CN117082688A - Tunnel intelligent dimming system, method and storage medium - Google Patents
Tunnel intelligent dimming system, method and storage medium Download PDFInfo
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- H—ELECTRICITY
- H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
- H05B—ELECTRIC HEATING; ELECTRIC LIGHT SOURCES NOT OTHERWISE PROVIDED FOR; CIRCUIT ARRANGEMENTS FOR ELECTRIC LIGHT SOURCES, IN GENERAL
- H05B47/00—Circuit arrangements for operating light sources in general, i.e. where the type of light source is not relevant
- H05B47/10—Controlling the light source
- H05B47/105—Controlling the light source in response to determined parameters
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- H05B47/10—Controlling the light source
- H05B47/165—Controlling the light source following a pre-assigned programmed sequence; Logic control [LC]
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- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
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Abstract
The invention relates to the field of illumination dimming, and provides a tunnel intelligent dimming system, a method and a storage medium, wherein the system comprises a tunnel illumination terminal for providing an illumination light source for a tunnel, and is characterized by further comprising: the system comprises a data acquisition module, a rule engine module, a machine learning module, a logic control module and an intelligent regulation and control module, wherein the data acquisition module acquires tunnel environment data and calculated vehicle arrival time, an optimal illumination intensity regulation rule is calculated by using a machine learning model established by data mined from massive historical data by the machine learning module, a logic control instruction is generated based on the illumination intensity regulation rule, and a tunnel illumination terminal is controlled by the logic control instruction, so that different illumination requirements can be responded quickly, illumination changes felt by a driver are smooth and natural, visual fatigue of the driver is effectively reduced, and driving safety is improved.
Description
Technical Field
The invention relates to the field of illumination dimming, in particular to a tunnel intelligent dimming system, a tunnel intelligent dimming method and a storage medium.
Background
In modern traffic construction, along with development of domestic expressways, expressways are an important link, mountain tunnels are excavated more and more, along with gradual increase of tunnel mileage, improvement in tunnel illumination is more and more important, and a tunnel illumination system is mainly used for illuminating roads and environments in tunnels. As the number of automobiles increases, the demand for tunnel lighting systems is also increasing.
Current tunnel lighting systems mainly consider illuminance adjustment. Due to the adaptability of human eyes, if the brightness difference between the inside and the outside of a tunnel hole is too large, a driver can generate a black hole effect (black before eyes) and a white hole effect (white before eyes) when entering the tunnel and leaving the tunnel, so that great hidden danger is caused to driving safety. How to scientifically and reasonably adjust the illumination in the hole according to seasons, time periods in one day, weather conditions and the brightness outside the hole as required, so as to ensure the driving safety, and the method is a direction needing important study.
In recent years, with the rapid development of artificial intelligence and machine learning technologies, more and more enterprises and scientific research institutions begin to explore and apply the technologies to tunnel illumination dimming systems, but the existing tunnel illumination systems have low dimming response speed and are difficult to adapt to different illumination requirements of tunnels, such as different illumination conditions of inlets and outlets, etc., so that illumination cannot be caused to naturally and smoothly transition, and different degrees of visual fatigue and potential safety hazards are caused to drivers.
Disclosure of Invention
Aiming at the problems, the invention provides a tunnel intelligent dimming system and a tunnel intelligent dimming method, and the system, the method and a storage medium are helpful for rapidly responding to different lighting demands, so that lighting changes felt by a driver are smooth and natural, visual fatigue of the driver is effectively reduced, and driving safety is improved.
The specific technical scheme provided by the invention is as follows: the intelligent dimming system for the tunnel comprises a tunnel illumination terminal, a data acquisition module, a rule engine module, a machine learning module, a logic control module and an intelligent regulation and control module, wherein the tunnel illumination terminal is used for providing an illumination light source for the tunnel;
the vehicle detection module is used for detecting the speed and the flow rate of the vehicle before the vehicle enters the tunnel;
the rule engine module is connected with the vehicle detection module, and calculates the time for the vehicle to reach the tunnel according to a predefined rule and the speed and the flow of the vehicle before the vehicle enters the tunnel, which are detected by the vehicle detection module;
the machine learning module is connected with the rule engine module and the data acquisition module, and calculates tunnel environment data acquired by the data acquisition module and the time of the vehicle reaching the tunnel calculated by the rule engine module through a machine learning model generated in advance to obtain a tunnel illumination intensity regulation rule;
the logic control module is connected with the machine learning module and generates a logic control instruction according to the obtained tunnel illumination intensity regulation rule;
the intelligent regulation and control module is connected with the logic control module and regulates and controls the illumination intensity of the tunnel illumination terminal based on the logic control instruction generated by the logic control module.
Further, the tunnel environment data includes the illumination intensity, temperature and humidity and visibility at the tunnel exit, at the tunnel entrance and within the tunnel.
Further, the system also comprises a communication module, a remote monitoring module and an alarm module;
the data acquisition module is also used for acquiring the current value, the voltage value and the illumination intensity of the tunnel illumination terminal;
the data acquisition module is connected with the remote monitoring module through the communication module and sends the acquired current value, voltage value and illumination intensity of the tunnel illumination terminal to the remote monitoring module;
the remote monitoring module is connected with the alarm module, and the alarm module alarms when any one of the current value, the voltage value and the illumination intensity received by the remote monitoring module does not accord with a preset threshold range.
Further, the data acquisition module further comprises a data cleaning unit and a data preprocessing unit;
the data cleaning unit is used for removing the abnormal tunnel environment data acquired by the data acquisition module and denoising the acquired tunnel environment data;
the data preprocessing unit is used for counting the tunnel environment data acquired by the data acquisition module.
In another aspect, the present invention provides a tunnel intelligent dimming method, including:
step one, a vehicle detection module detects the speed and the flow rate of a vehicle before the vehicle enters a tunnel;
step two, the data acquisition module acquires tunnel environment data;
step three, the rule engine module calculates the time for the vehicle to reach the tunnel according to the predefined rule and the speed and flow of the vehicle before the vehicle enters the tunnel, which are detected by the vehicle detection module;
fourthly, the machine learning module calculates tunnel environment data and the time of the vehicle reaching the tunnel through a machine learning model which is generated in advance, and a tunnel illumination intensity regulation rule is obtained;
step five, the logic control module generates a logic control instruction according to the obtained tunnel illumination intensity regulation rule;
and step six, the intelligent regulation and control module regulates and controls the illumination intensity of the tunnel illumination terminal based on the logic control instruction generated by the logic control module.
Further, before the fourth step, constructing a machine learning model; the generating a machine learning model includes the steps of:
q1, cleaning, denoising and missing value processing are carried out on the historical tunnel environment data of the data acquisition module and the time of the vehicle reaching the tunnel calculated by the rule engine module, so as to obtain a clean data set;
q2, extracting characteristics which have influence on classification from the data set as main parameters, and simultaneously taking other characteristics as auxiliary parameters to jointly construct high-dimensional characteristics;
and Q3, generating a machine learning model according to the high-dimensional characteristics.
Further, generating the machine learning model further includes: model training, model evaluation and model optimization are carried out on the generated machine learning module.
Further, the method further comprises: the current value, the voltage value and the illumination intensity of the tunnel illumination terminal acquired by the data acquisition module are sent to a remote monitoring module;
and when any one of the current value, the voltage value and the illumination intensity received by the remote monitoring module does not accord with the preset threshold range, the alarm module alarms.
Further, the method further comprises: the data acquisition module performs data cleaning and data preprocessing on the acquired data.
In yet another aspect, the present invention provides a computer readable storage medium storing a computer program which when executed by a processor implements the method described above.
The invention has the beneficial effects that:
according to the method, the optimal illumination intensity adjustment rule is calculated by utilizing a machine learning model established by the machine learning module from data mined from massive historical data according to the collected tunnel environment data and the calculated vehicle reaching time, and a logic control instruction is generated based on the illumination intensity adjustment rule, so that the tunnel illumination terminal is controlled by the logic control instruction, different illumination requirements can be responded quickly, illumination changes felt by a driver are smooth and natural, visual fatigue of the driver is effectively reduced, and driving safety is improved.
In addition, the invention has reliable design principle, simple structure and very wide application prospect.
Drawings
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 schematic block diagram of an electrical schematic diagram of a tunnel lighting intelligent dimming system according to an embodiment of the present invention.
Fig. 2 shows a flowchart of a tunnel lighting intelligent dimming method according to another embodiment of the present invention.
1-a tunnel lighting terminal; 2-a vehicle detection module; 3-a rule engine module; 4-a data acquisition module; a 5-machine learning module; 6-a logic control module; 7-an intelligent regulation module; 8-a communication module; 9-a remote monitoring module; 10-an alarm module; 4-1-a data cleaning unit; 4-2-data preprocessing unit.
Detailed Description
In order to make the technical solution of the present invention better understood by those skilled in the art, the technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
It should be noted that, tunnel lighting terminal and data acquisition module are provided with a plurality of, can be according to the number of data acquisition module of actual need tunnel lighting terminal.
As shown in fig. 1, the present embodiment provides a tunnel intelligent dimming system, which includes a tunnel lighting terminal 1 for providing a lighting source for a tunnel, a vehicle detection module 2, a data acquisition module 4, a rule engine module 3, a machine learning module 5, a logic control module 6, and an intelligent regulation module 7.
The vehicle detection module 2, such as a microwave vehicle detector, is used to detect the speed and flow of the vehicle before it enters the tunnel. The rule engine module 3 is connected with the vehicle detection module 2, and calculates the time for the vehicle to reach the tunnel according to a predefined rule, which may be an rule based on experience or an rule based on expert knowledge, and the vehicle speed and the vehicle flow before the vehicle is driven into the tunnel, which are detected by the vehicle detection module 2.
The machine learning module 5 is connected with the rule engine module 3 and the data acquisition module 4, the machine learning module 5 calculates tunnel environment data acquired by the data acquisition module 4 and the time of the vehicle reaching the tunnel calculated by the rule engine module 3 through a machine learning model generated in advance, and a tunnel illumination intensity adjustment rule is obtained, wherein the tunnel environment data comprises: illumination intensity, temperature and humidity and visibility at the tunnel exit; illumination intensity, temperature and humidity and visibility at the entrance of the tunnel; and the illumination intensity, temperature and humidity and visibility within the tunnel. The illumination intensity adjustment rule includes an illumination intensity adjustment time and an illumination intensity magnitude.
For example, the machine learning module 5 performs regression operation on the tunnel environment data acquired by the data acquisition module 4 and the time for the vehicle to reach the tunnel calculated by the rule engine module 3 through a previously generated illumination intensity regression model, so as to obtain a tunnel illumination intensity rule. For example, the vehicle reaches the tunnel entrance after five minutes, and the illumination intensity, the temperature and the humidity and the visibility at the tunnel exit are different from those at the tunnel entrance because the topography at the tunnel exit and the topography at the tunnel entrance are different, so that the tunnel illumination intensity adjustment rule may be that after five minutes, the illumination intensity of the illumination terminal at the tunnel entrance is adjusted to 15 lux, the illumination intensity of the illumination terminal in the tunnel is adjusted to 30 lux, and the illumination intensity of the illumination terminal at the tunnel exit is adjusted to 20 lux.
The logic control module 6 is connected with the machine learning module 5, and generates logic control instructions according to the obtained tunnel illumination intensity adjustment rules. The intelligent regulation and control module 7 is connected with the logic control module 6, based on the logic control instruction generated by the logic control module, the illumination intensity of the tunnel illumination terminal 1 is regulated and controlled, so that the illumination intensity of the tunnel can meet the condition that the illumination change sensed by a driver is smooth and natural, the visual field of the driver is clearer and brighter, excessive or insufficient illumination is avoided, the complex illumination requirement inside the tunnel is better met, and the driving safety is improved.
The illumination intensity adjustment rule further includes a vehicle departure time and a magnitude of illumination intensity after the vehicle has departed.
According to the method and the device, according to the collected tunnel environment data and the calculated vehicle reaching time, the optimal illumination intensity adjustment rule is calculated by utilizing a machine learning model established by the machine learning module from data mined from massive historical data, so that different illumination requirements can be responded quickly, illumination changes felt by a driver are smooth and natural, visual fatigue of the driver is effectively reduced, and driving safety is improved.
The tunnel intelligent dimming system further comprises a communication module 8, a remote monitoring module 9 and an alarm module 10.
The data acquisition module 4 is connected with the tunnel illumination terminal 1 to acquire the current value, the voltage value and the illumination intensity provided by the tunnel illumination terminal, and the data acquisition module 4 is connected with the remote monitoring module 9 through the communication module 8 to transmit the acquired current value, voltage value and illumination intensity provided by the tunnel illumination terminal to the remote monitoring module 9. The remote monitoring module 9 is connected with the alarm module 10, and when any one of the current value, the voltage value and the illumination intensity received by the remote monitoring module 9 does not accord with a preset threshold range, the alarm module 10 alarms, so that when the tunnel illumination terminal 1 fails, the tunnel illumination terminal can be maintained in time, and the illumination requirement of the tunnel is further improved, and the driving safety is improved.
Illustratively, the data acquisition module 4 further includes a data cleansing unit 4-1 and a data preprocessing unit 4-2; the data cleaning unit 4-1 is used for removing abnormal tunnel environment data collected by the data collection module and denoising the collected tunnel environment data; the data preprocessing unit 4-2 is used for counting the tunnel environment data acquired by the data acquisition module.
As shown in fig. 2, this embodiment provides a tunnel intelligent dimming method, including:
s1, detecting the speed and the flow of the vehicle before the vehicle enters a tunnel by a vehicle detection module.
S2, the data acquisition module acquires tunnel environment data.
The tunnel environment data comprise illumination intensity, temperature and humidity and visibility at the tunnel outlet; illumination intensity, temperature and humidity and visibility at the entrance of the tunnel; and the illumination intensity, temperature and humidity and visibility within the tunnel.
And S3, calculating the time for the vehicle to reach the tunnel according to a predefined rule and the speed and the flow of the vehicle before the vehicle is driven into the tunnel, which are detected by the vehicle detection module.
And S4, the machine learning module calculates tunnel environment data and the time of the vehicle reaching the tunnel through a machine learning model which is generated in advance, and a tunnel illumination intensity regulation rule is obtained.
Wherein the step of generating the machine learning model comprises:
a1, cleaning, denoising and missing value processing are carried out on the historical tunnel environment data of the data acquisition module and the time of the vehicle reaching the tunnel calculated by the rule engine module, so as to obtain a clean data set.
A2, extracting characteristics which have influence on classification from the data set as main parameters, and simultaneously taking other characteristics as auxiliary parameters to jointly construct high-dimensional characteristics, wherein the main parameters comprise illumination intensity, vehicle speed, vehicle flow and the like inside and outside a tunnel.
A3, generating a machine learning model according to the high-dimensional characteristics.
A4, performing model training on the generated machine learning model;
specifically, a plurality of supervised learning algorithms, such as decision trees, logistic regression, support vector machines, etc., are used to train the high-dimensional features and select the optimal algorithms and parameters according to the evaluation results of the test set. The test set is real-time tunnel environment data and real-time for the vehicle to arrive in the tunnel.
And A5, evaluating the machine learning model.
Specifically, the trained machine learning model is evaluated using a test set, and evaluation indexes include accuracy, recall, precision, F1 score, and the like.
And A6, optimizing the machine learning model.
Specifically, the model is optimized according to the evaluation result, including parameter adjustment, feature selection, data amplification and the like.
The regression operation can be carried out on the acquired real-time data through the trained machine learning model, so that the optimal illumination intensity in the current environment can be rapidly and accurately obtained. Therefore, corresponding dimming measures can be timely adopted, and the running efficiency of the tunnel lighting system is improved on the premise of ensuring safety.
S5, the logic control module generates a logic control instruction according to the obtained tunnel illumination intensity adjustment rule.
S6, the intelligent regulation and control module regulates and controls the illumination intensity of the tunnel illumination terminal based on the logic control instruction generated by the logic control module.
Wherein, before step S1, rules are predefined in the rule engine module 3, wherein the predefined rules may be rules based on experience or rules based on expert knowledge.
Illustratively, the method further comprises: the current value, the voltage value and the illumination intensity of the tunnel illumination terminal acquired by the data acquisition module are sent to a remote monitoring module;
and when any one of the current value, the voltage value and the illumination intensity received by the remote monitoring module does not accord with the preset threshold range, the alarm module alarms.
Illustratively, the method further comprises: the data acquisition module performs data cleaning and data preprocessing on the acquired data.
The data cleaning comprises data cleaning, noise removal, abnormal collected data removal and the like, and the data preprocessing comprises the steps of counting equipment with specific numerical values according to actual numerical values, for example, counting actually detected data by a sensor; the pool, battery, etc. display the remaining capacity; the signal transmission type displays intensity and the like, and for equipment without specific values, such as a controller, non-networking equipment and the like, 0-1 is used for indicating whether the equipment can work normally, 1 is used for indicating faults, and 0 is used for indicating normal.
The present invention also provides a computer storage medium in which a program may be stored, which program may include some or all of the steps in the embodiments provided by the present invention when executed. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a random-access memory (random access memory, RAM), or the like.
It will be apparent to those skilled in the art that the techniques of embodiments of the present invention may be implemented in software plus a necessary general purpose hardware platform. Based on such understanding, the technical solution in the embodiments of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium such as a U-disc, a mobile hard disc, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, etc. various media capable of storing program codes, including several instructions for causing a computer terminal (which may be a personal computer, a server, or a second terminal, a network terminal, etc.) to execute all or part of the steps of the method described in the embodiments of the present invention.
Although the present invention has been described in detail by way of preferred embodiments with reference to the accompanying drawings, the present invention is not limited thereto. Various equivalent modifications and substitutions may be made in the embodiments of the present invention by those skilled in the art without departing from the spirit and scope of the present invention, and it is intended that all such modifications and substitutions be within the scope of the present invention/be within the scope of the present invention as defined by the appended claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. The utility model provides a tunnel intelligent dimming system, includes the tunnel illumination terminal that is used for providing illumination light source for the tunnel, its characterized in that still includes: the system comprises a data acquisition module, a rule engine module, a machine learning module, a logic control module and an intelligent regulation module;
the vehicle detection module is used for detecting the speed and the flow rate of the vehicle before the vehicle enters the tunnel;
the rule engine module is connected with the vehicle detection module, and calculates the time for the vehicle to reach the tunnel according to a predefined rule and the speed and the flow of the vehicle before the vehicle enters the tunnel, which are detected by the vehicle detection module;
the machine learning module is connected with the rule engine module and the data acquisition module, and calculates tunnel environment data acquired by the data acquisition module and the time of the vehicle reaching the tunnel calculated by the rule engine module through a machine learning model generated in advance to obtain a tunnel illumination intensity regulation rule;
the logic control module is connected with the machine learning module and generates a logic control instruction according to the obtained tunnel illumination intensity regulation rule;
the intelligent regulation and control module is connected with the logic control module and regulates and controls the illumination intensity of the tunnel illumination terminal based on the logic control instruction generated by the logic control module.
2. The tunnel intelligent dimming system of claim 1, wherein the tunnel environment data comprises the intensity, temperature, humidity, and visibility of illumination at the tunnel exit, at the tunnel entrance, and within the tunnel.
3. The tunnel intelligent dimming system of claim 1, further comprising a communication module, a remote monitoring module, and an alarm module;
the data acquisition module is also used for acquiring the current value, the voltage value and the illumination intensity of the tunnel illumination terminal;
the data acquisition module is connected with the remote monitoring module through the communication module and sends the acquired current value, voltage value and illumination intensity of the tunnel illumination terminal to the remote monitoring module;
the remote monitoring module is connected with the alarm module, and the alarm module alarms when any one of the current value, the voltage value and the illumination intensity received by the remote monitoring module does not accord with a preset threshold range.
4. The tunnel intelligent dimming system of claim 1, wherein the data acquisition module further comprises a data cleaning unit and a data preprocessing unit;
the data cleaning unit is used for removing the abnormal tunnel environment data acquired by the data acquisition module and denoising the acquired tunnel environment data;
the data preprocessing unit is used for counting the tunnel environment data acquired by the data acquisition module.
5. The intelligent dimming method for the tunnel is characterized by comprising the following steps of:
step one, a vehicle detection module detects the speed and the flow rate of a vehicle before the vehicle enters a tunnel;
step two, the data acquisition module acquires tunnel environment data;
step three, the rule engine module calculates the time for the vehicle to reach the tunnel according to the predefined rule and the speed and flow of the vehicle before the vehicle enters the tunnel, which are detected by the vehicle detection module;
fourthly, the machine learning module calculates tunnel environment data and the time of the vehicle reaching the tunnel through a machine learning model which is generated in advance, and a tunnel illumination intensity regulation rule is obtained;
step five, the logic control module generates a logic control instruction according to the obtained tunnel illumination intensity regulation rule;
and step six, the intelligent regulation and control module regulates and controls the illumination intensity of the tunnel illumination terminal based on the logic control instruction generated by the logic control module.
6. The tunnel intelligent dimming method according to claim 5, further comprising constructing a machine learning model before the fourth step; the generating a machine learning model includes the steps of:
q1, cleaning, denoising and missing value processing are carried out on the historical tunnel environment data of the data acquisition module and the time of the vehicle reaching the tunnel calculated by the rule engine module, so as to obtain a clean data set;
q2, extracting characteristics which have influence on classification from the data set as main parameters, and simultaneously taking other characteristics as auxiliary parameters to jointly construct high-dimensional characteristics;
and Q3, generating a machine learning model according to the high-dimensional characteristics.
7. The tunnel intelligent dimming method of claim 6, wherein generating a machine learning model further comprises: model training, model evaluation and model optimization are carried out on the generated machine learning module.
8. The tunnel intelligent dimming method of claim 5, further comprising: the current value, the voltage value and the illumination intensity of the tunnel illumination terminal acquired by the data acquisition module are sent to a remote monitoring module;
and when any one of the current value, the voltage value and the illumination intensity received by the remote monitoring module does not accord with the preset threshold range, the alarm module alarms.
9. The tunnel intelligent dimming method of claim 8, further comprising: the data acquisition module performs data cleaning and data preprocessing on the acquired data.
10. A computer readable storage medium storing a computer program, which when executed by a processor implements the method of any one of claims 5-9.
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| CN202311033240.7A CN117082688A (en) | 2023-08-16 | 2023-08-16 | Tunnel intelligent dimming system, method and storage medium |
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| CN202311033240.7A CN117082688A (en) | 2023-08-16 | 2023-08-16 | Tunnel intelligent dimming system, method and storage medium |
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