CN117251751A - Machine room monitoring method and device, electronic equipment and storage medium - Google Patents
Machine room monitoring method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN117251751A CN117251751A CN202311295756.9A CN202311295756A CN117251751A CN 117251751 A CN117251751 A CN 117251751A CN 202311295756 A CN202311295756 A CN 202311295756A CN 117251751 A CN117251751 A CN 117251751A
- Authority
- CN
- China
- Prior art keywords
- target
- sample
- machine room
- samples
- parameters
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000012544 monitoring process Methods 0.000 title claims abstract description 29
- 238000003860 storage Methods 0.000 title claims abstract description 11
- 238000012806 monitoring device Methods 0.000 claims abstract description 6
- 230000002159 abnormal effect Effects 0.000 claims description 21
- 230000004044 response Effects 0.000 claims description 19
- 238000012545 processing Methods 0.000 claims description 17
- 238000012216 screening Methods 0.000 claims description 14
- 238000013507 mapping Methods 0.000 claims description 9
- 238000004422 calculation algorithm Methods 0.000 description 16
- 238000004891 communication Methods 0.000 description 11
- 238000001514 detection method Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 238000005457 optimization Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 2
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000000926 separation method Methods 0.000 description 2
- 206010000369 Accident Diseases 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 238000004378 air conditioning Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000007621 cluster analysis Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000000151 deposition Methods 0.000 description 1
- 229910003460 diamond Inorganic materials 0.000 description 1
- 239000010432 diamond Substances 0.000 description 1
- 238000003708 edge detection Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000004451 qualitative analysis Methods 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/211—Selection of the most significant subset of features
- G06F18/2113—Selection of the most significant subset of features by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
- G06F18/24133—Distances to prototypes
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention discloses a machine room monitoring method, a machine room monitoring device, electronic equipment and a storage medium. Wherein the method comprises the following steps: obtaining target parameters of a target machine room in a preset time period to obtain a target sample set, wherein the target parameters comprise temperature parameters and humidity parameters; clustering is carried out on the target sample set to obtain a clustering result; based on the clustering result, the temperature and the humidity of the target machine room are monitored. The invention solves the technical problem that the environment inside the machine room is difficult to effectively monitor in the related art.
Description
Technical Field
The invention relates to the field of data centers, in particular to a machine room monitoring method, a device, electronic equipment and a storage medium.
Background
Along with the continuous promotion of communication computer lab installed volume and equipment power density, the local hot spot that has led to the computer lab to list cabinet frequently, the sensor has been very extensive in data center monitoring application, can monitor the temperature data of the inside environment of computer lab through the sensor in real time, the control to the computer lab at present has mostly only gathered the temperature data in the computer lab, but in the practical application process, there are other factors to the equipment operation in the computer lab to influence in the time of temperature to equipment operation safety, consequently, be difficult to effectively monitor the inside environment of computer lab through the method in the correlation technique.
In view of the above problems, no effective solution has been proposed at present.
Disclosure of Invention
The embodiment of the invention provides a machine room monitoring method, a device, electronic equipment and a storage medium, which are used for at least solving the technical problem that the environment inside a machine room is difficult to effectively monitor in the related art.
According to an aspect of the embodiment of the present invention, there is provided a machine room monitoring method, including: obtaining target parameters of a target machine room in a preset time period to obtain a target sample set, wherein the target parameters comprise temperature parameters and humidity parameters; clustering is carried out on the target sample set to obtain a clustering result; based on the clustering result, the temperature and the humidity of the target machine room are monitored.
Optionally, clustering is performed on the target sample set to obtain a clustering result, including: traversing the target sample set to determine a core object, wherein the core object is a sample meeting preset conditions in the target sample set; sorting the first samples based on the distance between the core object and the first sample to obtain a seed set corresponding to the core object, wherein the first sample is other samples except the core object in the target sample set; and screening samples in the seed set based on the preset distance to obtain a clustering result.
Optionally, traversing the target sample set to determine the core object includes: determining a target distance between a target sample in a target sample set and a second sample, wherein the target sample is any sample in the target sample set, and the second sample is other samples in the target sample set except the target sample; determining the second sample as a threshold sample of the target sample in response to the target distance being less than or equal to the preset distance; and determining the target sample as a core object in response to the number of the critical domain samples being greater than or equal to a number threshold.
Optionally, based on a preset distance, screening samples in the seed set to obtain a clustering result, including: in response to the distance between the core object and the first sample being greater than a preset distance, eliminating the first sample from the seed set to obtain a target seed set corresponding to the core object; and determining the target seed subset corresponding to the core object as a clustering result.
Optionally, obtaining the target parameters of the target machine room in the preset time period to obtain the target sample set includes: constructing a target coordinate system, wherein the target coordinate system is used for representing the mapping relation between different temperatures and different humidities in a target machine room in a preset time period; mapping temperature parameters and humidity parameters acquired at the same moment in the target parameters to a target coordinate system to obtain samples at the same moment; summarizing samples at different moments in a preset time period to obtain a target sample set.
Optionally, the method further comprises: calibrating the core object in response to the obtained seed set corresponding to the core object; and screening samples in the seed set based on the preset distance in response to the core objects having been calibrated.
Optionally, based on the clustering result, monitoring the temperature and the humidity of the target machine room includes: evaluating the clustering result based on the profile coefficient to obtain an abnormal sample in the target sample set, wherein the abnormal sample is used for representing that the temperature or the humidity of the target machine room is abnormal; and displaying the abnormal sample through the target image.
According to another aspect of the embodiment of the present invention, there is also provided a machine room monitoring device, including: the acquisition module is used for acquiring target parameters of the target machine room in a preset time period to obtain a target sample set, wherein the target parameters comprise temperature parameters and humidity parameters; the processing module is used for carrying out clustering processing on the target sample set to obtain a clustering result; and the monitoring module is used for monitoring the temperature and the humidity of the target machine room based on the clustering result.
According to another aspect of the embodiment of the present invention, there is also provided an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of the above.
According to another aspect of embodiments of the present invention, there is also provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method of any one of the above.
In the embodiment of the invention, a target sample set is obtained by acquiring target parameters of a target machine room in a preset time period, wherein the target parameters comprise temperature parameters and humidity parameters; clustering is carried out on the target sample set to obtain a clustering result; based on the clustering result, the temperature and the humidity of the target machine room are monitored. It is easy to notice that the temperature parameter and the humidity parameter of the target machine room in the preset time period can be obtained, and the temperature parameter and the humidity parameter in the target machine room can be clustered, so that the temperature in the target machine room can be monitored, the humidity in the target machine room can be monitored, the target machine room can be effectively monitored, and the technical problem that the environment inside the machine room is difficult to effectively monitor in the related art is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
FIG. 1 is a flow chart of a machine room monitoring method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an alternative target sample set according to an embodiment of the invention;
FIG. 3 is an alternative temperature humidity cloud according to an embodiment of the invention;
fig. 4 is a schematic diagram of a machine room monitoring device according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
According to an embodiment of the present invention, there is provided an embodiment of a machine room monitoring method, it should be noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different from that herein.
Fig. 1 is a flowchart of a machine room monitoring method according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step S102, obtaining target parameters of a target machine room in a preset time period to obtain a target sample set, wherein the target parameters comprise temperature parameters and humidity parameters.
The above-mentioned target computer lab can be communication computer lab, and wherein, communication computer lab can be for depositing and managing the room of various communication equipment, is usually located large-scale building or data center, and communication computer lab's main function is the normal operation and the security of assurance communication equipment to support network communication and data transmission, communication computer lab generally includes following component parts:
server rack: the method is used for storing network equipment such as servers, switches, routers and the like. These devices are responsible for processing and forwarding network data.
A power supply device: including uninterruptible power supplies, generators, etc., to ensure that the communication device remains in normal operation during a power outage.
Network wiring system: for connecting the communication device to an external network, including optical fibers, cables, etc.
An air conditioning system: is used for keeping the temperature and the constant humidity in the machine room so as to ensure the stable operation of the equipment.
Fire alarm and extinguishing system: is used for monitoring and preventing fire accidents.
Safety monitoring system: the system comprises a video monitoring system and an access control system so as to ensure the safety in a machine room.
The length of the preset time period can be set by a person skilled in the art according to the requirement, and the length of the preset time period is not particularly limited in the present invention.
In an alternative embodiment, the temperature sensor and the humidity sensor may be deployed in the target machine room, so that the temperature parameter and the humidity parameter in the target machine room may be collected in real time, alternatively, a period of time may be specified by a person skilled in the art, that is, a preset period of time, and the temperature parameter and the humidity parameter at each moment in the preset period of time may be collected to obtain the target parameter, where the unit of the moment may be minutes or seconds, and specifically may be set by a person skilled in the art according to the requirement. Further, the target parameter may be determined as a monitoring sample, resulting in a target sample set.
And step S104, clustering is carried out on the target sample set to obtain a clustering result.
The above-mentioned clustering process may be an unsupervised learning algorithm, which is used to group samples in a target sample set according to similarity, and common clustering algorithms include hierarchical clustering, density-based clustering algorithm (Density-Based Spatial Clustering of Applications with Noise, abbreviated as DBSCAN), and the like.
Data preprocessing: and cleaning, processing and converting the original data so as to facilitate the application of the clustering algorithm.
Feature selection: and selecting proper characteristics for clustering processing to improve the accuracy and the interpretability of the clustering result.
Selecting a proper clustering algorithm: and selecting a proper clustering algorithm according to the characteristics and the requirements of the data.
Setting the clustering number: for an algorithm requiring the specification of the number of clusters, the number of clusters needs to be set.
And (3) running a clustering algorithm: and running the selected clustering algorithm to perform clustering processing.
Evaluating the clustering result: the quality and effectiveness of the clustering results are evaluated using appropriate evaluation indicators.
Interpretation and application of results: and explaining the clustering result, and carrying out subsequent data analysis and decision-making according to the requirement.
In an alternative embodiment, after the target sample set is determined, samples in the target sample set can be clustered by using a DBSCAN optimization algorithm, so that target parameters can be divided into different categories to obtain a clustering result, and further, the environment in the machine room can be monitored in two dimensions of temperature and humidity according to the clustering result, and effective monitoring of the machine room is achieved.
And step S106, monitoring the temperature and the humidity of the target machine room based on the clustering result.
In an optional embodiment, the temperature parameters and the humidity parameters corresponding to different classes in the clustering result can be displayed through different cloud charts, and the change condition of the temperature and the humidity in the target machine room can be observed through the cloud deck, so that the temperature and the humidity of the target machine room are monitored. Optionally, in the conventional temperature monitoring, after temperature data are acquired through a sensor, the condition of singular value is directly judged, an early warning threshold is met, early warning is generated, but in the normal operation process of a target machine room, the humidity of the target machine room is required to be controlled within a normal range, more importantly, the temperature and the humidity have strong correlation, the conventional anomaly detection method based on the singular index threshold is not applicable, the temperature and the humidity data of each detection point of the target machine room are simultaneously used, the anomaly detection method based on a DBSCAN optimization algorithm is used for detecting the temperature and the humidity of the detection point, the temperature and the humidity data set in a cold and hot channel is divided into different clusters through a clustering algorithm, and parameters are adjusted through the optimization algorithm, so that the similarity and the difference between the clusters can be found, and the environment inside the target machine room is monitored according to the similarity and the difference between the different clusters.
In the embodiment of the invention, a target sample set is obtained by acquiring target parameters of a target machine room in a preset time period, wherein the target parameters comprise temperature parameters and humidity parameters; clustering is carried out on the target sample set to obtain a clustering result; based on the clustering result, the temperature and the humidity of the target machine room are monitored. It is easy to notice that the temperature parameter and the humidity parameter of the target machine room in the preset time period can be obtained, and the temperature parameter and the humidity parameter in the target machine room can be clustered, so that the temperature in the target machine room can be monitored, the humidity in the target machine room can be monitored, the target machine room can be effectively monitored, and the technical problem that the environment inside the machine room is difficult to effectively monitor in the related art is solved.
Optionally, clustering is performed on the target sample set to obtain a clustering result, including: traversing the target sample set to determine a core object, wherein the core object is a sample meeting preset conditions in the target sample set; sorting the first samples based on the distance between the core object and the first sample to obtain a seed set corresponding to the core object, wherein the first sample is other samples except the core object in the target sample set; and screening samples in the seed set based on the preset distance to obtain a clustering result.
The core object may be a sample in the target sample set, and optionally, the number of other samples around the sample that are closer to the sample is greater than a certain value.
The length of the preset distance can be set by a person skilled in the art according to the need, optionally, the preset distance can be a critical radius of the core object, and clustering processing can be performed on the core object in the target sample set and other samples within the critical radius of the core object, where the critical radius refers to a radius size used for defining a neighborhood range around a pixel point in image processing, and the size of the critical radius determines the number of considered pixels when performing image processing operations such as filtering and edge detection, and in general, the larger the critical radius, the more the number of considered pixels, the smoother and global the processing effect on the image will be, however, the larger the critical radius will also cause an increase in the calculation amount and may cause a loss of some detail information, so when selecting the critical radius, the weighted needs to be performed according to specific image processing tasks and requirements on image details.
In an alternative embodiment, the core object may be determined at a point pile with a higher density in the target sample set, further, the reachable distances between the core object and other samples in the target sample set except the core object may be calculated, and the other samples in the target sample set except the core object, that is, the first samples, are sorted according to the sequence of the reachable distances from near to far or from far to near, so as to obtain a seed set corresponding to the core object, further, a preset distance may be set according to the requirements of a person skilled in the art, and samples, in the seed set, with the reachable distances between the samples and the core object being greater than the preset distance, are removed, so as to obtain a clustering result.
Optionally, traversing the target sample set to determine the core object includes: determining a target distance between a target sample in a target sample set and a second sample, wherein the target sample is any sample in the target sample set, and the second sample is other samples in the target sample set except the target sample; determining the second sample as a threshold sample of the target sample in response to the target distance being less than or equal to the preset distance; and determining the target sample as a core object in response to the number of the critical domain samples being greater than or equal to a number threshold.
The target distance may be the achievable distance between the target sample and the second sample.
In an alternative embodiment, a sample may be optionally determined from the target sample set and marked as a target sample, and the reachable distance between the target sample and other samples in the target sample set may be further calculated, where the calculated reachable distance may be compared with a preset distance, that is, a preset critical radius, and if the reachable distance between the target sample and other samples in the target sample set is less than or equal to the preset distance, the other samples in the target sample set that calculate the reachable distance from the target sample may be determined as critical samples, further, the number of critical samples may be compared with a number threshold set according to the needs of those skilled in the art, and if the number of critical samples is greater than or equal to the number threshold, the target sample may be determined as a core object, and optionally, after determining the core object, the core object may be stored in a set.
Optionally, based on a preset distance, screening samples in the seed set to obtain a clustering result, including: in response to the distance between the core object and the first sample being greater than a preset distance, eliminating the first sample from the seed set to obtain a target seed set corresponding to the core object; and determining the target seed subset corresponding to the core object as a clustering result.
In an alternative embodiment, after the seed set is obtained, the reachable distance between the core object and the first sample in the seed set may be compared with a preset distance, and if the reachable distance between the core object and a certain first sample in the seed set is greater than the preset distance, the first object may be considered as being far from the core object, so that the first object may be removed from the seed set, thereby obtaining the target seed set, that is, the clustering result.
Optionally, obtaining the target parameters of the target machine room in the preset time period to obtain the target sample set includes: constructing a target coordinate system, wherein the target coordinate system is used for representing the mapping relation between different temperatures and different humidities in a target machine room in a preset time period; mapping temperature parameters and humidity parameters acquired at the same moment in the target parameters to a target coordinate system to obtain samples at the same moment; summarizing samples at different moments in a preset time period to obtain a target sample set.
The target coordinate system can be a coordinate system of two dimensions of temperature and humidity, and optionally, the temperature parameter and the humidity parameter at the same time can be clearly displayed in the target coordinate system.
In an alternative embodiment, after acquiring the temperature parameter and the humidity parameter in the preset time period, a plurality of different moments can be determined in the preset time period, for example, one minute and one second are taken as a moment, or each whole minute moment is taken as a moment, further, the temperature parameter and the humidity parameter corresponding to the moment can be determined from the temperature parameter and the humidity parameter in the preset time period, and a coordinate system of two dimensions of the temperature and the humidity is established, so that a target sample set is obtained, and optionally, the temperature parameter and the humidity parameter of the machine room in the past 24 hours can be used as monitoring data, and cluster analysis and anomaly detection analysis can be performed on the temperature data and the humidity data of the cold channel and the hot channel of the current machine room respectively.
Fig. 2 is a schematic diagram of an alternative target sample set according to an embodiment of the present invention, and as shown in fig. 2, temperature values and humidity values at the same time can be clearly observed from the target sample set, and the temperature values and the humidity values at most times exhibit regular distribution, but abnormal conditions occur in the temperature values and the humidity values at individual times.
Optionally, the method further comprises: calibrating the core object in response to the obtained seed set corresponding to the core object; and screening samples in the seed set based on the preset distance in response to the core objects having been calibrated.
In an alternative embodiment, after the core object has been determined and stored in the set, if the seed set corresponding to the core object has been determined, the core object may be calibrated in the set, that is, the core object is calibrated to be clustered, and further, another core object may be determined from the set, and the seed set corresponding to the core object may be determined. Optionally, under the condition that all the core objects are calibrated, the obtained seed set can be filtered, that is, the first object with the reachable distance greater than the preset distance between the first object and the core object in the seed set is removed, so that the target seed set is determined.
Optionally, based on the clustering result, monitoring the temperature and the humidity of the target machine room includes: evaluating the clustering result based on the profile coefficient to obtain an abnormal sample in the target sample set, wherein the abnormal sample is used for representing that the temperature or the humidity of the target machine room is abnormal; and displaying the abnormal sample through the target image.
The contour coefficient can be a commonly used index for evaluating the clustering result, can measure the compactness and the separation degree of the clustering result, and has a value range generally between [ -1,1], wherein the closer the value is to 1, the better the clustering result is, and the closer the value is to-1, the worse the clustering result is.
The step of calculating the contour coefficients in the present invention is as follows:
for each core object, calculate its average distance a (i) from other sample points in the corresponding seed set.
For each core object, calculating the average distance between the core object and other sample points in other seed sets, and taking the minimum value.
For each core object, its contour coefficient s (i) = (b (i) -a (i))/max (a (i), b (i)) is calculated.
And (3) taking average values of the contour coefficients of all the core objects to obtain the contour coefficient of the whole clustering result.
Furthermore, the clustering result can be evaluated according to the value range of the contour coefficient, and optionally, if the contour coefficient is close to 1, the clustering result is better, the sample points are clustered tightly and are separated from other clusters obviously, if the contour coefficient is close to 0, the clustering result is common, the compactness and the separation degree of the sample point clusters are more consistent, and if the contour coefficient is close to-1, the clustering result is worse, and the overlapping or the unobvious clustering exists between the sample points.
The above-mentioned target image may be a point cloud distribution diagram, fig. 3 is an optional temperature-humidity cloud graph according to an embodiment of the present invention, where, as shown in fig. 3, a white sphere represents a parameter value of a cold channel, a black sphere represents a parameter value of a hot channel, and a diamond represents an abnormal value, and it can be clearly seen from the graph that the temperature values and the humidity values in the cold channel and the hot channel at different moments, where, a profile parameter of the cold channel is 0.436, a normal proportion of the cold channel is 0.6, an abnormal proportion of the cold channel is 0.4, a profile parameter of the hot channel is 0.753, a whole vehicle proportion of the hot channel is 0.7, and an abnormal proportion of the hot channel is 0.3. Optionally, qualitative analysis is performed on the machine room temperature and humidity indexes through different shapes, modeling analysis of the machine room temperature and humidity at different moments is realized based on a DBSCAN optimization algorithm, and quantitative analysis of the machine room temperature and humidity is realized by using algorithm indexes such as profile coefficients, abnormal point proportions and the like of the DBSCAN clustering algorithm, so that closed-loop management of the machine room environment indexes can be realized.
Example 2
According to the embodiment of the present invention, a machine room monitoring device is further provided, where the device may execute the method for detecting the state of the transformer in the foregoing embodiment, and a specific implementation manner and a preferred application scenario are the same as those of the foregoing embodiment, and are not described herein.
Fig. 4 is a schematic diagram of a machine room monitoring device according to an embodiment of the present invention, as shown in fig. 4, the device includes:
the obtaining module 402 is configured to obtain a target sample set by obtaining target parameters of the target machine room in a preset time period, where the target parameters include a temperature parameter and a humidity parameter.
And the processing module 404 is used for carrying out clustering processing on the target sample set to obtain a clustering result.
And the monitoring module 406 is configured to monitor the temperature and the humidity of the target machine room based on the clustering result.
Optionally, the processing module 404 includes: the determining unit is used for traversing the target sample set to determine a core object, wherein the core object is a sample meeting preset conditions in the target sample set; the sorting unit is used for sorting the first samples based on the distance between the core object and the first samples to obtain seed sets corresponding to the core object, wherein the first samples are other samples except the core object in the target sample set; and the screening unit is used for screening samples in the seed set based on the preset distance to obtain a clustering result.
Optionally, the determining unit includes: a first determining subunit, configured to determine a target distance between a target sample in the target sample set and a second sample, where the target sample is any one sample in the target sample set, and the second sample is another sample in the target sample set except the target sample; the second determining subunit is used for determining the second sample as a clinical domain sample of the target sample in response to the target distance being smaller than or equal to the preset distance; and the third determining subunit is used for determining that the target sample is a core object in response to the number of the clinical domain samples being greater than or equal to the number threshold.
Optionally, the screening unit includes: the rejecting subunit is used for rejecting the first sample from the seed set to obtain a target seed set corresponding to the core object in response to the fact that the distance between the core object and the first sample is greater than the preset distance; and the fourth determining subunit is used for determining the target seed subset corresponding to the core object as a clustering result.
Optionally, the acquiring module 402 includes: the construction unit is used for constructing a target coordinate system, wherein the target coordinate system is used for representing the mapping relation between different temperatures and different humidities in a target machine room in a preset time period; the mapping unit is used for mapping the temperature parameter and the humidity parameter which are acquired at the same time in the target parameters to a target coordinate system to obtain samples at the same time; and the summarizing unit is used for summarizing samples at different moments in a preset time period to obtain a target sample set.
Optionally, the apparatus further comprises: the calibration module is used for calibrating the core object in response to the obtained seed set corresponding to the core object; and the screening module is used for screening samples in the seed set based on the preset distance in response to the fact that the core objects are calibrated.
Optionally, the monitoring module 406 includes: the evaluation unit is used for evaluating the clustering result based on the contour coefficient to obtain an abnormal sample in the target sample set, wherein the abnormal sample is used for representing the abnormal occurrence of the temperature or the humidity of the target machine room; and the display unit is used for displaying the abnormal sample through the target image.
Example 3
According to an embodiment of the present invention, there is also provided an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of the above.
Example 4
There is also provided, in accordance with an embodiment of the present invention, a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method of any one of the above.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (10)
1. A machine room monitoring method, comprising:
obtaining target parameters of a target machine room in a preset time period to obtain a target sample set, wherein the target parameters comprise temperature parameters and humidity parameters;
clustering the target sample set to obtain a clustering result;
and monitoring the temperature and the humidity of the target machine room based on the clustering result.
2. The method of claim 1, wherein clustering the target sample set to obtain a clustered result comprises:
traversing the target sample set to determine a core object, wherein the core object is a sample meeting a preset condition in the target sample set;
sorting the first samples based on the distance between the core object and the first samples to obtain seed sets corresponding to the core object, wherein the first samples are other samples except the core object in the target sample set;
and screening samples in the seed set based on a preset distance to obtain the clustering result.
3. The method of claim 2, wherein traversing the set of target samples, determining a core object, comprises:
determining a target distance between a target sample in the target sample set and a second sample, wherein the target sample is any sample in the target sample set, and the second sample is other samples in the target sample set except the target sample;
determining that the second sample is a clinical domain sample of the target sample in response to the target distance being less than or equal to the preset distance;
and determining that the target sample is the core object in response to the number of critical domain samples being greater than or equal to a number threshold.
4. The method of claim 2, wherein screening the samples in the seed set based on a preset distance to obtain the clustering result comprises:
in response to the distance between the core object and the first sample being greater than a preset distance, eliminating the first sample from the seed set to obtain a target seed set corresponding to the core object;
and determining a target seed subset corresponding to the core object as the clustering result.
5. The method of claim 1, wherein obtaining the target parameters of the target machine room within the preset time period to obtain the target sample set comprises:
constructing a target coordinate system, wherein the target coordinate system is used for representing the mapping relation between different temperatures and different humidities in the target machine room in a preset time period;
mapping temperature parameters and humidity parameters acquired at the same moment in the target parameters to the target coordinate system to obtain samples at the same moment;
summarizing samples at different moments in the preset time period to obtain the target sample set.
6. The method of claim 2, wherein the method further comprises:
calibrating the core object in response to the obtained seed set corresponding to the core object;
and responding to the core objects being calibrated, and screening samples in the seed set based on the preset distance.
7. The method of claim 1, wherein monitoring the temperature and humidity of the target machine room based on the clustering result comprises:
evaluating the clustering result based on a contour coefficient to obtain an abnormal sample in the target sample set, wherein the abnormal sample is used for representing that the temperature or the humidity of the target machine room is abnormal;
and displaying the abnormal sample through the target image.
8. A machine room monitoring device, comprising:
the acquisition module is used for acquiring target parameters of the target machine room in a preset time period to obtain a target sample set, wherein the target parameters comprise temperature parameters and humidity parameters;
the processing module is used for carrying out clustering processing on the target sample set to obtain a clustering result;
and the monitoring module is used for monitoring the temperature and the humidity of the target machine room based on the clustering result.
9. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.
10. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311295756.9A CN117251751A (en) | 2023-10-08 | 2023-10-08 | Machine room monitoring method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311295756.9A CN117251751A (en) | 2023-10-08 | 2023-10-08 | Machine room monitoring method and device, electronic equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117251751A true CN117251751A (en) | 2023-12-19 |
Family
ID=89132800
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311295756.9A Pending CN117251751A (en) | 2023-10-08 | 2023-10-08 | Machine room monitoring method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117251751A (en) |
-
2023
- 2023-10-08 CN CN202311295756.9A patent/CN117251751A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112598343B (en) | Risk inspection method, device, equipment and storage medium for data center | |
| CN113344134B (en) | Low-voltage distribution monitoring terminal data acquisition abnormality detection method and system | |
| EP1958034B1 (en) | Use of sequential clustering for instance selection in machine condition monitoring | |
| CN116633816A (en) | Media display terminal safety supervision early warning system based on enterprise digitization | |
| CN111782484B (en) | Anomaly detection method and device | |
| CN109919135A (en) | Behavioral value method, apparatus based on deep learning | |
| CN109063885A (en) | A kind of substation's exception metric data prediction technique | |
| CN110830467A (en) | Network suspicious asset identification method based on fuzzy prediction | |
| CN111813644B (en) | Evaluation method and device for system performance, electronic equipment and computer readable medium | |
| CN107276779B (en) | Monitoring method, system and equipment | |
| CN113806171A (en) | Server health assessment method, system, equipment and medium | |
| CN110717551A (en) | Training method and device of flow identification model and electronic equipment | |
| CN115372816A (en) | Power distribution switchgear operation fault prediction system and method based on data analysis | |
| CN116684878A (en) | 5G information transmission data safety monitoring system | |
| KR102158100B1 (en) | Auto monitoring method and apparatus by using anomaly detection | |
| CN117406027A (en) | Distribution network fault distance measurement method and system | |
| CN116986246A (en) | Intelligent inspection system and method for coal conveying belt | |
| CN116914917A (en) | Big data-based monitoring and management system for operation state of power distribution cabinet | |
| CN112202630A (en) | Network quality abnormity detection method and device based on unsupervised model | |
| CN117251751A (en) | Machine room monitoring method and device, electronic equipment and storage medium | |
| CN114338351B (en) | Network anomaly root cause determination method and device, computer equipment and storage medium | |
| CN112732773B (en) | Method and system for checking uniqueness of relay protection defect data | |
| CN115314424A (en) | Method and device for rapidly detecting network signals | |
| CN113806495B (en) | Outlier machine detection method and device | |
| CN113992496B (en) | Abnormal alarm method and device based on quartile algorithm and computing equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |