Intelligent data center operation and maintenance optimization method based on dynamic learning algorithm

Abstract

The invention discloses an intelligent data center operation and maintenance optimization method based on a dynamic learning algorithm, and relates to the technical field of data operation and maintenance optimization. The direction and basic content of the data obtained this time are input, and related data meeting requirements are subjected to arrangement visualization processing; by setting the operation and maintenance management platform, the algorithm integration center, the database and the data acquisition port, the platform can be integrated with existing operation and maintenance tools and systems, supports automatic execution of an optimization strategy, facilitates separate management of hardware, software and personnel, and achieves the purpose that after the system is started, as long as factors of the system are involved, the operation and maintenance efficiency is greatly improved. All the subsystems can be inquired and managed, operation and maintenance data in different fields can be conveniently optimized through access of the subsystems, operation and maintenance optimization data and corresponding analysis results are managed, visualized and stored, operation and maintenance optimization management can be achieved through internet cloud management and control, and the intelligent level of operation and maintenance optimization management is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data operation and maintenance optimization, and specifically to an intelligent data center operation and maintenance optimization method based on a dynamic learning algorithm. Background Art

[0002] A data center usually refers to network equipment used to transmit, accelerate, display, calculate, and store data information on the internet network infrastructure. With the widespread application of data centers, artificial intelligence, network security, etc. have also emerged one after another. With the increase in servers and data volume, the maintenance of data centers has also received more and more attention.

[0003] In the relevant technologies, the daily operation and maintenance of the data center is mainly reflected in the following contents: server delivery, server and computer room monitoring, server maintenance, and server life cycle management.

[0004] In actual use, a large amount of manpower is required to manage each link one by one, resulting in low overall operation and maintenance efficiency. At the same time, there are problems in personnel communication, hardware management, software management, and personnel management, which will also affect the operation and maintenance optimization management. The adaptability to the operation and maintenance optimization management of data in different fields is poor. Therefore, the present invention needs to design an intelligent data center operation and maintenance optimization method based on a dynamic learning algorithm to solve the above-mentioned problems. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent data center operation and maintenance optimization method based on a dynamic learning algorithm in order to solve the above problems, thereby solving the problems mentioned in the background technology.

[0006] In order to solve the above problems, the present invention provides a technical solution: An intelligent data center operation and maintenance optimization method based on a dynamic learning algorithm, the data center operation and maintenance optimization method comprising: S1. Input the direction and basic content of the data to be acquired this time, arrange and visualize the relevant data that meet the requirements, obtain data information from different sources in each subsystem, pre-process the collected raw data, remove noise data, and record the relevant information of the operation and maintenance data acquired this time; S2. Filter the algorithms required for this operation and maintenance optimization by searching by entering keywords, import data after entering keywords, calculate different algorithms separately, list the calculation and analysis results in a table, and arrange them in the order that best meets the requirements; S3. Record, query, and monitor in real time all the hardware equipment information required to be installed for the data center operation and maintenance optimization system, access operation and maintenance data and optimization plans from different industries, search for corresponding historical data based on the keywords that need to be optimized this time, use it as a reference, optimize on the corresponding historical data, and finally optimize the plan that has completed this optimization.

[0007] As a preferred technical solution, the operation and maintenance data related information in step S1 includes acquisition time, acquisition source, and acquisition staff.

[0008] As a preferred technical solution, in step S1, before inputting the direction and basic content of the data to be acquired this time, it is necessary to build a data center operation and maintenance optimization system, which is used to effectively solve the operation and maintenance optimization problem input this time by optimizing data processing and improving the generalization ability of the model.

[0009] As a preferred technical solution, the final optimization in step S3 includes cost optimization, equipment optimization, and energy efficiency optimization.

[0010] As a preferred technical solution, all the hardware equipment information in step S3 includes equipment installation location, equipment maintenance records, and equipment real-time operation data.

[0011] As a preferred technical solution, the data center operation and maintenance optimization system includes an operation and maintenance management platform, an algorithm integration center, a database and a data acquisition port. The output end of the data acquisition port is communicatively connected to the input end of the algorithm integration center, the output end of the algorithm integration center is communicatively connected to the input end of the database, and the output end of the database is communicatively connected to the input end of the operation and maintenance management platform.

[0012] As a preferred technical solution, the operation and maintenance management platform includes a hardware management unit, a software management unit and a personnel management unit; The hardware management unit is used to record, query and monitor in real time all the hardware equipment information required to be installed by the data center operation and maintenance optimization system, including equipment installation location, equipment maintenance records and equipment real-time operation data; The software management unit is used to regularly maintain the software installed in the data center operation and maintenance optimization system to reduce the intrusion of virus data and external unknown information; The personnel management unit is used to manage the scheduling, job rotation, salary management, and each operation and maintenance data optimization information of the staff.

[0013] As a preferred technical solution, the algorithm integration center includes an algorithm screening unit, an algorithm simulation unit and an algorithm database, and the algorithm screening unit and the algorithm simulation unit are respectively connected to the algorithm database in two-way communication.

[0014] The algorithm screening unit is used to screen the algorithm required for this operation and maintenance optimization by inputting keyword search. The algorithm can be a single algorithm or a combination of multiple algorithms; The algorithm simulation unit is used to import data after inputting keywords, calculate different algorithms separately, list the calculation and analysis results in a table format, and arrange them in the order that best meets the needs, so as to facilitate comparison by staff; The algorithm database is used to supplement, delete, and update different algorithms, including the content definition, basic formula, and actual cases of the algorithms. The algorithms include: Dynamic learning algorithms are used to dynamically adjust the learning strategies or model parameters of data center operation and maintenance data according to changes in the environment or data feedback, adapt to new information or changes, and gradually optimize their prediction or decision-making capabilities; Online learning algorithms are used to gradually receive data center operation and maintenance data and update the model without waiting until all data is collected. This is particularly useful for data sets that are large or cannot be fully stored. Adaptive learning algorithms are used to adjust their own data center operation and maintenance learning speed or learning method according to the external environment or the performance of the model. When the model finds that its error is large, it will adjust the learning rate to make the training process more stable. Incremental learning algorithms are used to allow the model to fine-tune or supplement the existing model when receiving new data center operation and maintenance data, which is very important in the case of limited resources, so that the model can be continuously improved without starting from scratch. Genetic algorithm is an optimization algorithm used to simulate the natural selection process. It continuously optimizes the solution to the problem through operations such as "crossover", "mutation" and "selection". Although the genetic algorithm itself does not completely belong to dynamic learning, it is often used to solve complex problems that require adaptive optimization. Natural language processing algorithms are used for text classification, machine translation, and speech recognition of new operation and maintenance data in the data. Dynamic learning algorithms can update models in real time based on new corpora and contextual information to improve the performance of models in changing data.

[0015] As a preferred technical solution, the database includes an integrated data center, a security control unit and a subsystem data access unit, and the output ends of the security control unit and the subsystem data access unit are both communicatively connected to the input end of the integrated data center; The integrated data center is used to comprehensively manage the data center operation and maintenance optimization system, query specific links, and finally optimize the solution for completing this optimization, including cost optimization, equipment optimization, and energy efficiency optimization; The security control unit is used to add differential privacy protection during data use to ensure that personal privacy is not leaked, and use data encryption and access control security technology to protect sensitive information; The subsystem data access unit is used to access operation and maintenance data and optimization solutions from different industries, search for corresponding historical data based on the keywords that need to be optimized this time, and optimize on the corresponding historical data as a reference.

[0016] As a preferred technical solution, the data acquisition port includes a human-computer interaction unit, a data acquisition unit and an instruction input unit, the output end of the instruction input unit is communicatively connected to the input end of the human-computer interaction unit, and the human-computer interaction unit and the data acquisition unit are bidirectionally communicatively connected; The human-computer interaction unit is used to improve the manual operation interface, manually set various command delivery keywords, generate a command optimization model, and record the process and results of each search, so as to discard irrelevant information in subsequent searches and conduct continuous learning; The operation and maintenance data acquisition unit is used to acquire data information from different sources in each subsystem, pre-process the collected raw data, remove noise data, fill in missing data, and perform standardization, summarize the same type, and record the relevant information of the operation and maintenance data acquired this time, including acquisition time, acquisition source, and acquisition staff; The instruction input unit is used to input the direction and basic content of the data to be acquired this time, and to arrange and visualize the relevant data that meets the requirements.

[0017] The beneficial effects of the present invention are as follows: the present invention constructs a complete data center operation and maintenance optimization system by setting up an operation and maintenance management platform, an algorithm integration center, a database and a data acquisition port. When in use, the direction and basic content of the data to be acquired this time are input, and the relevant data that meets the requirements are arranged and visualized, data information from different sources is acquired in each subsystem, the collected raw data is preprocessed, noise data is removed, and information related to the operation and maintenance data acquired this time is recorded. The algorithm required for this operation and maintenance optimization is screened by inputting a keyword search method, data is imported after the keyword is input, different algorithms are calculated separately, and the calculation and analysis results are listed in a table format, arranged in the order that best meets the requirements, and operation and maintenance data and optimization solutions from different industries are accessed. According to the keyword to be optimized this time, the corresponding historical data is searched for, and optimization is performed on the corresponding historical data as a reference. At the same time, the solution that completes this optimization is finally optimized, and a visual operation interface is provided to help operation and maintenance personnel monitor the status of the data center in real time, view prediction results and optimization suggestions, The platform can be integrated with existing operation and maintenance tools and systems to support automated execution of optimization strategies. It also facilitates the separate management of hardware, software, and personnel. After the system is enabled, any factors related to the system can be queried and managed. Through access to various subsystems, it is convenient to optimize the operation and maintenance data in different fields, manage, visualize, and store the operation and maintenance optimization data and corresponding analysis results, which helps to achieve operation and maintenance optimization management through Internet cloud control and improve the intelligence level of operation and maintenance optimization management. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] For ease of explanation, the present invention is described in detail with reference to the following specific implementations and the accompanying drawings.

[0019] FIG. 1 is a diagram showing the overall steps of the intelligent data center operation and maintenance optimization method based on a dynamic learning algorithm of the present invention.

[0020] FIG. 2 is a flowchart of the intelligent data center operation and maintenance optimization method based on a dynamic learning algorithm of the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0022] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings, and "multiple" generally includes at least two.

[0023] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0024] It should be understood that although the terms first, second, third, etc. may be used to describe in the embodiments of the present application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of the present application, the first may also be referred to as the second, and similarly, the second may also be referred to as the first.

[0025] As used herein, the words “if” and “if” may be interpreted as “when” or “when” or “in response to determining” or “in response to detecting”, depending on the context. Similarly, the phrases “if it is determined” or “if (stated condition or event) is detected” may be interpreted as “upon determination” or “in response to determining” or “upon detecting (stated condition or event)”, depending on the context. or “in response to detecting (a stated condition or event)”.

[0026] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such commodity or device. In the absence of further restrictions, the elements defined by the sentence "comprises a ..." do not exclude the presence of other identical elements in the commodity or device including the elements.

[0027] It should be particularly noted that any symbols and / or numbers in the specification that are not marked in the accompanying drawings are not drawing marks.

[0028] The optional embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0029] As shown in Figure 1 and Figure 2As shown, this specific implementation adopts the following technical solutions: An intelligent data center operation and maintenance optimization method based on a dynamic learning algorithm, the data center operation and maintenance optimization method comprising: S1. Construct a data center operation and maintenance optimization system, which is used to effectively solve the operation and maintenance optimization problem input this time by optimizing data processing and improving the generalization ability of the model. Input the direction and basic content of the data to be acquired this time, arrange and visualize the relevant data that meet the requirements, acquire data information from different sources in each subsystem, pre-process the collected raw data, remove noise data, and record the information related to the operation and maintenance data acquired this time. The information related to the operation and maintenance data includes the acquisition time, acquisition source, and acquisition staff; S2. Filter the algorithms required for this operation and maintenance optimization by searching by entering keywords, import data after entering keywords, calculate different algorithms separately, list the calculation and analysis results in a table, and arrange them in the order that best meets the requirements; S3. Real-time recording, querying, and monitoring of all hardware equipment information required to be installed by the data center operation and maintenance optimization system, access to operation and maintenance data and optimization solutions from different industries, search for corresponding historical data based on the keywords that need to be optimized this time, and optimize on the corresponding historical data as a reference, and finally optimize the solution that has completed this optimization. All hardware equipment information includes equipment installation location, equipment maintenance records, and equipment real-time operation data. The final optimization includes cost optimization, equipment optimization, and energy efficiency optimization; The data center operation and maintenance optimization system includes an operation and maintenance management platform, an algorithm integration center, a database and a data acquisition port, wherein the output end of the data acquisition port is communicatively connected to the input end of the algorithm integration center, the output end of the algorithm integration center is communicatively connected to the input end of the database, and the output end of the database is communicatively connected to the input end of the operation and maintenance management platform; The operation and maintenance management platform includes a hardware management unit, a software management unit and a personnel management unit; the hardware management unit is used to record, query and monitor in real time all the hardware equipment information required to be installed for the data center operation and maintenance optimization system, including equipment installation location, equipment maintenance records and equipment real-time operation data; the software management unit is used to regularly maintain the software installed inside the data center operation and maintenance optimization system to reduce the intrusion of virus data and external unknown information; the personnel management unit is used to dispatch, change positions, manage salaries and manage each operation and maintenance data optimization information of the staff; The algorithm integration center includes an algorithm screening unit, an algorithm simulation unit and an algorithm database, and the algorithm screening unit and the algorithm simulation unit are respectively connected to the algorithm database in a two-way communication manner; The algorithm screening unit is used to screen the algorithms required for this operation and maintenance optimization by inputting keywords for retrieval. The algorithm can be a single algorithm or a combination of multiple algorithms. The algorithm simulation unit is used to import data after inputting keywords, calculate different algorithms separately, list the calculation and analysis results in a table, and arrange them in the order that best meets the needs, so as to facilitate comparison by staff. The algorithm database is used to supplement, delete, and update different algorithms, including the content definition, basic formula, and actual cases of the algorithm. The algorithm includes: Dynamic learning algorithms are used to dynamically adjust the learning strategies or model parameters of data center operation and maintenance data according to changes in the environment or data feedback, adapt to new information or changes, and gradually optimize their prediction or decision-making capabilities; Online learning algorithms are used to gradually receive data center operation and maintenance data and update the model without waiting until all data is collected. This is particularly useful for data sets that are large or cannot be fully stored. Adaptive learning algorithm, which is used to adjust its own data center operation and maintenance learning speed or learning method according to the external environment or model performance. When the model finds that its error is large, it will adjust the learning rate to make the training process more stable; Incremental learning algorithms are used to allow the model to fine-tune or supplement the existing model without starting from scratch when receiving new data center operation and maintenance data. This is very important when resources are limited, and the model can be continuously improved without starting from scratch. Genetic algorithm is an optimization algorithm used to simulate the natural selection process. It continuously optimizes the solution to the problem through operations such as "crossover", "mutation" and "selection". Although the genetic algorithm itself does not completely belong to dynamic learning, it is often used to solve complex problems that require adaptive optimization. Natural language processing algorithms are used for text classification, machine translation, and speech recognition of new operation and maintenance data in the data. Dynamic learning algorithms can perform real-time model updates based on new corpora and context information to improve the performance of models in changing data. The database includes an integrated data center, a security control unit and a subsystem data access unit. The output ends of the security control unit and the subsystem data access unit are both communicatively connected to the input end of the integrated data center. The integrated data center is used to comprehensively manage the data center operation and maintenance optimization system, query specific links, and finally optimize the optimization plan, including cost optimization, equipment optimization, and energy efficiency optimization. The security control unit is used to add differential privacy protection during data use to ensure that personal privacy is not leaked, and use data encryption and access control security technology to protect sensitive information. The subsystem data access unit is used to access operation and maintenance data and optimization plans from different industries, search for corresponding historical data according to the keywords that need to be optimized this time, and optimize on the corresponding historical data as a reference. The data acquisition port includes a human-computer interaction unit, a data acquisition unit and an instruction input unit, wherein the output end of the instruction input unit is communicatively connected to the input end of the human-computer interaction unit, and the human-computer interaction unit and the data acquisition unit are bidirectionally communicatively connected; the human-computer interaction unit is used to improve the manual operation interface, manually set various instruction delivery keywords, and generate an instruction optimization model, record the process and results of each retrieval, so as to discard irrelevant information in subsequent retrievals and conduct continuous learning; the operation and maintenance data acquisition unit is used to obtain data information from different sources in each subsystem, pre-process the collected raw data, remove noise data, fill in missing data, and perform standardization processing, perform the same type of aggregation, and record the relevant information of the operation and maintenance data acquired this time, including the acquisition time, acquisition source, and acquisition staff; the instruction input unit is used to input the direction and basic content of the data acquired this time, and arrange and visualize the relevant data that meets the requirements. Example

[0030] When dynamic learning algorithms are connected to intelligent data center operation and maintenance optimization management: S1. The staff will check the required hardware and software equipment one by one, and start the intelligent data center operation and maintenance optimization system after checking that everything is correct; S11. The instruction input unit is used to input the direction and basic content of the data to be acquired, and to arrange and visualize the relevant data that meets the requirements; S12. The operation and maintenance data acquisition unit acquires data information from different sources in each subsystem, pre-processes the collected raw data, removes noise data, fills in missing data, and performs standardization, summarizes the same type, and records the operation and maintenance data acquisition related information, including acquisition time, acquisition source, and acquisition staff; S13. The human-computer interaction unit improves the manual operation interface, manually sets various command delivery keywords, generates a command optimization model, and records the process and results of each search, so as to discard irrelevant information during subsequent searches and continue to learn; S2. The algorithm database is used to supplement, delete, and update different algorithms, including the algorithm's content definition, basic formulas, and actual cases; S21. Dynamic learning algorithms are used to dynamically adjust the learning strategies or model parameters of data center operation and maintenance data according to changes in the environment or data feedback, adapt to new information or changes, and gradually optimize their prediction or decision-making capabilities; Online learning algorithms are used to gradually receive data center operation and maintenance data and update the model without waiting until all data is collected. This is particularly useful for data sets that are large or cannot be fully stored. Adaptive learning algorithm, which is used to adjust its own data center operation and maintenance learning speed or learning method according to the external environment or model performance. When the model finds that its error is large, it will adjust the learning rate to make the training process more stable; Incremental learning algorithms are used to allow the model to fine-tune or supplement the existing model without starting from scratch when receiving new data center operation and maintenance data. This is very important when resources are limited, and the model can be continuously improved without starting from scratch. Genetic algorithm is an optimization algorithm used to simulate the natural selection process. It continuously optimizes the solution to the problem through operations such as "crossover", "mutation" and "selection". Although the genetic algorithm itself does not completely belong to dynamic learning, it is often used to solve complex problems that require adaptive optimization. Natural language processing algorithms are used for text classification, machine translation, and speech recognition of new operation and maintenance data in the data. Dynamic learning algorithms can perform real-time model updates based on new corpora and context information to improve the performance of models in changing data. S22. The algorithm screening unit is used to screen the algorithm required for this operation and maintenance optimization by inputting a keyword search method. The algorithm may be a single algorithm or a combination of multiple algorithms; S23. The algorithm simulation unit imports data after inputting keywords, calculates different algorithms separately, lists the calculation and analysis results in a table format, and arranges them in the order that best meets the needs, so that the staff can compare them easily; S3. Comprehensive data center comprehensive management data center operation and maintenance optimization system, query the specific links, and finally optimize the optimization plan, including cost optimization, equipment optimization, and energy efficiency optimization; S31. The security control unit is used to add differential privacy protection during data use to ensure that personal privacy is not leaked, and use data encryption and access control security technology to protect sensitive information; S32. The subsystem data access unit is used to access operation and maintenance data and optimization solutions from different industries, search for corresponding historical data based on the keywords that need to be optimized this time, and optimize the corresponding historical data as a reference; S33. Subsystem includes: Environmental monitoring system, used to monitor environmental parameters within the operating range of each hardware device, such as temperature and humidity, air circulation, smoke, water immersion, etc., to provide real-time environmental data to avoid hardware failures caused by abnormal temperature or humidity; Infrastructure management system, used to better manage hardware and software infrastructure, including servers, storage devices, and network devices, provides real-time monitoring of device health status, helps operation and maintenance personnel to promptly detect hardware failures, and achieves remote control and fault handling of devices through automated management; Automated operation and maintenance platform, used to realize automated task scheduling, resource management and fault response, such as automatic configuration, automatic deployment, automatic repair, reduce manual operations, improve efficiency and accuracy, quickly respond to system failures, and optimize resource configuration and utilization; Asset management system, which is used to track and manage all hardware and software assets in the data center and provide asset lifecycle management; Other field subsystems are used to access subsystems in fields such as enterprise, finance, and environmental management, and update corresponding operation and maintenance data in a timely manner; S4. The hardware management unit is used to record, query and monitor all hardware equipment information required to be installed by the data center operation and maintenance optimization system in real time, including equipment installation location, equipment maintenance records and real-time equipment operation data; S41. The software management unit regularly maintains the software installed in the data center operation and maintenance optimization system to reduce the intrusion of virus data and external unknown information; S42. The personnel management unit manages the staff scheduling, job rotation, salary management, and each operation and maintenance data optimization information; S43. When the access field is an enterprise, the subsystem in Figure 1 is an enterprise management system, and the keywords are changed to enterprise-related keywords. When the database itself exists, the internal data can be arranged first. When the database itself does not have data in this field, a no data report will be generated, a log will be generated, and this process will be recorded to end the operation and maintenance optimization; When data in this field exists in the database, continue to process the data, select the corresponding algorithm, import the corresponding file, and continuously improve and learn the operation and maintenance optimization model by referring to historical data to achieve the final optimization effect and complete this process; Specifically: In actual applications, there are multiple data acquisition ports, which are respectively used in conjunction with the operation and maintenance management platform, the algorithm integration center, and the database. The multiple data acquisition ports are located in different geographical locations. The present invention constructs a complete data center operation and maintenance optimization system by setting up the operation and maintenance management platform, the algorithm integration center, the database and the data acquisition port. When using it, the direction and basic content of the data to be acquired this time are input, and the relevant data that meets the requirements are arranged and visualized. Data information from different sources is acquired in each subsystem, and the collected original data is pre-processed to remove noise data, and the relevant information of the operation and maintenance data acquired this time is recorded. The algorithm required for this operation and maintenance optimization is screened by inputting keyword retrieval, and data is imported after the keyword is input. Different algorithms are calculated separately, and the calculation and analysis results are listed in a table format and arranged in the order that best meets the needs. Operation and maintenance data and optimization solutions from different industries are accessed, and corresponding historical data are searched according to the keywords that need to be optimized this time. As a reference, optimization is performed on the corresponding historical data. At the same time, the optimization plan is finally optimized, and a visual operation interface is provided to help operation and maintenance personnel monitor the status of the data center in real time, view prediction results and optimization suggestions. The platform can be integrated with existing operation and maintenance tools and systems, support automatic execution of optimization strategies, and facilitate the separate management of hardware, software and personnel. After the system is enabled, as long as the system factors are involved, they can be queried and managed. Through the access of various subsystems, it is convenient to optimize the operation and maintenance data in different fields, manage, visualize and store the operation and maintenance optimization data and corresponding analysis results, which helps to realize operation and maintenance optimization management through Internet cloud control and improve the intelligence level of operation and maintenance optimization management. Those skilled in the art may appreciate that the modules and method steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware; Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution; Professional technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention; Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices, equipment and modules can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here; In several embodiments provided in this application, it should be understood that the disclosed devices, systems and methods can be implemented in other ways; The system embodiments described above are merely illustrative. For example, the division of the modules is merely a logical function division. There may be other division methods in actual implementation. For example, multiple modules or units may be combined or integrated into another device, or some features may be ignored or not executed. In another aspect, the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or equipment, which may be electrical, mechanical or other forms; The modules serving as the operation and maintenance management platform, the algorithm integration center, the database and the data acquisition port may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units; Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment; In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module; If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium; Based on such understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention; The aforementioned storage media include: U disk, mobile hard disk, read-only storage server, random access storage server, disk or CD and other media that can store program instructions; In addition, it should be noted that the combination of the technical features in this case is not limited to the combination described in the claims of this case or the combination described in the specific embodiments. All technical features described in this case can be freely combined or combined in any way unless there is a contradiction between them. It should be noted that the above examples are only specific embodiments of the present invention. Obviously, the present invention is not limited to the above examples, and there are many similar variations. All variations directly derived or associated from the contents disclosed in the present invention by those skilled in the art should fall within the protection scope of the present invention.

[0031] The above are only preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent data center operation and maintenance optimization method based on a dynamic learning algorithm, characterized in that: The data center operation and maintenance optimization method comprises: S1. Input the direction and basic content of the data to be acquired this time, arrange and visualize the relevant data that meet the requirements, obtain data information from different sources in each subsystem, pre-process the collected raw data, remove noise data, and record the relevant information of the operation and maintenance data acquired this time; S2. Filter the algorithms required for this operation and maintenance optimization by searching by entering keywords, import data after entering keywords, calculate different algorithms separately, list the calculation and analysis results in a table, and arrange them in the order that best meets the requirements; S3. Record, query, and monitor in real time all the hardware equipment information required to be installed for the data center operation and maintenance optimization system, access operation and maintenance data and optimization plans from different industries, search for corresponding historical data based on the keywords that need to be optimized this time, use it as a reference, optimize on the corresponding historical data, and finally optimize the plan that has completed this optimization.

2. The intelligent data center operation and maintenance optimization method based on dynamic learning algorithm according to claim 1 is characterized in that: The operation and maintenance data related information in step S1 includes acquisition time, acquisition source, and acquisition staff.

3. The intelligent data center operation and maintenance optimization method based on dynamic learning algorithm according to claim 1 is characterized in that: In step S1, before inputting the direction and basic content of the data to be acquired this time, it is necessary to build a data center operation and maintenance optimization system, which is used to effectively solve the operation and maintenance optimization problem input this time by optimizing data processing and improving the generalization ability of the model.

4. The intelligent data center operation and maintenance optimization method based on dynamic learning algorithm according to claim 3 is characterized in that: The final optimization in step S3 includes cost optimization, equipment optimization, and energy efficiency optimization.

5. The intelligent data center operation and maintenance optimization method based on dynamic learning algorithm according to claim 1 is characterized in that: The entire hardware equipment information in step S3 includes equipment installation location, equipment maintenance records, and equipment real-time operation data.

6. The intelligent data center operation and maintenance optimization method based on dynamic learning algorithm according to claim 3 is characterized by: The data center operation and maintenance optimization system includes an operation and maintenance management platform, an algorithm integration center, a database and a data acquisition port. The output end of the data acquisition port is communicatively connected to the input end of the algorithm integration center, the output end of the algorithm integration center is communicatively connected to the input end of the database, and the output end of the database is communicatively connected to the input end of the operation and maintenance management platform.

7. The intelligent data center operation and maintenance optimization method based on dynamic learning algorithm according to claim 6 is characterized in that: The operation and maintenance management platform includes a hardware management unit, a software management unit and a personnel management unit; The hardware management unit is used to record, query and monitor in real time all the hardware equipment information required to be installed by the data center operation and maintenance optimization system; The software management unit is used to regularly maintain the software installed in the data center operation and maintenance optimization system; The personnel management unit is used to manage the scheduling, job rotation, salary management, and each operation and maintenance data optimization information of the staff.

8. The intelligent data center operation and maintenance optimization method based on dynamic learning algorithm according to claim 6 is characterized in that: The algorithm integration center includes an algorithm screening unit, an algorithm simulation unit and an algorithm database, and the algorithm screening unit and the algorithm simulation unit are respectively connected to the algorithm database in a two-way communication manner; The algorithm screening unit is used to screen the algorithms required for this operation and maintenance optimization by inputting keywords for retrieval; The algorithm simulation unit is used to import data after inputting keywords and to simulate different algorithms. Calculate each row separately and list the calculation and analysis results in a table; The algorithm database is used to supplement, delete, and update different algorithms, including the content definition, basic formulas, and actual cases of the algorithms.

9. The intelligent data center operation and maintenance optimization method based on dynamic learning algorithm according to claim 6 is characterized in that: The database includes an integrated data center, a security control unit and a subsystem data access unit, and the output ends of the security control unit and the subsystem data access unit are both communicatively connected to the input end of the integrated data center; The integrated data center is used to comprehensively manage the data center operation and maintenance optimization system, query specific links, and finally optimize the solution to complete this optimization; The security control unit is used to add differential privacy protection during data use; The subsystem data access unit is used to access operation and maintenance data and optimization solutions from different industries, and search for corresponding historical data according to the keywords that need to be optimized this time.

10. The intelligent data center operation and maintenance optimization method based on dynamic learning algorithm according to claim 6 is characterized in that: The data acquisition port includes a human-computer interaction unit, a data acquisition unit and an instruction input unit, the output end of the instruction input unit is communicatively connected to the input end of the human-computer interaction unit, and the human-computer interaction unit and the data acquisition unit are bidirectionally communicatively connected; The human-computer interaction unit is used to improve the manual operation interface, manually set various instruction delivery keywords, and generate an instruction optimization model; The operation and maintenance data acquisition unit is used to acquire data information from different sources in each subsystem, pre-process the collected raw data, remove noise data, and fill in missing data; The instruction input unit is used to input the direction and basic content of the data to be acquired this time, and to arrange and visualize the relevant data that meets the requirements.

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