A monitoring method, device and electronic equipment for electrical equipment in a computer room

Abstract

The present invention belongs to the field of electrical measurement technology, and specifically discloses a monitoring method, device and electronic equipment for electrical equipment in a computer room. The method acquires monitoring data of a cooling tower in real time through a sensor network and a power factor detection module, constructs a sliding time window, performs time window segmentation processing on the first monitoring data, and obtains a monitoring sequence; based on the first monitoring data, the instantaneous power of the cooling water pump is calculated in real time to obtain a first power sequence in the same time window; the power change rate of the cooling water pump is calculated according to the first power sequence and a power change rate sequence is obtained; the power abnormality monitoring value in each time window is calculated respectively through the power change rate sequence; the first monitoring abnormality feature and the second monitoring abnormality feature of the cooling water pump are analyzed and calculated; through multi-indicator fusion and intelligent data analysis, the safe operation of the power supply equipment in the computer room is ensured while effectively reducing energy consumption and maintenance risks.

Description

Technical Field

[0001] The present invention relates to the field of electrical measurement technology, and more particularly to a monitoring method, device and electronic equipment for electrical equipment in a computer room. Background Art

[0002] In a high-density production environment, there are multiple high-performance cooling towers to ensure the temperature control of key equipment in the refrigeration room. The existing literature (Mo Zifang. Research on efficient operation of refrigeration room system based on cooling tower operating parameters [D]. Guangzhou University, 2024. DOI: 10.27040 / d.cnki.ggzdu.2024.000711.) takes the air conditioning refrigeration room of a seasoning processing plant as the research object, such as Figure 2 This paper provides a two-dimensional plan of the refrigeration room system in the energy management system. It analyzes the energy-saving optimization of the cooling tower operation in the refrigeration room system while meeting the cooling load of the air-conditioning terminal. The purpose is to achieve low-energy operation of the optimized refrigeration room system and provide a reference for the optimization control strategy of the refrigeration room system.

[0003] When the power supply system of the cooling tower fluctuates, causing significant changes in current and power, the existing monitoring system fails to capture the instantaneous power fluctuations in time due to its slow response speed. The cooling water pump suddenly becomes overloaded due to unstable voltage, and the power surges. However, the traditional monitoring system has a low sampling frequency or a long data processing delay, and fails to identify this abnormal fluctuation in time. During the equipment overload, the system fails to trigger an early warning or alarm at the real-time data level. At this time, the system does not respond in time, and the overload state will continue for a period of time, which may cause the cooling water pump to overheat or even damage key components, thereby affecting the normal operation of the entire cooling system, increasing the difficulty of maintenance, and even shutting down for repairs, resulting in production losses. At the same time, due to excessive energy consumption and the system's lack of early warning, the cooling load exceeds its capacity, which may lead to abnormal overall temperature control in the computer room and increase electricity waste. In order to solve the above problems, a technical solution is now provided. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a monitoring method, device and electronic equipment for electrical equipment in a computer room. By analyzing the first monitoring abnormality characteristics and the second monitoring abnormality characteristics of the cooling water pump, the problem of excessive energy consumption and the system not issuing an early warning, which causes the cooling load to exceed the capacity, may be solved, so as to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for monitoring electrical equipment in a computer room, comprising the following steps:

[0007] Step 1: The monitoring data of the cooling tower is collected in real time through the sensor network and the power factor detection module, and the monitoring data is filtered, smoothed and normalized to obtain the first monitoring data. A sliding time window is constructed to segment the first monitoring data into time windows to obtain a monitoring sequence. ,in, is the first monitoring data in the i-th time window, is the number of time window segments, ,in, is the jth first monitoring data, for The number of the first monitoring data in ;

[0008] Step 2: Calculate the instantaneous power of the cooling water pump in real time based on the first monitoring data to obtain the first power sequence within the same time window. ,in, is the jth instantaneous power, is the mth instantaneous power, calculate the power change rate of the cooling water pump according to the first power sequence and obtain the power change rate sequence ,in, is the jth power change rate, is the m-1th power change rate, and the power abnormality monitoring value in each time window is calculated respectively through the power change rate sequence; the power abnormality monitoring value includes the mean and standard deviation;

[0009] Step 3: extracting temperature data from the first monitoring data, calculating the temperature change rate in each time window according to the temperature data to generate a temperature change rate sequence, and calculating the first monitoring abnormality feature of the cooling water pump according to the temperature change rate sequence;

[0010] Step 4: obtaining a second monitoring abnormality feature of the cooling water pump according to the cluster distribution result between the power change rates in each time window;

[0011] Step 5: Obtain the first monitoring abnormality feature and the second monitoring abnormality feature, compare them with a preset abnormality threshold, and output an abnormality indication based on the comparison result.

[0012] As a further solution of the present invention, in step 1, monitoring data is collected in real time through a sensor network and a power factor detection module. The sensor network includes a voltage sensor, a current sensor and a temperature sensor. The voltage data of the cooling water pump is obtained in real time through the voltage sensor, the current data of the cooling water pump is obtained in real time through the current sensor, and the temperature data of the cooling water pump is obtained in real time through the temperature sensor. The power factor detection module is used to monitor the power factor of the cooling water pump.

[0013] In step 2, the instantaneous power of the cooling water pump is calculated in real time based on the first monitoring data. The calculation formula of the instantaneous power is:

[0014] ;

[0015] Where: is the instantaneous power at time t, is the voltage data at time t, is the current data at time t, is the power factor at time t;

[0016] is the jth power change rate, and the calculation formula is:

[0017] ;

[0018] Where: is the jth instantaneous power, is the j+1th instantaneous power, is the time interval between adjacent sampling points;

[0019] The mean and standard deviation in each time window are calculated using the power change rate sequence. The specific formula is:

[0020] ;

[0021] ;

[0022] Where: for The mean corresponding to the sequence, for The corresponding standard deviation of the series.

[0023] As a further solution of the present invention, in step 3, the first monitoring abnormality feature of the cooling water pump is calculated according to the temperature change rate sequence, and the specific steps are:

[0024] Step 31: extracting temperature data from the first monitoring data , calculate the temperature change rate in each time window based on the temperature data Generate a temperature change rate series ;

[0025] Step 32: Calculate the first monitoring abnormality feature of the cooling water pump based on the temperature change rate sequence, determine the temperature change rate of three consecutive sampling points within the time window, and take the current temperature change rate minus twice the temperature change rate at the previous moment, plus the temperature change rate at the previous moment as the numerator;

[0026] Step 33: The product of the reciprocal of the square of the time interval between adjacent sampling points and the numerator is used as the first monitoring abnormality feature. .

[0027] As a further solution of the present invention, in step 4, the second monitoring abnormality feature of the cooling water pump is obtained according to the cluster distribution result between the power change rates in each time window, and the specific steps are:

[0028] Step 41, arranging the power change rate sequence composed of the power change rate in each time window in chronological order, and automatically dividing the data in the power change rate sequence into a plurality of clusters according to a clustering algorithm that does not preset the number of clusters;

[0029] Step 42: for each data point in the power rate change sequence, a fixed-length time period including the data point and centered at the time at which the data point is located is used as an observation time window;

[0030] Step 43: Based on each data point in the power change rate sequence, a sliding time window of fixed size is constructed with it as the center point. The distribution of elements in the observation time window of each power change rate and the distribution characteristics of the data in the cluster are used to calculate the second monitoring abnormality feature of the cooling water pump.

[0031] As a further solution of the present invention, in step 43, the second monitoring abnormality feature of the cooling water pump is calculated based on the distribution of elements in the observation time window of each power change rate and the distribution characteristics of the data in the cluster. The specific steps are:

[0032] In each observation time window, based on the obtained clustering results, the number of different clusters to which all power change rates in the observation time window belong is counted, the power anomaly monitoring value of the power change rate in the observation time window is calculated, the difference between the mean and the variance of the power anomaly monitoring value is calculated, and the product of the number of clusters and the difference is used as the numerator;

[0033] For each power change rate within the observation time window, count its frequency in the entire power change rate sequence, and accumulate all frequencies to obtain the cumulative frequency of the data in the observation time window in the global difference sequence;

[0034] The normalized product of the numerator and the inverse of the accumulated frequency in the global difference sequence is used as the second monitoring abnormality feature of the cooling water pump.

[0035] A monitoring device for electrical equipment in a computer room, comprising a data acquisition module, a data processing module, a first monitoring anomaly analysis module, a second monitoring anomaly analysis module, and an alarm and control module; the data acquisition module is connected to the data processing module, the data processing module is connected to the first monitoring anomaly analysis module and the second monitoring anomaly analysis module, respectively; the first monitoring anomaly analysis module and the second monitoring anomaly analysis module are respectively connected to the alarm and control module;

[0036] The data acquisition module is used to acquire the monitoring data of the cooling tower in real time through the sensor network and the power factor detection module, and to filter, smooth and normalize the monitoring data to obtain the first monitoring data, and to construct a sliding time window to perform time window segmentation processing on the first monitoring data to obtain a monitoring sequence;

[0037] The data processing module is used to calculate the instantaneous power of the cooling water pump in real time based on the first monitoring data to obtain a first power sequence within the same time window, calculate the power change rate of the cooling water pump according to the first power sequence and obtain a power change rate sequence, and calculate the power abnormality monitoring value within each time window respectively through the power change rate sequence;

[0038] The first monitoring anomaly analysis module is configured to extract temperature data from the first monitoring data, calculate the temperature change rate in each time window according to the temperature data to generate a temperature change rate sequence, and calculate the first monitoring anomaly feature of the cooling water pump according to the temperature change rate sequence;

[0039] The second monitoring abnormality analysis module obtains the second monitoring abnormality feature of the cooling water pump according to the cluster distribution result between the power change rates in each time window;

[0040] The alarm and control module is used to obtain the first monitoring abnormality feature and the second monitoring abnormality feature, compare them with the preset abnormality threshold, and output an abnormality instruction according to the comparison result.

[0041] An electronic device comprises: a memory; a processor; and a computer program; wherein the computer program is stored in the memory and configured to be executed by the processor to implement the above-mentioned monitoring method for electrical equipment in a computer room.

[0042] The present invention provides a monitoring method, device and electronic equipment for electrical equipment in a computer room. The present invention acquires monitoring data of a cooling tower in real time through a sensor network and a power factor detection module, constructs a sliding time window to perform time window segmentation processing on the first monitoring data to obtain a monitoring sequence; calculates the instantaneous power of a cooling water pump in real time based on the first monitoring data to obtain a first power sequence in the same time window, calculates the power change rate of the cooling water pump according to the first power sequence and obtains a power change rate sequence, calculates the power abnormality monitoring value in each time window through the power change rate sequence, analyzes and calculates the first monitoring abnormality feature and the second monitoring abnormality feature of the cooling water pump, and effectively reduces energy consumption and maintenance risks while ensuring the safe operation of the power supply equipment in the computer room through multi-indicator fusion and intelligent data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A schematic diagram showing the change in power rate of the cooling water pump over time provided by an embodiment of the present invention;

[0044] Figure 2 A two-dimensional plan view of a refrigeration room system in an energy management system in the prior art provided by an embodiment of the present invention;

[0045] Figure 3 A schematic diagram showing the temperature change rate of a cooling water pump over time according to an embodiment of the present invention;

[0046] Figure 4 A flow chart of a method for monitoring electrical equipment in a computer room provided by an embodiment of the present invention;

[0047] Figure 5 A flowchart of step 3 of a method for monitoring electrical equipment in a computer room provided by an embodiment of the present invention;

[0048] Figure 6 This is a flow chart of step 4 in a method for monitoring electrical equipment in a computer room provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the accompanying drawings. Obviously, the technical solutions described are only part of the present invention, not the entire invention. Based on the technical solutions of the present invention, all other technical solutions obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0050] Example 1

[0051] In an embodiment of the present invention, there is provided Figure 1The flowchart of a method for monitoring electrical equipment in a computer room is shown, and the method for monitoring electrical equipment in a computer room includes the following steps:

[0052] Step 1: The monitoring data of the cooling tower is collected in real time through the sensor network and the power factor detection module, and the monitoring data is filtered, smoothed and normalized to obtain the first monitoring data. A sliding time window is constructed to segment the first monitoring data into time windows to obtain a monitoring sequence. ,in, is the first monitoring data in the i-th time window, is the number of time window segments, ,in, is the jth first monitoring data, for The number of the first monitoring data in ;

[0053] Step 2: Calculate the instantaneous power of the cooling water pump in real time based on the first monitoring data to obtain the first power sequence within the same time window. ,in, is the jth instantaneous power, is the mth instantaneous power, calculate the power change rate of the cooling water pump according to the first power sequence and obtain the power change rate sequence ,in, is the jth power change rate, is the m-1th power change rate, and the power abnormality monitoring value in each time window is calculated respectively through the power change rate sequence; the power abnormality monitoring value includes the mean and standard deviation;

[0054] Step 3: extracting temperature data from the first monitoring data, calculating the temperature change rate in each time window according to the temperature data to generate a temperature change rate sequence, and calculating the first monitoring abnormality feature of the cooling water pump according to the temperature change rate sequence;

[0055] Step 4: obtaining a second monitoring abnormality feature of the cooling water pump according to the cluster distribution result between the power change rates in each time window;

[0056] Step 5: Obtain the first monitoring abnormality feature and the second monitoring abnormality feature, compare them with a preset abnormality threshold, and output an abnormality indication based on the comparison result.

[0057] Figure 1 A schematic diagram of the power change rate of the cooling water pump over time provided in an embodiment of the present invention; the horizontal axis is time (from 0:00 to 12:00), the vertical axis is the power change rate, each data point in the curve represents the magnitude of the power change rate of the cooling water pump monitored at the corresponding time point, and the points are connected by a smooth curve.

[0058] Figure 3 This diagram shows the cooling water pump temperature change rate over time, as provided by an embodiment of the present invention. The horizontal axis of the graph also represents time (from 0:00 to 12:00), and the vertical axis represents the temperature change rate. Each data point in the curve represents the cooling water pump temperature change rate monitored at that point in time.

[0059] Specifically, in step 1, monitoring data is collected in real time through a sensor network and a power factor detection module. The sensor network includes a voltage sensor, a current sensor, and a temperature sensor. The voltage data of the cooling water pump is obtained in real time through the voltage sensor, the current data of the cooling water pump is obtained in real time through the current sensor, and the temperature data of the cooling water pump is obtained in real time through the temperature sensor. The power factor detection module is used to monitor the power factor of the cooling water pump.

[0060] Specifically, in step 2, the instantaneous power of the cooling water pump is calculated in real time based on the first monitoring data. The calculation formula of the instantaneous power is:

[0061] ;

[0062] Where: is the instantaneous power at time t, is the voltage data at time t, is the current data at time t, is the power factor at time t;

[0063] is the jth power change rate, and the calculation formula is:

[0064] ;

[0065] Where: is the jth instantaneous power, is the j+1th instantaneous power, is the time interval between adjacent sampling points;

[0066] The mean and standard deviation in each time window are calculated using the power change rate sequence. The specific formula is:

[0067] ;

[0068] ;

[0069] Where: for The mean corresponding to the sequence, for The corresponding standard deviation of the series.

[0070] By collecting voltage, current, temperature, and power factor data in real time through a sensor network and applying high-frequency sliding time window processing, the system can capture dynamic changes in device status within milliseconds, enabling timely detection of power and temperature anomalies in cooling water pumps. The system uses the primary monitoring data to calculate instantaneous power, power change rate, and temperature change rate, and constructs power anomaly monitoring values ​​(mean and standard deviation) and temperature anomaly signatures, effectively combining electrical and thermal indicators to identify anomalies from multiple perspectives. The primary monitoring anomaly signature extracts local temperature fluctuations through the second-order difference of the temperature change rate. The secondary monitoring anomaly signature, based on the clustered distribution of the power change rate and combined with statistical indicators (mean and standard deviation) and global frequency information, more accurately reflects local load changes and power fluctuation anomalies. By comparing the monitoring anomaly signature with preset thresholds, an alarm is issued once an anomaly is detected. This provides early warning and allows for proactive action before the cooling water pumps experience temperature control anomalies in the equipment room due to high load or insufficient heat dissipation, mitigating the risk of system failure.

[0071] Specifically, Figure 4 This is a flow chart of step 3 of a method for monitoring electrical equipment in a computer room provided by an embodiment of the present invention. In step 3, a first monitoring abnormality feature of a cooling water pump is calculated based on a temperature change rate sequence. The specific steps are:

[0072] Step 31: extracting temperature data from the first monitoring data , calculate the temperature change rate in each time window based on the temperature data Generate a temperature change rate series , is the kth temperature change rate, is the number of temperature change rates within the time window; , is the k+1th temperature data in the time window, is the kth temperature data in the time window, is the time interval between adjacent sampling points;

[0073] Step 32: Calculate the first monitoring abnormality feature of the cooling water pump based on the temperature change rate sequence, determine the temperature change rate of three consecutive sampling points within the time window, and take the current temperature change rate minus twice the temperature change rate at the previous moment, plus the temperature change rate at the previous moment as the numerator;

[0074] Step 33: The product of the reciprocal of the square of the time interval between adjacent sampling points and the numerator is used as the first monitoring abnormality feature. .

[0075] The calculation formula for the first monitoring abnormality feature is:

[0076] ;

[0077] Where: The first monitoring abnormality feature, is the temperature change rate at time t, for The temperature change rate at time, for The temperature change rate at time, is the time interval between adjacent sampling points.

[0078] By calculating the temperature change rate of three consecutive sampling points, a quantitative indicator α1 was constructed, which can accurately describe the amplitude and trend of local temperature fluctuations, facilitating comparison and judgment with preset thresholds. The second-order difference can reflect the acceleration of temperature change and is extremely sensitive to abnormal conditions such as sudden temperature changes, steep rises or falls, thereby enabling early identification of potential equipment failures or heat dissipation anomalies. After preprocessing such as filtering and smoothing, the features extracted using the second-order difference method can further reduce the risk of false alarms caused by measurement noise or small fluctuations, and improve the robustness of anomaly detection. Based on the continuous calculation of sampling data, the α1 value can be updated in real time, thereby continuously monitoring the dynamic temperature changes of the cooling water pump and promptly detecting temperature control anomalies caused by load or insufficient heat dissipation.

[0079] Specifically, Figure 5 This is a flow chart of step 4 of a method for monitoring electrical equipment in a computer room provided by an embodiment of the present invention. In step 4, a second monitoring abnormality feature of a cooling water pump is obtained based on the cluster distribution results between the power change rates within each time window. The specific steps are:

[0080] Step 41, arranging the power change rate sequence composed of the power change rate in each time window in chronological order, and automatically dividing the data in the power change rate sequence into a plurality of clusters according to a clustering algorithm that does not preset the number of clusters;

[0081] Step 42: for each data point in the power rate change sequence, a fixed-length time period including the data point and centered at the time at which the data point is located is used as an observation time window;

[0082] Step 43: Based on each data point in the power change rate sequence, a sliding time window of fixed size is constructed with it as the center point. The distribution of elements in the observation time window of each power change rate and the distribution characteristics of the data in the cluster are used to calculate the second monitoring abnormality feature of the cooling water pump.

[0083] Specifically, in step 43, the second monitoring abnormality feature of the cooling water pump is calculated based on the distribution of elements in the observation time window of each power change rate and the distribution characteristics of the data in the cluster. The specific steps are:

[0084] In each observation time window, based on the obtained clustering results, the number of different clusters to which all power change rates in the observation time window belong is counted, the power anomaly monitoring value of the power change rate in the observation time window is calculated, the difference between the mean and the variance of the power anomaly monitoring value is calculated, and the product of the number of clusters and the difference is used as the numerator;

[0085] For each power change rate within the observation time window, count its frequency in the entire power change rate sequence, and accumulate all frequencies to obtain the cumulative frequency of the data in the observation time window in the global difference sequence;

[0086] The normalized product of the numerator and the inverse of the accumulated frequency in the global difference sequence is used as the second monitoring abnormality feature of the cooling water pump.

[0087] The calculation formula for the second monitoring abnormality characteristic of the cooling water pump is:

[0088] ;

[0089] Where: The second monitoring abnormality feature, for The mean corresponding to the sequence, for The standard deviation of the sequence, is the number of clusters, is the cumulative frequency in the difference sequence.

[0090] By counting the number of clusters to which the power change rate belongs within the observation time window and calculating the statistical indicators within the window, α2 can reflect the diversity and fluctuation of the local data distribution. Combined with the normalization of the cumulative frequency of the window data in the global difference sequence, the local abnormal characteristics can be combined with the overall data distribution, thereby more accurately identifying abnormal states. The clustering algorithm without presetting the number of clusters is adopted, so that the method can adapt to the diversity and complexity of data under different working conditions and avoid the deviation introduced by artificially setting the number of clusters. When the power change rate within the local window shows diverse and abnormal change patterns, the number of clusters will increase. At the same time, the change in the difference between the mean and the standard deviation will also reflect the instability of the data, thereby significantly increasing the α2 value. By fusing local distribution characteristics with the global data frequency, normal fluctuations and abnormal changes can be effectively distinguished, thereby improving the detection accuracy and response speed of abnormal power states of cooling water pumps.

[0091] During the monitoring of the cooling water pump in the computer room, the sensor network collected the following data in real time:

[0092] Each sampling point records the voltage U (unit: V), current I (unit: A), power factor cosφ, and temperature T (unit: °C). For example, the data from time 0 to 5 seconds is as follows:

[0093] Time 0 seconds: U = 230 V, I = 10.0 A, cosφ = 0.95, T = 35.0 °C;

[0094] Time 1 second: U = 231 V, I = 10.2 A, cosφ = 0.95, T = 35.2 °C;

[0095] Time 2 seconds: U = 232 V, I = 10.1 A, cosφ = 0.96, T = 35.5°C;

[0096] Time 3 seconds: U = 233 V, I = 10.3 A, cosφ = 0.94, T = 35.8°C;

[0097] Time 4 seconds: U = 230 V, I = 10.2 A, cosφ = 0.95, T = 36.0°C;

[0098] Time 5 seconds: U = 229 V, I = 10.0 A, cosφ = 0.96, T = 36.2°C.

[0099] Calculation of instantaneous power and power change rate: Calculate the instantaneous power at each moment using the formula:

[0100] Time 0: P0 = 230 × 10.0 × 0.95 ≈ 2185 W;

[0101] Time 1: P1 ≈ 2238 W;

[0102] Time 2: P2≈2250 W;

[0103] Time 3: P3 ≈ 2256 W;

[0104] Time 4: P4 ≈ 2233 W;

[0105] Time 5: P5 ≈ 2198 W;

[0106] Then calculate the power change rate between adjacent sampling points:

[0107] get:

[0108] ΔP0=P1-P0=2238-2185=+53 W;

[0109] ΔP1=P2-P1=2250-2238=+12 W;

[0110] ΔP2=P3-P2=2256-2250=+6 W;

[0111] ΔP3=P4-P3=2233-2256=-23 W;

[0112] ΔP4=P5-P4=2198-2233=-35 W;

[0113] Calculate the mean and standard deviation of the power rate of change within this window:

[0114] ;

[0115] ;

[0116] Calculation of temperature change rate and first monitoring abnormality characteristics:

[0117] Calculate the rate of temperature change from temperature data:

[0118] Time 0-1: 0.2 °C;

[0119] Time 1-2: 0.3 °C;

[0120] Time 2-3: 0.3 °C;

[0121] Time 3-4: 0.2 °C;

[0122] Time 4-5: 0.2 °C;

[0123] For example, at 3 seconds:

[0124] ;

[0125] Similarly, the The absolute value is around 0.1.

[0126] Specifically, in step 5, the first monitoring abnormality feature and the second monitoring abnormality feature are obtained and compared with the preset abnormality threshold, and an abnormality instruction is output according to the comparison result, specifically:

[0127] Compare the first monitoring abnormality feature with a preset first abnormality threshold, and if the first monitoring abnormality feature is greater than or equal to the preset first abnormality threshold, output an abnormality instruction; if the first monitoring abnormality feature is less than the preset first abnormality threshold, no output is given;

[0128] The second monitoring abnormality feature is compared with the preset second abnormality threshold. If the second monitoring abnormality feature is greater than or equal to the preset second abnormality threshold, an abnormality instruction is output; if the second monitoring abnormality feature is less than the preset second abnormality threshold, no output is given.

[0129] The embodiment of the present invention acquires the monitoring data of the cooling tower in real time through a sensor network and a power factor detection module, constructs a sliding time window to perform time window segmentation processing on the first monitoring data to obtain a monitoring sequence; calculates the instantaneous power of the cooling water pump in real time based on the first monitoring data to obtain a first power sequence in the same time window, calculates the power change rate of the cooling water pump according to the first power sequence and obtains a power change rate sequence, calculates the power abnormality monitoring value in each time window through the power change rate sequence, analyzes and calculates the first monitoring abnormality feature and the second monitoring abnormality feature of the cooling water pump, and effectively reduces energy consumption and maintenance risks while ensuring the safe operation of the power supply equipment in the computer room through multi-indicator fusion and intelligent data analysis.

[0130] Example 2

[0131] A monitoring device for electrical equipment in a computer room, comprising a data acquisition module, a data processing module, a first monitoring anomaly analysis module, a second monitoring anomaly analysis module, and an alarm and control module; the data acquisition module is connected to the data processing module, the data processing module is connected to the first monitoring anomaly analysis module and the second monitoring anomaly analysis module, respectively; the first monitoring anomaly analysis module and the second monitoring anomaly analysis module are respectively connected to the alarm and control module;

[0132] The data acquisition module is used to acquire the monitoring data of the cooling tower in real time through the sensor network and the power factor detection module, and to filter, smooth and normalize the monitoring data to obtain the first monitoring data, and to construct a sliding time window to perform time window segmentation processing on the first monitoring data to obtain a monitoring sequence;

[0133] The data processing module is used to calculate the instantaneous power of the cooling water pump in real time based on the first monitoring data to obtain a first power sequence within the same time window, calculate the power change rate of the cooling water pump according to the first power sequence and obtain a power change rate sequence, and calculate the power abnormality monitoring value within each time window respectively through the power change rate sequence;

[0134] The first monitoring anomaly analysis module is configured to extract temperature data from the first monitoring data, calculate the temperature change rate in each time window according to the temperature data to generate a temperature change rate sequence, and calculate the first monitoring anomaly feature of the cooling water pump according to the temperature change rate sequence;

[0135] The second monitoring abnormality analysis module obtains the second monitoring abnormality feature of the cooling water pump according to the cluster distribution result between the power change rates in each time window;

[0136] The alarm and control module is used to obtain the first monitoring abnormality feature and the second monitoring abnormality feature, compare them with the preset abnormality threshold, and output an abnormality instruction according to the comparison result.

[0137] Example 3

[0138] Based on the same inventive concept, an embodiment of the present invention provides an electronic device, comprising: a processor; a memory; and a computer program; wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the monitoring method for electrical equipment in a computer room as provided above, the method comprising: acquiring monitoring data of a cooling tower in real time through a sensor network and a power factor detection module, filtering, smoothing, and normalizing the monitoring data to obtain first monitoring data, constructing a sliding time window to perform time window segmentation processing on the first monitoring data to obtain a monitoring sequence ; Based on the first monitoring data, the instantaneous power of the cooling water pump is calculated in real time to obtain the first power sequence in the same time window , calculate the power change rate of the cooling water pump according to the first power sequence and obtain the power change rate sequence , the power abnormality monitoring value in each time window is calculated respectively through the power change rate sequence; the power abnormality monitoring value includes the mean and the standard deviation; by extracting temperature data from the first monitoring data, the temperature change rate in each time window is calculated according to the temperature data to generate a temperature change rate sequence, and the first monitoring abnormality feature of the cooling water pump is calculated according to the temperature change rate sequence; the second monitoring abnormality feature of the cooling water pump is obtained according to the clustering distribution result between the power change rates in each time window; the first monitoring abnormality feature and the second monitoring abnormality feature are obtained and compared with the preset abnormality threshold, and an abnormality instruction is output according to the comparison result.

[0139] Finally, it should be understood that the embodiments described in this specification are intended only to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly described and illustrated in this specification.

Claims

1. A method for monitoring electrical equipment in a computer room, characterized in that: The steps include: Step 1: Acquire monitoring data in real time through a sensor network and a power factor detection module. The sensor network includes a voltage sensor, a current sensor, and a temperature sensor. The monitoring data is filtered, smoothed, and normalized to obtain first monitoring data. A sliding time window is constructed to segment the first monitoring data into time windows to obtain a monitoring sequence I1. Step 2: Based on the first monitoring data, the instantaneous power of the cooling water pump is calculated in real time to obtain a first power sequence P1 within the same time window. The power change rate of the cooling water pump is calculated based on the first power sequence to obtain a power change rate sequence ΔP1. The power abnormality monitoring value within each time window is calculated using the power change rate sequence. The power abnormality monitoring value includes a mean and a standard deviation. Step 3: extracting temperature data from the first monitoring data, calculating the temperature change rate in each time window according to the temperature data to generate a temperature change rate sequence, and calculating the first monitoring abnormality feature of the cooling water pump according to the temperature change rate sequence; Step 4: obtaining a second monitoring abnormality feature of the cooling water pump according to the cluster distribution result between the power change rates in each time window; Step 5: Obtain the first monitoring abnormality feature and the second monitoring abnormality feature, compare them with a preset abnormality threshold, and output an abnormality indication based on the comparison result.

2. A method for monitoring electrical equipment in a computer room according to claim 1, characterized in that: In step 1, the voltage data of the cooling water pump is obtained in real time through the voltage sensor, the current data of the cooling water pump is obtained in real time through the current sensor, and the temperature data of the cooling water pump is obtained in real time through the temperature sensor. The power factor detection module is used to monitor the power factor of the cooling water pump.

3. A method for monitoring electrical equipment in a computer room according to claim 1, characterized in that: In step 3, the first monitoring abnormality feature of the cooling water pump is calculated according to the temperature change rate sequence. The specific steps are as follows: Step 31: extracting temperature data T from the first monitoring data k , calculate the temperature change rate ΔT in each time window based on the temperature data k Generate temperature change rate sequence ΔT1; Step 32: Calculate the first monitoring abnormality feature of the cooling water pump based on the temperature change rate sequence, determine the temperature change rate of three consecutive sampling points within the time window, and take the current temperature change rate minus twice the temperature change rate at the previous moment, plus the temperature change rate at the previous moment as the numerator; Step 33: The product of the reciprocal of the square of the time interval between adjacent sampling points and the numerator is used as the first monitoring abnormality feature α1.

4. A method for monitoring electrical equipment in a computer room according to claim 1, characterized in that: In step 4, the second monitoring abnormality feature of the cooling water pump is obtained according to the cluster distribution results between the power change rates in each time window. The specific steps are: Step 41, arranging the power change rate sequence composed of the power change rate in each time window in chronological order, and automatically dividing the data in the power change rate sequence into a plurality of clusters according to a clustering algorithm that does not preset the number of clusters; Step 42: for each data point in the power rate change sequence, a fixed-length time period including the data point and centered at the time at which the data point is located is used as an observation time window; Step 43, based on each data point in the power change rate sequence, a fixed-size sliding time window is constructed with it as the center point, and the second monitoring abnormality feature of the cooling water pump is calculated based on the distribution of elements in the observation time window of each power change rate and the distribution characteristics of the data in the cluster.

5. A method for monitoring electrical equipment in a computer room according to claim 4, characterized in that: In step 43, the second monitoring abnormality feature of the cooling water pump is calculated based on the distribution of elements in the observation time window of each power change rate and the distribution characteristics of the data in the cluster. The specific steps are: In each observation time window, based on the obtained clustering results, the number of different clusters to which all power change rates in the observation time window belong is counted, the power anomaly monitoring value of the power change rate in the observation time window is calculated, the difference between the mean and the variance of the power anomaly monitoring value is calculated, and the product of the number of clusters and the difference is used as the numerator; For each power change rate within the observation time window, count its frequency in the entire power change rate sequence, and accumulate all frequencies to obtain the cumulative frequency of the data in the observation time window in the global difference sequence; The normalized product of the numerator and the inverse of the accumulated frequency in the global difference sequence is used as the second monitoring abnormality feature of the cooling water pump.

6. A monitoring device for electrical equipment in a computer room, comprising a data acquisition module, a data processing module, a first monitoring anomaly analysis module, a second monitoring anomaly analysis module, and an alarm and control module, characterized in that: The data acquisition module is used to acquire the monitoring data of the cooling tower in real time through the sensor network and the power factor detection module, and to filter, smooth and normalize the monitoring data to obtain the first monitoring data, and to construct a sliding time window to perform time window segmentation processing on the first monitoring data to obtain a monitoring sequence; The data processing module is used to calculate the instantaneous power of the cooling water pump in real time based on the first monitoring data to obtain a first power sequence within the same time window, calculate the power change rate of the cooling water pump according to the first power sequence and obtain a power change rate sequence, and calculate the power abnormality monitoring value within each time window respectively through the power change rate sequence; The first monitoring anomaly analysis module is configured to extract temperature data from the first monitoring data, calculate the temperature change rate in each time window according to the temperature data to generate a temperature change rate sequence, and calculate the first monitoring anomaly feature of the cooling water pump according to the temperature change rate sequence; The second monitoring abnormality analysis module obtains the second monitoring abnormality feature of the cooling water pump according to the cluster distribution result between the power change rates in each time window; The alarm and control module is used to obtain the first monitoring abnormality feature and the second monitoring abnormality feature, compare them with the preset abnormality threshold, and output an abnormality instruction according to the comparison result.

7. An electronic device, characterized in that: include: Memory; Processor; And a computer program; wherein, the computer program is stored in the memory and is configured to be executed by the processor to implement the monitoring method for electrical equipment in a computer room according to any one of claims 1 to 5.

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