Management method and system for integrated intelligent machine room

Abstract

The invention provides a management method and system for an integrated intelligent machine room, and the method comprises the steps: obtaining a key factor recognition result through a state information flow from a distributed sensor network, constructing a dynamic risk assessment model to generate a region prediction report, obtaining a digital twin environment according to the region prediction report, and carrying out the recognition of a key factor. The method comprises the following steps: combining a security policy library and an adaptive learning algorithm, generating an optimal adjustment instruction, defining a network controller by using software, generating an intelligent regulation and control record, performing field operation guidance by using a virtual reality guidance tool, obtaining a complete operation guidance log, generating an operation record chain through an intelligent contract mechanism, and based on the operation record chain, obtaining a complete operation guidance log. Generating an optimal candidate case library, and confirming and learning an optimal candidate case according to the operation record chain and the optimal candidate case library in combination with a swarm intelligence algorithm to obtain an intelligent machine room alliance network; according to the technical scheme provided by the invention, the error risk caused by manual operation is reduced, and the capability of managing the global state of the machine room is improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of integrated intelligent computer rooms, and in particular to a management method for an integrated intelligent computer room. Background Art

[0002] With the rapid development of information technology, as the core facility for data storage and processing, the management complexity and requirements of intelligent computer rooms are also increasing day by day; Existing intelligent computer room management solutions have multiple limitations. There is a lack of effective real-time data analysis means, resulting in untimely identification of key factors, which affects the accuracy of risk assessment and the timeliness of early warning. Moreover, the current emergency response plans are often based on fixed rules, lacking flexibility and adaptability, and cannot be optimized and adjusted according to the actual situation. Traditional methods are not precise enough when generating operation instructions, which may lead to resource waste or operation errors. Finally, the existing maintenance task assignment, resource scheduling, and permission management are mostly manual operations, with low efficiency and prone to errors, lacking automation and intelligent support. These defects limit the overall performance and reliability of the intelligent computer room management system. Summary of the Invention

[0003] The embodiments of the present invention provide a management method and system for an integrated intelligent computer room to solve the problems in the prior art, such as lacking flexibility and adaptability, not being able to be optimized and adjusted according to the actual situation, resulting in resource waste or operation errors, low efficiency and being prone to errors, and lacking automation and intelligent support.

[0004] In a first aspect, the embodiments of the present invention provide a management method for an integrated intelligent computer room, including: Based on the status information flow from a distributed sensor network, obtaining the key factor identification result, constructing a dynamic risk assessment model based on the key factor identification result, generating a regional prediction report, and generating a multi-dimensional digital twin mapping model integrating physical layout, logical architecture, and time series dimensions according to the regional prediction report to obtain a digital twin environment; Based on the digital twin environment, combining a preset security policy library and an adaptive learning algorithm, simulating emergency response plans in different scenarios, selecting a target emergency response plan as an intervention measure, and optimizing the intervention measure to generate an optimal adjustment instruction; Using a software-defined network controller, sending an operation instruction to a target execution unit according to the optimal adjustment instruction, generating an intelligent regulation record, and at the same time using a virtual reality guidance tool for on-site operation guidance and recording all operation guidance processes to obtain a complete operation guidance log; Based on the intelligent regulation records and complete operation guidance logs, through the smart contract mechanism, automate the processing of maintenance task allocation, resource scheduling, and permission management to generate an operation record chain. Based on the operation record chain, generate an optimal candidate case library; According to the operation record chain and the optimal candidate case library, combined with the swarm intelligence algorithm, through comparison and optimization, confirm and learn the optimal candidate cases to obtain the intelligent computer room alliance network.

[0005] Optionally, based on the intelligent regulation records and complete operation guidance logs, through the smart contract mechanism, automate the processing of maintenance task allocation, resource scheduling, and permission management to generate an operation record chain. Based on the operation record chain, generate an optimal candidate case library, including: Based on the intelligent regulation records and complete operation guidance logs, use the zero-knowledge proof protocol combined with the hash chain technology for integration processing to obtain a blockchain ledger; According to the blockchain ledger, through the smart contract mechanism and graph algorithm optimization processing, generate an operation record chain; Use the operation record chain, combined with the time series analysis algorithm for effect evaluation, generate best practice cases, and merge the best practice cases to obtain a preliminary candidate case library; Based on the preliminary candidate case library, apply data mining algorithms and association rule learning algorithms for in-depth analysis, and use the Bayesian optimization algorithm for optimization processing to generate an optimal candidate case library.

[0006] Optionally, based on the preliminary candidate case library, apply data mining algorithms and association rule learning algorithms for in-depth analysis, and use the Bayesian optimization algorithm for optimization processing to generate an optimal candidate case library, including: Based on the preliminary candidate case library, use data mining algorithms and association rule learning algorithms to analyze the best practice cases, extract key operation patterns and success factors to obtain common characteristics; According to the common characteristics, use the Bayesian optimization algorithm combined with the random forest model for optimization processing to generate an optimized candidate case set; Use the support vector machine algorithm to classify the candidate cases in the optimized candidate case set to obtain a classification result, and use the K-means clustering algorithm to refine the classification result to obtain the target optimization category; Use the target optimization category to construct a multi-layer evaluation framework and integrate multiple evaluation indicators. Based on the analytic hierarchy process, calculate the weights of each evaluation indicator to evaluate the candidate cases in each target optimization category, and select the best candidate cases to generate the best candidate case library. The evaluation indicators include: cost-benefit ratio, response speed, and resource utilization rate.

[0007] Optionally, based on the preliminary candidate case library, use data mining algorithms and association rule learning algorithms to analyze the best practice cases, extract key operation patterns and success factors, and obtain common features, including: Based on the preliminary candidate case library, preprocess the data of each best practice case, remove redundant information and standardize the data format to obtain a standardized best practice case dataset; Use frequent pattern mining techniques to perform pattern recognition on the standardized best practice case dataset, extract the key operation patterns therein, and generate an operation pattern set; Combine the Apriori algorithm to perform in-depth association analysis on the operation pattern set, identify the success factors in the operation instances, and obtain the initial common features; According to the initial common features, construct a high-dimensional feature vector space, and introduce the principal component analysis method to map each best practice case into the high-dimensional feature vector space to obtain the target common features.

[0008] Optionally, based on the state information flow from the distributed sensing network, obtain the key factor identification result, construct a dynamic risk assessment model based on the key factor identification result, generate a regional prediction report, and generate a multi-dimensional digital twin mapping model integrating the physical layout, logical architecture, and time series dimensions to obtain a digital twin environment, including: Based on the state information flow from the distributed sensing network, perform real-time parsing and processing on the state information flow, use data mining and machine learning algorithms to extract and identify key factors, and obtain the key factor identification result. The key factors include: temperature, humidity, power consumption, and equipment operating status; According to the key factor identification result, use edge computing nodes to deeply analyze the key factors in combination with historical data and preset risk assessment rules, construct a dynamic risk assessment model, and the dynamic risk assessment model is used to predict possible risk points and the influence range of the risk points, and generate a regional prediction report; Based on the regional prediction report, integrate the physical layout, logical architecture, and time series dimensions to construct a multi-dimensional digital twin mapping model; According to the multi-dimensional digital twin mapping model, provide a visual interface through augmented reality technology to obtain a digital twin environment.

[0009] Optionally, use a software-defined network controller to send operation instructions to the target execution unit according to the optimal adjustment instruction, generate an intelligent regulation record, and at the same time use a virtual reality guidance tool to conduct on-site operation guidance and record all operation guidance processes to obtain a complete operation guidance log, including: Based on the optimal adjustment instruction, use the software-defined network controller to parse and transform the operation instruction, and generate specific control commands for different target execution units; According to the specific control commands, send the operation instructions to each target execution unit through the automation control system, realize the real-time adjustment of the environmental control devices, security systems, and IT equipment in the computer room, and generate real-time intelligent regulation records; Based on the real-time intelligent regulation records, use the virtual reality guidance tool to provide an immersive operation guidance interface for remote operation and maintenance personnel, support the operation and maintenance personnel to conduct on-site operation guidance through the virtual environment, and record all interaction data during the remote guidance process to obtain a complete operation guidance log; Combined with augmented reality technology, according to the complete operation guidance log, overlay virtual guidance information in the actual environment to assist on-site operation and maintenance personnel in understanding the operation process, make the operation guidance accurate, and incorporate key operation points and exception handling methods during the operation guidance process into the complete operation guidance log.

[0010] Optionally, according to the operation record chain and the best candidate case library, combined with the swarm intelligence algorithm, through comparison and optimization, confirm and learn the best candidate cases to obtain an intelligent computer room alliance network, including: Based on the operation record chain and the candidate case library, use the swarm intelligence algorithm such as particle swarm optimization to perform preliminary screening and classification processing on the candidate cases to obtain a set of high-potential cases; According to the set of high-potential cases, construct a distributed verification framework, so that multiple intelligent computer rooms located in different geographical locations can share and verify high-potential cases with each other through a peer-to-peer network to generate local verification results; Utilize the local verification results, combined with the federated learning mechanism, to train a shared model. The shared model integrates the feedback adjustment of each intelligent computer room and the selection criteria for optimizing the best candidate cases to generate an optimized set of best candidate cases; Based on the optimized set of best candidate cases, construct an initial intelligent computer room alliance network, and apply the reinforcement learning algorithm to dynamically update the policies of each node in the initial intelligent computer room alliance network to select the target operation plan; According to the target operation plan, construct a multi-layer evaluation system, integrate multiple evaluation indicators, use the analytic hierarchy process to determine the weights of each evaluation indicator, evaluate each best candidate case to obtain an evaluation result, and optimize the initial intelligent computer room alliance network based on the evaluation result to obtain the target intelligent computer room alliance network.

[0011] In a second aspect, an embodiment of the present invention provides a management system for an integrated intelligent computer room, including: A construction module for obtaining key factor identification results based on the state information flow from a distributed sensing network, constructing a dynamic risk assessment model based on the key factor identification results, generating a regional prediction report, and generating a multi-dimensional digital twin mapping model integrating physical layout, logical architecture, and time series dimensions according to the regional prediction report to obtain a digital twin environment; A simulation module for simulating emergency response plans in different scenarios based on the digital twin environment, combining a preset security policy library and an adaptive learning algorithm, selecting a target emergency response plan as an intervention measure, and optimizing the intervention measure to generate an optimal adjustment instruction; A recording module for using a software-defined network controller to send operation instructions to a target execution unit according to the optimal adjustment instruction to generate an intelligent regulation record, and at the same time using a virtual reality guidance tool for on-site operation guidance and recording all operation guidance processes to obtain a complete operation guidance log; A generation module for automatically processing maintenance task allocation, resource scheduling, and permission management through an intelligent contract mechanism based on the intelligent regulation record and the complete operation guidance log to generate an operation record chain, and generating an optimal candidate case library based on the operation record chain; A confirmation module for confirming and learning the optimal candidate cases through comparison and optimization based on the operation record chain and the optimal candidate case library, combining a swarm intelligence algorithm, to obtain an intelligent computer room alliance network.

[0012] In a third aspect, an embodiment of the present invention provides a computing device, including a processor and a memory, where a computer program is stored in the memory, and the processor is configured to run the computer program to execute the management of an integrated intelligent computer room according to any one of the first aspects.

[0013] In a fourth aspect, an embodiment of the present invention provides a computer storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the management of an integrated intelligent computer room according to any one of the first aspects is implemented.

[0014] In an embodiment of the present invention, based on the state information flow from a distributed sensing network, a key factor identification result is obtained. Based on the key factor identification result, a dynamic risk assessment model is constructed, and a regional prediction report is generated. According to the regional prediction report, a multi-dimensional digital twin mapping model integrating physical layout, logical architecture, and time series dimensions is generated to obtain a digital twin environment. Based on the digital twin environment, combined with a preset security policy library and an adaptive learning algorithm, emergency response scenarios in different situations are simulated, a target emergency response scenario is selected as an intervention measure, and the intervention measure is optimized to generate an optimal adjustment instruction. Using a software-defined network controller, according to the optimal adjustment instruction, an operation instruction is sent to a target execution unit to generate an intelligent regulation record. At the same time, a virtual reality guidance tool is used for on-site operation guidance, and all operation guidance processes are recorded to obtain a complete operation guidance log. Based on the intelligent regulation record and the complete operation guidance log, through an intelligent contract mechanism, maintenance task allocation, resource scheduling, and permission management are automatically processed to generate an operation record chain. Based on the operation record chain, an optimal candidate case library is generated. According to the operation record chain and the optimal candidate case library, combined with a swarm intelligence algorithm, through comparison and optimization, the optimal candidate cases are confirmed and learned to obtain an intelligent computer room alliance network; The technical solution provided by the present invention improves the timeliness of risk warning, reduces the risk of faults occurring, enhances the understanding and control ability of management personnel for the overall state of the computer room, supports more efficient management and decision-making, reduces the risk of errors caused by manual operations, ensures the improvement space in the long-term operation of the system, and realizes the continuous improvement of management level; Furthermore, preprocess the data of each best practice case in the preliminary candidate case library to remove redundant information and standardize the data format, obtaining a standardized best practice case dataset. This step ensures the quality of the basic data for subsequent analysis, improving the reliability and accuracy of the analysis results. Use frequent pattern mining technology to perform pattern recognition on the standardized best practice case dataset, extract the key operation patterns therein, and generate an operation pattern set. Through this method, recurring and effective operation patterns can be identified, providing an important basis for subsequent analysis. Combine the Apriori algorithm to conduct in-depth association analysis on the operation pattern set, identify the success factors in the operation instances, and obtain the initial common features. This association analysis helps discover the internal connections between operation patterns and reveals the successful operation rules. According to the initial common features, construct a high-dimensional feature vector space and introduce the principal component analysis (PCA) method to map each best practice case into this space to obtain the target common features. Through dimensionality reduction processing, not only is the computational efficiency improved, but also the most important feature information is retained, enhancing the expressiveness of the model. The finally obtained target common features make the classification of best practice cases more accurate, facilitating subsequent comparison and optimization, ensuring that the selected cases are highly representative, and providing a solid foundation for the learning and application of the intelligent computer room alliance network. These aspects or other aspects of the present invention will be more clearly understood in the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0016] Figure 1 It is a flowchart of a management method for an integrated intelligent computer room provided by an embodiment of the present invention; Figure 2 It is a schematic structural diagram of a management system for an integrated intelligent computer room provided by an embodiment of the present invention; Figure 3 It is a schematic structural diagram of a computing device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] In order to enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention.

[0018] In some processes described in the specification, claims and above-mentioned drawings of the present invention, a plurality of operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear herein or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit that "first" and "second" are of different types.

[0019] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention.

[0020] Figure 1 The following is a flowchart of a management method for an integrated intelligent computer room provided for an embodiment of the present invention. As Figure 1 shown, the method includes: Step 101: Based on the state information flow from the distributed sensing network, obtain the key factor identification result, construct a dynamic risk assessment model based on the key factor identification result, generate a regional prediction report, and generate a multi-dimensional digital twin mapping model integrating the physical layout, logical architecture, and time series dimension according to the regional prediction report to obtain a digital twin environment; In this step, the distributed sensing network refers to a sensor network deployed everywhere in the computer room, which is used to collect data such as temperature, humidity, power consumption, and equipment status in real time. These data constitute the state information flow, which is the basis for subsequent analysis. The key factor identification result is obtained by analyzing the state information flow and using machine learning algorithms to extract the key parameters affecting the operation of the computer room. The dynamic risk assessment model is based on these key factors, combined with historical data and preset rules, to predict possible future risk points and their influence ranges. The regional prediction report is a specific presentation of these risk assessment results. The multi-dimensional digital twin mapping model integrates the physical layout (such as equipment location), logical architecture (such as network topology), and time series dimension (such as historical operation records) of the computer room, and provides an intuitive visualization interface through augmented reality technology to form a digital twin environment; First, obtain the real-time status information flow from the distributed sensing network, parse and process it, and identify the key factors. Then, use the edge computing nodes to construct a dynamic risk assessment model by combining historical data and preset risk assessment rules, and generate a detailed regional prediction report. Finally, based on this report, generate a multi-dimensional digital twin mapping model that integrates physical layout, logical architecture, and time series dimensions, providing a comprehensive and intuitive digital twin environment for managers to monitor and make decisions in real time; Suppose, in a circuit breaker control system based on wireless communication, sensors installed on each circuit breaker continuously send status information such as current and voltage to the central control unit. The central control unit parses this information and identifies the key factors that may cause overload or short circuit. Then, use these key factors to construct a dynamic risk assessment model, predict potential fault points, and generate a regional prediction report. According to the report, the system generates a multi-dimensional digital twin mapping model to display the structure and real-time status of the entire power system, helping maintenance personnel to detect and prevent potential problems in a timely manner.

[0021] Step 102: Based on the digital twin environment, combine the preset security policy library and the adaptive learning algorithm to simulate emergency response plans in different scenarios, select the target emergency response plan as the intervention measure, and optimize the intervention measure to generate the optimal adjustment instruction; In this step, the digital twin environment is a virtualized computer room model that integrates physical layout, logical architecture, and time series dimensions, and is used to simulate and display the real state of the computer room. The security policy library is a series of predefined operating procedures and emergency response plans, aiming to ensure the safe operation of the computer room in various situations. The adaptive learning algorithm is a machine learning method that can automatically adjust and optimize strategies according to the actual situation. The emergency response plan is a series of measures taken in the event of an emergency, including but not limited to shutting down certain devices, adjusting load distribution, etc. The optimal adjustment instruction refers to the operation command that is most matched to the current computer room state and operation goal after optimization; In the digital twin environment, combine the preset security policy library and the adaptive learning algorithm to simulate the emergency response plans to be taken in various possible scenarios. By evaluating and optimizing these plans, select the plan that is most suitable for the current computer room state and operation goal as the intervention measure. The finally generated optimal adjustment instruction will guide the actual operation to ensure the effectiveness and timeliness of the emergency response; In the aforementioned circuit breaker control system, when an abnormal increase in current in a certain area is detected, the system enters the digital twin environment for simulation. Combine the preset plan in the security policy library and the adaptive learning algorithm to simulate the effects of different circuit breaker switch combinations, select a plan that can protect the circuit without affecting the overall power supply as the intervention measure, generate the optimal adjustment instruction, and instruct a specific circuit breaker to act immediately to avoid the expansion of potential faults.

[0022] Step 103: Using the software-defined network controller, send operation instructions to the target execution unit according to the optimal adjustment instruction, generate an intelligent regulation record, and at the same time use the virtual reality guidance tool for on-site operation guidance and record all operation guidance processes to obtain a complete operation guidance log; In this step, the software-defined network (SDN) controller is the core component for centralized management and configuration of network resources, capable of precisely controlling each node in the network according to the optimal adjustment instruction. The target execution unit refers to the device or system that specifically executes the operation instruction, such as an air conditioning system, a power supply device, etc. The intelligent regulation record is a detailed record after each execution of the operation instruction, including the time, content, and result of the operation. The virtual reality guidance tool is a technical means to assist maintenance personnel in remote operation, providing an immersive operation guidance interface. The complete operation guidance log is a detailed record of all remote guidance processes, used for subsequent review and training; According to the generated optimal adjustment instruction, the SDN controller sends operation instructions to the target execution unit to achieve intelligent regulation of the air conditioning system, power supply, network connection, and server load. At the same time, use the virtual reality guidance tool to provide immersive operation guidance for on-site maintenance personnel and record all operation guidance processes to ensure that every operation is traceable and generate a complete operation guidance log; Continuing with the example of the circuit breaker control system above, once the system generates the optimal adjustment instruction, the SDN controller immediately sends the instruction to the designated circuit breaker execution unit to ensure its rapid response. At the same time, technicians receive detailed remote operation guidance through the virtual reality guidance tool to ensure that each step is accurately executed according to the optimal adjustment instruction. All operation processes are completely recorded to form an intelligent regulation record and a complete operation guidance log, which are convenient for subsequent review and experience summary.

[0023] Step 104: Based on the intelligent regulation record and the complete operation guidance log, automate the processing of maintenance task allocation, resource scheduling, and permission management through the smart contract mechanism to generate an operation record chain, and based on the operation record chain, generate an optimal candidate case library; In this step, the intelligent regulation record and the complete operation guidance log are detailed records of each operation, including the time, content, and result of the operation. A smart contract is an automatically executed contract clause that can trigger corresponding operations automatically when conditions are met. Maintenance task allocation, resource scheduling, and permission management refer to the automatic arrangement of maintenance work, allocation of resources, and management of operation permissions under the smart contract mechanism. The operation record chain is a chain formed by linking all operation records in chronological order, ensuring the characteristics of non-tamperability and distribution of the records. The optimal candidate case library is a collection of best practice cases selected by comparing and optimizing existing cases; Based on intelligent control records and complete operation guidance logs, through the smart contract mechanism, the maintenance task assignment, resource scheduling, and permission management are automatically processed to ensure that all operations are carried out in an orderly manner. The generated operation record chain not only records the details of each operation but also has the characteristics of immutability and distributed storage. Based on these record chains, the system can generate an optimal candidate case library to provide reference for future operations; In the circuit breaker control system, after each operation is completed, the system automatically assigns subsequent maintenance tasks, schedules the required resources, and manages the operation permissions of relevant personnel according to the intelligent control records and complete operation guidance logs. All these operations are recorded in the operation record chain to ensure the security and transparency of the records. Over time, the system accumulates a large number of operation records, from which the best practice cases are selected to form an optimal candidate case library, providing valuable experience and guidance for future circuit breaker operations.

[0024] Step 105: According to the operation record chain and the optimal candidate case library, combined with the swarm intelligence algorithm, through comparison and optimization, confirm and learn the optimal candidate cases to obtain the intelligent computer room alliance network; In this step, the operation record chain is a chain formed by linking all operation records in chronological order, ensuring the immutability and distributed characteristics of the records. The optimal candidate case library is a set of best practice cases selected by comparing and optimizing existing cases. The swarm intelligence algorithm is an optimization algorithm that simulates the behavior of biological groups and can work collaboratively among multiple agents to find the optimal solution. The intelligent computer room alliance network is a collaborative network composed of multiple geographically dispersed intelligent computer rooms, which can jointly improve the management level by sharing the best practice cases; According to the operation record chain and the optimal candidate case library, combined with the swarm intelligence algorithm for comparison and optimization, confirm and learn the optimal candidate cases. Each node in the intelligent computer room alliance network can jointly improve its own management level by sharing these best practice cases, forming a continuously evolving collaborative network; In multiple geographically dispersed circuit breaker control systems, the operation record chains and optimal candidate case libraries of each site are uploaded to a central platform. Through the swarm intelligence algorithm, these cases are further compared and optimized, and the best practice cases are confirmed and learned from. Each site in the intelligent computer room alliance network can not only access these best practice cases but also make personalized adjustments according to its own situation, thereby improving the reliability and efficiency of the overall circuit breaker control system. This collaborative mechanism ensures that all sites can benefit from the latest technologies and experiences and realizes the common improvement of the management level.

[0025] Since existing maintenance task allocation, resource scheduling, and permission management are mostly manual operations, which are inefficient and error-prone, and lack an automated processing mechanism, resulting in slow response speed and increased operation and maintenance costs. Based on this, the present invention provides a specific embodiment. In step 104, based on the intelligent regulation record and the complete operation guidance log, the maintenance task allocation, resource scheduling, and permission management are automatically processed through the smart contract mechanism to generate an operation record chain. Based on the operation record chain, an optimal candidate case library is generated, which specifically includes the following steps: Step 201: Based on the intelligent regulation record and the complete operation guidance log, use the zero-knowledge proof protocol combined with the hash chain technology for integration processing to obtain a blockchain ledger; In this step, the intelligent regulation record and the complete operation guidance log are detailed records after each operation instruction is executed, including the time, content, and result of the operation. The zero-knowledge proof protocol is a cryptographic technology that allows one party (the prover) to prove the truth of a certain statement to another party (the verifier) without revealing any additional information. The hash chain technology links data blocks in chronological order and uses a hash function to ensure the integrity of each data block, thus forming an immutable data chain. The blockchain ledger is a distributed database that records all verified operation records and has the characteristics of transparency, security, and immutability; First, collect all the intelligent regulation records and the complete operation guidance logs. Then, use the zero-knowledge proof protocol to ensure that these records do not disclose sensitive information during the integration process, and link the records in chronological order through the hash chain technology to form an immutable data chain. Finally, these integrated records are written into the blockchain ledger, providing a reliable basis for subsequent operations; Suppose, in an intelligent computer room management scenario, the system generates a large number of intelligent regulation records and operation guidance logs. To ensure the security of these records, the zero-knowledge proof protocol is used to encrypt the records, and then these records are linked into an immutable data chain through the hash chain technology. Finally, these records are integrated into the blockchain ledger, which not only guarantees the integrity and security of the records but also provides a reliable data basis for subsequent automated processing.

[0026] Step 202: According to the blockchain ledger, generate an operation record chain through the smart contract mechanism and graph algorithm optimization processing; In this step, the blockchain ledger is a distributed and immutable database that records all verified operation records. A smart contract is an automatically executable contract clause that can trigger corresponding operations automatically when conditions are met. Graph algorithms are a class of algorithms used for analyzing network structures and path optimization, which can effectively identify key nodes and paths in complex relationships. The operation record chain is all operation records arranged in chronological order, ensuring that every step of the operation is traceable; Based on the records in the blockchain ledger, the maintenance task assignment, resource scheduling, and permission management are automated through the smart contract mechanism. At the same time, graph algorithms are applied to optimize these records, identify the most effective operation paths and nodes, and generate an operation record chain. This approach not only improves the efficiency of operations but also ensures the accuracy and transparency of the records; In the aforementioned intelligent computer room management scenario, the records in the blockchain ledger are automatically processed through the smart contract mechanism to ensure the orderly progress of maintenance tasks, resource scheduling, and permission management. In addition, graph algorithms are used to optimize these records, identify the best operation paths and key nodes, and generate an operation record chain. For example, when it is necessary to adjust the load of a certain server, the system can select the optimal adjustment path based on historical records and the current state to ensure efficient and error-free operations.

[0027] Step 203: Utilize the operation record chain, combine it with time series analysis algorithms for effectiveness evaluation, generate best practice cases, and merge the best practice cases to obtain a preliminary candidate case library; In this step, the operation record chain is all operation records arranged in chronological order, ensuring that every step of the operation is traceable. Time series analysis algorithms are a technique for analyzing data that changes over time, which can help identify trends and patterns. Best practice cases refer to operation instances that perform excellently in specific situations, usually containing successful operation models and elements. The preliminary candidate case library is a collection composed of multiple best practice cases for subsequent comparison and optimization; Utilize the data in the operation record chain, combine it with time series analysis algorithms to evaluate the effectiveness of each operation, identify the key success factors and operation models. Based on these evaluation results, generate best practice cases and merge them into the preliminary candidate case library. This approach not only ensures the representativeness and effectiveness of the cases but also provides a solid foundation for subsequent optimization; Continuing with the above intelligent computer room management scenario, the system utilizes the data in the operation record chain and combines time series analysis algorithms to evaluate the effectiveness of each operation, such as temperature regulation of the air conditioning system and load distribution of the power supply. Through these evaluations, the system identifies which operation modes are the most successful and generates best practice cases. For example, if a temperature regulation operation of the air conditioning system performs excellently under specific conditions, the system saves it as a best practice case. All these best practice cases are merged into the preliminary candidate case library, providing rich references for subsequent optimization.

[0028] Step 204: Based on the preliminary candidate case library, apply data mining algorithms and association rule learning algorithms for in-depth analysis, and use the Bayesian optimization algorithm for optimization processing to generate the best candidate case library; In this step, the preliminary candidate case library is a set composed of multiple best practice cases for subsequent comparison and optimization. Data mining algorithms are a class of techniques used to extract useful information from large amounts of data, capable of discovering hidden patterns and relationships. Association rule learning algorithms are a method for identifying the association relationships between data items, capable of revealing the internal connections between different operations. The Bayesian optimization algorithm is an optimization method based on probability models, capable of finding the optimal solution under uncertain conditions. The best candidate case library is a set composed of best practice cases that have undergone in-depth analysis and optimization, used to guide future operations; Based on the preliminary candidate case library, apply data mining algorithms and association rule learning algorithms for in-depth analysis to extract key operation modes and success factors. Then, use the Bayesian optimization algorithm to optimize these modes and factors to generate the best candidate case library. This approach not only improves the representativeness and applicability of the cases but also provides more accurate guidance for future operations; In the intelligent computer room management scenario, the system, based on the preliminary candidate case library, applies data mining algorithms and association rule learning algorithms to deeply analyze each best practice case and extract key operation modes and success factors. For example, some temperature regulation operations of the air conditioning system perform excellently under specific conditions, and these conditions are significantly associated with other environmental factors (such as humidity and external air temperature). Through the Bayesian optimization algorithm, the system further optimizes these modes and factors to generate the best candidate case library. These cases not only represent the optimal operation methods but can also be flexibly adjusted according to the actual situation, providing valuable references and guidance for future operations.

[0029] Based on this, the present invention also provides a specific embodiment. The step 204, based on the preliminary candidate case library, applies data mining algorithms and association rule learning algorithms for in-depth analysis, and uses the Bayesian optimization algorithm for optimization processing to generate the best candidate case library, specifically includes the following steps: Step 301: Based on the preliminary candidate case library, analyze the best practice cases using data mining algorithms and association rule learning algorithms, extract key operation patterns and success factors, and obtain common features; In this step, the preliminary candidate case library is a set composed of multiple best practice cases for subsequent comparison and optimization. Data mining algorithms are a class of technologies used to extract useful information from large amounts of data, capable of discovering hidden patterns and relationships. Association rule learning algorithms are a method for identifying the association relationships between data items, capable of revealing the internal connections between different operations. Common features refer to the key operation patterns and success factors that repeatedly appear in multiple best practice cases; Based on the data in the preliminary candidate case library, apply data mining algorithms and association rule learning algorithms, deeply analyze each best practice case, and extract the key operation patterns and success factors therein. These common features not only reflect the successful operation rules but also provide a solid foundation for subsequent optimization; Suppose, in an intelligent computer room management scenario, the system is based on the preliminary candidate case library, applies data mining algorithms and association rule learning algorithms, and analyzes each best practice case. For example, the temperature adjustment operations of some air conditioning systems perform excellently under specific conditions, and these conditions are significantly associated with other environmental factors (such as humidity, external temperature). Through this analysis, the system extracts key operation patterns and success factors, such as how to adjust the temperature to achieve the best effect in a high humidity environment. These common features will provide valuable references for subsequent optimization.

[0030] Step 302: According to the common features, use the Bayesian optimization algorithm combined with the random forest model for optimization processing to generate an optimized candidate case set; In this step, the common features are the key operation patterns and success factors extracted by data mining and association rule learning algorithms. The Bayesian optimization algorithm is an optimization method based on probability models, capable of finding the optimal solution under uncertain conditions. The random forest model is an ensemble learning method that makes predictions and classifications by constructing multiple decision trees and synthesizing their results. The optimized candidate case set is a set composed of best practice cases that have undergone in-depth analysis and optimization, used to guide future operations; According to the extracted common features, use the Bayesian optimization algorithm combined with the random forest model for optimization processing. The Bayesian optimization algorithm helps determine the optimal operation parameters, while the random forest model is used to evaluate the effectiveness and applicability of these parameters. The finally generated optimized candidate case set not only improves the representativeness and applicability of the cases but also provides more accurate guidance for future operations; Continuing with the above intelligent computer room management scenario, the system uses the Bayesian optimization algorithm to find the optimal operating parameters based on the extracted common features, such as the optimal range for temperature adjustment. Meanwhile, the random forest model is used to evaluate the effectiveness and applicability of these parameters under different environmental conditions. For example, the system may find that a certain temperature adjustment range is most effective under specific humidity and external air temperature conditions. In this way, the system generates an optimized candidate case set, providing detailed guidance for future air conditioning system operations.

[0031] Step 303: Use the support vector machine algorithm to classify the candidate cases in the optimized candidate case set to obtain a classification result, and use the K-means clustering algorithm to refine the classification result to obtain the target optimization category; In this step, the optimized candidate case set is a set composed of best practice cases that have undergone in-depth analysis and optimization. The support vector machine (SVM) algorithm is a supervised learning method for classification and regression analysis, which can effectively distinguish different types of cases. The K-means clustering algorithm is an unsupervised learning method used to divide data points into several clusters, and the data points within each cluster have similar characteristics. The target optimization category refers to the category that best represents the successful operation mode after classification and refinement; First, use the support vector machine algorithm to classify the candidate cases in the optimized candidate case set to obtain a preliminary classification result. Then, use the K-means clustering algorithm to further refine these classification results to ensure that the cases in each category have similar operation modes and success factors. The finally obtained target optimization category not only improves the accuracy of classification but also provides a clear grouping basis for subsequent evaluations; In the intelligent computer room management scenario, the system uses the support vector machine algorithm to classify the optimized candidate case set to distinguish different types of operation modes, such as efficient energy consumption control and fast response adjustment. Then, use the K-means clustering algorithm to further refine these classification results to ensure that the cases in each category have similar success factors. For example, a certain category may concentrate all the temperature adjustment cases that perform well in high humidity environments. In this way, the system obtains the target optimization category, providing a clear grouping basis for subsequent evaluations.

[0032] Step 304: Use the target optimization category to construct a multi-layer evaluation framework and integrate multiple evaluation indicators. Based on the analytic hierarchy process, calculate the weights of each evaluation indicator to evaluate the candidate cases in each target optimization category, and select the best candidate cases to generate the best candidate case library. The evaluation indicators include: cost-benefit ratio, response speed, and resource utilization rate; In this step, the target optimization category refers to the category that, after classification and refinement, best represents the successful operation mode. The multi-layer evaluation framework is a multi-level evaluation system that can comprehensively evaluate the performance in different dimensions. The evaluation indicators are the criteria used to measure the quality of candidate cases, such as the cost-benefit ratio, response speed, and resource utilization rate. The Analytic Hierarchy Process (AHP) is a multi-criteria decision analysis method used to determine the relative importance or weights of each evaluation indicator. The best candidate case library is a set composed of the optimal candidate cases selected through the multi-layer evaluation framework, which is used to guide future operations; Based on the target optimization category, a multi-layer evaluation framework is constructed, integrating multiple evaluation indicators such as the cost-benefit ratio, response speed, and resource utilization rate. The weights of each evaluation indicator are calculated using the Analytic Hierarchy Process to ensure that the importance of each indicator is reflected. By comprehensively evaluating the candidate cases in each target optimization category, the best candidate cases are selected to form the best candidate case library. This approach not only improves the comprehensiveness and accuracy of the evaluation but also provides optimal guidance for future operations; In the intelligent computer room management scenario, the system constructs a multi-layer evaluation framework based on the target optimization category, integrating multiple evaluation indicators such as the cost-benefit ratio, response speed, and resource utilization rate. The weights of each evaluation indicator are calculated using the Analytic Hierarchy Process to ensure that the importance of each indicator is reflected. For example, the system may find that in some cases, the response speed is more important than the cost-benefit. By comprehensively evaluating the candidate cases in each target optimization category, the system selects the best candidate cases to form the best candidate case library. These cases not only represent the optimal operation methods but can also be flexibly adjusted according to the actual situation, providing valuable reference and guidance for future operations.

[0033] Based on this, the present invention also provides a specific embodiment. In step 301, based on the preliminary candidate case library, the best practice cases are analyzed using data mining algorithms and association rule learning algorithms to extract key operation modes and success factors, obtaining common characteristics, which specifically include the following steps: Step 401: Based on the preliminary candidate case library, preprocess the data of each best practice case, remove redundant information, and standardize the data format to obtain a standardized best practice case dataset; In this step, the preliminary candidate case base is a set composed of multiple best practice cases for subsequent comparison and optimization. Preprocessing refers to operations such as cleaning and transforming the original data to improve data quality and consistency. Removing redundant information is to eliminate unnecessary data items and reduce the impact of noise on the analysis results. Standardizing the data format means unifying data from different sources into the same format and unit to ensure the consistency and accuracy of subsequent analysis. The standardized best practice case data set is a data set formed after preprocessing, which is convenient for further analysis; First, extract the data of each best practice case from the preliminary candidate case base. Then, by removing redundant information (such as duplicate records, irrelevant variables) and standardizing the data format (such as unifying timestamps, unit conversion), ensure that all data is consistent and of high quality. The finally obtained standardized best practice case data set provides a solid foundation for subsequent pattern recognition and feature extraction; Suppose, in an intelligent computer room management scenario, the system extracts the data of each best practice case from the preliminary candidate case base. For example, the temperature adjustment records of the air conditioning system may contain redundant fields or inconsistent time formats. By preprocessing this data, removing redundant information (such as duplicate operation logs) and unifying the time format to the ISO standard, the system obtains a high-quality standardized data set. This not only improves the data quality but also lays a foundation for subsequent frequent pattern mining and technology application.

[0034] Step 402: Use frequent pattern mining technology to perform pattern recognition on the standardized best practice case data set, extract the key operation patterns therein, and generate an operation pattern set; In this step, the frequent pattern mining technology is a data mining method aimed at discovering patterns or rules that frequently appear in the data set. The standardized best practice case data set is a data set formed after preprocessing, which is convenient for further analysis. The key operation pattern refers to the important operation steps or combinations that repeatedly appear in multiple best practice cases. The operation pattern set is the summary of these key operation patterns for subsequent association analysis and optimization; Based on the standardized best practice case data set, use frequent pattern mining technology to identify the frequently occurring operation patterns. These patterns may include a series of operation steps or combinations of operation parameters under specific conditions. In this way, the system can extract the key operation patterns and generate an operation pattern set, providing a basis for further in-depth analysis; Continuing with the above intelligent computer room management scenario, the system uses frequent pattern mining technology to analyze the standardized best practice case dataset. For example, in the temperature adjustment records of the air conditioning system, the system identifies temperature adjustment patterns that frequently occur under certain specific conditions (such as in a high humidity environment). These patterns may include a series of specific operation steps (such as gradually decreasing the temperature set value). Through this pattern recognition, the system generates a set of operation patterns, providing a detailed reference for subsequent association analysis and optimization.

[0035] Step 403: Combine with the Apriori algorithm to conduct in-depth association analysis on the set of operation patterns, identify the success factors in the operation instances, and obtain the initial common characteristics; In this step, the Apriori algorithm is a classical association rule learning algorithm used to discover the association relationships between data items. The set of operation patterns is a summary of the key operation patterns extracted by the frequent pattern mining technology. The success factors refer to the factors that play a key role in the operation instances, such as operation parameters or steps under specific conditions. The initial common characteristics refer to the success factors that commonly appear in multiple operation instances, reflecting the successful operation rules; Based on the set of operation patterns, use the Apriori algorithm for in-depth association analysis to identify the success factors in the operation instances. These success factors may be effective operation parameter or step combinations under specific conditions. In this way, the system can extract the initial common characteristics, providing a basis for further optimization processing; In the intelligent computer room management scenario, the system uses the Apriori algorithm to conduct in-depth association analysis on the set of operation patterns. For example, in the temperature adjustment records of the air conditioning system, the system identifies the successful operation patterns that frequently occur under certain specific conditions (such as in a high humidity environment). These patterns may include a series of specific operation steps (such as gradually decreasing the temperature set value) and the corresponding environmental conditions (such as external air temperature, humidity). Through this association analysis, the system extracts the initial common characteristics, such as how to adjust the temperature to achieve the best effect in a high humidity environment. These characteristics not only reflect the successful operation rules but also provide valuable references for subsequent optimization.

[0036] Step 404: According to the initial common characteristics, construct a high-dimensional feature vector space, and introduce the principal component analysis method to map each best practice case into the high-dimensional feature vector space to obtain the target common characteristics; In this step, the initial common features are the success factors that commonly appear in multiple operation instances, reflecting the successful operation rules. The high-dimensional feature vector space is a multi-dimensional space used to represent complex data structures. Principal Component Analysis (PCA) is a dimensionality reduction technique used to reduce the data dimension while retaining the most important feature information. The target common features refer to the features that can best represent the successful operation mode after dimensionality reduction processing, and are used to guide future operations; Based on the initial common features, a high-dimensional feature vector space is constructed to represent the features of each best practice case. Then, Principal Component Analysis (PCA) is introduced to map each best practice case into this high-dimensional feature vector space. Through dimensionality reduction processing, the system can retain the most important feature information while reducing the data dimension, and finally obtain the target common features. This method not only improves the efficiency of feature representation but also provides accurate reference for subsequent operation guidance; In the intelligent computer room management scenario, the system constructs a high-dimensional feature vector space based on the initial common features to represent the features of each best practice case. For example, in the temperature adjustment records of the air conditioning system, the system uses Principal Component Analysis (PCA) to map each case into this high-dimensional feature vector space. Through dimensionality reduction processing, the system retains the most important feature information, such as the change trend of the temperature setting value, humidity conditions, etc. The finally obtained target common features not only reflect the successful operation mode but also provide detailed guidance for future air conditioning system operations. This method not only improves the efficiency of feature representation but also provides a more intuitive and accurate reference for the operators.

[0037] Moreover, since traditional computer room management usually relies on regular inspections and manual records, it cannot achieve real-time data analysis and rapid identification of key factors, and it is difficult to dynamically reflect the potential risks in the computer room operation, resulting in untimely or inaccurate warnings. Based on this, the present invention provides a specific embodiment. In step 101, based on the status information flow from the distributed sensing network, the key factor identification result is obtained, a dynamic risk assessment model is constructed based on the key factor identification result, a regional prediction report is generated, and according to the regional prediction report, a multi-dimensional digital twin mapping model integrating the physical layout, logical architecture, and time series dimension is generated to obtain the digital twin environment, which specifically includes the following steps: Step 501: Based on the status information flow from the distributed sensing network, perform real-time analysis and processing on the status information flow, extract and identify key factors using data mining and machine learning algorithms to obtain the key factor identification result. The key factors include: temperature, humidity, power consumption, and equipment operation status; In this step, the distributed sensing network refers to the sensor network deployed throughout the intelligent computer room, which is used to collect data on the environment and equipment status in real time. The status information flow is the data stream continuously sent by these sensors, including information such as temperature, humidity, power consumption, and equipment operating status. Real-time parsing and processing refers to the immediate analysis and processing of this data to ensure the timeliness of the data. Data mining and machine learning algorithms are a class of technologies used to extract useful information from large amounts of data and can discover hidden patterns and relationships. The key factor identification result refers to the extraction of key parameters affecting the operation of the computer room by analyzing the status information flow; First, the system receives the real-time status information flow from the distributed sensing network. Then, data mining and machine learning algorithms are used to parse and process this data, extract and identify key factors such as temperature, humidity, power consumption, and equipment operating status. The final key factor identification result provides a basis for subsequent risk assessment and decision-making; In a scenario of intelligent computer room management, the system receives the status information flow from various sensors (such as temperature and humidity sensors, ammeters, and equipment monitors) in real time. Through data mining and machine learning algorithms, the system parses this data and identifies key factors such as an abnormal increase in temperature, excessive humidity, increased power consumption, or a fault warning for a specific device in a certain area. These key factor identification results not only help managers understand the current status of the computer room in a timely manner but also provide an important basis for subsequent risk assessment and decision-making.

[0038] Step 502: According to the key factor identification result, use the edge computing node to combine historical data and preset risk assessment rules to deeply analyze the key factors, construct a dynamic risk assessment model, which is used to predict possible risk points and the influence range of the risk points, and generate a regional prediction report; In this step, the key factor identification result is the key parameter affecting the operation of the computer room extracted from the status information flow. The edge computing node refers to the computing resource located near the data source, which can quickly process data locally. Historical data refers to the accumulated operation records and status information in the past, which is used for auxiliary analysis. The preset risk assessment rules are a series of predefined conditions and thresholds used to judge whether there are potential risks. The dynamic risk assessment model is a model used to predict future risks and can reflect potential problems in the operation of the computer room. The regional prediction report is a specific presentation of these risk assessment results; Based on the key factor identification result, use the edge computing node to combine historical data and preset risk assessment rules to deeply analyze the key factors. In this way, the system constructs a dynamic risk assessment model, which is used to predict possible risk points and their influence range, and generate a detailed regional prediction report. This not only improves the accuracy and forward-looking of risk warning but also provides a basis for subsequent countermeasures; Continuing with the above intelligent computer room management scenario, the system, based on the identified key factors (such as abnormal temperature increase), uses edge computing nodes to conduct in-depth analysis in combination with historical data and preset risk assessment rules. For example, if the temperature in a certain area continuously exceeds the preset threshold, the system will combine historical data to evaluate the possible overheating risk and its impact scope in that area in the next period of time. By constructing a dynamic risk assessment model, the system generates a regional prediction report to early warn of potential problems and help management personnel take preventive measures.

[0039] Step 503: Based on the regional prediction report, integrate the physical layout, logical architecture, and time series dimension to construct a multi-dimensional digital twin mapping model; In this step, the regional prediction report is a specific presentation of the dynamic risk assessment results, showing the possible risk points and their impact scopes. The physical layout refers to the equipment locations and spatial distributions in the computer room. The logical architecture refers to the internal network topology structure and other logical connections in the computer room. The time series dimension refers to the time series information of historical operation records and prediction trends. The multi-dimensional digital twin mapping model is a virtualized computer room model that integrates the physical layout, logical architecture, and time series dimension and is used to simulate and display the real state of the computer room; Based on the regional prediction report, the system integrates the physical layout, logical architecture, and time series dimension of the computer room to construct a multi-dimensional digital twin mapping model. This model not only shows the current state of the computer room but also predicts possible future changes, providing a comprehensive and intuitive virtualized environment. This helps management personnel monitor and make decisions more efficiently; In the intelligent computer room management scenario, the system, based on the regional prediction report, integrates the physical layout, logical architecture, and time series dimension of the computer room to construct a multi-dimensional digital twin mapping model. For example, when it is predicted that there may be an overheating risk in a certain area, the model not only shows the current temperature distribution but also predicts the change trend in the next period of time. This comprehensive and intuitive display method helps management personnel better understand and respond to potential problems.

[0040] Step 504: According to the multi-dimensional digital twin mapping model, provide a visualization interface through augmented reality technology to obtain a digital twin environment; In this step, the multi-dimensional digital twin mapping model is a virtualized computer room model that integrates physical layout, logical architecture, and time series dimensions to simulate and display the real state of the computer room. Augmented reality (AR) technology is a technology that superimposes virtual information onto the real-world view and can provide an immersive operation guidance interface. A visualization interface refers to presenting complex data and information in a graphical way to facilitate user understanding and operation. The digital twin environment is a virtualized environment jointly constructed by the multi-dimensional digital twin mapping model and augmented reality technology for real-time monitoring and decision support; Based on the multi-dimensional digital twin mapping model, the system provides an intuitive visualization interface through augmented reality technology to form a digital twin environment. This environment not only shows the current state of the computer room but also predicts possible future changes, supporting managers for real-time monitoring and decision-making. The application of augmented reality technology enables managers to view and operate the virtualized model of the computer room in real time through mobile devices or other display devices, improving work efficiency and accuracy; In the scenario of intelligent computer room management, the system, based on the multi-dimensional digital twin mapping model, provides an intuitive visualization interface for managers through augmented reality technology, forming a digital twin environment. For example, managers can use a tablet computer or smart glasses to view the temperature distribution, device status, and network connection status of the computer room in real time. When it is predicted that there may be an overheating risk in a certain area, the system will highlight that area on the visualization interface and provide detailed prediction information and recommended operation steps. This immersive operation guidance interface not only improves management efficiency but also reduces the possibility of misoperations.

[0041] Secondly, traditional methods lack optimization when generating operation instructions, which may lead to unnecessary resource waste or operation errors, and lack refined management and guidance for the operation process, further affecting the execution effect. Based on this, the present invention provides a specific embodiment. In step 103, a software-defined network controller is used to send operation instructions to the target execution unit according to the optimal adjustment instruction, generate an intelligent regulation record, and at the same time use a virtual reality guidance tool for on-site operation guidance and record all operation guidance processes to obtain a complete operation guidance log, which specifically includes the following steps: Step 601: Based on the optimal adjustment instruction, use the software-defined network controller to parse and transform the operation instruction to generate specific control commands for different target execution units; In this step, the optimal adjustment instruction refers to the operation command that is most matched to the current computer room status and operation objectives after optimization. The software-defined network (SDN) controller is the core component for centralized management and configuration of network resources, and can precisely control each node in the network according to the optimal adjustment instruction. Operation instruction parsing refers to decomposing high-level operation instructions into specific and executable commands. Conversion processing refers to adapting these specific commands to the interfaces and protocols of different target execution units to ensure that the commands can be correctly understood and executed. Specific control commands refer to low-level commands that directly act on target execution units (such as air conditioning systems, power supply devices, etc.); Based on the optimal adjustment instruction, the SDN controller parses and converts it, generating specific control commands for different target execution units. These commands not only contain the content and parameters of the operation, but also adapt to the interfaces and protocols of each execution unit to ensure that the commands can be correctly executed. This method improves the accuracy and efficiency of the operation; For example, when it is necessary to adjust the load of a certain server, the system will generate specific control commands, such as "increase the fan speed of the cooling system to 80%". These commands adapt to the interfaces and protocols of each execution unit to ensure that each device can respond accurately. This refined command generation mechanism not only improves the accuracy of the operation, but also reduces the possibility of misoperation.

[0042] Step 602: According to the specific control commands, the operation instructions are sent to each target execution unit through the automated control system to achieve real-time adjustment of the environmental control devices, security systems, and IT devices in the computer room, and generate real-time intelligent control records; In this step, the specific control commands refer to low-level commands that directly act on target execution units. The automated control system is a set of hardware and software systems for automatically executing operation instructions, which can ensure the fast and accurate execution of commands. The environmental control devices include air conditioning systems, ventilation equipment, etc., which are used to adjust environmental conditions such as temperature and humidity in the computer room. The security system includes access control, surveillance cameras, etc., which are used to ensure the security of the computer room. IT devices refer to key information infrastructure such as servers and network devices. The real-time intelligent control record refers to the detailed record after each operation instruction is executed, including the time, content, and result of the operation; According to the specific control commands, the automated control system sends the operation instructions to each target execution unit to achieve real-time adjustment of the environmental control devices, security systems, and IT devices in the computer room. After each operation is completed, the system generates a detailed real-time intelligent control record to ensure that every step of the operation is traceable. This method not only improves the operation efficiency, but also ensures the integrity and accuracy of the records; For example, when the temperature setting value of the air conditioning system needs to be adjusted, the system will immediately send this instruction to the corresponding air conditioning equipment to ensure its prompt response. At the same time, the system will also adjust the access control permissions of the security system to allow technicians to enter specific areas for maintenance. All these operations are detailedly recorded to form a real-time intelligent regulation record, which is convenient for subsequent review and experience summary.

[0043] Step 603: Based on the real-time intelligent regulation record, use a virtual reality guidance tool to provide an immersive operation guidance interface for remote operation and maintenance personnel, support the operation and maintenance personnel to conduct on-site operation guidance through the virtual environment, and record all interaction data during the remote guidance process to obtain a complete operation guidance log; In this step, the real-time intelligent regulation record refers to the detailed record after each operation instruction is executed, including the time, content, and result of the operation. The virtual reality (VR) guidance tool is a technical means to assist operation and maintenance personnel in remote operation, providing an immersive operation guidance interface. The immersive operation guidance interface is a virtualized environment that enables operation and maintenance personnel to simulate and guide actual operations in the virtual environment. Interaction data refers to all interactive information generated during the remote guidance process, including voice, text, and operation steps. The complete operation guidance log is a detailed record of all remote guidance processes, including the data of each interaction; Based on the real-time intelligent regulation record, the system uses a virtual reality guidance tool to provide an immersive operation guidance interface for remote operation and maintenance personnel. The operation and maintenance personnel can conduct on-site operation guidance through the virtual environment to ensure that each operation is accurately executed according to the optimal adjustment instruction. At the same time, the system records all interaction data during the remote guidance process to generate a complete operation guidance log. This method not only improves the accuracy of the operation but also provides valuable data support for subsequent training and review; For example, when technicians need to adjust the settings of a certain server, remote operation and maintenance personnel can conduct operation guidance through the virtual environment to ensure that technicians are accurate in every step. All interaction data during these remote guidance processes, such as voice instructions, operation steps, etc., are detailedly recorded to form a complete operation guidance log. This not only improves the accuracy of the operation but also provides valuable materials for future training and review.

[0044] Step 604: Combine augmented reality technology, and based on the complete operation guidance log, overlay virtual guidance information in the actual environment to assist on-site operation and maintenance personnel in understanding the operation process, make the operation guidance accurate, and incorporate key operation points and abnormal handling methods during the operation guidance process into the complete operation guidance log; In this step, augmented reality (AR) technology is a technology that superimposes virtual information on the real-world view, which can provide an immersive operation guidance interface. The complete operation guidance log is a detailed record of all remote guidance processes, containing data of each interaction. Key operation points refer to particularly important or complex steps during the operation process, and the exception handling method refers to the countermeasures taken when problems are encountered. Through AR technology, the system can superimpose virtual guidance information in the actual environment to help on-site operation and maintenance personnel better understand the operation process; Combined with augmented reality technology, the system superimposes virtual guidance information in the actual environment according to the complete operation guidance log to assist on-site operation and maintenance personnel in understanding the operation process. This method not only improves the accuracy of the operation but also ensures that each step of the operation is carried out strictly in accordance with the guidance. At the same time, the system incorporates the key operation points and exception handling methods in the operation guidance process into the complete operation guidance log, further enhancing the detail and practicality of the record; For example, when technicians adjust the settings of a certain server on-site, the system will display virtual guidance information on their mobile devices, such as arrows indicating the operation direction and text explaining the operation steps. These information help technicians more intuitively understand the operation process and ensure that each step is accurate. In addition, the system incorporates the key operation points and exception handling methods in the operation guidance process into the complete operation guidance log, such as immediately shutting down the power supply and checking the cooling system when the server overheats. This not only improves the accuracy of the operation but also provides valuable reference for future operations.

[0045] Based on this, the present invention also provides a specific embodiment. In step 105, according to the operation record chain and the best candidate case library, combined with the swarm intelligence algorithm, through comparison and optimization, the best candidate cases are confirmed and learned to obtain the intelligent computer room alliance network, which specifically includes the following steps: Step 701: Based on the operation record chain and the candidate case library, use the swarm intelligence algorithm such as particle swarm optimization to conduct preliminary screening and classification processing on the candidate cases to obtain a set of high-potential cases; In this step, the operation record chain is all operation records arranged in chronological order to ensure that each step of the operation is traceable. The candidate case library is a set composed of multiple best practice cases for subsequent comparison and optimization. The swarm intelligence algorithm is a class of optimization algorithms that simulate the behavior of biological groups and can work collaboratively among multiple agents to find the optimal solution. Particle swarm optimization (PSO) is a commonly used swarm intelligence algorithm that finds the global optimal solution by simulating the foraging behavior of birds. The set of high-potential cases refers to the set of cases that are considered to have high application value after preliminary screening and classification; Based on the operation record chain and the candidate case library, swarm intelligence algorithms such as particle swarm optimization are used to preliminarily screen and classify the candidate cases. These algorithms simulate swarm behavior to identify the cases most likely to succeed, forming a set of high-potential cases. This approach not only improves the screening efficiency but also ensures that the selected cases have high application value.

[0046] Step 702: According to the set of high-potential cases, construct a distributed verification framework so that multiple intelligent computer rooms located in different geographical locations can share and verify the high-potential cases with each other through a peer-to-peer network, generating local verification results; In this step, the set of high-potential cases is a set of cases that are considered to have high application value after preliminary screening and classification. The distributed verification framework is a distributed collaboration mechanism that allows multiple intelligent computer rooms to jointly participate in case verification. The peer-to-peer network is a decentralized network architecture where nodes can communicate directly. The local verification result refers to the feedback information generated by each intelligent computer room during the verification process, which is used to evaluate the effectiveness of the high-potential cases; According to the set of high-potential cases, construct a distributed verification framework so that multiple intelligent computer rooms located in different geographical locations can share and verify these cases through a peer-to-peer network. Each intelligent computer room conducts actual tests on the high-potential cases according to its own conditions and environment and generates local verification results. This approach not only improves the comprehensiveness and accuracy of verification but also promotes experience exchange and technology sharing among different computer rooms.

[0047] Step 703: Use the local verification results and combine them with the federated learning mechanism to train a shared model. The shared model integrates the feedback from each intelligent computer room to adjust and optimize the selection criteria for the best candidate cases, generating an optimized set of the best candidate cases; In this step, the local verification result refers to the feedback information generated by each intelligent computer room during the verification process, which is used to evaluate the effectiveness of the high-potential cases. The federated learning mechanism is a machine learning method that allows multiple participants to jointly train a model without sharing data. The shared model is a model jointly trained by multiple intelligent computer rooms, which can integrate the feedback from each computer room to adjust and optimize the selection criteria for the best candidate cases. The optimized set of the best candidate cases is a set composed of best practice cases that have undergone in-depth analysis and optimization, which is used to guide future operations; Use the local verification results and combine them with the federated learning mechanism to train a shared model. This model not only integrates the feedback adjustments of each intelligent computer room but also optimizes the selection criteria for the best candidate cases. The finally generated optimized set of the best candidate cases not only improves the representativeness and applicability of the cases but also provides more accurate guidance for future operations.

[0048] Step 704: Based on the optimized best candidate case set, construct an initial intelligent computer room alliance network, and apply the reinforcement learning algorithm to dynamically update the policies of each node in the initial intelligent computer room alliance network to select the target operation plan; In this step, the intelligent computer room alliance network is a collaborative network composed of multiple geographically dispersed intelligent computer rooms. By sharing best practice cases, the management level can be jointly improved. The reinforcement learning algorithm is a method of learning through trial and error, aiming to find the optimal behavior strategy. Dynamic policy update refers to continuously adjusting and optimizing the operation policy according to real-time feedback. The target operation plan refers to the optimal operation steps or combinations in the current situation; Based on the optimized best candidate case set, construct an initial intelligent computer room alliance network. Apply the reinforcement learning algorithm to dynamically update the policies of each node in the alliance network, continuously adjust and optimize the operation policy according to real-time feedback, and select the target operation plan. This method not only improves the flexibility and adaptability of the operation but also ensures that each node can operate in the optimal state.

[0049] Step 705: According to the target operation plan, construct a multi-layer evaluation system, integrate multiple evaluation indicators, use the analytic hierarchy process to determine the weights of each evaluation indicator, evaluate each best candidate case to obtain the evaluation results, and optimize the initial intelligent computer room alliance network based on the evaluation results to obtain the target intelligent computer room alliance network; In this step, the multi-layer evaluation system is a multi-level evaluation framework that can comprehensively evaluate the performance of different dimensions. The evaluation indicator is a standard used to measure the quality of candidate cases, such as cost-benefit ratio, response speed, and resource utilization rate. The analytic hierarchy process (AHP) is a multi-criteria decision-making analysis method used to determine the relative importance or weights of each evaluation indicator. The evaluation result is a comprehensive evaluation of each best candidate case. The target intelligent computer room alliance network is a collaborative network composed of multiple optimized intelligent computer rooms, with higher efficiency and reliability; According to the target operation plan, construct a multi-layer evaluation system, integrate multiple evaluation indicators, such as cost-benefit ratio, response speed, and resource utilization rate. Use the analytic hierarchy process to calculate the weights of each evaluation indicator to ensure that the importance of each indicator is reflected. By comprehensively evaluating each best candidate case, the system obtains detailed evaluation results. Based on these evaluation results, the system further optimizes the initial intelligent computer room alliance network to form the target intelligent computer room alliance network. This method not only improves the comprehensiveness and accuracy of the evaluation but also provides the optimal guidance for future operations.

[0050] Figure 2 The following is a schematic structural diagram of a management system for an integrated intelligent computer room provided by an embodiment of the present invention, as Figure 2 shown, the system includes: The construction module 21 is configured to obtain the key factor identification result based on the status information flow from the distributed sensing network, construct a dynamic risk assessment model based on the key factor identification result, generate a regional prediction report, and generate a multi-dimensional digital twin mapping model integrating the physical layout, logical architecture, and time series dimension according to the regional prediction report to obtain a digital twin environment; The simulation module 22 is configured to simulate emergency response plans in different scenarios based on the digital twin environment, combine a preset security policy library and an adaptive learning algorithm, select a target emergency response plan as an intervention measure, and optimize the intervention measure to generate an optimal adjustment instruction; The recording module 23 is configured to use a software-defined network controller to send an operation instruction to a target execution unit according to the optimal adjustment instruction, generate an intelligent regulation record, and simultaneously use a virtual reality guidance tool for on-site operation guidance and record all operation guidance processes to obtain a complete operation guidance log; The generation module 24 is configured to automate the processing of maintenance task allocation, resource scheduling, and permission management through a smart contract mechanism based on the intelligent regulation record and the complete operation guidance log, generate an operation record chain, and generate an optimal candidate case library based on the operation record chain; The confirmation module 25 is configured to confirm and learn the optimal candidate cases according to the operation record chain and the optimal candidate case library, combine a swarm intelligence algorithm, and obtain an intelligent computer room alliance network through comparison and optimization.

[0051] Figure 2 The described management system of an integrated intelligent computer room can execute Figure 1 The management method of an integrated intelligent computer room described in the illustrated embodiment, and its implementation principle and technical effects will not be elaborated further. For the management system of an integrated intelligent computer room in the above embodiment, the specific manners in which each module and unit perform operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0052] In a possible design, Figure 2 The management system of an integrated intelligent computer room in the illustrated embodiment can be implemented as a computing device, such as Figure 3 shown, and the computing device can include a storage component 31 and a processing component 32; The storage component 31 stores one or more computer instructions, and the one or more computer instructions are called and executed by the processing component 32.

[0053] The processing component 32 is configured to obtain a key factor identification result based on the status information flow from the distributed sensing network, construct a dynamic risk assessment model based on the key factor identification result, generate a regional prediction report, and generate a multi-dimensional digital twin mapping model integrating physical layout, logical architecture, and time series dimensions according to the regional prediction report, so as to obtain a digital twin environment; Based on the digital twin environment, in combination with a preset security policy library and an adaptive learning algorithm, simulate emergency response plans in different scenarios, select a target emergency response plan as an intervention measure, and optimize the intervention measure to generate an optimal adjustment instruction; Use a software-defined network controller to send an operation instruction to a target execution unit according to the optimal adjustment instruction, generate an intelligent regulation record, and at the same time use a virtual reality guidance tool to conduct on-site operation guidance and record all operation guidance processes to obtain a complete operation guidance log; Based on the intelligent regulation record and the complete operation guidance log, automate the processing of maintenance task allocation, resource scheduling, and permission management through a smart contract mechanism to generate an operation record chain, and generate an optimal candidate case library based on the operation record chain; According to the operation record chain and the optimal candidate case library, in combination with a swarm intelligence algorithm, confirm and learn the optimal candidate cases through comparison and optimization to obtain an intelligent computer room alliance network.

[0054] Among them, the processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components for executing the above method.

[0055] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component 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 memory, flash memory, magnetic disk or optical disk.

[0056] Of course, the computing device may also necessarily include other components, such as input / output interfaces, display components, communication components, etc.

[0057] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above-mentioned peripheral interface module can be an output device, an input device, etc.

[0058] The communication component is configured to facilitate communication between the computing device and other devices in a wired or wireless manner, etc.

[0059] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. At this time, the computing device can refer to a cloud server, and the above-mentioned processing component, storage component, etc. can be basic server resources leased or purchased from a cloud computing platform.

[0060] The embodiment of the present invention also provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the above Figure 1 management method of an integrated intelligent computer room shown in the embodiment.

[0061] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0062] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0063] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0064] Finally, 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 foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A management method for an integrated intelligent computer room, characterized in that: include: Based on the state information flow from the distributed sensor network, a key factor identification result is obtained, a dynamic risk assessment model is constructed based on the key factor identification result, a regional prediction report is generated, and according to the regional prediction report, a multi-dimensional digital twin mapping model integrating physical layout, logical architecture and time series dimensions is generated to obtain a digital twin environment; Based on the digital twin environment, combined with a preset security policy library and an adaptive learning algorithm, emergency response plans under different scenarios are simulated, a target emergency response plan is selected as an intervention measure, and the intervention measure is optimized to generate an optimal adjustment instruction; Using a software-defined network controller, an operation instruction is sent to a target execution unit according to the optimal adjustment instruction to generate an intelligent control record. At the same time, a virtual reality guidance tool is used to provide on-site operation guidance, and all operation guidance processes are recorded to obtain a complete operation guidance log; Based on the intelligent control records and complete operation guidance logs, maintenance task allocation, resource scheduling and authority management are automatically processed through the intelligent contract mechanism to generate an operation record chain, and based on the operation record chain, the best candidate case library is generated; According to the operation record chain and the best candidate case library, combined with the swarm intelligence algorithm, the best candidate cases are confirmed and learned through comparison and optimization to obtain the intelligent computer room alliance network.

2. The method according to claim 1, characterized in that Based on the intelligent control record and complete operation guidance log, the maintenance task allocation, resource scheduling and authority management are automatically processed through the intelligent contract mechanism to generate an operation record chain. Based on the operation record chain, the best candidate case library is generated, including: Based on the intelligent control records and complete operation guidance logs, the zero-knowledge proof protocol is combined with hash chain technology for integration and processing to obtain the blockchain account book; According to the blockchain ledger, an operation record chain is generated through intelligent contract mechanism and graph algorithm optimization processing; Using the operation record chain in combination with a time series analysis algorithm to perform effect evaluation, generate best practice cases, and merge the best practice cases to obtain a preliminary candidate case library; Based on the preliminary candidate case library, data mining algorithms and association rule learning algorithms are used to perform in-depth analysis, and Bayesian optimization algorithms are used for optimization processing to generate the best candidate case library.

3. The method according to claim 2, characterized in that Based on the preliminary candidate case library, the data mining algorithm and the association rule learning algorithm are used for in-depth analysis, and the Bayesian optimization algorithm is used for optimization processing to generate the best candidate case library, including: Based on the preliminary candidate case library, the best practice cases are analyzed using data mining algorithms and association rule learning algorithms to extract key operation modes and success factors and obtain common features; According to the common characteristics, a Bayesian optimization algorithm is used in combination with a random forest model to perform optimization processing to generate an optimized candidate case set; Using a support vector machine algorithm to classify the candidate cases in the optimized candidate case set to obtain a classification result, and using a K-means clustering algorithm to refine the classification result to obtain a target optimization category; Using the target optimization category, a multi-layer evaluation framework is constructed to integrate multiple evaluation indicators. Based on the hierarchical analysis method, the weight of each evaluation indicator is calculated to evaluate the candidate cases in each target optimization category, and the best candidate case is selected to generate the best candidate case library. The evaluation indicators include: cost-effectiveness ratio, response speed and resource utilization.

4. The method according to claim 3, characterized in that Based on the preliminary candidate case library, the best practice cases are analyzed using data mining algorithms and association rule learning algorithms to extract key operating modes and success factors, and obtain common features, including: Based on the preliminary candidate case library, preprocess the data of each best practice case, remove redundant information and standardize the data format to obtain a standardized best practice case data set; Using frequent pattern mining technology, pattern recognition is performed on the standardized best practice case data set, key operation patterns are extracted therein, and an operation pattern set is generated; Combined with the Apriori algorithm, a deep correlation analysis is performed on the operation mode set to identify the success factors in the operation instance and obtain the initial common features; According to the initial common features, a high-dimensional feature vector space is constructed, and the principal component analysis method is introduced to map each best practice case into the high-dimensional feature vector space to obtain the target common features.

5. The method according to claim 1, characterized in that Based on the state information flow from the distributed sensor network, a key factor identification result is obtained, a dynamic risk assessment model is constructed based on the key factor identification result, and a regional prediction report is generated. According to the regional prediction report, a multi-dimensional digital twin mapping model integrating physical layout, logical architecture and time series dimensions is generated to obtain a digital twin environment, including: Based on the state information flow from the distributed sensor network, the state information flow is analyzed and processed in real time, and key factors are extracted and identified using data mining and machine learning algorithms to obtain key factor identification results. The key factors include: temperature, humidity, power consumption, and equipment operating status; According to the key factor identification results, the key factors are deeply analyzed by using edge computing nodes in combination with historical data and preset risk assessment rules to build a dynamic risk assessment model. The dynamic risk assessment model is used to predict possible risk points and the impact range of risk points, and generate a regional prediction report; Based on the regional forecast report, a multi-dimensional digital twin mapping model is constructed by integrating physical layout, logical architecture, and time series dimensions; According to the multi-dimensional digital twin mapping model, a visualization interface is provided by augmented reality technology to obtain a digital twin environment.

6. The method according to claim 1, characterized in that Using a software-defined network controller, an operation instruction is sent to the target execution unit according to the optimal adjustment instruction to generate an intelligent control record. At the same time, a virtual reality guidance tool is used for on-site operation guidance, and all operation guidance processes are recorded to obtain a complete operation guidance log, including: Based on the optimal adjustment instruction, the software-defined network controller is used to parse and convert the operation instruction to generate specific control commands for different target execution units; According to the specific control command, the operation instruction is sent to each target execution unit through the automatic control system, so as to realize the real-time adjustment of the environment control device, security system and IT equipment in the computer room and generate real-time intelligent control records; Based on the real-time intelligent control record, a virtual reality guidance tool is used to provide an immersive operation guidance interface for remote operation and maintenance personnel, supporting operation and maintenance personnel to provide on-site operation guidance through a virtual environment, and recording all interactive data during the remote guidance process to obtain a complete operation guidance log; Combined with augmented reality technology, virtual guidance information is superimposed in the actual environment based on the complete operation guidance log to assist on-site operation and maintenance personnel in understanding the operation process, making the operation guidance accurate, and incorporating key operation points and exception handling methods in the operation guidance process into the complete operation guidance log.

7. The method according to claim 1, characterized in that According to the operation record chain and the best candidate case library, combined with the swarm intelligence algorithm, the best candidate cases are confirmed and learned through comparison and optimization to obtain the smart computer room alliance network, including: Based on the operation record chain and the candidate case library, a swarm intelligence algorithm such as particle swarm optimization is used to preliminarily screen and classify the candidate cases to obtain a high-potential case set; Based on the high-potential case set, a distributed verification framework is constructed to enable multiple intelligent computer rooms distributed in different geographical locations to share and verify high-potential cases through a peer-to-peer network and generate local verification results; Using the local verification results and combining with the federated learning mechanism, a shared model is trained. The shared model integrates the feedback of each intelligent computer room to adjust and optimize the selection criteria of the best candidate cases, and generates an optimized set of best candidate cases. Based on the optimized best candidate case set, an initial smart computer room alliance network is constructed, and a reinforcement learning algorithm is applied to dynamically update the strategy of each node in the initial smart computer room alliance network to select a target operation plan; According to the target operation plan, a multi-layer evaluation system is constructed, multiple evaluation indicators are integrated, and the weight of each evaluation indicator is determined using the hierarchical analysis method. Each best candidate case is evaluated to obtain the evaluation results. Based on the evaluation results, the initial smart computer room alliance network is optimized to obtain the target smart computer room alliance network.

8. An integrated intelligent computer room management system, characterized in that: include: A construction module is used to obtain a key factor identification result based on a state information flow from a distributed sensor network, construct a dynamic risk assessment model based on the key factor identification result, generate a regional prediction report, and generate a multi-dimensional digital twin mapping model integrating physical layout, logical architecture, and time series dimensions according to the regional prediction report to obtain a digital twin environment; A simulation module is used to simulate emergency response plans under different scenarios based on the digital twin environment, in combination with a preset security policy library and an adaptive learning algorithm, select a target emergency response plan as an intervention measure, and optimize the intervention measure to generate an optimal adjustment instruction; A recording module is used to use a software-defined network controller to send an operation instruction to a target execution unit according to the optimal adjustment instruction, generate an intelligent control record, and use a virtual reality guidance tool to provide on-site operation guidance, and record all operation guidance processes to obtain a complete operation guidance log; A generation module, which is used to automatically process maintenance task allocation, resource scheduling and authority management through a smart contract mechanism based on the smart control record and the complete operation guidance log, generate an operation record chain, and generate an optimal candidate case library based on the operation record chain; The confirmation module is used to confirm and learn the best candidate cases through comparison and optimization based on the operation record chain and the best candidate case library, combined with the swarm intelligence algorithm, to obtain the intelligent computer room alliance network.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement an integrated intelligent computer room management method as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, an integrated intelligent computer room management method as described in any one of claims 1 to 7 is implemented.

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