Intelligent Operation and Maintenance Management System, Method and Device for Model Simulation and Data Optimization

Abstract

The present invention belongs to the field of intelligent operation and maintenance management, involves data analysis technology, and is used to solve the problems that the existing operation and maintenance management systems cannot find out the core operation and maintenance data from a large amount of operation and maintenance data, nor can they accurately alarm the running state of the system according to the priority of the core operation and maintenance data. Specifically, it is an intelligent operation and maintenance management system for model simulation and data optimization, including a model simulation module, an operation and maintenance data analysis module, an operation and maintenance alarm module, and an operation and maintenance optimization evaluation module; the model simulation module, the operation and maintenance data analysis module, the operation and maintenance alarm module, and the operation and maintenance optimization evaluation module are communicatively connected in sequence; the present invention can find out the core operation and maintenance data from a large amount of operation and maintenance data, and conduct joint monitoring on the core operation and maintenance data, so as to accurately judge the risk degree of the system and alarm in time, be able to discover potential problems in advance, effectively reduce the occurrence probability of system failures, ensure the continuous and normal operation of the business, and improve the operation and maintenance efficiency and resource utilization rate at the same time.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent operation and maintenance management, relates to data analysis technology, and specifically is an intelligent operation and maintenance management system, method and device for model simulation and data optimization. Background Art

[0002] An intelligent operation and maintenance management system is a platform that uses technologies such as artificial intelligence, big data, and machine learning to realize automated and intelligent monitoring, management, and maintenance of IT systems, network devices, servers, application programs, etc.; with the rapid development of information technology, the business operations of enterprises and organizations have become increasingly dependent on information systems, and the stable operation and efficient management of information systems have become key factors in ensuring business continuity and competitiveness.

[0003] With the growth of business and continuous technological updates, the scale of infrastructure such as servers, network devices, and storage devices in information systems has been continuously expanding, and the connection relationships and dependencies among them have become more complex. Traditional operation and maintenance management methods have many deficiencies such as low accuracy and difficulty in dealing with massive data when dealing with these complex systems.

[0004] Existing operation and maintenance management systems often cannot perform model simulation and optimization analysis on massive operation and maintenance data to find the core operation and maintenance data reflecting the system state, nor can they accurately alarm the running state of the system according to the priority of the core operation and maintenance data, resulting in operation and maintenance personnel being unable to respond and handle problems quickly and effectively.

[0005] In view of the above technical problems, the present application proposes a solution. Summary of the Invention

[0006] The purpose of the present invention is to provide an intelligent operation and maintenance management system, method and device for model simulation and data optimization, which are used to solve the problems that existing operation and maintenance management systems cannot find the core operation and maintenance data reflecting the system state from massive operation and maintenance data, nor can they accurately alarm the running state of the system according to the priority of the core operation and maintenance data;

[0007] The technical problem to be solved by the present invention is: how to provide an intelligent operation and maintenance management system, method and device for model simulation and data optimization that can find the core operation and maintenance data reflecting the system state from massive operation and maintenance data, and at the same time can accurately alarm the running state of the system according to the priority of the core operation and maintenance data.

[0008] The purpose of the present invention can be achieved through the following technical solutions:

[0009] An intelligent operation and maintenance management system for model simulation and data optimization, including a model simulation module, an operation and maintenance data analysis module, an operation and maintenance alarm module, and an operation and maintenance optimization evaluation module; the model simulation module, the operation and maintenance data analysis module, the operation and maintenance alarm module, and the operation and maintenance optimization evaluation module are sequentially communicatively connected;

[0010] The model simulation module is used for model construction and simulation of operation and maintenance management: marking the infrastructure to be operated and maintained as an operation and maintenance component, using simulation software to model the operation and maintenance component, generating a simulation period with a fixed duration of T1, simulating the actual workload within the simulation period, and calculating the load factor FZ within the simulation period;

[0011] The operation and maintenance data analysis module is used for analyzing and optimizing the classification of operation and maintenance data according to the simulation model: marking the data collected during model simulation within the simulation period as operation and maintenance data YWi, where i is the number of types of operation and maintenance data, i = 1, 2... m, and m is a positive integer; calculating the compliance ratio FH for the operation and maintenance data YWi and the load factor FZ within n simulation periods, and classifying the operation and maintenance data YWi into core operation and maintenance data HX and general operation and maintenance data through the compliance ratio FH;

[0012] The operation and maintenance alarm module is used for jointly monitoring and alarming the operation and maintenance components according to the optimized core operation and maintenance data: in the operation and maintenance work, obtaining the abnormal value YC of the core operation and maintenance data HX, calculating the abnormal coefficient YX through the abnormal values YC of all the core operation and maintenance data HX, and monitoring and alarming the operation and maintenance components according to the abnormal coefficient YX;

[0013] The operation and maintenance optimization evaluation module is used for evaluating the operation and maintenance optimization according to the service availability coefficient before and after optimization: generating a service period with a fixed duration of T2, obtaining the service availability coefficient FW and the historical service availability coefficient LW within the service period, and judging whether the operation and maintenance optimization meets the conditions by comparing the service availability coefficient FW and the historical service availability coefficient LW.

[0014] Further, the operation and maintenance components include servers, network devices, and storage devices, and the connection relationship, dependency relationship, configuration information, and performance parameters between the operation and maintenance components are defined according to the actual working state.

[0015] Further, the workload includes the user request volume QQ, the data traffic volume LL, and the concurrent access volume BF; the user request volume QQ is the number of times users request the server within the simulation period; the data traffic volume LL is the total traffic of different types of request data within the simulation period, and the request data includes text data, image data, and audio data; the concurrent access volume BF is the average number of users accessing the server at the same moment within the simulation period; the user request volume QQ, the data traffic volume LL, and the concurrent access volume BF are numerically calculated to obtain the load factor FZ within the simulation period.

[0016] Further, obtain the operation and maintenance data YWi within n simulation periods, and establish a rectangular coordinate system with the operation and maintenance data YWi as the Y-axis of the coordinate system and the simulation period as the X-axis of the coordinate system. Plot points in the rectangular coordinate system and draw the operation and maintenance data YWi - period scatter plot; denote the n coordinate points on the operation and maintenance data YWi - period scatter plot as (X n , Y n ), and denote the angle between the line connecting two adjacent coordinate points and the positive direction of the X-axis as θ n , then θ n = arctan[(Y n+1 - Y n ) / (X n+1 - X n )]; similarly, obtain the load factor FZ within n simulation periods and draw the load factor FZ - period scatter plot, and calculate the angle β n between the lines connecting two adjacent coordinate points and the positive direction of the X-axis; compare the angle β n and the corresponding angle θ n of the simulation period with the preset angle α: if |β n - θ n | ≤ α, then it is determined that the change trends between the two sets of corresponding coordinate points on the two scatter plots meet the requirements; if |β n - θ n | > α, then it is determined that the change trends between the two sets of corresponding coordinate points on the two scatter plots do not meet the requirements.

[0017] Further, for the total of (n - 1) groups of angles β n , θ nThe ratio of the number meeting the requirements to (n - 1) is denoted as the compliance ratio FH, and the ratio of the number not meeting the requirements to (n - 1) is denoted as the non - compliance ratio BH. Then FH + BH = 1. Compare the compliance ratio FHi corresponding to the operation and maintenance data YWi with the preset compliance threshold FHmax: If the compliance ratio FHi is greater than or equal to the compliance threshold FHmax, it is determined that the operation and maintenance data YWi can represent the actual workload, and the operation and maintenance data YWi is denoted as the core operation and maintenance data HX; If the compliance ratio FHi is less than the compliance threshold FHmax, it is determined that the operation and maintenance data YWi cannot represent the actual workload, and the operation and maintenance data YWi is denoted as the general operation and maintenance data.

[0018] Furthermore, the core operation and maintenance data HX is obtained in real - time and compared with the preset core operation and maintenance data threshold HXmax: If the core operation and maintenance data HX is less than the core operation and maintenance data threshold HXmax, it is determined that the core operation and maintenance data HX is normal and no processing is required; If the core operation and maintenance data HX is greater than or equal to the core operation and maintenance data threshold HXmax, it is determined that the core operation and maintenance data HX is abnormal, and an abnormal value YC is assigned to the core operation and maintenance data HX. The assignment method of the abnormal value YC is as follows: Arrange the core operation and maintenance data HX in descending order according to the compliance ratio FHi to obtain the core operation and maintenance sequence, and denote the number of elements in the core operation and maintenance sequence as the core number S. Denote the position order of the core operation and maintenance data HX in the core operation and maintenance sequence as the serial number P. Then the abnormal value YC = S - P + 1; Calculate the sum of the abnormal values YC of all the core operation and maintenance data HX and take the average to obtain the abnormal coefficient YX. Compare the abnormal coefficient YX with the preset abnormal threshold YXmax: If the abnormal coefficient YX is less than the abnormal threshold YXmax, it is determined that the risk of the system is low, generate a low - risk warning signal and send the signal to the mobile terminal of the management personnel; If the abnormal coefficient YX is greater than or equal to the abnormal threshold YXmax, it is determined that the risk of the system is high, generate a high - risk warning signal and send the signal to the mobile terminal of the management personnel.

[0019] Further, obtain the normal time ZC and the failure time GZ within the service cycle; the normal time ZC is the total normal running time within the service cycle, and the failure time GZ is the total failure time within the service cycle; perform numerical calculation on the normal time ZC and the failure time GZ to obtain the service availability coefficient FW within the service cycle; obtain historical service data before the operation and maintenance warning using the core operation and maintenance data through the historical database. The historical service data includes the historical total normal running time and the historical total failure time. Similarly, perform numerical calculation on the historical total normal running time and the historical total failure time to obtain the historical service availability coefficient LW; compare the service availability coefficient FW with the historical service availability coefficient LW: if the service availability coefficient FW is greater than the historical service availability coefficient LW, it is determined that the generation condition of the warning signal meets the requirements and no adjustment is needed; if the service availability coefficient FW is less than or equal to the historical service availability coefficient LW, it is determined that the generation condition of the warning signal does not meet the requirements, generate an operation and maintenance optimization signal and send the signal to the mobile terminal of the management personnel.

[0020] An intelligent operation and maintenance management method for model simulation and data optimization includes the following steps:

[0021] Step 1: Mark the infrastructure that needs to be managed for operation and maintenance as an operation and maintenance component, use simulation software to model the operation and maintenance component, generate a simulation cycle with a fixed duration of T1, simulate the actual user request volume QQ, data traffic LL, and concurrent access volume BF within the simulation cycle, and perform numerical calculation on the user request volume QQ, data traffic LL, and concurrent access volume BF to obtain the load coefficient FZ within the simulation cycle;

[0022] Step 2: Obtain the operation and maintenance data YWi within n simulation cycles, plot points in a rectangular coordinate system and draw the operation and maintenance data YWi - cycle scatter plot, and calculate the angle θ between the line connecting two adjacent coordinate points and the positive direction of the X-axis n , similarly, obtain the load coefficient FZ within n simulation cycles and calculate the angle β between the line connecting two adjacent coordinate points and the positive direction of the X-axis n , and calculate the compliance ratio FH according to the angle β n , θ n and divide the operation and maintenance data YWi into core operation and maintenance data and general operation and maintenance data;

[0023] Step 3: In actual operation and maintenance work, compare the core operation and maintenance data HX with the preset core operation and maintenance data threshold HXmax, determine whether the core operation and maintenance data HX is abnormal and calculate the abnormality coefficient YX, and perform low-risk warning or high-risk warning by judging the abnormality coefficient YX;

[0024] Step 4: Generate a service cycle with a fixed duration of T2. Perform numerical calculations on the normal time ZC and the failure time GZ within the service cycle to obtain the service availability factor FW. Compare the service availability factor FW with the historical service availability factor LW to determine whether the operation and maintenance optimization meets the requirements.

[0025] An intelligent operation and maintenance management device for model simulation and data optimization, which is applied to an intelligent operation and maintenance management system for model simulation and data optimization.

[0026] The present invention has the following beneficial effects:

[0027] 1. Through the model simulation module, accurate model construction and simulation of operation and maintenance management are carried out, which can more realistically reflect the system's workload and operating status, providing an accurate basis for subsequent data analysis and optimization.

[0028] 2. Through the operation and maintenance data analysis module, operation and maintenance data can be deeply analyzed, and optimized classification can be carried out according to its compliance with the actual workload. A large amount of operation and maintenance data is classified into core operation and maintenance data and general operation and maintenance data, enabling operation and maintenance personnel to pay more targeted attention to key data.

[0029] 3. The operation and maintenance alarm module conducts joint monitoring based on the core operation and maintenance data, assigns abnormal values to the core operation and maintenance data that are abnormal through calculation and comparison, thereby accurately judging the risk level of the system. Through accurate analysis of the operation and maintenance data for timely alarm, potential problems can be discovered and solved in advance, effectively reducing the occurrence probability of system failures, ensuring the continuous normal operation of the business, and at the same time reducing unnecessary troubleshooting time and resource waste, improving operation and maintenance efficiency and resource utilization rate.

[0030] 4. Through intelligent operation and maintenance management, the costs brought by manual monitoring and misjudgment are reduced, and at the same time, the availability of the system is improved, indirectly reducing the business loss costs caused by system failures. Description of the Drawings

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0032] Figure 1 It is the overall system block diagram of Embodiment 1 of the present invention;

[0033] Figure 2 It is the method flow chart of Embodiment 2 of the present invention. Detailed Embodiments

[0034] 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 a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0035] Embodiment 1: As Figure 1 shown, an intelligent operation and maintenance management system for model simulation and data optimization includes a model simulation module, an operation and maintenance data analysis module, an operation and maintenance alarm module, and an operation and maintenance optimization evaluation module; the model simulation module, the operation and maintenance data analysis module, the operation and maintenance alarm module, and the operation and maintenance optimization evaluation module are sequentially communicatively connected;

[0036] The model simulation module is used to build a model and perform simulation for operation and maintenance management: mark the infrastructure to be operated and maintained as operation and maintenance components, use simulation software to model the operation and maintenance components, and the operation and maintenance components include servers, network devices, and storage devices. The connection relationships, dependency relationships, configuration information, and performance parameters among the operation and maintenance components are defined according to the actual working conditions; generate a simulation period with a fixed duration of T1, and simulate the actual workload within the simulation period. The workload includes the user request volume QQ, data traffic LL, and concurrent access volume BF; where the user request volume QQ is the number of users requesting the server within the simulation period; the data traffic LL is the total traffic of different types of request data within the simulation period, and the request data includes text data, image data, and audio data; the concurrent access volume BF is the average value of the number of users accessing the server at the same time within the simulation period; perform numerical calculation on the user request volume QQ, data traffic LL, and concurrent access volume BF through the formula FZ = k1*QQ + k2*LL + k3*BF to obtain the load coefficient FZ within the simulation period, where k1, k2, and k3 are all proportionality coefficients, and k1 > k2 > k3; through the model simulation module, accurate model building and simulation for operation and maintenance management can more realistically reflect the system's workload and operating status, providing an accurate basis for subsequent data analysis and optimization.

[0037] The operation and maintenance data analysis module is used to analyze and optimize the classification of operation and maintenance data according to the simulation model: the data collected during model simulation within the simulation period is marked as operation and maintenance data YWi, where i is the number of types of operation and maintenance data, i = 1, 2... m, and m is a positive integer; it should be noted that the operation and maintenance data YWi includes server data, network data, application data, etc. Server data includes CPU usage rate, memory usage rate, disk IO speed, etc. Network data includes network latency, packet loss rate, etc. Application data includes application response time, error logs, warning messages, etc.; the operation and maintenance data YWi can be obtained by searching and filtering log data from different sources; obtain the operation and maintenance data YWi within n simulation periods, where n is a numerical constant, and the specific value of n is set by the management personnel themselves. Taking the operation and maintenance data YWi as the Y-axis of the coordinate system and the simulation period as the X-axis of the coordinate system, plot points in the rectangular coordinate system and draw the operation and maintenance data YWi - period scatter plot; mark the n coordinate points on the operation and maintenance data YWi - period scatter plot as (X n ,Y n ), and denote the angle between the connection line of two adjacent coordinate points and the positive direction of the X-axis as θ n ,then θ n = arctan[(Y n+1 - Y n ) / (X n+1 - X n ); similarly, obtain the load factor FZ within n simulation periods and draw the load factor FZ - period scatter plot, and calculate the angle β n between the connection line of two adjacent coordinate points and the positive direction of the X-axis; compare the angle β n and the corresponding angle θ n of the simulation period with the preset angle α: if |β n - θ n | ≤ α, then it is judged that the change trend between the two groups of corresponding coordinate points on the two scatter plots meets the requirements; if |β n - θ n | > α, then it is judged that the change trend between the two groups of corresponding coordinate points on the two scatter plots does not meet the requirements;

[0038] Among the n simulation periods, a total of (n - 1) groups of angles β n 、θ nThe ratio of the number meeting the requirements to (n - 1) is denoted as the compliance ratio FH, and the ratio of the number not meeting the requirements to (n - 1) is denoted as the non-compliance ratio BH. Then FH + BH = 1. Compare the compliance ratio FHi corresponding to the operation and maintenance data YWi with the preset compliance threshold FHmax: If the compliance ratio FHi is greater than or equal to the compliance threshold FHmax, it is determined that the operation and maintenance data YWi can represent the actual workload, and the operation and maintenance data YWi is denoted as the core operation and maintenance data HX; If the compliance ratio FHi is less than the compliance threshold FHmax, it is determined that the operation and maintenance data YWi cannot represent the actual workload, and the operation and maintenance data YWi is denoted as the general operation and maintenance data. Through the operation and maintenance data analysis module, the operation and maintenance data can be deeply analyzed, and optimized classification can be carried out according to its compliance with the actual workload, so as to distinguish the massive operation and maintenance data into core operation and maintenance data and general operation and maintenance data, enabling the operation and maintenance personnel to focus on the key data more pertinently.

[0039] The operation and maintenance alarm module is used to jointly monitor and alarm the operation and maintenance components according to the optimized core operation and maintenance data: In actual operation and maintenance work, the core operation and maintenance data HX is obtained in real time and compared with the preset core operation and maintenance data threshold HXmax: If the core operation and maintenance data HX is less than the core operation and maintenance data threshold HXmax, it is determined that the core operation and maintenance data HX is normal and does not need to be processed; If the core operation and maintenance data HX is greater than or equal to the core operation and maintenance data threshold HXmax, it is determined that the core operation and maintenance data HX is abnormal, and an abnormal value YC is assigned to the core operation and maintenance data HX. The assignment method of the abnormal value YC is as follows: Arrange the core operation and maintenance data HX in descending order according to the compliance ratio FHi to obtain the core operation and maintenance sequence, and denote the number of elements in the core operation and maintenance sequence as the core number S. Denote the position order of the core operation and maintenance data HX in the core operation and maintenance sequence as the serial number P. Then the abnormal value YC = S - P + 1. Calculate the sum and average of the abnormal values YC of all core operation and maintenance data HX to obtain the abnormal coefficient YX, and compare the abnormal coefficient YX with the preset abnormal threshold YXmax: If the abnormal coefficient YX is less than the abnormal threshold YXmax, it is determined that the risk of the system is low, and a low-risk alarm signal is generated and sent to the mobile terminal of the management personnel; If the abnormal coefficient YX is greater than or equal to the abnormal threshold YXmax, it is determined that the risk of the system is high, and a high-risk alarm signal is generated and sent to the mobile terminal of the management personnel. The operation and maintenance alarm module conducts joint monitoring based on the core operation and maintenance data, accurately judges the risk degree of the system by calculating and comparing the abnormal core operation and maintenance data and assigning abnormal values, and conducts timely alarm through the accurate analysis of the operation and maintenance data, which can discover and solve potential problems in advance, effectively reduce the occurrence probability of system failures, ensure the continuous and normal operation of the business, and at the same time reduce unnecessary troubleshooting time and resource waste, improving the operation and maintenance efficiency and resource utilization rate.

[0040] The operation and maintenance optimization evaluation module is used to evaluate the operation and maintenance optimization based on the service availability coefficient before and after optimization: generate a service cycle with a fixed duration of T2, and obtain the normal time ZC and the failure time GZ within the service cycle; the normal time ZC is the total normal operation time within the service cycle, and the failure time GZ is the total failure time within the service cycle; through the formula Perform numerical calculations on the normal time ZC and the failure time GZ to obtain the service availability coefficient FW within the service cycle; obtain historical service data before operation and maintenance alarms using core operation and maintenance data through the historical database. The historical service data includes the total historical normal operation time and the total historical failure time. Similarly, perform numerical calculations on the total historical normal operation time and the total historical failure time to obtain the historical service availability coefficient LW; compare the service availability coefficient FW with the historical service availability coefficient LW: if the service availability coefficient FW is greater than the historical service availability coefficient LW, it is judged that the generation condition of the alarm signal meets the requirements and no adjustment is required; if the service availability coefficient FW is less than or equal to the historical service availability coefficient LW, it is judged that the generation condition of the alarm signal does not meet the requirements, generate an operation and maintenance optimization signal and send the signal to the mobile terminal of the management personnel.

[0041] Embodiment 2: As Figure 2 shown, the intelligent operation and maintenance management method for model simulation and data optimization includes the following steps:

[0042] Step 1: Mark the infrastructure that needs to be operated and maintained as an operation and maintenance component, use simulation software to model the operation and maintenance component, generate a simulation cycle with a fixed duration of T1, simulate the actual user request volume QQ, data traffic LL, and concurrent access volume BF within the simulation cycle, and perform numerical calculations on the user request volume QQ, data traffic LL, and concurrent access volume BF to obtain the load coefficient FZ within the simulation cycle;

[0043] Step 2: Obtain the operation and maintenance data YWi within n simulation cycles, plot points in a rectangular coordinate system and draw the operation and maintenance data YWi - cycle scatter diagram, and calculate the angle θ between the connection line of adjacent two coordinate points and the positive direction of the X-axis n , similarly, obtain the load coefficient FZ within n simulation cycles and calculate the angle β between the connection line of adjacent two coordinate points and the positive direction of the X-axis n , and calculate the compliance ratio FH according to the angle β n , θ n , and divide the operation and maintenance data YWi into core operation and maintenance data and general operation and maintenance data;

[0044] Step 3: In actual operation and maintenance work, compare the core operation and maintenance data HX with the preset core operation and maintenance data threshold HXmax, judge whether the core operation and maintenance data HX is abnormal and calculate the abnormal coefficient YX, and perform low-risk alarms or high-risk alarms by judging the abnormal coefficient YX;

[0045] Step 4: Generate a service cycle with a fixed duration of T2, perform numerical calculations on the normal time ZC and the failure time GZ within the service cycle to obtain the service availability factor FW; compare the service availability factor FW with the historical service availability factor LW to determine whether the operation and maintenance optimization meets the requirements.

[0046] For the intelligent operation and maintenance management system, method and device for model simulation and data optimization, during operation, mark the infrastructure that needs to be operated and maintained as an operation and maintenance component, use simulation software to model the operation and maintenance component, generate a simulation cycle with a fixed duration, and calculate the load factor within the simulation cycle. Obtain the operation and maintenance data and load factors within n simulation cycles and calculate the compliance ratio. Divide the operation and maintenance data into core operation and maintenance data and general operation and maintenance data according to the compliance ratio; in actual operation and maintenance work, compare the core operation and maintenance data with the threshold to determine whether the core operation and maintenance data is abnormal, calculate the abnormal coefficient, and issue a low-risk alarm or a high-risk alarm by judging the abnormal coefficient; finally, perform numerical calculations on the normal time and the failure time within the service cycle to obtain the service availability factor, and compare the service availability factor with the historical service availability factor to determine whether the operation and maintenance optimization meets the requirements.

[0047] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of this technology make various modifications or supplements to the described specific embodiments or use similar methods to replace them. As long as they do not deviate from the structure of the invention or exceed the scope defined by this claim book, they should all fall within the protection scope of the present invention.

[0048] The above formulas are all obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value. The coefficients in the formula are set by those skilled in the art according to the actual situation; for example: the formula FZ = k1 * QQ + k2 * LL + k3 * BF; those skilled in the art collect multiple groups of sample data and set corresponding load factors for each group of sample data; substitute the set load factors and the collected sample data into the formula, and any three formulas form a system of linear equations with three variables. Screen and take the average value of the calculated coefficients to obtain the values of k1, k2, and k3 as 2.15, 1.87, and 1.65 respectively;

[0049] The magnitude of the coefficient is a specific value obtained by quantifying each parameter for subsequent comparison. Regarding the magnitude of the coefficient, it depends on the amount of sample data and the load factors initially set by those skilled in the art for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantified value, for example, the load factor is proportional to the value of the user request volume.

[0050] In the description of this specification, the descriptions referring to the terms "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0051] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the present invention to only the specific embodiments. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. An intelligent operation and maintenance management system for model simulation and data optimization, characterized in that, It includes a model simulation module, an operation and maintenance data analysis module, an operation and maintenance alarm module, and an operation and maintenance optimization evaluation module; the model simulation module, the operation and maintenance data analysis module, the operation and maintenance alarm module, and the operation and maintenance optimization evaluation module are communicatively connected in sequence; The model simulation module is used to build a model and conduct simulation for operation and maintenance management: mark the infrastructure that needs to be managed for operation and maintenance as operation and maintenance components, use simulation software to model the operation and maintenance components, generate a simulation period with a fixed duration of T1, simulate the actual workload within the simulation period, and calculate the load factor FZ within the simulation period; The operation and maintenance data analysis module is used to analyze and optimize the classification of operation and maintenance data according to the simulation model: mark the data collected during model simulation within the simulation period as operation and maintenance data YWi, where i is the number of types of operation and maintenance data, i = 1, 2... m, and m is a positive integer; calculate the compliance ratio FH for the operation and maintenance data YWi and the load factor FZ within n simulation periods, and classify the operation and maintenance data YWi into core operation and maintenance data HX and general operation and maintenance data through the compliance ratio FH; The operation and maintenance alarm module is used to jointly monitor and alarm the operation and maintenance components according to the optimized core operation and maintenance data: during operation and maintenance work, obtain the abnormal value YC of the core operation and maintenance data HX, calculate the abnormal coefficient YX through the abnormal values YC of all the core operation and maintenance data HX, and monitor and alarm the operation and maintenance components according to the abnormal coefficient YX; The operation and maintenance optimization evaluation module is used to evaluate the operation and maintenance optimization according to the service availability coefficients before and after optimization: generate a service period with a fixed duration of T2, obtain the service availability coefficient FW and the historical service availability coefficient LW within the service period, and judge whether the operation and maintenance optimization meets the conditions by comparing the service availability coefficient FW and the historical service availability coefficient LW; Obtain the operation and maintenance data YWi within n simulation cycles, and establish a rectangular coordinate system with the operation and maintenance data YWi as the Y-axis of the coordinate system and the simulation cycle as the X-axis of the coordinate system. Plot points in the rectangular coordinate system and draw the scatter plot of operation and maintenance data YWi - cycle; Denote the n coordinate points on the scatter plot of operation and maintenance data YWi - cycle as (X n , Y n ), and denote the angle between the line connecting two adjacent coordinate points and the positive direction of the X-axis as θ n , then θ n = arctan[(Y n+1 - Y n ) / (X n+1 - X n ); Similarly, obtain the load factor FZ within n simulation cycles and draw the scatter plot of load factor FZ - cycle, and calculate the angle β n between the line connecting two adjacent coordinate points and the positive direction of the X-axis; Compare the angle β n and the corresponding angle θ n of the simulation cycle with the preset angle α: If |β n - θ n | ≤ α, then it is judged that the change trends between the two sets of corresponding coordinate points on the two scatter plots meet the requirements. If |β n - θ n | > α, then it is judged that the change trends between the two sets of corresponding coordinate points on the two scatter plots do not meet the requirements; The total of (n - 1) groups of included angles β n , θ n in n simulation cycles, the ratio of the number that meets the requirements to (n - 1) is denoted as the compliance ratio FH, and the ratio of the number that does not meet the requirements to (n - 1) is denoted as the non-compliance ratio BH. Then FH + BH = 1. Compare the compliance ratio FHi corresponding to the operation and maintenance data YWi with the preset compliance threshold FHmax: If the compliance ratio FHi is greater than or equal to the compliance threshold FHmax, it is determined that the operation and maintenance data YWi can reflect the actual workload, and the operation and maintenance data YWi is denoted as the core operation and maintenance data HX; If the compliance ratio FHi is less than the compliance threshold FHmax, it is determined that the operation and maintenance data YWi cannot reflect the actual workload, and the operation and maintenance data YWi is denoted as the general operation and maintenance data.

2. The intelligent operation and maintenance management system for model simulation and data optimization according to claim 1, characterized in that The operation and maintenance components include servers, network devices, and storage devices, and the connection relationships, dependency relationships, configuration information, and performance parameters among the operation and maintenance components are defined according to the actual working status.

3. The intelligent operation and maintenance management system for model simulation and data optimization according to claim 2, characterized in that, The workload includes the number of user requests QQ, data traffic LL, and concurrent access volume BF; the number of user requests QQ is the number of times users request the server within the simulation period; The data traffic LL is the total traffic of different types of request data within the simulation period, and the request data includes text data, image data, and audio data; the concurrent access volume BF is the average value of the number of users accessing the server at the same time within the simulation period; perform numerical calculations on the number of user requests QQ, the data traffic LL, and the concurrent access volume BF to obtain the load factor FZ within the simulation period.

4. The intelligent operation and maintenance management system for model simulation and data optimization according to claim 1, characterized in that Obtain the core operation and maintenance data HX in real time and compare the core operation and maintenance data HX with the preset core operation and maintenance data threshold HXmax: if the core operation and maintenance data HX is less than the core operation and maintenance data threshold HXmax, it is judged that the core operation and maintenance data HX is normal and does not need to be processed; if the core operation and maintenance data HX is greater than or equal to the core operation and maintenance data threshold HXmax, it is judged that the core operation and maintenance data HX is abnormal, and an abnormal value YC is assigned to the core operation and maintenance data HX.

5. The intelligent operation and maintenance management system for model simulation and data optimization according to claim 4, wherein Obtain the normal time ZC and the fault time GZ within the service cycle; the normal time ZC is the total time of normal operation within the service cycle, and the fault time GZ is the total time of faults occurring within the service cycle; Perform numerical calculations on the normal time ZC and the fault time GZ to obtain the service availability coefficient FW within the service cycle; obtain historical service data before operation and maintenance alarms using core operation and maintenance data through the historical database. The historical service data includes the total historical normal operation time and the total historical fault time. Similarly, perform numerical calculations on the total historical normal operation time and the total historical fault time to obtain the historical service availability coefficient LW.

6. The intelligent operation and maintenance management system for model simulation and data optimization according to claim 5, characterized in that Compare the service availability coefficient FW with the historical service availability coefficient LW: If the service availability coefficient FW is greater than the historical service availability coefficient LW, it is determined that the generation condition of the alarm signal meets the requirements and no adjustment is required; if the service availability coefficient FW is less than or equal to the historical service availability coefficient LW, it is determined that the generation condition of the alarm signal does not meet the requirements, generate an operation and maintenance optimization signal and send the signal to the mobile terminal of the management personnel.

7. An intelligent operation and maintenance management method for model simulation and data optimization, characterized in that This intelligent operation and maintenance management method is applied to the system described in any one of claims 1-6. This intelligent operation and maintenance management method includes the following steps: Step 1: Mark the infrastructure that needs to be operated and maintained as an operation and maintenance component, use simulation software to model the operation and maintenance component, generate a simulation cycle with a fixed duration of T1, simulate the actual user request volume QQ, data traffic LL, and concurrent access volume BF within the simulation cycle, and perform numerical calculations on the user request volume QQ, data traffic LL, and concurrent access volume BF to obtain the load coefficient FZ within the simulation cycle; Step 2: Obtain the operation and maintenance data YWi within n simulation cycles, plot points in a rectangular coordinate system and draw the operation and maintenance data YWi - cycle scatter plot, and calculate the angle θ between the line connecting two adjacent coordinate points and the positive direction of the X-axis n Similarly, obtain the load factor FZ within n simulation cycles and calculate the angle β between the line connecting two adjacent coordinate points and the positive direction of the X-axis n and, based on the angle β n and θ n calculate the compliance ratio FH, and classify the operation and maintenance data YWi into core operation and maintenance data and general operation and maintenance data; Step 3: In actual operation and maintenance work, compare the core operation and maintenance data HX with the preset core operation and maintenance data threshold HXmax, determine whether the core operation and maintenance data HX is abnormal and calculate the abnormality coefficient YX, and perform low-risk alarms or high-risk alarms by judging the abnormality coefficient YX; Step 4: Generate a service cycle with a fixed duration of T2, perform numerical calculations on the normal time ZC and the fault time GZ within the service cycle to obtain the service availability coefficient FW; compare the service availability coefficient FW with the historical service availability coefficient LW to determine whether the operation and maintenance optimization meets the requirements.

8. An intelligent operation and maintenance management device for model simulation and data optimization, characterized in that, This intelligent operation and maintenance management device for model simulation and data optimization is applied to the system described in claim 1.

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