Remote information system operation and maintenance optimization scheduling method

Abstract

The invention discloses a remote information system operation and maintenance optimization scheduling method, and relates to the technical field of system operation and maintenance management. The method specifically comprises the following steps: system monitoring and data acquisition: acquiring indexes of a remote information system in real time through a sensor type configuration tool and an automatic script; data analysis and prediction: processing and analyzing the collected data, identifying a trend of abnormal operation or performance reduction of the system, giving historical data and a current state, and establishing a prediction model; resource optimization scheduling: dynamically adjusting resource allocation according to a data analysis result so as to optimize system performance; and carrying out fault early warning and response. According to the method, through dynamic optimization of resource allocation, intelligent fault prediction and efficient cooperative work, the efficiency, safety and reliability of remote operation and maintenance are remarkably improved, multiple advanced scheduling algorithms and remote operation and maintenance technologies are fused, comprehensive optimization scheduling of a remote information system is achieved, and powerful support is provided for IT operation and maintenance of enterprises.

Description

Technical Field

[0001] The present invention relates to the technical field of system operation and maintenance management, and in particular to a remote information system operation and maintenance optimization scheduling method. Background Art

[0002] A remote computer network system refers to a network system that is applicable to a large area. It has the functions of fast and reliable data transmission, resource sharing, cooperation between users, load balancing, decentralized equipment, safe and reliable, and easy to use. Network hardware is composed of computer systems, data communication systems, and terminal equipment; network software includes network operating systems, network communication protocols, and network access methods.

[0003] After searching, the Chinese patent application number CN116708175B discloses a remote information system operation and maintenance optimization scheduling method, including S1: the operation and maintenance system receives the operation and maintenance instruction information of the remote control terminal, parses the operation and maintenance instruction information and executes the operation and maintenance command corresponding to the instruction information, and analyzes the operation and maintenance instruction information and the operation and maintenance command corresponding to the execution instruction information to obtain the operation and maintenance instruction priority coefficient; S2: according to the operation and maintenance instruction priority coefficient, the operation and maintenance command corresponding to the operation and maintenance instruction information is matched with the channel flow of different levels; S3: the execution status data of the operation and maintenance instruction is tracked to realize the identification of the operation status of the operation and maintenance system.

[0004] The above patents have the following shortcomings: low communication efficiency, asynchronous operation and maintenance interface, unreasonable resource allocation and other problems. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a remote information system operation and maintenance optimization scheduling method.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A remote information system operation and maintenance optimization scheduling method comprises the following steps:

[0008] S1: System monitoring and data collection, through "sensor" configuration tools and automated scripts, real-time collection of telematics system indicators;

[0009] S2: Data analysis and prediction: Process and analyze the collected data, identify the trend of abnormal system operation or performance degradation, and establish a prediction model based on historical data and current status;

[0010] S3: Resource optimization scheduling, dynamically adjust resource allocation according to data analysis results to optimize system performance;

[0011] S4: Fault warning and response: Establish a fault warning mechanism. When the system detects an abnormal event, it generates an alarm in real time and classifies and notifies through a multi-level alarm mechanism.

[0012] S5: Remote operation and maintenance collaboration, by configuring the intranet operation and maintenance server and the remote operation and maintenance server, the remote operation and maintenance interface can be synchronized and communicated efficiently; the intranet operation and maintenance server is connected to the computer room server, and the remote operation and maintenance server is connected through the Internet. The signal conversion and isolation between the two are achieved through a one-way transmission VGA signal acquisition card.

[0013] As a preferred embodiment of the present invention: in the step S1, the configuration tools include Ansible and Puppet, the automation scripts include Jenkins and Rundeck, and the system indicators include CPU usage, memory usage, disk space, and network latency.

[0014] As a preferred embodiment of the present invention: in the step S2, the formula of the prediction model is Where P(F t ) represents the probability of failure at time t, i (t) represents the i-th eigenvalue at time t, w i Represents the weight of the i-th eigenvalue.

[0015] As a preferred embodiment of the present invention: in the step S2, ΔP o =P o -P o-1 , P o is the value of the i-th eigenvalue at time o of the time series.

[0016] As a preferred embodiment of the present invention: the step S3 comprises the following steps:

[0017] S31: Divide tasks into multiple priority queues;

[0018] S32: Scheduling tasks based on their urgency and importance;

[0019] S33: Balance the execution time of long jobs and short jobs.

[0020] As a preferred embodiment of the present invention: in the step S31, the priority determination formula is: Priority(J k ) indicates character J k The current priority, InitialPriority(J k ) represents task J k The initial priority, Penalty(J k , Q i) represents task J k In the queue Q i The penalty value obtained while waiting.

[0021] As a preferred embodiment of the present invention: in the step S2, the data processing includes filling missing values ​​and removing duplicate values.

[0022] As a preferred embodiment of the present invention: in the step S2, the missing value filling includes the following steps:

[0023] S21a: Establish a two-dimensional coordinate system with the time axis as the horizontal axis and the data as the vertical axis;

[0024] S22a: Taking the data collection frequency as the period, select the corresponding node on the horizontal axis;

[0025] S23: Fill the acquired data into the ordinate of each node, and leave a blank if the node data is missing;

[0026] S24a: According to the formula f(x i-1 , x i+1 ) fills the missing data at the i-th node, x i is the data of the i-th node, and f(x) is the filling function logic.

[0027] As a preferred embodiment of the present invention: in the step S24a, or where k o , k′ o is the weight, which satisfies

[0028] As a preferred embodiment of the present invention: the removal of duplicate values ​​comprises the following steps:

[0029] S21b: remove all duplicate values ​​to form missing data;

[0030] S22b: Then perform data filling according to the missing value filling method.

[0031] The beneficial effects of the present invention are:

[0032] 1. The present invention significantly improves the efficiency, safety and reliability of remote operation and maintenance by dynamically optimizing resource allocation, intelligently predicting faults and working in collaboration. It also integrates a variety of advanced scheduling algorithms and remote operation and maintenance technologies to achieve comprehensive optimization and scheduling of remote information systems, providing strong support for enterprise IT operation and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 The present invention provides a flowchart of a remote information system operation and maintenance optimization scheduling method. DETAILED DESCRIPTION

[0034] The technical solution of this patent is further described in detail below in conjunction with specific implementation methods.

[0035] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0036] Embodiment 1:

[0037] A remote information system operation and maintenance optimization scheduling method, comprising the following steps:

[0038] S1: System monitoring and data collection, through "sensor" configuration tools and automated scripts, real-time collection of telematics system indicators;

[0039] S2: Data analysis and prediction: Process and analyze the collected data, identify the trend of abnormal system operation or performance degradation, and establish a prediction model based on historical data and current status;

[0040] S3: Resource optimization scheduling, dynamically adjust resource allocation according to data analysis results to optimize system performance;

[0041] S4: Fault warning and response: Establish a fault warning mechanism. When the system detects an abnormal event, it generates an alarm in real time and classifies and notifies through a multi-level alarm mechanism.

[0042] S5: Remote operation and maintenance collaboration, by configuring the intranet operation and maintenance server and the remote operation and maintenance server, the remote operation and maintenance interface can be synchronized and communicated efficiently; the intranet operation and maintenance server is connected to the computer room server, and the remote operation and maintenance server is connected through the Internet. The signal conversion and isolation between the two are achieved through a one-way transmission VGA signal acquisition card.

[0043] In the S1 step, the configuration tools include Ansible and Puppet, the automation scripts include Jenkins and Rundeck, and the system indicators include CPU usage, memory usage, disk space, and network latency.

[0044] Embodiment 2:

[0045] A remote information system operation and maintenance optimization scheduling method, comprising the following steps:

[0046] S1: System monitoring and data collection, through "sensor" configuration tools and automated scripts, real-time collection of telematics system indicators;

[0047] S2: Data analysis and prediction: Process and analyze the collected data, identify the trend of abnormal system operation or performance degradation, and establish a prediction model based on historical data and current status;

[0048] S3: Resource optimization scheduling, dynamically adjust resource allocation according to data analysis results to optimize system performance;

[0049] S4: Fault warning and response: Establish a fault warning mechanism. When the system detects an abnormal event, it generates an alarm in real time and classifies and notifies through a multi-level alarm mechanism.

[0050] S5: Remote operation and maintenance collaboration, by configuring the intranet operation and maintenance server and the remote operation and maintenance server, the remote operation and maintenance interface can be synchronized and communicated efficiently; the intranet operation and maintenance server is connected to the computer room server, and the remote operation and maintenance server is connected through the Internet. The signal conversion and isolation between the two are achieved through a one-way transmission VGA signal acquisition card.

[0051] In the S1 step, the configuration tools include Ansible and Puppet, the automation scripts include Jenkins and Rundeck, and the system indicators include CPU usage, memory usage, disk space, and network latency.

[0052] In step S2, the formula of the prediction model is: Where P(F t ) represents the probability of failure at time t, i (t) represents the i-th eigenvalue at time t, w i Represents the weight of the i-th eigenvalue.

[0053] In the step S2, ΔP o =P o -P o-1 , P o is the value of the i-th eigenvalue at time o of the time series.

[0054] Embodiment 3:

[0055] A remote information system operation and maintenance optimization scheduling method, comprising the following steps:

[0056] S1: System monitoring and data collection, through "sensor" configuration tools and automated scripts, real-time collection of telematics system indicators;

[0057] S2: Data analysis and prediction: Process and analyze the collected data, identify the trend of abnormal system operation or performance degradation, and establish a prediction model based on historical data and current status;

[0058] S3: Resource optimization scheduling, dynamically adjust resource allocation according to data analysis results to optimize system performance;

[0059] S4: Fault warning and response: Establish a fault warning mechanism. When the system detects an abnormal event, it generates an alarm in real time and classifies and notifies through a multi-level alarm mechanism.

[0060] S5: Remote operation and maintenance collaboration, by configuring the intranet operation and maintenance server and the remote operation and maintenance server, the remote operation and maintenance interface can be synchronized and communicated efficiently; the intranet operation and maintenance server is connected to the computer room server, and the remote operation and maintenance server is connected through the Internet. The signal conversion and isolation between the two are achieved through a one-way transmission VGA signal acquisition card.

[0061] In the S1 step, the configuration tools include Ansible and Puppet, the automation scripts include Jenkins and Rundeck, and the system indicators include CPU usage, memory usage, disk space, and network latency.

[0062] In the step S2, the formula of the prediction model is: Where P(F t ) represents the probability of failure at time t, i (t) represents the i-th eigenvalue at time t, w i Represents the weight of the i-th eigenvalue.

[0063] In the step S2, ΔP o =P o -P o-1 , P o is the value of the i-th eigenvalue at time o of the time series.

[0064] The step S3 includes the following steps:

[0065] S31: Divide tasks into multiple priority queues;

[0066] S32: Scheduling tasks based on their urgency and importance;

[0067] S33: Balance the execution time of long jobs and short jobs.

[0068] Embodiment 4:

[0069] A remote information system operation and maintenance optimization scheduling method, comprising the following steps:

[0070] S1: System monitoring and data collection, through "sensor" configuration tools and automated scripts, real-time collection of telematics system indicators;

[0071] S2: Data analysis and prediction: Process and analyze the collected data, identify the trend of abnormal system operation or performance degradation, and establish a prediction model based on historical data and current status;

[0072] S3: Resource optimization scheduling, dynamically adjust resource allocation according to data analysis results to optimize system performance;

[0073] S4: Fault warning and response: Establish a fault warning mechanism. When the system detects an abnormal event, it generates an alarm in real time and classifies and notifies through a multi-level alarm mechanism.

[0074] S5: Remote operation and maintenance collaboration, by configuring the intranet operation and maintenance server and the remote operation and maintenance server, the remote operation and maintenance interface can be synchronized and communicated efficiently; the intranet operation and maintenance server is connected to the computer room server, and the remote operation and maintenance server is connected through the Internet. The signal conversion and isolation between the two are achieved through a one-way transmission VGA signal acquisition card.

[0075] In the S1 step, the configuration tools include Ansible and Puppet, the automation scripts include Jenkins and Rundeck, and the system indicators include CPU usage, memory usage, disk space, and network latency.

[0076] In the step S2, the formula of the prediction model is: Where P(F t ) represents the probability of failure at time t, i (t) represents the i-th eigenvalue at time t, w i Represents the weight of the i-th eigenvalue.

[0077] In the step S2, ΔP o =P o -P o-1 , P o is the value of the i-th eigenvalue at time o of the time series.

[0078] The step S3 includes the following steps:

[0079] S31: Divide tasks into multiple priority queues;

[0080] S32: Scheduling tasks based on their urgency and importance;

[0081] S33: Balance the execution time of long jobs and short jobs.

[0082] In the step S31, the priority determination formula is: Priority(J k ) indicates character J kThe current priority, InitialPriority(J k ) represents task J k The initial priority, Penalty(J k , Q i ) represents task J k In the queue Q i The penalty value obtained while waiting.

[0083] Embodiment 5:

[0084] A remote information system operation and maintenance optimization scheduling method, comprising the following steps:

[0085] S1: System monitoring and data collection, through "sensor" configuration tools and automated scripts, real-time collection of telematics system indicators;

[0086] S2: Data analysis and prediction: Process and analyze the collected data, identify the trend of abnormal system operation or performance degradation, and establish a prediction model based on historical data and current status;

[0087] S3: Resource optimization scheduling, dynamically adjust resource allocation according to data analysis results to optimize system performance;

[0088] S4: Fault warning and response: Establish a fault warning mechanism. When the system detects an abnormal event, it generates an alarm in real time and classifies and notifies through a multi-level alarm mechanism.

[0089] S5: Remote operation and maintenance collaboration, by configuring the intranet operation and maintenance server and the remote operation and maintenance server, the remote operation and maintenance interface can be synchronized and communicated efficiently; the intranet operation and maintenance server is connected to the computer room server, and the remote operation and maintenance server is connected through the Internet. The signal conversion and isolation between the two are achieved through a one-way transmission VGA signal acquisition card.

[0090] In the S1 step, the configuration tools include Ansible and Puppet, the automation scripts include Jenkins and Rundeck, and the system indicators include CPU usage, memory usage, disk space, and network latency.

[0091] In step S2, the formula of the prediction model is: Where P(F t ) represents the probability of failure at time t, i (t) represents the i-th eigenvalue at time t, w i Represents the weight of the i-th eigenvalue.

[0092] In the step S2, ΔP o =P o-P o-1 , P o is the value of the i-th eigenvalue at time o of the time series.

[0093] The step S3 includes the following steps:

[0094] S31: Divide tasks into multiple priority queues;

[0095] S32: Scheduling tasks based on their urgency and importance;

[0096] S33: Balance the execution time of long jobs and short jobs.

[0097] In the step S31, the priority determination formula is: Priority(J k ) indicates character J k The current priority, InitialPriority(J k ) represents task J k The initial priority, Penalty(J k , Q i ) represents task J k In the queue Q i The penalty value obtained while waiting.

[0098] In the step S2, data processing includes filling missing values ​​and removing duplicate values.

[0099] In the S2 step, the missing value filling includes the following steps:

[0100] S21a: Establish a two-dimensional coordinate system with the time axis as the horizontal axis and the data as the vertical axis;

[0101] S22a: Taking the data collection frequency as the period, select the corresponding node on the horizontal axis;

[0102] S23: Fill the acquired data into the ordinate of each node, and leave a blank if the node data is missing;

[0103] S24a: According to the formula f(x i-1 , x i+1 ) fills the missing data at the i-th node, x i is the data of the i-th node, and f(x) is the filling function logic.

[0104] Embodiment 6:

[0105] A remote information system operation and maintenance optimization scheduling method, comprising the following steps:

[0106] S1: System monitoring and data collection, through "sensor" configuration tools and automated scripts, real-time collection of telematics system indicators;

[0107] S2: Data analysis and prediction: Process and analyze the collected data, identify the trend of abnormal system operation or performance degradation, and establish a prediction model based on historical data and current status;

[0108] S3: Resource optimization scheduling, dynamically adjust resource allocation according to data analysis results to optimize system performance;

[0109] S4: Fault warning and response: Establish a fault warning mechanism. When the system detects an abnormal event, it generates an alarm in real time and classifies and notifies through a multi-level alarm mechanism.

[0110] S5: Remote operation and maintenance collaboration, by configuring the intranet operation and maintenance server and the remote operation and maintenance server, the remote operation and maintenance interface can be synchronized and communicated efficiently; the intranet operation and maintenance server is connected to the computer room server, and the remote operation and maintenance server is connected through the Internet. The signal conversion and isolation between the two are achieved through a one-way transmission VGA signal acquisition card.

[0111] In the S1 step, the configuration tools include Ansible and Puppet, the automation scripts include Jenkins and Rundeck, and the system indicators include CPU usage, memory usage, disk space, and network latency.

[0112] In the step S2, the formula of the prediction model is: Where P(F t ) represents the probability of failure at time t, i (t) represents the i-th eigenvalue at time t, w i Represents the weight of the i-th eigenvalue.

[0113] In the step S2, ΔP o =P o -P o-1 , P o is the value of the i-th eigenvalue at time o of the time series.

[0114] The step S3 includes the following steps:

[0115] S31: Divide tasks into multiple priority queues;

[0116] S32: Scheduling tasks based on their urgency and importance;

[0117] S33: Balance the execution time of long jobs and short jobs.

[0118] In the step S31, the priority determination formula is: Priority(J k ) indicates character J k The current priority, InitialPriority(J k ) represents task J k The initial priority, Penalty(J k , Q i ) represents task J k In the queue Q i The penalty value obtained while waiting.

[0119] In the step S2, data processing includes filling missing values ​​and removing duplicate values.

[0120] In the S2 step, the missing value filling includes the following steps:

[0121] S21a: Establish a two-dimensional coordinate system with the time axis as the horizontal axis and the data as the vertical axis;

[0122] S22a: Taking the data collection frequency as the period, select the corresponding node on the horizontal axis;

[0123] S23: Fill the acquired data into the ordinate of each node, and leave a blank if the node data is missing;

[0124] S24a: According to the formula f(x i-1 , x i+1 ) fills the missing data at the i-th node, x i is the data of the i-th node, and f(x) is the filling function logic.

[0125] In the step S24a,

[0126] Embodiment 7:

[0127] A remote information system operation and maintenance optimization scheduling method, comprising the following steps:

[0128] S1: System monitoring and data collection, through "sensor" configuration tools and automated scripts, real-time collection of telematics system indicators;

[0129] S2: Data analysis and prediction: Process and analyze the collected data, identify the trend of abnormal system operation or performance degradation, and establish a prediction model based on historical data and current status;

[0130] S3: Resource optimization scheduling, dynamically adjust resource allocation according to data analysis results to optimize system performance;

[0131] S4: Fault warning and response: Establish a fault warning mechanism. When the system detects an abnormal event, it generates an alarm in real time and classifies and notifies through a multi-level alarm mechanism.

[0132] S5: Remote operation and maintenance collaboration, by configuring the intranet operation and maintenance server and the remote operation and maintenance server, the remote operation and maintenance interface can be synchronized and communicated efficiently; the intranet operation and maintenance server is connected to the computer room server, and the remote operation and maintenance server is connected through the Internet. The signal conversion and isolation between the two are achieved through a one-way transmission VGA signal acquisition card.

[0133] In the S1 step, the configuration tools include Ansible and Puppet, the automation scripts include Jenkins and Rundeck, and the system indicators include CPU usage, memory usage, disk space, and network latency.

[0134] In the step S2, the formula of the prediction model is: Where P(F t ) represents the probability of failure at time t, i (t) represents the i-th eigenvalue at time t, w i Represents the weight of the i-th eigenvalue.

[0135] In the step S2, ΔP o =P o -P o-1 , P o is the value of the i-th eigenvalue at time o of the time series.

[0136] The step S3 includes the following steps:

[0137] S31: Divide tasks into multiple priority queues;

[0138] S32: Scheduling tasks based on their urgency and importance;

[0139] S33: Balance the execution time of long jobs and short jobs.

[0140] In the step S31, the priority determination formula is: Priority(J k ) indicates character J k The current priority, InitialPriority(J k ) represents task J k The initial priority, Penalty(J k , Q i ) represents task J k In the queue Q i The penalty value obtained while waiting.

[0141] In the step S2, data processing includes filling missing values ​​and removing duplicate values.

[0142] In the S2 step, the missing value filling includes the following steps:

[0143] S21a: Establish a two-dimensional coordinate system with the time axis as the horizontal axis and the data as the vertical axis;

[0144] S22a: Taking the data collection frequency as the period, select the corresponding node on the horizontal axis;

[0145] S23: Fill the acquired data into the ordinate of each node, and leave a blank if the node data is missing;

[0146] S24a: According to the formula f(x i-1 , x i+1 ) fills the missing data at the i-th node, x i is the data of the i-th node, and f(x) is the filling function logic.

[0147] In the step S24a, where k o , k′ o is the weight, which satisfies

[0148] Embodiment 8:

[0149] A remote information system operation and maintenance optimization scheduling method, comprising the following steps:

[0150] S1: System monitoring and data collection, through "sensor" configuration tools and automated scripts, real-time collection of telematics system indicators;

[0151] S2: Data analysis and prediction: Process and analyze the collected data, identify the trend of abnormal system operation or performance degradation, and establish a prediction model based on historical data and current status;

[0152] S3: Resource optimization scheduling, dynamically adjust resource allocation according to data analysis results to optimize system performance;

[0153] S4: Fault warning and response: Establish a fault warning mechanism. When the system detects an abnormal event, it generates an alarm in real time and classifies and notifies through a multi-level alarm mechanism.

[0154] S5: Remote operation and maintenance collaboration, by configuring the intranet operation and maintenance server and the remote operation and maintenance server, the remote operation and maintenance interface can be synchronized and communicated efficiently; the intranet operation and maintenance server is connected to the computer room server, and the remote operation and maintenance server is connected through the Internet. The signal conversion and isolation between the two are achieved through a one-way transmission VGA signal acquisition card.

[0155] In the S1 step, the configuration tools include Ansible and Puppet, the automation scripts include Jenkins and Rundeck, and the system indicators include CPU usage, memory usage, disk space, and network latency.

[0156] In the step S2, the formula of the prediction model is: Where P(F t ) represents the probability of failure at time t, i (t) represents the i-th eigenvalue at time t, w i Represents the weight of the i-th eigenvalue.

[0157] In the step S2, ΔP o =P o -P o-1 , P o is the value of the i-th eigenvalue at time o of the time series.

[0158] The step S3 includes the following steps:

[0159] S31: Divide tasks into multiple priority queues;

[0160] S32: Scheduling tasks based on their urgency and importance;

[0161] S33: Balance the execution time of long jobs and short jobs.

[0162] In the step S31, the priority determination formula is: Priority(J k ) indicates character J k The current priority, InitialPriority(J k ) represents task J k The initial priority, Penalty(J k , Q i ) represents task J k In the queue Q i The penalty value obtained while waiting.

[0163] In the step S2, data processing includes filling missing values ​​and removing duplicate values.

[0164] In the S2 step, the missing value filling includes the following steps:

[0165] S21a: Establish a two-dimensional coordinate system with the time axis as the horizontal axis and the data as the vertical axis;

[0166] S22a: Taking the data collection frequency as the period, select the corresponding node on the horizontal axis;

[0167] S23: Fill the acquired data into the ordinate of each node, and leave a blank if the node data is missing;

[0168] S24a: According to the formula f(x i-1 , x i+1 ) fills the missing data at the i-th node, x i is the data of the i-th node, and f(x) is the filling function logic.

[0169] In the step S24a, or where k o , k′ o is the weight, which satisfies

[0170] The duplicate value removal comprises the following steps:

[0171] S21b: remove all duplicate values ​​to form missing data;

[0172] S22b: Then perform data filling according to the missing value filling method.

[0173] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A remote information system operation and maintenance optimization scheduling method, characterized in that: The following steps are involved: S1: System monitoring and data collection, through "sensor" configuration tools and automated scripts, real-time collection of telematics system indicators; S2: Data analysis and prediction: Process and analyze the collected data, identify the trend of abnormal system operation or performance degradation, and establish a prediction model based on historical data and current status; S3: Resource optimization scheduling, dynamically adjust resource allocation according to data analysis results to optimize system performance; S4: Fault warning and response: Establish a fault warning mechanism. When the system detects an abnormal event, it generates an alarm in real time and classifies and notifies through a multi-level alarm mechanism. S5: Remote operation and maintenance collaboration, by configuring the intranet operation and maintenance server and the remote operation and maintenance server, the remote operation and maintenance interface can be synchronized and communicated efficiently; the intranet operation and maintenance server is connected to the computer room server, and the remote operation and maintenance server is connected through the Internet. The signal conversion and isolation between the two are achieved through a one-way transmission VGA signal acquisition card.

2. A remote information system operation and maintenance optimization scheduling method according to claim 1, characterized in that: In the S1 step, the configuration tools include Ansible and Puppet, the automation scripts include Jenkins and Rundeck, and the system indicators include CPU usage, memory usage, disk space, and network latency.

3. A remote information system operation and maintenance optimization scheduling method according to claim 1, characterized in that: In the step S2, the formula of the prediction model is: Where P(F t ) represents the probability of failure at time t, i (t) represents the i-th eigenvalue at time t, w i Represents the weight of the i-th eigenvalue.

4. A remote information system operation and maintenance optimization scheduling method according to claim 3, characterized in that: In the step S2, ΔP o =P o -P o-1 , P o is the value of the i-th eigenvalue at time o of the time series.

5. A remote information system operation and maintenance optimization scheduling method according to claim 1, characterized in that: The step S3 includes the following steps: S31: Divide tasks into multiple priority queues; S32: Scheduling tasks based on their urgency and importance; S33: Balance the execution time of long jobs and short jobs.

6. A remote information system operation and maintenance optimization scheduling method according to claim 5, characterized in that: In the step S31, the priority determination formula is: Priority(J k ) indicates character J k The current priority, InitialPriority(J k ) represents task J k The initial priority, Penalty(J k , Q i ) represents task J k In the queue Q i The penalty value obtained while waiting.

7. A remote information system operation and maintenance optimization scheduling method according to claim 1, characterized in that: In the step S2, data processing includes filling missing values ​​and removing duplicate values.

8. A remote information system operation and maintenance optimization scheduling method according to claim 7, characterized in that: In the S2 step, the missing value filling includes the following steps: S21a: Establish a two-dimensional coordinate system with the time axis as the horizontal axis and the data as the vertical axis; S22a: Taking the data collection frequency as the period, select the corresponding node on the horizontal axis; S23: Fill the acquired data into the ordinate of each node, and leave a blank if the node data is missing; S24a: According to the formula f(x i-1 , x i+1 ) fills the missing data at the i-th node, x i is the data of the i-th node, and f(x) is the filling function logic.

9. A remote information system operation and maintenance optimization scheduling method according to claim 8, characterized in that: In the step S24a, or where k o , k′ o is the weight, which satisfies 10. A remote information system operation and maintenance optimization scheduling method according to claim 9, characterized in that: The duplicate value removal comprises the following steps: S21b: remove all duplicate values ​​to form missing data; S22b: Then perform data filling according to the missing value filling method.

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