Prefabricated cabin type data center monitoring system

Abstract

The invention relates to the technical field of data center management, and discloses a prefabricated cabin type data center monitoring system, which comprises a data acquisition module arranged in a prefabricated cabin and used for acquiring environment data, electric power data and IT equipment operation state data in the cabin, converting the acquired data into a standardized data format, and transmitting the standardized data format to a server through a protocol conversion interface; and uniformly converting the acquired data of different communication protocols into a preset standard data format. A data cleaning and compression mechanism is designed in an edge calculation module, collected environment data, power data and IT equipment operation state data are preprocessed in real time, abnormal data are rapidly judged based on set upper and lower limit values, the abnormal data are marked as alarm data and are preferentially transmitted to a subsequent module, transmission of invalid data is reduced, and the efficiency is improved. The network bandwidth utilization rate is improved, rapid fault detection and response are realized, and the fault diagnosis and repair time is shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of data center management, and in particular to a prefabricated cabin-type data center monitoring system. Background Art

[0002] With the rapid growth of data center demand, the traditional data center construction model is gradually facing many challenges, including long construction period, large floor space and high energy consumption. To this end, modular and prefabricated data center solutions have emerged. Among them, prefabricated cabin data centers, as a highly integrated modular solution, have significant advantages of rapid deployment, space optimization, energy saving and environmental protection, and have become an important trend in the development of the industry.

[0003] Prefabricated cabin data centers usually integrate power systems, air conditioning systems, communication equipment, and IT equipment operating environment monitoring systems. The operating environment is complex, which places higher requirements on the monitoring and operation and maintenance systems. However, the existing monitoring systems have the following technical problems: Prefabricated cabin data centers integrate sensors, devices, and systems from different manufacturers. The devices use different communication protocols, which leads to compatibility issues in data collection and management. Traditional monitoring systems cannot achieve efficient access and unified management of heterogeneous devices, limiting the scalability and flexibility of the system.

[0004] Most existing monitoring systems use a centralized data processing architecture, and data needs to be transmitted to a central server for processing. When faced with large amounts of data, this can easily lead to excessive bandwidth usage, increased latency, and even data congestion. In addition, the lack of edge computing capabilities leads to delayed fault detection and response speeds, which cannot meet the data center's requirements for real-time and efficiency.

[0005] Critical data in data centers are vulnerable to security threats during transmission, such as data leakage, tampering or loss. At the same time, in a complex network environment, link failures or network congestion will affect the continuity and reliability of data transmission, increasing the operation and maintenance risks of the system.

[0006] Traditional monitoring systems rely more on manually set thresholds for monitoring and alarming, and lack intelligent analysis capabilities. The trend analysis and fault prediction capabilities of equipment operation status are weak, and the operation and maintenance process relies on experience-based operations, which cannot achieve active management and precise optimization.

[0007] Therefore, those skilled in the art provide a prefabricated cabin-type data center monitoring system to solve the above-mentioned problems. Summary of the invention

[0008] In view of the deficiencies of the prior art, the present invention provides a prefabricated cabin-type data center monitoring system to solve the problems raised in the above-mentioned background technology.

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions: A prefabricated cabin-type data center monitoring system, comprising: The data acquisition module is arranged inside the prefabricated cabin and is used to obtain the environmental data, power data and IT equipment operation status data in the cabin, and then convert the acquired data into a standardized data format. Through the protocol conversion interface, the collected data of different communication protocols are uniformly converted into a predetermined standard data format. The environmental data includes temperature, humidity, particulate matter concentration and smoke concentration, the power data includes voltage and current, and the IT equipment operation status data includes CPU utilization and memory occupancy; The edge computing module receives the standardized data provided by the data acquisition module to clean and compress the standardized data. In the compressed data generated after data cleaning and compression, it is determined whether the data exceeds the range according to the set upper and lower limits. If data exceeding the range is detected, it is marked as alarm data, and then the processed compressed data and alarm data are sent to the data transmission module; The data transmission module is used to receive the compressed data and alarm data transmitted by the edge computing module, and send them to the centralized monitoring module through industrial Ethernet or wireless communication link. During the data transmission process, the transmitted data is encrypted by a preset encryption protocol; The centralized monitoring module receives the encrypted data sent by the data transmission module, decrypts it and stores it in the time series database to visualize the stored data, including real-time display of environmental data, power data and IT equipment operation status data, and then generates alarm information based on the alarm data and pushes it to the operation and maintenance terminal. Finally, it periodically generates historical operation reports based on the received data and transmits them to the decision optimization module. The decision optimization module analyzes the historical operation reports and real-time data provided by the centralized monitoring module, conducts trend analysis on the operating status of the equipment in the prefabricated cabin, and adjusts the operating parameters of the air-conditioning and power equipment in the prefabricated cabin to optimize energy consumption by combining environmental data, power data, and IT equipment operating status data. At the same time, the module predicts equipment failures based on the trend analysis results and feeds the prediction results back to the centralized monitoring module to prompt the operation and maintenance personnel to take preventive measures.

[0010] Preferably, the data acquisition module includes: The environmental data acquisition submodule collects cabin environmental data, including temperature, humidity, particulate matter concentration, and smoke concentration, and then converts the collected data into a standardized data format and provides it to the power data acquisition submodule; The power data acquisition submodule collects the voltage and current of the power system in the cabin, calculates the real-time power, and then verifies whether the environmental data affects the reliability of the power data, completing data calibration; The IT equipment operation status data collection submodule collects the operation status of the servers and storage devices in the cabin, including CPU utilization and memory occupancy, and uses power data to analyze the energy consumption of IT equipment and complete load calibration.

[0011] Preferably, the environmental data collection submodule includes: Temperature and humidity collection, use temperature and humidity sensors to measure temperature T and humidity H: , in, represents the environment state vector at time t, is the temperature at time t, is the humidity at time t; Particle concentration, using PM2.5 sensor to measure particle concentration P: , in, is the number of particles per unit volume at time t, is the sample gas volume, is the particle concentration; Smoke density, use the smoke sensor to measure the smoke density S: , in, is the smoke concentration at time t, is the output voltage of the smoke sensor at time t, The sensitivity factor of the sensor.

[0012] Preferably, the power data acquisition submodule includes: Voltage and current acquisition, using voltage sensors and current transformers to measure voltage and current : , in, represents the power state vector at time t, is the voltage at time t, The current at time t; Power calculation, based on voltage and current , calculate the instantaneous power : , in, is the power at time t, is the voltage at time t, The current at time t; Power data calibration uses environmental data to verify the reliability of power data. The formula is as follows: , in, It is the power deviation caused by environmental factors. is the current temperature, is the reference temperature, is the maximum power deviation, is the temperature sensitivity coefficient.

[0013] Preferably, the IT equipment operation status data collection submodule includes: CPU utilization, use the server management tool to obtain CPU utilization : , in, is the CPU utilization at time t, is the CPU core i usage time at time t, is the total time of the CPU core i at time t; Memory usage, use system monitoring tools to obtain memory usage : , in, is the memory usage at time t, is the used memory at time t, is the total system memory; Equipment energy consumption analysis, calculate the energy consumption of IT equipment based on power data : , in, is the energy consumption of IT equipment, is the total power at time t, It is the energy consumption ratio of IT equipment.

[0014] Preferably, the edge computing module includes: The data compression submodule uses a sliding window average algorithm to compress data: , in, represents the compressed data at time t, represents the sampled data at time i, represents the size of the sliding window, represents the time variable; The data cleaning submodule cleans the collected data through outlier detection: , in, is the cleaned data set, Represents the 25th percentile value of the data, Represents the 75th percentile value of the data, = - is the interquartile range, represents a data sample, The lower boundary, used to identify outliers, The upper boundary, used to identify outliers; The fast alarm submodule implements fault alarm by setting alarm thresholds: , in, represents the alarm status at time t, Indicates the maximum allowed threshold of monitoring data. Indicates the minimum allowed threshold of monitoring data, Represents the dynamic value at time t.

[0015] Preferably, the data transmission module includes a data receiving submodule, a data encryption submodule, a link transmission submodule and a data decryption submodule: The data receiving submodule receives compressed data and alarm data from the edge computing module and performs data format verification and buffering; First, verify the integrity of the transmitted data and determine whether there is any data loss through checksum calculation: , in, is the check code, is the content of the ith data packet, is the weight of the ith packet, is the preset verification benchmark value, and n represents the total number of data items; The received data is then stored in the buffer, and the buffer capacity is calculated as: , in, is the buffer capacity, is the receive data rate, is the maximum buffer time allowed; Finally, the received and verified data is passed to the encryption submodule for security processing.

[0016] Preferably, the data encryption submodule encrypts the received compressed data and alarm data: First, use the AES encryption algorithm to encrypt the data. The formula is as follows: , in, is the encrypted data. is the original data, is the encryption key; Then estimate the delay time of the encryption process: , in, is the encryption delay time, is the size of the data to be encrypted, is the encryption rate; Finally, the encrypted data is handed over to the link transmission submodule for transmission; The link transmission submodule transmits data through the main link and the backup link, and dynamically switches according to the link status, and then decrypts the encrypted data transmitted by the link and hands it over to the data decryption submodule for processing; Based on link bandwidth and delay Determine the link status: , in, is the link status, Indicates that the main link is used. Indicates switching to the backup link. is the bandwidth of the current link, is the current link delay, is the minimum bandwidth threshold, is the maximum latency threshold.

[0017] Preferably, the data decryption submodule decrypts the encrypted data after transmission and restores it to a readable original data format; First, use the AES algorithm that is consistent with encryption to decrypt, the formula is as follows: , in, is the decrypted data, is encrypted data, is the decryption key; Estimated latency of the decryption process: , in, is the decryption delay time, is the size of the data to be decrypted, is the decryption rate; The decrypted data is processed by the centralized monitoring module.

[0018] Preferably, when the centralized monitoring module stores the decrypted data, a time series database is used to store the data, and the data structure is represented as follows: , in, Indicates the timestamp, Indicates time The corresponding data value, n represents the total number of data items; The centralized monitoring module displays the stored data through visualization tools, including line graphs of real-time data, bar graphs of alarm data, and heat maps of historical data; The decision optimization module dynamically adjusts the operating parameters of air conditioners and power equipment based on the historical operation reports and real-time data provided by the centralized monitoring module, and optimizes energy consumption through the following formula: , in, represents the total energy consumption of the system, Indicates the cabin ambient temperature. and are the minimum and maximum values ​​of temperature.

[0019] The present invention provides a prefabricated cabin-type data center monitoring system, which has the following beneficial effects: 1. The present invention designs a data cleaning and compression mechanism in the edge computing module to perform real-time pre-processing on the collected environmental data, power data and IT equipment operating status data, and quickly judges abnormal data based on the set upper and lower limits. The abnormal data is marked as alarm data and transmitted to subsequent modules in priority, reducing the transmission of invalid data, improving network bandwidth utilization, achieving rapid detection and response to faults, shortening fault diagnosis and repair time, and optimizing system operation efficiency, reducing network transmission burden and improving fault response capabilities.

[0020] 2. The present invention integrates the AES symmetric encryption algorithm in the data transmission module to perform end-to-end encryption protection on compressed data and alarm data, thereby preventing data from being tampered with or stolen during transmission and improving data security. At the same time, a redundant switching mechanism for the main link and the backup link is designed. When the main link cannot be used due to failure or network congestion, the system can automatically switch to the backup link to ensure the continuity and stability of data transmission, achieve high security and high reliability in the data transmission process, and protect data privacy, enhance system fault tolerance, and ensure stable operation of the data center in a complex network environment.

[0021] 3. The present invention uses a decision optimization module, combined with historical operation reports and real-time data provided by a centralized monitoring module, and a machine learning algorithm to perform trend analysis and fault prediction on the operating status of the equipment in the prefabricated cabin. According to the equipment operation trend, the decision optimization module can dynamically adjust the operating parameters of the air-conditioning system and the power equipment to optimize the cabin temperature and key indicators of power load. At the same time, it can predict equipment failures in advance, generate early warning information and feed it back to the centralized monitoring module, prompting the operation and maintenance personnel to take corresponding measures, so as to realize active equipment management, optimize energy consumption and reduce the risk of failure, thereby improving equipment operation efficiency, extending equipment service life and reducing operation and maintenance costs.

[0022] 4. The present invention adopts a modular design to make the data acquisition, edge computing, data transmission, centralized monitoring and decision optimization modules run independently of each other. At the same time, through a unified protocol conversion interface, data from different communication protocols are converted into a standardized format, allowing independent upgrades and flexible expansions of each functional module, supporting plug-and-play of heterogeneous devices and dynamic access of new modules. The system can adapt to the deployment needs of prefabricated cabin-type data centers of different scales, and reserves interfaces for future system expansion and functional optimization, achieving the goals of high flexibility, high compatibility and high scalability, and simplifying system maintenance, supporting rapid business changes and adapting to multi-scenario requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a system framework diagram of the present invention. DETAILED DESCRIPTION

[0024] In order to make the technical personnel in the technical field understand the scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in combination with the drawings in the embodiment of the present invention. Obviously, the described embodiment is a partial embodiment of the present invention, not a complete embodiment. Based on the embodiment of the present invention, other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present invention.

[0025] The present invention is described in detail below in conjunction with the accompanying drawings:

[0026] Example: Please refer to the attached Figure 1 The embodiment of the present invention provides a prefabricated cabin-type data center monitoring system, including: The data acquisition module is arranged inside the prefabricated cabin and is used to obtain the environmental data, power data and IT equipment operation status data in the cabin, and then convert the acquired data into a standardized data format. Through the protocol conversion interface, the collected data of different communication protocols are uniformly converted into a predetermined standard data format. The environmental data includes temperature, humidity, particulate matter concentration and smoke concentration, the power data includes voltage and current, and the IT equipment operation status data includes CPU utilization and memory occupancy; The edge computing module receives the standardized data provided by the data acquisition module to clean and compress the standardized data. In the compressed data generated after data cleaning and compression, it is determined whether the data exceeds the range according to the set upper and lower limits. If data exceeding the range is detected, it is marked as alarm data, and then the processed compressed data and alarm data are sent to the data transmission module; The data transmission module is used to receive the compressed data and alarm data transmitted by the edge computing module, and send them to the centralized monitoring module through industrial Ethernet or wireless communication link. During the data transmission process, the transmitted data is encrypted by a preset encryption protocol; The centralized monitoring module receives the encrypted data sent by the data transmission module, decrypts it and stores it in the time series database to visualize the stored data, including real-time display of environmental data, power data and IT equipment operation status data, and then generates alarm information based on the alarm data and pushes it to the operation and maintenance terminal. Finally, it periodically generates historical operation reports based on the received data and transmits them to the decision optimization module. The decision optimization module analyzes the historical operation reports and real-time data provided by the centralized monitoring module, conducts trend analysis on the operating status of the equipment in the prefabricated cabin, and adjusts the operating parameters of the air-conditioning and power equipment in the prefabricated cabin to optimize energy consumption by combining environmental data, power data, and IT equipment operating status data. At the same time, the module predicts equipment failures based on the trend analysis results and feeds the prediction results back to the centralized monitoring module to prompt the operation and maintenance personnel to take preventive measures.

[0027] Benefits of data acquisition module: It can comprehensively cover environmental data, power data and IT equipment operating status data, realize accurate acquisition of multi-dimensional data in the cabin, and provide high-quality input for subsequent processing; Through the protocol conversion interface, data from different communication protocols are uniformly converted into a standardized data format to solve the compatibility problem between heterogeneous devices and improve the scalability and access flexibility of the system; Quickly obtain the operating parameters in the cabin and provide real-time data support for subsequent modules to meet the data center's needs for rapid response and high-precision monitoring.

[0028] Benefits of edge computing modules: Through data cleaning and compression functions, the edge computing module can effectively remove invalid or abnormal data, reduce data redundancy, significantly reduce the amount of transmitted data, and improve the overall processing efficiency of the system; Rapidly judge abnormal data based on the set upper and lower limits, generate alarm data and send it to subsequent modules first, shorten fault detection and response time, and enhance the real-time and security of the system; Complete preprocessing at the data source to reduce the data processing pressure of the centralized monitoring module and optimize the system's resource allocation.

[0029] Benefits of Data Transfer Module: End-to-end encryption of transmitted data is performed through a preset encryption protocol to prevent data from being stolen or tampered with during transmission, thus improving the data security of the system; Industrial Ethernet is used as the main link, and 5G wireless communication link is used as the backup link to provide dynamic redundant switching function to ensure the continuity and reliability of data transmission, and to maintain stable operation of the system when the network environment is unstable.

[0030] Benefits of centralized monitoring module: The decrypted data is stored in a time series database, and the real-time changes of environmental data, power data, and IT equipment operating status data are intuitively displayed through visualization tools, so that operation and maintenance personnel can fully understand the operating status of the cabin; Generate alarm information based on alarm data and push it to the operation and maintenance terminal to achieve instant notification and response to faults, improve fault handling efficiency, and reduce the work pressure of operation and maintenance personnel; Historical operation reports are generated regularly and passed to the decision-making optimization module to provide key data support for trend analysis and optimization and regulation, thereby improving the intelligence level of the system.

[0031] Benefits of the Decision Optimization Module: By combining historical operation reports and real-time data, machine learning algorithms are used to analyze the operating status of equipment in the cabin, proactively predict potential failures and generate early warning information to avoid downtime and losses caused by sudden equipment failures; Dynamically adjust the operating parameters of air conditioning and power equipment to achieve balanced control of cabin environment and energy, reduce the energy consumption cost of the system, and improve the operating efficiency and economic benefits of the data center; By feeding back the prediction results to the centralized monitoring module, operation and maintenance personnel are prompted to take preventive measures, reduce the frequency of manual intervention, reduce operation and maintenance costs, and extend the service life of the equipment.

[0032] In summary, through the collaborative work of the above modules, the prefabricated cabin data center monitoring system of the present invention improves the efficiency of data collection and processing, achieves high security and stability of data transmission, and at the same time, through intelligent monitoring and optimized regulation, further reduces energy consumption and operation and maintenance costs, providing comprehensive support for the efficient operation of the data center.

[0033] The data acquisition module includes: The environmental data acquisition submodule collects cabin environmental data, including temperature, humidity, particulate matter concentration, and smoke concentration, and then converts the collected data into a standardized data format and provides it to the power data acquisition submodule; The power data acquisition submodule collects the voltage and current of the power system in the cabin, calculates the real-time power, and then verifies whether the environmental data affects the reliability of the power data, completing data calibration; The IT equipment operation status data collection submodule collects the operation status of the servers and storage devices in the cabin, including CPU utilization and memory occupancy, and uses power data to analyze the energy consumption of IT equipment and complete load calibration.

[0034] Benefits of the environmental data acquisition submodule: It can accurately collect key environmental data such as cabin temperature, humidity, particulate matter concentration and smoke concentration, covering the core parameters that have a direct impact on cabin environmental quality and safety, and provide a reliable environmental monitoring foundation for subsequent modules; Convert the collected environmental data into a standardized format to ensure the compatibility and availability of the data in subsequent submodules and modules, and simplify the processing of heterogeneous data; The converted environmental data is directly provided to the power data acquisition submodule to verify the impact of cabin environmental factors on the accuracy of power data and provide support for the reliability calibration of power data.

[0035] Benefits of the power data acquisition submodule: It can accurately collect voltage and current data in the cabin, and by calculating real-time power, fully grasp the operating status of the power system in the cabin, providing a reliable basis for subsequent optimization of power equipment operating parameters; Based on the reference of environmental data, the power data acquisition submodule can verify the impact of environmental factors on power parameters, complete data calibration, and ensure the accuracy and reliability of power data; The calibrated power data processed by the power data acquisition submodule provides key input for the IT equipment operation status data acquisition submodule to analyze equipment energy consumption, thereby enhancing the collaboration between the submodules.

[0036] Benefits of IT equipment operation status data collection submodule: It can collect the operating parameters of the servers and storage devices in the cabin in real time, intuitively reflect the load and performance status of the equipment, and provide important data support for fault prediction and energy consumption optimization; By linking with the real-time power data of the power data acquisition submodule, the IT equipment operation status data acquisition submodule can quantify the actual energy consumption of IT equipment, provide accurate energy consumption analysis results, and provide a basis for energy consumption optimization decisions; Combined with power data and equipment operating status, load calibration is completed to further improve the accuracy of data collection and provide reliable data input for subsequent modules.

[0037] In summary, the environmental data acquisition submodule provides an environmental calibration basis for power data, the power data acquisition submodule provides key support for energy consumption analysis of IT equipment operation status, and the IT equipment operation status data acquisition submodule further improves the accuracy of the overall data through load calibration, achieving all-round monitoring of the environment, energy, and equipment operation, laying a solid foundation for subsequent data processing and decision optimization.

[0038] The environmental data collection submodule includes: Temperature and humidity collection, use temperature and humidity sensors to measure temperature T and humidity H: , in, represents the environment state vector at time t, is the temperature at time t, is the humidity at time t; Particle concentration, using PM2.5 sensor to measure particle concentration P: , in, is the number of particles per unit volume at time t, is the sample gas volume, is the particle concentration; Smoke density, use the smoke sensor to measure the smoke density S: , in, is the smoke concentration at time t, is the output voltage of the smoke sensor at time t, The sensitivity factor of the sensor.

[0039] The power data acquisition submodule includes: Voltage and current acquisition, using voltage sensors and current transformers to measure voltage and current : , in, represents the power state vector at time t, is the voltage at time t, The current at time t; Power calculation, based on voltage and current , calculate the instantaneous power : , in, is the power at time t, is the voltage at time t, The current at time t; Power data calibration uses environmental data to verify the reliability of power data. The formula is as follows: , in, It is the power deviation caused by environmental factors. is the current temperature, is the reference temperature, is the maximum power deviation, is the temperature sensitivity coefficient.

[0040] The IT equipment operation status data collection submodule includes: CPU utilization, use the server management tool to obtain CPU utilization : , in, is the CPU utilization at time t, is the CPU core i usage time at time t, is the total time of the CPU core i at time t; Memory usage, use system monitoring tools to obtain memory usage : , in, is the memory usage at time t, is the used memory at time t, is the total system memory; Equipment energy consumption analysis, calculate the energy consumption of IT equipment based on power data : , in, is the energy consumption of IT equipment, is the total power at time t, It is the energy consumption ratio of IT equipment.

[0041] Benefits of temperature and humidity collection: Using temperature and humidity sensors to collect temperature and humidity and form an environmental state vector can help you understand the changes in temperature and humidity in the cabin in real time and provide an environmental adaptability reference for equipment operation. Temperature and humidity data, as the basis for regulating the air-conditioning system, can effectively prevent equipment from operating failures due to overheating or abnormal humidity.

[0042] Benefits of particle concentration measurement: Using PM2.5 sensors to measure particle concentration directly reflects the number of particles per unit volume, providing a quantitative basis for air quality for operation and maintenance personnel. Particle concentration data helps to determine the effectiveness of the cabin filtration system, ensure the cleanliness of the equipment operating environment, and reduce the risk of equipment loss due to particle pollution.

[0043] Benefits of smoke concentration measurement: Smoke concentration can be measured by smoke sensors, which can directly reflect the smoke concentration level, quickly detect fire or abnormal smoke, and improve safety protection capabilities. Smoke data, temperature, humidity, and particle concentration data form a complete environmental monitoring matrix to ensure the stability and safety of the cabin environment.

[0044] Benefits of voltage and current collection: Use voltage sensors and current transformers to collect voltage and current to form a power state vector, which directly reflects the real-time status of the power system in the cabin. Through real-time monitoring of voltage and current, abnormalities can be quickly identified to ensure the safe operation of equipment in the cabin.

[0045] Benefits of power calculation: Calculate instantaneous power and provide key data for equipment load status and energy consumption analysis. Real-time power data provides a basis for energy allocation for subsequent modules, which helps optimize the energy consumption distribution in the cabin.

[0046] Benefits of power data calibration: Use environmental data to calibrate power data, quantify the impact of temperature fluctuations on power deviation, and ensure the accuracy of power data. Calibration eliminates the potential impact of temperature and humidity changes on power data, and improves the accuracy and stability of system monitoring.

[0047] Benefits of CPU utilization collection: Calculating CPU utilization can directly reflect the current operating pressure of the device and provide data support for load optimization. Real-time monitoring of CPU usage helps to detect performance bottlenecks caused by overload or improper resource allocation, and warn of failures in advance.

[0048] Benefits of collecting memory usage: Calculating memory usage can intuitively display the current memory resource usage and provide a basis for optimizing device resources. Memory usage data and CPU utilization are analyzed in coordination to provide more comprehensive information support for device resource scheduling and task allocation.

[0049] Benefits of equipment energy consumption analysis: Calculate the energy consumption of IT equipment, quantify the actual energy consumption of the equipment, and support refined energy consumption management. Equipment energy consumption analysis combines power data and equipment operating status to provide key input for energy consumption optimization and dynamic adjustment of subsequent modules.

[0050] In summary, the data of each sub-module complement and verify each other, forming an efficient and reliable in-cabin monitoring basic system, providing high-quality data input for subsequent edge computing and decision optimization, and further improving the monitoring accuracy and operation efficiency of the data center.

[0051] The edge computing module includes: The data compression submodule uses a sliding window average algorithm to compress data: , in, represents the compressed data at time t, represents the sampled data at time i, represents the size of the sliding window, represents the time variable; The data cleaning submodule cleans the collected data through outlier detection: , in, is the cleaned data set, Represents the 25th percentile value of the data, Represents the 75th percentile value of the data, = - is the interquartile range, represents a data sample, The lower boundary, used to identify outliers, The upper boundary, used to identify outliers; The fast alarm submodule implements fault alarm by setting alarm thresholds: , in, represents the alarm status at time t, Indicates the maximum allowed threshold of monitoring data. Indicates the minimum allowed threshold of monitoring data, Represents the dynamic value at time t.

[0052] Benefits of the data compression submodule: Compressing the collected data through the sliding window average algorithm to generate compressed data can effectively reduce the amount of redundant data and significantly reduce the bandwidth requirements for data transmission. Data compression retains the trends and key characteristics of time series data, providing high-quality simplified data for subsequent monitoring and analysis, while reducing the use of computing resources. Reducing storage space usage provides a more efficient solution for data storage in centralized monitoring modules and extends the service life of the storage system.

[0053] Benefits of the data cleaning submodule: Outliers are removed through outlier detection algorithms to ensure that the cleaned data set contains valid values. Noise data is eliminated to improve data accuracy and consistency. The upper and lower boundaries are dynamically set using the interquartile range method to effectively cope with dynamic data fluctuations, ensure the adaptability of data cleaning, and enhance the reliability of data analysis results. The cleaned data is smaller and more accurate, which helps to reduce the computational burden of subsequent analysis, alarm, and transmission modules, and improve the overall performance of the system.

[0054] Benefits of the fast alarm submodule: Whether the dynamic data of the off-time t exceeds the upper and lower threshold ranges, an alarm status is quickly generated to achieve real-time response to faults. When an abnormality is detected, an alarm is immediately marked to directly trigger the rapid processing of subsequent modules, shorten the time for fault detection and processing, and improve the agility of the system. By setting the dynamic threshold range, potential faults or abnormal behaviors can be captured in time, reducing the risk of system downtime or loss caused by the spread of faults. The fast alarm mechanism provides an immediate feedback path for the system, enabling the data center to dynamically adjust and optimize during operation to maintain high stability and reliability.

[0055] In summary, the sub-modules in the edge computing module work together to achieve efficient data processing and real-time alarms: The data compression submodule reduces data redundancy and improves transmission and storage efficiency through the sliding window averaging algorithm; the data cleaning submodule removes outliers through outlier detection to improve data quality and reliability; the rapid alarm submodule implements real-time alarms based on set dynamic thresholds, shortens fault response time and reduces operational risks.

[0056] Overall, the edge computing module significantly improves the real-time, reliability and efficiency of the data center monitoring system, provides high-quality input for subsequent modules, and optimizes the system's resource utilization.

[0057] The data transmission module includes a data receiving submodule, a data encryption submodule, a link transmission submodule and a data decryption submodule: The data receiving submodule receives compressed data and alarm data from the edge computing module and performs data format verification and buffering; First, verify the integrity of the transmitted data and determine whether there is any data loss through checksum calculation: , in, is the check code, is the content of the ith data packet, is the weight of the ith packet, is the preset verification benchmark value, and n represents the total number of data items; The received data is then stored in the buffer, and the buffer capacity is calculated as: , in, is the buffer capacity, is the receive data rate, is the maximum buffer time allowed; Finally, the received and verified data is passed to the encryption submodule for security processing.

[0058] The data encryption submodule encrypts the received compressed data and alarm data: First, use the AES encryption algorithm to encrypt the data. The formula is as follows: , in, is the encrypted data. is the original data, is the encryption key; Then estimate the delay time of the encryption process: , in, is the encryption delay time, is the size of the data to be encrypted, is the encryption rate; Finally, the encrypted data is handed over to the link transmission submodule for transmission; The link transmission submodule transmits data through the main link and the backup link, and dynamically switches according to the link status, and then decrypts the encrypted data transmitted by the link and hands it over to the data decryption submodule for processing; Based on link bandwidth and delay Determine the link status: , in, is the link status, Indicates that the main link is used. Indicates switching to the backup link. is the bandwidth of the current link, is the current link delay, is the minimum bandwidth threshold, is the maximum latency threshold.

[0059] The data decryption submodule decrypts the encrypted data after transmission and restores it to the readable original data format; First, use the AES algorithm that is consistent with encryption to decrypt, the formula is as follows: , in, is the decrypted data, is encrypted data, is the decryption key; Estimated latency of the decryption process: , in, is the decryption delay time, is the size of the data to be decrypted, is the decryption rate; The decrypted data is processed by the centralized monitoring module.

[0060] Benefits of the data receiving submodule: The integrity of the transmitted data can be verified by the check code formula, and data loss or damage can be quickly identified to ensure the quality of the transmitted data. The buffer mechanism is used to dynamically allocate buffer resources to effectively deal with transmission rate fluctuations and instantaneous congestion, and improve the stability of data transmission. By performing format verification and buffer management on the received data, the validity and orderliness of the data before entering the encryption submodule are guaranteed, and the operating efficiency of the subsequent processing module is optimized.

[0061] Benefits of the data encryption submodule: The data encryption submodule uses the AES encryption algorithm to perform end-to-end encryption on the received compressed data and alarm data to prevent the data from being stolen or tampered with during transmission, thereby improving the security of the system. By estimating the encryption delay time and evaluating the performance of the encryption process, the encryption rate and data block strategy can be dynamically adjusted to ensure efficient encryption processing. The data encryption submodule supports unified encryption of compressed data and alarm data to ensure the consistency and security of different data types during transmission.

[0062] Benefits of link transmission submodule: The link transmission submodule implements a redundant transmission mechanism through the main link and the backup link. It can automatically switch to the backup link when the main link is unavailable, ensuring the continuity and reliability of the transmission process. The link status judgment formula is dynamically adjusted based on the link bandwidth and delay to ensure the adaptability of the system in different network environments. The redundant link design reduces the impact of single link failure on data transmission and improves the stability and fault tolerance of the data center monitoring system.

[0063] Benefits of the data decryption submodule: The data decryption submodule uses the same AES algorithm as the encryption submodule to completely restore the original data format and provide readable high-quality data for the centralized monitoring module. The decryption delay time formula can quantify the decryption performance and optimize the decryption rate to ensure that the decryption process is efficient and stable. The decryption submodule ensures the integrity of the encrypted data during transmission, and combines the data reception and encryption submodules to form an end-to-end secure transmission chain, improving the security and reliability of the data center system.

[0064] In summary, the data transmission module builds an efficient, secure and stable data transmission mechanism through the division of labor and cooperation among various sub-modules: The data receiving submodule ensures the integrity and stability of the data and provides reliable input for subsequent processing; the data encryption submodule implements data security protection during transmission through encryption algorithms; the link transmission submodule ensures the continuity and reliability of data transmission through redundant link design; the data decryption submodule restores the readability of the data and provides high-quality data for subsequent centralized monitoring and decision-making modules.

[0065] The modular design significantly improves the security, stability and adaptability of the data center monitoring system, and provides comprehensive technical support for data transmission in complex network environments.

[0066] When the centralized monitoring module stores the decrypted data, it uses a time series database to store the data. The data structure is expressed as follows: , in, Indicates the timestamp, Indicates time The corresponding data value, n represents the total number of data items; The centralized monitoring module displays the stored data through visualization tools, including line graphs of real-time data, bar graphs of alarm data, and heat maps of historical data.

[0067] Benefits of using a time series database to store data: The decrypted data is stored in a time series database, and the data structure associates the timestamp with the corresponding data value one by one. It is suitable for time-continuous data monitoring scenarios and can significantly improve storage efficiency and query performance; The time series database optimizes the writing and retrieval of high-frequency collected data, and can record rapidly changing monitoring parameters in real time, such as environmental data, power data, and IT equipment status data, to ensure data integrity; Through the time-indexed data structure, historical data can be quickly located, providing reliable support for system operation trend analysis and historical anomaly backtracking.

[0068] Benefits of Data Visualization: The stored real-time data is displayed in the form of a line graph, which can intuitively reflect the changing trends of key parameters such as temperature and humidity, power status, and equipment operating load, helping operation and maintenance personnel to quickly understand the operating status of the cabin; Line charts can effectively display fluctuations in real-time data, help quickly locate abnormal data points, and provide immediate decision-making basis for operation and maintenance personnel; The alarm data is displayed in the form of a bar chart, which can clearly show the frequency and distribution of abnormal data, helping operation and maintenance personnel to quickly identify high-risk time periods and abnormal types; Different types of alarm information are classified and summarized through bar charts, supporting multi-dimensional analysis of alarm conditions by device, time, and region, improving the system's abnormality handling capabilities; Heat maps can condense and display large-scale historical data. They can quickly reflect the data density and change range in different time periods through color changes, providing a comprehensive view for long-term trend analysis. The heat map intuitively reveals the regularity of environmental parameters, power load and equipment operation, provides key data support for system operation optimization, and helps achieve more efficient energy allocation and operation and maintenance management.

[0069] In summary, the centralized monitoring module provides the following benefits by storing data in a time series database and displaying data using visualization tools: Time series databases improve data storage and query efficiency, support high-frequency data collection and historical tracking, and provide powerful data management capabilities for monitoring systems; Visual display presents key data intuitively in the form of real-time line graphs, alarm bar graphs and historical heat maps, helping operation and maintenance personnel to quickly understand system status, identify problems and optimize operation strategies.

[0070] Overall, this module significantly enhances the system's data management and analysis capabilities, providing strong support for the efficient operation and intelligent decision-making of prefabricated cabin data centers.

[0071] The decision optimization module dynamically adjusts the operating parameters of air conditioners and power equipment based on the historical operation reports and real-time data provided by the centralized monitoring module, and optimizes energy consumption through the following formula: , in, represents the total energy consumption of the system, Indicates the cabin ambient temperature. and are the minimum and maximum values ​​of temperature.

[0072] The decision optimization module dynamically adjusts the operating parameters of air conditioners and power equipment and combines the optimization formula to finely manage total energy consumption, bringing the following benefits: Accurate energy consumption management: optimize equipment operating parameters and reduce energy waste; Efficient and flexible regulation: adapt to changing environmental conditions and achieve a balance between operating efficiency and energy consumption; Intelligent optimization decision-making: Based on historical and real-time data, it provides highly accurate and real-time optimization solutions; Cost savings: Reduce total energy consumption and labor operation and maintenance costs, and provide reliable support for the long-term stable operation of the data center.

[0073] In summary, the module not only improves energy efficiency, but also significantly reduces the operating costs of data centers, providing important technical support for the construction of green and energy-saving future data centers.

[0074] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A prefabricated cabin data center monitoring system, characterized in that: include: The data acquisition module is arranged inside the prefabricated cabin and is used to obtain the environmental data, power data and IT equipment operation status data in the cabin, and then convert the acquired data into a standardized data format. Through the protocol conversion interface, the collected data of different communication protocols are uniformly converted into a predetermined standard data format. The environmental data includes temperature, humidity, particulate matter concentration and smoke concentration, the power data includes voltage and current, and the IT equipment operation status data includes CPU utilization and memory occupancy; The edge computing module receives the standardized data provided by the data acquisition module to clean and compress the standardized data. In the compressed data generated after data cleaning and compression, it is determined whether the data exceeds the range according to the set upper and lower limits. If data exceeding the range is detected, it is marked as alarm data, and then the processed compressed data and alarm data are sent to the data transmission module; The data transmission module is used to receive the compressed data and alarm data transmitted by the edge computing module, and send them to the centralized monitoring module through industrial Ethernet or wireless communication link. During the data transmission process, the transmitted data is encrypted by a preset encryption protocol; The centralized monitoring module receives the encrypted data sent by the data transmission module, decrypts it and stores it in the time series database to visualize the stored data, including real-time display of environmental data, power data and IT equipment operation status data, and then generates alarm information based on the alarm data and pushes it to the operation and maintenance terminal. Finally, it periodically generates historical operation reports based on the received data and transmits them to the decision optimization module. The decision optimization module analyzes the historical operation reports and real-time data provided by the centralized monitoring module, conducts trend analysis on the operating status of the equipment in the prefabricated cabin, and adjusts the operating parameters of the air-conditioning and power equipment in the prefabricated cabin to optimize energy consumption by combining environmental data, power data, and IT equipment operating status data. At the same time, the module predicts equipment failures based on the trend analysis results and feeds the prediction results back to the centralized monitoring module to prompt the operation and maintenance personnel to take preventive measures.

2. The prefabricated cabin data center monitoring system according to claim 1, characterized in that: The data acquisition module comprises: The environmental data acquisition submodule collects cabin environmental data, including temperature, humidity, particulate matter concentration, and smoke concentration, and then converts the collected data into a standardized data format and provides it to the power data acquisition submodule; The power data acquisition submodule collects the voltage and current of the power system in the cabin, calculates the real-time power, and then verifies whether the environmental data affects the reliability of the power data, completing data calibration; The IT equipment operation status data collection submodule collects the operation status of the servers and storage devices in the cabin, including CPU utilization and memory occupancy, and uses power data to analyze the energy consumption of IT equipment and complete load calibration.

3. The prefabricated cabin data center monitoring system according to claim 2, characterized in that: The environmental data acquisition submodule includes: Temperature and humidity collection, use temperature and humidity sensors to measure temperature T and humidity H: ， in, represents the environmental state vector at time t, is the temperature at time t, is the humidity at time t; Particle concentration, using PM2.5 sensor to measure particle concentration P: ， in, is the number of particles per unit volume at time t, is the sample gas volume, is the particle concentration; Smoke density, use the smoke sensor to measure the smoke density S: ， in, is the smoke concentration at time t, is the output voltage of the smoke sensor at time t, The sensitivity factor of the sensor.

4. The prefabricated cabin data center monitoring system according to claim 2, characterized in that: The power data acquisition submodule includes: Voltage and current acquisition, using voltage sensors and current transformers to measure voltage and current : ， in, represents the power state vector at time t, is the voltage at time t, The current at time t; Power calculation, based on voltage and current , calculate the instantaneous power : ， in, is the power at time t, is the voltage at time t, The current at time t; Power data calibration uses environmental data to verify the reliability of power data. The formula is as follows: ， in, It is the power deviation caused by environmental factors. is the current temperature, is the reference temperature, is the maximum power deviation, is the temperature sensitivity coefficient.

5. The prefabricated cabin data center monitoring system according to claim 2, characterized in that: The IT equipment operation status data collection submodule includes: CPU utilization, use the server management tool to obtain CPU utilization : ， in, is the CPU utilization at time t, is the CPU core i usage time at time t, is the total time of the CPU core i at time t; Memory usage, use system monitoring tools to obtain memory usage : ， in, is the memory usage at time t, is the used memory at time t, is the total system memory; Equipment energy consumption analysis, calculate the energy consumption of IT equipment based on power data : ， in, is the energy consumption of IT equipment, is the total power at time t, It is the energy consumption ratio of IT equipment.

6. The prefabricated cabin data center monitoring system according to claim 1, characterized in that: The edge computing module includes: The data compression submodule uses a sliding window average algorithm to compress data: ， in, represents the compressed data at time t, represents the sampled data at time i, represents the size of the sliding window, represents the time variable; The data cleaning submodule cleans the collected data through outlier detection: ， in, is the cleaned data set, Represents the 25th percentile value of the data, Represents the 75th percentile value of the data, = - is the interquartile range, represents a data sample, The lower boundary, used to identify outliers, The upper boundary, used to identify outliers; The fast alarm submodule implements fault alarm by setting alarm thresholds: ， in, represents the alarm status at time t, Indicates the maximum allowed threshold of monitoring data. Indicates the minimum allowed threshold of the monitoring data. Represents the dynamic value at time t.

7. The prefabricated cabin data center monitoring system according to claim 1, characterized in that: The data transmission module includes a data receiving submodule, a data encryption submodule, a link transmission submodule and a data decryption submodule: The data receiving submodule receives compressed data and alarm data from the edge computing module and performs data format verification and buffering; First, verify the integrity of the transmitted data and determine whether there is any data loss through checksum calculation: ， in, is the check code, is the content of the ith data packet, is the weight of the ith packet, is the preset verification benchmark value, and n represents the total number of data items; The received data is then stored in the buffer, and the buffer capacity is calculated as: ， in, is the buffer capacity, is the receive data rate, is the maximum buffer time allowed; Finally, the received and verified data is passed to the encryption submodule for security processing.

8. The prefabricated cabin-type data center monitoring system according to claim 7, characterized in that: The data encryption submodule encrypts the received compressed data and alarm data: First, use the AES encryption algorithm to encrypt the data. The formula is as follows: ， in, is the encrypted data. is the original data, is the encryption key; Then estimate the delay time of the encryption process: ， in, is the encryption delay time, is the size of the data to be encrypted, is the encryption rate; Finally, the encrypted data is handed over to the link transmission submodule for transmission; The link transmission submodule transmits data through the main link and the backup link, and dynamically switches according to the link status, and then decrypts the encrypted data transmitted by the link and hands it over to the data decryption submodule for processing; Based on link bandwidth and delay Determine the link status: ， in, is the link status, Indicates that the main link is used. Indicates switching to the backup link. is the bandwidth of the current link, is the current link delay, is the minimum bandwidth threshold, is the maximum latency threshold.

9. The prefabricated cabin-type data center monitoring system according to claim 7, characterized in that: The data decryption submodule decrypts the encrypted data after transmission and restores it to a readable original data format; First, use the AES algorithm that is consistent with encryption to decrypt, the formula is as follows: ， in, is the decrypted data, is encrypted data, is the decryption key; Estimated latency of the decryption process: ， in, is the decryption delay time, is the size of the data to be decrypted, is the decryption rate; The decrypted data is processed by the centralized monitoring module.

10. The prefabricated cabin data center monitoring system according to claim 1, characterized in that: When the centralized monitoring module stores the decrypted data, it uses a time series database to store the data, and the data structure is represented as follows: ， in, Indicates the timestamp, Indicates time The corresponding data value, n represents the total number of data items; The centralized monitoring module displays the stored data through visualization tools, including line graphs of real-time data, bar graphs of alarm data, and heat maps of historical data; The decision optimization module dynamically adjusts the operating parameters of air conditioners and power equipment based on the historical operation reports and real-time data provided by the centralized monitoring module, and optimizes energy consumption through the following formula: ， in, represents the total energy consumption of the system, Indicates the cabin ambient temperature. and are the minimum and maximum values ​​of temperature.

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