Intelligent power grid optimal dispatching system based on artificial intelligence

Abstract

The invention discloses an intelligent power grid optimal scheduling system based on artificial intelligence, which comprises a data acquisition module, a scheduling monitoring module and an output module, and is characterized in that the data acquisition module is used for acquiring related information of a highway section where a street lamp is erected; the scheduling monitoring module is used for acquiring illumination intensity requirements of the street lamps; according to the system, the driving condition of vehicles on the highway at night is monitored, the real-time driving condition of a vehicle driver is obtained through the scheduling analysis module, and meanwhile, the lane selection trend of the driver at a road intersection is predicted; the illumination intensity of the street lamps on different roads is adjusted through the power grid power dispatching system, a driver is prompted to select a correct driving route in time, the safety of high-speed driving is improved, the driving route of the driver is corrected in time, the application efficiency of power grid dispatching is optimized, and the system has the advantages of being high in monitoring capability and accuracy and high in real-time illumination efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power dispatching management, and in particular to an intelligent power grid optimization dispatching system based on artificial intelligence. Background Art

[0002] Power grid dispatching is an effective management method to ensure the stable operation of the dark area of ​​the power grid, reliable external power supply, and orderly progress of various power production work. The specific work content of power dispatching is to comprehensively consider the development of various production work based on the data information fed back by various information collection equipment or the information provided by monitoring personnel, combined with the actual operation parameters of the power grid, such as voltage, current, frequency, load, etc. With the continuous development of science and technology, modern monitoring and control methods are constantly improving, and the technical support for power dispatching is becoming increasingly powerful.

[0003] In recent years, in order to further increase the safety of road driving, street lights have been newly installed on some sections of highways. The power supply of street lights at different locations on highways by the power grid is crucial. In the prior art, the street lights on both sides of the road will be turned on and off in time after the preset time or the weather brightness reaches the rated value. The system monitors the driving conditions of the vehicles in real time and adjusts the intensity of the lights in time. However, on the highway at night, there are fewer vehicles passing through. When the driver drives at high speed for a long time, due to fatigue and bright surroundings, the driver's concentration on the road signs ahead will be greatly reduced. Even if the road section is familiar, the target intersection may be missed or the fork in the wrong direction may be taken. At the same time, the rear vehicles may also be in danger due to the sudden lane change of the vehicle in front. Therefore, it is necessary to design an intelligent grid optimization dispatching system and method based on artificial intelligence with high monitoring capability accuracy and high real-time lighting efficiency. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent grid optimization dispatching system and method based on artificial intelligence to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: an intelligent grid optimization dispatching system and method based on artificial intelligence, comprising a data acquisition module, a dispatching monitoring module and an output module, wherein the data acquisition module is used to obtain relevant information of the highway section where street lights are installed; the dispatching monitoring module is used to obtain the light intensity requirements of the street lights by monitoring the driving conditions of drivers on the highway; and the output module is used to control the light intensity of the street lights in real time according to the analysis results of the dispatching monitoring module.

[0006] According to the above technical solution, the data acquisition module includes a road section information acquisition module and a street light information acquisition module. The road section information acquisition module is used to extract and screen the road section conditions of the target highway section; the street light information acquisition module is used to obtain detailed information on the street lights set up in the target highway section.

[0007] According to the above technical solution, the scheduling monitoring module includes a driving monitoring module and a lighting demand analysis module; the driving monitoring module is used to monitor the vehicle driving conditions in real time and obtain real-time data of the vehicle driving; the lighting demand analysis module is used to analyze the street lamp lighting demand near the target vehicle through the monitoring results of the driving monitoring module.

[0008] According to the above technical solution, the driving monitoring module further includes a driving speed detection submodule and a driving time detection submodule. The driving speed detection submodule is used to obtain the real-time driving speed of the vehicle; the driving time detection submodule is used to obtain the driver's continuous driving time as required. The lighting demand analysis module further includes a visibility distance detection submodule and a light adjustment submodule. The visibility distance detection submodule is used to obtain the minimum visibility distance required by the driver at the current driving speed and driving time; the light adjustment submodule is used to analyze the required street light illumination intensity near the vehicle and obtain the corresponding highway power adjustment plan.

[0009] According to the above technical solution, the output module includes a power scheduling module and a prompt module. The power scheduling module is used to apply the power adjustment scheme obtained by the light adjustment submodule; the prompt module is used to prompt the driver of the brightness changes of the safety street lights when the power load in the current area is large, and output the brightness adjustment information of the relevant area.

[0010] According to the above technical solution, the smart grid optimization scheduling method mainly includes the following steps:

[0011] Step S1: When the smart grid optimization dispatching system detects that a vehicle enters a highway, the driving monitoring module starts to obtain relevant information about the vehicle and the driver, and monitors the vehicle in real time to analyze the minimum visibility distance required by the driver in the current situation;

[0012] Step S2: The light adjustment submodule analyzes the required street light illumination intensity near the vehicle, obtains the corresponding highway power adjustment scheme, and dynamically adjusts the illumination intensity of the lights on both sides so that the lighting effect meets the normal driving lighting requirements preset by the driver in real time;

[0013] Step S3: When the driver arrives at the lane change intersection, the light adjustment submodule analyzes the driving trend of the target vehicle and further adjusts the light control;

[0014] Step S4: If the power load in the current area is large, the prompt module prompts the driver to change the brightness of the safety street lights and outputs the brightness adjustment information of the street lights in the relevant area.

[0015] According to the above technical solution, step S1 further includes:

[0016] Step S11: the vehicle speed detection submodule obtains the real-time speed of the vehicle as V, where the unit of V is m / s;

[0017] Step S12: The driving time after the driver enters the highway is X hours. If it is detected that the driver enters a service area and stays there for more than half an hour, the driving time is recalculated when the driver exits the service area.

[0018] Step S13: The minimum visibility distance L required by the driver in the current situation is L=V[0.2γ*(ηX)]+R, where R is the braking distance of the driver when braking immediately at a speed of V obtained by the system through the database, η is the fatigue coefficient of the driver after X hours of driving relative to normal driving conditions, γ is a unit conversion parameter, and the unit of L is meter.

[0019] According to the above technical solution, step S3 further includes:

[0020] Step S31: When the system detects that the distance from the front of the vehicle to the road crossing is less than L meters, the previous driving record of the current vehicle on the highway is obtained and fitted with the road on which the current vehicle is traveling. If the overlap of the vehicle's driving road is higher than 80% and the through direction of the current road on which the vehicle is traveling does not overlap with the through direction in the driving record, it is determined that the current vehicle may need to change lanes, and the vehicle speed at this time is obtained as V1. If V1 ≥ μV, it is determined that the current vehicle has no obvious lane change tendency, where μ is the error ratio of the normal speed change of the vehicle. The light adjustment submodule transmits the information to the output module, and the power dispatching module increases the brightness of the lane change lamp in front of the current vehicle through the power dispatching system and reduces the brightness of the current lane road lamp;

[0021] Step S32: When the system detects that a vehicle appears within m meters behind the target vehicle at this time, the distance that the driver can see through the reflector is A meters, where m is the safe detection distance that needs to be maintained between the speeds of the two vehicles obtained by the system. The street lights A meters behind the target vehicle increase their brightness synchronously with the road change section in step S31 until there is no vehicle within m meters behind the target vehicle and no subsequent vehicles are detected, and the brightness of the lights within the current distance is reduced to a normal level.

[0022] According to the above technical solution, in step S4, when the total load of the highway power grid where the street light section is set up is greater than or equal to 80% of the maximum load of the power grid in the target road area, the system extracts the highway section information in the database, and while ensuring that the street lights in all sections maintain normal levels of light brightness standards, it gives priority to increasing the brightness of street lights in sections with a high history of accidents and sections with difficult driving.

[0023] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention monitors the driving conditions of vehicles on highways at night, obtains the real-time driving conditions of vehicle drivers through a scheduling analysis module, and predicts the driver's lane selection tendency at road intersections, and adjusts the light intensity of street lamps on different roads through the power grid power scheduling system to prompt the driver to choose the correct driving route in time, thereby improving the safety of high-speed driving, timely correcting the driver's driving route, and optimizing the application efficiency of power grid scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0025] Figure 1 It is a schematic diagram of the system module composition of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] See also Figure 1 The present invention provides a technical solution: an intelligent power grid optimization dispatching system and method based on artificial intelligence, including:

[0028] Data acquisition module, dispatching and monitoring module and output module. The data acquisition module is used to obtain relevant information of the highway section where street lights are installed; the dispatching and monitoring module is used to obtain the light intensity requirements of street lights by monitoring the driving conditions of drivers on the highway; the output module is used to control the light intensity of street lights in real time through the analysis results of the dispatching and monitoring module.

[0029] The present invention monitors the driving conditions of vehicles on highways at night. The dispatching and analysis module obtains the real-time driving conditions of vehicle drivers and predicts the driver's route selection tendency at road intersections. The power grid power dispatching system adjusts the light intensity of street lamps on different roads to prompt the driver to choose the correct driving route in time, thereby improving the safety of high-speed driving, correcting the driver's driving route in time, and optimizing the application efficiency of power grid dispatching.

[0030] The data collection module includes a road section information collection module and a street light information collection module. The road section information collection module is used to extract and screen the road section conditions of the target highway section; the street light information collection module is used to obtain detailed information on the street lights set up in the target highway section.

[0031] The dispatch monitoring module includes a driving monitoring module and a lighting demand analysis module; the driving monitoring module is used to monitor the vehicle driving conditions in real time and obtain real-time data of the vehicle driving; the lighting demand analysis module is used to analyze the street lighting demand near the target vehicle through the monitoring results of the driving monitoring module.

[0032] The driving monitoring module further includes a driving speed detection submodule and a driving time detection submodule. The driving speed detection submodule is used to obtain the real-time driving speed of the vehicle; the driving time detection submodule is used to obtain the driver's continuous driving time as required. The lighting demand analysis module further includes a visibility distance detection submodule and a light adjustment submodule. The visibility distance detection submodule is used to obtain the minimum visibility distance required by the driver at the current driving speed and driving time; the light adjustment submodule is used to analyze the required street light illumination intensity near the vehicle and obtain the corresponding highway power adjustment plan.

[0033] The output module includes a power dispatching module and a prompting module. The power dispatching module is used to apply the power regulation scheme obtained by the light regulation submodule; the prompting module is used to prompt the driver of the brightness changes of the safety street lights when the power load in the current area is large, and output the brightness adjustment information of the relevant area.

[0034] In a preferred embodiment, the smart grid optimization scheduling method mainly includes the following steps:

[0035] Step S1: When the smart grid optimization dispatching system detects that a vehicle enters a highway, the driving monitoring module starts to obtain relevant information about the vehicle and the driver, and monitors the vehicle in real time to analyze the minimum visibility distance required by the driver in the current situation;

[0036] Step S2: The light adjustment submodule analyzes the required street light illumination intensity near the vehicle, obtains the corresponding highway power adjustment scheme, and dynamically adjusts the illumination intensity of the lights on both sides so that the lighting effect meets the normal driving lighting requirements preset by the driver in real time;

[0037] Step S3: When the driver arrives at the lane change intersection, the light adjustment submodule analyzes the driving trend of the target vehicle and further adjusts the light control;

[0038] Step S4: If the power load in the current area is large, the prompt module prompts the driver to change the brightness of the safety street lights and outputs the brightness adjustment information of the street lights in the relevant area.

[0039] In this embodiment, step S1 further includes:

[0040] Step S11: The vehicle speed detection submodule obtains the real-time speed of the vehicle as V, where the unit of V is m / s; since the street lights on both sides of the road will provide the driver with lights of different intensities and directions when the vehicle is driving, it is necessary to judge the intensity of the light in a certain range under the current vehicle speed based on the driver's visual visibility so that the driver can pass through the area without being affected.

[0041] Step S12: The driving time after the driver enters the highway is X hours. If it is detected that the driver enters a service area and stays there for more than half an hour, the driving time is recalculated when the driver exits the service area.

[0042] After driving for a long time, the driver may experience visual fatigue. At this time, the driver's demand for lights will be greater than when driving without fatigue.

[0043] Step S13: The minimum visibility distance L required by the driver in the current situation is L=V[0.2γ*(ηX)]+R, where R is the braking distance of the driver when braking immediately at a speed of V obtained by the system through the database, η is the fatigue coefficient of the driver after X hours of driving relative to normal driving conditions, γ is a unit conversion parameter, and the unit of L is meter.

[0044] Visibility distance refers to the distance at which the driver can observe the road conditions ahead while driving a vehicle. When observing the road conditions ahead, the driver can see vehicles on the road ahead or notice other road hazards and respond in a timely manner.

[0045] In this embodiment, step S3 further includes:

[0046] Step S31: When the system detects that the distance from the front of the vehicle to the road crossing is less than L meters, the previous driving record of the current vehicle on the highway is obtained and fitted with the road on which the current vehicle is traveling. If the overlap of the vehicle's driving road is higher than 80% and the through direction of the current road on which the vehicle is traveling does not overlap with the through direction in the driving record, it is determined that the current vehicle may need to change lanes, and the vehicle speed at this time is obtained as V1. If V1 ≥ μV, it is determined that the current vehicle has no obvious lane change tendency, where μ is the error ratio of the normal speed change of the vehicle. The light adjustment submodule transmits the information to the output module, and the power dispatching module increases the brightness of the lane change lamp in front of the current vehicle through the power dispatching system, and reduces the brightness of the current lane lamp, prompting the driver to change lanes in time;

[0047] Step S32: When the system detects that a vehicle appears within m meters behind the target vehicle at this time, the distance that the driver can see through the reflector is A meters, where m is the safe detection distance that needs to be maintained between the speeds of the two vehicles obtained by the system. The street lights A meters behind the target vehicle increase their brightness synchronously with the road change section in step S31 until there is no vehicle within m meters behind the target vehicle and no subsequent vehicles are detected, and the brightness of the lights within the current distance is reduced to a normal level.

[0048] In step S4 of the present embodiment, when the total load of the highway power grid where the streetlight section is set up is greater than or equal to 80% of the maximum load of the power grid in the target road area, the system extracts the highway section information in the database, and, while ensuring that the streetlights on all sections maintain normal levels of light brightness standards, gives priority to increasing the brightness of streetlights in sections with a high history of accidents and sections with difficult driving.

[0049] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0050] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent grid optimization dispatching system based on artificial intelligence, characterized by: It includes a data acquisition module, a dispatch monitoring module and an output module, wherein the data acquisition module is used to obtain information related to the highway section where the street lights are installed; The dispatch monitoring module is used to obtain the light intensity requirements of the street lamps by monitoring the driving conditions of drivers on the highway; the output module is used to control the light intensity of the street lamps in real time according to the analysis results of the dispatch monitoring module; The dispatch monitoring module includes a driving monitoring module and a lighting demand analysis module; the driving monitoring module is used to monitor the driving conditions of the vehicle in real time and obtain real-time data of the vehicle driving; the lighting demand analysis module is used to analyze the lighting demand of street lamps near the target vehicle based on the monitoring results of the driving monitoring module; The driving monitoring module further includes a driving speed detection submodule and a driving time detection submodule, wherein the driving speed detection submodule is used to obtain the real-time driving speed of the vehicle; The driving time detection submodule is used to obtain the driver's continuous driving time according to requirements. The lighting demand analysis module further includes a visibility distance detection submodule and a light adjustment submodule. The visibility distance detection submodule is used to obtain the minimum visibility distance required by the driver at the current driving speed and driving time; The light adjustment submodule is used to analyze the required street light intensity near the vehicle and obtain the corresponding highway power adjustment plan; The output module includes a power dispatching module and a prompting module. The power dispatching module is used to apply the power adjustment scheme obtained by the light adjustment submodule; the prompting module is used to prompt the driver of the brightness change of the safety street light when the power load in the current area is large, and output the brightness adjustment information of the relevant area; The dispatching method of the smart grid optimization dispatching system mainly includes the following steps: Step S1: When the smart grid optimization dispatching system detects that a vehicle enters a highway, the driving monitoring module starts to obtain relevant information about the vehicle and the driver, and monitors the vehicle in real time to analyze the minimum visibility distance required by the driver in the current situation; Step S2: The light adjustment submodule analyzes the required street light illumination intensity near the vehicle, obtains the corresponding highway power adjustment scheme, and dynamically adjusts the illumination intensity of the lights on both sides so that the lighting effect meets the normal driving lighting requirements preset by the driver in real time; Step S3: When the driver arrives at the lane change intersection, the light adjustment submodule analyzes the driving trend of the target vehicle and further adjusts the light control; Step S4: If the power load in the current area is large, the prompt module prompts the driver to change the brightness of the safety street lights and outputs the brightness adjustment information of the street lights in the relevant area; The step S1 further comprises: Step S11: the vehicle speed detection submodule obtains the real-time speed of the vehicle as V, where the unit of V is m / s; The step S3 further comprises: Step S31: When the system detects that the distance from the front of the vehicle to the road crossing is less than L meters, the previous driving record of the current vehicle on the highway is obtained and fitted with the road on which the current vehicle is traveling. If the overlap of the vehicle's driving road is higher than 80% and the through direction of the current road on which the vehicle is traveling does not overlap with the through direction in the driving record, it is determined that the current vehicle may need to change lanes, and the vehicle speed at this time is obtained as V1. If V1 ≥ μV, it is determined that the current vehicle has no obvious lane change tendency, where μ is the error ratio of the normal speed change of the vehicle. The light adjustment submodule transmits the information to the output module, and the power dispatching module increases the brightness of the lane change lamp in front of the current vehicle through the power dispatching system and reduces the brightness of the current lane road lamp; Step S32: When the system detects that a vehicle appears within m meters behind the target vehicle at this time, the distance that the driver can see through the reflector is A meters, where m is the safe detection distance that needs to be maintained between the speeds of the two vehicles obtained by the system. The street lights A meters behind the target vehicle increase their brightness synchronously with the road change section in step S31 until there is no vehicle within m meters behind the target vehicle and no subsequent vehicles are detected, and the brightness of the lights within the current distance is reduced to a normal level.

2. The intelligent grid optimization dispatching system based on artificial intelligence according to claim 1 is characterized in that: In step S4, when the total load of the highway power grid where the street light section is set up is greater than or equal to 80% of the maximum load of the power grid in the target road area, the system extracts the highway section information in the database, and while ensuring that the street lights in all sections maintain normal light brightness standards, gives priority to increasing the brightness of street lights in sections with a high history of accidents and difficult driving sections.

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