the CPU usage rate at a certain monitoring time point in the server is within the set reference CPU usage rate range, the monitoring time point in the server is recorded as a qualified monitoring time point.

[0055] G1-3. Count the number of qualified monitoring time points, and use the ratio of the number of qualified monitoring time points to the number of monitoring time points as the CPU usage qualification index of the server.

[0056] G2. Extract the memory usage, disk space usage and network bandwidth usage of the server at each monitoring time point from the device operation data of the server in the smart city data center.

[0057] G3. The server's memory utilization rate qualification index, disk space utilization rate qualification index and network bandwidth utilization rate qualification index are calculated in the same way as the server's CPU utilization rate qualification index.

[0058] G4. Filter out the minimum value from the server's CPU utilization qualification index, memory utilization qualification index, disk space utilization qualification index and network bandwidth utilization qualification index as the equipment operation qualification index of the server in the smart city data center, and record it as α2.

[0059] The business application monitoring module is used to monitor the business application data of the servers in the smart city data center.

[0060] It should be added that the business application data of the servers in the smart city data center include: the response time and throughput of the servers during each operation.

[0061] It should be added that the response time and throughput of the server during each run are monitored by the application performance management (APM) tool installed on the server.

[0062] The business application analysis module is used to analyze the business application qualification index of the servers in the smart city data center according to the business applications of the servers in the smart city data center.

[0063] See also Figure 3 As shown, exemplarily, the analysis of the business application qualification index of the server in the smart city data center includes: R1, extracting the response time of the server at each operation from the business application data of the server in the smart city data center, and then calculating the response time qualification index δ1 of the server in the smart city data center.

[0064] Furthermore, the statistical response time qualification index of the server in the smart city data center includes: R1-1, comparing the response time of the server during each operation with the set permitted response time, and then counting the number of operations in which the operation response time is less than or equal to the set permitted response time, and the ratio of the operation time to the total number of operations is used as the response time qualification ratio of the server, recorded as

[0065] In a specific embodiment, the qualified response time ratio is obtained by dividing the number of runs in which the operation response time is less than or equal to the set permitted response time by the total number of runs. For example, if within a monitoring period, the server runs 1000 times, of which the number of runs in which the operation response time is less than or equal to the set permitted response time is 800 times, then the qualified response time ratio is 800 / 1000=0.8.

[0066] R1-2, the response time of each operation of the server in the set monitoring period is calculated by the calculation formula of the month-on-month growth rate to obtain the month-on-month growth rate of each operation of the server in the set monitoring period, and the result of the average calculation is used as the average month-on-month growth rate of the server, recorded as

[0067] It should be added that the month-on-month growth rate refers to the ratio of the difference in response time between two adjacent time periods to the response time of the previous time period. The calculation formula is: month-on-month growth rate = (current period response time - previous period response time) / previous period response time.

[0068] In a specific embodiment, the current response time is 1.5 seconds, and the previous response time is 1.2 seconds, then the month-on-month growth rate is (1.5-1.2) / 1.2×=0.25.

[0069] R1-3, calculate the qualified index δ1 of the response time of the server in the smart city data center, q1 and q2 are the weights of the qualified response time ratio and the month-on-month growth rate of operation respectively, q1+q2=1, q1>q2.

[0070] It should be added that the qualified response time ratio directly reflects the timeliness of the server in meeting user requests. In the application scenarios of smart cities, many services, such as real-time traffic query in intelligent transportation systems and rapid command issuance in urban emergency response, have extremely high requirements for response speed. If the response time is unqualified, it may cause chaos in urban operation or service failure. For example, in the scenario of traffic congestion relief, if the server of the traffic command center cannot respond to the data from the sensors of each road section and give instructions in time, it will not be able to effectively alleviate the congestion. The month-on-month growth rate of operation mainly reflects the growth trend of server performance in adjacent time periods. Although it is of certain significance for evaluating the long-term development and optimization effect of the server, it is not the most critical indicator for the real-time operation and service provision of smart cities. For example, a server has a good month-on-month growth rate of operation, but if the current response time is unqualified, it still cannot meet the needs of real-time operation of the city. Therefore, q1>q2 is set. For the convenience of analysis, q1 can be specifically taken as 0.6 and q2 can be specifically taken as 0.4.

[0071] R2. Extract the throughput of the server at each operation from the business application data of the server in the smart city data center, and then calculate the throughput qualification index δ2 of the server in the smart city data center.

[0072] Furthermore, the statistical throughput qualification index of the servers in the smart city data center includes: R2-1, comparing the throughput of the server at each operation with a set reference throughput range.

[0073] R2-2. If the throughput of the server during this run is within the set reference throughput range, the throughput qualification index of the server during this run is recorded as 1.

[0074] R2-3. If the throughput of the server during a certain operation is less than the lower limit of the reference throughput interval, the throughput during the operation and the lower limit of the reference throughput interval are recorded as θ1 and θ2 respectively.

[0075] R2-4, calculate the throughput qualification index ξ1 during this operation,

[0076] R2-5. If the throughput of the server during a certain operation is greater than the upper limit of the reference throughput interval, the upper limit of the reference throughput interval is recorded as θ3.

[0077] R2-6, calculate the throughput qualification index ξ2 during this operation, In this way, the throughput qualification index of the server at each operation is obtained, and the average is calculated to obtain the throughput qualification index of the server in the smart city data center, which is recorded as δ2.

[0078] R3. Filter out the minimum value from the response time qualification index and throughput qualification index of the server in the smart city data center as the business application qualification index of the server in the smart city data center, and record it as α3.

[0079] The smart city analysis module is used to analyze the data management information of the servers in the smart city data center according to the infrastructure qualification index, equipment operation qualification index and business application qualification index of the servers in the smart city data center.

[0080] Exemplarily, the analyzing data management information of the servers in the smart city data center includes: extracting the infrastructure qualification index, equipment operation qualification index and business application qualification index of the servers in the smart city data center.

[0081] If the infrastructure qualification index of the server in the smart city data center is less than the set reference infrastructure qualification index, the infrastructure failure will be used as data management information for the server in the smart city data center.

[0082] If the equipment operation qualification index of the server in the smart city data center is less than the set reference equipment operation qualification index, the equipment operation failure will be regarded as the data management information of the server in the smart city data center.

[0083] If the business application qualification index of the server in the smart city data center is less than the set reference business application qualification index, the business application failure will be regarded as the data management information of the server in the smart city data center.

[0084] The embodiment of the present invention performs smart city operation and maintenance management through three dimensions: infrastructure, equipment operation, and business application. It avoids the current shortcomings of not considering the infrastructure level, equipment operation level, and business application level of the equipment, and realizes multi-dimensional monitoring and analysis of the smart city data center, thereby ensuring the comprehensiveness and precision of data monitoring and analysis, and further ensuring the timeliness of the discovery of potential problems in the equipment, while providing targeted decision-making support for the operation and maintenance management of the smart city data center.

[0085] The smart city management terminal is used to perform corresponding management according to the data management information of the server in the smart city data center.

[0086] The embodiments of the present invention break the current problem of insufficient attention to the operation and maintenance management of smart city data centers by performing operation and maintenance management on smart city data centers, ensure the timeliness of maintenance of data center infrastructure, and at the same time ensure the application attention of business applications, thereby reducing the frequency of equipment failures and improving user satisfaction.

[0087] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A smart city data center operation and maintenance management system based on big data, characterized by: The system includes: Infrastructure monitoring module, used to monitor the infrastructure data of servers in smart city data centers; An infrastructure analysis module, used for analyzing the infrastructure qualification index of the servers in the smart city data center according to the infrastructure data of the servers in the smart city data center; Equipment operation monitoring module, used to monitor equipment operation data of servers in smart city data centers; The equipment operation analysis module is used to analyze the equipment operation qualification index of the servers in the smart city data center according to the equipment operation data of the servers in the smart city data center; Business application monitoring module, used to monitor business application data of servers in smart city data centers; A business application analysis module, used to analyze the business application qualification index of the servers in the smart city data center according to the business applications of the servers in the smart city data center; A smart city analysis module, used to analyze data management information of servers in the smart city data center according to the infrastructure qualification index, equipment operation qualification index and business application qualification index of the servers in the smart city data center; The smart city management terminal is used to perform corresponding management based on the data management information of the server in the smart city data center.

2. According to the big data-based smart city data center operation and maintenance management system of claim 1, it is characterized by: The analysis of the infrastructure qualification index of the server in the smart city data center includes: F1. Extract the input voltage of the server at each monitoring time point from the infrastructure data of the smart city data center, and then calculate the input voltage qualification index β1 of the server; F2. Extract the output voltage, current and power of the server at each monitoring time point from the infrastructure data of the smart city data center, and then calculate the output voltage qualification index, current qualification index and power qualification index of the server in the same way as β1, and record them as β2, β3 and β4 respectively; F3. Calculate the infrastructure qualification index α1 of the server in the smart city data center, α1=β1*r1+β2*r2+β3*r3+β4*r4, r1, r2, r3 and r4 are the weights of the set input voltage qualification index, output voltage qualification index, current qualification index and power qualification index respectively, r1+r2+r3+r4=1, r4>r1>r2>r3.

3. According to the big data-based smart city data center operation and maintenance management system of claim 2, it is characterized by: The input voltage qualification index of the statistical server includes: Compare the input voltage of the server at each monitoring time point with the set input voltage range; If the input voltage of the server at a certain monitoring time point is within the set input voltage range, the input voltage of the server at the monitoring time point is recorded as a qualified input voltage, and then the number of qualified voltages is counted, and the ratio of the qualified voltage to the number of input voltages is taken as the input voltage qualified ratio of the server, which is recorded as K; With the monitoring time point as the horizontal axis and the input voltage as the horizontal axis, the input voltage change curve of the server is constructed, and then the input voltage change trend qualification index φ of the server is calculated: Statistical server input voltage qualification index β1, K′ and φ′ are the input voltage qualified ratio and input voltage change trend qualified index of the set reference respectively.

4. According to the big data-based smart city data center operation and maintenance management system of claim 3, it is characterized by: The input voltage variation trend qualification index of the statistical server includes: Extract the number of fluctuation points and the horizontal and vertical distances between each fluctuation point and the next fluctuation point from the input voltage variation curve of the server; Compare the horizontal distance and vertical distance of each fluctuation point to the next fluctuation point with the horizontal distance interval and vertical distance interval of the reference set respectively; The fluctuation point whose horizontal distance from the fluctuation point to the next fluctuation point is within the set reference horizontal distance interval is recorded as a qualified fluctuation point for horizontal distance, thereby obtaining the number of qualified fluctuation points for horizontal distance, and the number of qualified fluctuation points for vertical distance is obtained in the same way as the number of qualified fluctuation points for horizontal distance; The ratios of the number of qualified fluctuation points in the horizontal distance and the number of qualified fluctuation points in the vertical distance to the number of fluctuation points are respectively used as the horizontal distance qualified index and the vertical distance qualified index of the server, and are respectively recorded as and Statistical server input voltage change trend qualified index φ 5. According to the big data-based smart city data center operation and maintenance management system of claim 1, it is characterized by: The analysis of the equipment operation qualification index of the server in the smart city data center includes: G1. Extract the CPU usage rate of the server at each monitoring time point from the device operation data of the server in the smart city data center, and then calculate the CPU usage rate qualification index of the server; G2. Extract the memory usage, disk space usage and network bandwidth usage of the server at each monitoring time point from the device operation data of the server in the smart city data center; G3. Calculate the server's memory utilization rate, disk space utilization rate and network bandwidth utilization rate indices in the same way as the server's CPU utilization rate. G4. Filter out the minimum value from the server's CPU utilization qualification index, memory utilization qualification index, disk space utilization qualification index and network bandwidth utilization qualification index as the equipment operation qualification index of the server in the smart city data center, and record it as α2.

6. According to the big data-based smart city data center operation and maintenance management system of claim 5, it is characterized by: The CPU usage qualification index of the statistical server includes: Compare the CPU usage of the server at each monitoring time point with the set reference CPU usage range; If the CPU usage rate at a certain monitoring time point in the server is within the set reference CPU usage rate range, the monitoring time point in the server is recorded as a qualified monitoring time point; The number of qualified monitoring time points is counted, and the ratio of the qualified number of qualified monitoring time points to the number of monitoring time points is used as the CPU usage qualification index of the server.

7. The big data-based smart city data center operation and maintenance management system according to claim 1, characterized in that: The analysis of the business application qualification index of the server in the smart city data center includes: R1, extract the response time of the server at each operation from the business application data of the server in the smart city data center, and then calculate the response time qualification index δ1 of the server in the smart city data center; R2, extract the throughput of the server at each operation from the business application data of the server in the smart city data center, and then calculate the throughput qualification index δ2 of the server in the smart city data center; R3. Filter out the minimum value from the response time qualification index and throughput qualification index of the server in the smart city data center as the business application qualification index of the server in the smart city data center, and record it as α3.

8. The big data-based smart city data center operation and maintenance management system according to claim 7, characterized in that: The statistical response time qualification index of the server in the smart city data center includes: Compare the response time of the server in each run with the set permitted response time, and then count the number of runs in which the response time is less than or equal to the set permitted response time, and use the ratio of the response time to the total number of runs as the qualified response time ratio of the server, recorded as The response time of each operation of the server in the set monitoring period is calculated by the calculation formula of the month-on-month growth rate to obtain the month-on-month growth rate of each operation of the server in the set monitoring period, and the result of the average calculation is taken as the average month-on-month growth rate of the server, recorded as Calculate the qualified index δ1 of the response time of the server in the smart city data center, q1 and q2 are the weights of the qualified response time ratio and the average month-on-month growth rate of operation, respectively, q1+q2=1, q1>q2.

9. The big data-based smart city data center operation and maintenance management system according to claim 7, characterized in that: The statistical throughput qualification index of the server in the smart city data center includes: Compare the server's throughput at each run time with the set reference throughput range; If the throughput of the server during a certain operation is within the set reference throughput range, the throughput qualification index of the server during this operation is recorded as 1; If the throughput of the server in a certain operation is less than the lower limit of the reference throughput interval, the throughput of the operation and the lower limit of the reference throughput interval are recorded as and The throughput qualification index ζ1 during this operation is calculated. If the throughput of the server during a certain operation is greater than the upper limit of the reference throughput interval, the upper limit of the reference throughput interval is recorded as The throughput qualification index ζ2 of this operation is calculated. In this way, the throughput qualification index of the server at each operation is obtained, and the average is calculated to obtain the throughput qualification index of the server in the smart city data center, which is recorded as δ2.

10. The big data-based smart city data center operation and maintenance management system according to claim 9, characterized in that: The analyzing of data management information of the server in the smart city data center includes: Extract the infrastructure qualification index, equipment operation qualification index and business application qualification index of the servers in the smart city data center; If the infrastructure qualification index of the server in the smart city data center is less than the set reference infrastructure qualification index, the infrastructure failure will be used as the data management information of the server in the smart city data center; If the equipment operation qualification index of the server in the smart city data center is less than the set reference equipment operation qualification index, the equipment operation failure is regarded as the data management information of the server in the smart city data center; If the business application qualification index of the server in the smart city data center is less than the set reference business application qualification index, the business application failure will be regarded as the data management information of the server in the smart city data center.

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