CN117632664B - Machine room equipment monitoring method and system based on automatic comparison - Google Patents
Machine room equipment monitoring method and system based on automatic comparison Download PDFInfo
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Abstract
The invention provides a machine room equipment monitoring method and system based on automatic comparison, which relate to the technical field of artificial intelligence, and the method comprises the following steps: collecting historical environmental parameters, historical fault reports and real-time environmental parameters of all devices in a target machine room; analyzing and processing the historical environmental parameters and the fault report by using a machine learning algorithm to determine an optimal environmental parameter threshold; comparing the current real-time environment parameter with the environment parameter threshold value in real time to obtain a comparison result; and automatically adjusting the environment control equipment in the target machine room according to the comparison result so as to maintain the optimal environment condition. The invention can effectively improve the environmental monitoring and management level of equipment and a machine room and ensure the safe and stable operation of the equipment.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a machine room equipment monitoring method and system based on automatic comparison.
Background
With the development of technology, various devices and machine rooms have increasingly high requirements on environmental parameters, such as temperature, voltage, pressure, and the like. Conventional environmental monitoring methods typically require manual setting of alarm thresholds, which are time consuming and labor intensive, and difficult to adapt in time to environmental changes and equipment performance changes. Therefore, there is a need for an intelligent monitoring method that can automatically learn and adjust.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a machine room equipment monitoring method and system based on automatic comparison.
In order to achieve the above object, the present invention provides the following solutions:
A machine room equipment monitoring method based on automatic comparison comprises the following steps:
Collecting historical environmental parameters, historical fault reports and real-time environmental parameters of all devices in a target machine room;
Analyzing and processing the historical environmental parameters and the fault report by using a machine learning algorithm to determine an optimal environmental parameter threshold;
Comparing the current real-time environment parameter with the environment parameter threshold value in real time to obtain a comparison result;
And automatically adjusting the environment control equipment in the target machine room according to the comparison result so as to maintain the optimal environment condition.
Preferably, the historical environmental parameters include temperature data, voltage data, and pressure data.
Preferably, a plurality of data acquisition devices are distributed in the target machine room; the data acquisition device is used for acquiring the environmental parameters of the target machine room.
Preferably, after collecting the historical environmental parameters, the historical fault reports and the real-time environmental parameters of each device in the target machine room, the method further comprises:
carrying out data cleaning on the temperature data acquired by each data acquisition device to obtain cleaned temperature data;
and weighting the cleaned temperature data by using a weighting coefficient to obtain the real temperature data of the whole target machine room.
Preferably, the data cleaning is performed on the temperature data collected by each data collecting device to obtain cleaned temperature data, including:
calculating the variance of the temperature value of the data acquisition device in each acquisition period;
constructing a temperature acquisition model by utilizing the variance;
calculating the degree of association between each data acquisition device according to the temperature acquisition model;
constructing a contact degree matrix according to the contact degree, and determining the weighted contact degree of each data acquisition device;
Judging whether the weighted contact degree is larger than a preset threshold value or not;
and if the weighted contact degree is greater than a preset threshold value, removing the temperature information acquired by the corresponding data acquisition device to obtain cleaned temperature data.
Preferably, constructing a temperature acquisition model using the variance includes:
Using the formula Constructing a temperature acquisition model; wherein,Representing the variance of the temperature values of the data acquisition device during the ith acquisition period,Representing the variance of the temperature value of the data acquisition device during the jth acquisition period,Representing the average value of the temperature values of the data acquisition device in the ith acquisition period,Representing the acquisition model of the ith data acquisition device,Representing the acquisition model of the jth data acquisition device.
Preferably, calculating the degree of association between each data acquisition device according to the temperature acquisition model includes:
Determining the trust degree among all the data acquisition devices by using a temperature acquisition model; the trust degree calculation formula is as follows: ; wherein, Indicating the degree of trust between the ith and jth data acquisition devices,Representing the degree of trust between the jth data acquisition device and the ith data acquisition device;
Calculating the degree of association between each data acquisition device based on the degree of trust between each data acquisition device; the calculation formula of the contact degree is as follows ; Wherein,Representing the degree of association between the ith and jth data acquisition devices.
Preferably, weighting the cleaned temperature data by using a weighting coefficient to obtain real temperature data of the whole target machine room, including:
calculating a weighting coefficient according to the variance of the cleaned temperature data in each acquisition period;
And carrying out weighted average on the cleaned temperature data based on the weighting coefficient to obtain the real temperature data of the whole target machine room.
Preferably, the machine learning algorithm is any one of a neural network, a decision tree, and a support vector machine.
Machine room equipment monitoring system based on automatic comparison includes:
The acquisition module is used for acquiring historical environment parameters, historical fault reports and real-time environment parameters of all the devices in the target machine room;
The machine learning module is used for analyzing and processing the historical environmental parameters and the fault report by utilizing a machine learning algorithm so as to determine an optimal environmental parameter threshold;
the comparison module is used for comparing the current real-time environment parameters with the environment parameter threshold value in real time to obtain a comparison result;
And the adjusting module is used for automatically adjusting the environment control equipment in the target machine room according to the comparison result so as to maintain the optimal environment condition.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
The invention provides a machine room equipment monitoring method and system based on automatic comparison, wherein the method comprises the following steps: collecting historical environmental parameters, historical fault reports and real-time environmental parameters of all devices in a target machine room; analyzing and processing the historical environmental parameters and the fault report by using a machine learning algorithm to determine an optimal environmental parameter threshold; comparing the current real-time environment parameter with the environment parameter threshold value in real time to obtain a comparison result; and automatically adjusting the environment control equipment in the target machine room according to the comparison result so as to maintain the optimal environment condition. The invention can effectively improve the environmental monitoring and management level of equipment and a machine room and ensure the safe and stable operation of the equipment.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a machine room equipment monitoring method and system based on automatic comparison, which can effectively improve the environmental monitoring and management level of equipment and a machine room and ensure the safe and stable operation of the equipment.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Fig. 1 is a flowchart of a method provided by an embodiment of the present invention, and as shown in fig. 1, the present invention provides a method for monitoring equipment in a machine room based on automatic comparison, including:
Step 100: collecting historical environmental parameters, historical fault reports and real-time environmental parameters of all devices in a target machine room;
step 200: analyzing and processing the historical environmental parameters and the fault report by using a machine learning algorithm to determine an optimal environmental parameter threshold;
step 300: comparing the current real-time environment parameter with the environment parameter threshold value in real time to obtain a comparison result;
Step 400: and automatically adjusting the environment control equipment in the target machine room according to the comparison result so as to maintain the optimal environment condition.
Preferably, the historical environmental parameters include temperature data, voltage data, and pressure data.
Optionally, the overall technical flow in this embodiment is as follows:
The process is as follows: the deployment data acquisition unit is arranged in each device and each machine room, acquires environmental parameters in real time and transmits data to the intelligent learning module.
A second flow: the intelligent learning module uses machine learning algorithms (such as neural networks, decision trees, support vector machines, etc.) to analyze the historical acquisition data, automatically learn and determine the optimal environmental parameter threshold.
And a process III: and continuously receiving the current environmental parameter data, and comparing the current environmental parameter data with a threshold value determined by the intelligent learning module to judge whether adjustment is needed.
The process is four: when the environmental parameter is detected to exceed the threshold value, corresponding environmental control equipment is activated, such as starting an air conditioner to cool down, adjusting the voltage of a voltage stabilizer, adjusting the pressure of a pressure controller and the like, so as to quickly restore to an optimal state.
Preferably, a plurality of data acquisition devices are distributed in the target machine room; the data acquisition device is used for acquiring the environmental parameters of the target machine room.
The number of the data acquisition devices is at least 2, and the distance between the data acquisition devices and the data acquisition devices is as far as possible, so that the overall distribution condition of the monitoring parameters in the machine room can be acquired.
Specifically, the data acquisition device in this embodiment is integrated with a semiconductor sensor (such as a thermistor, an integrated circuit sensor): the measurement is performed using the characteristics of the resistance or voltage of the semiconductor material as a function of temperature.
Further, the data acquisition device in this embodiment further includes a Current Transformer (CT): using the transformer principle, a secondary current proportional to the primary current is induced by the magnetic field generated by the current being measured, and then the measurement is performed.
Furthermore, the data acquisition device in this embodiment is further integrated with a strain gauge pressure sensor, which is used for processing the pressure environment parameters in the target machine room.
Preferably, after collecting the historical environmental parameters, the historical fault reports and the real-time environmental parameters of each device in the target machine room, the method further comprises:
carrying out data cleaning on the temperature data acquired by each data acquisition device to obtain cleaned temperature data;
and weighting the cleaned temperature data by using a weighting coefficient to obtain the real temperature data of the whole target machine room.
Specifically, because the data collection device collects a large amount of data, before weighting the temperature data measured by the plurality of data collection devices, data cleaning is required to remove abnormal data.
Preferably, the data cleaning is performed on the temperature data collected by each data collecting device to obtain cleaned temperature data, including:
calculating the variance of the temperature value of the data acquisition device in each acquisition period;
constructing a temperature acquisition model by utilizing the variance;
calculating the degree of association between each data acquisition device according to the temperature acquisition model;
constructing a contact degree matrix according to the contact degree, and determining the weighted contact degree of each data acquisition device;
Judging whether the weighted contact degree is larger than a preset threshold value or not;
and if the weighted contact degree is greater than a preset threshold value, removing the temperature information acquired by the corresponding data acquisition device to obtain cleaned temperature data.
Preferably, constructing a temperature acquisition model using the variance includes:
Using the formula Constructing a temperature acquisition model; wherein,Representing the variance of the temperature values of the data acquisition device during the ith acquisition period,Representing the variance of the temperature value of the data acquisition device during the jth acquisition period,Representing the average value of the temperature values of the data acquisition device in the ith acquisition period,Representing the acquisition model of the ith data acquisition device,Representing the acquisition model of the jth data acquisition device.
Preferably, calculating the degree of association between each data acquisition device according to the temperature acquisition model includes:
Determining the trust degree among all the data acquisition devices by using a temperature acquisition model; the trust degree calculation formula is as follows: ; wherein, Indicating the degree of trust between the ith and jth data acquisition devices,Representing the degree of trust between the jth data acquisition device and the ith data acquisition device;
Calculating the degree of association between each data acquisition device based on the degree of trust between each data acquisition device; the calculation formula of the contact degree is as follows ; Wherein,Representing the degree of association between the ith and jth data acquisition devices.
Above mentionedReflecting the support degree of the data acquisition device i to the data acquisition device j, and knowing 0-0 according to a trust degree calculation formula≤1，A larger value indicates a lower confidence in the data acquisition device measurements,The smaller the value, the closer the measured values of the two data acquisition devices are to the true value (i.e., the difference between the temperatures acquired by the two data acquisition devices is small).
Preferably, weighting the cleaned temperature data by using a weighting coefficient to obtain real temperature data of the whole target machine room, including:
calculating a weighting coefficient according to the variance of the cleaned temperature data in each acquisition period;
And carrying out weighted average on the cleaned temperature data based on the weighting coefficient to obtain the real temperature data of the whole target machine room.
Preferably, the machine learning algorithm is any one of a neural network, a decision tree, and a support vector machine.
Specifically, in this embodiment, the determination of the optimal environmental parameter threshold is performed by using a CNN-LSTM combined neural network model. The fault report in the embodiment can be used as labeling data, namely, the combination parameters of a certain environment are labeled, so that the prediction result can be obtained more accurately. The CNN-LSTM combined neural network model comprises: a CNN neural network model and an LSTM network; the CNN neural network model comprises a plurality of stacked convolution layers, a ReLU activation function and a pooling layer which are sequentially arranged; the CNN neural network model is used for outputting the data after feature extraction; the LSTM network is used for receiving the data after the feature extraction, and the predicted environmental parameter threshold value is obtained through an LSTM unit updating and transmitting process and a linear layer conversion process.
Optionally, a user interface is also provided in this embodiment, allowing the user to view real-time data, historical data, system status, and manual intervention system operation.
Corresponding to the above method, the embodiment also provides a machine room equipment monitoring system based on automatic comparison, which comprises:
The acquisition module is used for acquiring historical environment parameters, historical fault reports and real-time environment parameters of all the devices in the target machine room;
The machine learning module is used for analyzing and processing the historical environmental parameters and the fault report by utilizing a machine learning algorithm so as to determine an optimal environmental parameter threshold;
the comparison module is used for comparing the current real-time environment parameters with the environment parameter threshold value in real time to obtain a comparison result;
And the adjusting module is used for automatically adjusting the environment control equipment in the target machine room according to the comparison result so as to maintain the optimal environment condition.
The beneficial effects of the invention are as follows:
(1) According to the invention, through intelligent learning, the optimal environmental parameter threshold value is automatically determined, and the workload and error rate of manual setting are reduced.
(2) The invention can monitor and automatically adjust in real time, ensure that the equipment always operates under the optimal environmental condition, and improve the stability and the service life of the equipment.
(3) The user interface provides visual data display and operation interfaces, and is convenient for user management and intervention.
(4) The system has self-adaptability and can automatically adjust the threshold value along with environmental change and equipment performance change.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (4)
1. The machine room equipment monitoring method based on automatic comparison is characterized by comprising the following steps of:
Collecting historical environmental parameters, historical fault reports and real-time environmental parameters of all devices in a target machine room;
Analyzing and processing the historical environmental parameters and the fault report by using a machine learning algorithm to determine an optimal environmental parameter threshold;
Comparing the current real-time environment parameter with the environment parameter threshold value in real time to obtain a comparison result;
automatically adjusting environmental control equipment in a target machine room according to the comparison result so as to maintain optimal environmental conditions;
The historical environmental parameters include temperature data, voltage data, and pressure data;
A plurality of data acquisition devices are distributed in the target machine room; the data acquisition device is used for acquiring environmental parameters of the target machine room;
after collecting the historical environmental parameters, the historical fault reports and the real-time environmental parameters of each device in the target machine room, the method further comprises the following steps:
carrying out data cleaning on the temperature data acquired by each data acquisition device to obtain cleaned temperature data;
weighting the cleaned temperature data by using a weighting coefficient to obtain real temperature data of the whole target machine room;
carrying out data cleaning on the temperature data acquired by each data acquisition device to obtain cleaned temperature data, wherein the data cleaning method comprises the following steps:
calculating the variance of the temperature value of the data acquisition device in each acquisition period;
constructing a temperature acquisition model by utilizing the variance;
calculating the degree of association between each data acquisition device according to the temperature acquisition model;
constructing a contact degree matrix according to the contact degree, and determining the weighted contact degree of each data acquisition device;
Judging whether the weighted contact degree is larger than a preset threshold value or not;
If the weighted contact degree is greater than a preset threshold value, removing the temperature information acquired by the corresponding data acquisition device to obtain cleaned temperature data;
Constructing a temperature acquisition model using the variance, comprising:
Using the formula Constructing a temperature acquisition model; wherein/>Representing the variance of the temperature value of the data acquisition device in the ith acquisition period,/>Representing the variance of the temperature value of the data acquisition device in the j acquisition period,/>Mean value of temperature values of the data acquisition device in the ith acquisition period,/>, is representedAn acquisition model representing an i-th data acquisition device,/>Representing an acquisition model of a j-th data acquisition device;
calculating the degree of association between each data acquisition device according to the temperature acquisition model, including:
Determining the trust degree among all the data acquisition devices by using a temperature acquisition model; the trust degree calculation formula is as follows: ; wherein/> Representing the degree of trust between the ith and jth data acquisition devices,/>Representing the degree of trust between the jth data acquisition device and the ith data acquisition device;
Calculating the degree of association between each data acquisition device based on the degree of trust between each data acquisition device; the calculation formula of the contact degree is as follows ; Wherein/>Representing the degree of association between the ith and jth data acquisition devices.
2. The automatic comparison-based machine room equipment monitoring method according to claim 1, wherein weighting the cleaned temperature data by using a weighting coefficient to obtain real temperature data of the whole target machine room comprises:
calculating a weighting coefficient according to the variance of the cleaned temperature data in each acquisition period;
And carrying out weighted average on the cleaned temperature data based on the weighting coefficient to obtain the real temperature data of the whole target machine room.
3. The machine room equipment monitoring method based on automatic comparison according to claim 1, wherein the machine learning algorithm is any one of a neural network, a decision tree and a support vector machine.
4. Computer lab equipment monitoring system based on automatic comparison, characterized by, include:
The acquisition module is used for acquiring historical environment parameters, historical fault reports and real-time environment parameters of all the devices in the target machine room;
The machine learning module is used for analyzing and processing the historical environmental parameters and the fault report by utilizing a machine learning algorithm so as to determine an optimal environmental parameter threshold;
the comparison module is used for comparing the current real-time environment parameters with the environment parameter threshold value in real time to obtain a comparison result;
the adjusting module is used for automatically adjusting the environment control equipment in the target machine room according to the comparison result so as to maintain the optimal environment condition;
The historical environmental parameters include temperature data, voltage data, and pressure data;
A plurality of data acquisition devices are distributed in the target machine room; the data acquisition device is used for acquiring environmental parameters of the target machine room;
after collecting the historical environmental parameters, the historical fault reports and the real-time environmental parameters of each device in the target machine room, the method further comprises the following steps:
carrying out data cleaning on the temperature data acquired by each data acquisition device to obtain cleaned temperature data;
weighting the cleaned temperature data by using a weighting coefficient to obtain real temperature data of the whole target machine room;
carrying out data cleaning on the temperature data acquired by each data acquisition device to obtain cleaned temperature data, wherein the data cleaning method comprises the following steps:
calculating the variance of the temperature value of the data acquisition device in each acquisition period;
constructing a temperature acquisition model by utilizing the variance;
calculating the degree of association between each data acquisition device according to the temperature acquisition model;
constructing a contact degree matrix according to the contact degree, and determining the weighted contact degree of each data acquisition device;
Judging whether the weighted contact degree is larger than a preset threshold value or not;
If the weighted contact degree is greater than a preset threshold value, removing the temperature information acquired by the corresponding data acquisition device to obtain cleaned temperature data;
Constructing a temperature acquisition model using the variance, comprising:
Using the formula Constructing a temperature acquisition model; wherein/>Representing the variance of the temperature value of the data acquisition device in the ith acquisition period,/>Representing the variance of the temperature value of the data acquisition device in the j acquisition period,/>Mean value of temperature values of the data acquisition device in the ith acquisition period,/>, is representedAn acquisition model representing an i-th data acquisition device,/>Representing an acquisition model of a j-th data acquisition device;
calculating the degree of association between each data acquisition device according to the temperature acquisition model, including:
Determining the trust degree among all the data acquisition devices by using a temperature acquisition model; the trust degree calculation formula is as follows: ; wherein/> Representing the degree of trust between the ith and jth data acquisition devices,/>Representing the degree of trust between the jth data acquisition device and the ith data acquisition device;
Calculating the degree of association between each data acquisition device based on the degree of trust between each data acquisition device; the calculation formula of the contact degree is as follows ; Wherein/>Representing the degree of association between the ith and jth data acquisition devices.
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