A big data-based computer room operation and maintenance management system

Abstract

The present invention discloses a computer room operation and maintenance management system based on big data, which relates to the technical field of operation and maintenance management, and includes: a plurality of data acquisition modules, each of which is matched with a group of computer room equipment, and each includes a transmission unit and a comparison unit; the transmission unit collects the real-time data of the corresponding computer room; the comparison unit is built with a comparison model, and after receiving the real-time data, it mobilizes the corresponding comparison model data and synchronously transmits it with the real-time data; an analysis module, which receives the real-time data and the comparison model data and cleans them; a relationship network module, which generates a relationship network for each computer room equipment according to the relationship of the work process and the area. By synchronously comparing the real-time data of the data acquisition module with the comparison model data, the analysis module of the present invention can quickly and accurately detect the abnormal state of the equipment, reduce the lag of traditional manual monitoring, and at the same time, by using the process relationship and area relationship modules, it can perform multi-dimensional analysis and monitoring on the states of different equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of operation and maintenance management, and specifically to a computer room operation and maintenance management system based on big data. Background Technique

[0002] With the development of data-driven technologies, the operation and maintenance management of computer room equipment is gradually shifting towards intelligence and automation. Traditional computer room operation and maintenance management methods usually rely on manual inspections and static threshold judgments, making it difficult to detect equipment failures in a timely and accurate manner. With the development of Internet of Things and big data technologies, real-time monitoring and intelligent analysis means provide new solutions for equipment operation and maintenance. How to achieve comprehensive monitoring of equipment status through real-time data collection and intelligent analysis has become an important research direction for current operation and maintenance management systems.

[0003] After retrieval, a Chinese patent (Publication No.: CN117575559A) discloses an equipment operation and maintenance management system, which includes: an operation and maintenance personnel library for recording and updating operation and maintenance personnel information; a trigger module for parsing the user terminal information to obtain semantic information and matching the operation and maintenance commands corresponding to the semantic information, and triggering new work tasks according to the operation and maintenance commands; a work order generation module for generating corresponding work orders according to the work tasks; an assignment module for assigning the work orders to appropriate operation and maintenance personnel according to the work tasks and the skill information and work arrangement information of the operation and maintenance personnel in the operation and maintenance personnel library; a recording module for recording the execution process of the work tasks; a confirmation module for confirming the completion of the work tasks; an evaluation module for evaluating the completion effect of the work tasks and generating an operation and maintenance score; and an end module for ending the work tasks.

[0004] In the prior art, if the methods of manual inspection and regular maintenance are adopted, it is easy to cause lag in fault detection, making it difficult to detect equipment anomalies in a timely manner, affecting the stability and security of the system, and the equipment status monitoring is mostly based on static threshold judgments, unable to flexibly cope with dynamic environmental changes, easily causing false alarms or missed alarms, thus reducing the accuracy of fault detection. Therefore, the present invention proposes a computer room operation and maintenance management system based on big data. Summary of the Invention

[0005] The purpose of the present invention is to provide a computer room operation and maintenance management system based on big data to solve the problems mentioned in the above background technique.

[0006] The present invention can be realized through the following technical solutions: A computer room operation and maintenance management system based on big data, including a data collection module, an analysis module, a relationship network module, and an operation and maintenance platform;

[0007] Multiple said data collection modules are respectively matched with a group of computer room equipment, and it includes:

[0008] A transmission unit for collecting real-time data of the corresponding computer room;

[0009] A comparison unit with a built-in comparison model. After receiving real-time data, it retrieves the corresponding comparison model data and synchronously transmits it with the real-time data;

[0010] The analysis module is used to receive real-time data and comparison model data, and after receiving the real-time data, it cleans the real-time data;

[0011] The relationship network module generates a relationship network for each computer room device according to the corresponding relationships, including a work process relationship unit and a regional relationship unit;

[0012] The process relationship unit locates the working order and data flow between each node according to the corresponding work process of each computer room device, and generates a directed graph for each computer room device according to the working order nodes;

[0013] The regional relationship unit divides into different regions based on the physical layout of the computer room and the actual positions of the computer room devices, and assigns each computer room device to the corresponding position nodes according to the positions. And the regional relationship unit generates a regional relationship model representing the spatial distribution based on the positions of each computer room device;

[0014] The process relationship unit processes the real-time data and comparison model data in each work process in the directed graph to obtain a process data set;

[0015] The regional relationship unit processes the real-time data and comparison model data in each region to obtain a regional data set;

[0016] The analysis module receives the process data set and the regional data set, and aggregates and multi-dimensionally analyzes the process data set and the regional data set to obtain an analysis result;

[0017] The operation and maintenance platform includes a prediction unit and a decision support unit;

[0018] The prediction unit predicts the working data and data change trends of each computer room device based on the analysis result to obtain a prediction result;

[0019] The decision support unit provides decision-making opinions to the operation and maintenance personnel based on the prediction result using a recommendation algorithm based on big data, so as to facilitate the dynamic adjustment of the maintenance strategy and improve the management efficiency.

[0020] A further technical improvement of the present invention is that the cleaning of the real-time data by the analysis module includes duplicate data removal, data type conversion and format correction, standardization, outlier detection and processing, missing data processing, and verification of the cleaned data.

[0021] A further technical improvement of the present invention lies in that when processing missing data, it is obtained by calculating the historical data of the missing data and the difference between the corresponding historical data and the comparison model data on the basis of the corresponding data in the comparison model data. The formula used is:

[0022]

[0023] Wherein, X m is the value of the missing data at time point T m , that is, the numerical value used for the missing data;

[0024] is the value of the comparison model data at time point T m , serving as the reference value for filling the missing data;

[0025] X i is the value of the historical data at time point T i ;

[0026] α is the attenuation factor, used to control the rate of time attenuation, that is, the greater the time from the missing data, the smaller the weight;

[0027] T m is the time point of the missing data, and T i is the time point of the historical data used to fill the missing data;

[0028] |T i -T m | is the time difference between time point T i and T m , that is, the distance between the historical data and the time point of the missing data;

[0029] X i is the value of the historical data at time point T i ;

[0030] is the weighting coefficient, ensuring that the historical data closer to the time point T m of the missing data has a greater impact on the filling result.

[0031] A further technical improvement of the present invention lies in that in a directed graph, the input data of the initial computer room equipment in each work process is used as a matching point to match the corresponding work model, and the computer room equipment corresponding to each node in the work process is matched with the corresponding part in the work model;

[0032] After matching, the comparison model of the comparison unit in each data acquisition module is established, and the comparison model data of the comparison model is used for subsequent synchronous transmission with the corresponding real-time data.

[0033] A further technical improvement of the present invention lies in that: the analysis method of the analysis module for the process data set includes the following steps:

[0034] A1. Taking each node of the work process as an axis and using the comparison model data corresponding to each node as a base point to establish a comparison curve;

[0035] A2. Matching the real-time data corresponding to each node to the base point of the corresponding comparison curve, and through the formula: Obtaining the total difference value Dt between the comparison model data of each computer room device and the corresponding work model on the corresponding work process;

[0036] Wherein, X i is the real-time data of the i-th node, and M i is the comparison model data of the i-th node, that is, the corresponding base point on the comparison curve;

[0037] N is the total number of nodes in the work process;

[0038] A3. Comparing the total difference value Dt with a preset difference threshold T;

[0039] a1. If the total difference value Dt is not greater than the difference threshold T, then mark the failure degree of each computer room device in this work process as level one;

[0040] a2. If the total difference value Dt is greater than the difference threshold T, then mark the failure degree of each computer room device in this work process as level two.

[0041] A further technical improvement of the present invention lies in that: on the basis of a2, identifying the distribution state of each real-time data on the comparison curve;

[0042] b1. If the difference between individual real-time data and the corresponding comparison model data is in an irregular discrete state, then mark the computer room device corresponding to the irregular discrete real-time data;

[0043] b2. If the differences between multiple real-time data and the corresponding comparison model data are in a continuous state, then identify each real-time data with a continuous state difference to obtain an identified data segment;

[0044] Mark based on the computer room device corresponding to the starting point of the identified data segment.

[0045] A further technical improvement of the present invention lies in that: the analysis method of the analysis module for the regional data set includes:

[0046] Y1. Difference detection;

[0047] Presetting a comparison threshold G for judging whether the real-time data of nodes in the corresponding area exceeds the reasonable range of the comparison model data;

[0048] Compare the real-time data of each node in the corresponding area with the comparison threshold G. If it exceeds the comparison threshold G, mark it as an abnormal node, and record each abnormal node as a set S;

[0049] Y2, Status mode recognition;

[0050] Perform the following operations on each abnormal node in the set S;

[0051] u1. Retrieve records similar to the abnormal node from the big data storage, and use the similarity matching algorithm to compare the real-time data of the abnormal node with the similar data in the big data storage, and determine whether there is the same data for the abnormal status of the abnormal node in the big data storage;

[0052] u2. When there is the same data, match the abnormal status of the abnormal node with the result of the big data storage data;

[0053] Y3. Generate analysis results;

[0054] For each abnormal node, generate a corresponding analysis report, mark the degree of failure of the computer room equipment of each corresponding node as level two, and at the same time summarize the analysis reports of all abnormal nodes in the area to generate an area analysis report;

[0055] The area analysis report includes the number of abnormal nodes in the area and their specific locations, the status classification of each node, and the overall health status of the summary area;

[0056] If there is the same type of abnormal status in the analysis reports of each abnormal node in the area, mark the area as an abnormal area.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] Through the synchronous comparison of the real-time data of the data acquisition module with the data of the comparison model, the analysis module of the present invention can quickly and accurately detect the abnormal status of the equipment, reduce the lag of traditional manual monitoring. At the same time, by using the process relationship and area relationship modules, the status of different equipment can be analyzed and monitored from multiple dimensions, adapting to complex operation and maintenance environments and meeting the needs of associated monitoring between different equipment;

[0059] And through the accumulation of historical data and the continuous optimization of the comparison model, not only can equipment failures be detected, but also trend prediction can be performed, providing decision support for operation and maintenance personnel and taking preventive maintenance measures in advance. Brief Description of the Drawings

[0060] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings.

[0061] Figure 1 This is the system block diagram of the present invention. Detailed implementation manners

[0062] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following will, in conjunction with the accompanying drawings and preferred embodiments, describe in detail the specific implementation manners, structures, features and their effects according to the present invention.

[0063] Please refer to Figure 1 As shown, the present invention provides a computer room operation and maintenance management system based on big data, including a data acquisition module, an analysis module, a relationship network module and an operation and maintenance platform;

[0064] Multiple data acquisition modules are respectively matched with a group of computer room devices, and it includes:

[0065] A transmission unit for collecting real-time data of the corresponding computer room;

[0066] A comparison unit, which has a built-in comparison model, and after receiving real-time data, retrieves the corresponding comparison model data and synchronously transmits it with the real-time data;

[0067] The analysis module is used to receive real-time data and comparison model data, and after receiving the real-time data, clean it;

[0068] The cleaning of the real-time data by the analysis module includes duplicate data removal, data type conversion and format correction, standardization, outlier detection and processing, missing data processing and verification of the cleaned data;

[0069] When performing missing data processing, it is obtained based on the corresponding data in the comparison model data by calculating the historical data of the missing data and the difference between the corresponding historical data and the comparison model data. The formula used is:

[0070]

[0071] Wherein, X m is the value of the missing data at time point T m , that is, the numerical value used for the missing data;

[0072] is the value of the comparison model data at time point T m , serving as the reference value for filling the missing data;

[0073] X i is the value of the historical data at time point T i ;

[0074] α is the attenuation factor, which is used to control the rate of time attenuation, that is, the greater the time from the missing data, the smaller the weight;

[0075] T m is the time point of missing data, T i is the historical data time point for filling in the missing data;

[0076] |T i -T m | is the time difference between time point T i and T m i.e., the distance between the historical data and the missing data time point;

[0077] X i is the value of the historical data at time point T i ;

[0078] is the weighting coefficient, ensuring that the historical data closer to the missing data time point T m has a greater impact on the filling result;

[0079] The relationship network module generates a relationship network for each computer room device according to the corresponding relationships, including a process relationship unit and a regional relationship unit;

[0080] The process relationship unit locates the working order and data flow between each node according to the corresponding working process of each computer room device, and generates a directed graph for each computer room device according to the working order nodes;

[0081] In the directed graph, taking the input data of the initial computer room device in each working process as the matching point, matching the corresponding working model, and matching the computer room device corresponding to each node in the working process with the corresponding part in the working model;

[0082] After matching, the comparison model in the comparison unit of each data acquisition module is established, and the comparison model data of the comparison model is used for subsequent synchronous transmission with the corresponding real-time data;

[0083] The regional relationship unit divides into different regions based on the physical layout of the computer room and the actual positions of the computer room devices, and assigns each computer room device to the corresponding position node according to the position, and the regional relationship unit generates a regional relationship model representing the spatial distribution based on the positions of each computer room device;

[0084] The process relationship unit processes the real-time data and comparison model data in each working process in the directed graph to obtain a process data set;

[0085] The regional relationship unit processes the real-time data and comparison model data in each region to obtain a regional data set;

[0086] The analysis module receives the process data set and the regional data set, and aggregates and multi-dimensionally analyzes the process data set and the regional data set to obtain an analysis result;

[0087] The analysis method of the analysis module for the process data set includes the following steps:

[0088] A1. Taking each node of the work process as the axis and using the corresponding comparison model data of each node as the base point to establish a comparison curve;

[0089] A2. Matching the real-time data corresponding to each node to the base point of the corresponding comparison curve, and through the formula: Obtain the total difference value Dt of the comparison model data between each computer room device and the corresponding work model on the corresponding work process;

[0090] Wherein, X i is the real-time data of the i-th node, and M i is the comparison model data of the i-th node, that is, the corresponding base point on the comparison curve;

[0091] N is the total number of nodes in the work process;

[0092] A3. Comparing the total difference value Dt with the preset difference threshold T;

[0093] a1. If the total difference value Dt is not greater than the difference threshold T, then mark the fault degree of each computer room device in this work process as level one;

[0094] a2. If the total difference value Dt is greater than the difference threshold T, then mark the fault degree of each computer room device in this work process as level two.

[0095] On the basis of a2, identify the distribution state of each real-time data on the comparison curve;

[0096] b1. If the difference between individual real-time data and the corresponding comparison model data is in an irregular discrete state, then mark the computer room device corresponding to the irregular discrete real-time data;

[0097] b2. If the differences between multiple real-time data and the corresponding comparison model data are in a continuous state, then identify each real-time data with a continuous state difference to obtain an identified data segment;

[0098] Mark based on the computer room device corresponding to the starting point of the identified data segment;

[0099] In this embodiment, the marking method based on the identified data segment includes:

[0100] z1. Preset a fluctuation threshold Y;

[0101] z2. Calculate the difference value V between each node and the fluctuation threshold Y;

[0102] z3. Detect continuous difference segments;

[0103] Traverse all nodes of the comparison curve to find a continuous set of nodes such that the difference value V of each node in the set of nodes is greater than the fluctuation threshold Y, that is, convert the corresponding set of nodes into an identified data segment;

[0104] z4. In the identified data segment, select the first node as the starting point. Assume that this node is a continuous difference segment from node i to node j. Then, taking the first node i as the starting point of the identified data segment, mark the computer room equipment corresponding to node i and its first s previous nodes;

[0105] The analysis method of the analysis module for the process data set includes the following steps:

[0106] A1. Taking each node of the work process as the axis and using the comparison model data corresponding to each node as the base point to establish a comparison curve;

[0107] A2. Match the real-time data corresponding to each node to the base point of the corresponding comparison curve, and through the formula: Obtain the total difference value Dt between the comparison model data of each computer room equipment on the corresponding work process and the corresponding work model;

[0108] Among them, X i is the real-time data of the i-th node, and M i is the comparison model data of the i-th node, that is, the corresponding base point on the comparison curve;

[0109] N is the total number of nodes in the work process;

[0110] A3. Compare the total difference value Dt with the preset difference threshold T;

[0111] a1. If the total difference value Dt is not greater than the difference threshold T, mark the fault degree of each computer room equipment in this work process as level one;

[0112] a2. If the total difference value Dt is greater than the difference threshold T, mark the fault degree of each computer room equipment in this work process as level two.

[0113] After the corresponding computer room equipment is marked as level two, it means that the corresponding computer room equipment has a fault problem and needs to be processed;

[0114] On the basis of a2, identify the distribution state of each real-time data on the comparison curve;

[0115] b1. If the difference between individual real-time data and the corresponding comparison model data is in an irregular discrete state, mark the computer room equipment corresponding to the irregular discrete real-time data;

[0116] b2. If the differences between multiple real-time data and the corresponding comparison model data are in a continuous state, then identify each real-time data with continuous-state differences to obtain an identified data segment;

[0117] Mark based on the computer room equipment corresponding to the starting point of the identified data segment;

[0118] In this embodiment, the marking method based on the identified data segment includes:

[0119] z1. A preset fluctuation threshold Y;

[0120] z2. Calculate the difference value V between each node and the fluctuation threshold Y;

[0121] z3. Detect continuous difference segments;

[0122] Traverse all nodes of the comparison curve to find a continuous set of nodes such that the difference value V of each node within the set of nodes is greater than the fluctuation threshold Y, that is, convert the corresponding set of nodes into an identified data segment;

[0123] z4. In the identified data segment, select the first node as the starting point. Suppose this node is a continuous difference segment from node i to node j. Then, using the first node i as the starting point of the identified data segment, mark the computer room equipment corresponding to node i and its first s previous nodes;

[0124] The analysis method of the analysis module for the regional data set includes:

[0125] Y1. Difference detection;

[0126] A preset comparison threshold G is used to determine whether the real-time data of the nodes in the corresponding area exceeds the reasonable range of the comparison model data;

[0127] Compare the real-time data of each node in the corresponding area with the comparison threshold G. If it exceeds the comparison threshold G, mark it as an abnormal node, and record all abnormal nodes as set S;

[0128] Y2. State mode recognition;

[0129] Perform the following operations on each abnormal node in set S;

[0130] u1. Retrieve records similar to this abnormal node from the big data storage, and use the similarity matching algorithm to compare the real-time data of the abnormal node with the similar data in the big data storage, and determine whether the abnormal state of this abnormal node exists in the big data storage;

[0131] u2. When there is the same data, match the abnormal state of this abnormal node with the result of the big data storage data;

[0132] Y3. Generate analysis results;

[0133] For each abnormal node, generate a corresponding analysis report, mark the failure degree of the computer room equipment of each corresponding node as level two, and at the same time summarize the analysis reports of all abnormal nodes in the area to generate an area analysis report;

[0134] The area analysis report includes the number of abnormal nodes in the area and their specific locations, the status classification of each node, and the overall health status of the summary area, and provides area-level operation and maintenance suggestions. The operation and maintenance suggestions include "suggest immediate inspection" and "suggest continuous monitoring";

[0135] If there is the same type of abnormal status in the analysis reports of each abnormal node in the area, mark the area as an abnormal area;

[0136] In the analysis results of the process data set and the area data set by the analysis module, if a certain computer room equipment is marked as level two, the detection priority of the computer room equipment is increased, and the system preferentially recommends detecting the computer room equipment;

[0137] In this embodiment, a certain area includes 5 nodes, its real-time data P = {15, 18, 30, 12, 22}, the comparison model data L = {10, 18, 25, 14, 20}, and the comparison threshold G = 5;

[0138] Calculate the difference value B between each node and the comparison threshold G:

[0139] Node 1: B1 = |15 - 10| = 5;

[0140] Node 2: B2 = |18 - 18| = 0;

[0141] Node 3: B3 = |30 - 25| = 5;

[0142] Node 4: B4 = |12 - 14| = 2;

[0143] Node 5: B5 = |22 - 20| = 2;

[0144] Among them, the difference value B of Node 1 and Node 3 is equal to 5, so they are marked as abnormal nodes;

[0145] Query the historical status data of Node 1 and Node 3, and compare the current status with the historical records through the pattern matching algorithm. It is found that the situation of Node 1 is similar to the "overload" pattern in the history, and the situation of Node 3 is similar to the "temperature fluctuation" pattern;

[0146] Generate an analysis report, Node 1: Marked as the "overload" pattern, and the status classification is "potential failure".

[0147] Node 3: Marked as the "temperature fluctuation" mode, and the status is classified as "normal fluctuation";

[0148] Summarize the number of abnormal nodes and the health status in the area, generate an overall analysis report and provide decision support, and recommend that the operation and maintenance personnel conduct further inspections on Node 1;

[0149] The operation and maintenance platform includes a prediction unit and a decision support unit;

[0150] The prediction unit predicts the working data and data change trends of each computer room device based on the analysis results to obtain prediction results;

[0151] The decision support unit provides decision-making opinions to the operation and maintenance personnel based on the prediction results and using a recommendation algorithm based on big data, so as to facilitate the dynamic adjustment of maintenance strategies and improve management efficiency;

[0152] When using the recommendation algorithm, corresponding decision-making opinions are generated based on the processing solutions of the corresponding historical data in the big data, so as to assist the operation and maintenance personnel in quick processing.

[0153] The above are only the preferred embodiments of the present invention, and do not impose any form of limitation on the present invention. Although the present invention has been disclosed above with preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments by using the disclosed technical content within the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A computer room operation and maintenance management system based on big data, characterized in that: include: A data acquisition module, wherein a plurality of the data acquisition modules are respectively matched with a group of equipment in the computer room, and each of the modules comprises a transmission unit and a comparison unit; The transmission unit collects the real-time data of the corresponding computer room; The comparison unit has a built-in comparison model. After receiving real-time data, it retrieves the corresponding comparison model data and transmits it synchronously with the real-time data. The analysis module receives real-time data and comparison model data and cleans them; The relationship network module generates a relationship network for each equipment room according to the relationship between work processes and regions, including process relationship units and regional relationship units; The process relationship unit locates the working order and data flow between each node of each computer room equipment according to the corresponding work process, and generates a directed graph according to the working order node of each computer room equipment. At the same time, the process relationship unit processes the real-time data and comparison model data of each computer room equipment to obtain a process data set; The regional relationship unit divides the computer room into different areas based on the physical layout of the computer room and the actual location of the equipment in the computer room, and allocates each computer room equipment to a corresponding location node according to the location to generate a regional relationship model. At the same time, the regional relationship unit processes the real-time data and comparison model data of the computer room equipment in each area to obtain a regional data set; The analysis module receives the process data set and the regional data set, and analyzes the process data set and the regional data set to obtain analysis results; Operation and maintenance platform, including prediction unit and decision support unit; The prediction unit predicts the working data and data change trend of each equipment in the computer room based on the analysis result to obtain the prediction result; The decision support unit provides decision suggestions to the operation and maintenance personnel based on the prediction results and the recommendation algorithm based on big data.

2. According to the big data-based computer room operation and maintenance management system of claim 1, it is characterized in that: The analysis module cleans real-time data by removing duplicate data, converting data types and correcting formats, standardizing, detecting and processing outliers, processing missing data, and verifying cleaned data.

3. The computer room operation and maintenance management system based on big data according to claim 2 is characterized in that: When processing missing data, the missing data at time point T is obtained by calculating the difference between the historical data of the missing data and the corresponding historical data and the comparison model data based on the corresponding data in the comparison model data. m The value of is calculated using the formula: in, To compare the model data at time point T m The value of is used as the benchmark value to fill in the missing data; X i is the historical data at time point T i The value of α is the decay factor, which is used to control the rate of time decay; T m is the time point at which the data is missing, T i The time points of historical data used to fill in missing data; |T i -T m | is the time point T i With T m The time difference between X i is the historical data at time point T i The value of is the weighting coefficient.

4. According to the big data-based computer room operation and maintenance management system of claim 1, it is characterized in that: In the directed graph, the input data of the initial computer room equipment in each workflow is used as the matching point to match the corresponding work model, and the computer room equipment corresponding to each node in the workflow is matched with the corresponding part in the work model; After matching, the comparison model of the comparison unit in each data acquisition module is established, and the comparison model data of the comparison model is used for subsequent synchronous transmission with the corresponding real-time data.

5. The computer room operation and maintenance management system based on big data according to claim 4 is characterized in that: The analysis module's method for analyzing the process data set includes the following steps: A1. Take each node of the workflow as the axis and the comparison model data corresponding to each node as the base point to establish a comparison curve; A2. Compare the real-time data matching values ​​corresponding to each node with the base point corresponding to the curve, and use the formula: Obtain the total difference value Dt of the comparison model data of each computer room equipment and the corresponding working model in the corresponding workflow; Among them, X i is the real-time data of the i-th node, M i is the comparison model data of the ith node, i.e., the corresponding base point on the comparison curve; N is the total number of nodes in the workflow; A3, comparing the total difference value Dt with a preset difference threshold value T; a1. If the total difference value Dt is not greater than the difference threshold T, the fault degree of each equipment room in the workflow is marked as level one; a2. If the total difference value Dt is greater than the difference threshold T, the failure degree of each equipment room in the workflow is marked as level 2.

6. The computer room operation and maintenance management system based on big data according to claim 5 is characterized in that: Based on a2, identify the distribution status of each real-time data in the comparison curve; b1. If the difference between individual real-time data and the corresponding comparison model data is in an irregular discrete state, the equipment in the computer room corresponding to the irregular discrete real-time data will be marked; b2. If the differences between the multiple real-time data and the corresponding comparison model data are in a continuous state, each real-time data with a continuous difference is identified to obtain an identification data segment; The marking is based on the equipment in the computer room corresponding to the starting point of the identified data segment.

7. The computer room operation and maintenance management system based on big data according to claim 1 is characterized in that: The analysis module analyzes the regional data set, including: Y1, difference detection; The preset comparison threshold G is used to determine whether the real-time data of the nodes in the corresponding area exceeds the reasonable range of the comparison model data; The real-time data of each node in the corresponding area is compared with the comparison threshold G. If it exceeds the comparison threshold G, it is marked as an abnormal node, and each abnormal node is recorded as a set S; Y2, state pattern recognition; Perform the following operations on each abnormal node in the set S: u1. Retrieve similar records of the abnormal node from the big data storage, and use a similarity matching algorithm to compare the real-time data of the abnormal node with similar data in the big data storage, and determine whether the abnormal state of the abnormal node exists in the same data in the big data storage; u2. When the same data exists, the abnormal state of the abnormal node is matched with the result of storing the data in the big data; Y3. Generate analysis results; For each abnormal node, a corresponding analysis report is generated, and the fault level of the equipment in the corresponding node is marked as level 2. At the same time, the analysis reports of all abnormal nodes in the region are summarized to generate a regional analysis report; If the analysis reports of all abnormal nodes in the area have the same type of abnormal status, the area is marked as an abnormal area.

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