CN117370917B - Urban intelligent street lamp service life prediction method and system - Google Patents
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Abstract
The invention relates to the technical field of data prediction, in particular to a life prediction method and system of an urban intelligent street lamp, comprising the following steps: acquiring actual illumination intensity time sequence data and rated illumination intensity time sequence data; acquiring the abnormal degree of the illumination intensity of the intelligent street lamp at each moment, and acquiring the first abnormal degree and the second abnormal degree of the illumination intensity of the intelligent street lamp at each moment; acquiring a plurality of intervals to obtain the fluctuation period of data points of the intelligent street lamp at each moment, and obtaining the abnormal fluctuation degree of the intelligent street lamp at each moment and the third abnormal degree of the illumination intensity of the intelligent street lamp at each moment; and correcting the actual illumination intensity time sequence data of the intelligent street lamp according to the third abnormal degree of the illumination intensity of the intelligent street lamp at each moment to obtain corrected actual illumination intensity time sequence data of the intelligent street lamp, and finally predicting the service life of the intelligent street lamp. The invention improves the accuracy of data anomaly detection on the actual illumination intensity time sequence data.
Description
Technical Field
The invention relates to the technical field of data prediction, in particular to a life prediction method and system for an urban intelligent street lamp.
Background
With the development of cities, intelligent street lamps play a key role in providing road illumination and improving urban safety as an important component of modern urban illumination systems. In order to achieve efficient street lamp management, it becomes important to accurately predict the life of intelligent street lamps. In the life prediction method of the intelligent street lamp, the light intensity attenuation of the street lamp is important data. When the light intensity attenuation of the intelligent street lamp is calculated, the light intensity data of the street lamp are required to be collected, but due to a series of reasons such as sensor errors, the collected light intensity data have a series of data noise, the accuracy of data prediction is affected, and data cleaning is required.
In order to eliminate the influence of abnormal data on intelligent street lamp life prediction, a random initial threshold value is selected through an iterative threshold algorithm in conventional implementation, and a final optimal threshold value is obtained through continuous iteration to obtain an optimal group of abnormal data.
Disclosure of Invention
The invention provides a life prediction method and system for an urban intelligent street lamp, which aims to solve the existing problems.
The invention relates to a life prediction method and a life prediction system for an urban intelligent street lamp, which adopts the following technical scheme:
the embodiment of the invention provides a life prediction method of an urban intelligent street lamp, which comprises the following steps:
acquiring actual illumination intensity time sequence data of the intelligent street lamp and corresponding rated illumination intensity time sequence data;
obtaining the abnormal degree of the illumination intensity of the intelligent street lamp at each moment according to the actual illumination intensity time sequence data and the rated illumination intensity time sequence data of the intelligent street lamp, obtaining the first abnormal degree of the illumination intensity of the intelligent street lamp at each moment according to the abnormal degree of the illumination intensity of the intelligent street lamp at each moment and the used time of the intelligent street lamp at each moment, and obtaining the second abnormal degree of the illumination intensity of the intelligent street lamp at each moment according to the first abnormal degree of the illumination intensity of the intelligent street lamp at each moment;
obtaining an actual illumination intensity curve according to actual illumination intensity time sequence data of the intelligent street lamp, obtaining a plurality of intervals according to the actual illumination intensity curve, obtaining a fluctuation period of data points of the intelligent street lamp at each moment according to the plurality of intervals, obtaining abnormal fluctuation degree of the intelligent street lamp at each moment according to the fluctuation period of the data points of the intelligent street lamp at each moment, and obtaining a third abnormal degree of the illumination intensity of the intelligent street lamp at each moment according to the abnormal fluctuation degree of the intelligent street lamp at each moment and the second abnormal degree of the illumination intensity of the intelligent street lamp at each moment;
and correcting the actual illumination intensity time sequence data of the intelligent street lamp according to the third abnormal degree of the illumination intensity of the intelligent street lamp at each moment to obtain corrected actual illumination intensity time sequence data of the intelligent street lamp, and predicting the service life of the intelligent street lamp according to the corrected actual illumination intensity time sequence data of the intelligent street lamp.
Further, the specific acquisition steps of the abnormal degree of the illumination intensity of the intelligent street lamp at each moment are as follows:
the degree of abnormality of the illumination intensity of the intelligent street lamp at each moment is equal to the absolute value of the difference value between the actual illumination intensity data and the rated illumination intensity data at each moment.
Further, the calculation formula of the first abnormal degree of the illumination intensity of the intelligent street lamp at each moment is as follows:
in the method, in the process of the invention,indicating the abnormal degree of the illumination intensity of the intelligent street lamp at the kth moment, < >>Indicating the time that the intelligent street lamp has been used up to the kth moment,/>Indicating rated life of intelligent street lamp->For a preset threshold value, ++>And the first abnormal degree of the illumination intensity of the intelligent street lamp at the kth moment is represented.
Further, the calculation formula of the second abnormal degree of the illumination intensity of the intelligent street lamp at each moment is as follows:
in the method, in the process of the invention,indicating a first abnormality degree of the illumination intensity of the intelligent street lamp at the kth time,/for the intelligent street lamp>Mean value of first abnormality degree of illumination intensity of intelligent street lamp at all time points in data point neighborhood of kth time point, +.>Variance of first abnormality degree indicating illumination intensity of intelligent street lamp at all times in data point neighborhood of kth time, +.>Indicating a second degree of abnormality of the illumination intensity of the intelligent street lamp at the kth time,/for the intelligent street lamp>Representing absolute value symbols.
Further, the step of obtaining an actual illumination intensity curve according to the actual illumination intensity time sequence data of the intelligent street lamp and obtaining a plurality of intervals according to the actual illumination intensity curve comprises the following specific steps:
performing curve fitting on the actual illumination intensity time sequence data of the intelligent street lamp by using a least square method to obtain an actual illumination intensity curve; and dividing the intervals according to extreme points in the actual illumination intensity curve to obtain a plurality of intervals.
Further, the step of obtaining the fluctuation period of the data points of the intelligent street lamp at each moment according to a plurality of intervals comprises the following specific steps:
the time length of each interval is 2 times as long as the fluctuation period of each time data point in the interval.
Further, the calculation formula of the abnormal fluctuation degree of the intelligent street lamp at each moment is as follows:
in the method, in the process of the invention,indicates the fluctuation period of the data point at the kth moment, < >>Mean value of fluctuation period of all time data points in the data point neighborhood of kth time of intelligent street lamp,/>Representing absolute value symbols, ++>And (5) representing the abnormal fluctuation degree of the intelligent street lamp at the kth moment.
Further, the calculation formula of the third abnormal degree of the illumination intensity of the intelligent street lamp at each moment is as follows:
in the method, in the process of the invention,indicating a first abnormality degree of the illumination intensity of the intelligent street lamp at the kth time,/for the intelligent street lamp>Indicating a second degree of abnormality of the illumination intensity of the intelligent street lamp at the kth time,/for the intelligent street lamp>Indicating the degree of abnormal fluctuation of the intelligent street lamp at the kth time,/->Indicating the third abnormality degree of the illumination intensity of the intelligent street lamp at the kth time,/day>Representing a linear normalization function.
Further, the correcting the actual illumination intensity time sequence data of the intelligent street lamp according to the third abnormal degree of the illumination intensity of the intelligent street lamp at each moment to obtain the corrected actual illumination intensity time sequence data of the intelligent street lamp comprises the following specific steps:
(1) Selecting the average actual illumination intensity of the actual illumination intensity corresponding to the maximum value of the third abnormal degree and the actual illumination intensity corresponding to the minimum value of the third abnormal degree in the third abnormal degree of the intelligent street lamp at all times as initial thresholds in an iterative threshold algorithm, dividing the actual illumination intensity time sequence data into two groups according to the initial thresholds, marking the two groups as an array A and an array B, obtaining the average value of the third abnormal degree of the actual illumination intensity at all times in the array A, marking the average value asObtaining the average value of the third abnormal degree of the actual illumination intensity at all times in the array B, and marking the average value as +.>Obtain->And->Is recorded as the average value ofSelecting->The actual illumination intensity which is the smallest in difference from the third abnormality degree of the actual illumination intensity is taken as a new threshold +.>；
(2) When the difference between the new threshold and the initial threshold is greater than or equal to the preset threshold G, then according to the new thresholdDividing the time sequence data of the actual illumination intensity into two groups, marking the two groups as an array C and an array D, obtaining the average value of the third abnormal degree of the actual illumination intensity at all times in the array C, marking the average value as +.>Obtaining the average value of the third abnormal degree of the actual illumination intensity at all times in the array D, and marking the average value as +.>Obtain->And->Is recorded as->Selecting->The actual illumination intensity which is the smallest in difference from the third abnormality degree of the actual illumination intensity is taken as a new threshold +.>；
(3) And similarly, stopping iteration until the difference of the thresholds of two adjacent iterations is smaller than a preset threshold G, and taking the threshold of the last iteration as an optimal threshold;
dividing actual illumination intensity time sequence data of the intelligent street lamp into two groups according to an optimal threshold value in the actual illumination intensity, calculating the average value of all the actual illumination intensities in each group, marking one group with the largest average value of the actual illumination intensities as abnormal data, marking the other group as normal data, deleting all the abnormal data in the actual illumination intensity time sequence data of the intelligent street lamp, and marking the deleted position of the abnormal data as a point to be interpolated; the data of the points to be interpolated are obtained through linear interpolation by two normal data at two ends, so that the corrected real illumination intensity time sequence data of the intelligent street lamp is obtained.
The invention also provides a life prediction system of the urban intelligent street lamp, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes any one of the steps when executing the computer program.
The technical scheme of the invention has the beneficial effects that: according to the invention, the first abnormal degree and the second abnormal degree of the illumination intensity of the intelligent street lamp at each moment are obtained by analyzing the actual illumination intensity time sequence data and the rated illumination intensity time sequence data of the intelligent street lamp, so that the accuracy of detecting the abnormal data in the actual illumination intensity time sequence data is improved; and acquiring an optimal threshold according to the third abnormal degree of the illumination intensity of the intelligent street lamp at each moment, correcting the actual illumination intensity time sequence data of the intelligent street lamp according to the optimal threshold, and predicting the service life of the intelligent street lamp according to the corrected actual illumination intensity time sequence data of the intelligent street lamp, so that the accuracy of data prediction is improved. In the process of acquiring the optimal threshold value through the iterative threshold algorithm, acquiring the initial threshold value through the third abnormal degree, determining the initial threshold value in the iterative threshold algorithm, reducing the iterative times, further reducing the calculated data quantity and shortening the time consumed by a computer in processing the data.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart showing the steps of a life prediction method for an urban intelligent street lamp.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following detailed description is given below of a life prediction method and system for an urban intelligent street lamp according to the invention, which are specific embodiments, structures, features and effects thereof, with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The invention provides a service life prediction method and a service life prediction system for urban intelligent street lamps, which are specifically described below with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of steps of a method for predicting life of an urban intelligent street lamp according to an embodiment of the invention is shown, the method includes the following steps:
step S001: and acquiring actual illumination intensity time sequence data of the intelligent street lamp and corresponding rated illumination intensity time sequence data.
It should be noted that, because the illumination intensity of the intelligent street lamp can change according to the change of the traffic flow of the road, and the rated illumination intensity of the street lamp can be obtained according to the data of the traffic flow of the road and the power of the street lamp, the illumination intensity of the street lamp is adjusted, and then the illumination intensity time sequence data of each intelligent street lamp needs to be acquired for analysis.
Specifically, the illumination intensity of each intelligent street lamp is acquired by using an illumination measuring instrument at a time interval of one minute, and all data in two days are acquired to obtain actual illumination intensity time sequence data of each intelligent street lamp; and obtaining the rated illumination intensity of each intelligent street lamp at each moment according to the road traffic flow at each acquisition moment, the power of the street lamp and other factors, obtaining the rated illumination intensity of each intelligent street lamp at all moments within two days, and recording the rated illumination intensity time sequence data of each intelligent street lamp. The obtaining of the rated illumination intensity of each intelligent street lamp is a known technology, and detailed description is omitted herein. In this embodiment, an intelligent street lamp is used for analysis.
So far, the actual illumination intensity time sequence data and the rated illumination intensity time sequence data of the intelligent street lamp are obtained.
Step S002: obtaining abnormal degrees of illumination intensity of the intelligent street lamp at each moment according to actual illumination intensity time sequence data and rated illumination intensity time sequence data of the intelligent street lamp, obtaining first abnormal degrees of illumination intensity of the intelligent street lamp at each moment according to the abnormal degrees of illumination intensity of the intelligent street lamp at each moment and the time that each intelligent street lamp is used at each moment, and obtaining second abnormal degrees of illumination intensity of the intelligent street lamp at each moment according to the first abnormal degrees of illumination intensity of the intelligent street lamp at each moment.
When the iterative threshold algorithm is used for identifying and detecting abnormal data, the difference between the actual illumination intensity and the corresponding rated illumination intensity is generally used for representing the abnormal degree of each data point, and the abnormal data is identified through threshold iteration of the abnormal degree.
(1) And obtaining the abnormal degree of the illumination intensity of the intelligent street lamp at each moment according to the actual illumination intensity time sequence data and the rated illumination intensity time sequence data of the intelligent street lamp.
It should be further noted that, in the actual situation, the rated illumination intensity of the intelligent street lamp is generally determined by some known parameters around the street lamp, but because the intelligent street lamp has loss in each element in the intelligent street lamp during the use process, the loss of each element in the intelligent street lamp cannot be specifically measured, so that a difference exists between the actual illumination intensity and the rated illumination intensity of the intelligent street lamp, therefore, the analysis is performed according to the difference between the actual illumination intensity and the rated illumination intensity in the intelligent street lamp, and the abnormal data point in the abnormal data point is obtained.
Specifically, the abnormal degree of the illumination intensity at each moment is obtained according to the actual illumination intensity time sequence data and the rated illumination intensity time sequence data of each intelligent street lamp, and is expressed as follows:
in the method, in the process of the invention,representing the actual illumination intensity at the kth time in the actual illumination intensity time sequence data,/for>Rated light intensity of kth moment in the time sequence data of the rated light intensity is represented by +.>Indicating the abnormal degree of the illumination intensity of the intelligent street lamp at the kth moment, < >>Representing absolute value symbols.
Wherein, when the difference between the actual illumination intensity and the rated illumination intensity of the intelligent street lamp at each moment is larger, the data point at the moment is abnormal. The difference represents the absolute value of the difference.
(2) The first abnormal degree of the illumination intensity of the intelligent street lamp at each moment is obtained according to the abnormal degree of the illumination intensity of the intelligent street lamp at each moment and the used time of each intelligent street lamp at each moment.
It should be noted that, in the use process of the intelligent street lamp, the service life of the intelligent street lamp is gradually attenuated, and along with the attenuation of the service life of the intelligent street lamp, the actual illumination intensity of the intelligent street lamp is gradually attenuated, and the difference between the intelligent street lamp and the rated illumination intensity is gradually increased, so that the degree of abnormality is larger, and therefore, the degree of abnormality of the street lamp needs to be corrected according to the service time of the intelligent street lamp and the rated service life of the intelligent street lamp.
Specifically, a threshold value is presetWherein the present embodiment is +.>The embodiment is not particularly limited, and is described by taking 0.8 as an example, wherein +.>Depending on the particular implementation. According to the abnormal degree of the illumination intensity of the intelligent street lamp at each moment, the service time of the intelligent street lamp and the rated service life of the intelligent street lamp, the first abnormal degree of the illumination intensity of the intelligent street lamp at each moment is obtained, and the first abnormal degree is expressed as follows:
in the method, in the process of the invention,indicating the abnormal degree of the illumination intensity of the intelligent street lamp at the kth moment, < >>Indicating the time that the intelligent street lamp has been used up to the kth moment,/>Indicating rated life of intelligent street lamp->Is a preset threshold value, namely a correction coefficient,and the first abnormal degree of the illumination intensity of the intelligent street lamp at the kth moment is represented.
When the service time of the intelligent street lamp is closer to the rated service life, the illumination intensity attenuation degree of the intelligent street lamp is larger, and corresponding data points are more biased to abnormal data points.
(3) Obtaining a second abnormal degree of the illumination intensity of the intelligent street lamp at each moment according to the first abnormal degree of the illumination intensity of the intelligent street lamp at each moment.
It should be noted that, during the use of the intelligent street lamp, the illumination intensity of the intelligent street lamp gradually decays along with the use time, but the intelligent street lamp may locally violate the overall performance due to accidental factors and environmental factors. The method is characterized in that the greater the fluctuation degree of data around a data point is, the greater the abnormality degree of the data is; the greater the fluctuation around the data, the greater the degree of abnormality of the data.
It should be further noted that specific data in terms of data fluctuation are expressed as: the greater the fluctuation of the degree of abnormality near the data point, the greater the variance of the degree of abnormality around the data point, which means that the greater the fluctuation of the degree of abnormality around the data point, the more abnormal the actual illumination intensity data here; meanwhile, the greater the difference between the average value of the abnormality degree of the data point and the surrounding abnormality degree, the greater the abnormality degree of the actual illumination intensity of the data point.
Specifically, a threshold N is preset, where the embodiment is described by taking n=20 as an example, and the embodiment is not specifically limited, where N may be determined according to the specific implementation situation. Taking each data point in the actual illumination intensity time sequence data as a center point, selecting 2N+1 data points, recording the data points as data in the neighborhood of each data point, analyzing according to the data in the neighborhood of each data point to obtain a second abnormal degree of the illumination intensity of the intelligent street lamp at each moment, and expressing the second abnormal degree as follows by a formula:
in the method, in the process of the invention,indicating a first abnormality degree of the illumination intensity of the intelligent street lamp at the kth time,/for the intelligent street lamp>Mean value of first abnormality degree of illumination intensity of intelligent street lamp at all time points in data point neighborhood of kth time point, +.>Variance of first abnormality degree indicating illumination intensity of intelligent street lamp at all times in data point neighborhood of kth time, +.>Indicating a second degree of abnormality of the illumination intensity of the intelligent street lamp at the kth time,/for the intelligent street lamp>Representing absolute value symbols.
Wherein,the difference between the first abnormal degree of the illumination intensity of the intelligent street lamp at the kth moment and the average value of the first abnormal degree of the illumination intensity at all moments in the corresponding neighborhood is represented, and the larger the difference is, the more abnormal the data point is represented; />The larger the value is for the variance of the first degree of abnormality of the illumination intensity at all times in the neighborhood, the more abnormal the corresponding data point is.
So far, the second abnormal degree of the illumination intensity of the intelligent street lamp at each moment is obtained.
Step S003: obtaining an actual illumination intensity curve according to actual illumination intensity time sequence data of the intelligent street lamp, obtaining a plurality of intervals according to the actual illumination intensity curve, obtaining fluctuation periods of data points of the intelligent street lamp at each moment according to the intervals, obtaining abnormal fluctuation degrees of the intelligent street lamp at each moment according to the fluctuation periods of the data points of the intelligent street lamp at each moment, and obtaining third abnormal degrees of the illumination intensity of the intelligent street lamp at each moment according to the abnormal fluctuation degrees of the intelligent street lamp at each moment and the second abnormal degrees of the illumination intensity of the intelligent street lamp at each moment.
(1) And obtaining an actual illumination intensity curve according to the actual illumination intensity time sequence data of the intelligent street lamp, and obtaining a plurality of intervals according to the actual illumination intensity curve.
It should be noted that, when the abnormal data points appear, they generally show irregular or show strong regularity due to some specific factors, that is, the actual illumination intensity time sequence data can be curve fitted to obtain a curve, the fluctuation period is determined according to the adjacent extreme points in the curve, and the abnormal data points are analyzed according to the fluctuation period.
Specifically, performing curve fitting on actual illumination intensity time sequence data of the intelligent street lamp through a quintic polynomial by using a least square method to obtain an actual illumination intensity curve; dividing the intervals according to extreme points in the actual illumination intensity curve, namely forming one interval by adjacent extreme points, and obtaining a plurality of intervals, wherein each interval is left-open and right-closed, and the first interval is left-closed and right-closed. Wherein the extreme points include a maximum point and a minimum point. However, the fitting is not particularly limited in this embodiment, and the practitioner may be according to the specific case.
(2) And obtaining the fluctuation period of the data point of the intelligent street lamp at each moment according to the plurality of intervals, and obtaining the abnormal fluctuation degree of the intelligent street lamp at each moment according to the fluctuation period of the data point of the intelligent street lamp at each moment.
It should be noted that specific data in terms of fluctuation frequency are expressed as: the greater the frequency of fluctuation of the degree of abnormality near the data point, the faster the fluctuation of the degree of abnormality around the data point, the less stable the degree of abnormality at this time, namely the faster the variation of the difference between the actual illumination intensity and the rated illumination intensity, the more abnormal the actual illumination intensity data acquired here; meanwhile, the greater the difference between the data period where the data point is located and the surrounding fluctuation period, the greater the degree of abnormality of the illumination intensity of the data point.
Specifically, a threshold value M is preset, where the embodiment is described by taking m=10 as an example, and the embodiment is not specifically limited, where M may be determined according to the specific implementation situation. The time length of each interval is 2 times as long as the fluctuation period of each time data point in the interval. And taking each data point in the actual illumination intensity time sequence data as a center point, selecting 2M+1 data points, and recording the data points as data in the neighborhood of each data point. And obtaining the abnormal fluctuation degree of the intelligent street lamp at each moment according to the fluctuation period of the data point at each moment. Expressed by the formula:
in the method, in the process of the invention,indicates the fluctuation period of the data point at the kth moment, < >>Mean value of fluctuation period of all time data points in the data point neighborhood of kth time of intelligent street lamp,/>Representing absolute value symbols, ++>And (5) representing the abnormal fluctuation degree of the intelligent street lamp at the kth moment.
Wherein,representing the degree of difference between the fluctuation period of the kth time data point and the average value of the fluctuation periods of all the time data points in the corresponding neighborhood, wherein the larger the value is, the more abnormal the period of the data point is; />The frequency of the data point at the kth time is represented, and the larger the value is, the faster the data change around the data point is, the faster the data fluctuation is, and the higher the degree of abnormality is.
(3) And obtaining a third abnormal degree of the illumination intensity of the intelligent street lamp at each moment according to the abnormal fluctuation degree of the intelligent street lamp at each moment and the second abnormal degree of the illumination intensity of the intelligent street lamp at each moment.
Obtaining a third abnormal degree of the illumination intensity of the intelligent street lamp at each moment according to the abnormal fluctuation degree of the intelligent street lamp at each moment, the second abnormal degree and the first abnormal degree of the illumination intensity of the intelligent street lamp at each moment, and expressing the third abnormal degree as follows by a formula:
in the method, in the process of the invention,indicating a first abnormality degree of the illumination intensity of the intelligent street lamp at the kth time,/for the intelligent street lamp>Indicating a second degree of abnormality of the illumination intensity of the intelligent street lamp at the kth time,/for the intelligent street lamp>Indicating the degree of abnormal fluctuation of the intelligent street lamp at the kth time,/->Indicating the third abnormality degree of the illumination intensity of the intelligent street lamp at the kth time,/day>Representation lineAnd (5) a sex normalization function.
So far, the third abnormal degree of the illumination intensity of the intelligent street lamp at each moment is obtained.
Step S004: and correcting the actual illumination intensity time sequence data of the intelligent street lamp according to the third abnormal degree of the actual illumination intensity of the intelligent street lamp at each moment to obtain corrected actual illumination intensity time sequence data of the intelligent street lamp, and predicting the service life of the intelligent street lamp according to the corrected actual illumination intensity time sequence data of the intelligent street lamp.
A threshold value G is preset, where the embodiment is described by taking g=300 as an example, and the embodiment is not specifically limited, where G may be determined according to the specific implementation situation.
(1) Selecting the average actual illumination intensity of the actual illumination intensity corresponding to the maximum value of the third abnormal degree and the actual illumination intensity corresponding to the minimum value of the third abnormal degree in the third abnormal degree of the intelligent street lamp at all times as initial thresholds in an iterative threshold algorithm, dividing the actual illumination intensity time sequence data into two groups according to the initial thresholds, marking the two groups as an array A and an array B, obtaining the average value of the third abnormal degree of the actual illumination intensity at all times in the array A, marking the average value asObtaining the average value of the third abnormal degree of the actual illumination intensity at all times in the array B, and marking the average value as +.>Obtain->And->Is recorded as the average value ofSelecting->Actual illumination with minimum difference from third abnormality degree of actual illumination intensityIntensity as new threshold ∈ ->；
(2) When the difference between the new threshold and the initial threshold is greater than or equal to the preset threshold G, then according to the new thresholdDividing the time sequence data of the actual illumination intensity into two groups, marking the two groups as an array C and an array D, obtaining the average value of the third abnormal degree of the actual illumination intensity at all times in the array C, marking the average value as +.>Obtaining the average value of the third abnormal degree of the actual illumination intensity at all times in the array D, and marking the average value as +.>Obtain->And->Is recorded as->Selecting->The actual illumination intensity which is the smallest in difference from the third abnormality degree of the actual illumination intensity is taken as a new threshold +.>；
(3) And similarly, stopping iteration until the difference of the thresholds of two adjacent iterations is smaller than a preset threshold G, and taking the threshold of the last iteration as an optimal threshold.
Wherein the difference represents an absolute value of the difference; the iterative threshold algorithm is a well-known technique and will not be described in detail here.
Dividing actual illumination intensity time sequence data of the intelligent street lamp into two groups according to an optimal threshold value in the actual illumination intensity, calculating average values of all the actual illumination intensities in each group, marking one group with the largest average value of the actual illumination intensity as abnormal data, marking the other group as normal data, deleting all the abnormal data in the actual illumination intensity time sequence data of the intelligent street lamp, and marking the deleted position of the abnormal data as a point to be interpolated.
Obtaining all points to be interpolated in the actual illumination intensity time sequence data of the intelligent street lamp, wherein the points to be interpolated exist in two types in the actual illumination intensity time sequence data of the intelligent street lamp, the first type is that one point to be interpolated is contained in the middle of two normal data points, the second type is that a plurality of continuous points to be interpolated are contained in the middle of the two normal data points, namely, the data of the middle point to be interpolated obtains the data value of each point to be interpolated through linear interpolation of the two normal data at two ends, and thus, the corrected actual illumination intensity time sequence data of the intelligent street lamp is obtained.
And predicting the service life of the intelligent street lamp by using an exponential decay model according to the corrected actual illumination intensity time sequence data of the intelligent street lamp.
The embodiment provides a life prediction system of an urban intelligent street lamp, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes steps S001 to S004 when executing the computer program.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the invention, but any modifications, equivalent substitutions, improvements, etc. within the principles of the present invention should be included in the scope of the present invention.
Claims (8)
1. A life prediction method of an urban intelligent street lamp is characterized by comprising the following steps:
acquiring actual illumination intensity time sequence data of the intelligent street lamp and corresponding rated illumination intensity time sequence data;
obtaining the abnormal degree of the illumination intensity of the intelligent street lamp at each moment according to the actual illumination intensity time sequence data and the rated illumination intensity time sequence data of the intelligent street lamp, obtaining the first abnormal degree of the illumination intensity of the intelligent street lamp at each moment according to the abnormal degree of the illumination intensity of the intelligent street lamp at each moment and the used time of the intelligent street lamp at each moment, and obtaining the second abnormal degree of the illumination intensity of the intelligent street lamp at each moment according to the first abnormal degree of the illumination intensity of the intelligent street lamp at each moment;
obtaining an actual illumination intensity curve according to actual illumination intensity time sequence data of the intelligent street lamp, obtaining a plurality of intervals according to the actual illumination intensity curve, obtaining a fluctuation period of data points of the intelligent street lamp at each moment according to the plurality of intervals, obtaining abnormal fluctuation degree of the intelligent street lamp at each moment according to the fluctuation period of the data points of the intelligent street lamp at each moment, and obtaining a third abnormal degree of the illumination intensity of the intelligent street lamp at each moment according to the abnormal fluctuation degree of the intelligent street lamp at each moment and the second abnormal degree of the illumination intensity of the intelligent street lamp at each moment;
correcting the actual illumination intensity time sequence data of the intelligent street lamp according to the third abnormal degree of the illumination intensity of the intelligent street lamp at each moment to obtain corrected actual illumination intensity time sequence data of the intelligent street lamp, and predicting the service life of the intelligent street lamp according to the corrected actual illumination intensity time sequence data of the intelligent street lamp;
the calculation formula of the first abnormal degree of the illumination intensity of the intelligent street lamp at each moment is as follows:
in the method, in the process of the invention,indicating the abnormal degree of the illumination intensity of the intelligent street lamp at the kth moment, < >>Indicating the time that the intelligent street lamp has been used up to the kth moment,/>Indicating rated life of intelligent street lamp->For a preset threshold value, ++>The first abnormal degree of the illumination intensity of the intelligent street lamp at the kth moment is represented;
the method comprises the following specific steps of:
(1) Selecting the average actual illumination intensity of the actual illumination intensity corresponding to the maximum value of the third abnormal degree and the actual illumination intensity corresponding to the minimum value of the third abnormal degree in the third abnormal degree of the intelligent street lamp at all times as initial thresholds in an iterative threshold algorithm, dividing the actual illumination intensity time sequence data into two groups according to the initial thresholds, marking the two groups as an array A and an array B, obtaining the average value of the third abnormal degree of the actual illumination intensity at all times in the array A, marking the average value asObtaining the average value of the third abnormal degree of the actual illumination intensity at all times in the array B, and marking the average value as +.>Obtain->And->Is recorded as->Selecting->The actual illumination intensity which is the smallest in difference from the third abnormality degree of the actual illumination intensity is taken as a new threshold +.>；
(2) When the difference between the new threshold and the initial threshold is greater than or equal to the preset threshold G, then according to the new thresholdDividing the time sequence data of the actual illumination intensity into two groups, marking the two groups as an array C and an array D, obtaining the average value of the third abnormal degree of the actual illumination intensity at all times in the array C, marking the average value as +.>Obtaining the average value of the third abnormal degree of the actual illumination intensity at all times in the array D, and marking the average value as +.>Obtain->And->Is recorded as->Selecting->The actual illumination intensity which is the smallest in difference from the third abnormality degree of the actual illumination intensity is taken as a new threshold +.>；
(3) And similarly, stopping iteration until the difference of the thresholds of two adjacent iterations is smaller than a preset threshold G, and taking the threshold of the last iteration as an optimal threshold;
dividing actual illumination intensity time sequence data of the intelligent street lamp into two groups according to an optimal threshold value in the actual illumination intensity, calculating the average value of all the actual illumination intensities in each group, marking one group with the largest average value of the actual illumination intensities as abnormal data, marking the other group as normal data, deleting all the abnormal data in the actual illumination intensity time sequence data of the intelligent street lamp, and marking the deleted position of the abnormal data as a point to be interpolated; the data of the points to be interpolated are obtained through linear interpolation by two normal data at two ends, so that the corrected real illumination intensity time sequence data of the intelligent street lamp is obtained.
2. The life prediction method of urban intelligent street lamps according to claim 1, wherein the specific acquisition steps of the degree of abnormality of the illumination intensity of the intelligent street lamps at each moment are as follows:
the degree of abnormality of the illumination intensity of the intelligent street lamp at each moment is equal to the absolute value of the difference value between the actual illumination intensity data and the rated illumination intensity data at each moment.
3. The urban intelligent street lamp life prediction method according to claim 1, wherein the calculation formula of the second degree of abnormality of the illumination intensity of the intelligent street lamp at each moment is:
in the method, in the process of the invention,indicating a first abnormality degree of the illumination intensity of the intelligent street lamp at the kth time,/for the intelligent street lamp>Mean value of first abnormality degree of illumination intensity of intelligent street lamp at all time points in data point neighborhood of kth time point, +.>Variance of first abnormality degree indicating illumination intensity of intelligent street lamp at all times in data point neighborhood of kth time, +.>Indicating a second degree of abnormality of the illumination intensity of the intelligent street lamp at the kth time,/for the intelligent street lamp>Representing absolute value symbols.
4. The method for predicting the life of an intelligent street lamp according to claim 1, wherein the steps of obtaining an actual illumination intensity curve according to the actual illumination intensity time sequence data of the intelligent street lamp and obtaining a plurality of intervals according to the actual illumination intensity curve include the following steps:
performing curve fitting on the actual illumination intensity time sequence data of the intelligent street lamp by using a least square method to obtain an actual illumination intensity curve; and dividing the intervals according to extreme points in the actual illumination intensity curve to obtain a plurality of intervals.
5. The urban intelligent street lamp life prediction method according to claim 1, wherein the step of obtaining the fluctuation period of the intelligent street lamp data point at each moment according to a plurality of intervals comprises the following specific steps:
the time length of each interval is 2 times as long as the fluctuation period of each time data point in the interval.
6. The urban intelligent street lamp life prediction method according to claim 1, wherein the calculation formula of the abnormal fluctuation degree of the intelligent street lamp at each moment is as follows:
in the method, in the process of the invention,indicates the fluctuation period of the data point at the kth moment, < >>Mean value of fluctuation period of all time data points in the data point neighborhood of kth time of intelligent street lamp,/>Representing absolute value symbols, ++>And (5) representing the abnormal fluctuation degree of the intelligent street lamp at the kth moment.
7. The urban intelligent street lamp life prediction method according to claim 1, wherein the calculation formula of the third degree of abnormality of the illumination intensity of the intelligent street lamp at each moment is:
in the method, in the process of the invention,indicating a first abnormality degree of the illumination intensity of the intelligent street lamp at the kth time,/for the intelligent street lamp>Indicating a second degree of abnormality of the illumination intensity of the intelligent street lamp at the kth time,/for the intelligent street lamp>The abnormal fluctuation degree of the intelligent street lamp at the kth moment is represented,indicating the third abnormality degree of the illumination intensity of the intelligent street lamp at the kth time,/day>Representing a linear normalization function.
8. A city intelligent street lamp life prediction system comprising a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor, when executing the computer program, implements the steps of a city intelligent street lamp life prediction method as claimed in any one of claims 1-7.
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