CN115968088A - Intelligent tunnel dimming method and system and computer storage medium - Google Patents
Intelligent tunnel dimming method and system and computer storage medium Download PDFInfo
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Abstract
The invention discloses a tunnel intelligent dimming method and system based on an isolated forest and a 3Sigma principle and a computer storage medium, and relates to the technical field of tunnel lighting control. The method comprises the following steps: s1, acquiring historical data of a tunnel, and extracting historical characteristic data from the historical data; s2, constructing a tunnel light intensity abnormity judgment model Y by using the historical characteristic data and an isolated forest algorithm; s3, eliminating abnormal data in the historical characteristic data by using the tunnel light intensity abnormality judgment model Y, and calculating dimming reference values with different characteristics by adopting a 3Sigma principle; and S4, acquiring real-time characteristic data in the tunnel, judging the real-time data set X by using the light intensity abnormity judgment model Y, and outputting the intelligent dimming grade according to the judgment result. Whether historical and real-time light intensity data are abnormal or not can be judged, correct dimming levels are given, the average energy-saving proportion of the total power consumption after intelligent dimming is used reaches 48.39%, and the fact that the model provided has important significance on reduction of tunnel operation cost is shown.
Description
Technical Field
The invention relates to the technical field of tunnel lighting control, in particular to a tunnel intelligent dimming method and system based on an isolated forest and a 3Sigma principle and a computer storage medium.
Background
In the field of tunnel lighting control, because a 'black hole effect' and a 'white hole effect' exist when a vehicle enters or exits a tunnel, traffic accidents caused by poor tunnel lighting account for more than half of the tunnel accidents every year, and therefore the tunnel lighting quality directly determines the driving safety. The illumination adjustment of the tunnel is scientifically and reasonably carried out, so that a good tunnel driving environment can be built, the occurrence of accidents is reduced, and the energy loss and the operation cost of the tunnel can be reduced. Currently, tunnel dimming faces the following problems:
(1) Most tunnels are only dimmed according to time intervals, for example, the tunnel is uniformly turned on to the maximum illumination brightness in the daytime, and only basic illumination is turned on at night, so that energy consumption is wasted, and white and black hole effects can be caused to cause traffic accidents.
(2) The utilization rate of tunnel data is low, for example, the light intensity data outside the hole is basically idle, and cannot generate corresponding value.
(3) The light intensity value measured by the device is easily influenced by various factors, so that the light intensity data is inaccurate, and partial data is even greatly different from the actual data.
Disclosure of Invention
The tunnel intelligent dimming method, system and computer storage medium based on the isolated forest and the 3Sigma principle can utilize tunnel data resources to the maximum extent, judge abnormal light intensity data and output intelligent dimming levels according to judgment results.
In order to achieve the technical purpose, the invention mainly adopts the following technical scheme:
in a first aspect, the application provides a tunnel intelligent dimming method based on an isolated forest and a 3Sigma principle, which includes the following steps:
s1, acquiring historical data of a tunnel, and extracting historical characteristic data from the historical data;
s2, constructing a tunnel light intensity abnormity judgment model Y by using an isolated forest algorithm according to historical characteristic data;
s3, eliminating abnormal data in the historical characteristic data by using the tunnel light intensity abnormality judgment model Y, and calculating dimming reference values of different characteristics by adopting a 3Sigma principle;
and S4, collecting real-time characteristic data in the tunnel, judging the real-time data set X by using the light intensity abnormity judgment model Y, and outputting the intelligent dimming level according to the judgment result.
In some embodiments, in step S1, the characteristic data includes light intensity, season, time point, and climate characteristic data.
In some embodiments, in step S2, the construction of the tunnel light intensity abnormality determination model Y includes the following steps:
collecting key index data influencing light intensity;
cleaning and preprocessing the key index data in sequence, and extracting characteristic data;
and constructing a light intensity abnormity judgment model Y by using an IForest algorithm.
In some embodiments, in step S3, rejecting abnormal data in the historical feature data by using the tunnel light intensity abnormality determination model Y includes the following steps:
using the light intensity abnormality determination model Y to determine the historical feature data set Z 0 Judging, screening abnormal data needing to be eliminated, and reserving a cleaned historical characteristic data set Z 1 。
In some embodiments, the calculating of the dimming reference value in step S3 includes the steps of:
using historical feature data set Z 1 The dimming reference values with different characteristics are calculated by adopting a 3sigma principle and are reserved as a data set B, and the specific format of the data set B is as follows:
B＝{s i ,h j ,w k ,b n }
wherein:
s i denotes a first feature, h j Denotes a second feature, w k Representing a third characteristic, wherein the value ranges of i, j and k are determined according to the attributes or the classifications of the first characteristic, the second characteristic and the third characteristic respectively;
b n and n represents the dimming reference value, and ranges from 1 to i j k, and represents the dimming reference values with different characteristics respectively.
Preferably, the specific format of the data set B is as follows:
B＝{s i ,h j ,w k ,b n }
wherein:
s i representing seasonal characteristics, the value range of i is 1 to 4, which represents 4 seasons of a year;
h j representing the time point characteristics, wherein j has a value ranging from 0 to 23 and represents 24 hours in a day;
w k the climate characteristics are represented, the value range of k is 1 to k, and k climates are represented, such as sunny days, rainy days, cloudy days, foggy days and the like;
b n and the value range of n is 1 to i j k, and represents the dimming reference values of different seasons, different time points and different climates.
In some embodiments, the step S4, the determining of the real-time data set includes the following steps:
judging the real-time data set X by using the light intensity abnormity judging model Y, and calling light intensity data in the dimming reference value data set B if the real-time data set X is judged to be abnormal; if the real-time data set X is judged to be normal, the light intensity data of the real-time data set X is reserved, and the normal light intensity data returned in the step is recorded as Lo.
In some embodiments, in step S4, the determining of the smart dimming level comprises the steps of:
and comparing the normal light intensity data Lo with the execution level to determine the intelligent dimming level.
In a second aspect, the present application discloses a tunnel intelligent dimming system based on isolated forest and 3Sigma principle, the system includes:
a data acquisition module: the system is used for acquiring historical characteristic data and real-time characteristic data in the tunnel;
a data analysis module: analyzing historical characteristic data acquired by the data acquisition module, and constructing a tunnel light intensity abnormity judgment model according to an analysis result;
a calculation module: calculating dimming reference values of different characteristics in the tunnel according to the tunnel light intensity abnormity judgment model;
the light source regulation and control module: and regulating and controlling a light source in the tunnel according to the real-time data in the tunnel acquired by the data acquisition module so that the illuminance in the tunnel reaches the execution level specified in the road tunnel lighting design rules (JTG/T D70/2-01-2014).
In a third aspect, the present application discloses a computer storage medium comprising a stored program, wherein the program, when executed, controls an apparatus in which the storage medium is located to perform the method according to the first aspect.
Compared with the prior art, the invention has the following beneficial effects:
according to the tunnel intelligent dimming method based on the isolated forest and the 3Sigma principle, the characteristics of historical light intensity data, seasonal data, climate data, time point data and the like are extracted from tunnel data resources, whether the historical and real-time light intensity data are abnormal or not can be judged, the correct dimming grade is given, the average energy-saving ratio of the total power consumption after intelligent dimming is used reaches 48.39%, and the provided model has important significance for reduction of tunnel operation cost.
Drawings
FIG. 1 is a flowchart of a tunnel smart dimming method based on isolated forest and 3Sigma principle;
FIG. 2 is a flow chart of a light intensity abnormality determination model according to the present application;
FIG. 3 is a schematic diagram of light intensity reference values and execution levels thereof at different time points in a sunny day state in the present application;
fig. 4 is a graph showing a comparison of power consumption before and after the smart dimming of each tunnel in the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Example 1
As shown in fig. 1, the tunnel smart dimming method based on the isolated forest and the 3Sigma principle provided in this embodiment specifically includes the following steps:
(1) And acquiring historical data of the tunnel, and extracting historical characteristic data from the historical data.
Characteristics such as historical light intensity data, seasonal data, climate data, time point data and the like are extracted from tunnel data resources; the extraction time at least comprises one year of historical data, and under the condition that the condition and the expense are allowed, the years of historical data are extracted as much as possible, so that the extracted historical characteristic data are more accurate.
(2) And constructing a tunnel light intensity abnormity judgment model Y by using an isolated forest algorithm according to historical characteristic data.
An isolated Forest algorithm (IForest) is a rapid outlier detection method based on Ensemble, and has the characteristics of small calculated amount, linear time complexity, high precision and the like. In order to improve the accuracy of the light intensity data outside the tunnel, key indexes affecting the light intensity, such as light intensity, time points, seasons, climate and the like, are collected firstly, then the processes of data cleaning, data preprocessing, feature extraction and the like are carried out, and finally a light intensity abnormity judgment model Y is constructed by using an IForest algorithm, wherein the specific flow is shown in figure 2.
(3) And eliminating abnormal data in the historical characteristic data by using the tunnel light intensity abnormality judgment model Y, and calculating the dimming reference values with different characteristics by adopting a 3Sigma principle.
Firstly, the historical characteristic data collected in the step (1) is collected, including historical data such as light intensity, time point, season, climate and the like, and a historical characteristic data set Z is obtained 0 Then, the historical characteristic data set Z is judged by using the tunnel light intensity abnormity judgment model Y 0 Judging, screening abnormal data to be eliminated, and reserving cleaned historical characteristic data set Z 1 。
Using historical feature data set Z 1 The 3sigma principle is adopted to calculate the dimming reference values in different seasons, different time points and different climates, and the dimming reference values are reserved as a data set B, and the specific format of the data set B is as follows:
B＝{s i ,h j ,w k ,b n }
wherein:
s i representing seasonal characteristics, the value range of i is 1 to 4, which represents 4 quarters of a year;
h j representing the time point characteristics, wherein j has a value ranging from 0 to 23 and represents 24 hours in a day;
w k representing climate characteristics, wherein the value range of k is 1 to k, and represents k types of climates, such as sunny days, rainy days, cloudy days, foggy days and the like;
b n and the value range of n is 1 to i x j x k, and the value range represents the dimming reference value of different seasons, different time points and different climates.
The 3sigma principle is also called a Laviand criterion, the method assumes that the data set follows normal distribution or approximately normal distribution, then calculates the mean value mu and the standard deviation sigma of the data set, and the probability of the data set outside the (mu-3 sigma, mu +3 sigma) interval is lower than 0.3%, so the data value outside the interval can be defined as a critical abnormal value.
In order to ensure the safety of tunnel driving and reduce the energy consumption of the tunnel, the mean value and the variance of different seasons, different time points and different climates in the data set B are respectively calculated, and mu + 3sigma is taken as B n The reference value of (1).
(4) And acquiring real-time characteristic data in the tunnel, judging the real-time characteristic data set X by using the light intensity abnormity judgment model Y, and outputting the intelligent dimming level according to the judgment result.
And judging the real-time characteristic data set X by using the light intensity abnormity judging model Y. If the real-time characteristic data set X is judged to be abnormal, calling light intensity data in the dimming reference value data set B; and if the real-time characteristic data set X is judged to be normal, keeping the light intensity data of the real-time data set. Note that the normal intensity data returned by this step is Lo.
The standard for dimming tunnels with different light intensity ranges is shown in Table 1, according to the rules of the rules for design of Highway Tunnel Lighting (JTG/T D70/2-01-2014).
TABLE 1 Tunnel dimming standard table in different light intensity ranges
And calculating the dimming Level required by the tunnel according to the normal light intensity data Lo returned in the previous step, and recording the dimming Level as Level.
The characteristic data may further include other characteristics, such as an air environment in the tunnel, a dust dispersion condition, and the like, and generally, if there is a lot of dust in the tunnel, the dust will adhere to the lamp, which may also result in a bad lighting environment in the tunnel. The characteristic parameters are selected as much as possible according to actual conditions, dust dispersion condition parameters can be selected for areas with large pollution, and the parameters can be selected for simplifying steps for places with good environment.
Application example
The method mainly selects the data of 7 tunnels from 20 days at 4 months and 15 days at 2021 years and 22 days at 4 months and 22 days at 2021 years in 4 months in Guangdong province as experimental tests, the data acquisition granularity is 1 minute, the climate type in the time period is a sunny day, and the main acquisition indexes comprise road section names, tunnel names, time, weather types and light intensity values. And then, analyzing the data of 7 tunnels according to the tunnel light intensity evaluation model of the previous section, firstly discovering abnormal light intensity data, then calculating a reference value of the time period, determining the dimming execution level of the time period, and comparing the power saving conditions after intelligent dimming.
(1) Abnormal light intensity value rejection and reference value determination
The python toolkit is used for modeling the key index data of 7 tunnels, and two parameters, namely nesting and registration, are set, so that abnormal light intensity result values shown in the table 2 can be obtained.
TABLE 2 Tunnel abnormal light intensity result table
The abnormal light intensity data in table 2 are removed, and the light intensity reference values at different time points of the tunnel (where 22.
As can be seen from fig. 3: the light intensity execution levels from 19 pm to 6 pm in the next morning of the tunnel are all 5, i.e. the intensive lighting is in a full-off state; in addition, the light intensity of 11 to 14 points is performed at the highest level, and the intensive illumination needs to be turned on to the maximum. From the overall trend, the change of the light intensity reference value in all days is in normal distribution, and the actual illumination condition in sunny days is met.
(2) Result comparison analysis before and after intelligent dimming
The tunnel data of 9/4/2021 (without using smart dimming) and 19/4/2021 (with smart dimming) are mainly used for comparative analysis, and the specific results are shown in fig. 4.
As can be seen from fig. 4: after the intelligent dimming is used for the Chiling tunnel, the saving proportion of the power consumption is the largest and reaches 67.51 percent; in addition, for 7 high-speed tunnels, the average energy-saving proportion of the total power consumption reaches 48.39 percent after intelligent dimming is used. The model provided by the method is significant to reduction of the tunnel operation cost.
Example 2
The tunnel intelligent dimming system based on the isolated forest and the 3Sigma principle provided by the embodiment specifically includes,
a data acquisition module: the system is used for acquiring historical characteristic data and real-time characteristic data in the tunnel; the method comprises the steps of collecting collected historical characteristic data to obtain a historical characteristic data set, wherein the historical characteristic data set comprises characteristics of historical light intensity data, seasonal data, climate data, time point data and the like; and collecting the collected real-time characteristic data to obtain a real-time characteristic data set.
A data analysis module: and analyzing the historical characteristic data acquired by the data acquisition module, and constructing a tunnel light intensity abnormity judgment model according to the analysis result.
In this embodiment, the data analysis module is mainly used for performing steps such as data cleaning, data preprocessing, feature extraction and the like on historical feature data, and constructing a tunnel light intensity abnormality determination model according to the obtained data.
A calculation module: and calculating dimming reference values of different characteristics in the tunnel according to the tunnel light intensity abnormity judgment model.
In the embodiment, the calculation module mainly uses the tunnel light intensity abnormity determination model Y to perform comparison on the historical characteristic data set Z 0 Judging, screening abnormal data to be eliminated, and reserving cleaned historical characteristic data set Z 1 Then using the historical feature data set Z 1 And calculating dimming reference values of different seasons, different time points and different climates by adopting a 3sigma principle, and reserving the dimming reference values as a data set B.
And finally, judging the real-time characteristic data set X acquired by the data acquisition module by using the light intensity abnormity judgment model Y. If the real-time characteristic data set X is judged to be abnormal, calling light intensity data in the dimming reference value data set B; if the real-time characteristic data set X is judged to be normal, the light intensity data of the real-time data set is reserved. Note that the normal intensity data returned by this step is Lo.
The light source regulation and control module: and regulating and controlling a light source in the tunnel according to the real-time data in the tunnel acquired by the data acquisition module so that the illuminance in the tunnel reaches the execution level specified in the road tunnel lighting design rules (JTG/T D70/2-01-2014).
In this embodiment, the light source adjusting and controlling module adjusts the light source mainly according to the feedback result of the above steps, so that the illuminance in the tunnel reaches the execution level specified in the road tunnel lighting design rules (JTG/T D70/2-01-2014).
Example 3
The present invention also provides a computer-readable storage medium comprising a stored program, wherein when the program is run, an apparatus in which the storage medium is located is controlled to perform the method as described in any one of the above.
The computer storage medium disclosed by the embodiment of the invention intelligently adjusts the illumination in the tunnel, realizes that the illumination intensity in the tunnel is maintained above the stable illumination intensity meeting the requirement, and achieves the purposes of intelligently controlling the light and saving the power resource.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (Hyper Text Transfer Protocol), and may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
Claims (10)
1. A tunnel intelligent dimming method based on an isolated forest and a 3Sigma principle is characterized by comprising the following steps:
s1, acquiring historical data of a tunnel, and extracting historical characteristic data from the historical data;
s2, constructing a tunnel light intensity abnormity judgment model Y by using the historical characteristic data and an isolated forest algorithm;
s3, eliminating abnormal data in the historical characteristic data by using the tunnel light intensity abnormality judgment model Y, and calculating dimming reference values of different characteristics by adopting a 3Sigma principle;
and S4, collecting real-time characteristic data in the tunnel, judging the real-time data set X by using the light intensity abnormity judgment model Y, and outputting the intelligent dimming level according to the judgment result.
2. The smart dimming method for tunnels based on solitary forest and 3Sigma principle as claimed in claim 1, wherein: in step S1, the characteristic data includes light intensity, season, time point, and climate characteristic data.
3. The intelligent tunnel dimming method based on the isolated forest and the 3Sigma principle as claimed in claim 1, wherein the step S2 of constructing the abnormal tunnel light intensity judgment model Y comprises the following steps:
collecting key index data influencing light intensity;
cleaning and preprocessing the key index data in sequence, and extracting characteristic data;
and constructing a light intensity abnormity judgment model Y by using an IForest algorithm.
4. The tunnel intelligent dimming method based on the isolated forest and the 3Sigma principle as claimed in claim 1, wherein in the step S3, the abnormal data in the historical characteristic data are removed by using the tunnel light intensity abnormality determination model Y, and the method comprises the following steps:
using the light intensity abnormality determination model Y to determine the historical feature data set Z 0 Judging, screening abnormal data needing to be eliminated, and reserving a cleaned historical characteristic data set Z 1 。
5. The smart dimming method for tunnels based on solitary forest and 3Sigma principle as claimed in claim 4, wherein the step S3, the calculating of the dimming reference value comprises the steps of:
using historical feature data set Z 1 The dimming reference values with different characteristics are calculated by adopting a 3sigma principle and are reserved as a data set B, and the specific format of the data set B is as follows:
B＝{s i ，h j ，w k ，b n }
wherein:
s i denotes a first feature, h j Denotes a second feature, w k Representing a third characteristic, wherein the value ranges of i, j and k are respectively carried out according to the attributes or the classifications of the first characteristic, the second characteristic and the third characteristicDetermining;
b n and the value range of n is 1 to i x j x k, and the values respectively represent the dimming reference values with different characteristics.
6. The smart tunnel dimming method based on isolated forest and 3Sigma principle as claimed in claim 5, wherein the specific format of the data set B is as follows:
B＝{s i ，h j ，w k ，b n }
wherein:
s i representing seasonal characteristics, the value range of i is 1 to 4, which represents 4 quarters of a year;
h j representing the time point characteristics, wherein j has a value ranging from 0 to 23 and represents 24 hours in a day;
w k representing climate characteristics, wherein the value range of k is 1 to k, and represents k types of climates, such as sunny days, rainy days, cloudy days, foggy days and the like;
b n and the value range of n is 1 to i j k, and represents the dimming reference values of different seasons, different time points and different climates.
7. The smart tunnel dimming method based on isolated forest and 3Sigma principle as claimed in claim 5, wherein the step S4, the real-time data set judgment comprises the following steps:
judging the real-time data set X by using the light intensity abnormity judging model Y, and calling light intensity data in the dimming reference value data set B if the real-time data set X is judged to be abnormal; if the real-time data set X is judged to be normal, the light intensity data of the real-time data set X is reserved, and the normal light intensity data returned in the step is recorded as Lo.
8. The smart dimming method for tunnels based on isolated forest and 3Sigma principle as claimed in claim 7, wherein the step S4, the determination of the smart dimming level comprises the steps of: and comparing the normal light intensity data Lo with the execution level to determine the intelligent dimming level.
9. A tunnel intelligent dimming system based on isolated forest and 3Sigma principle, characterized in that the system comprises:
a data acquisition module: the system is used for acquiring historical characteristic data and real-time characteristic data in the tunnel;
a data analysis module: analyzing historical characteristic data acquired by the data acquisition module, and constructing a tunnel light intensity abnormity judgment model according to an analysis result;
a calculation module: calculating dimming reference values of different characteristics in the tunnel according to the tunnel light intensity abnormity judgment model;
the light source regulation and control module: and regulating and controlling a light source in the tunnel according to the real-time data in the tunnel acquired by the data acquisition module so that the illuminance in the tunnel reaches the execution level specified in the road tunnel lighting design rules (JTG/T D70/2-01-2014).
10. A computer storage medium, characterized in that the storage medium comprises a stored program, wherein the program, when executed, controls an apparatus in which the storage medium is located to perform the method according to any one of claims 1-8.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116596703A (en) * | 2023-07-17 | 2023-08-15 | 吉林省骅涛科技有限公司 | Electricity saver and intelligent control method thereof |
| CN117390379A (en) * | 2023-12-11 | 2024-01-12 | 博睿康医疗科技(上海)有限公司 | On-line signal measuring device and confidence measuring device for signal characteristics |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116596703A (en) * | 2023-07-17 | 2023-08-15 | 吉林省骅涛科技有限公司 | Electricity saver and intelligent control method thereof |
| CN116596703B (en) * | 2023-07-17 | 2023-09-19 | 吉林省骅涛科技有限公司 | Electricity saver and intelligent control method thereof |
| CN117390379A (en) * | 2023-12-11 | 2024-01-12 | 博睿康医疗科技(上海)有限公司 | On-line signal measuring device and confidence measuring device for signal characteristics |
| CN117390379B (en) * | 2023-12-11 | 2024-03-19 | 博睿康医疗科技(上海)有限公司 | On-line signal measuring device and confidence measuring device for signal characteristics |
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