A computer room management method and management device based on digital twin

Abstract

The present invention provides a computer room management method and management device based on digital twins, which relate to the field of digital twin technology. By constructing a digital twin model that includes the spatial layout structure of each device in the computer room and the correlation relationship between operating parameters, real-time perception data is obtained and a state mapping relationship is established. The operating state of the computer room is simulated to generate a dynamic operating representation. The dynamic operating representation is further analyzed for abnormal characteristics, the abnormal associated equipment and the impact range are determined, and operation and maintenance adjustment instructions are generated to drive the physical computer room equipment to perform state adjustment operations. Real-time and accurate simulation and intelligent operation and maintenance of the computer room are realized, the stability and reliability of the computer room are improved, and an efficient and intuitive solution is provided for computer room management.

Description

Technical Field

[0001] The present invention relates to the field of digital twin technology, and in particular to a computer room management method and management device based on digital twins. Background Art

[0002] With the rapid development of information technology, the stability and reliability of computer rooms, as core locations for data storage and processing, are crucial. Traditional computer room management relies primarily on manual inspections and scheduled maintenance. This approach is not only inefficient but also makes it difficult to monitor the operating status of each device in real time, making it difficult to detect and address potential faults in a timely manner. Although some computer rooms have begun to introduce automated monitoring systems, these systems often only provide real-time data from equipment and lack the ability to comprehensively analyze and simulate the overall operating status of the computer room, making it difficult to provide early warning and intervention before faults occur. Summary of the Invention

[0003] In view of the above-mentioned problems, in combination with the first aspect of the present invention, the present invention provides a computer room management method based on digital twins, the method comprising:

[0004] Construct a digital twin model of the physical entity of the computer room, which includes the spatial layout structure information of each device in the computer room and the correlation relationship information of the operating parameters between devices;

[0005] Acquire real-time perception data of each device in the computer room, and establish a state mapping relationship based on the real-time perception data and the spatial layout structure information and operating parameter association relationship information in the digital twin model;

[0006] Simulating the equipment operating status in the digital twin model according to the state mapping relationship to generate a dynamic operating representation reflecting the real-time operating status of the physical computer room;

[0007] Performing abnormal feature analysis on the dynamic operation representation to determine abnormal associated devices and the impact range of the abnormal associated devices;

[0008] An operation and maintenance adjustment instruction is generated according to the abnormal associated equipment and the impact range, and the operation and maintenance adjustment instruction is used to drive the corresponding equipment in the physical computer room to perform an operation state adjustment operation.

[0009] On the other hand, the present invention also provides a computer room management device, including a processor and a machine-readable storage medium, the machine-readable storage medium is connected to the processor, the machine-readable storage medium is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the machine-readable storage medium to implement the above method.

[0010] Based on the above aspects, the present invention realizes the accurate modeling of the spatial layout structure and operating parameter correlation of each device in the computer room by constructing a digital twin model of the physical entity of the computer room. Furthermore, by obtaining the real-time perception data of each device in the computer room and establishing a state mapping relationship in the digital twin model, it can simulate the operating status of the computer room in real time and generate a dynamic operating representation that reflects the real-time operating status of the physical computer room. It not only improves the real-time and accuracy of computer room management, but also provides an intuitive and comprehensive decision-making basis for the operation and maintenance management of the computer room. By performing abnormal feature analysis and processing on the dynamic operation representation, it can quickly determine the abnormal related equipment and its impact range, and then generate operation and maintenance adjustment instructions to drive the corresponding equipment in the physical computer room to perform operation status adjustment operations, thereby realizing intelligent operation and maintenance and fault prevention of the computer room, and significantly improving the stability and reliability of the computer room. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a schematic diagram of the execution flow of the computer room management method based on digital twins provided in an embodiment of the present invention.

[0012] Figure 2 Schematic diagram of exemplary hardware and software components of a computer room management device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0013] The present invention will be described in detail below with reference to the accompanying drawings. Figure 1 This is a flow chart of a computer room management method based on digital twins provided by an embodiment of the present invention. The computer room management method based on digital twins is introduced in detail below.

[0014] Step S110: Construct a digital twin model of the physical entity of the computer room, which includes the spatial layout structure information of each device in the computer room and the association relationship information of the operating parameters between the devices.

[0015] In this example, a comprehensive enterprise computer room is used as an example, which contains various types of equipment, including servers, storage devices, network equipment, power supplies, etc. To build a digital twin model, you need to obtain relevant information about each device in the computer room. For details, see the following steps.

[0016] Step S111: spatial data collection is performed on the physical entity of the computer room to obtain the installation position coordinates of each device, device dimensions and path information of the connection lines between devices as spatial layout structure information.

[0017] In this company's computer room, 3D laser scanning technology was first used to scan the entire space. 3D laser scanning can accurately capture the three-dimensional spatial information of each device within the room. For each device, such as a server, the scanning device records the coordinates of its installation location within the room's 3D coordinate system. Assuming the server's installation location coordinates are represented by a set of coordinate values ​​(X, Y, Z), the coordinate values ​​for different servers will vary depending on their actual location within the room. The scanning device also captures the server's physical dimensions, including length, width, and height. These parameters are recorded in a set unit of measurement to ensure dimensional consistency. Line tracing equipment is used to detect the connections between devices. Line tracing equipment can trace connections such as network cables and power cables, recording their path information. For example, for a network cable connecting a server to a switch, the line tracing equipment records the coordinates of each path point along the cable, starting from the server port and ending at the switch port. These path point coordinates constitute the path information for the connection.

[0018] Step S112: performing correlation analysis on the historical operating data of each device in the computer room to extract the changing pattern of the device operating parameters, which include device temperature parameters, power consumption parameters, and data processing rate parameters.

[0019] In order to accurately extract the changing patterns of equipment operating parameters, it is necessary to conduct a comprehensive and in-depth analysis of the historical operating data of each device in the computer room. First, various historical operating data of the equipment during the continuous operation cycle are collected.

[0020] Step S1121: collecting the temperature parameter history records, power consumption parameter history records and data processing rate parameter history records of each device in the computer room during a continuous operation cycle.

[0021] In enterprise computer rooms, temperature sensors installed on and inside equipment collect real-time temperature data. This data is stored in a data storage system, creating a historical record of temperature parameters. Similarly, power consumption monitoring devices record power consumption data during operation, creating a historical record of power consumption parameters. For data processing rate parameters, built-in performance monitoring tools or network traffic monitoring devices are used to collect data processing rate information, creating a historical record of data processing rate parameters. This historical record is arranged chronologically for subsequent analysis and processing.

[0022] Step S1122: Perform time series analysis on the temperature parameter history records to identify the change trend of the temperature parameters with the equipment operation time, which includes time nodes of the temperature rising stage, stable stage and falling stage.

[0023] After acquiring historical temperature parameter records, we process them using time series analysis. By analyzing the temperature data over time, we can identify trends in device temperature. During device startup, the temperature typically rises as the device operates for extended periods, forming a temperature rise phase. Once the device reaches the set stable operating state, the temperature fluctuates within a relatively stable range, representing the temperature stabilization phase. When the device stops operating or experiences an abnormality, the temperature gradually decreases, entering the temperature drop phase. By analyzing historical data, we can determine the timeframes for these phases. For example, statistical analysis of a large amount of historical data revealed that after a certain server model starts up normally, it enters the temperature stabilization phase after a period of time, T1. After stopping, the temperature drops to near the ambient temperature after a period of time, T2.

[0024] Step S1123: performing correlation analysis on the power consumption parameter history records, calculating the correlation coefficient between the power consumption parameter and the device data processing rate parameter, and determining the linear or nonlinear relationship between the power consumption parameter and the data processing rate.

[0025] When performing a correlation analysis on the historical records of power consumption parameters and data processing rate parameters, a set statistical analysis method is used. First, the power consumption data and data processing rate data at the same time point are paired to form a data pair. Then, the correlation coefficient calculation method is used to calculate the correlation coefficient between the power consumption parameters and the data processing rate parameters. If the correlation coefficient is close to 1 or -1, it means that there is a strong linear relationship between the two; if the correlation coefficient deviates from 1 or -1, further analysis is required to determine whether there is a nonlinear relationship. For example, through the calculation and analysis of a large amount of data, it was found that there is a linear relationship between the power consumption parameters and the data processing rate parameters of a certain type of server, and it can be expressed by a linear equation, where power consumption increases linearly with the increase of data processing rate.

[0026] Step S1124: Periodically analyze the historical records of data processing rate parameters to extract the peak frequency and valley frequency of the data processing rate in different service time periods.

[0027] When performing periodic analysis on historical data processing rate parameter records, methods such as spectrum analysis are used. First, arrange the data processing rate data in chronological order. Then, use spectrum analysis tools to analyze the data and identify periodic components. By analyzing these periodic components, the frequency of peak and valley data processing rate values ​​during different business hours can be determined. For example, during the daytime on weekdays, when business activity is high, the data processing rate may experience multiple peaks. Analysis can determine the frequency of these peaks. Meanwhile, during nighttime and holidays, when business activity is relatively low, the data processing rate may experience valleys. The frequency of these valleys can also be determined.

[0028] Step S1125: Integrate the change trend of the temperature parameters, the correlation between the power consumption parameters and the data processing rate, and the periodic characteristics of the data processing rate to generate a description of the change pattern of the equipment operation parameters, which includes the time dependence characteristics of the parameter changes and the mutual influence characteristics between the parameters.

[0029] After completing the separate analysis of temperature parameters, power consumption parameters, and data processing rate parameters, these analysis results need to be integrated. First, the changing trend of the temperature parameters, the correlation between the power consumption parameters and the data processing rate, and the periodic characteristics of the data processing rate are comprehensively considered. For example, when the data processing rate is at its peak, based on the correlation between the power consumption parameters and the data processing rate, it can be predicted that the power consumption of the device will increase accordingly; at the same time, due to the increase in power consumption, the temperature of the device may also rise based on the changing trend of the temperature parameters. Through the above comprehensive analysis, a description of the changing patterns of the device operating parameters is generated. This description includes the time-dependent characteristics of the parameter changes, that is, the changes of different parameters at different time points, and the mutual influence characteristics between the parameters, that is, how the change of one parameter affects the change of other parameters.

[0030] Step S113: establishing a causal relationship description between different equipment operating parameters, where the causal relationship description includes triggering conditions for parameter changes and transmission direction information of parameter changes.

[0031] In an enterprise computer room, the operating parameters of different devices interact with each other. For example, the operating status of a server affects the network traffic of the connected switch, and the network traffic of the switch in turn affects the stability of the entire network. To accurately describe these relationships, it is necessary to establish a causal relationship description between the operating parameters of different devices. First, by analyzing a large amount of historical data, the trigger conditions for parameter changes are determined. For example, when the data processing rate of a server exceeds a set threshold, it can trigger an increase in network traffic on the connected switch. Then, the transmission direction of the parameter change is determined. For example, a change in the data processing rate of a server is transmitted to the switch through the network connection, affecting the switch's network traffic. Through this approach, a causal relationship description is established between the operating parameters of different devices.

[0032] Step S114: Fusion modeling is performed on the spatial layout structure information and the association relationship information of the equipment operation parameters to generate a basic framework of a digital twin model including a three-dimensional spatial coordinate system and parameter association rules.

[0033] After obtaining the relationship between the spatial layout structure information and the device operating parameters, these two pieces of information need to be integrated and modeled. First, the spatial layout structure information, such as the device installation location coordinates and external dimensions, is associated with the relationship between the device operating parameters. For example, the server's installation location coordinates are associated with operating parameters such as the server's temperature, power consumption, and data processing rate. Then, in a three-dimensional spatial coordinate system, a three-dimensional model of the device is constructed based on the device's installation location coordinates and external dimensions. Simultaneously, the relationship between the device operating parameters is converted into parameter association rules and embedded into the three-dimensional model. For example, when the server temperature rises, according to the parameter association rules, the cooling power of the connected air conditioning equipment will increase accordingly. Through the above method, a basic framework for a digital twin model is generated, which includes a three-dimensional spatial coordinate system and parameter association rules.

[0034] Step S115: Perform compatibility verification on the spatial location information and operating parameter information of the newly connected device through the digital twin model basic framework, and update the spatial layout structure information and operating parameter association relationship information of the digital twin model.

[0035] During the operation of an enterprise computer room, new devices may be connected. When a new device is connected, the digital twin model infrastructure framework is used to perform a compatibility check on the new device's spatial location information and operating parameter information. First, check whether the new device's spatial location conflicts with the spatial layout structure of existing devices. For example, whether the new device's installation location will affect the heat dissipation or maintenance access of other devices. Then, check whether the new device's operating parameter information is compatible with the operating parameter associations of existing devices. For example, whether parameters such as the new device's data processing rate and power consumption will affect the existing network and power supply systems. If the new device's spatial location information and operating parameter information pass the compatibility check, the new device information is added to the digital twin model, and the digital twin model's spatial layout structure information and operating parameter association information are updated. If the compatibility check fails, the new device's installation location or operating parameters need to be adjusted until they pass the check.

[0036] Step S120: Acquire real-time perception data of each device in the computer room, and establish a state mapping relationship based on the real-time perception data and the spatial layout structure information and operating parameter association information in the digital twin model.

[0037] After building the digital twin model, it's necessary to obtain real-time sensor data from each device in the computer room and establish a state mapping relationship between this data and the digital twin model. In an enterprise computer room, real-time sensor data can reflect the current operating status of the device, and the state mapping relationship can accurately map the operating status of the physical device to the digital twin model.

[0038] Step S121: The real-time temperature value, real-time power consumption value and real-time data processing rate value of the device are collected as real-time perception data through sensor components deployed on the surface of the device and on the connection lines.

[0039] In enterprise computer rooms, sensor components are deployed on equipment surfaces and connecting lines to accurately capture the real-time operating status of devices. For example, a temperature sensor is installed on the server casing to collect real-time server temperature values; a power consumption sensor is installed on the power cord to collect real-time server power consumption values; and a flow sensor is installed at the network interface to collect real-time server data processing rates. These sensor components transmit the collected data to the data acquisition system in real time, generating real-time perception data.

[0040] Step S122: Perform format standardization processing on the real-time perception data to generate standardized perception data that is consistent with the operating parameter data format in the digital twin model.

[0041] Because real-time sensor data collected by different sensor components may have different data formats, it is necessary to standardize the format of this real-time sensor data to facilitate matching and processing with the operating parameter data in the digital twin model. For example, temperature data collected by temperature sensors of different brands may have different precisions and units, and this data needs to be uniformly converted to the precision and units specified in the digital twin model. By standardizing the format of real-time sensor data, standardized sensor data is generated that is consistent with the operating parameter data format in the digital twin model.

[0042] Step S123: Extract the spatial layout structure information in the digital twin model and determine the device identification and device spatial position coordinates corresponding to each sensor component.

[0043] Extract spatial layout information from the digital twin model, including the device's installation location coordinates, external dimensions, and other information. Then, determine the device ID and device spatial location coordinates corresponding to each sensor component based on its installation location. For example, the temperature sensor installed on server A has the device ID "Server A" and its device spatial location coordinates are the installation location coordinates of server A.

[0044] Step S124: Bind the standardized perception data with the corresponding device identification and device spatial position coordinates to generate multi-dimensional status data including the device identification, spatial position coordinates and standardized perception data.

[0045] After standardizing the format of real-time sensor data and associating sensor components with devices, the standardized sensor data is then bound to the corresponding device identifier and device spatial location coordinates. For example, server A's standardized temperature, power consumption, and data processing rate values ​​are bound to server A's device identifier and spatial location coordinates to generate multi-dimensional status data containing server A's device identifier, spatial location coordinates, and standardized sensor data. This approach integrates real-time sensor data with the device's spatial information, creating more comprehensive multi-dimensional status data.

[0046] Step S125: Based on the operating parameter association information in the digital twin model, establish parameter mapping rules between the multi-dimensional status data of different devices, and the parameter mapping rules include parameter synchronization frequency and parameter change threshold.

[0047] In order to accurately reflect the operating status relationship between different devices, it is necessary to establish parameter mapping rules between the multi-dimensional status data of different devices based on the operating parameter association information in the digital twin model.

[0048] Step S1251: Extract the causal relationship description of the operating parameters between devices from the operating parameter association information of the digital twin model.

[0049] Extract causal relationships between operating parameters of devices from the operating parameter association information of the digital twin model. For example, extract the causal relationship between the operating parameters of the server and the switch from the digital twin model, that is, how changes in the server's data processing rate affect the network traffic of the switch.

[0050] Step S1252: Determine the synchronization order of the master device parameters and the slave device parameters according to the parameter change triggering condition in the causal relationship description. The master device parameters are the starting parameters that trigger the parameter change, and the slave device parameters are the follow-up parameters affected by the master device parameters.

[0051] The synchronization order of master and slave device parameters is determined based on the parameter change trigger conditions described in the causal relationship. For example, in the relationship between a server and a switch, the server's data processing rate is the master parameter, and the switch's network traffic is the slave parameter. A change in the server's data processing rate can trigger a corresponding change in the switch's network traffic. Based on this causal relationship, the synchronization order of master and slave device parameters is determined, with the server's data processing rate changing first and the switch's network traffic changing later.

[0052] Step S1253: Based on the synchronization order of the master device parameters and the slave device parameters, the parameter synchronization frequency is set. The parameter synchronization frequency is the time interval for the slave device parameters to be updated after the master device parameters are updated.

[0053] After determining the synchronization order for master and slave device parameters, set the parameter synchronization frequency. For example, based on the actual operating conditions of the server and switch, set the server's data processing rate to update, and the switch's network traffic to update within a set interval. This interval is the parameter synchronization frequency. By setting the parameter synchronization frequency, you ensure that operating parameters between different devices are synchronized in a timely manner, accurately reflecting the device's operating status.

[0054] Step S1254: Analyze the parameter change transmission direction information in the causal relationship description, determine the maximum acceptable fluctuation range of the parameter change, and use the maximum acceptable fluctuation range as the parameter change threshold.

[0055] Analyze the parameter change direction information in the causal relationship description to determine the maximum acceptable fluctuation range of the parameter change. For example, when the server's data processing rate changes, analyze the maximum acceptable fluctuation range of the switch's network traffic. If the switch's network traffic fluctuates beyond this range, it may affect network stability. This maximum acceptable fluctuation range is used as the parameter change threshold for subsequent state mapping and anomaly detection.

[0056] Step S1255: Combine the parameter synchronization sequence, parameter synchronization frequency, and parameter change threshold to generate parameter mapping rules between multi-dimensional status data of different devices.

[0057] After determining the parameter synchronization order, frequency, and change threshold, these three elements are combined to generate parameter mapping rules between the multi-dimensional status data of different devices. For example, the parameter synchronization order, frequency, and change threshold between a server and a switch are combined to form a parameter mapping rule between the server and the switch. This approach establishes an accurate mapping relationship between the multi-dimensional status data of different devices.

[0058] Step S126: Map the multi-dimensional state data to the corresponding device nodes of the digital twin model according to the parameter mapping rules, and generate a state mapping relationship that reflects the one-to-one correspondence between the physical device and the digital twin model node.

[0059] After establishing the parameter mapping rules, the multi-dimensional state data is mapped to the corresponding device nodes in the digital twin model according to the parameter mapping rules. For example, the multi-dimensional state data of server A is mapped to the node corresponding to server A in the digital twin model according to the parameter mapping rules. This method generates a state mapping relationship that reflects the one-to-one correspondence between physical devices and digital twin model nodes. This state mapping relationship accurately transfers the operating status of the physical device to the digital twin model.

[0060] Step S130: Simulate the equipment operating status in the digital twin model according to the status mapping relationship, and generate a dynamic operation representation that reflects the real-time operating status of the physical computer room.

[0061] After establishing the state mapping relationship, the relationship can be used to simulate the equipment operation status in the digital twin model to generate a dynamic operation representation that reflects the real-time operation status of the physical computer room.

[0062] Step S131: Extract the multi-dimensional state data corresponding to each digital twin model node from the state mapping relationship.

[0063] Extract the multi-dimensional state data corresponding to each digital twin model node from the state mapping relationship. For example, extract the multi-dimensional state data corresponding to server A node in the digital twin model from the state mapping relationship, including information such as server A's device identification, spatial location coordinates, standardized temperature value, standardized power consumption value, and standardized data processing rate value.

[0064] Step S132: According to the parameter synchronization frequency in the state mapping relationship, the multi-dimensional state data is input into the operation simulation module of the corresponding digital twin model node.

[0065] Based on the parameter synchronization frequency in the state mapping relationship, multi-dimensional state data is input into the operation simulation module of the corresponding digital twin model node. For example, based on the parameter synchronization frequency between the server and the switch, the multi-dimensional state data of the server is input into the operation simulation module of the server node in the digital twin model at a specified time interval. This approach ensures that the device operation status in the digital twin model can keep pace with the actual operation status of the physical device.

[0066] Step S133: The operation simulation module deduces the parameter change trend of the input multi-dimensional status data based on the operation parameter association information in the digital twin model, and generates a simulation value of the equipment operation status.

[0067] After receiving the multi-dimensional status data, the operation simulation module deduces the parameter change trend of the input multi-dimensional status data based on the operation parameter association information in the digital twin model.

[0068] Step S1331: input the real-time temperature value, real-time power consumption value and real-time data processing rate value in the multi-dimensional state data into the parameter input layer of the operation simulation module.

[0069] The real-time temperature, power consumption, and data processing rate values ​​from the multi-dimensional status data are input into the parameter input layer of the running simulation module. For example, the real-time temperature, power consumption, and data processing rate values ​​of the server are input into the parameter input layer of the running simulation module. The parameter input layer performs preliminary processing on these input real-time parameter values, such as checking the integrity and accuracy of the data to ensure that the input data meets the requirements of subsequent processing. Because the format of the real-time sensing data has been standardized, the real-time temperature, power consumption, and data processing rate values ​​input here are consistent in dimension and format with the relevant data stored in the digital twin model, ensuring the accuracy and consistency of subsequent calculations.

[0070] Step S1332: The parameter input layer of the running simulation module matches the input real-time parameter value with the description of the change law of the equipment operation parameters stored in the digital twin model to determine the stage of the current equipment operation, which includes the temperature rising stage, stable stage or falling stage, and the peak stage or valley stage of the data processing rate.

[0071] After receiving real-time parameter values, the parameter input layer can perform a detailed match with the device operating parameter variation pattern description stored in the digital twin model. For a server, for example, the real-time temperature value can be compared with the characteristics of the temperature rising phase, stable phase, and falling phase described in the temperature parameter variation pattern description. If the real-time temperature value shows a continuous upward trend and meets the characteristic range of the temperature rising phase, the server can be determined to be in the temperature rising phase. If the real-time temperature value fluctuates within a relatively stable range, consistent with the characteristics of the stable phase, the server is in the stable phase. If the real-time temperature value begins to decline and meets the characteristic conditions of the falling phase, the server is in the temperature falling phase. For the real-time data processing rate value, it can be matched with the peak and valley phases of the data processing rate periodicity characteristics. If the real-time data processing rate value reaches or approaches the value range corresponding to the peak frequency determined in the previous analysis, the server is in the peak data processing rate phase; if the real-time data processing rate value is within the value range corresponding to the valley frequency, the server is in the valley data processing rate phase.

[0072] Step S1333: Based on the equipment operation stage, the corresponding change trend model in the description of the operation parameter change law is called, and the change trend model includes a temperature change trend model, a power consumption and data processing rate correlation model, and a data processing rate periodicity model.

[0073] After determining the stage in which the device is currently operating, the operation simulation module will call the corresponding change trend model according to that stage. If the server is in the temperature rising stage, the specific model of the temperature rising stage in the temperature change trend model can be called. This model may be fitted based on a large amount of historical data and can describe the trend of temperature changes over time in this stage. For the power consumption and data processing rate correlation model, if the server is in the peak stage of the data processing rate, since there is a certain correlation between power consumption and data processing rate, the part of the correlation model that is applicable to the peak stage can be called to more accurately predict changes in power consumption. Similarly, for the data processing rate periodicity model, the corresponding periodic feature part of the model can be called according to the data processing rate stage that the server is currently in to predict future changes in the data processing rate.

[0074] Step S1334: deduce the temperature prediction value at the future time point through the temperature change trend model, deduce the power consumption prediction value at the future time point through the power consumption and data processing rate correlation model, and deduce the data processing rate prediction value at the future time point through the data processing rate periodicity model.

[0075] The temperature, power consumption, and data processing rate at future points in time are predicted using the called change trend model. For temperature prediction, the temperature change trend model will take into account the time factor based on the current real-time temperature value and the temperature change pattern of this stage, and calculate the temperature prediction value at a certain point in the future. For example, based on the change pattern of the temperature rise stage, combined with the current temperature rise rate and time interval, the temperature value after a period of time is predicted. For power consumption prediction, the power consumption and data processing rate correlation model will predict the impact of data processing rate changes on power consumption at future points in time based on the current data processing rate value and the correlation between the two, thereby deriving the power consumption prediction value at future points in time. The data processing rate periodicity model predicts the data processing rate prediction value at future points in time based on the stage and periodic characteristics of the current data processing rate. For example, based on the periodic fluctuation pattern of the data processing rate, it predicts the time point when the next peak or valley value will appear and the corresponding rate value.

[0076] Step S1335: Combine the temperature prediction value, power consumption prediction value, and data processing rate prediction value to generate a simulation value of the device operating status.

[0077] After obtaining the predicted temperature, power consumption, and data processing rate values ​​for a future point in time, these three predicted values ​​are combined to form a simulated value representing the device's operating status. This simulated value encompasses multiple operating parameter information for the device at a specific point in the future, providing a more comprehensive reflection of the device's operating status. For example, the predicted temperature, power consumption, and data processing rate values ​​for a server at a specific point in the future are combined to form a multi-dimensional simulated value vector, representing the simulated operating status of the server at that moment.

[0078] Step S134: Perform consistency check on the simulation value and the real-time perception data in the multi-dimensional state data. If the check passes, the simulation value is retained. If the check fails, the parameter change trend is re-derived.

[0079] In order to ensure the accuracy and reliability of the simulation value, it is necessary to perform consistency verification on the generated simulation value and the real-time perception data in the multi-dimensional state data. Taking the server as an example, the temperature prediction value obtained by simulation can be compared with the real-time temperature value, the power consumption prediction value with the real-time power consumption value, and the data processing rate prediction value with the real-time data processing rate value. During the comparison process, it can be judged whether the difference between the two is within an acceptable range based on the parameter change threshold set previously. If the difference between the simulation value and the real-time perception data is within the parameter change threshold, it means that the simulation value is relatively consistent with the actual situation, the verification passes, and the simulation value is retained at this time; if the difference between the simulation value and the real-time perception data of a certain parameter exceeds the parameter change threshold, it is considered that the verification fails. In this case, it is necessary to start again from step S133, and deduce the parameter change trend of the input multi-dimensional state data again, and adjust the relevant parameters and models in the prediction process to obtain more accurate simulation values.

[0080] Step S135: The simulation value that has passed the consistency check is visualized and rendered in the three-dimensional space coordinate system of the digital twin model according to the spatial position coordinates of the device, generating a three-dimensional visualization scene including the color identification of the device operation status, the dynamic curve of the parameter change and the connection line status indication.

[0081] After the simulated values ​​pass consistency verification, they can be visualized in the 3D spatial coordinate system of the digital twin model based on the device's spatial location coordinates. For example, for devices such as servers and switches in a computer room, the simulated values ​​of each device can be visualized based on their operating parameter status. Device operating status color coding can be assigned different colors based on operating parameters such as temperature and power consumption. For example, when the server temperature is within the normal range, the server may appear green in the 3D visualization; if the temperature approaches or exceeds the safety threshold, the server may appear red, visually alerting managers to potential device anomalies. For parameter change dynamic curves, dynamic curves of parameters such as temperature, power consumption, and data processing rate over time can be plotted based on the device's simulated values ​​and real-time sensor data. These curves can visually display the changing trends of device operating parameters in the 3D visualization, facilitating analysis and forecasting by managers. For connection line status indication, connection lines can be visualized based on the connection relationships between devices and network traffic. If network traffic is normal, the line may appear blue; if abnormal conditions such as network congestion occur, the line may appear yellow or red. Through the above-mentioned visual rendering, a three-dimensional visual scene is generated, which includes color identification of equipment operation status, dynamic curve of parameter changes and connection line status indication, allowing managers to understand the real-time operation status of the physical computer room more intuitively.

[0082] Step S136: synchronize the three-dimensional visualization scene with the real-time updated simulation value in time series to generate a dynamic operation representation that changes continuously over time.

[0083] After generating the 3D visualization scene, it needs to be synchronized with the real-time updated simulation values ​​in a time series. Since the operating status of the equipment changes continuously over time, the simulation values ​​are also updated in real time. To ensure that the 3D visualization scene accurately reflects the real-time operating status of the equipment, information such as the equipment operating status, parameter change dynamic curves, and connection line status in the 3D visualization scene needs to be synchronized with the real-time updated simulation values. Specifically, the latest simulation values ​​can be updated to the 3D visualization scene at set time intervals, such as at regular intervals (this time interval is related to the previously set parameter synchronization frequency). In this way, information such as the device operating status color code, parameter change dynamic curves, and connection line status indications in the 3D visualization scene will change in real time as the simulation values ​​are updated, thereby generating a dynamic operating representation that continuously changes over time. This dynamic operating representation can accurately reflect the operating status and changes of the equipment in the physical computer room in real time.

[0084] Step S140: performing abnormal feature analysis on the dynamic operation representation to determine abnormal associated devices and the impact range of the abnormal associated devices.

[0085] After obtaining the dynamic operation representation that reflects the real-time operation status of the physical computer room, it is necessary to analyze and process the abnormal characteristics to discover possible abnormal conditions during the operation of the equipment and determine the abnormal associated equipment and the impact range of the abnormal associated equipment.

[0086] Step S141: extracting the simulation value time series data of each device and the color identification information in the three-dimensional visualization scene from the dynamic operation representation.

[0087] Extract the simulation value time series data of each device and the color identification information in the three-dimensional visualization scene from the dynamic operation representation. For the simulation value time series data, the simulation value of each device at different time points can be extracted in chronological order to form a multi-dimensional time series data set. For example, for a server, its temperature simulation value, power consumption simulation value and data processing rate simulation value at each moment in a period of time can be extracted. These values ​​arranged in chronological order constitute the simulation value time series data of the server. For the color identification information in the three-dimensional visualization scene, the color identification corresponding to each device can be extracted. The color identification represents the operating status of the device. For example, a server displayed in red may indicate that its operating status is abnormal, while green indicates normal.

[0088] Step S142: Based on the operating parameter association information in the digital twin model, set the normal variation range of each device operating parameter and the normal display rules of the color identification.

[0089] Based on the operating parameter association information in the digital twin model, a normal variation range is set for the operating parameters of each device. Taking the server as an example, for temperature parameters, a normal temperature variation range can be set based on the temperature variation pattern obtained in the previous analysis. This range takes into account the different operating stages and environmental factors of the device. Corresponding normal variation ranges are also set for power consumption parameters and data processing rate parameters. At the same time, normal display rules are set for color identification in the three-dimensional visualization scene. For example, when the operating parameters of the device are all within the normal variation range, the device is displayed in green; when a parameter approaches or exceeds the normal variation range, the device is displayed in yellow; when the parameter seriously exceeds the normal variation range, the device is displayed in red. The above normal variation range and color identification normal display rules are important basis for determining whether the device is abnormal.

[0090] Step S143: Compare the simulation value time series data with the normal variation range to identify abnormal parameter values ​​that exceed the normal variation range.

[0091] The extracted analog value time series data for each device is compared against the set normal variation range. For example, for the temperature analog value in the analog value time series data for a server, each time point can be checked to see if the temperature analog value is within the normal temperature variation range. If the temperature analog value at a certain time point exceeds the normal variation range, it is identified as an abnormal parameter value. Similar comparisons are performed for the power consumption analog value and the data processing rate analog value. Through these comparisons, any parameter values ​​that may be abnormal during device operation can be identified.

[0092] Step S144: Analyze the matching between the color identification in the 3D visualization scene and the normal display rules, and identify the device nodes with abnormal color identification.

[0093] Perform a detailed analysis of the color identification of each device in the 3D visualization scene and its normal display rules. Check whether each device's color identification complies with normal display rules. For example, if a server's operating parameters are within normal range, but its color identification is red, this indicates that the color identification does not match the normal display rules and the server node's color identification is abnormal. This analysis can identify device nodes with abnormal color identification, which may indicate potential device anomalies.

[0094] Step S145: Determine the device node with abnormal parameter value or color identification as the initial abnormal device.

[0095] After identifying device nodes with abnormal parameter values ​​or color codes outside the normal range, these nodes are identified as initial abnormal devices. For example, if the simulated temperature value of server A exceeds the normal range, or the color code of server B is abnormal, then servers A and B are identified as initial abnormal devices. These initial abnormal devices serve as the starting point for further analysis of associated devices and the scope of impact.

[0096] Step S146: According to the operating parameter association relationship information in the digital twin model, the parameter change triggering conditions and transmission direction information of the initial abnormal equipment are analyzed, and the set of associated equipment affected by the parameter change of the initial abnormal equipment is derived.

[0097] After determining the initial abnormal device, it is necessary to analyze the parameter change triggering conditions and transmission direction information of the initial abnormal device based on the operating parameter association relationship information in the digital twin model to deduce the set of associated devices affected by it.

[0098] Step S1461: Extract the causal relationship description of the initial abnormal equipment from the operating parameter association information of the digital twin model.

[0099] The causal relationship description of the initial abnormal device is extracted from the operating parameter association information of the digital twin model. Taking the initial abnormal device server A as an example, the causal relationship description of the operating parameters between server A and other devices can be extracted. This includes information such as how parameter changes in server A affect the operating parameters of other devices and the reaction of parameter changes in other devices on server A. This causal relationship description includes information on the triggering conditions and transmission direction of parameter changes, which is an important basis for deducing the set of associated devices.

[0100] Step S1462: Analyze the parameter change triggering conditions in the causal relationship description to determine whether the abnormal parameter value of the initial abnormal device meets the conditions for triggering parameter changes of other devices.

[0101] The parameter change trigger conditions in the extracted causal relationship description are parsed. For server A's abnormal parameter value, check whether it meets the conditions for triggering parameter changes in other devices. For example, if server A's data processing rate increases abnormally, according to the causal relationship description, when the data processing rate increases to a set threshold, it can trigger an increase in network traffic on the connected switch. At this point, it is necessary to determine whether the abnormal increase in server A's data processing rate has reached this threshold. If so, the trigger condition is met, which may affect parameter changes in other devices. If not, there will be no impact on other devices for the time being.

[0102] Step S1463: If the trigger condition is met, the first-level associated device to which the parameter change is transferred is determined according to the parameter change transfer direction information in the causal relationship description. The first-level associated device is a device that has a direct parameter association relationship with the initial abnormal device.

[0103] If the abnormal parameter value of the initially abnormal device meets the conditions for triggering parameter changes in other devices, the first-level associated devices to which the parameter change is transferred are determined based on the parameter change transmission direction information in the causal relationship description. For example, if Server A's data processing rate abnormally increases, meeting the triggering conditions, and the causal relationship description indicates that its parameter change will directly affect the connected switch, then the switch is the first-level associated device for parameter change transmission. First-level associated devices have a direct parameter relationship with the initially abnormal device, and their operating parameters will be the first to be affected by the parameter change of the initially abnormal device.

[0104] Step S1464: Perform the same analysis on the causal relationship description of the first-level associated devices to determine the second-level associated devices to which the parameter changes are transmitted. The second-level associated devices are devices that have a parameter association relationship with the first-level associated devices.

[0105] After identifying the first-level associated devices, the same analysis is performed on the causal relationship descriptions of the first-level associated devices. For example, the causal relationship description of the switch can be examined to determine whether changes in its parameters trigger parameter changes in other devices. If the switch's network traffic increases due to an abnormal increase in the data processing rate of server A, and according to the switch's causal relationship description, when the network traffic increases to a certain level, it can affect the network connection status of other connected servers, then these affected servers are the second-level associated devices to which the parameter change is transmitted. Second-level associated devices have parameter associations with first-level associated devices, and their operating parameters are indirectly affected by the parameter changes of the initial abnormal device.

[0106] Step S1465: Repeat the above analysis process until no new associated devices are identified or the preset transfer level limit is reached.

[0107] The above analysis process of the causal relationship description of the associated devices will be repeated continuously to determine more levels of associated devices. The determination of each level of associated devices is based on the parameter changes and causal relationship descriptions of the associated devices at the previous level. For example, after determining the second-level associated devices, the causal relationship descriptions of the second-level associated devices can be analyzed to determine the third-level associated devices, and so on. This process will continue until no new associated devices are identified, that is, all affected devices have been determined; or the preset transfer level limit is reached. In order to avoid the analysis process being too complicated and time-consuming, a transfer level upper limit can be set. When the transfer level upper limit is reached, the derivation process of the associated devices is stopped.

[0108] Step S1466: The initial abnormal device, the first-level associated devices, and the subsequent levels of associated devices are merged to generate a set of associated devices affected by the parameter change of the initial abnormal device.

[0109] After all associated devices are derived, the initial abnormal device, the first-level associated devices, and the associated devices at all subsequent levels are merged together to form a set of associated devices affected by the parameter change of the initial abnormal device. This set of associated devices includes all devices directly or indirectly affected by the parameter change of the initial abnormal device.

[0110] Step S147: Count the number of devices in the associated device set and their spatial distribution positions, and generate influence range description information including an associated device identification list and a spatial distribution area.

[0111] Perform statistical analysis on the associated device set, count the number of devices in the set, and determine the spatial distribution of each device. Taking the computer room as an example, the coordinate position of each associated device in the three-dimensional spatial coordinate system of the computer room can be recorded. Then, based on the number of devices and their spatial distribution, generate impact range description information. The impact range description information contains a list of associated device identifiers, which lists the identifiers of all associated devices, making it convenient for management personnel to quickly identify affected devices. At the same time, based on the spatial distribution of the devices, a spatial distribution area can be determined, which represents the distribution range of the affected devices in the computer room. For example, associated devices may be concentrated in a certain area of ​​the computer room, or scattered across multiple areas. By determining the spatial distribution area, the impact range of the abnormal situation can be intuitively understood.

[0112] Step S148: The initial abnormal device is merged with the associated device set to determine it as an abnormal associated device, and the impact range description information is used as the impact range of the abnormal associated device.

[0113] The initial abnormal device is merged with the associated device set to form a larger set of devices, which is the abnormal associated device set. At the same time, the previously generated impact range description information is used as the impact range of the abnormal associated device. This clarifies which devices in the computer room are affected by the abnormal situation and the scope of the abnormal situation.

[0114] Step S150: Generate an operation and maintenance adjustment instruction according to the abnormal related equipment and the affected scope, and drive the corresponding equipment in the physical computer room to perform an operation to adjust the operating status through the operation and maintenance adjustment instruction.

[0115] After determining the abnormal associated devices and the impact range of the abnormal associated devices, it is necessary to generate operation and maintenance adjustment instructions based on the above information, and use the operation and maintenance adjustment instructions to drive the corresponding devices in the physical computer room to perform operation status adjustment operations to restore the normal operating status of the devices.

[0116] For example, step S151: extracting the device identification of the abnormal associated device and the corresponding abnormal parameter value.

[0117] Extract each device's device ID and corresponding abnormal parameter value from the set of abnormally associated devices. For example, for a server, the server's device ID and corresponding abnormal temperature, power consumption, or data processing rate values ​​can be extracted. The device ID accurately identifies the abnormally associated device, while the abnormal parameter value is crucial for developing subsequent adjustment strategies. These abnormal parameter values, previously identified during the abnormality feature analysis as exceeding the normal range, ensure data accuracy and relevance.

[0118] Step S152: Obtain an adjustment strategy library corresponding to the abnormal parameter value from the operating parameter association relationship information in the digital twin model. The adjustment strategy library contains parameter correction target values ​​and adjustment operation steps.

[0119] Based on the extracted abnormal parameter values, the corresponding adjustment strategy library is obtained from the operating parameter association information in the digital twin model. Taking the abnormal temperature value of the server as an example, the digital twin model will store adjustment strategies for different temperature anomalies. The adjustment strategy library contains the parameter correction target value, that is, the normal range value to which the abnormal parameter value is to be adjusted, such as adjusting the abnormally high server temperature to the normal temperature range. At the same time, it also includes adjustment operation steps, which detail how to operate the equipment to achieve parameter correction. In the case of abnormally high server temperature, the adjustment operation steps may include increasing the air conditioning cooling power of the area where the server is located, checking whether the server's cooling fan is operating normally, and other specific operations.

[0120] Step S153: Determine the priority order of the adjustment operation based on the number of associated devices and the spatial distribution area in the impact range description information. The priority order is based on the rule that areas with a large number of associated devices are adjusted first and areas with concentrated spatial distribution are adjusted first.

[0121] The priority order of adjustment operations is determined based on the number of associated devices and the spatial distribution area in the impact range description information. If there are a large number of associated devices in a certain area, it means that the area is greatly affected by the abnormal situation, which may have more serious consequences for the operation of the entire computer room. Therefore, it is necessary to prioritize the equipment in this area. For example, if a large number of associated servers are concentrated in a corner of the computer room and the operating parameters of these servers are abnormal, then this area should be the priority object for adjustment. For areas with concentrated spatial distribution, priority adjustment can solve the problem more efficiently and reduce resource waste and time consumption during the adjustment process. According to this rule, the adjustment operations of different areas are sorted to determine a reasonable priority order.

[0122] Step S154: Integrate the device identification, abnormal parameter value, parameter correction target value, adjustment operation steps and priority order of the abnormal associated device to generate an operation and maintenance adjustment instruction containing device control instructions and operation guidance information.

[0123] Comprehensively integrate the device identification, abnormal parameter values, parameter correction target values, adjustment operation steps, and priority order of the abnormally associated devices. Taking the server as an example, the server's device identification, abnormal temperature, power consumption and other parameter values, the corresponding parameter correction target values, such as the normal temperature range and power consumption standards, the adjustment operation steps, such as turning on backup cooling equipment, adjusting the power supply, etc., and the adjustment priority order of the area where the server is located are combined. The integrated information will generate an operation and maintenance adjustment instruction that contains device control instructions and operation guidance information. Device control instructions are used to directly control the operating status of the device, such as sending a power reduction instruction to the server; the operation guidance information provides operation and maintenance personnel with specific operation steps and precautions to guide them in performing equipment adjustment operations.

[0124] Step S155: Send the operation and maintenance adjustment instruction to the controller of the abnormal related equipment through the equipment control system in the computer room.

[0125] The computer room is equipped with a dedicated device control system responsible for accurately transmitting generated O&M adjustment instructions to the controllers of associated devices. For example, using a server as an example, the device control system uses the server's device ID to transmit O&M adjustment instructions to the server's controller via network communication and other means. This transmission process ensures the accuracy and completeness of O&M adjustment instructions, preventing data loss or errors. A stable communication link is established between the device control system and the controllers of associated devices, ensuring timely and effective transmission of instructions.

[0126] Step S156: After receiving the operation and maintenance adjustment instruction, the controller performs the parameter correction operation according to the adjustment operation steps and priority order to adjust the equipment operation parameters to the parameter correction target values.

[0127] After receiving the operation and maintenance adjustment instruction, the controller of the abnormally associated device strictly follows the adjustment operation steps and priority order to perform parameter correction operations. Taking a server as an example, if the operation and maintenance adjustment instruction requires reducing the server's power consumption, the controller will gradually adjust the server's operating mode according to the preset program, such as shutting down unnecessary service processes and reducing the CPU operating frequency, in order to adjust the server's power consumption to the parameter correction target value. During the execution of the operation, the priority order can be followed, and high-priority adjustment tasks can be processed first to ensure that the abnormal problem can be resolved efficiently. At the same time, the controller will monitor the changes in the equipment's operating parameters in real time and adjust the operation steps in a timely manner based on the parameter feedback to ensure the accuracy and stability of the adjustment process.

[0128] Step S157: During the execution of the adjustment operation, the adjusted equipment operating parameters are obtained in real time through the state mapping relationship, and the dynamic operation representation is synchronously updated in the digital twin model until there are no abnormal features in the dynamic operation representation.

[0129] During the adjustment process, the previously established state mapping relationships are used to obtain the adjusted device operating parameters in real time. For example, sensors installed on servers collect parameters such as temperature, power consumption, and data processing rate after the adjustments are made. These adjusted parameters are then transmitted to the digital twin model via the state mapping relationships, and the dynamic operating representation within the digital twin model is updated simultaneously. The digital twin model regenerates the simulated operating state of the device based on the new parameter values ​​and presents a visual rendering. During this process, the dynamic operating representation can be continuously checked for any abnormalities. If abnormalities persist, the adjustment operation has not achieved the expected results, requiring further analysis and possibly readjustment of the adjustment strategy or operation steps. The adjustment operation should continue until the abnormalities are eliminated from the dynamic operating representation, indicating that the device operating parameters have returned to normal ranges and the entire computer room's operation has returned to normal.

[0130] Figure 2 The schematic diagram shows exemplary hardware and software components of the computer room management device 100 provided in some embodiments of the present application that can implement the concept of the present application. For example, the processor 120 can be used in the computer room management device 100 and used to perform the functions of the present application.

[0131] The computer room management device 100 can be a general-purpose server or a special-purpose server, both of which can be used to implement the computer room management method based on digital twins of this application. Although only one server is shown in this application, for convenience, the functions described in this application can be implemented in a distributed manner on multiple similar platforms to balance the processing load.

[0132] For example, the computer room management device 100 may include a network port 110 connected to a network, one or more processors 120 for executing program instructions, a communication bus 130, and storage media 140 in various forms, such as a disk, ROM, or RAM, or any combination thereof. Exemplarily, the computer room management device 100 may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The method of the present application may be implemented based on these program instructions. The computer room management device 100 also includes an I / O interface 150 between the computer and other input and output devices.

[0133] For ease of explanation, only one processor is described in the computer room management device 100. However, it should be noted that the computer room management device 100 in this application may also include multiple processors, so the steps performed by one processor described in this application may also be performed jointly or individually by multiple processors. For example, if the processor of the computer room management device 100 performs steps A and B, it should be understood that steps A and B may also be performed jointly by two different processors or individually in one processor. For example, the first processor performs step A and the second processor performs step B, or the first processor and the second processor perform steps A and B together.

[0134] In addition, an embodiment of the present invention also provides a readable storage medium, in which computer-executable instructions are preset. When the processor executes the computer-executable instructions, the above-mentioned computer room management method based on digital twins is implemented.

[0135] It should be noted that in order to simplify the description of the present invention and thus help understand one or more embodiments of the invention, in the foregoing description of the embodiments of the present invention, multiple features are sometimes combined into one embodiment, figure or description thereof.

Claims

1. A computer room management method based on digital twins, characterized in that: The method comprises: Construct a digital twin model of the physical entity of the computer room, which includes the spatial layout structure information of each device in the computer room and the correlation relationship information of the operating parameters between devices; Acquire real-time perception data of each device in the computer room, and establish a state mapping relationship based on the real-time perception data and the spatial layout structure information and operating parameter association relationship information in the digital twin model; Simulating the equipment operating status in the digital twin model according to the state mapping relationship to generate a dynamic operating representation reflecting the real-time operating status of the physical computer room; Performing abnormal feature analysis on the dynamic operation representation to determine abnormal associated devices and the impact range of the abnormal associated devices; Generate an operation and maintenance adjustment instruction according to the abnormal associated device and the affected range, and drive the corresponding device in the physical computer room to perform an operation state adjustment operation through the operation and maintenance adjustment instruction; The acquiring of real-time perception data of each device in the computer room and establishing a state mapping relationship based on the real-time perception data and the spatial layout structure information and operating parameter association relationship information in the digital twin model include: The sensor components deployed on the device surface and connection lines collect the device's real-time temperature value, real-time power consumption value, and real-time data processing rate value as real-time perception data; Performing format standardization processing on the real-time perception data to generate standardized perception data that is consistent with the operating parameter data format in the digital twin model; Extracting spatial layout structure information from the digital twin model and determining the device identification and device spatial position coordinates corresponding to each sensor component; Binding the standardized perception data with the corresponding device identification and device spatial position coordinates to generate multi-dimensional status data including the device identification, spatial position coordinates and standardized perception data; Based on the operating parameter association information in the digital twin model, establish parameter mapping rules between the multi-dimensional status data of different devices, wherein the parameter mapping rules include parameter synchronization frequency and parameter change threshold; Mapping the multi-dimensional state data to corresponding device nodes of the digital twin model according to the parameter mapping rule to generate a state mapping relationship reflecting a one-to-one correspondence between physical devices and digital twin model nodes; The parameter mapping rules between the multi-dimensional status data of different devices are established based on the operating parameter association relationship information in the digital twin model, and the parameter mapping rules include parameter synchronization frequency and parameter change threshold, including: Extracting a causal relationship description of operating parameters between devices from the operating parameter association relationship information of the digital twin model; Determining a synchronization order of master device parameters and slave device parameters according to the parameter change triggering condition in the causal relationship description, wherein the master device parameters are the starting parameters that trigger the parameter change, and the slave device parameters are the follow-up parameters affected by the master device parameters; Based on the synchronization order of the master device parameters and the slave device parameters, a parameter synchronization frequency is set, where the parameter synchronization frequency is the time interval for the slave device parameters to be updated after the master device parameters are updated; Analyzing parameter change transmission direction information in the causal relationship description, determining a maximum acceptable fluctuation range of the parameter change, and using the maximum acceptable fluctuation range as a parameter change threshold; Combining the parameter synchronization sequence, parameter synchronization frequency, and parameter change threshold to generate parameter mapping rules between multi-dimensional status data of different devices; The performing abnormal feature analysis on the dynamic operation representation to determine abnormal associated devices and the impact range of the abnormal associated devices includes: Extracting the simulation value time series data of each device and the color identification information in the three-dimensional visualization scene from the dynamic operation representation; Based on the operating parameter association information in the digital twin model, set the normal variation range of each device operating parameter and the normal display rules of the color identification; Comparing the simulated value time series data with the normal variation range to identify abnormal parameter values ​​that exceed the normal variation range; Analyzing the matching between the color identification in the three-dimensional visualization scene and the normal display rules, and identifying the device nodes with abnormal color identification; Determine the device node with abnormal parameter value or color identification as the initial abnormal device; Analyze the parameter change triggering conditions and transmission direction information of the initial abnormal device based on the operating parameter association relationship information in the digital twin model, and deduce the set of associated devices affected by the parameter change of the initial abnormal device; Counting the number and spatial distribution positions of devices in the associated device set, and generating impact range description information including a list of associated device identifiers and spatial distribution areas; Merging the initial abnormal device with the associated device set to determine it as an abnormal associated device, and using the impact range description information as the impact range of the abnormal associated device; The step of analyzing the parameter change triggering conditions and transmission direction information of the initial abnormal device based on the operating parameter association relationship information in the digital twin model, and deducing the set of associated devices affected by the parameter change of the initial abnormal device, includes: Extracting a causal relationship description of the initial abnormal device from the operating parameter association relationship information of the digital twin model; Analyze the parameter change triggering conditions in the causal relationship description to determine whether the abnormal parameter value of the initial abnormal device meets the conditions for triggering parameter changes of other devices; If the trigger condition is met, determining the first-level associated device for parameter change transmission based on the parameter change transmission direction information in the causal relationship description, where the first-level associated device is a device that has a direct parameter association relationship with the initial abnormal device; Perform the same analysis on the causal relationship description of the first-level associated devices to determine the second-level associated devices to which the parameter change is transmitted, where the second-level associated devices are devices that have a parameter association relationship with the first-level associated devices; Repeat the above analysis process until no new associated devices are identified or the preset transfer level limit is reached; The initial abnormal device, the first-level associated devices and the subsequent levels of associated devices are merged to generate a set of associated devices affected by the parameter change of the initial abnormal device.

2. The computer room management method based on digital twin according to claim 1 is characterized in that: The digital twin model of the physical entity of the computer room is constructed, and the digital twin model contains the spatial layout structure information of each device in the computer room and the correlation relationship information of the operating parameters between the devices, including: Collect spatial data of the physical entity of the computer room to obtain the installation location coordinates of each device, device dimensions, and the path information of the connection lines between devices as spatial layout structure information; Perform correlation analysis on the historical operating data of each device in the computer room to extract the changing patterns of the device operating parameters, including device temperature parameters, power consumption parameters, and data processing rate parameters; Establishing a causal relationship description between different equipment operating parameters, wherein the causal relationship description includes the triggering conditions for parameter changes and the transmission direction information of the parameter changes; The spatial layout structure information and the association relationship information of the equipment operation parameters are integrated and modeled to generate a basic framework of a digital twin model including a three-dimensional spatial coordinate system and parameter association rules; The digital twin model basic framework is used to perform compatibility verification on the spatial location information and operating parameter information of the newly connected device, and the spatial layout structure information and operating parameter association relationship information of the digital twin model are updated.

3. The computer room management method based on digital twin according to claim 2 is characterized in that: The historical operating data of each device in the computer room is correlated and analyzed to extract the changing patterns of the device operating parameters, which include device temperature parameters, power consumption parameters, and data processing rate parameters, including: Collect the temperature parameter history, power consumption parameter history and data processing rate parameter history of each device in the computer room during the continuous operation cycle; Performing a time series analysis on the temperature parameter history records to identify the changing trend of the temperature parameters over the operating time of the equipment, wherein the changing trend includes the time nodes of the temperature rising stage, the stable stage and the falling stage; Performing a correlation analysis on the power consumption parameter history, calculating a correlation coefficient between the power consumption parameter and a device data processing rate parameter, and determining a linear or nonlinear relationship between the power consumption parameter and the data processing rate; Perform periodic analysis on the historical records of the data processing rate parameters to extract the peak frequency and valley frequency of the data processing rate in different business time periods; The change trend of the temperature parameters, the correlation between the power consumption parameters and the data processing rate, and the periodic characteristics of the data processing rate are integrated to generate a description of the change rules of the equipment operation parameters, which includes the time dependence characteristics of the parameter changes and the mutual influence characteristics between the parameters.

4. The computer room management method based on digital twin according to claim 1 is characterized in that: The simulating the equipment operation status in the digital twin model according to the state mapping relationship to generate a dynamic operation representation reflecting the real-time operation status of the physical computer room includes: Extracting multi-dimensional state data corresponding to each digital twin model node from the state mapping relationship; According to the parameter synchronization frequency in the state mapping relationship, the multi-dimensional state data is input into the operation simulation module of the corresponding digital twin model node; The operation simulation module deduces parameter change trends of the input multi-dimensional status data based on the operation parameter association relationship information in the digital twin model to generate a simulation value of the equipment operation status; Performing a consistency check between the simulation value and the real-time sensing data in the multi-dimensional state data; if the check passes, retaining the simulation value; if the check fails, re-deriving the parameter change trend; The simulated values ​​that have passed the consistency check are visualized and rendered in the three-dimensional spatial coordinate system of the digital twin model according to the spatial position coordinates of the equipment, generating a three-dimensional visualization scene including color identification of the equipment operation status, dynamic curves of parameter changes, and connection line status indications; The three-dimensional visualization scene is synchronized with the real-time updated simulation value in time series to generate a dynamic operation representation that changes continuously over time.

5. The computer room management method based on digital twin according to claim 4 is characterized in that: The operation simulation module deduces parameter change trends of the input multi-dimensional status data based on the operation parameter association relationship information in the digital twin model to generate a simulation value of the equipment operation status, including: Inputting the real-time temperature value, real-time power consumption value and real-time data processing rate value in the multi-dimensional state data into the parameter input layer of the operation simulation module; The parameter input layer of the operation simulation module matches the input real-time parameter values ​​with the description of the equipment operation parameter change law stored in the digital twin model to determine the stage of the current equipment operation, which includes the temperature rising stage, stable stage or falling stage, and the peak stage or valley stage of the data processing rate; Based on the device operation stage, calling the corresponding change trend model in the description of the operation parameter change law, the change trend model includes a temperature change trend model, a power consumption and data processing rate correlation model, and a data processing rate periodicity model; Deducing a temperature prediction value at a future time point using the temperature change trend model, deducing a power consumption prediction value at a future time point using the power consumption and data processing rate correlation model, and deducing a data processing rate prediction value at a future time point using the data processing rate periodicity model; The temperature prediction value, power consumption prediction value and data processing rate prediction value are combined to generate a simulation value of the device operation status.

6. A computer room management device, characterized in that: It includes a processor and a memory, the memory is connected to the processor, the memory is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the memory to implement the digital twin-based computer room management method described in any one of claims 1 to 5.

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