You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 regional_sales
【Schema】
# Table: Customers
[
(CustomerID:INTEGER, Primary Key, Examples: [1, 2, 3]),
(Customer Names:TEXT, Examples: [Avon Corp, WakeFern , Elorac, Corp])
]
# Table: Sales Team
[
(SalesTeamID:INTEGER, Primary Key, Examples: [1, 2, 3]),
(Sales Team:TEXT, Examples: [Adam Hernandez, Keith Griffin, Jerry Green]),
(Region:TEXT, Examples: [Northeast, West, South])
]
# Table: Sales Orders
[
(OrderNumber:TEXT, Primary Key, Examples: [SO - 0001000, SO - 0001001, SO - 0001002]),
(Sales Channel:TEXT, Examples: [In-Store, Online, Distributor]),
(WarehouseCode:TEXT, Examples: [WARE-UHY1004, WARE-NMK1003, WARE-PUJ1005]),
(ProcuredDate:TEXT, Examples: [12/31/17, 4/10/18, 7/19/18]),
(OrderDate:TEXT, Examples: [5/31/18, 6/1/18, 6/2/18]),
(ShipDate:TEXT, Examples: [6/14/18, 6/22/18, 6/21/18]),
(DeliveryDate:TEXT, Examples: [6/19/18, 7/2/18, 7/1/18]),
(CurrencyCode:TEXT, Examples: [USD]),
(_SalesTeamID:INTEGER, Examples: [6, 14, 21]),
(_CustomerID:INTEGER, Examples: [15, 20, 16]),
(_StoreID:INTEGER, Examples: [259, 196, 213]),
(_ProductID:INTEGER, Examples: [12, 27, 16]),
(Order Quantity:INTEGER, Examples: [5, 3, 1]),
(Discount Applied:REAL, Examples: [0.075, 0.05, 0.1]),
(Unit Price:TEXT, Examples: [1,963.10, 3,939.60, 1,775.50]),
(Unit Cost:TEXT, Examples: [1,001.18, 3,348.66, 781.22])
]
# Table: Products
[
(ProductID:INTEGER, Primary Key, Examples: [1, 2, 3]),
(Product Name:TEXT, Examples: [Cookware, Photo Frames, Table Lamps])
]
# Table: Regions
[
(StateCode:TEXT, Primary Key, Examples: [AL, AR, AZ]),
(State:TEXT, Examples: [Alabama, Arkansas, Arizona]),
(Region:TEXT, Examples: [South, West, Northeast])
]
# Table: Store Locations
[
(StoreID:INTEGER, Primary Key, Examples: [1, 2, 3]),
(City Name:TEXT, Examples: [Birmingham, Huntsville, Mobile]),
(County:TEXT, Examples: [Shelby County/Jefferson County]),
(StateCode:TEXT, Examples: [AL, AR, AZ]),
(State:TEXT, Examples: [Alabama, Arkansas, Arizona]),
(Type:TEXT, Examples: [City, Town, CDP]),
(Latitude:REAL, Examples: [33.52744, 34.69901, 30.69436]),
(Longitude:REAL, Examples: [-86.79905, -86.67298, -88.04305]),
(AreaCode:INTEGER, Examples: [205, 256, 251]),
(Population:INTEGER, Examples: [212461, 190582, 194288]),
(Household Income:INTEGER, Examples: [89972, 78554, 76170]),
(Median Income:INTEGER, Examples: [31061, 48775, 38776]),
(Land Area:INTEGER, Examples: [378353942, 552604579, 361044263]),
(Water Area:INTEGER, Examples: [6591013, 3452021, 105325210]),
(Time Zone:TEXT, Examples: [America/Chicago, America/Phoenix, America/Los Angeles])
]
【Foreign keys】
Sales Orders._ProductID=Products.ProductID
Sales Orders._StoreID=Store Locations.StoreID
Sales Orders._CustomerID=Customers.CustomerID
Sales Orders._SalesTeamID=Sales Team.SalesTeamID
Store Locations.StateCode=Regions.StateCode

    【Question】
    Find the average yearly order by customer Weimei Corp for 2018, 2019 and 2020. "Weimei Corp" is the Customer Names; in 2018, 2019 and 2020 refers to SUBSTR (OrderDate, -2) IN ('18', '19', '20') : Average order = Divide (Count (OrderNumber), 3)

    【Evidence】
    "Weimei Corp" is the Customer Names; in 2018, 2019 and 2020 refers to SUBSTR (OrderDate, -2) IN ('18', '19', '20') : Average order = Divide (Count (OrderNumber), 3)

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT COUNT(T1.OrderNumber) / 3 FROM `Sales Orders` AS T1 INNER JOIN Customers AS T2 ON T2.CustomerID = T1._CustomerID WHERE (T1.OrderDate LIKE '%/%/18' AND T2.`Customer Names` = 'Weimei Corp') OR (T1.OrderDate LIKE '%/%/19' AND T2.`Customer Names` = 'Weimei Corp') OR (T1.OrderDate LIKE '%/%/20' AND T2.`Customer Names` = 'Weimei Corp')