Cloud computing data center cabinet energy consumption intelligent monitoring management system

Abstract

The invention discloses a cloud computing data center cabinet energy consumption intelligent monitoring management system, and relates to the technical field of energy consumption intelligent monitoring. The management center is connected with a state data acquisition module, a data processing module, an energy consumption evaluation module and an intelligent regulation and control module; a state data acquisition module is provided with a monitoring index acquisition node to acquire monitoring index data of a data center cabinet; performing node mapping on the monitoring index data based on digital twinning to construct a three-dimensional monitoring index model of the data center cabinet, labeling the index type of the monitoring index data in the three-dimensional monitoring index model, and segmenting and processing the three-dimensional monitoring index model according to the index type to obtain time sequence data; performing parallel fusion processing on each time sequence data to obtain a cabinet energy consumption data value in real time; and performing intelligent regulation and control on the data center cabinet according to the cabinet energy consumption data value.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent energy consumption monitoring, and in particular to an intelligent energy consumption monitoring and management system for cabinets in a cloud computing data center. Background Art

[0002] Cloud computing is a type of distributed computing that involves breaking down a massive data processing program into countless smaller programs over a network called a "cloud." These programs are then processed and analyzed by a system of multiple servers, generating results that are then returned to the user. In its early days, cloud computing was simply distributed computing, distributing tasks and consolidating results. Therefore, cloud computing is also known as grid computing. This technology allows processing of tens of thousands of data points in a fraction of a second, resulting in powerful network services.

[0003] Data center cabinets are one of the basic equipment in a data center, used to store key information technology equipment such as servers, network equipment, and storage devices. In a data center, cabinets are not just a simple tool for storing and managing equipment, but also provide rich functions and scalability;

[0004] However, it is highly dependent on manual inspections or single-point sensors, lacks fine-grained real-time monitoring of electrical parameters (voltage, current, power) and environmental indicators (temperature, humidity, airflow, pressure), and is difficult to accurately locate abnormal energy consumption nodes; and lacks cabinet-level three-dimensional visualization modeling based on digital twins, which cannot intuitively present energy consumption distribution and heat flow characteristics, resulting in insufficient monitoring clarity and data independence; therefore, in order to solve the above technical problems, the present invention provides an intelligent energy consumption monitoring and management system for cloud computing data center cabinets. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides an intelligent monitoring and management system for energy consumption of cabinets in a cloud computing data center;

[0006] The object of the present invention can be achieved by the following technical solutions: A cloud computing data center cabinet energy consumption intelligent monitoring and management system, comprising a management center, the management center is connected to a state data acquisition module, a data processing module, an energy consumption evaluation module and an intelligent control module;

[0007] The state data acquisition module is provided with a monitoring index acquisition node for collecting monitoring index data of the data center cabinet;

[0008] The data processing module is used to map the monitoring indicator data to nodes based on the digital twin to build a three-dimensional monitoring indicator model of the data center cabinet, mark the indicator type of the monitoring indicator data in the three-dimensional monitoring indicator model, and segment the three-dimensional monitoring indicator model according to the indicator type and process it to obtain time series data;

[0009] The energy consumption evaluation module is used to perform parallel fusion processing on each time series data to obtain the cabinet energy consumption data value in real time;

[0010] The intelligent control module is used to intelligently control the data center cabinets according to the cabinet energy consumption data values.

[0011] Furthermore, the status data collection module is provided with a monitoring index collection node, and the process of collecting monitoring index data of the data center cabinet includes:

[0012] Key monitoring locations are set in data center cabinets. The key monitoring locations include edge key monitoring locations and indicator monitoring locations, and edge monitoring acquisition nodes and monitoring indicator acquisition nodes are generated respectively. The edge monitoring acquisition nodes are wirelessly connected to the monitoring indicator nodes, and data filters and acquisition devices are set at the edge monitoring acquisition nodes and the monitoring indicator acquisition nodes respectively. The detection indicator acquisition nodes are numbered and recorded as i, where i=1, 2, ..., j, and j is a positive integer; the monitoring indicator data include electrical parameters, temperature, humidity, airflow and pressure; and the electrical parameters include voltage, current and power.

[0013] Furthermore, the process of the data processing module mapping the monitoring indicator data into nodes based on the digital twin to construct a three-dimensional monitoring indicator model of the data center cabinet includes:

[0014] The data center cabinet is three-dimensionally modeled, and the corresponding collection nodes are mapped according to the key monitoring positions to obtain the corresponding structural three-dimensional model of the data center cabinet. The corresponding monitoring indicator data is mapped to the monitoring indicator collection nodes corresponding to the structural three-dimensional model, and the equipment parameters are associated with the corresponding collection equipment to generate a three-dimensional monitoring indicator model.

[0015] Furthermore, the process of marking the indicator type of the monitoring indicator data in the three-dimensional monitoring indicator model, segmenting the three-dimensional monitoring indicator model according to the indicator type, and processing to obtain time series data includes:

[0016] Obtain the indicator type corresponding to each monitoring indicator collection node in the three-dimensional monitoring indicator model, wherein the indicator type includes internal indicator type and environmental indicator type; the internal indicator type includes voltage, current and power; the environmental indicator type includes temperature, humidity, airflow and pressure;

[0017] Mark the corresponding indicator type at the corresponding monitoring indicator collection node; then split the monitoring indicator collection nodes corresponding to the internal indicator type and the environmental indicator type to generate a corresponding single-line three-dimensional monitoring indicator model; the single-line three-dimensional monitoring indicator model includes an internal indicator three-dimensional monitoring indicator model and an environmental indicator three-dimensional monitoring indicator model;

[0018] According to the indicator type, the collection time points corresponding to the internal three-dimensional monitoring indicator model and the environmental indicator three-dimensional monitoring indicator model are set to collect the monitoring indicator data of each monitoring indicator collection node in each single-row three-dimensional monitoring indicator model according to the collection cycle and integrate them to generate time series data of the data center cabinet.

[0019] Furthermore, the energy consumption evaluation module performs parallel fusion processing on each time series data to obtain the cabinet energy consumption data value in real time, including:

[0020] Obtain internal energy consumption data values ​​based on the voltage, current, and power of the time series data corresponding to the internal indicator three-dimensional monitoring indicator model;

[0021] That is, the specific formula is:

[0022]

[0023] Where E(t) represents the internal energy consumption data value of the data center cabinet corresponding to the collection time t; Δt represents the interval time between two collection time points; V i (t), I i (t) and P i (t) represents the voltage, current and power of the i-th monitoring indicator collection node at the corresponding collection time point.

[0024] Furthermore, the weight coefficients corresponding to the temperature, humidity, airflow and pressure of the time series data of the three-dimensional monitoring indicator model of the environmental indicators are set, and the environmental energy consumption data value is obtained according to each weight coefficient;

[0025] That is, the specific formula is:

[0026]

[0027] Among them, Q(t) represents the environmental energy consumption data value corresponding to the collection time point; T i (t), S i (t), L i (t) and Y i (t) represents the temperature, humidity, airflow and pressure of the i-th monitoring indicator collection node at the collection time point; a, b, c and d represent the weight coefficients corresponding to temperature, humidity, airflow and pressure respectively.

[0028] Furthermore, the internal energy consumption data value and the environmental energy consumption data value are fused to obtain the cabinet energy consumption data value of the data center cabinet;

[0029] That is, the specific formula is:

[0030] P(t) = α × E(t) + β × Q(t);

[0031] Among them, P(t) represents the cabinet energy consumption data value of the data center cabinet corresponding to the collection time point; α and β represent the weight coefficients corresponding to the internal energy consumption data value and the environmental energy consumption data value, respectively.

[0032] Furthermore, the process of the intelligent control module intelligently controlling the data center cabinets according to the energy consumption data value includes:

[0033] Setting the level threshold range of the cabinet energy consumption value corresponding to the data center cabinet, the level threshold range includes the first level threshold range, the second level threshold range and the third level threshold range;

[0034] If the cabinet energy consumption value is within the first-level threshold range, the corresponding cabinet energy consumption data value will be marked as a first-level energy consumption warning;

[0035] If the cabinet energy consumption value is within the second-level threshold range, the corresponding cabinet energy consumption data value will be marked as a second-level energy consumption warning;

[0036] If the cabinet energy consumption value is within the third-level threshold range, the corresponding cabinet energy consumption data value will be marked as a third-level energy consumption warning;

[0037] The first-level energy consumption warning, the second-level energy consumption warning and the third-level energy consumption warning are sent to the preset management center for management, and intelligent control is performed according to the preset intelligent control parameters.

[0038] Compared with the existing technology, the beneficial effects of the present invention are: a monitoring indicator collection node is set up through the status data collection module to collect the monitoring indicator data of the data center cabinet; based on the digital twin, the monitoring indicator data is node mapped to construct a three-dimensional monitoring indicator model of the data center cabinet, the indicator type of the monitoring indicator data is marked in the three-dimensional monitoring indicator model, and the three-dimensional monitoring indicator model is segmented and processed according to the indicator type to obtain time series data; each time series data is parallelly fused and processed to obtain the cabinet energy consumption data value in real time; the data center cabinet is intelligently controlled according to the cabinet energy consumption data value; and the accuracy of energy consumption monitoring of the data center cabinet is effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0040] Figure 1 This is a schematic diagram of the present invention. DETAILED DESCRIPTION

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0042] like Figure 1 As shown, a cloud computing data center cabinet energy consumption intelligent monitoring and management system includes a management center, the management center is connected to a state data acquisition module, a data processing module, an energy consumption evaluation module and an intelligent control module;

[0043] The state data acquisition module is provided with a monitoring index acquisition node for collecting monitoring index data of the data center cabinet;

[0044] The data processing module is used to map the monitoring indicator data to nodes based on the digital twin to build a three-dimensional monitoring indicator model of the data center cabinet, mark the indicator type of the monitoring indicator data in the three-dimensional monitoring indicator model, and segment the three-dimensional monitoring indicator model according to the indicator type and process it to obtain time series data;

[0045] The energy consumption evaluation module is used to perform parallel fusion processing on each time series data to obtain the cabinet energy consumption data value in real time;

[0046] The intelligent control module is used to intelligently control the data center cabinets according to the cabinet energy consumption data values.

[0047] The status data collection module is provided with a monitoring index collection node. The process of collecting monitoring index data of the data center cabinet needs further explanation:

[0048] Key monitoring locations are set in data center cabinets, including edge key monitoring locations and indicator monitoring locations, and edge monitoring collection nodes and monitoring indicator collection nodes are generated respectively. The edge monitoring collection nodes are wirelessly connected to the monitoring indicator nodes, and data filters and collection devices are set at the edge monitoring collection nodes and the monitoring indicator collection nodes respectively. The detection indicator collection nodes are numbered and recorded as i, where i=1, 2, ..., j, and j is a positive integer; the monitoring indicator data include electrical parameters, temperature, humidity, airflow, and pressure; and the electrical parameters include voltage, current, and power;

[0049] Obtain the device parameter sensor of the collection device corresponding to the monitoring indicator collection node, which is used to collect the device parameters in real time and send them to the data filter corresponding to the edge key monitoring collection node for real-time monitoring of the device parameters of the collection device;

[0050] In the above embodiment, it needs to be further explained that the device parameters of the collection device are monitored by the edge monitoring collection node, and this monitoring is to improve the accuracy of the collection of monitoring indicator data of the data center cabinet by the collection device.

[0051] The process of mapping the monitoring indicator data into nodes based on the digital twin by the data processing module to construct a three-dimensional monitoring indicator model of the data center cabinet requires further explanation:

[0052] The data center cabinet is 3D modeled, and the corresponding acquisition nodes are mapped according to the key monitoring positions to obtain the corresponding structural 3D model of the data center cabinet. The corresponding monitoring indicator data is mapped to the monitoring indicator acquisition nodes corresponding to the structural 3D model, and the equipment parameters are associated with the corresponding acquisition equipment to generate a 3D monitoring indicator model.

[0053] The process of labeling the indicator types of monitoring indicator data in the three-dimensional monitoring indicator model, segmenting the three-dimensional monitoring indicator model according to the indicator type, and processing to obtain time series data requires further explanation:

[0054] Obtain the indicator type corresponding to each monitoring indicator collection node in the three-dimensional monitoring indicator model, wherein the indicator type includes internal indicator type and environmental indicator type; the internal indicator type includes voltage, current and power; the environmental indicator type includes temperature, humidity, airflow and pressure;

[0055] Mark the corresponding indicator type at the corresponding monitoring indicator collection node; then split the monitoring indicator collection nodes corresponding to the internal indicator type and the environmental indicator type to generate a corresponding single-line three-dimensional monitoring indicator model; the single-line three-dimensional monitoring indicator model includes an internal indicator three-dimensional monitoring indicator model and an environmental indicator three-dimensional monitoring indicator model;

[0056] In the above embodiment, it should be further explained that segmenting the data center cabinet into three-dimensional models of different indicator types can not only improve the clarity of monitoring but also improve the independence of data monitoring;

[0057] According to the indicator type, the corresponding collection time point of the internal three-dimensional monitoring indicator model and the environmental indicator three-dimensional monitoring indicator model is set to collect the monitoring indicator data of each monitoring indicator collection node in each single-row three-dimensional monitoring indicator model according to the collection cycle and integrate them to generate time series data of the data center cabinet;

[0058] In the above embodiment, it needs to be further explained that the single-row three-dimensional monitoring indicator model of different indicator types is collected at different collection time nodes to obtain the time series data corresponding to the monitoring indicator data of the single-row three-dimensional monitoring indicator model, thereby effectively improving the collection efficiency.

[0059] The process of performing parallel fusion processing on each time series data by the energy consumption assessment module to obtain the cabinet energy consumption data value in real time needs further explanation;

[0060] Obtain internal energy consumption data values ​​based on the voltage, current, and power of the time series data corresponding to the internal indicator three-dimensional monitoring indicator model;

[0061] That is, the specific formula is:

[0062]

[0063] Where E(t) represents the internal energy consumption data value of the data center cabinet corresponding to the collection time t; Δt represents the interval time between two collection time points; V i (t), I i (t) and P i (t) represents the voltage, current and power of the i-th monitoring indicator collection node at the corresponding collection time point;

[0064] Set the weight coefficients corresponding to the temperature, humidity, airflow, and pressure of the time series data of the three-dimensional monitoring indicator model of the environmental indicators, and obtain the environmental energy consumption data value according to each weight coefficient;

[0065] That is, the specific formula is:

[0066]

[0067] Among them, Q(t) represents the environmental energy consumption data value corresponding to the collection time point; T i (t), S i (t), L i (t) and Y i(t) represents the temperature, humidity, airflow and pressure of the i-th monitoring indicator collection node at the corresponding collection time point; a, b, c and d represent the weight coefficients corresponding to temperature, humidity, airflow and pressure respectively;

[0068] In the above embodiment, it should be further explained that a single row of data corresponding to the internal three-dimensional monitoring index model and the environmental index three-dimensional monitoring index model can better monitor the energy consumption of the data center cabinet;

[0069] The internal energy consumption data value and the environmental energy consumption data value are integrated to obtain the cabinet energy consumption data value of the data center cabinet;

[0070] That is, the specific formula is:

[0071] That is, the specific formula is:

[0072] P(t) = α × E(t) + β × Q(t);

[0073] Where P(t) represents the cabinet energy consumption data value of the data center cabinet at the collection time point; α and β represent the weight coefficients corresponding to the internal energy consumption data value and the environmental energy consumption data value respectively;

[0074] In the above embodiment, it needs to be further explained that the cabinet energy consumption data value of the data center cabinet is obtained by fusing the internal energy consumption data value with the environmental energy consumption data value, thereby improving the accuracy.

[0075] The process of the intelligent control module intelligently controlling the data center cabinets according to the energy consumption data value needs further explanation:

[0076] Setting the level threshold range of the cabinet energy consumption value corresponding to the data center cabinet, the level threshold range includes the first level threshold range, the second level threshold range and the third level threshold range;

[0077] If the cabinet energy consumption value is within the first-level threshold range, the corresponding cabinet energy consumption data value will be marked as a first-level energy consumption warning;

[0078] If the cabinet energy consumption value is within the second-level threshold range, the corresponding cabinet energy consumption data value will be marked as a second-level energy consumption warning;

[0079] If the cabinet energy consumption value is within the third-level threshold range, the corresponding cabinet energy consumption data value will be marked as a third-level energy consumption warning;

[0080] The first-level energy consumption warning, the second-level energy consumption warning and the third-level energy consumption warning are sent to the preset management center for management, and intelligent control is performed according to the preset intelligent control parameters.

[0081] Working principle: A monitoring indicator collection node is set up through the status data collection module to collect the monitoring indicator data of the data center cabinet; based on the digital twin, the monitoring indicator data is mapped to nodes to build a three-dimensional monitoring indicator model of the data center cabinet, and the indicator type of the monitoring indicator data is marked in the three-dimensional monitoring indicator model. The three-dimensional monitoring indicator model is segmented and processed according to the indicator type to obtain time series data; each time series data is parallelly fused and processed to obtain the cabinet energy consumption data value in real time; the data center cabinet is intelligently controlled according to the cabinet energy consumption data value; and the accuracy of energy consumption monitoring of the data center cabinet is effectively improved.

[0082] The features and exemplary embodiments of various aspects of the present application are described in detail above. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail in combination with the drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without some of these specific details. The above description of the embodiments is merely to provide a better understanding of the present application by showing examples of the present application.

[0083] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A cloud computing data center cabinet energy consumption intelligent monitoring and management system, including a management center, characterized in that: The management center is connected to a state data acquisition module, a data processing module, an energy consumption evaluation module and an intelligent control module; The state data acquisition module is provided with a monitoring index acquisition node for collecting monitoring index data of the data center cabinet; The data processing module is used to map the monitoring indicator data to nodes based on the digital twin to build a three-dimensional monitoring indicator model of the data center cabinet, mark the indicator type of the monitoring indicator data in the three-dimensional monitoring indicator model, and segment the three-dimensional monitoring indicator model according to the indicator type and process it to obtain time series data; The energy consumption evaluation module is used to perform parallel fusion processing on each time series data to obtain the cabinet energy consumption data value in real time; The intelligent control module is used to intelligently control the data center cabinets according to the cabinet energy consumption data values.

2. The intelligent monitoring and management system for energy consumption of cabinets in a cloud computing data center according to claim 1 is characterized in that: The state data collection module is provided with a monitoring index collection node, and the process of collecting monitoring index data of the data center cabinet includes: Key monitoring locations are set in data center cabinets. The key monitoring locations include edge key monitoring locations and indicator monitoring locations, and edge monitoring acquisition nodes and monitoring indicator acquisition nodes are generated respectively. The edge monitoring acquisition nodes are wirelessly connected to the monitoring indicator nodes, and data filters and acquisition devices are set at the edge monitoring acquisition nodes and the monitoring indicator acquisition nodes respectively. The detection indicator acquisition nodes are numbered and recorded as i, where i=1, 2, ..., j, and j is a positive integer; the monitoring indicator data include electrical parameters, temperature, humidity, airflow and pressure; and the electrical parameters include voltage, current and power.

3. The intelligent monitoring and management system for energy consumption of cabinets in a cloud computing data center according to claim 2 is characterized in that: The process of the data processing module mapping the monitoring indicator data into nodes based on the digital twin to construct a three-dimensional monitoring indicator model for the data center cabinet includes: The data center cabinet is three-dimensionally modeled, and the corresponding collection nodes are mapped according to the key monitoring positions to obtain the corresponding structural three-dimensional model of the data center cabinet. The corresponding monitoring indicator data is mapped to the monitoring indicator collection nodes corresponding to the structural three-dimensional model, and the equipment parameters are associated with the corresponding collection equipment to generate a three-dimensional monitoring indicator model.

4. The intelligent monitoring and management system for energy consumption of cabinets in a cloud computing data center according to claim 3 is characterized in that: The process of marking the indicator type of the monitoring indicator data in the three-dimensional monitoring indicator model, segmenting the three-dimensional monitoring indicator model according to the indicator type, and processing to obtain time series data includes: Obtain the indicator type corresponding to each monitoring indicator collection node in the three-dimensional monitoring indicator model, wherein the indicator type includes internal indicator type and environmental indicator type; the internal indicator type includes voltage, current and power; the environmental indicator type includes temperature, humidity, airflow and pressure; Mark the corresponding indicator type at the corresponding monitoring indicator collection node; then split the monitoring indicator collection nodes corresponding to the internal indicator type and the environmental indicator type to generate a corresponding single-line three-dimensional monitoring indicator model; the single-line three-dimensional monitoring indicator model includes an internal indicator three-dimensional monitoring indicator model and an environmental indicator three-dimensional monitoring indicator model; According to the indicator type, the collection time points corresponding to the internal three-dimensional monitoring indicator model and the environmental indicator three-dimensional monitoring indicator model are set to collect the monitoring indicator data of each monitoring indicator collection node in each single-row three-dimensional monitoring indicator model according to the collection cycle and integrate them to generate time series data of the data center cabinet.

5. The intelligent monitoring and management system for energy consumption of cabinets in a cloud computing data center according to claim 4 is characterized in that: The process of the energy consumption evaluation module performing parallel fusion processing on each time series data to obtain the cabinet energy consumption data value in real time includes: Obtain internal energy consumption data values ​​based on the voltage, current, and power of the time series data corresponding to the internal indicator three-dimensional monitoring indicator model; That is, the specific formula is: Where E(t) represents the internal energy consumption data value of the data center cabinet corresponding to the collection time t; Δt represents the interval time between two collection time points; V i (t), I i (t) and P i (t) represents the voltage, current and power of the i-th monitoring indicator collection node at the corresponding collection time point.

6. The intelligent monitoring and management system for energy consumption of cabinets in a cloud computing data center according to claim 5 is characterized in that: Set the weight coefficients corresponding to the temperature, humidity, airflow, and pressure of the time series data of the three-dimensional monitoring indicator model of the environmental indicators, and obtain the environmental energy consumption data value according to each weight coefficient; That is, the specific formula is: Among them, Q(t) represents the environmental energy consumption data value corresponding to the collection time point; T i (t), S i (t), L i (t) and Y i (t) represents the temperature, humidity, airflow and pressure of the i-th monitoring indicator collection node at the collection time point; a, b, c and d represent the weight coefficients corresponding to temperature, humidity, airflow and pressure respectively.

7. The intelligent monitoring and management system for energy consumption of cabinets in a cloud computing data center according to claim 6 is characterized in that: The internal energy consumption data value and the environmental energy consumption data value are integrated to obtain the cabinet energy consumption data value of the data center cabinet; That is, the specific formula is: P(t) = α × E(t) + β × Q(t); Among them, P(t) represents the cabinet energy consumption data value of the data center cabinet corresponding to the collection time point; α and β represent the weight coefficients corresponding to the internal energy consumption data value and the environmental energy consumption data value, respectively.

8. The intelligent monitoring and management system for energy consumption of cabinets in a cloud computing data center according to claim 7 is characterized in that: The process of the intelligent control module intelligently controlling the data center cabinets according to the energy consumption data value includes: Setting the level threshold range of the cabinet energy consumption value corresponding to the data center cabinet, the level threshold range includes the first level threshold range, the second level threshold range and the third level threshold range; If the cabinet energy consumption value is within the first-level threshold range, the corresponding cabinet energy consumption data value will be marked as a first-level energy consumption warning; If the cabinet energy consumption value is within the second-level threshold range, the corresponding cabinet energy consumption data value will be marked as a second-level energy consumption warning; If the cabinet energy consumption value is within the third-level threshold range, the corresponding cabinet energy consumption data value will be marked as a third-level energy consumption warning; The first-level energy consumption warning, the second-level energy consumption warning and the third-level energy consumption warning are sent to the preset management center for management, and intelligent control is performed according to the preset intelligent control parameters.

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