Machine room intelligent temperature control monitoring system and early warning method based on situation awareness

Abstract

The invention relates to the technical field of machine room temperature monitoring, and particularly discloses a machine room intelligent temperature control monitoring system based on situation awareness and an early warning method. According to an evaluation result, a collection network of machine room temperature control data is intelligently adjusted and fed back, meanwhile, according to the collected machine room temperature control data, abnormal sensing risk factors of the machine room environment are deeply analyzed, potential problems can be found and solved in time, and on the basis of analysis of the risk factors, safety is improved. And further matching to obtain an environment early warning adjustment set of the machine room operation environment, providing a scientific basis for optimization of the machine room environment, monitoring the operation environment parameters of the machine room again after execution of the environment early warning adjustment set is completed, and judging the optimization quality index of the machine room operation environment according to the parameters. The closed-loop monitoring mechanism also performs early warning feedback on the intelligent temperature control monitoring of the machine room, so that the continuous optimization of the temperature control of the machine room is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of computer room temperature monitoring, and in particular to a computer room intelligent temperature control monitoring system and an early warning method based on situation awareness. Background Art

[0002] With the rapid development of information technology, data centers, as the "heart" of the information age, are crucial to ensuring the continuity of various businesses through stable operation. Computer rooms, as the core area of ​​data centers, are particularly critical to the control of environmental conditions. Temperature is an important factor affecting the performance and life of equipment in computer rooms. Its monitoring and regulation level directly determines the operating efficiency and safety of data centers. Therefore, intelligent temperature control monitoring of computer rooms is of great significance.

[0003] For example, the invention patent with publication number CN118759948A discloses a centralized monitoring system and method for a computer room based on the Internet of Things technology, which belongs to the field of computer room monitoring technology. The method specifically includes: obtaining abnormal dynamic environment monitoring data through data processing according to dynamic environment monitoring data, obtaining a fault warning model through neural network training according to the historical operation data of power equipment, obtaining equipment fault warning information through fault warning model processing according to the abnormal power equipment operation data, constructing a state space coefficient matrix, input vector and state vector according to the equipment cooling system operation information, constructing a temperature prediction model according to the state space coefficient matrix, input vector and state vector, obtaining equipment thermal environment abnormality information through temperature prediction model processing according to the equipment cooling system operation information, uploading the equipment fault warning information and equipment thermal environment abnormality information to the terminal, realizing the monitoring of the equipment operation status and equipment thermal environment of the computer room, and improving the efficiency and safety of computer room monitoring.

[0004] For example, the invention patent with publication number CN114690672A discloses an intelligent temperature monitoring wireless early warning device and control system, which belongs to the field of temperature monitoring technology, including monitoring equipment, wireless transceiver unit, data receiving and processing module, cloud server, memory, comparison unit, user equipment and early warning equipment. The monitoring equipment communicates with the data receiving and processing module through the wireless transceiver unit, the data receiving and processing module communicates with the cloud server through the 5G network, the cloud server interacts with the memory through the RS232 protocol, the cloud server communicates with the user equipment through the wireless network, and the user equipment communicates with the early warning equipment through the 5G network.

[0005] However, in the process of implementing the embodiments of the present application, it was found that the above technology has at least the following technical problems: in the existing computer room temperature control technology, only abnormal warnings are given for temperature control monitoring, and the warning method is manual abnormal control adjustment, which will cause the computer room temperature control to be inflexible and inefficient, and difficult to adapt to complex environmental changes. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides an intelligent temperature control monitoring system and early warning method for a computer room based on situation awareness, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: In the first aspect, the present invention provides a computer room intelligent temperature control monitoring system based on situational awareness, including: a data collection and feedback module, which is used to collect network environment parameters to which the computer room temperature control data belongs, evaluate the collection network quality index of the computer room temperature control data, and compare it with the collection network quality threshold. If the collection network quality index of the computer room temperature control data is greater than the collection network quality threshold, the environmental risk analysis module is executed, otherwise, the collection network of the computer room temperature control data is adjusted and fed back; the environmental risk analysis module is used to analyze the abnormal perception risk factor of the computer room environment according to the computer room temperature control data, thereby matching and obtaining the environmental warning adjustment set of the computer room operating environment; the environmental optimization adjustment module is used to execute the environmental warning adjustment set of the computer room operating environment, and the operating environment parameters of the computer room after the situational awareness is executed, thereby determining the optimization quality index of the computer room operating environment, and providing early warning feedback for the intelligent temperature control monitoring of the computer room.

[0008] As a further solution, the collection network for the computer room temperature control data is adjusted and fed back. The specific adjustment and feedback process is: the collection network quality threshold is differenced with the collection network quality index of the computer room temperature control data to obtain the collection network quality difference of the computer room temperature control data, and matched with the collection network adjustment set corresponding to each collection network quality difference interval stored in the computer room database, thereby obtaining and executing the collection network adjustment set for the computer room temperature control data, and completing the adjustment of the computer room temperature control data collection network; by completing the adjustment of the computer room temperature control data collection network, the computer room temperature control data is collected again, and the data processing platform updates the computer room temperature control data, thereby completing the adjustment feedback of the collection network for the computer room temperature control data.

[0009] As a further solution, the matching obtains an environmental warning adjustment set for the computer room operating environment. The specific matching process is: matching the abnormal perception risk factor of the computer room environment with the environmental warning adjustment set corresponding to each abnormal perception risk factor interval stored in the computer room database, thereby obtaining the environmental warning adjustment set for the computer room operating environment.

[0010] As a further solution, the operating environment parameters of the computer room after the situation awareness is completed, specifically the operating parameters of each device belonging to the situation awareness computer room and the environmental parameters of the environment to which the computer room belongs, are comprehensively marked as the operating environment parameters of the computer room. Thus, by analyzing the operating parameters of each device belonging to the computer room and the environmental parameters of the environment to which the computer room belongs, the optimization quality index of the computer room operating environment is comprehensively determined.

[0011] As a further solution, the intelligent temperature control monitoring of the computer room is provided with early warning feedback. The specific early warning feedback process is as follows: the optimization quality index of the computer room operating environment is compared with the optimization quality threshold. If the optimization quality index of the computer room operating environment is less than the optimization quality threshold, the optimization quality index of the computer room operating environment is ratio-processed with the optimization quality threshold to obtain the optimization quality index ratio of the computer room operating environment, and the ratio is matched with the temperature control early warning adjustment set corresponding to each optimization quality index ratio interval stored in the computer room database, thereby obtaining and executing the temperature control early warning adjustment set of the computer room operating environment, and providing feedback to the intelligent temperature control monitoring of the computer room; if the optimization quality index of the computer room operating environment is greater than or equal to the optimization quality threshold, the situation awareness monitoring of the intelligent temperature control of the computer room is continuously performed.

[0012] The second aspect of the present invention provides an early warning method for the situation-aware computer room intelligent temperature control monitoring system, comprising: S1. Collecting network environment parameters of the computer room temperature control data, evaluating the collection network quality index of the computer room temperature control data, and comparing it with the collection network quality threshold. If the collection network quality index of the computer room temperature control data is greater than the collection network quality threshold, the environmental risk analysis module is executed, otherwise, the collection network of the computer room temperature control data is adjusted and fed back; S2. According to the computer room temperature control data, the abnormal perception risk factor of the computer room environment is analyzed, and the environmental early warning adjustment set of the computer room operating environment is obtained by matching; S3. Execute the environmental early warning adjustment set of the computer room operating environment, and the operating environment parameters of the computer room after the situation awareness execution is completed, thereby determining the optimized quality index of the computer room operating environment, and providing early warning feedback for the computer room intelligent temperature control monitoring.

[0013] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0014] (1) The present invention provides a computer room intelligent temperature control monitoring system and early warning method based on situational awareness. By collecting network environment parameters to which computer room temperature control data belongs, the quality index of the collection network of computer room temperature control data is evaluated, and the collection network of computer room temperature control data is intelligently adjusted and feedback is given according to the evaluation results, thereby ensuring the accuracy and reliability of computer room temperature control data collection and effectively improving the accuracy of the computer room temperature control system. At the same time, according to the collected computer room temperature control data, the abnormal perception risk factors of the computer room environment are deeply analyzed, and potential problems can be discovered in time, further improving the safety and stability of computer room operation. Based on the analysis of risk factors, an environmental early warning adjustment set of the computer room operating environment is further matched, providing a scientific basis for the optimization of the computer room environment. After the execution of the environmental early warning adjustment set is completed, the operating environment parameters of the computer room are monitored again, and the optimization quality index of the computer room operating environment is determined accordingly. This closed-loop monitoring mechanism also ensures the continuous optimization of computer room temperature control by early warning feedback of the intelligent temperature control monitoring of the computer room.

[0015] (2) The present invention analyzes the temperature control data of the computer room and matches it in a targeted manner to obtain the environmental warning adjustment set of the computer room operating environment. This shows that it is possible to match a suitable environmental warning adjustment plan according to the specific conditions of the computer room and the temperature control data, thereby improving the accuracy and targeting of the environmental warning adjustment, and effectively reducing the problems of excessive resource consumption and poor results that may be caused by adopting inappropriate adjustment strategies.

[0016] (3) The present invention analyzes the operating environment parameters after the environmental warning adjustment set of the computer room operating environment is completed, that is, the average operating power consumption, total cooling capacity, temperature drop rate and other parameters are analyzed, thereby intuitively reflecting the actual effect of the computer room environment after temperature control optimization, providing data support for optimizing the temperature control of the computer room, and facilitating the improvement and perfection of the subsequent temperature control adjustment strategy of the computer room. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0018] Figure 1 It is a schematic diagram of system module connection of the present invention.

[0019] Figure 2 The figure is a schematic flow chart of the method steps of the present invention. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0021] Reference Figure 1 As shown, the first aspect of the present invention provides an intelligent temperature control monitoring system for a computer room based on situational awareness, including: a data collection and feedback module, an environmental risk analysis module and an environmental optimization and adjustment module.

[0022] The first aspect of the present invention provides an intelligent temperature control monitoring system for a computer room based on situational awareness, which also includes a computer room database, wherein the computer room database is used to store an average command response time limit value, an influencing factor corresponding to a unit value of transmission congestion, an influencing factor corresponding to a unit value of an average transmission delay time, an influencing factor corresponding to a unit value of an upload success frequency, a collection network adjustment set corresponding to each collection network quality difference interval, an influencing factor corresponding to a unit value of an average energy efficiency ratio, an environmental warning adjustment set corresponding to each abnormal perception risk factor interval, a reference average operating power consumption, a reference total cooling capacity, a weight factor corresponding to an abnormal perception risk factor, an influencing factor corresponding to a unit value of a temperature drop rate, a temperature control warning adjustment set corresponding to each optimized quality index ratio interval, a collection network quality threshold, and an optimized quality threshold.

[0023] The data collection and feedback module is connected to the environmental risk analysis module, the environmental risk analysis module is connected to the environmental optimization and adjustment module, and the data collection and feedback module, the environmental risk analysis module and the environmental optimization and adjustment module are all connected to the computer room database.

[0024] The data collection and feedback module is used to collect the network environment parameters of the computer room temperature control data, evaluate the collection network quality index of the computer room temperature control data, and compare it with the collection network quality threshold. If the collection network quality index of the computer room temperature control data is greater than the collection network quality threshold, the environmental risk analysis module is executed; otherwise, the collection network of the computer room temperature control data is adjusted and feedback is provided.

[0025] The network environment parameters to which the temperature control data of the above-mentioned computer room belongs include the response time of each command, the actual data transmission volume and the maximum data transmission volume. In addition to being applicable to the above-mentioned parameters, this example can also be applied to other parameters.

[0026] The above-mentioned collection network quality threshold represents the minimum value of the reasonable range of the collection network quality index of the computer room temperature control data, which is extracted from the computer room database.

[0027] Specifically, the collection network quality index of the temperature control data of the computer room, in this embodiment, is obtained by comprehensively analyzing the average command response time, average transmission delay time, upload success frequency of the equipment in the computer room during the temperature control monitoring period, and the transmission congestion of the network to which the temperature control data of the computer room belongs during the temperature control monitoring period, and is used to determine the value of the collection network quality of the temperature control data of the computer room. The specific evaluation method is:

[0028]

[0029] CBUR is the average command response time of the equipment in the computer room during the temperature control monitoring cycle, which refers to the average time from the issuance of the command to the completion of the execution of the intelligent device after the intelligent device in the computer room receives the temperature control command.

[0030] TCL is the transmission congestion of the network to which the temperature control data of the computer room belongs during the temperature control monitoring cycle, which refers to the numerical value of the data transmission busyness of the network to which the temperature control data of the computer room belongs during a specific time period (such as the temperature control monitoring cycle).

[0031] ATDT is the average transmission delay of the temperature control data in the computer room during the temperature control monitoring cycle, which refers to the time required for the temperature control data to be generated from monitoring to reach the receiving end (such as the data processing platform).

[0032] USF is the frequency of successful upload of the temperature control data of the computer room during the temperature control monitoring cycle, which refers to the ratio of the number of temperature control data of the computer room successfully uploaded to the data processing platform during the temperature control monitoring cycle to the duration corresponding to the temperature control monitoring cycle.

[0033] CBUR ΔJ It is the average command response time limit value preset in the computer room database, indicating the maximum value of the average command response time of the equipment in the analysis room during the temperature control monitoring cycle.

[0034] e is a natural constant, er1 is an influence factor corresponding to the transmission congestion unit value preset in the computer room database, and represents the influence degree of the transmission congestion unit value on the collection network quality index of the computer room temperature control data. When used, the influence factor corresponding to the transmission congestion unit value can be directly obtained from the computer room database, and the corresponding relationship can be a preset mapping relationship. For example, the transmission congestion and the influence factor corresponding to the transmission congestion unit value preset in the computer room database form a mapping set, and the real-time transmission congestion is input into the mapping set to obtain the influence factor corresponding to the transmission congestion unit value. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0035] Er2 is the impact factor corresponding to the unit value of the average transmission delay time preset in the computer room database, which represents the numerical value of the impact of the unit value of the average transmission delay time on the collection network quality index of the computer room temperature control data. When used, the impact factor corresponding to the unit value of the average transmission delay time can be directly obtained from the computer room database, and the corresponding relationship can be a preset mapping relationship. For example, the average transmission delay time and the impact factor corresponding to the unit value of the average transmission delay time preset in the computer room database form a mapping set, and the real-time average transmission delay time is input into the mapping set to obtain the impact factor corresponding to the unit value of the average transmission delay time. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0036] Er3 is the impact factor corresponding to the preset upload success frequency unit value in the computer room database, which represents the influence degree of the upload success frequency unit value on the collection network quality index of the computer room temperature control data. When used, the impact factor corresponding to the upload success frequency unit value can be directly obtained from the computer room database, and the corresponding relationship can be a preset mapping relationship. For example, the upload success frequency and the impact factor corresponding to the upload success frequency unit value preset in the computer room database form a mapping set, and the real-time upload success frequency is input into the mapping set to obtain the impact factor corresponding to the upload success frequency unit value. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0037] Among them, ZQ is the network quality index for collecting the temperature control data in the computer room. When the network environment in the computer room is poor, a series of chain reactions will be triggered, as shown below: due to the poor network environment, the equipment may encounter delays when requesting or receiving instructions to be processed, which will significantly increase the average instruction response time of the equipment in the computer room. It also shows that during the transmission of the temperature control data in the computer room, the data packet needs to wait longer in the network before it can be transmitted to the destination data processing platform for integration and processing. This waiting will not only reduce the efficiency of the temperature control data transmission in the computer room and increase the network transmission congestion, but also increase the risk of temperature control data loss in the computer room. If the risk of temperature control data loss in the computer room is high, the temperature control data in the computer room cannot be successfully received and processed, which further reduces the frequency of successful upload of the temperature control data in the computer room. Due to the increase in network transmission congestion, the transmission speed of the temperature control data in the computer room will slow down, and then Increase the average transmission delay time, because data takes longer to be transmitted and received. The longer average transmission delay time will also reduce the number of successful uploads of the computer room temperature control data within the temperature control monitoring cycle. Therefore, within a given cycle, the amount of data that can be successfully uploaded will be reduced accordingly, thereby reducing the frequency of successful uploads of the computer room temperature control data. In summary, analyzing the relevant parameters of the computer room temperature control data from multiple aspects can clearly reveal the quality of the computer room temperature control data collection network, which not only helps to timely discover and solve potential problems in the network environment, improve the reliability and efficiency of the computer room temperature control data transmission, but also ensure the accuracy and completeness of the computer room temperature control data. High-quality computer room temperature control data provides a solid foundation for subsequent judgments and decisions, helps to more accurately grasp the operating status of the computer room temperature control, and take necessary regulatory measures in time, thereby ensuring the stable operation and efficient management of the computer room temperature control.

[0038] In this exemplary embodiment, the collection network quality index of the temperature control data of the equipment room and the change table of its corresponding parameters are shown in Table 1:

[0039] Table 1 Collection of temperature control data of computer room network quality index and its corresponding parameter changes

[0040]

[0041]

[0042] In this example embodiment, the average command response time limit is set to 40 milliseconds, the value of the impact factor corresponding to the unit value of the transmission congestion is set to 0.78, the value of the impact factor corresponding to the unit value of the transmission delay time is set to 0.65, and the value of the impact factor corresponding to the unit value of the upload success frequency is set to 0.71. It can be seen from Table 1 that when the average command response time is much smaller than the average command response time limit (that is, when the average command response time is 23 milliseconds), the network's transmission congestion and average transmission delay time will be smaller (47% and 41 milliseconds respectively), and the frequency of successful data upload will be at a higher level (the upload success frequency is 68 per second). This can comprehensively reflect that the collection network quality is good, which is manifested in that the collection network quality index is at a high level of 108%.

[0043] Specifically, the collection network quality index of the evaluation room temperature control data is evaluated, and the specific evaluation process is as follows:

[0044] The response time of each command of each device in the computer room during the temperature control monitoring period is extracted from the network environment parameters of the computer room temperature control data, and the average response time of each command of the equipment in the computer room during the temperature control monitoring period is obtained by averaging. The temperature control monitoring period refers to the time period for monitoring the temperature control of the computer room, and its specific duration is determined by the temperature control administrator of the computer room. The equipment in the computer room refers to the intelligent equipment in the computer room, including refrigeration equipment, power supply equipment and intelligent operation equipment. It should be explained that the computer room is equipped with monitoring platforms such as the dynamic environment monitoring system, which can monitor the computer room in real time. The status of each device in the computer room is monitored, and the time for each device to receive commands and respond is recorded. Therefore, the response time of each command of each device belonging to the above-mentioned computer room within the temperature control monitoring period can be monitored by monitoring platforms such as the dynamic environment monitoring system to obtain the average response time of the equipment in the above-mentioned computer room within the temperature control monitoring period, which means that the response time of each command of each device belonging to the computer room within the temperature control monitoring period is first averaged to obtain the average response time of each device in the computer room within the temperature control monitoring period, and then the average processing is continued to obtain the average response time of the equipment in the computer room within the temperature control monitoring period.

[0045] The actual data transmission volume of the network to which the temperature control data of the computer room belongs during the temperature control monitoring period and the maximum data transmission volume of the network to which the temperature control data of the computer room belongs during the temperature control monitoring period are extracted from the network environment parameters to which the temperature control data of the computer room belongs, and ratio processing is performed, and the ratio processing result is marked as the transmission congestion of the network to which the temperature control data of the computer room belongs during the temperature control monitoring period; the actual data transmission volume of the network to which the temperature control data of the computer room belongs during the temperature control monitoring period is obtained by setting monitoring rules in a network performance monitoring tool (such as SolarWinds) to capture network data packets during the temperature control monitoring period, counting the sizes of these network data packets, and accumulating the actual data transmission volume; the maximum data transmission volume of the network to which the temperature control data of the computer room belongs during the temperature control monitoring period is obtained by using a network bandwidth measurement tool (such as Speedtest) to test the available bandwidth of the computer room network during the temperature control monitoring period, and integrating the available bandwidth of the computer room network during the temperature control monitoring period to obtain the maximum data transmission volume.

[0046] The platform receiving time point and data collection time point of each sampled temperature control data belonging to the computer room temperature control data within the temperature control monitoring period are obtained, and difference and mean processing are performed in turn, and the processing result is marked as the average transmission delay duration of the computer room temperature control data within the temperature control monitoring period; each sampled temperature control data belonging to the above-mentioned computer room temperature control data refers to the sampling analysis of the collected computer room temperature control data. Due to the huge amount of computer room temperature control data, if all data are comprehensively analyzed, it will not only consume a lot of computing resources and time, but also may reduce the accuracy of the analysis due to data redundancy. At the same time, since the temperature control data is uploaded in the same network environment and the upload process is consistent, it can be considered that the interference and influence on the data during the transmission process are similar, based on For these reasons, it is reasonable and efficient to use a sampling analysis method to process the temperature control data of the computer room; the platform reception time point and data collection time point of each sampled temperature control data belonging to the above-mentioned computer room temperature control data within the temperature control monitoring period, wherein the platform reception time point is extracted from the data processing platform time log, and the data collection time point is extracted through the timestamp data recorded by the built-in clock of the acquisition sensor corresponding to each sampled temperature control data, wherein each sampled data includes but is not limited to the temperature of each temperature detection area belonging to the computer room during the temperature control monitoring period, the cooling capacity of each refrigeration equipment belonging to the computer room during the temperature control monitoring period, and the total power consumption of each refrigeration equipment belonging to the computer room during the temperature control monitoring period, and the average air flow speed of the computer room environment during the temperature control monitoring period.

[0047] The number of successful uploads of the temperature control data of the computer room within the temperature control monitoring period is obtained, and the ratio is processed with the duration corresponding to the temperature control monitoring period to obtain the frequency of successful uploads of the temperature control data of the computer room within the temperature control monitoring period, and finally the evaluation method of the collection network quality index of the temperature control data of the computer room is comprehensively obtained; the number of successful uploads of the temperature control data of the computer room within the temperature control monitoring period is extracted from the data log received by the data processing platform.

[0048] Furthermore, the collection network of the temperature control data of the computer room is adjusted and fed back, and the specific adjustment and feedback process is as follows:

[0049] The collection network quality threshold and the collection network quality index of the computer room temperature control data are processed by difference to obtain the collection network quality difference of the computer room temperature control data, and the difference is matched with the collection network adjustment set corresponding to each collection network quality difference interval stored in the computer room database, thereby obtaining and executing the collection network adjustment set of the computer room temperature control data, and completing the adjustment of the collection network of the computer room temperature control data; in an example embodiment, assuming that the collection network quality difference of the computer room temperature control data is X, in the collection network quality difference interval [X-16%, X+14%] stored in the computer room database, the collection network adjustment set corresponding to the collection network quality difference interval [X-16%, X+14%] includes: through the built-in traffic shaping and rate limiting functions of the network equipment (such as the switch), the traffic is prioritized and the rate is controlled, the bandwidth requirements of important services are given priority, and the traffic of non-critical services is limited to reduce network congestion, and the bandwidth allocation of the network equipment is automatically adjusted according to the real-time monitoring results of the network traffic, Increase bandwidth allocation during peak traffic hours. The load balancer deployed on the network device distributes traffic to multiple servers or links according to certain algorithms (such as polling or the least number of connections, etc.) to achieve balanced distribution of traffic, reduce overload of a single server or link, and improve the throughput and response speed of the overall network. Therefore, the network adjustment set for collecting temperature control data in the computer room includes: through the built-in traffic shaping and rate limiting functions of network devices (such as switches), prioritize and control the rate of traffic, give priority to ensuring the bandwidth requirements of important services, and limit the traffic of non-critical services to reduce network congestion. According to the real-time monitoring results of network traffic, automatically adjust the bandwidth allocation of network devices, increase bandwidth allocation during peak traffic hours, and the load balancer deployed on the network device distributes traffic to multiple servers or links according to certain algorithms (such as polling or the least number of connections, etc.) to achieve balanced distribution of traffic, reduce overload of a single server or link, and improve the throughput and response speed of the overall network.

[0050] The above-mentioned collection network adjustment set for executing the temperature control data of the computer room refers to automatic execution by the network management system of the computer room or the intelligent management system of the network equipment in the computer room itself; it should be explained that the adjustments in the collection network adjustment set can automatically and accurately adjust the network configuration according to the real-time monitoring results of the network traffic, so as to ensure the stable transmission and efficient collection of data. Therefore, after executing the collection network adjustment set, the collection network quality index of the temperature control data of the computer room can meet the requirement of being greater than the collection network quality threshold.

[0051] By completing the adjustment of the computer room temperature control data collection network, the computer room temperature control data is collected again, and the data processing platform updates the computer room temperature control data, completing the adjustment feedback of the computer room temperature control data collection network; the above-mentioned data processing platform refers to a system platform that integrates and analyzes the network environment parameters to which the computer room temperature control data belongs, the computer room temperature control data, and the situation-aware computer room operating environment parameters in real time, and automatically triggers the early warning adjustment mechanism when an abnormality is detected, and adjusts and improves the computer room temperature control by calling the corresponding adjustment mechanism, so as to ensure the effectiveness of the computer room temperature control and the stability of the computer room operation; the above-mentioned collection of computer room temperature control data again refers to the collection of computer room temperature control data within a predetermined time period after completing the adjustment of the computer room temperature control data collection network, and the predetermined time period is equal to the time period corresponding to the computer room temperature control cycle, so the data processing platform updates the computer room temperature control data and continues to analyze.

[0052] The environmental risk analysis module is used to analyze the abnormal perception risk factors of the computer room environment based on the computer room temperature control data, thereby matching and deriving the environmental warning adjustment set of the computer room operating environment.

[0053] In a specific embodiment, the present invention analyzes the temperature control data of the computer room and matches it in a targeted manner to obtain an environmental warning adjustment set for the computer room's operating environment. This shows that a suitable environmental warning adjustment plan can be matched according to the specific conditions of the computer room and the temperature control data, thereby improving the accuracy and targeting of the environmental warning adjustment, and effectively reducing the problems of excessive resource consumption and poor results that may be caused by adopting inappropriate adjustment strategies.

[0054] Specifically, the matching obtains the environmental warning adjustment set of the computer room operating environment, and the specific matching process is:

[0055] The abnormal perception risk factor of the computer room environment is matched with the environmental warning adjustment set corresponding to each abnormal perception risk factor interval stored in the computer room database, thereby obtaining the environmental warning adjustment set of the computer room operating environment; in an example embodiment, assuming that the abnormal perception risk factor of the computer room environment is Y, in the abnormal perception risk factor interval [Y-25%, Y+35%] stored in the computer room database, the environmental warning adjustment set corresponding to the abnormal perception risk factor interval [Y-25%, Y+35%] includes: each cooling device increases the cooling capacity by 25% per unit time, automatically turns on three smart fans for air circulation and cooling, and automatically turns off unnecessary lighting.

[0056] Furthermore, the abnormal perception risk factor of the computer room environment is analyzed, and the specific analysis process is as follows:

[0057] The temperatures of each temperature detection area belonging to the computer room during the temperature control monitoring period are extracted from the temperature control data of the computer room, and average processing is performed, and the processing result is marked as the average temperature of the computer room environment during the temperature control monitoring period; the temperatures of each temperature detection area belonging to the computer room during the temperature control monitoring period refer to a number of temperature detection areas randomly arranged in the computer room, and the average temperature of each temperature detection area belonging to the computer room during the temperature control monitoring period is measured by a temperature sensor, and is marked as the temperature of each temperature detection area belonging to the computer room during the temperature control monitoring period.

[0058] The cooling capacity of each refrigeration equipment in the computer room during the temperature control monitoring period and the total power consumption of each refrigeration equipment in the computer room during the temperature control monitoring period are extracted from the temperature control data of the computer room, and the cooling capacity of each refrigeration equipment in the computer room during the temperature control monitoring period is ratio-processed with the corresponding total power consumption to obtain the energy efficiency ratio of each refrigeration equipment in the computer room during the temperature control monitoring period, and the average energy efficiency ratio of the refrigeration equipment in the computer room during the temperature control monitoring period is obtained by average processing; the cooling capacity of each refrigeration equipment in the computer room during the temperature control monitoring period is obtained by analyzing the average cooling power of each refrigeration equipment during the temperature control monitoring period from the data recorded in the power table of each refrigeration equipment, and the cooling capacity of each refrigeration equipment in the computer room during the temperature control monitoring period is obtained by multiplying the average cooling power by the duration corresponding to the temperature control monitoring period; the total power consumption of each refrigeration equipment in the computer room during the temperature control monitoring period is extracted from the data recorded in the power metering device (such as a multimeter).

[0059] The average air flow speed of the computer room environment during the temperature control monitoring period is extracted from the computer room temperature control data. The average air flow speed of the computer room environment during the temperature control monitoring period can be measured by several air flow velocity sensors installed in the computer room, and the average value is processed. The processing result is marked as the average air flow speed of the computer room environment during the temperature control monitoring period.

[0060] At the same time, combined with the average command response time of the equipment in the computer room during the temperature control monitoring period, an analysis method for the abnormal perception risk factor of the computer room environment is comprehensively obtained. In this embodiment, the abnormal perception risk factor of the computer room environment is obtained by comprehensively analyzing the average temperature of the computer room environment during the temperature control monitoring period, the average air flow speed, the average energy efficiency ratio of the refrigeration equipment in the computer room during the temperature control monitoring period, and the average command response time of the equipment in the computer room during the temperature control monitoring period. The value used to determine the abnormal perception risk degree of the computer room environment is obtained. The specific analysis method is:

[0061]

[0062] PD is the average temperature of the equipment room environment during the temperature control monitoring period, which refers to the average value of the ambient temperature of the equipment room during the temperature control monitoring period.

[0063] BN is the average energy efficiency ratio of the refrigeration equipment in the computer room during the temperature control monitoring period, which refers to the average value of the ratio of the cooling capacity generated by the refrigeration equipment in the computer room during the temperature control monitoring period to the electrical energy consumed.

[0064] VL is the average air flow velocity in the computer room environment during the temperature control monitoring cycle. It refers to the average air flow velocity in the computer room environment during the temperature control monitoring cycle. It is an important indicator to measure the air circulation effect in the computer room. It helps to evaluate the ventilation effect and air distribution of the computer room, which in turn affects the temperature and humidity control of the computer room.

[0065] PD ΔC The reference average temperature represents the reference value of the average temperature of the analysis room environment during the temperature control monitoring period.

[0066] V L ΔC The reference air flow average speed represents the reference value of the average air flow speed of the analysis room environment during the temperature control monitoring period.

[0067] CBUR is the average command response time of the equipment in the computer room during the temperature control monitoring cycle. ΔJ It is the limit value of the average command response time preset in the computer room database. In the expression of the abnormal perception risk factor of the computer room environment, the average command response time affects the execution of the subsequent environmental warning adjustment set. If the average command response time is long, the environmental warning adjustment set cannot be sent for execution in time, which aggravates the abnormal perception risk level of the computer room environment.

[0068] e is a natural constant, rn1 is an impact factor corresponding to the average energy efficiency ratio unit value preset in the computer room database, and represents the numerical value of the impact degree of the average energy efficiency ratio unit value on the abnormal perception risk factor of the computer room environment. When used, the impact factor corresponding to the average energy efficiency ratio unit value can be directly obtained from the computer room database, and the corresponding relationship can be a preset mapping relationship. For example, the average energy efficiency ratio and the impact factor corresponding to the average energy efficiency ratio unit value preset in the computer room database form a mapping set, and the real-time average energy efficiency ratio is input into the mapping set to obtain the impact factor corresponding to the average energy efficiency ratio unit value. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0069] Among them, ARF is the abnormal perception risk factor of the computer room environment. When the average air flow speed of the computer room environment deviates significantly from the reference average air flow speed, it indicates that the temperature control system of the computer room performs poorly, so that the heat in the computer room cannot be effectively removed in time. Local overheating may occur in the computer room, causing the average temperature of the computer room to deviate significantly from the reference average temperature. The average temperature of the computer room is a key factor affecting the performance and energy efficiency of the equipment in the computer room. Once the average temperature of the computer room deviates significantly from the reference average temperature, the refrigeration equipment needs to consume more electricity to maintain the normal temperature of the computer room, which will not only reduce the energy efficiency ratio of the refrigeration equipment, but also accelerate the aging of the refrigeration equipment. The process causes the refrigeration equipment to generate more heat, further increasing the average temperature of the computer room, thereby shortening its service life. In addition, the high temperature environment will also reduce the operating efficiency of the electronic components inside other equipment in the computer room, resulting in an increase in the response time of all equipment commands in the computer room, which directly affects the operation and maintenance efficiency of the computer room and the continuity of business. Therefore, the above parameters will increase the risk of abnormal perception of the computer room environment from different angles, posing a severe challenge to the stability of the computer room temperature control, the safety of the computer room operation and the operation and maintenance efficiency. Therefore, regular monitoring of these parameters and timely detection and handling of abnormalities are key measures to ensure the effectiveness of the computer room temperature control, the stability of the computer room environment and the continuity of the computer room business.

[0070] The environment optimization and adjustment module is used to execute the environment warning adjustment set of the computer room operating environment, and the operating environment parameters of the computer room after the situation awareness is completed, thereby determining the optimization quality index of the computer room operating environment and providing early warning feedback for the intelligent temperature control monitoring of the computer room.

[0071] The operating environment parameters of the above-mentioned computer room include: operating power consumption, cooling capacity and average temperature. In addition to being applicable to the above-mentioned parameters, this example can also be used for other parameters; the above-mentioned execution of the environmental warning adjustment set of the computer room operating environment refers to the data processing platform automatically classifying the environmental warning adjustment set of the computer room operating environment into various instructions, and transmitting them to various cooling devices and other related intelligent devices through the intelligent network in the computer room. After receiving the instructions, each cooling device and related intelligent device will immediately perform corresponding operations, such as increasing cooling capacity and adjusting wind speed, etc., to quickly respond to changes in the computer room environment.

[0072] In a specific embodiment, the present invention analyzes the operating environment parameters after completing the execution of the environmental warning adjustment set of the computer room operating environment, that is, analyzes parameters such as average operating power consumption, total cooling capacity, and temperature drop rate, thereby intuitively reflecting the actual effect of the computer room environment after temperature control optimization, providing data support for optimizing the temperature control of the computer room, and facilitating the improvement and perfection of subsequent computer room temperature control adjustment strategies.

[0073] Specifically, the early warning feedback of the intelligent temperature control monitoring of the computer room is performed, and the specific early warning feedback process is:

[0074] The optimization quality index of the computer room operating environment is compared with the optimization quality threshold. If the optimization quality index of the computer room operating environment is less than the optimization quality threshold, the optimization quality index of the computer room operating environment is compared with the optimization quality threshold to obtain the optimization quality index ratio of the computer room operating environment, and the optimization quality index ratio is matched with the temperature control warning adjustment set corresponding to each optimization quality index ratio interval stored in the computer room database, thereby obtaining and executing the temperature control warning adjustment set of the computer room operating environment, and providing feedback for the intelligent temperature control monitoring of the computer room; the above-mentioned optimization quality threshold represents the minimum value of the reasonable range of the optimization quality index of the computer room operating environment, which is extracted from the computer room database; in an example embodiment, assuming that the optimization quality index ratio of the computer room operating environment is R, in the optimization quality index ratio interval [R-21%, R+19%] stored in the computer room database, the optimization quality index ratio interval [R- 21%, R+19%] The corresponding temperature control warning adjustment set includes: each cooling device increases the cooling capacity by 15% per unit time, automatically turns off one smart fan according to the temperature distribution and airflow conditions in the computer room, and intelligently adjusts the speed and wind direction of the remaining two smart fans; then the temperature control warning adjustment set of the computer room operating environment includes: each cooling device increases the cooling capacity by 15% per unit time, automatically turns off one smart fan according to the temperature distribution and airflow conditions in the computer room, and intelligently adjusts the speed and wind direction of the remaining two smart fans; the above-mentioned execution of the temperature control warning adjustment set of the computer room operating environment is consistent with the process of executing the environmental warning adjustment set of the computer room operating environment; the above-mentioned feedback on the intelligent temperature control monitoring of the computer room refers to the feedback of the temperature control adjustment success log in a visual form, and the temperature control adjustment success log contains the temperature distribution map in the computer room and the operating status of each smart device in the computer room.

[0075] If the optimization quality index of the computer room operating environment is greater than or equal to the optimization quality threshold, situation awareness monitoring of the computer room intelligent temperature control is continuously performed.

[0076] It should be explained that the environmental warning adjustment set is a universal adjustment strategy, and the temperature control warning adjustment set is a targeted adjustment measure formulated according to the specific adjustment status of the temperature control system of different computer rooms. Therefore, after executing the temperature control warning adjustment set, the optimization quality index of the computer room operating environment can meet the optimization quality threshold requirements.

[0077] Specifically, the operating environment parameters of the computer room after the situation awareness is completed, specifically refer to the operating parameters of each device belonging to the situation awareness computer room and the environmental parameters of the environment to which the computer room belongs, which are comprehensively marked as the operating environment parameters of the computer room. Thus, by analyzing the operating parameters of each device belonging to the computer room and the environmental parameters of the environment to which the computer room belongs, the optimization quality index of the operating environment of the computer room is comprehensively determined; the operating parameters of each device belonging to the situation awareness computer room and the environmental parameters of the environment to which the computer room belongs refer to the operating parameters of each device belonging to the computer room and the environmental parameters of the environment to which the computer room belongs obtained by monitoring different sensors, such as the operating power consumption obtained by detecting the power consumption monitoring sensor.

[0078] Furthermore, the optimization quality index of the computer room operating environment is determined by the following specific determination process:

[0079] The operating power consumption of each device in the computer room during the optimization detection period is extracted from the operating parameters of each device in the computer room, and the average operating power consumption of the equipment in the computer room during the optimization detection period is obtained by averaging. The above-mentioned optimization detection period refers to the period for detecting the operating environment of the computer room after the environmental warning adjustment set is adjusted and optimized, and the specific duration is determined by the temperature control administrator of the computer room. The operating power consumption of each device in the above-mentioned computer room during the optimization detection period is measured from the power consumption monitoring sensors installed on each device.

[0080] The cooling capacity of each refrigeration equipment belonging to the computer room during the optimization detection period is extracted from the operating parameters of each equipment belonging to the computer room, and the total cooling capacity of the refrigeration equipment belonging to the computer room during the optimization detection period is accumulated; the cooling capacity of each refrigeration equipment belonging to the computer room during the optimization detection period is consistent with the cooling capacity of each refrigeration equipment belonging to the computer room during the temperature control monitoring period.

[0081] The average temperature of the computer room environment at the start time point of the optimization detection cycle and the average temperature at the end time point of the optimization detection cycle are extracted from the environmental parameters of the environment to which the computer room belongs, and difference processing is performed. The difference processing result is ratio processed with the average temperature of the computer room environment at the start time point of the optimization detection cycle, and the processing result is marked as the temperature drop rate of the computer room environment within the optimization detection cycle; the average temperature of the computer room environment at the start time point of the optimization detection cycle and the average temperature at the end time point of the optimization detection cycle are consistent with the method of obtaining the average temperature of the computer room environment within the temperature control monitoring cycle.

[0082] At the same time, combined with the abnormal perception risk factor of the computer room environment, a method for determining the optimized quality index of the computer room operating environment is comprehensively obtained. In this embodiment, the optimized quality index of the computer room operating environment is obtained by comprehensively analyzing the average operating power consumption of the equipment belonging to the computer room during the optimization detection period, the total cooling capacity of the refrigeration equipment belonging to the computer room during the optimization detection period, and the temperature drop rate of the computer room environment during the optimization detection period. It is used to determine the value of the adjusted and optimized quality of the computer room operating environment. The specific determination method is:

[0083]

[0084] ARF is the abnormal perception risk factor of the computer room environment. It is obtained by comprehensive analysis of the average temperature of the computer room environment during the temperature control monitoring period, the average air flow speed, the average energy efficiency ratio of the refrigeration equipment belonging to the computer room during the temperature control monitoring period, and the average command response time of the equipment belonging to the computer room during the temperature control monitoring period. It is a numerical value used to determine the abnormal perception risk level of the computer room environment. Incorporating the abnormal perception risk factor of the computer room environment into the optimization quality index of the computer room operating environment can significantly improve the accuracy of determining the optimization quality. For example, under the same optimization degree, when the abnormal perception risk factor of the computer room environment is smaller, the optimization quality of the computer room environment will be higher.

[0085] PD is the average operating power consumption of the equipment in the computer room during the optimization detection cycle, which refers to the average energy consumption level of each device in the computer room (such as servers, storage devices, and network devices) during the optimization detection cycle.

[0086] CT is the total cooling capacity of the refrigeration equipment in the computer room during the optimization detection period, which refers to the total cooling capacity generated by all refrigeration equipment in the computer room during the optimization detection period.

[0087] LD is the temperature drop rate of the equipment room environment during the optimization detection cycle, which refers to the ratio of the change in the temperature of the equipment room environment from the initial temperature at the beginning of the optimization detection cycle to the temperature reached at the end of the optimization detection cycle relative to the initial temperature.

[0088] PD ΔC It is the reference average operating power consumption preset in the computer room database, which represents the reference value of the average operating power consumption of the equipment in the computer room during the optimization detection cycle.

[0089] CT ΔC It is the reference total cooling capacity preset in the computer room database, which represents the reference value of the total cooling capacity of the refrigeration equipment in the analyzed computer room during the optimization detection period.

[0090] D is the weight factor corresponding to the abnormal perception risk factor preset in the computer room database, which represents the proportion of the abnormal perception risk factor to the optimized quality index of the computer room operating environment. When in use, the weight factor corresponding to the abnormal perception risk factor can be directly obtained from the computer room database, and the corresponding relationship can be a preset mapping relationship. For example, the average temperature, average air flow speed, average energy efficiency ratio and average command response time form a mapping set with the weight factors corresponding to the abnormal perception risk factors preset in the computer room database. The real-time average temperature, average air flow speed, average energy efficiency ratio and average command response time are input into the mapping set to obtain the weight factor corresponding to the abnormal perception risk factor. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0091] ec1 is the impact factor corresponding to the unit value of the temperature drop rate preset in the computer room database, which represents the numerical value of the impact degree of the unit value of the temperature drop rate on the optimization quality index of the computer room operating environment. When used, the impact factor corresponding to the unit value of the temperature drop rate can be directly obtained from the computer room database, and the corresponding relationship can be a preset mapping relationship. For example, the temperature drop rate and the impact factor corresponding to the unit value of the temperature drop rate preset in the computer room database form a mapping set, and the real-time temperature drop rate is input into the mapping set to obtain the impact factor corresponding to the unit value of the temperature drop rate. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0092] Among them, HRH is the optimization quality index of the computer room operating environment. The total cooling capacity of the computer room refrigeration equipment during the optimization detection cycle is an important indicator to measure the refrigeration system's ability to control the computer room environment. When the computer room temperature control strategy has been adjusted and optimized, if the total cooling capacity of the refrigeration equipment deviates significantly from the preset reference total cooling capacity, the specific manifestation is that the refrigeration equipment needs to abnormally increase the cooling capacity to cope with the computer room temperature. This indicates that the adjustment of the computer room temperature control has not yet reached the expected target reference value. As a result, the computer room temperature is difficult to be effectively controlled, and the temperature drop rate decreases accordingly. At the same time, the operating power consumption of each device in the computer room will also deviate significantly. With reference to the average operating power consumption, each device will generate more heat, which will further reduce the temperature drop rate, thus reflecting the abnormal operating status of the computer room. At the same time, combined with the abnormal risk level of the computer room environment, that is, the abnormal risk factor of the computer room environment, it jointly reflects the effectiveness of the temperature control adjustment of the computer room and the optimization quality of the computer room operating environment. Therefore, continuous monitoring and analysis of these parameters is an important means to evaluate the temperature control adjustment effect of the computer room and continuously optimize the operating environment of the computer room. Through this process, problems in the temperature control system can be discovered and solved in a timely manner, thereby ensuring the continuous and effective operation of the temperature control system of the computer room.

[0093] In a specific embodiment, the present invention provides a situation-awareness-based intelligent temperature control monitoring system and early warning method for a computer room. The system collects network environment parameters to which the temperature control data of the computer room belongs, evaluates the collection network quality index of the temperature control data of the computer room, and intelligently adjusts and feedbacks the collection network of the temperature control data of the computer room according to the evaluation results, thereby ensuring the accuracy and reliability of the temperature control data collection of the computer room, and effectively improving the accuracy of the temperature control system of the computer room. At the same time, according to the collected temperature control data of the computer room, the abnormal perception risk factors of the computer room environment are deeply analyzed, and potential problems can be discovered in time, further improving the safety and stability of the operation of the computer room. Based on the analysis of the risk factors, an environmental early warning adjustment set of the computer room operating environment is further matched, providing a scientific basis for the optimization of the computer room environment. After the execution of the environmental early warning adjustment set is completed, the operating environment parameters of the computer room are monitored again, and the optimization quality index of the operating environment of the computer room is determined accordingly. This closed-loop monitoring mechanism also ensures the continuous optimization of the temperature control of the computer room by early warning feedback on the intelligent temperature control monitoring of the computer room.

[0094] Reference Figure 2 As shown, the second aspect of the present invention provides an early warning method applied to the situational awareness-based computer room intelligent temperature control monitoring system, including: S1. Collecting network environment parameters to which the computer room temperature control data belongs, evaluating the collection network quality index of the computer room temperature control data, and comparing it with the collection network quality threshold. If the collection network quality index of the computer room temperature control data is greater than the collection network quality threshold, the environmental risk analysis module is executed; otherwise, the collection network of the computer room temperature control data is adjusted and fed back.

[0095] S2. Analyze the abnormal perception risk factors of the computer room environment based on the temperature control data of the computer room, and thereby match and derive the environmental warning adjustment set of the computer room operating environment.

[0096] S3. Execute the environmental warning adjustment set of the computer room operating environment, and the operating environment parameters of the computer room after the situation awareness is completed, thereby determining the optimization quality index of the computer room operating environment, and providing early warning feedback for the intelligent temperature control monitoring of the computer room.

[0097] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. An intelligent temperature control monitoring system for a computer room based on situational awareness, characterized in that: include: The data collection and feedback module is used to collect the network environment parameters of the temperature control data of the computer room, evaluate the collection network quality index of the temperature control data of the computer room, and compare it with the collection network quality threshold. If the collection network quality index of the temperature control data of the computer room is greater than the collection network quality threshold, the environmental risk analysis module is executed. Otherwise, the collection network of the temperature control data of the computer room is adjusted and fed back. The environmental risk analysis module is used to analyze the abnormal perception risk factors of the computer room environment based on the computer room temperature control data, and thus match the environmental warning adjustment set of the computer room operating environment; The environment optimization and adjustment module is used to execute the environmental warning adjustment set of the computer room operating environment. After the situation awareness is completed, the operating environment parameters of the computer room are determined, thereby determining the optimization quality index of the computer room operating environment and providing early warning feedback for the intelligent temperature control monitoring of the computer room.

2. According to claim 1, a situation-awareness-based intelligent temperature control monitoring system for a computer room is characterized by: The specific evaluation process of the collection network quality index of the evaluation room temperature control data is as follows: Extract the response time of each command of each device in the computer room during the temperature control monitoring period from the network environment parameters of the computer room temperature control data, and perform average processing to obtain the average response time of each command of the device in the computer room during the temperature control monitoring period; Extract the actual data transmission volume of the network to which the temperature control data of the computer room belongs during the temperature control monitoring period and the maximum data transmission volume of the network to which the temperature control data of the computer room belongs during the temperature control monitoring period from the network environment parameters to which the temperature control data of the computer room belongs, perform ratio processing, and mark the ratio processing result as the transmission congestion degree of the network to which the temperature control data of the computer room belongs during the temperature control monitoring period; Obtain the platform receiving time point and data collection time point of each sampled temperature control data of the computer room temperature control data within the temperature control monitoring period, perform difference and mean processing in sequence, and mark the processing result as the average transmission delay duration of the temperature control data of the computer room within the temperature control monitoring period; The number of successful uploads of the temperature control data of the computer room within the temperature control monitoring cycle is obtained, and the ratio is processed with the duration corresponding to the temperature control monitoring cycle, thereby obtaining the successful upload frequency of the temperature control data of the computer room within the temperature control monitoring cycle, and finally comprehensively obtaining an evaluation method for the collection network quality index of the temperature control data of the computer room.

3. According to claim 2, a situation-awareness-based intelligent temperature control monitoring system for a computer room, characterized in that: The collection network quality index of the room temperature control data is evaluated by the following specific method: Among them, ZQ is the collection network quality index of the computer room temperature control data, CBUR is the average command response time of the equipment in the computer room during the temperature control monitoring period, TCL is the transmission congestion of the network to which the computer room temperature control data belongs during the temperature control monitoring period, ATDT is the average transmission delay time of the computer room temperature control data during the temperature control monitoring period, USF is the upload success frequency of the computer room temperature control data during the temperature control monitoring period, and CBUR is the average transmission delay of the computer room temperature control data during the temperature control monitoring period. ΔJ is the limit value of the average command response time preset in the computer room database, e is a natural constant, er1 is the impact factor corresponding to the unit value of the transmission congestion preset in the computer room database, er2 is the impact factor corresponding to the unit value of the average transmission delay time preset in the computer room database, and er3 is the impact factor corresponding to the unit value of the upload success frequency preset in the computer room database.

4. According to claim 1, a situation-awareness-based intelligent temperature control monitoring system for a computer room is characterized by: The adjustment and feedback of the collection network of the temperature control data of the computer room is performed, and the specific adjustment and feedback process is as follows: Perform difference processing on the collection network quality threshold and the collection network quality index of the computer room temperature control data to obtain the collection network quality difference of the computer room temperature control data, and match it with the collection network adjustment set corresponding to each collection network quality difference interval stored in the computer room database, thereby obtaining and executing the collection network adjustment set of the computer room temperature control data, and completing the adjustment of the collection network of the computer room temperature control data; By completing the adjustment of the computer room temperature control data collection network, the computer room temperature control data is collected again, and the data processing platform updates the computer room temperature control data, completing the adjustment feedback of the computer room temperature control data collection network.

5. According to the situation awareness-based intelligent temperature control monitoring system for a computer room according to claim 1, it is characterized by: The abnormal perception risk factors of the computer room environment are analyzed, and the specific analysis process is as follows: Extract the temperature of each temperature detection area of ​​the computer room within the temperature control monitoring period from the computer room temperature control data, perform average processing, and mark the processing result as the average temperature of the computer room environment within the temperature control monitoring period; Extract the cooling capacity of each refrigeration device in the computer room during the temperature control monitoring period and the total power consumption of each refrigeration device in the computer room during the temperature control monitoring period from the temperature control data of the computer room, perform ratio processing on the cooling capacity of each refrigeration device in the computer room during the temperature control monitoring period and the corresponding total power consumption to obtain the energy efficiency ratio of each refrigeration device in the computer room during the temperature control monitoring period, and perform average processing to obtain the average energy efficiency ratio of the refrigeration devices in the computer room during the temperature control monitoring period; The average air flow speed of the computer room environment during the temperature control monitoring cycle is extracted from the computer room temperature control data. At the same time, the average command response time of the equipment in the computer room during the temperature control monitoring cycle is combined to comprehensively obtain an analysis method for the abnormal perception risk factors of the computer room environment.

6. According to claim 1, a situation-awareness-based intelligent temperature control monitoring system for a computer room, characterized in that: The matching obtains the environmental warning adjustment set of the computer room operating environment. The specific matching process is as follows: The abnormal perception risk factor of the computer room environment is matched with the environmental warning adjustment set corresponding to each abnormal perception risk factor interval stored in the computer room database, thereby obtaining the environmental warning adjustment set of the computer room operating environment.

7. According to claim 1, a situation-awareness-based intelligent temperature control monitoring system for a computer room, characterized in that: The operating environment parameters of the computer room after the situation awareness is completed, specifically the operating parameters of each device belonging to the situation awareness computer room and the environmental parameters of the environment to which the computer room belongs, are comprehensively marked as the operating environment parameters of the computer room. Therefore, by analyzing the operating parameters of each device belonging to the computer room and the environmental parameters of the environment to which the computer room belongs, the optimization quality index of the computer room operating environment is comprehensively determined.

8. The intelligent temperature control monitoring system for a computer room based on situational awareness according to claim 7 is characterized in that: The optimization quality index of the computer room operating environment is determined by the following specific determination process: The operating power consumption of each device in the computer room during the optimization detection period is extracted from the operating parameters of each device in the computer room, and the average operating power consumption of the equipment in the computer room during the optimization detection period is obtained by averaging. Extract the cooling capacity of each refrigeration device in the computer room during the optimization detection period from the operating parameters of each device in the computer room, and add them up to obtain the total cooling capacity of the refrigeration devices in the computer room during the optimization detection period; Extract the average temperature of the computer room environment at the start time of the optimization detection cycle and the average temperature at the end time of the optimization detection cycle from the environmental parameters of the environment to which the computer room belongs, perform difference processing, perform ratio processing on the difference processing result and the average temperature of the computer room environment at the start time of the optimization detection cycle, and mark the processing result as the temperature drop rate of the computer room environment within the optimization detection cycle; At the same time, combined with the abnormal perception risk factors of the computer room environment, a method for determining the optimized quality index of the computer room operating environment is obtained.

9. The intelligent temperature control monitoring system for a computer room based on situational awareness according to claim 1, characterized in that: The specific early warning feedback process of the intelligent temperature control monitoring of the computer room is as follows: The optimization quality index of the computer room operating environment is compared with the optimization quality threshold. If the optimization quality index of the computer room operating environment is less than the optimization quality threshold, the optimization quality index of the computer room operating environment is processed by ratio with the optimization quality threshold to obtain the optimization quality index ratio of the computer room operating environment, and the ratio is matched with the temperature control early warning adjustment set corresponding to each optimization quality index ratio interval stored in the computer room database, thereby obtaining and executing the temperature control early warning adjustment set of the computer room operating environment, and providing feedback to the intelligent temperature control monitoring of the computer room; If the optimization quality index of the computer room operating environment is greater than or equal to the optimization quality threshold, situation awareness monitoring of the computer room intelligent temperature control is continuously performed.

10. An early warning method for a computer room intelligent temperature control monitoring system based on situational awareness according to any one of claims 1 to 9, characterized in that: include: S1. Collect the network environment parameters of the temperature control data of the computer room, evaluate the collection network quality index of the temperature control data of the computer room, and compare it with the collection network quality threshold. If the collection network quality index of the temperature control data of the computer room is greater than the collection network quality threshold, the environmental risk analysis module is executed. Otherwise, the collection network of the temperature control data of the computer room is adjusted and fed back; S2. Analyze the abnormal perception risk factors of the computer room environment based on the temperature control data of the computer room, and thereby match and obtain the environmental warning adjustment set of the computer room operating environment; S3. Execute the environmental warning adjustment set of the computer room operating environment, and the operating environment parameters of the computer room after the situation awareness is completed, thereby determining the optimization quality index of the computer room operating environment, and providing early warning feedback for the intelligent temperature control monitoring of the computer room.

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