CN117650447A - Intelligent real-time monitoring method based on automatic control of air conditioning unit of distribution box - Google Patents
Intelligent real-time monitoring method based on automatic control of air conditioning unit of distribution box Download PDFInfo
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Abstract
The invention relates to the technical field of monitoring and controlling of distribution boxes, in particular to an intelligent real-time monitoring method based on automatic control of an air conditioning unit of a distribution box, which comprises the following steps: environmental awareness and data acquisition; data transmission and preliminary analysis, namely transmitting monitoring data of environmental awareness and data acquisition to a central processing unit, and performing preliminary analysis; the intelligent decision and regulation are carried out, the central processing unit comprehensively analyzes the data, and whether the running state of the air conditioner needs to be adjusted is judged; automatic adjustment and real-time feedback; the method comprises the steps of carrying out acoustic monitoring and fault prediction, integrating a high-sensitivity acoustic sensor, monitoring an acoustic signal in the distribution box in real time, identifying early signs of mechanical component wear and circuit abnormality problems by analyzing a specific acoustic mode, predicting potential mechanical and circuit problems and positioning fault sources by combining acoustic analysis and thermal image data. The invention not only improves the accuracy of fault detection, but also enables maintenance personnel to quickly and directly respond to specific problems.
Description
Technical Field
The invention relates to the technical field of monitoring and controlling of distribution boxes, in particular to an intelligent real-time monitoring method based on automatic control of an air conditioning unit of a distribution box.
Background
In modern power systems, distribution boxes are critical electrical devices, the stable and safe operation of which is critical to the reliability of the entire power grid. Conventional block terminal monitoring methods typically rely on basic temperature monitoring or periodic manual checks, which cannot respond in real time to changes in equipment status, nor can they predict potential fault problems. Especially in complex or harsh environments, such passive monitoring methods are highly prone to ignore some of the incipient signs of failure, thereby increasing the risk of system failure and shutdown.
The existing automatic monitoring technology improves the frequency and accuracy of data collection, but most of the automatic monitoring technology still focuses on data analysis in a single dimension, for example, the automatic monitoring technology only depends on temperature or vibration data, and comprehensive analysis and intelligent processing of various monitoring data are not realized. This results in limitations of the monitoring system in terms of fault prediction and timely response. Meanwhile, an automatic regulation and control system in the prior art often lacks flexibility and adaptability, and cannot effectively carry out intelligent decision and adjustment according to real-time monitoring data.
Therefore, there is an urgent need to develop a more efficient and intelligent monitoring and control method for a distribution box, which can realize real-time monitoring of multi-dimensional data, comprehensively analyze various monitoring data, discover and predict potential faults in time, and intelligently adjust system parameters to optimize operation states and improve reliability of equipment.
Disclosure of Invention
Based on the above purpose, the invention provides an intelligent real-time monitoring method based on automatic control of an air conditioner unit of a distribution box.
An intelligent real-time monitoring method based on automatic control of an air conditioner unit of a distribution box comprises the following steps:
s1: environmental perception and data acquisition, wherein a high-precision temperature sensor is combined with a thermal imager technology, so that the temperature and the heat distribution in the power distribution box are monitored in real time, and the thermal imager technology is used for detecting hot spots in the power distribution box and identifying overheated parts or potential circuit problems;
s2: data transmission and preliminary analysis, namely transmitting monitoring data of environmental awareness and data acquisition to a central processing unit, performing preliminary analysis, and identifying an abnormal heat distribution mode and analyzing equipment faults or potential safety hazards by the central processing unit in combination with temperature data and thermal image data;
s3: the intelligent decision and regulation, the central processing unit comprehensively analyzes the data, judges whether the running state of the air conditioner needs to be regulated, evaluates the health condition of equipment, automatically adjusts the parameters of the air conditioner to reduce local overheating if abnormal heat distribution is found, and reminds maintenance personnel to check;
s4: automatically adjusting and feeding back in real time, automatically adjusting the output of the air conditioner according to the decision of the central processing unit, taking preventive measures on potential safety risks, and feeding back monitoring data, adjusting actions and equipment states to a user side in real time through a wireless network;
s5: the method comprises the following steps of acoustic monitoring and fault prediction, integrating a high-sensitivity acoustic sensor, monitoring an acoustic signal in the distribution box in real time, identifying early signs of mechanical component wear and circuit abnormality problems by analyzing a specific acoustic mode, predicting potential mechanical and circuit problems in advance by combining acoustic analysis and thermal image data, and positioning fault sources.
Further, the S1 specifically includes:
the high-precision temperature sensor is deployed at a key position of the distribution box and is used for continuously monitoring and recording temperature changes in the box, so that the accuracy and the continuity of temperature data are ensured, and the key position comprises a position close to an inlet cable and a position close to an outlet cable; a circuit breaker and a protection device; a transformer and a capacitor; dense connection areas; the proximity of the controller and other sensitive electronics;
the thermal imager technology is applied to the inside of the distribution box, and captures heat distribution images of various components in the inside of the distribution box in a non-contact mode, wherein the heat distribution images reveal hot spots hidden in the inside of the distribution box;
by analyzing the temperature gradients and hot spot locations in the thermographic image, overheated components that lead to equipment damage or dysfunction are identified.
Further, the step S2 specifically includes:
the central processing unit receives data from a high-precision temperature sensor and a thermal imager, wherein the temperature sensor provides real-time temperature data of each key position in the distribution box, and the thermal imager provides a thermal distribution image in the distribution box;
the central processing unit performs time sequence analysis on the received temperature data, identifies abnormal fluctuation or continuous rising trend of the temperature, and indicates that overheat or abnormal equipment operation exists;
the central processing unit performs image processing and pattern recognition on the thermal image data, and comprises the steps of recognizing an abnormal high-temperature area in a thermal distribution image, comparing heat map changes at different time points and recognizing the specific position and shape of a hot spot;
and combining the temperature data and the thermal image analysis result, and identifying potential equipment faults or potential safety hazards by a central processing unit through a machine learning algorithm or an expert system.
Further, the time series analysis specifically includes:
data preprocessing, including denoising and data smoothing, is performed on data collected from a high-precision temperature sensor, and the temperature data is smoothed by using a moving average formula:wherein, SMA t Is the moving average temperature at time point T, T i The measured temperature at time i, n is the time window length of the moving average;
abnormality detection, time series analysis is aimed at detecting abnormal values, and is considered to be abnormal if a certain temperature value exceeds 3 standard deviations of an average value using statistical method box graph analysis or standard deviation analysis: wherein T is t Is the temperature at a specific time point t, μ is the average value of the temperature data, σ is the targetThe accuracy is poor;
trend analysis, using regression analysis or other trend recognition techniques to analyze the trend of temperature data over time, a linear regression equation is expressed as: t (T) t ＝β 0 +β 1 t+∈ t ；
Wherein T is t Is the temperature at time t, beta 0 And beta 1 Is a regression coefficient, E t Is an error term.
Further, the step S3 specifically includes:
the central processing unit comprehensively analyzes the temperature data and the thermal image data according to a preset rule, wherein the preset rule is based on typical parameters of equipment operation and a known fault mode, and a threshold value is set for the temperature data to identify temperature abnormality;
the thermal image data is used for detecting hot spots in a specific area in the distribution box, and if the thermal image shows that the temperature of a certain area is obviously higher than that of surrounding areas, the thermal image data is marked as a potential fault area;
when the data analysis indicates that adjustment is needed, the central processing unit automatically adjusts parameters of the air conditioning unit, including reducing the set temperature or increasing the fan speed, to optimize the environmental conditions and prevent overheating, while assessing the health of the equipment based on the analysis results.
Further, the calculation of the intelligent decision and regulation in the step S3 is as follows:
data definition, T: temperature data acquired from a high-precision temperature sensor in real time, H: thermal profile data acquired from a thermal imager;
threshold setting, T safe : safety temperature threshold, H warn : a hot spot warning temperature difference threshold;
data analysis and judgment, judging whether T is more than T safe If yes, consider that enhanced cooling is needed, judge whether H has area exceeding H warn If yes, marking as a hot spot area;
air conditioner adjustment decision: if T > T safe Adjusting the set temperature of the air conditioner:
T set ＝max(T set -ΔT，T min )；
if there is a hot spot in H exceeding H warn Increasing the fan speed:
F speed ＝min(F speed +ΔF，F max )；
wherein DeltaT and DeltaF respectively represent the adjustment amounts of temperature and wind speed, F min And F max Respectively the minimum value of the set temperature of the air conditioner and the maximum value of the speed of the wind ;
when the real-time temperature T exceeds the set safety threshold T safe When the temperature inside the distribution box is too high, the set temperature T of the air conditioner needs to be reduced set To enhance cooling, the adjustment amount DeltaT should be set based on actual demand while ensuring that the adjusted temperature is not lower than the minimum set temperature T min The method comprises the steps of carrying out a first treatment on the surface of the If the thermal image data H shows that the temperature of a certain area exceeds the hot spot warning threshold H warn Then it is necessary to increase the fan speed by a large speed F max 。
Further, the step S5 specifically includes:
the sound signal acquisition is carried out, and a high-sensitivity acoustic sensor is used for fixing the key parts of the distribution box, wherein the key parts comprise a near-circuit breaker, a transformer and a fan;
analyzing the sound signal, wherein the collected sound signal is focused on noise level and frequency change, and the sound signal comprises decibel level and spectrum analysis for continuously monitoring the sound signal;
for noise levels, any sustained increase above a threshold may be considered abnormal by comparing the real-time data to a preset normal operating sound level baseline;
for frequency changes, detecting changes within a preset frequency range using a spectrum analyzer, abnormal frequency changes, including unusual high frequency or low frequency signals, indicative of wear of mechanical components or circuit failure;
and (3) identifying and early warning faults, combining the result of sound signal analysis, and if abnormal noise level or frequency change is found, marking the abnormal noise level or frequency change as potential faults and sending out early warning, wherein frequent high-frequency noise is used for indicating that a certain mechanical component (such as a fan bearing) is about to be out of work, and continuous low-frequency noise is used for indicating a circuit problem.
Further, the step S5 further includes an auxiliary algorithm to analyze the sound signal, specifically as follows:
root Mean Square (RMS) value of the sound signal is calculated by the following formula:
wherein x is i Representing the sound signal intensity of the i-th sample, N being the number of samples;
spectral analysis of sound signals: to converting a time-domain sound signal into the frequency domain using fourier transforms:
where x (n) is the nth sound signal sample over the time domain and F (k) is the spectral value at frequency k;
noise level anomaly detection: first, a baseline noise level N is set baseline Comparing the real-time calculated RMS value N RMS From the baseline value, if a certain threshold is exceeded, it is considered abnormal:
frequency anomaly detection: analyzing real-time spectrum F current (k) Spectrum F from baseline baseline (k) By comparing the magnitudes at specific frequencies to determine if an anomaly is present:
further, the system also comprises user interaction and remote control, the user side receives system feedback in real time, remote monitoring and manual intervention are carried out, and a user interface provides detailed data analysis, equipment state report and heat distribution images, so that a user can make more intelligent maintenance decisions.
The invention has the beneficial effects that:
according to the invention, through integrating the high-precision temperature sensor, the thermal imager technology and the acoustic sensor, comprehensive monitoring is provided for the distribution box, and the comprehensive monitoring can not only capture abnormal temperature and heat distribution in real time, but also identify the abrasion or circuit problem of mechanical parts through sound signal analysis. This multi-dimensional monitoring approach greatly enhances the early detection capability of faults, thereby making maintenance work more preventive and efficient.
According to the invention, the intelligent decision and automatic regulation mechanism of the central processing unit can timely respond to abnormal conditions in the monitoring data, the temperature, the thermal image and the acoustic data are comprehensively analyzed through the preset algorithm, the running state of the air conditioning unit can be automatically regulated by the system, the environmental condition in the distribution box is optimized, the energy efficiency of the air conditioning unit is improved, and the equipment loss caused by unstable environment is reduced.
According to the invention, by combining acoustic analysis and thermal image data, accurate fault source positioning is provided, and the fusion method not only improves the accuracy of fault detection, but also enables maintenance personnel to quickly and directly respond to specific problems, reduces equipment downtime, and improves the reliability and stability of the whole power system.
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In order to more clearly illustrate the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only of the invention and that other drawings can be obtained from them without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a monitoring control method according to an embodiment of the present invention;
fig. 2 is a schematic perspective view of a distribution box according to an embodiment of the present invention;
fig. 3 is a schematic perspective view of another side of the distribution box according to an embodiment of the present invention.
Marked in the figure as:
1. a distribution box body; 2. a thermal imager; 3. a high-precision temperature sensor; 4. an acoustic sensor; 5. an air conditioning unit.
Detailed Description
The present invention will be further described in detail with reference to specific embodiments in order to make the objects, technical solutions and advantages of the present invention more apparent.
It is to be noted that unless otherwise defined, technical or scientific terms used herein should be taken in a general sense as understood by one of ordinary skill in the art to which the present invention belongs. The terms "first," "second," and the like, as used herein, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
As shown in fig. 1, an intelligent real-time monitoring method based on automatic control of an air conditioner unit of a distribution box comprises the following steps:
s1: environmental perception and data acquisition, wherein a high-precision temperature sensor is combined with a thermal imager technology, so that the temperature and the heat distribution in the power distribution box are monitored in real time, and the thermal imager technology is used for detecting hot spots in the power distribution box and identifying overheated parts or potential circuit problems;
s2: data transmission and preliminary analysis, namely transmitting monitoring data of environmental awareness and data acquisition to a central processing unit, performing preliminary analysis, and identifying an abnormal heat distribution mode and analyzing equipment faults or potential safety hazards by the central processing unit in combination with temperature data and thermal image data;
s3: the intelligent decision and regulation, the central processing unit comprehensively analyzes the data, judges whether the running state of the air conditioner needs to be regulated, evaluates the health condition of equipment, automatically adjusts the parameters of the air conditioner to reduce local overheating if abnormal heat distribution is found, and reminds maintenance personnel to check;
s4: automatically adjusting and feeding back in real time, automatically adjusting the output (such as temperature and wind speed) of an air conditioner according to the decision of a central processing unit, taking preventive measures on potential safety risks, and feeding back monitoring data, adjusting actions and equipment states to a user side in real time through a wireless network;
s5: the method comprises the following steps of acoustic monitoring and fault prediction, integrating a high-sensitivity acoustic sensor, monitoring an acoustic signal in the distribution box in real time, identifying early signs of mechanical component wear and circuit abnormality problems by analyzing specific acoustic modes (such as noise level and frequency change), and predicting potential mechanical and circuit problems in advance and positioning fault sources by combining acoustic analysis and thermal image data;
the method integrates various sensing technologies, realizes comprehensive monitoring of the air conditioning unit of the distribution box, improves energy efficiency and equipment safety, and reduces maintenance cost through early fault detection.
S1 specifically comprises:
the high accuracy temperature sensor is disposed in the key position of block terminal for the temperature variation in the continuous monitoring and the record case ensures temperature data's accuracy and continuity, and the key position includes nearly entry and export cable department: these locations are typically the primary channels for current to enter or leave the electrical box and therefore may generate high levels of heat, and monitoring the temperature of these areas helps to timely detect overheating problems due to overload or cable failure; the circuit breaker and the protection device are near: circuit breakers and protection devices are critical safety elements in electrical distribution boxes that shut down power to protect the entire system in the event of a fault, and deployment of temperature sensors near these components can timely detect overheating conditions due to abnormal loads or equipment faults; near the transformer and capacitor: transformers and capacitors are common components in electrical distribution boxes that may generate heat during operation, and monitoring the temperature of these components may help prevent performance degradation or equipment damage due to overheating; dense connection area: dense connection areas inside the distribution box, such as wire joints and connectors, are potential hot spot areas where deployment of temperature sensors helps to detect increased heat due to poor connection or overload; the controller and other sensitive electronics are in proximity to: controllers and other electronic components are sensitive to temperature, excessive temperatures may affect their performance or cause failure, and monitoring the temperature of these areas helps to ensure that these sensitive components are operating within safe temperature ranges;
thermal imager technology is applied to the interior of the electrical box to capture in a non-contact manner a thermal distribution image of the various components inside the electrical box revealing hot spots hidden inside the electrical box, especially those areas of overheating that are not visible to the naked eye;
identifying overheating components that cause equipment damage or dysfunction by analyzing temperature gradients and hot spot locations in the thermographic image, which may be due to circuit problems, overload, or equipment aging;
by combining the temperature sensor data with the thermal imager image, the system can detect the current temperature abnormality, can predict the possible problems in the future, and provides basis for preventive maintenance.
S2 specifically comprises:
the central processing unit receives data from a high-precision temperature sensor and a thermal imager, wherein the temperature sensor provides real-time temperature data of each key position in the distribution box, and the thermal imager provides a thermal distribution image in the distribution box;
the central processing unit performs time sequence analysis on the received temperature data, identifies abnormal fluctuation or continuous rising trend of the temperature, and indicates that overheat or abnormal equipment operation exists;
the central processing unit performs image processing and pattern recognition on the thermal image data, and comprises the steps of recognizing an abnormal high-temperature area in a thermal distribution image, comparing heat map changes at different time points and recognizing the specific position and shape of a hot spot;
and combining the temperature data and the thermal image analysis result, and identifying potential equipment faults or potential safety hazards by a central processing unit through a machine learning algorithm or an expert system.
The time series analysis specifically includes:
data preprocessing, including denoising and data smoothing, is performed on data collected from a high-precision temperature sensor, and the temperature data is smoothed by using a moving average formula:wherein, SMA t Is the moving average temperature at time point T, T i The measured temperature at time i, n is the time window length of the moving average;
abnormality detection, time series analysis is aimed at detecting abnormal values, and is considered to be abnormal if a certain temperature value exceeds 3 standard deviations of an average value using statistical method box graph analysis or standard deviation analysis: wherein T is t Is the temperature at a specific time point t, μ is the average value of the temperature data, σ is the standard deviation;
trend analysis, using regression analysis or other trend recognition techniques to analyze the trend of temperature data over time, a linear regression equation is expressed as: t (T) t ＝β 0 +β 1 t+∈ t ；
Wherein T is t Is the temperature at time t, beta 0 And beta 1 Is a regression coefficient, E t Is an error term.
S3 specifically comprises:
the central processing unit comprehensively analyzes the temperature data and the thermal image data according to a preset rule, wherein the preset rule is based on typical parameters of equipment operation and a known fault mode, and a threshold value is set for the temperature data to identify temperature abnormality;
the thermal image data is used for detecting hot spots in a specific area in the distribution box, and if the thermal image shows that the temperature of a certain area is obviously higher than that of surrounding areas, the thermal image data is marked as a potential fault area;
when the data analysis indicates that adjustment is needed, the central processing unit automatically adjusts parameters of the air conditioning unit, including reducing the set temperature or increasing the fan speed, to optimize the environmental conditions and prevent overheating, while assessing the health of the equipment based on the analysis results.
The intelligent decision and regulation in S3 are calculated as follows:
data definition, T: temperature data acquired from a high-precision temperature sensor in real time, H: thermal profile data acquired from a thermal imager;
threshold setting, T safe : safety temperature threshold, H warn : a hot spot warning temperature difference threshold;
data analysis and judgment, judging whether T is more than T safe If yes, consider that enhanced cooling is needed, judge whether H has area exceeding H warn If yes, marking as a hot spot area;
air conditioner adjustment decision: if T > T safe Adjusting the set temperature of the air conditioner:
T set ＝max(T set -ΔT，T min )；
if there is a hot spot in H exceeding H warn Increasing the fan speed:
F speed ＝min(F speed +ΔF，F max )；
wherein DeltaT and DeltaF respectively represent the adjustment amounts of temperature and wind speed, T min And F max Respectively the minimum value of the set temperature of the air conditioner and the maximum value of the speed of the wind ;
when the real-time temperature T exceeds the set safety threshold T safe When the temperature inside the distribution box is too high, the set temperature T of the air conditioner needs to be reduced set To enhance cooling, the adjustment amount deltat should be set based on actual demand,at the same time, ensures that the adjusted temperature is not lower than the minimum set temperature T min The method comprises the steps of carrying out a first treatment on the surface of the If the thermal image data H shows that the temperature of a certain area exceeds the hot spot warning threshold H warn Then it is necessary to increase the fan speed by a large speed F max 。
In this way, the central processing unit can dynamically adjust the operation parameters of the air conditioner according to the real-time data, and optimize the temperature control of the distribution box, thereby improving the stability and efficiency of the system.
S5 specifically comprises the following steps:
the sound signal acquisition is carried out, and a high-sensitivity acoustic sensor is used for fixing the key parts of the distribution box, wherein the key parts comprise a near-circuit breaker, a transformer and a fan;
analyzing the sound signal, wherein the collected sound signal is focused on noise level and frequency change, and the sound signal comprises decibel level and spectrum analysis for continuously monitoring the sound signal;
for noise levels, any sustained increase above a threshold (e.g., 10% of normal level) can be considered abnormal by comparing the real-time data to a preset normal operating sound level baseline;
for frequency changes, detecting changes within a preset frequency range using a spectrum analyzer, abnormal frequency changes, including unusual high frequency or low frequency signals, indicative of wear of mechanical components or circuit failure;
the fault identification and early warning are combined with the result of the sound signal analysis, if abnormal noise level or frequency change is found, the fault is marked as potential fault, and early warning is sent out, wherein frequent high-frequency noise indicates that a certain mechanical component (such as a fan bearing) is about to be invalid, and the problem of a continuous low-frequency noise indication circuit is solved;
in this way, the central processing unit is able to effectively utilize the acoustic signals to predict faults inside the distribution box, identify potential problems early, and take precautions to reduce unexpected downtime and maintenance costs.
S5 further comprises an auxiliary algorithm to analyze the sound signal, in particular as follows:
algorithm-enhanced acoustic monitoring and fault prediction steps:
and (3) sound signal acquisition: the use of high sensitivity acoustic sensors is located at key locations of the distribution box.
Feature extraction: the collected sound signals are preprocessed, including noise reduction and normalization.
Key acoustic features are extracted, such as Root Mean Square (RMS) values of the sound signal, spectral energy distribution, etc.
Anomaly detection algorithm: description of the algorithm(s),
setting a baseline noise level N baseline And frequency distribution baseline F baseline 。
Calculating the RMS value N of the current Sound Signal RMS And frequency distribution F current 。
Comparison N RMS And F current Significant deviations from baseline values were detected.
Abnormality determination: if N RMS Over N baseline E.g., 10 \%) then the signature is an anomaly in noise level.
Fault prediction and diagnosis, namely predicting potential fault types, such as mechanical component wear or circuit problems, according to the abnormal detection result and combining the historical maintenance record and the typical fault mode of the equipment.
Long-term trend analysis, which tracks the long-term trend of the sound signal to identify the signs of progressive failure.
Root Mean Square (RMS) value of the sound signal is calculated by the following formula:
wherein x is i Representing the sound signal intensity of the i-th sample, N being the number of samples;
spectral analysis of sound signals: to converting a time-domain sound signal into the frequency domain using fourier transforms:
where x (n) is the nth sound signal sample over the time domain and F (k) is the spectral value at frequency k;
noise level anomaly detection: first, a baseline noise level N is set baseline Comparing the real-time calculated RMS value N RMS From the baseline value, if a certain threshold is exceeded, it is considered abnormal:
frequency anomaly detection: analyzing real-time spectrum F current (k) Spectrum F from baseline baseline (k) By comparing the magnitudes at specific frequencies to determine if an anomaly is present:
further comprises: acoustic data analysis, collecting sound signals in the distribution box by using a high-sensitivity acoustic sensor, and performing Root Mean Square (RMS) value calculation and spectrum analysis.
Noise level anomalies are detected by comparing the real-time calculated RMS value with a set baseline noise level.
For spectral analysis, variations within a preset frequency range are of interest for identifying wear of mechanical components or circuit problems.
Thermal image data analysis: thermal imager technology is used to capture thermal distribution images of various components inside the electrical box and to locate potential overheating components or circuit problems by analyzing abnormally high temperature areas in the thermal map.
Data fusion and fault prediction: and carrying out data fusion processing by combining acoustic data analysis and thermal image data. For example, if a region has both noise level anomalies and heat map anomalies, this may indicate that the region has a higher risk of failure.
An algorithm is used to compare the spatial locations of acoustic anomalies and thermal anomalies to improve the accuracy of fault localization.
Fault source localization: by analyzing the spatial correspondence of acoustic and thermal image data, the fault source is precisely located. For example, if the acoustic analysis shows an increase in high frequency noise and overheating is observed in the thermal image of the same location, it may be determined that a particular mechanical component (such as a fan) may be malfunctioning.
In this way, the central processing unit can integrate acoustic and thermal image data, not only can predict potential mechanical and circuit problems in advance, but also can accurately locate fault sources, thereby improving maintenance efficiency and system reliability.
The system also comprises user interaction and remote control, the user side receives system feedback in real time, remote monitoring and manual intervention are carried out, and a user interface provides detailed data analysis, equipment state report and heat distribution images, so that a user can make more intelligent maintenance decisions.
As shown in fig. 2-3, a power distribution box comprises a power distribution box body 1, wherein an air conditioning unit 5 is integrated in the power distribution box body 1, the air conditioning unit 5 is automatically controlled by using an intelligent real-time monitoring method, and a thermal imager 2, a high-precision temperature sensor 3 and an acoustic sensor 4 are installed on the power distribution box body 1.
Those of ordinary skill in the art will appreciate that: the discussion of any of the embodiments above is merely exemplary and is not intended to suggest that the scope of the invention is limited to these examples; the technical features of the above embodiments or in the different embodiments may also be combined within the idea of the invention, the steps may be implemented in any order and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.
The present invention is intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omission, modification, equivalent replacement, improvement, etc. of the present invention should be included in the scope of the present invention.
Claims (9)
1. An intelligent real-time monitoring method based on automatic control of an air conditioner unit of a distribution box is characterized by comprising the following steps:
s1: environmental perception and data acquisition, wherein a high-precision temperature sensor is combined with a thermal imager technology, so that the temperature and the heat distribution in the power distribution box are monitored in real time, and the thermal imager technology is used for detecting hot spots in the power distribution box and identifying overheated parts or potential circuit problems;
s2: data transmission and preliminary analysis, namely transmitting monitoring data of environmental awareness and data acquisition to a central processing unit, performing preliminary analysis, and identifying an abnormal heat distribution mode and analyzing equipment faults or potential safety hazards by the central processing unit in combination with temperature data and thermal image data;
s3: the intelligent decision and regulation, the central processing unit comprehensively analyzes the data, judges whether the running state of the air conditioner needs to be regulated, evaluates the health condition of equipment, automatically adjusts the parameters of the air conditioner to reduce local overheating if abnormal heat distribution is found, and reminds maintenance personnel to check;
s4: automatically adjusting and feeding back in real time, automatically adjusting the output of the air conditioner according to the decision of the central processing unit, taking preventive measures on potential safety risks, and feeding back monitoring data, adjusting actions and equipment states to a user side in real time through a wireless network;
s5: the method comprises the following steps of acoustic monitoring and fault prediction, integrating a high-sensitivity acoustic sensor, monitoring an acoustic signal in the distribution box in real time, identifying early signs of mechanical component wear and circuit abnormality problems by analyzing a specific acoustic mode, predicting potential mechanical and circuit problems in advance by combining acoustic analysis and thermal image data, and positioning fault sources.
2. The intelligent real-time monitoring method based on the automatic control of the air conditioner unit of the distribution box according to claim 1, wherein the S1 specifically comprises:
high precision temperature sensors are deployed at critical locations of the distribution box, including near-entrance and exit cables, near circuit breakers and protectors, near transformers and capacitors, dense connection areas, and near controllers and other sensitive electronic components, for continuously monitoring and recording temperature changes within the box, ensuring accuracy and continuity of temperature data.
3. The intelligent real-time monitoring method based on the automatic control of the air conditioner unit of the distribution box according to claim 2, wherein the S2 specifically comprises:
the central processing unit receives data from a high-precision temperature sensor and a thermal imager, wherein the temperature sensor provides real-time temperature data of each key position in the distribution box, and the thermal imager provides a thermal distribution image in the distribution box;
the central processing unit performs time sequence analysis on the received temperature data, identifies abnormal fluctuation or continuous rising trend of the temperature, and indicates that overheat or abnormal equipment operation exists;
the central processing unit performs image processing and pattern recognition on the thermal image data, and comprises the steps of recognizing an abnormal high-temperature area in a thermal distribution image, comparing heat map changes at different time points and recognizing the specific position and shape of a hot spot;
and combining the temperature data and the thermal image analysis result, and identifying potential equipment faults or potential safety hazards by a central processing unit through a machine learning algorithm or an expert system.
4. The intelligent real-time monitoring method based on automatic control of an air conditioner unit of a distribution box according to claim 3, wherein the time sequence analysis specifically comprises:
data preprocessing, including denoising and data smoothing, is performed on data collected from a high-precision temperature sensor, and the temperature data is smoothed by using a moving average formula:wherein, SMA t Is the moving average temperature at time point T, T i The measured temperature at time i, n is the time window length of the moving average;
abnormality detection, time series analysis is aimed at detecting abnormal values, and is considered to be abnormal if a certain temperature value exceeds 3 standard deviations of an average value using statistical method box graph analysis or standard deviation analysis: abnormality of|T t -μ|>3 sigma; wherein T is t Is the temperature at a specific time point t, μ is the average value of the temperature data, σ is the standard deviation;
trend analysis, using regression analysis or other trend recognition techniques to analyze the trend of temperature data over time, a linear regression equation is expressed as: t (T) t ＝β 0 +β 1 t+∈ t ；
Wherein T is t Is the temperature at time t, beta 0 And beta 1 Is a regression coefficient, E t Is an error term.
5. The intelligent real-time monitoring method based on the automatic control of the air conditioner unit of the distribution box according to claim 4, wherein the step S3 specifically comprises:
the central processing unit comprehensively analyzes the temperature data and the thermal image data according to a preset rule, wherein the preset rule is based on typical parameters of equipment operation and a known fault mode, and a threshold value is set for the temperature data to identify temperature abnormality;
the thermal image data is used for detecting hot spots in a specific area in the distribution box, and if the thermal image shows that the temperature of a certain area is obviously higher than that of surrounding areas, the thermal image data is marked as a potential fault area;
when the data analysis indicates that adjustment is needed, the central processing unit automatically adjusts parameters of the air conditioning unit, including reducing the set temperature or increasing the fan speed, to optimize the environmental conditions and prevent overheating, while assessing the health of the equipment based on the analysis results.
6. The intelligent real-time monitoring method based on the automatic control of the air conditioner unit of the distribution box according to claim 5, wherein the calculation of the intelligent decision and regulation in the step S3 is as follows:
the data definition comprises temperature data acquired from a high-precision temperature sensor in real time, and heat distribution data acquired from a thermal imager;
threshold setting, T safe Safety temperature threshold, H warn A hot spot warning temperature difference threshold;
data analysis and judgment, judging whether T is>T safe If yes, consider that enhanced cooling is needed, judge whether H has area exceeding H warn If yes, marking as a hot spot area;
air conditioner adjusting decision, if T>T safe Adjusting the set temperature of the air conditioner:
T set ＝max(T set -ΔT,T min )；
if there is a hot spot in H exceeding H warn Increasing the fan speed:
F speed ＝min(F speed +ΔF,F max )；
wherein DeltaT and DeltaF respectively represent the adjustment amounts of temperature and wind speed, T min And F max Respectively the minimum value of the set temperature of the air conditioner and the maximum value of the speed of the wind ;
when the real-time temperature T exceeds the set safety threshold T safe When the temperature inside the distribution box is too high, the set temperature T of the air conditioner needs to be reduced set To enhance cooling, the adjustment amount DeltaT should be set based on actual demand while ensuring that the adjusted temperature is not lower than the minimum set temperature T min The method comprises the steps of carrying out a first treatment on the surface of the If the thermal image data H shows that the temperature of a certain area exceeds the hot spot warning threshold H warn Then it is necessary to increase the fan speed by a large speed F max 。
7. The intelligent real-time monitoring method based on the automatic control of the air conditioner unit of the distribution box according to claim 6, wherein the step S5 specifically comprises:
the sound signal acquisition is carried out, and a high-sensitivity acoustic sensor is used for fixing the key parts of the distribution box, wherein the key parts comprise a near-circuit breaker, a transformer and a fan;
analyzing the sound signal, wherein the collected sound signal is focused on noise level and frequency change, and the sound signal comprises decibel level and spectrum analysis for continuously monitoring the sound signal;
for noise levels, any sustained increase above a threshold may be considered abnormal by comparing the real-time data to a preset normal operating sound level baseline;
for frequency changes, detecting changes within a preset frequency range using a spectrum analyzer, abnormal frequency changes, including unusual high frequency or low frequency signals, indicative of wear of mechanical components or circuit failure;
and (3) identifying and early warning faults, wherein if abnormal noise level or frequency change is found by combining the result of the sound signal analysis, the fault is marked as potential faults and early warning is sent out, and the frequent high-frequency noise indicates that a certain mechanical component is about to fail, and the problem of a continuous low-frequency noise indication circuit is solved.
8. The intelligent real-time monitoring method based on the automatic control of the air conditioner unit of the distribution box according to claim 7, wherein the step S5 further comprises an auxiliary algorithm for analyzing the sound signal, specifically comprising the following steps:
root mean square value of the sound signal is calculated, and the calculation formula is as follows:
wherein x is i Representing the sound signal intensity of the i-th sample, N being the number of samples;
spectral analysis of sound signals: to converting a time-domain sound signal into the frequency domain using fourier transforms:
where x (n) is the nth sound signal sample over the time domain and F (k) is the spectral value at frequency k;
noise level differenceAnd (3) constant detection: first, a baseline noise level N is set baseline Comparing the real-time calculated RMS value N RMS From the baseline value, if a certain threshold is exceeded, it is considered abnormal: abnormality ofN RMS ＞N baseline X (1 + threshold ratio);
frequency anomaly detection: analyzing real-time spectrum F current (k) Spectrum F from baseline baseline (k) By comparing the magnitudes at specific frequencies to determine if an anomaly is present: abnormal frequency|F current (k)-F baseline (k)|>A set frequency difference threshold.
9. The intelligent real-time monitoring method based on the automatic control of the air conditioner unit of the distribution box according to claim 8, further comprising user interaction and remote control, wherein the user side receives system feedback in real time to perform remote monitoring and manual intervention, and the user interface provides detailed data analysis, equipment status report and heat distribution images, so that a user can make more intelligent maintenance decisions.
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