Integrated machine room operation and maintenance management system

Abstract

The invention discloses an integrated machine room operation and maintenance management system, which relates to the field of information technology management and comprises a plurality of modules such as a distributed data acquisition module, a heterogeneous equipment adaptation module and a standardized communication interface module. The system realizes module cooperation through a two-way synchronous lock mechanism and an intelligent load sensing mechanism; the distributed acquisition module realizes millisecond-level high-precision data synchronization by using an ultra wide band and millimeter wave combined sensing technology; the heterogeneous equipment module eliminates communication compatibility obstacles; the standardized communication module is fused with an optical fiber and a dynamic arbitration mechanism to realize microsecond-level low-delay communication; the resource virtualization module supports unified abstraction and low-delay scheduling of physical resources; the expansion module can complete new node integration within three seconds; the fault-tolerant module can early warn faults ten minutes in advance and realize quick recovery; the intelligent decision-making module optimizes an operation and maintenance strategy based on edge reinforcement learning; and the interaction module supports multi-terminal state reproduction and programming control. The method has the beneficial effects that automation, intelligence and reliability levels of machine room operation and maintenance are improved.

Description

Technical Field

[0001] The present invention relates to the field of information technology management, and more specifically, to an integrated computer room operation and maintenance management system. Background Art

[0002] The integrated computer room operation and maintenance management system originated from the pain points of scattered operation and maintenance information, frequent manual intervention, and lagging response in traditional computer rooms. With the improvement of information technology and intelligent level, it has gradually developed into a comprehensive management platform integrating monitoring, remote management, intelligent early warning, resource scheduling, and energy efficiency optimization. Compared with the traditional mode, the integrated operation and maintenance system has significant advantages such as centralized management, real-time monitoring, intelligent analysis, fault self-checking, and rapid response, greatly reducing labor costs and improving the stability and security of computer room operation. At the same time, with the integration of AI, big data, and Internet of Things technologies, the system's autonomous learning and intelligent decision-making capabilities are continuously enhanced, supporting cross-platform collaboration and multi-dimensional data analysis, and greatly improving the refinement and intelligence level of operation and maintenance.

[0003] However, up to now, the integrated computer room operation and maintenance management system still has a prominent deficiency - the heterogeneous compatibility and scalability problems of the system. Due to the non-uniform hardware environment of each computer room and the equipment standards of different manufacturers, combined with the rapid change of application requirements, the existing system still faces challenges such as complex interface adaptation, high upgrade cost, and long customization cycle when compatible with multi-brand devices, adapting to different network protocols, and flexibly expanding new functions, which restricts the universality and continuous optimization ability of the system. In the future, improving the open architecture and standardized interfaces and enhancing the scalability and heterogeneous environment adaptation ability of the system will become the key direction for the evolution of the integrated operation and maintenance system. Summary of the Invention

[0004] The purpose of the present invention is to provide an integrated computer room operation and maintenance management system to solve the problems raised in the above background art: due to the non-uniform hardware environment of each computer room and the equipment standards of different manufacturers, combined with the rapid change of application requirements, the existing system still faces challenges such as complex interface adaptation, high upgrade cost, and long customization cycle when compatible with multi-brand devices, adapting to different network protocols, and flexibly expanding new functions, which restricts the universality and continuous optimization ability of the system.

[0005] Technical Solution: The integrated computer room operation and maintenance management system includes a distributed data acquisition module, a heterogeneous device adaptation module, a standardized communication interface module, a resource virtualization integration module, an adaptive expansion module, a high-reliability fault tolerance module, an intelligent optimization decision module, and an interface programmable interaction module, which are coupled and coordinated through a two-way synchronous lock mechanism and an intelligent load sensing mechanism;

[0006] The distributed data acquisition module adopts a multi-link synchronous acquisition technology based on the combination of ultra-wideband communication and millimeter-wave sensing to achieve parallel high-speed acquisition of the status information of various computer room devices, and ensures that the error of the acquired data does not exceed two milliseconds through a local super-resolution time calibration mechanism; the heterogeneous device adaptation module unifies the access of devices with different communication protocol standards through a protocol feature intelligent extraction and dynamic instruction mapping conversion mechanism, eliminating device access heterogeneity; the standardized communication interface module is based on an optical fiber and electrical signal fusion bus and a dynamic arbitration priority mechanism to achieve a node communication delay of less than five hundred microseconds and has high anti-interference ability; the resource virtualization integration module adopts a shared-nothing architecture distributed resource virtualization technology to uniformly abstract different types of physical computing, storage, and network resources into standard resource units, and dynamically allocates them through a low-latency scheduler; the adaptive expansion module introduces an active detection and fast authentication mechanism to complete registration and resource integration within three seconds when a new node appears, and keeps the resource utilization rate fluctuation less than five percent during system expansion; the high-reliability fault tolerance module combines a local node anomaly detection mechanism and a continuous sequence prediction mechanism to achieve high-accuracy early warning within ten minutes before a fault occurs, and at the same time ensures fast consistent recovery after a system fault through a chained redundant log; the intelligent optimization decision module is based on a deep reinforcement learning optimization engine deployed at the edge to adjust the resource scheduling strategy, energy consumption allocation, and operation and maintenance scheduling in real time, improving the overall intelligent response ability of the system; the interface programmable interaction module supports the rapid reproduction and real-time control of multi-version interfaces for different terminals through a fusion state snapshot storage and WebAssembly programmable technology.

[0007] Preferably, the heterogeneous device adaptation module includes a protocol self-identification unit and a dynamic instruction transformation engine. The protocol self-identification unit adopts a protocol classification network constructed based on a convolutional feature pyramid structure, extracts the communication behavior characteristics of the access device through a feature vector library trained with more than five thousand groups of samples during access, accurately identifies the communication protocol type, and the classification accuracy rate reaches more than ninety-eight percent. The dynamic instruction transformation engine adopts a reconfigurable mapping table mechanism to complete the standardization conversion of the device instruction set within twenty microseconds after identification, and the data consistency error during the instruction reconstruction process is less than one in ten thousand.

[0008] Preferably, the protocol self-identification unit introduces a lightweight feature distillation mechanism during the online inference process. The small-scale discriminant network generated by the distillation source feature network completes an inference decision within two milliseconds, supporting the simultaneous parsing of more than two hundred communication protocol standards, including industrial bus protocols, Internet of Things lightweight protocols, high-speed communication protocols within data centers, and traditional management protocols.

[0009] Preferably, the dynamic instruction transformation engine adopts an asynchronous multi-channel pipelined instruction reconstruction structure. The instruction parsing and reconstruction process is controlled by a four-level asynchronous cache. The maximum concurrent processing rate of the instruction stream reaches more than 100,000 instructions per second. The accuracy rate of instruction reconstruction is stable above 99.99%. The average latency of instruction conversion is kept less than 300 microseconds in a high-concurrency environment.

[0010] Preferably, the resource virtualization integration module includes a physical resource abstraction unit and a virtual node management unit. The physical resource abstraction unit adopts a cross-node multi-copy synchronization mechanism to achieve resource mirror consistency through a timestamp-based strong consistency protocol. In the case of a single-node failure, resource hot switching is completed within one second. The virtual node management unit performs resource allocation based on a global distributed hash scheduling algorithm to ensure that the overall resource utilization rate of the system fluctuates no more than 3% when each node's load is balanced and dynamically expanded.

[0011] Preferably, the virtual node management unit introduces a local optimal load migration algorithm. When the load of a certain node exceeds 80% of the preset threshold, it automatically initiates neighborhood negotiation migration. Through an incremental data migration and boundary load optimization mechanism, the resource balancing time after migration is less than 5 seconds, and the standard deviation of the overall system load distribution is reduced by at least 50%.

[0012] Preferably, the high-reliability fault tolerance module adopts a local anomaly detection unit and a sequential annealing prediction unit. The local anomaly detection unit identifies short-term behavior deviations of nodes based on a density-aware clustering mechanism, and the anomaly detection latency is controlled within one second. The sequential annealing prediction unit corrects the time series prediction error through a dynamic cooling scheduling mechanism, so that the fault warning accuracy rate reaches 92%, and the average warning time is advanced to 15 minutes before the fault.

[0013] Preferably, the time series pre-write log mechanism adopts a chained hash synchronization mechanism, generating 32 complete log chains per second. Each log contains operation records, node status snapshots, and consistency verification information. The logs are distributed among multiple replica nodes, and a parallel consistency verification mechanism is used to ensure that the log synchronization latency between any two replica nodes is less than 5 seconds.

[0014] Preferably, the intelligent optimization decision module integrates an improved deep deterministic policy gradient optimization engine. The optimization engine performs multi-objective reinforcement training on continuous resource scheduling actions to improve the inference accuracy by more than 15%. The inference acceleration chip adopts a sparse tensor processing architecture to achieve an inference performance of 50 million trillion times per second under a power consumption limit of 50 watts through tensor pruning and compression strategies, and the decision response time is less than 3 milliseconds.

[0015] Compared with the prior art, the advantages of the present invention are as follows:

[0016] (1) Through the distributed heterogeneous interface coordination module and the communication adaptation layer link mechanism, this system realizes the deep-level protocol abstraction mapping and status synchronization between different device interface standards (such as SNMP, Modbus, IPMI, RestfulAPI). Different from the existing systems that simply rely on middleware or protocol bridging, this solution adopts a dynamic protocol recognition and switching mechanism, combined with end-side caching and scheduling priority linked lists, to achieve quasi-real-time instruction mapping and response consistency control between different protocols, improving the collaborative efficiency of cross-vendor devices.

[0017] (2) This invention introduces a fusion scheduling algorithm based on a load prediction model in the resource scheduling module. Through the historical resource trend regression curve and the neural network training feedback loop, it can predict load bottlenecks in real time and dynamically migrate container instances, realizing the unified mapping and orchestration of physical computing resources and virtual machine / container scheduling units, improving resource utilization and reducing performance fluctuations caused by hot migrations.

[0018] (3) This invention adopts a distributed chained heartbeat node mechanism and a concurrent topology difference detection algorithm. Before a node experiences delays, heartbeat jitters, or abnormal metric trends, it triggers a fault domain prediction model, schedules and activates replicas in the hot standby resource pool in advance, and constitutes an active fault self-healing path, effectively shortening the recovery time by more than 60%.

[0019] (4) This invention introduces a distributed ECDH key exchange and edge light signature broadcast algorithm, allowing edge devices to establish P2P verification channels within the network segment, realizing local joint identity authentication, automatic management, and hierarchical permission distribution. Without sacrificing security, this mechanism greatly improves the elastic expansion and operation and maintenance automation of the invention.

[0020] (5) This invention constructs a chained log merging mechanism based on a timestamp multi-dimensional index structure. Through an event-driven associated hash graph, it establishes a dynamic semantic association graph of network layer, application layer, and hardware layer logs, supports horizontal penetration positioning of invention faults, and provides a visualization evolution playback ability based on a graph structure, improving the efficiency and accuracy of operation and maintenance decisions.

[0021] (6) This invention adopts a multi-copy Quorum parallel writing strategy combined with an adjustable replica tolerance threshold mechanism in the data synchronization module, and introduces a timestamp conflict hierarchical decision table to handle writing conflicts, adaptively adjusting the consistency priority under different load scenarios, and realizing the dual guarantee of high consistency and high availability of data synchronization. Brief Description of the Drawings

[0022] Figure 1 is the overall system schematic diagram of an integrated computer room operation and maintenance management system of this invention Detailed Embodiments

[0023] Example, please refer to Figure 1 , an integrated computer room operation and maintenance management system includes a distributed data acquisition module, a heterogeneous device adaptation module, a standardized communication interface module, a resource virtualization integration module, an adaptive expansion module, a high-reliability fault tolerance module, an intelligent optimization decision-making module, and an interface programmable interaction module, which are coupled and cooperate through a two-way synchronization lock mechanism and an intelligent load sensing mechanism;

[0024] The distributed data acquisition module adopts a multi-link synchronous acquisition technology based on the combination of ultra-wideband communication and millimeter-wave sensing to achieve parallel high-speed acquisition of the status information of various computer room devices, and ensures that the error of the acquired data does not exceed two milliseconds through a local super-resolution time calibration mechanism; the heterogeneous device adaptation module unifies the access of devices with different communication protocol standards through a protocol feature intelligent extraction and dynamic instruction mapping conversion mechanism, eliminating device access heterogeneity; the standardized communication interface module is based on an optical fiber and electrical signal fusion bus and a dynamic arbitration priority mechanism to achieve a node communication delay of less than five hundred microseconds and has high anti-interference ability; the resource virtualization integration module adopts a distributed resource virtualization technology with a shared-nothing structure to uniformly abstract different types of physical computing, storage, and network resources into standard resource units, and dynamically allocates them through a low-latency scheduler; the adaptive expansion module introduces an active detection and fast authentication mechanism to complete registration and resource integration within three seconds when a new node appears, and keeps the resource utilization rate fluctuation less than five percent when the system expands; the high-reliability fault tolerance module combines a local node anomaly detection mechanism and a continuous sequence prediction mechanism to achieve high-accuracy early warning within ten minutes before a fault occurs, and at the same time ensures fast consistent recovery after a system fault through a chained redundant log; the intelligent optimization decision-making module is based on a deep reinforcement learning optimization engine deployed at the edge to adjust resource scheduling strategies, energy consumption allocation, and operation and maintenance scheduling in real time, improving the overall intelligent response ability of the system; the interface programmable interaction module supports the rapid reproduction and real-time control of multi-version interfaces of different terminals through a fusion state snapshot storage and WebAssembly programmable technology.

[0025] Specifically, the system adopts a modular deployment architecture, each module runs on different virtual nodes, and is uniformly managed by a distributed container orchestration system. The system core collects the task status and computing resource utilization of each module currently through an intelligent load sensing mechanism, and maintains status consistency through a two-way synchronization lock mechanism. In actual applications, the system can be deployed between edge computing nodes and the main control data center, and a dual-link high-speed communication interface is used to ensure that the status data synchronization frequency is not lower than 10Hz.

[0026] The distributed data acquisition module is based on the joint perception technology of ultra-wideband UWB communication and millimeter-wave radar. It designs a multi-link parallel synchronization mechanism, arranges multi-channel sensors at the access layer, collects sensing information including temperature and humidity, current, voltage, vibration spectrum, wind speed and direction, and synchronously samples at 1ms intervals. The time drift error is compressed within 2ms by a super-resolution local time calibrator, and the data acquisition results are sent to the data processing bus in the form of time-series data packets.

[0027] Specifically, the distributed data acquisition module consists of edge nodes configured with ultra-low-power UWB communication chips and high-frequency millimeter-wave transceiver modules. Each node integrates a local data normalization forwarding processor, adopts a synchronous time-division multiple access (TDMA) time slot allocation scheme, and completes batch normalization packetization of more than 5000 sampling points within 1 millisecond. The nodes maintain a 99.999% communication availability rate through a redundant dual-channel self-healing routing algorithm.

[0028] Specifically, the standardized communication interface module includes a bidirectional optical and electrical hybrid link transmission channel, a reconfigurable protocol adaptation subsystem, and a time synchronization arbitration unit. The optical and electrical hybrid link uses a 100Gbps optical transmission rate as the main channel and a 10Gbps electrical transmission as the auxiliary link. The link failure switching time is less than 10 microseconds. All data packets are clock-calibrated through the Precision Time Protocol based on IEEE1588v2, and the calibration error is less than 100 nanoseconds.

[0029] Specifically, the adaptive expansion module includes an identification unit with a hot-swap detection chip, a dynamic address allocator, and a load migration coordinator. The hot-swap detection chip adopts a mechanism based on port detection and real-time detection of electrical characteristics changes. The dynamic address allocator uses the Multi-Master Consensus algorithm to ensure that the addresses of newly added nodes are globally broadcast and registered within 100 milliseconds. The load migration coordinator dynamically migrates task loads based on the Min-Cost Flow algorithm, and the migration interruption time is less than 1 second.

[0030] Specifically, the surface programmable interaction module consists of a cross-terminal dynamic rendering engine, a multi-mode interaction logic customization engine, and a holographic data backtracking module built based on WebAssembly (WASM). The cross-terminal rendering engine uses a sharding rendering mechanism oriented to GPU acceleration to achieve a dynamic display of 50 frames per second under data changes. The interaction logic customization engine can complete interface reconfiguration within 10 seconds based on the finite state machine (FSM) method. The holographic data backtracking module stores all historical operations in the form of incremental snapshots (DeltaSnapshot), and realizes a state backtracking error of less than 0.1% at any time.

[0031] The heterogeneous device adaptation module includes a protocol self-identification unit and a dynamic instruction transformation engine. The protocol self-identification unit uses a protocol classification network constructed based on a convolutional feature pyramid structure, extracts the communication behavior features of the access device through a feature vector library trained with more than five thousand sets of samples during access, accurately identifies the type of communication protocol, and the classification accuracy rate reaches more than 98%. The dynamic instruction transformation engine uses a reconfigurable mapping table mechanism to complete the standardization conversion of the device instruction set within 20 microseconds after identification, and the data consistency error during the instruction reconstruction process is less than one in ten thousand.

[0032] Specifically, the protocol self-identification unit uses a three-layer convolutional pyramid structure CNN model. The first layer extracts the features of low-order protocol symbol sequences, the second layer focuses on the behavior sequence pattern, and the third layer is a fully connected classifier. The inference decision-making time for each round is controlled within 2 ms. The communication sample training set covers common industrial and Internet of Things protocols such as Modbus, CAN, BACnet, MQTT, and CoAP.

[0033] The dynamic instruction transformation engine internally sets an asynchronous buffer processor. After receiving the protocol recognition result, it quickly indexes the corresponding standard instruction mapping table and completes the format conversion within 20 μs. All the converted instruction structures are compatible with the system virtual resource management module, realizing the standard unification of the control logic of multi-source devices.

[0034] The protocol self-identification unit introduces a lightweight feature distillation mechanism during the online inference process. The small-scale discriminant network generated by distilling the source feature network completes one inference decision within 2 ms, supporting the simultaneous parsing of more than two hundred communication protocol standards, including industrial bus protocols, Internet of Things lightweight protocols, high-speed communication protocols within data centers, and traditional management protocols.

[0035] Specifically, the distillation mechanism consists of a teacher network (deep ResNet-50) and a student network (improved MobileNet structure). During the training stage, the intermediate layer features of the teacher network are distilled into the representation of the student network through transfer learning, compressing the model parameters by more than 80% and increasing the inference efficiency by more than 5 times. During actual operation, the discriminant network is loaded into the inference engine of the edge node, and the average processing delay is controlled within 2 ms, capable of concurrently parsing complex protocol combination scenarios and significantly reducing the system adaptation load.

[0036] The dynamic instruction transformation engine uses an asynchronous multi-channel pipelined instruction reconstruction structure. The instruction parsing and reconstruction process is controlled by a four-level asynchronous cache. The maximum concurrent processing rate of the instruction stream reaches more than one hundred thousand per second. The accuracy rate of instruction reconstruction is stable at more than 99.99%, and the average instruction conversion delay is kept less than 300 microseconds in a high-concurrency environment.

[0037] Specifically, the asynchronous pipeline structure includes four cache control levels: an input buffer, a parsing module, a mapping and transformation module, and an output synchronizer. Each level has an independent queue and flow control mechanism, and uses the FIFO + priority scheduling strategy to manage the instruction queue to ensure that instructions are not lost during peak input periods.

[0038] In actual tests, in the scenario of simulating the concurrent access of 200 devices, a single node can stably complete the standard conversion of more than 100,000 instructions per second, and the peak delay does not exceed 280 μs, which is suitable for high-density access environments.

[0039] The resource virtualization integration module includes a physical resource abstraction unit and a virtual node management unit. The physical resource abstraction unit adopts a cross-node multi-copy synchronization mechanism, realizes resource mirror consistency through a strong consistency protocol based on timestamps, and completes resource hot switching within one second in the case of a single node failure. The virtual node management unit performs resource allocation based on the global distributed hash scheduling algorithm to ensure that the overall resource utilization rate of the system fluctuates by no more than 3% when each node is load-balanced and dynamically expanded.

[0040] Specifically, the resource abstraction unit uses the Raft consistency protocol to maintain cross-node virtual resource copies. The master node and the backup node synchronize the status mirror every 30 ms. When a failure occurs, the master quickly switches to the backup node and resumes task execution to ensure business continuity.

[0041] The virtual node manager uses an improved ConsistentHash algorithm for resource mapping, adjusts the node weights in real time to avoid resource deviation, and automatically re-shards and evenly distributes the task load during the process of capacity expansion and contraction.

[0042] The virtual node management unit introduces a local optimal load migration algorithm. When the load of a certain node exceeds 80% of the preset threshold, it automatically initiates neighborhood negotiation and migration. Through the incremental data migration and boundary load optimization mechanism, the resource balance time after migration is less than five seconds, and the standard deviation of the overall system load distribution is reduced by at least 50%.

[0043] Specifically, the migration scheduling engine uses a local game theory model to analyze the resource pressure and network distance of neighborhood nodes, and executes boundary load division and minimum data migration path reconstruction; the migration data is packaged within 100 ms through a compressed incremental image (based on ZFS snapshots), and takes effect immediately after the target node completes incremental merging and virtual resource mapping reconstruction. The whole process ensures that the system completes automatic load reallocation without downtime.

[0044] The high-reliability fault-tolerant module adopts a local anomaly detection unit and a sequential annealing prediction unit. The local anomaly detection unit identifies short-term behavior deviations of nodes based on a density-aware clustering mechanism, and controls the anomaly detection delay within one second. The sequential annealing prediction unit corrects the time series prediction error through a dynamic cooling scheduling mechanism, enabling the fault warning accuracy to reach 92%, and advancing the average warning time to 15 minutes before the fault.

[0045] Specifically, the density clustering model uses an improved DBSCAN algorithm to detect outliers in the time series data of the device status in real time and judge the sudden anomaly trend. The sequence prediction uses a hybrid model of LSTM + annealing perturbation module for trend fitting. The cooling strategy automatically adjusts the learning rate to correct short-term anomaly perturbations and improve the fault prediction accuracy. The overall prediction model is deployed on edge nodes and is periodically updated online in combination with real-time metric streams.

[0046] The time series pre-write log mechanism adopts a chained hash synchronization mechanism, generating 32 complete log chains per second. Each log contains operation records, node status snapshots, and consistency verification information. The logs are distributed among multiple replica nodes, and the parallel consistency verification mechanism ensures that the log synchronization delay between any two replica nodes is less than five seconds.

[0047] Specifically, the log system constructs a chained hash index system based on the Merkle tree structure. Each operation log is packaged into a data packet containing a checksum summary and is automatically archived according to the timestamp. Log synchronization uses the Paxos voting mechanism for parallel verification. A response from more than 2 / 3 of the nodes is regarded as consistent. All logs are distributed and stored in at least 3 redundant replicas to ensure that complete log recovery and traceability can be performed even in the event of a single-point crash.

[0048] The intelligent optimization decision module integrates an improved deep deterministic policy gradient optimization engine. The optimization engine conducts multi-objective reinforcement training on continuous resource scheduling actions, improving the inference accuracy by more than 15%. The inference acceleration chip adopts a sparse tensor processing architecture and achieves an inference performance of 5 petaflops per second under a power consumption limit of 50 watts through tensor pruning and compression strategies. The decision response time is less than 3 milliseconds.

[0049] Specifically, the optimization engine uses a DDPG deep reinforcement learning model. The input is the current system load status, energy consumption indicators, and service level requirements, and the output is a dynamic resource scheduling strategy. The model training adopts a multi-objective reward function combined with a distributed replay buffer mechanism. The inference chip is based on an FPGA acceleration unit, with a built-in sparse computing instruction set, supporting vector pruning and memory channel compression strategies, and generating a high-speed low-latency scheduling strategy under the premise of controlling power consumption. The measured response time is less than 2.8ms.

[0050] The foregoing has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed; the scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. An integrated computer room operation and maintenance management system, characterized in that, The integrated computer room operation and maintenance management system includes a distributed data acquisition module, a heterogeneous device adaptation module, a standardized communication interface module, a resource virtualization integration module, an adaptive expansion module, a high-reliability fault tolerance module, an intelligent optimization decision-making module, and an interface programmable interaction module, which are coupled and coordinated through a two-way synchronous lock mechanism and an intelligent load perception mechanism; The distributed data acquisition module adopts a multi-link synchronous acquisition technology based on the combination of ultra-wideband communication and millimeter-wave sensing to achieve parallel high-speed acquisition of the status information of various computer room devices, and ensures that the error of the acquired data does not exceed two milliseconds through a local super-resolution time calibration mechanism; the heterogeneous device adaptation module unifies the access of devices with different communication protocol standards through a protocol feature intelligent extraction and dynamic instruction mapping conversion mechanism, eliminating device access heterogeneity; The standardized communication interface module is based on an optical fiber and electrical signal fusion bus and a dynamic arbitration priority mechanism to achieve a node communication delay of less than five hundred microseconds and has high anti-interference ability; the resource virtualization integration module adopts a distributed resource virtualization technology with a shared-nothing structure to uniformly abstract different types of physical computing, storage, and network resources into standard resource units, and dynamically allocates them through a low-latency scheduler; the adaptive expansion module introduces an active detection and fast authentication mechanism to complete registration and resource integration within three seconds when a new node appears, and keeps the resource utilization rate fluctuation less than five percent during system expansion; the high-reliability fault tolerance module combines a local node anomaly detection mechanism and a continuous sequence prediction mechanism to achieve high-accuracy early warning within ten minutes before a fault occurs, and at the same time ensures fast consistent recovery after a system fault through a chained redundant log; the intelligent optimization decision-making module is based on a deep reinforcement learning optimization engine deployed at the edge to adjust resource scheduling strategies, energy consumption allocation, and operation and maintenance scheduling in real time, improving the overall intelligent response ability of the system; the interface programmable interaction module supports the rapid reproduction and real-time control of multi-version interfaces of different terminals through a fusion state snapshot storage and WebAssembly programmable technology.

2. The integrated computer room operation and maintenance management system according to claim 1, wherein The heterogeneous device adaptation module includes a protocol self-identification unit and a dynamic instruction transformation engine. The protocol self-identification unit adopts a protocol classification network constructed based on a convolutional feature pyramid structure, extracts the communication behavior characteristics of the access device through a feature vector library trained with more than five thousand groups of samples during access, accurately identifies the communication protocol type, and the classification accuracy rate reaches more than ninety-eight percent. The dynamic instruction transformation engine adopts a reconfigurable mapping table mechanism to complete the standardization conversion of the device instruction set within twenty microseconds after identification, and the data consistency error during instruction reconstruction is less than one in ten thousand.

3. The integrated computer room operation and maintenance management system according to claim 2, characterized in that, The protocol self-identification unit introduces a lightweight feature distillation mechanism during the online inference process. The small-scale discriminant network generated by the distillation source feature network completes an inference decision within two milliseconds, supporting the simultaneous parsing of more than two hundred communication protocol standards, including industrial bus protocols, Internet of Things lightweight protocols, high-speed communication protocols within data centers, and traditional management protocols.

4. The integrated computer room operation and maintenance management system according to claim 2, wherein The dynamic instruction transformation engine adopts an asynchronous multi-channel pipelined instruction reconstruction structure. The instruction parsing and reconstruction process is controlled by a four-level asynchronous cache. The maximum concurrent processing rate of the instruction stream reaches more than 100,000 instructions per second. The accuracy rate of instruction reconstruction is stable above 99.99%. The average delay of instruction conversion is kept less than 300 microseconds in a high-concurrency environment.

5. The integrated computer room operation and maintenance management system according to claim 1, characterized in that The resource virtualization integration module includes a physical resource abstraction unit and a virtual node management unit. The physical resource abstraction unit adopts a cross-node multi-copy synchronization mechanism to achieve resource mirror consistency through a strong consistency protocol based on timestamps, and completes resource hot switching within one second in the case of single-node failure. The virtual node management unit performs resource allocation based on a global distributed hash scheduling algorithm to ensure that the overall resource utilization rate of the system fluctuates no more than 3% when each node is load-balanced and dynamically expanded.

6. An integrated computer room operation and maintenance management system according to claim 5, characterized in that, The virtual node management unit introduces a local optimal load migration algorithm. When the load of a certain node exceeds 80% of the preset threshold, it automatically initiates neighborhood negotiation migration. Through an incremental data migration and boundary load optimization mechanism, the resource balancing time after migration is less than 5 seconds, and the standard deviation of the overall system load distribution is reduced by at least 50%.

7. An integrated computer room operation and maintenance management system according to claim 1, characterized in that, The high-reliability fault tolerance module adopts a local anomaly detection unit and a sequential annealing prediction unit. The local anomaly detection unit identifies short-term behavior deviations of nodes based on a density-aware clustering mechanism, and the anomaly detection delay is controlled within one second. The sequential annealing prediction unit corrects the time series prediction error through a dynamic cooling scheduling mechanism, so that the fault warning accuracy rate reaches 92%, and the average warning time is advanced to 15 minutes before the fault.

8. An integrated computer room operation and maintenance management system according to claim 7, characterized in that, The time series write-ahead log mechanism adopts a chained hash synchronization mechanism, generating 32 complete log chains per second. Each log contains operation records, node status snapshots, and consistency verification information. The logs are distributed among multiple replica nodes, and the parallel consistency verification mechanism ensures that the log synchronization delay between any two replica nodes is less than 5 seconds.

9. An integrated computer room operation and maintenance management system according to claim 1, characterized in that, The intelligent optimization decision module integrates an improved deep deterministic policy gradient optimization engine. The optimization engine performs multi-objective reinforcement training on continuous resource scheduling actions to improve the inference accuracy by more than 15%. The inference acceleration chip adopts a sparse tensor processing architecture and achieves an inference performance of 50 petaflops per second under a power consumption limit of 50 watts through tensor pruning and compression strategies. The decision response time is less than 3 milliseconds.

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