Information system integration technology service intelligent management and control platform and management and control method

Abstract

The invention discloses an information system integration technology service intelligent management and control platform and method, and relates to the technical field of information management and control, and the method comprises the steps: collecting real-time data and historical data of an information system through a sensor, carrying out the preprocessing, building an equipment fault prediction model and a system performance evaluation model through a machine learning algorithm, historical data are used for training the model, parameters of the model are dynamically adjusted, real-time data are input into the equipment fault prediction model, the model calculates the probability of equipment faults according to real-time features, the system performance evaluation model evaluates the current performance state of the system according to the real-time data, and finally a management and control platform interface is established. And the user is allowed to perform some operations on the interface. According to the method, the equipment fault probability can be calculated more accurately according to the real-time data, the reliability of the information system is greatly improved, the overall performance of the system can be effectively improved, and it is ensured that the information system runs in the optimal state.

Description

Technical Field

[0001] The present invention relates to the field of information management and control technology, and in particular to an information system integration technology service intelligent management and control platform and a management and control method. Background Art

[0002] With the rapid development of information technology, the application of information systems in various fields is becoming increasingly extensive and complex. Traditional information system management methods face many challenges. Information systems generate a large amount of real-time data and accumulate a massive amount of historical data. These data often lack effective integration and utilization. The data formats and standards of information systems of different departments within an enterprise or organization are inconsistent, making it difficult to manage data in a unified manner. In large enterprises, the information system data of the financial department focuses on capital flow, which is very different from the production process data focused on by the production department in terms of structure and purpose. When processing these data, traditional methods often only involve simple storage and basic statistical analysis, and fail to deeply explore the valuable information hidden in the data. Information systems rely on various hardware devices and software components to operate. Equipment failures can lead to serious problems such as system interruption and data loss. Traditional equipment failure management is mainly based on regular maintenance and post-maintenance. Although regular maintenance can prevent failures to a certain extent, there are problems of over-maintenance or under-maintenance. Post-maintenance is handled only after the failure occurs, which can cause a long system downtime and affect business continuity. In financial transaction systems, sudden server failures may cause transaction interruptions, causing huge losses to financial institutions and customers.

[0003] However, the common solutions currently available have many shortcomings, including: although there are some methods that attempt to predict equipment failures on existing management and control platforms, the accuracy is low; some rule-based fault prediction methods require manual setting of a large number of complex rules, and these rules are difficult to adapt to the dynamic changes in the equipment's operating status; existing system performance evaluation methods are relatively rough and cannot accurately reflect the system's true performance status; due to the lack of the ability to comprehensively consider multiple performance indicators and their interrelationships, the evaluation results often cannot accurately reflect the overall performance of the system. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above-mentioned problems existing in the existing information system integration technology service intelligent management and control platform and management and control method, the present invention is proposed.

[0006] Therefore, the purpose of the present invention is to provide an information system integrated technology service intelligent management and control platform and management method, which is suitable for solving the problems that the existing management and control platform has low accuracy in equipment failure prediction, requires manual setting of a large number of complex rules, and is difficult to adapt to the dynamic changes in equipment operating status. The existing system performance evaluation method is relatively rough and cannot accurately reflect the actual performance status of the system. The evaluation results often cannot accurately reflect the overall performance of the system.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In the first aspect, an embodiment of the present invention provides an intelligent management and control method for information system integrated technical services, which includes using sensors to collect real-time data and historical data of the information system and preprocessing them; using machine learning algorithms to build equipment failure prediction models and system performance evaluation models; inputting the real-time data into the equipment failure prediction model, and the model calculates the probability of equipment failure based on real-time characteristics; the system performance evaluation model evaluates the current performance status of the system based on the real-time data, and compares it with preset performance indicators; and establishing a management and control platform interface to allow users to perform some operations on the interface.

[0009] As a preferred solution of the intelligent management and control method of information system integration technical services described in the present invention, wherein: the real-time data includes system operating parameters, equipment status information and user operation records; the historical data includes operating status data, maintenance record data and configuration change data; the performance indicators include CPU utilization, memory utilization, disk I / O read and write speed and network bandwidth utilization; the management and control platform interface provides a device failure probability trend chart and a system performance indicator change chart; the operation includes setting security audit rules and adjusting model parameters.

[0010] As a preferred solution of the intelligent management and control method of information system integrated technical services described in the present invention, the specific steps of constructing the equipment failure prediction model and the system performance evaluation model are as follows: identifying the data sources related to equipment failure and system performance in the information system; using sensors to continuously collect equipment operation data; selecting a suitable machine learning algorithm according to data characteristics, equipment failure prediction requirements and performance evaluation requirements; initializing the structure and parameters of the model according to the selected algorithm; using historical data to train the equipment failure prediction model and the system performance evaluation model, and adjusting the parameters of the equipment failure prediction model and the system performance evaluation model according to the evaluation results; inputting real-time data into the trained equipment failure prediction model and the system performance evaluation model; the equipment failure prediction model and the system performance evaluation model calculate the probability of equipment failure and the performance status of the current system according to their internal algorithm logic based on the input real-time data.

[0011] As a preferred solution of the information system integration technology service intelligent management and control method of the present invention, the specific formula for constructing the equipment failure prediction model is:

[0012]

[0013] Among them, P H is the probability of equipment failure; n is the number of samples of historical data; m is the number of features contained in each historical data sample or real-time data; h ij is the jth eigenvalue in the i-th historical data sample; r j is the jth eigenvalue in the device data collected in real time.

[0014] As a preferred solution of the information system integration technology service intelligent management and control method of the present invention, the probability of the equipment failure prediction model is expressed as:

[0015] When P H When the value is close to 0, it means that the probability of equipment failure is extremely low, indicating that the current operating state of the equipment is highly similar to the normal operating state in history, and all parameters of the equipment are within the normal fluctuation range, without obvious deviation from the historical normal state;

[0016] When P H When the value is between 0.1 and 1, it indicates that the device has a certain risk of failure, indicating that there is a certain deviation between the real-time data and the average value of the historical data characteristics. At present, it does not strongly indicate that the device has failed, but it may be an early signal of failure or that the device has been slightly disturbed.

[0017] When P H When the value is greater than 1, it indicates that the probability of equipment failure is very high, indicating that there is a large deviation between the real-time data and the average value of the historical data characteristics, and that the key parameters of the equipment have serious abnormalities, indicating that the equipment has failed or is about to fail.

[0018] As a preferred solution of the information system integration technology service intelligent management and control method of the present invention, the specific formula for constructing the system performance evaluation model is:

[0019]

[0020] Among them, S d is the quantitative result of system performance evaluation; l is the number of system performance indicators; d i is the actual value of the i-th performance indicator of the system at that time; p i is the preset ideal value for the i-th performance indicator.

[0021] As a preferred solution of the information system integration technology service intelligent management and control method of the present invention, the quantitative result of the system performance evaluation model is expressed as:

[0022] When S d When the value is close to 0, it means that the real-time performance of the system is very close to the preset performance index, indicating that the current operating status of the system meets the preset requirements in all performance indicators;

[0023] When S d When the value is between 0 and 1, it means that the real-time performance of the system deviates from the preset performance index to a certain extent, but the degree of deviation is still within an acceptable range;

[0024] When S d When the value is greater than 1, it means that the real-time performance of the system deviates greatly from the preset performance indicators, the operating efficiency of the system will be significantly affected, and there will be problems such as response delays and service interruptions.

[0025] On the second aspect, in order to further solve the above-mentioned technical problems, the embodiments of the present invention provide an information system integrated technical service intelligent management and control platform, which includes: a data collection module, which is used to collect real-time data and historical data of the information system through sensors for preprocessing; a model building module, which is used to use machine learning algorithms to build equipment failure prediction models and system performance evaluation models; a fault prediction module, which is used to predict the probability of equipment failure through the equipment failure prediction model; a performance evaluation module, which is used to evaluate the current performance of the system in real time through the system performance evaluation model; an interface interaction module, which is used to allow users to perform some operations on the interface by establishing a management and control platform interface.

[0026] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of an information system integration technology service intelligent management and control method as described in the first aspect of the present invention.

[0027] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of an information system integration technology service intelligent management and control method as described in the first aspect of the present invention.

[0028] The beneficial effects of the present invention are as follows: the model constructed by the machine learning algorithm of the present invention utilizes historical data for training and dynamically adjusts parameters, and can more accurately calculate the probability of equipment failure based on real-time data. This has higher accuracy than traditional fault detection methods based on fixed thresholds or simple rules. Preventive measures can be taken by predicting equipment failures in advance, thereby avoiding information system interruptions caused by sudden equipment failures, greatly improving the reliability of the information system. The system performance evaluation model comprehensively evaluates the current performance status of the system based on real-time data and compares it with preset indicators. This comprehensive and dynamic evaluation method can timely discover performance bottlenecks. Compared with traditional single indicators or static evaluation methods, it can more accurately locate factors affecting system performance. By timely adjusting system configuration, optimizing algorithms, or reallocating resources, the overall performance of the system can be effectively improved to ensure that the information system runs in the optimal state. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0030] Figure 1 This is a flow chart for implementing the present invention in Example 1. DETAILED DESCRIPTION

[0031] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0032] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0033] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0034] Example 1

[0035] Reference Figure 1 , which is the first embodiment of the present invention, and provides an information system integration technology service intelligent management and control method, comprising the following steps:

[0036] S1: Use sensors to collect real-time and historical data of the information system and perform preprocessing.

[0037] Preferably, Figure 1 The figure shows the implementation process of the present invention. First, sensors are used to collect real-time data and historical data of the information system and pre-process them. Then, a machine learning algorithm is used to build an equipment failure prediction model and a system performance evaluation model. The real-time data is input into the equipment failure prediction model. The model calculates the probability of equipment failure based on real-time characteristics. The system performance evaluation model evaluates the current performance status of the system based on real-time data and compares it with preset performance indicators. Finally, a management and control platform interface is established to allow users to perform some operations on the interface.

[0038] Furthermore, real-time data includes system operating parameters, device status information, and user operation records.

[0039] Furthermore, historical data includes operating status data, maintenance record data, and configuration change data.

[0040] Specifically, preprocessing ensures the quality and consistency of data by preprocessing real-time data and historical data in real time at the data source, including data cleaning, noise reduction and standardization.

[0041] S2: Use machine learning algorithms to build equipment failure prediction models and system performance evaluation models.

[0042] Preferably, the specific steps of constructing the equipment failure prediction model and the system performance evaluation model are:

[0043] Identify data sources within the information system that are relevant to equipment failures and system performance;

[0044] Use sensors to continuously collect equipment operation data;

[0045] Select appropriate machine learning algorithms based on data characteristics, equipment failure prediction requirements, and performance evaluation needs;

[0046] Initialize the model structure and parameters according to the selected algorithm;

[0047] Use historical data to train the equipment failure prediction model and system performance evaluation model, and adjust the parameters of the equipment failure prediction model and system performance evaluation model according to the evaluation results;

[0048] Input real-time data into the trained equipment failure prediction model and system performance evaluation model;

[0049] The equipment failure prediction model and system performance evaluation model calculate the probability of equipment failure and the performance status of the current system based on the input real-time data and their internal algorithm logic.

[0050] Furthermore, the specific formula for constructing the equipment failure prediction model is:

[0051]

[0052] Among them, P H is the probability of equipment failure; n is the number of samples of historical data; m is the number of features contained in each historical data sample or real-time data; h ij is the jth eigenvalue in the i-th historical data sample; r j is the jth eigenvalue in the device data collected in real time.

[0053] Specifically, for the equipment failure prediction model, dynamically adjusting parameters can make it better adapt to changes in the information system over time. Through sufficient training of historical data, the model can learn more complex failure modes and improve the accuracy of prediction.

[0054] Specifically, for system performance evaluation models, in complex information systems, different software and hardware components influence each other, and dynamically adjusted models can more accurately evaluate the overall performance of the system.

[0055] S3: The real-time data is input into the equipment failure prediction model, and the model calculates the probability of equipment failure based on the real-time characteristics.

[0056] Preferably, the probability of the equipment failure prediction model is expressed as:

[0057] When P H When the value is close to 0, it means that the probability of equipment failure is extremely low, indicating that the current operating state of the equipment is highly similar to the normal operating state in history, and all parameters of the equipment are within the normal fluctuation range, without obvious deviation from the historical normal state;

[0058] When P H When the value is between 0.1 and 1, it indicates that the device has a certain risk of failure, indicating that there is a certain deviation between the real-time data and the average value of the historical data characteristics. At present, there is no strong indication that the device has failed, but it may be an early signal of failure or the device has been slightly disturbed. Further attention should be paid to the operating status of the device, and some simple inspections or monitoring are required;

[0059] When P HWhen the value is greater than 1, it indicates that the probability of equipment failure is very high, indicating that there is a large deviation between the real-time data and the average value of the historical data characteristics, and serious abnormalities have occurred in the key parameters of the equipment, indicating that the equipment has failed or is about to fail, and maintenance or other intervention measures need to be taken in time.

[0060] S4: The system performance evaluation model evaluates the current performance status of the system based on real-time data and compares it with the preset performance indicators.

[0061] Specifically, the specific formula for constructing the system performance evaluation model is:

[0062]

[0063] Among them, S d is the quantitative result of system performance evaluation; l is the number of system performance indicators; d i is the actual value of the i-th performance indicator of the system at that time; p i is the preset ideal value for the i-th performance indicator.

[0064] Preferably, the quantitative result of the system performance evaluation model is expressed as:

[0065] When S d When the value is close to 0, it means that the real-time performance of the system is very close to the preset performance index, indicating that the current operating status of the system meets the preset requirements in all performance indicators;

[0066] When S d When the value is between 0 and 1, it means that the real-time performance of the system deviates from the preset performance index to a certain extent, but the degree of deviation is still within an acceptable range. The system can still operate normally, but it is necessary to continue to pay attention to the changing trend of the performance index;

[0067] When S d When the value is greater than 1, it means that the real-time performance of the system deviates greatly from the preset performance indicators, the operating efficiency of the system will be significantly affected, and there will be problems such as response delays and service interruptions. The system needs to be adjusted or optimized.

[0068] S5: Establish a management and control platform interface to allow users to perform some operations on the interface.

[0069] Furthermore, the management and control platform interface provides equipment failure probability trend charts and system performance indicator change charts.

[0070] Further, operations include setting security audit rules and adjusting model parameters.

[0071] Specifically, the equipment failure probability trend chart and the system performance index change chart allow users to intuitively observe the operating trend of the system. Users can quickly identify the rising trend of failure probability or abnormal fluctuations in performance indicators, and take timely measures.

[0072] Specifically, users can set security audit rules on the platform interface to enhance the system's security management capabilities. The function of adjusting model parameters enables users to optimize the model according to actual conditions and improve the applicability of the model.

[0073] This embodiment also provides an information system integrated technical service intelligent management and control platform, including: a data collection module, which is used to collect real-time data and historical data of the information system through sensors for pre-processing; a model construction module, which is used to use machine learning algorithms to build equipment failure prediction models and system performance evaluation models; a fault prediction module, which is used to predict the probability of equipment failure through equipment failure prediction models; a performance evaluation module, which is used to evaluate the current performance of the system in real time through a system performance evaluation model; an interface interaction module, which is used to establish a management and control platform interface to allow users to perform some operations on the interface.

[0074] This embodiment also provides a computer device, which is suitable for a method for intelligent management and control of information system integrated technical services, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement a method for intelligent management and control of information system integrated technical services as proposed in the above embodiment.

[0075] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0076] The present embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for intelligent management and control of information system integrated technical services as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0077] In summary, the model constructed by the machine learning algorithm of the present invention uses historical data for training and dynamically adjusts parameters, and can more accurately calculate the probability of equipment failure based on real-time data. This has higher accuracy than traditional fault detection methods based on fixed thresholds or simple rules. Preventive measures can be taken by predicting equipment failures in advance, thereby avoiding information system interruptions caused by sudden equipment failures, greatly improving the reliability of the information system. The system performance evaluation model comprehensively evaluates the current performance status of the system based on real-time data and compares it with preset indicators. This comprehensive and dynamic evaluation method can promptly discover performance bottlenecks. Compared with traditional single indicators or static evaluation methods, it can more accurately locate factors that affect system performance. By timely adjusting system configuration, optimizing algorithms, or reallocating resources, the overall performance of the system can be effectively improved to ensure that the information system runs in the optimal state.

[0078] Example 2

[0079] Referring to Tables 1 to 3, the second embodiment of the present invention is shown. This embodiment is different from the first embodiment in that, in order to verify its beneficial effects, operating data and related instructions of the present invention in an actual environment are provided.

[0080] Tables 1 to 3 show the comparison between the existing technology and our technology in this example in terms of the technical characteristics of information system management and control, different stages of equipment fault handling, and different times of system performance evaluation.

[0081] Table 1 Comparison of technical characteristics of information system management and control

[0082] Compare Projects Prior art Our invention Data Collection Some hardware data Various data (operation, equipment, user, history, etc.) Failure prediction No, no probability Able to calculate, accuracy rate 85%+ Performance Evaluation Simple statistics of some indicators Comprehensive multi-data evaluation and classification Management and control platform Single function With trend chart and operation function

[0083] Table 2 Comparison of different stages of equipment fault handling

[0084] Time interval Fault handling of existing technical equipment Our invention equipment failure handling situation normal No prediction, no action Probabilistic monitoring Potential Failures Undetectable Exceeding threshold triggers audit Failure occurs Post-maintenance, long downtime Prepare in advance to reduce downtime

[0085] Table 3 Comparison of system performance evaluation at different times

[0086] Evaluation Moment Results of the performance evaluation of existing technologies Results of performance evaluation of our invention peak Some indicators exceed the threshold Comprehensive evaluation performance level smooth Look at the basic indicators Look at the fluctuations and optimize in advance

[0087] It can be seen from the above table that the present invention can comprehensively collect various types of data related to the operation of the information system, can calculate the probability of equipment failure, and can comprehensively evaluate the system performance status based on various data.

[0088] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An information system integration technology service intelligent management and control method, characterized by: include: Use sensors to collect real-time and historical data from information systems and perform preprocessing; Use machine learning algorithms to build equipment failure prediction models and system performance evaluation models; The real-time data is input into a device failure prediction model, and the model calculates the probability of device failure based on the real-time characteristics; The system performance evaluation model evaluates the current performance status of the information system based on real-time data and compares it with preset performance indicators; A management and control platform interface is established to allow users to perform some operations on the management and control interface.

2. The information system integration technology service intelligent management and control method according to claim 1, characterized in that: The real-time data includes system operation parameters, device status information and user operation records; The historical data includes operating status data, maintenance record data, and configuration change data; The performance indicators include CPU usage, memory usage, disk I / O read / write speed, and network bandwidth utilization; The control platform interface provides a device failure probability trend chart and a system performance indicator change chart; The operations include setting security audit rules and adjusting model parameters.

3. The information system integration technology service intelligent management and control method according to claim 2, characterized in that: The specific steps of constructing the equipment failure prediction model and the system performance evaluation model are as follows: Identify data sources within the information system that are relevant to equipment failures and system performance; Use sensors to continuously collect equipment operation data; Select appropriate machine learning algorithms based on data characteristics, equipment failure prediction requirements, and performance evaluation needs; Initialize the model structure and parameters according to the selected algorithm; Use historical data to train the equipment failure prediction model and system performance evaluation model, and adjust the parameters of the equipment failure prediction model and system performance evaluation model according to the evaluation results; Input real-time data into the trained equipment failure prediction model and system performance evaluation model; The equipment failure prediction model and system performance evaluation model calculate the probability of equipment failure and the performance status of the current system according to the input real-time data and their internal algorithm logic.

4. The information system integration technology service intelligent management and control method according to claim 3, characterized in that: The specific formula for constructing the equipment failure prediction model is: Among them, P H is the probability of equipment failure; n is the number of samples of historical data; m is the number of features contained in each historical data sample or real-time data; h ij is the jth eigenvalue in the i-th historical data sample; r j is the jth eigenvalue in the device data collected in real time.

5. The information system integration technology service intelligent management and control method according to claim 4, characterized in that: The probability of the equipment failure prediction model is expressed as: When P H When the value is close to 0, it means that the probability of equipment failure is extremely low, indicating that the current operating state of the equipment is highly similar to the normal operating state in history, and all parameters of the equipment are within the normal fluctuation range, without obvious deviation from the historical normal state; When P H When the value is between 0.1 and 1, it indicates that the device has a certain risk of failure, indicating that there is a certain deviation between the real-time data and the average value of the historical data characteristics. At present, it does not strongly indicate that the device has failed, but it may be an early signal of failure or that the device has been slightly disturbed. When P H When the value is greater than 1, it indicates that the probability of equipment failure is very high, indicating that there is a large deviation between the real-time data and the average value of the historical data characteristics, and that the key parameters of the equipment have serious abnormalities, indicating that the equipment has failed or is about to fail.

6. The information system integration technology service intelligent management and control method according to claim 5, characterized in that: The specific formula for constructing the system performance evaluation model is: Among them, S d is the quantitative result of system performance evaluation; l is the number of system performance indicators; d i is the actual value of the i-th performance indicator of the system at that time; p i is the preset ideal value for the i-th performance indicator.

7. The information system integration technology service intelligent management and control method according to claim 6, characterized in that: The quantitative results of the system performance evaluation model are expressed as: When S d When the value is close to 0, it means that the real-time performance of the system is very close to the preset performance index, indicating that the current operating status of the system meets the preset requirements in all performance indicators; When S d When the value is between 0 and 1, it means that the real-time performance of the system deviates from the preset performance index to a certain extent, but the degree of deviation is still within an acceptable range; When S d When the value is greater than 1, it means that the real-time performance of the system deviates greatly from the preset performance indicators, the operating efficiency of the system will be significantly affected, and there will be response delays or service interruptions.

8. An information system integration technology service intelligent management and control platform, based on an information system integration technology service intelligent management and control method according to any one of claims 1 to 7, characterized in that: include, A data collection module is used to collect real-time data and historical data of the information system through sensors for pre-processing; Model building module, used to build equipment failure prediction model and system performance evaluation model using machine learning algorithms; A fault prediction module is used to predict the probability of equipment failure through an equipment fault prediction model; The performance evaluation module is used to evaluate the current performance of the system in real time through the system performance evaluation model; The interface interaction module is used to establish a management and control platform interface to allow users to perform some operations on the interface.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the information system integration technology service intelligent management and control method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of an information system integration technology service intelligent management and control method as described in any one of claims 1 to 7 are implemented.

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