Shunting type gas station management method based on Internet of Things

Abstract

The invention relates to the technical field of gas station management, and discloses a distributed gas station management method based on Internet of Things, which comprises the following steps: collecting real-time data of vehicles in a gas station by using an Internet of Things sensor; calculating the vehicle distribution density according to the collected data; the cloud computing platform is used for analyzing the vehicle data and the vehicle density data, a prediction model of the vehicle flow and the refueling demand is generated, and an operation strategy of the gas station is adjusted; calculating a path from the current position to a specified oiling machine through an optimization algorithm, and guiding the vehicle; real-time navigation and predicted latency information are provided to a driver using a dynamic display screen. Through application of the Internet of Things technology, real-time data collection and processing of the vehicles in the gas station are achieved, the vehicle overstock condition can be quickly responded, the vehicle flow direction and oiling machine use are dynamically adjusted through an optimization algorithm, and the vehicle processing capacity and the service speed of the gas station are remarkably improved.

Description

Technical Field

[0001] The patent of this invention relates to the technical field of gas station management, specifically a diversion gas station management method based on the Internet of Things. Background Art

[0002] With the development of the economy and the increase in the number of cars, gas stations, as one of the important energy supply facilities, are facing tremendous pressure, especially during peak hours in urban central areas. Due to too many vehicles and congested roads, parking difficulties and long queues frequently occur, reducing the operating efficiency and service quality of gas stations.

[0003] In view of this, we propose a diversion gas station management method based on the Internet of Things.

[0004] Invention patent content

[0005] The purpose of this patent is to provide a diversion gas station management method based on the Internet of Things to solve the problems existing in the above-mentioned background technology.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] A diversion type gas station management method based on the Internet of Things, comprising:

[0008] Use IoT sensors to collect real-time data from vehicles at gas stations;

[0009] Calculate vehicle distribution density based on the collected data;

[0010] Use cloud computing platforms to analyze vehicle data and vehicle density data, generate prediction models for vehicle flow and refueling demand, and adjust the operating strategies of gas stations;

[0011] Calculate the path from the current location to the designated refueling station through the optimization algorithm and guide the vehicle;

[0012] Use dynamic displays to provide drivers with real-time navigation and estimated wait time information.

[0013] Preferably, the IoT sensor includes a vehicle detector, a video surveillance device and a fuel dispenser status monitor, and the real-time data includes the vehicle's location, speed, vehicle entry and exit data and type of fueling demand.

[0014] Preferably, the calculation formula for the vehicle distribution density is: F(x)=a*x^2+b*x+c, wherein x represents the time interval, and a, b, and c are adjustment coefficients.

[0015] Preferably, the cloud computing platform is used to analyze vehicle data and vehicle density data, generate a prediction model for vehicle flow and refueling demand, and adjust the operation strategy of the gas station, including:

[0016] Using cloud computing technology, data is processed in real time to identify peaks and valleys in vehicle traffic at gas stations;

[0017] Analyze historical data to identify trends and patterns in vehicle traffic, as well as seasonal variations in fueling demand;

[0018] Develop machine learning models to predict vehicle flow and refueling demand over a specific time period and train the models using historical vehicle flow and refueling data;

[0019] Dynamically adjust the operating status of the fuel dispensers based on the prediction results, including turning some fuel dispensers on or off;

[0020] Adjusting the opening strategy of gas station entrances and exits or adjusting traffic routes within gas stations to optimize vehicle flow in advance of predicted high traffic periods;

[0021] Provides forecast information and current vehicle flow status to gas station managers and customers through a user interface.

[0022] Preferably, the adjustment of the gas station entrance and exit is based on the formula: J(n)=k*log(n+1, where n represents the number of vehicles, k is the adjustment factor, and log(n+1) represents the common logarithm of (n+1), i.e., the logarithm with base 10.

[0023] Preferably, the optimization algorithm calculates the path from the current position to the designated gas station using the following formula: G(p)=\min(y*p+z), where p represents path selection, y and z are path weight parameters, and \min represents taking the minimum value.

[0024] Preferably, the use of a dynamic display screen to provide the driver with real-time navigation and estimated waiting time information further includes automatically sending coupons or discount information to vehicles with excessive waiting time, and the preferential issuance is calculated by the algorithm I(t)=t / T, where t is the waiting time and T is the threshold.

[0025] By means of the above technical solution, the present invention provides a diversion gas station management method based on the Internet of Things. It has at least the following beneficial effects:

[0026] Through the application of Internet of Things technology, real-time data collection and processing of vehicles in gas stations can be realized, which can quickly respond to vehicle backlogs. The vehicle flow direction and the use of gas pumps can be dynamically adjusted through optimization algorithms, which significantly improves the vehicle processing capacity and service speed of gas stations. Through mathematical models and optimization algorithms, such as the path selection algorithm G(p), it can effectively guide the reasonable diversion of vehicles, reduce traffic congestion inside the gas station, and improve the overall vehicle entry and exit efficiency. Through dynamic display screens, users are provided with real-time navigation, estimated waiting time, current oil prices and other information, so that users can obtain transparent and timely service information during the refueling process. Using cloud computing platforms and machine learning technologies to analyze large amounts of data, it is possible to predict and adjust gas station resources in real time, such as the number of open entrances / exits and the use status of gas pumps, to adapt to changes in vehicle flow in different time periods. By reducing vehicle queuing time and improving service speed, gas stations can handle more customers and increase operating income. At the same time, through the intelligent issuance of coupons and discounts, customer satisfaction and loyalty are increased, which helps to enhance long-term business value. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application:

[0028] Figure 1 It is a schematic diagram of the overall process of the patent of this invention. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the patent of the present invention to clearly and completely describe the technical solutions in the embodiments of the patent of the present invention. Obviously, the described embodiments are only part of the embodiments of the patent of the present invention, not all of them. Based on the embodiments in the patent 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 patent of the present invention.

[0030] See also Figure 1 As shown, the present invention provides a diversion type gas station management method based on the Internet of Things, comprising:

[0031] Step S1, using IoT sensors to collect real-time data of vehicles in a gas station;

[0032] The IoT sensors include vehicle detectors, video surveillance equipment, and fuel dispenser status monitors, and the real-time data includes the vehicle's location, speed, vehicle entry and exit data, and type of fueling demand.

[0033] Step S2: Calculate the vehicle distribution density based on the collected data;

[0034] The calculation formula of the vehicle distribution density is: F(x)=a*x^2+b*x+c, where x represents the time interval, and a, b, and c are adjustment coefficients.

[0035] Step S3: Analyze vehicle data and vehicle density data using the cloud computing platform, generate a prediction model for vehicle flow and refueling demand, and adjust the operation strategy of the gas station;

[0036] The cloud computing platform is used to analyze vehicle data and vehicle density data, generate a prediction model for vehicle flow and refueling demand, and adjust the operation strategy of the gas station, including:

[0037] Using cloud computing technology, data is processed in real time to identify peaks and valleys in vehicle traffic at gas stations;

[0038] Analyze historical data to identify trends and patterns in vehicle traffic, as well as seasonal variations in fueling demand;

[0039] Develop machine learning models to predict vehicle flow and refueling demand over a specific time period and train the models using historical vehicle flow and refueling data;

[0040] Dynamically adjust the operating status of the fuel dispensers based on the prediction results, including turning some fuel dispensers on or off;

[0041] Adjusting the opening strategy of gas station entrances and exits or adjusting traffic routes within the gas station in advance of predicted high-traffic periods to optimize vehicle flow, for example, increasing the number of available fuel dispensers during peak demand and reducing them during low demand;

[0042] Providing forecast information and current vehicle flow status, such as which fuel dispensers are available and estimated wait times, to gas station managers and customers through a user interface;

[0043] The adjustment of the gas station entrance and exit is based on the formula: J(n)=k*log(n+1), where n represents the number of vehicles, k is the adjustment factor, and log(n+1) represents the common logarithm of (n+1), that is, the logarithm with base 10. By adjusting the number of open entrances / exits and the usage status of the gas station, the changes in vehicle flow in different time periods can be adapted.

[0044] Step S4, calculating the path from the current position to the designated fuel dispenser through an optimization algorithm, and guiding the vehicle;

[0045] The optimization algorithm calculates the path from the current position to the designated gas station using the following calculation formula: G(p)=\min(y*p+z), where p represents path selection, y and z are path weight parameters, and \min represents the minimum value. This can effectively guide the reasonable diversion of vehicles, reduce traffic congestion inside the gas station, and improve the overall efficiency of vehicle entry and exit.

[0046] Step S5: Use a dynamic display screen to provide the driver with real-time navigation and estimated waiting time information, and the dynamic display screen additionally displays current oil prices, gas station status and special discount information.

[0047] The use of a dynamic display screen to provide the driver with real-time navigation and estimated waiting time information further includes automatically sending coupons or discount information to vehicles with excessive waiting time. The preferential issuance is calculated by the algorithm I(t)=t / T, where t is the waiting time and T is the threshold.

[0048] In this embodiment, it should be specifically explained that in step S1, multiple sensors of different types can be set to work simultaneously to accurately obtain data; a combination of vehicle detectors and video surveillance equipment is placed at the entrance of the gas station to obtain real-time information such as the speed and number of vehicles entering the station area. Inside the gas station, a camera can be used to record the refueling process, and the data can be transmitted to the cloud service end through the gas pump, which is convenient for staff to analyze and compile statistics, so that accurate real-time data can be obtained, thereby providing basic support for subsequent work.

[0049] For example: In a gas station with 10 gas pumps, vehicle detectors and video surveillance equipment are installed, and all data are transmitted to the cloud platform via a wireless network. Based on real-time data, the IoT control system automatically adjusts the opening status of 5 entrances and 3 exits, and provides navigation information to the driver through an LED display.

[0050] In this embodiment, it should be specifically explained that in step S4, the specific path planning algorithm adopts a depth-first search method, which can effectively solve certain NP-hard problems and will not produce backtracking on the search path, thus avoiding the possibility of duplication of work and waste of time. In addition, a greedy algorithm is used for optimization, making the path selection more reasonable and effective. At the same time, a genetic algorithm is used to improve the structure of the traditional neural network, thereby improving its generalization ability and robustness.

[0051] In this embodiment, it should be specifically explained that in step S5, during peak hours, the platform uses machine learning algorithms to optimize the overall vehicle flow. For vehicles that have been waiting for a long time, the system automatically provides coupons through the I(t) algorithm to improve user satisfaction.

[0052] 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.

[0053] Although the embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A diversion gas station management method based on the Internet of Things, characterized by: The method comprises: Use IoT sensors to collect real-time data from vehicles at gas stations; Calculate vehicle distribution density based on the collected data; Use cloud computing platforms to analyze vehicle data and vehicle density data, generate prediction models for vehicle flow and refueling demand, and adjust the operating strategies of gas stations; Calculate the path from the current location to the designated refueling station through the optimization algorithm and guide the vehicle; Use dynamic displays to provide drivers with real-time navigation and estimated wait time information.

2. The method for managing a diversion-type gas station based on the Internet of Things according to claim 1 is characterized in that: The IoT sensors include vehicle detectors, video surveillance equipment, and fuel dispenser status monitors, and the real-time data includes the vehicle's location, speed, vehicle entry and exit data, and type of fueling demand.

3. The method for managing a diversion-type gas station based on the Internet of Things according to claim 1 is characterized in that: The calculation formula of the vehicle distribution density is: F(x)=a*x^2+b*x+c, where x represents the time interval, and a, b, and c are adjustment coefficients.

4. The method for managing a diversion-type gas station based on the Internet of Things according to claim 1 is characterized in that: The cloud computing platform is used to analyze vehicle data and vehicle density data, generate a prediction model for vehicle flow and refueling demand, and adjust the operation strategy of the gas station, including: Using cloud computing technology, data is processed in real time to identify peaks and valleys in vehicle traffic at gas stations; Analyze historical data to identify trends and patterns in vehicle traffic, as well as seasonal variations in fueling demand; Develop machine learning models to predict vehicle flow and refueling demand over a specific time period and train the models using historical vehicle flow and refueling data; Dynamically adjust the operating status of the fuel dispensers based on the prediction results, including turning some fuel dispensers on or off; Adjusting the opening strategy of gas station entrances and exits or adjusting traffic routes within gas stations to optimize vehicle flow in advance of predicted high traffic periods; Provides forecast information and current vehicle flow status to gas station managers and customers through a user interface.

5. The method for managing a diversion-type gas station based on the Internet of Things according to claim 4 is characterized in that: The adjustment of the gas station entrance and exit is based on the formula: J(n)=k*log(n+1), where n represents the number of vehicles, k is the adjustment factor, and log(n+1) represents the common logarithm of (n+1), that is, the logarithm with base 10.

6. The method for managing a diversion-type gas station based on the Internet of Things according to claim 1, characterized in that: The optimization algorithm calculates the path from the current position to the designated refueling machine using the following calculation formula: G(p)=\min(y*p+z), where p represents the path selection, y and z are path weight parameters, and \min represents the minimum value.

7. The method for managing a diversion gas station based on the Internet of Things according to claim 1, characterized in that: The use of a dynamic display screen to provide the driver with real-time navigation and estimated waiting time information further includes automatically sending coupons or discount information to vehicles with excessive waiting time. The preferential issuance is calculated by the algorithm I(t)=t / T, where t is the waiting time and T is the threshold.

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