CN116559713A - Intelligent monitoring method and device for power supply of communication base station - Google Patents
Intelligent monitoring method and device for power supply of communication base station Download PDFInfo
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- CN116559713A CN116559713A CN202310389559.7A CN202310389559A CN116559713A CN 116559713 A CN116559713 A CN 116559713A CN 202310389559 A CN202310389559 A CN 202310389559A CN 116559713 A CN116559713 A CN 116559713A
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- 238000004891 communication Methods 0.000 title claims abstract description 27
- 238000012544 monitoring process Methods 0.000 title claims abstract description 24
- 238000000034 method Methods 0.000 title claims abstract description 17
- 230000002159 abnormal effect Effects 0.000 claims abstract description 42
- 238000012806 monitoring device Methods 0.000 claims abstract description 5
- 238000003062 neural network model Methods 0.000 claims description 20
- 230000005856 abnormality Effects 0.000 claims description 18
- 238000012549 training Methods 0.000 claims description 10
- 238000013528 artificial neural network Methods 0.000 claims description 8
- 238000012423 maintenance Methods 0.000 description 5
- 230000003247 decreasing effect Effects 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 238000004080 punching Methods 0.000 description 3
- 239000003990 capacitor Substances 0.000 description 2
- 230000007423 decrease Effects 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
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Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/40—Testing power supplies
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
- G01J5/0096—Radiation pyrometry, e.g. infrared or optical thermometry for measuring wires, electrical contacts or electronic systems
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/003—Environmental or reliability tests
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/40—Testing power supplies
- G01R31/42—AC power supplies
Abstract
The invention discloses an intelligent monitoring method for a communication base station power supply, which comprises the following steps: acquiring an infrared image in a power supply box, wherein the infrared image comprises temperature values of all pixel points; obtaining a visible light image in the power supply box; and determining the temperature value of each component in the power box according to the infrared image and the visible light image, and judging whether the power supply is abnormal according to the temperature value of each component. The invention also provides an intelligent monitoring device for the power supply of the communication base station. The invention can effectively and comprehensively monitor the power supply of the base station.
Description
Technical Field
The invention relates to the technical field related to communication base stations. More particularly, the invention relates to an intelligent monitoring method and device for a power supply of a communication base station.
Background
The number of 5G base stations has increased dramatically with the development of 5G communications, now reaching millions. So many base stations bring about the test for the monitoring and maintenance of the base stations. The 5G base station power supply is used for supplying power to communication equipment or other equipment, and is particularly important for monitoring and maintaining the 5G base station power supply. The 5G base station power supply is arranged in the power supply box, equipment in the power supply box is numerous, the heating value is large, various sensors are usually arranged in the power supply box to monitor at present, but the sensing range of the sensors is limited, and the monitoring effect is poor. Therefore, it is necessary to design a technical solution that can overcome the above-mentioned drawbacks to a certain extent.
Disclosure of Invention
The invention aims to provide an intelligent monitoring method and device for a communication base station power supply, which can effectively and comprehensively monitor the base station power supply.
To achieve these objects and other advantages and in accordance with the purpose of the invention, as embodied and broadly described herein, there is provided a method for intelligently monitoring a power supply of a communication base station, comprising: acquiring an infrared image in a power supply box, wherein the infrared image comprises temperature values of all pixel points; obtaining a visible light image in the power supply box; and determining the temperature value of each component in the power box according to the infrared image and the visible light image, and judging whether the power supply is abnormal according to the temperature value of each component.
Further, object recognition is carried out on the visible light image, components corresponding to the objects are determined, pixel points contained in the components are determined, the temperature value of the pixel points contained in the components is determined according to the temperature value of the pixel points in the infrared image, and the temperature value of the components is determined according to the temperature value of the pixel points contained in the components.
Further, feature points of the visible light image are identified, a reference point is selected according to the feature points, the infrared image and the visible light image are corresponding to each other according to the reference point, and pixel points contained in each component are determined.
Further, the temperature value of each component is a weighted average of the temperature values of the pixel points included in each component.
Further, the weight of the pixel points is sequentially reduced from the center to the periphery of each component.
Further, obtaining temperature values of all parts at all historical time points; constructing an expert group, and scoring the power supply of each historical time point to obtain a power supply abnormal value; training by using the temperature value and the power supply abnormal value of each component at each historical time point to obtain a neural network model; acquiring the temperature value of each component at the current time point, inputting a neural network model, and outputting a power supply abnormality predicted value; and comparing the power supply abnormality predicted value with a preset threshold value, and judging that the power supply is abnormal if the error is larger than a preset range.
Further, the method further comprises the following steps: and forming the temperature values of all the components at the current time point into a temperature sequence, calculating the characteristic values of the temperature sequence, re-acquiring if the characteristic values do not accord with a preset rule, and inputting the characteristic values into a neural network prediction model if the characteristic values accord with the preset rule, wherein the characteristic values comprise a mean value, a peak value and a variance.
According to another aspect of the present invention, there is also provided an intelligent monitoring apparatus for a power supply of a communication base station, including: the infrared camera is used for acquiring an infrared image in the power supply box, and the infrared image comprises temperature values of all pixel points; the camera is used for acquiring visible light images in the power supply box; and the processor is used for determining the temperature value of each component in the power supply box according to the infrared image and the visible light image, and judging whether the power supply is abnormal according to the temperature value of each component.
Further, object recognition is carried out on the visible light image, components corresponding to all objects are determined, pixel points contained in all the components are determined, the temperature value of the pixel point contained in each component is determined according to the temperature value of each pixel point in the infrared image, and the temperature value of each component is determined according to the temperature value of the pixel point contained in each component; identifying characteristic points of the visible light image, selecting reference points according to the characteristic points, corresponding the infrared image and the visible light image according to the reference points, and determining pixel points contained in each component; the temperature value of each component is a weighted average value of the temperature values of the pixel points contained in each component, and the weight values of the pixel points are sequentially reduced from the center to the periphery of each component.
Further, the processor inputs the temperature value of each component at the current time point into a neural network model and outputs a power supply abnormality predicted value; comparing the power supply abnormality predicted value with a preset threshold value, and judging that the power supply is abnormal if the error is larger than a preset range; training by using the temperature values and the abnormal power supply values of all the components at all the historical time points to obtain a neural network model, wherein the abnormal power supply values are determined by scoring the power supply at all the historical time points by an expert group; and forming the temperature values of all the components at the current time point into a temperature sequence, calculating the characteristic values of the temperature sequence, re-acquiring if the characteristic values do not accord with a preset rule, and inputting the characteristic values into a neural network prediction model if the characteristic values accord with the preset rule, wherein the characteristic values comprise a mean value, a peak value and a variance.
The invention at least comprises the following beneficial effects:
the invention determines the temperature value of each component in the power supply box according to the infrared image and the visible light image, then judges whether the power supply is abnormal according to the temperature value of each component, does not need to arrange a large number of temperature sensors in the power supply box, has comprehensive monitoring, good effect and low cost, and provides reference for maintenance management of the base station.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention.
Drawings
FIG. 1 is a flow chart of one embodiment of the present application.
Detailed Description
The present invention is described in further detail below with reference to the drawings to enable those skilled in the art to practice the invention by referring to the description.
It will be understood that terms, such as "having," "including," and "comprising," as used herein, do not preclude the presence or addition of one or more other elements or groups thereof.
As shown in fig. 1, an embodiment of the present application provides a method for intelligently monitoring a power supply of a communication base station, including:
s1: acquiring an infrared image in a power supply box, wherein the infrared image comprises temperature values of all pixel points; the power supply box comprises a switching power supply, a storage battery, a rectifying module, a communication module, an air switch, a switching tube, a capacitor, a resistor, a heat radiating unit and the like; the infrared camera can be used for acquiring infrared images in the power supply box, and can be arranged on the inner side of the power supply box door or on the outer side, and the infrared camera faces the inner side of the power supply box through punching holes in the box door; one or more infrared cameras can be arranged for acquiring the comprehensive infrared images in the power supply box;
s2: obtaining a visible light image in the power supply box; the camera can be arranged on the inner side of the power box door or on the outer side of the power box door, and the camera faces the inner side of the power box through punching holes on the box door; one or more cameras can be arranged for acquiring the comprehensive visible light images in the power supply box;
s3: determining the temperature value of each component in the power box according to the infrared image and the visible light image, and judging whether the power supply is abnormal according to the temperature value of each component; according to the corresponding relation of the pixel points in the infrared image and the visible light image, the temperature value of each part is obtained, whether the power supply is abnormal or not is judged according to the size and the distribution of the temperature values, for example, the power supply is abnormal when the temperature value is maximum, minimum or the distribution is abnormal, and maintenance personnel is reminded of processing;
it can be seen that, in this embodiment, a large number of temperature sensors are not required to be disposed in the power supply box to perform temperature detection, the structure is simple, the cost is low, and in the prior art, the temperature sensors are generally disposed only at the air outlet, and cannot be close to each component, and cannot detect the surface temperature of each component, so that the embodiment overcomes the defect, and the monitoring is comprehensive, the result is accurate, and references are provided for maintenance management of the base station; through tests, compared with the prior art that the temperature sensor is arranged at the air outlet, the judging accuracy of the power supply abnormality is improved by 20%.
In another embodiment, the visible light image is subject to identification, the components corresponding to the objects are determined, the pixel points contained in the components are determined, the temperature value of the pixel point contained in the components is determined according to the temperature value of the pixel point contained in the infrared image, and the temperature value of the components is determined according to the temperature value of the pixel point contained in the components; the feasible implementation mode is that components in the visible light image are identified and determined according to the characteristics of a switching power supply, a storage battery, a rectifying module, a communication module, an air switch, a switching tube, a capacitor, a resistor, a radiating unit, a color, a contour, a texture and the like, and the temperature value of each component corresponding to a pixel point is determined by combining the temperature value of the corresponding position of the infrared image, so that the temperature value of each component is determined.
In another embodiment, identifying characteristic points of the visible light image, selecting reference points according to the characteristic points, corresponding the infrared image and the visible light image according to the reference points, and determining pixel points contained in each component; one possible implementation manner is to use points at four corners or edges inside the power supply box as feature points, select a plurality of feature points as reference points, and use the reference points to correspond the visible light image and the infrared image, so as to determine which pixel points belong to which component.
In another embodiment, the temperature value of each component is a weighted average of the temperature values of the pixels included in each component, that is, the temperature values of the pixels are weighted differently and added.
In another embodiment, the weights of the pixel points are sequentially reduced from the center to the periphery of each component; one possible implementation manner is to determine the center of each component according to the contour of each component, and the weight decreases sequentially from near to far around the pixel point of the center, where the decreasing speed and the distance of the weight may be nonlinear logarithmic functions, i.e. the decreasing speed is fast and slow.
In another embodiment, temperature values of components at historical points in time are obtained; constructing an expert group, and scoring the power supply of each historical time point to obtain a power supply abnormal value; training by using the temperature value and the power supply abnormal value of each component at each historical time point to obtain a neural network model; acquiring the temperature value of each component at the current time point, inputting a neural network model, and outputting a power supply abnormality predicted value; comparing the power supply abnormality predicted value with a preset threshold value, and judging that the power supply is abnormal if the error is larger than a preset range;
in the above embodiment, the temperature values of the respective components at the historic time points are recorded, and the intervals of the respective time points may be selected from several hours, one day, and the like; the expert group can comprise 3-5 experts, each expert scores the power supply according to the temperature condition and other parameters of each part, and the scores of each expert are averaged to obtain abnormal values of the power supply; training the temperature value and the power supply abnormal value of each corresponding component, for example, inputting a BP neural network for training to obtain a neural network model; acquiring temperature values of all components at the current time point in real time, and inputting the temperature values into a neural network model to obtain a power supply abnormality predicted value; judging whether the power supply is abnormal or not according to the abnormal value of the power supply and the error of a preset threshold value, and judging that the abnormality occurs if the error exceeds 20%; the predetermined threshold may be set to a plurality according to the air temperature to accommodate different seasons.
In another embodiment, the method further comprises: the temperature values of all the components at the current time point are formed into a temperature sequence, the characteristic value of the temperature sequence is calculated, if the characteristic value does not accord with a preset rule, the characteristic value is obtained again, if the characteristic value accords with the preset rule, a neural network prediction model is input, and the characteristic value comprises a mean value, a peak value and a variance; and (3) forming a temperature sequence by the temperature values of all the components, performing characteristic analysis on the temperature sequence, calculating the mean value, the peak value, the variance and the like, and when the temperature sequence is obviously abnormal, not inputting the neural network model, and re-acquiring the temperature value so as to avoid inputting error data into the neural network model and generating an error monitoring result.
The embodiment of the application also provides an intelligent monitoring device for the power supply of the communication base station, which comprises: the infrared camera is used for acquiring an infrared image in the power supply box, and the infrared image comprises temperature values of all pixel points; the camera is used for acquiring visible light images in the power supply box; the processor is used for determining the temperature value of each component in the power supply box according to the infrared image and the visible light image, and judging whether the power supply is abnormal according to the temperature value of each component;
in the above embodiment, the infrared camera is used to obtain the infrared image in the power box, and the infrared camera may be disposed inside the power box door or outside the power box door, and by punching holes in the door, the infrared camera faces the inside of the power box; one or more infrared cameras can be arranged for acquiring the comprehensive infrared images in the power supply box; the camera can be arranged on the inner side of the power box door or on the outer side, and is punched on the box door to face the inside of the power box; one or more cameras can be arranged for acquiring the comprehensive visible light images in the power supply box; obtaining temperature values of all components according to the corresponding relation of pixel points in the infrared image and the visible light image, judging whether the power supply is abnormal or not according to the size and the distribution of the temperature values, and judging whether the power supply is abnormal if the temperature values are maximum, minimum or the distribution is abnormal, wherein the power supply abnormality can be judged; the processor can be arranged in a remote server or a cloud server, and an infrared image and a visible light image are obtained through the communication module and are processed; it can be seen that the embodiment does not need to set a large number of temperature sensors in the power supply box, has low cost, overcomes the defect that the temperature sensors cannot detect the temperature of each component respectively, has comprehensive monitoring and good effect, and provides references for maintenance and management of the base station.
In another embodiment, the visible light image is subject to identification, the components corresponding to the objects are determined, the pixel points contained in the components are determined, the temperature value of the pixel point contained in the components is determined according to the temperature value of the pixel point contained in the infrared image, and the temperature value of the components is determined according to the temperature value of the pixel point contained in the components; identifying characteristic points of the visible light image, selecting reference points according to the characteristic points, corresponding the infrared image and the visible light image according to the reference points, and determining pixel points contained in each component; the temperature value of each component is a weighted average value of the temperature values of the pixel points contained in each component, and the weight values of the pixel points are sequentially reduced from the center to the periphery of each component;
in the above embodiment, the components in the visible light image are identified and determined according to the characteristics of the color, the contour, the texture and the like of the components, and the temperature value of the pixel point corresponding to each component is determined in combination with the temperature value of the position corresponding to the infrared image, so that the temperature value of each component is determined; taking points at four corners or edges in the power supply box as characteristic points, selecting a plurality of characteristic points as reference points, and corresponding visible light images and infrared images by the reference points so as to determine which pixel points belong to which component; one possible implementation manner is to determine the center of each component according to the contour of each component, and the weight decreases sequentially from near to far around the pixel point of the center, where the decreasing speed of the weight may be a nonlinear logarithmic function, i.e. the decreasing speed is fast and slow.
In another embodiment, the processor inputs the temperature values of the components at the current time point into a neural network model and outputs a power supply abnormality predicted value; comparing the power supply abnormality predicted value with a preset threshold value, and judging that the power supply is abnormal if the error is larger than a preset range; training by using the temperature values and the abnormal power supply values of all the components at all the historical time points to obtain a neural network model, wherein the abnormal power supply values are determined by scoring the power supply at all the historical time points by an expert group; the temperature values of all the components at the current time point are formed into a temperature sequence, the characteristic value of the temperature sequence is calculated, if the characteristic value does not accord with a preset rule, the characteristic value is obtained again, if the characteristic value accords with the preset rule, a neural network prediction model is input, and the characteristic value comprises a mean value, a peak value and a variance;
in the above embodiment, the temperature values of the respective components at the historic time points are recorded, and the intervals of the respective time points may be selected from several hours, one day, and the like; 3-5 expert groups can be selected, each expert performs scoring on the power supply according to the temperature condition and other parameters of each component, and the scoring of each expert is averaged to obtain abnormal values of the power supply; training the temperature value and the power supply abnormal value of each corresponding component, for example, inputting a BP neural network for training to obtain a neural network model; acquiring temperature values of all components at the current time point in real time, and inputting the temperature values into a neural network model to obtain a power supply abnormality predicted value; judging whether the power supply is abnormal or not according to the abnormal value of the power supply and the error of a preset threshold value, and judging that the abnormality occurs if the error exceeds 20%; the preset threshold value can be set into a plurality of values according to the air temperature so as to adapt to different seasons; meanwhile, the temperature values of all the components are formed into a temperature sequence, the temperature sequence is subjected to characteristic analysis, the mean value, the peak value, the variance and the like are calculated, when the temperature sequence is obviously abnormal, a neural network model is not input, and acquisition is repeated, so that error data are not input into the neural network model, and an error monitoring result is generated.
The number of equipment and the scale of processing described herein are intended to simplify the description of the present invention. Applications, modifications and variations of the method and apparatus for intelligent monitoring of a communications base station power supply of the present invention will be apparent to those skilled in the art.
Although embodiments of the present invention have been disclosed above, it is not limited to the details and embodiments shown and described, it is well suited to various fields of use for which the invention would be readily apparent to those skilled in the art, and accordingly, the invention is not limited to the specific details and illustrations shown and described herein, without departing from the general concepts defined in the claims and their equivalents.
Claims (10)
1. The intelligent monitoring method for the power supply of the communication base station is characterized by comprising the following steps:
acquiring an infrared image in a power supply box, wherein the infrared image comprises temperature values of all pixel points;
obtaining a visible light image in the power supply box;
and determining the temperature value of each component in the power box according to the infrared image and the visible light image, and judging whether the power supply is abnormal according to the temperature value of each component.
2. The intelligent monitoring method of the communication base station power supply according to claim 1, wherein the visible light image is subject to identification, the component corresponding to each subject is determined, the pixel point contained in each component is determined, the temperature value of the pixel point contained in each component is determined according to the temperature value of each pixel point in the infrared image, and the temperature value of each component is determined according to the temperature value of the pixel point contained in each component.
3. The intelligent monitoring method of the communication base station power supply according to claim 2, wherein the characteristic points of the visible light image are identified, a reference point is selected according to the characteristic points, the infrared image and the visible light image are corresponding according to the reference point, and the pixel points included in each component are determined.
4. The intelligent monitoring method for power supply of communication base station according to claim 2, wherein the temperature value of each component is a weighted average of the temperature values of the pixels included in each component.
5. The intelligent monitoring method of communication base station power supply according to claim 4, wherein the weight of the pixel points is sequentially reduced from the center to the periphery of each component.
6. The intelligent monitoring method of the communication base station power supply according to claim 2, wherein the temperature values of the components at each historical time point are obtained;
constructing an expert group, and scoring the power supply of each historical time point to obtain a power supply abnormal value;
training by using the temperature value and the power supply abnormal value of each component at each historical time point to obtain a neural network model;
acquiring the temperature value of each component at the current time point, inputting a neural network model, and outputting a power supply abnormality predicted value;
and comparing the power supply abnormality predicted value with a preset threshold value, and judging that the power supply is abnormal if the error is larger than a preset range.
7. The intelligent monitoring method of a communication base station power supply according to claim 6, further comprising:
and forming the temperature values of all the components at the current time point into a temperature sequence, calculating the characteristic values of the temperature sequence, re-acquiring if the characteristic values do not accord with a preset rule, and inputting the characteristic values into a neural network prediction model if the characteristic values accord with the preset rule, wherein the characteristic values comprise a mean value, a peak value and a variance.
8. Communication base station power intelligent monitoring device, its characterized in that includes:
the infrared camera is used for acquiring an infrared image in the power supply box, and the infrared image comprises temperature values of all pixel points;
the camera is used for acquiring visible light images in the power supply box;
and the processor is used for determining the temperature value of each component in the power supply box according to the infrared image and the visible light image, and judging whether the power supply is abnormal according to the temperature value of each component.
9. The intelligent monitoring device for the communication base station power supply according to claim 8, wherein the visible light image is subject to recognition, the components corresponding to each subject are determined, the pixel points contained in each component are determined, the temperature value of the pixel point contained in each component is determined according to the temperature value of each pixel point in the infrared image, and the temperature value of each component is determined according to the temperature value of the pixel point contained in each component; identifying characteristic points of the visible light image, selecting reference points according to the characteristic points, corresponding the infrared image and the visible light image according to the reference points, and determining pixel points contained in each component; the temperature value of each component is a weighted average value of the temperature values of the pixel points contained in each component, and the weight values of the pixel points are sequentially reduced from the center to the periphery of each component.
10. The intelligent monitoring device for power supply of communication base station according to claim 9, wherein the processor inputs the temperature value of each component at the current time point into a neural network model and outputs the power supply abnormality predicted value;
comparing the power supply abnormality predicted value with a preset threshold value, and judging that the power supply is abnormal if the error is larger than a preset range;
training by using the temperature values and the abnormal power supply values of all the components at all the historical time points to obtain a neural network model, wherein the abnormal power supply values are determined by scoring the power supply at all the historical time points by an expert group; and forming the temperature values of all the components at the current time point into a temperature sequence, calculating the characteristic values of the temperature sequence, re-acquiring if the characteristic values do not accord with a preset rule, and inputting the characteristic values into a neural network prediction model if the characteristic values accord with the preset rule, wherein the characteristic values comprise a mean value, a peak value and a variance.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116739830A (en) * | 2023-08-15 | 2023-09-12 | 深圳市倍联德实业有限公司 | Detection method, system, terminal and storage medium for workstation power supply |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116739830A (en) * | 2023-08-15 | 2023-09-12 | 深圳市倍联德实业有限公司 | Detection method, system, terminal and storage medium for workstation power supply |
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