Use case is as follows:
The parser (through HTML parser) collects information from the website https://chargefinder.com/ at specified addresses (there can be from 2 to 20 charging columns at one address). The information is collected every 10 minutes and the information is the answers to the question - is the column occupied or available. The results are saved in a table - (for example, 0 - available and 1 - occupied) One column - one page of the table). The name of the column is the address and №1 (for example) - then by columns - the date is broken down into columns with time intervals of 10 minutes each, that is, 144 columns for one date.
The obtained information is received upon request in the Telegram bot. For example, we request information about a specific charging station - for this, we enter its name (for simplicity, we can enter the code name of the charging station (specific column) for example by the first two letters of the street (if it consists of two words - Baker Street, 13 in San Francisco - BSS-13-1-150) plus the first letter of the city, then the house number and then the column number, the power of the column (in kW) and the cost per kW/hour (if this information is not available on the website, then by default it is set to 55 cents per kW/hour). And when we enter - BSS-13-1-150, the bot provides information that this column was occupied 26% in the last 24 hours, 23% in the last 7 days, 19% in the last 30 days, and 17% for the entire period, and during this time it earned (income according to the tariff - before expenses) $514 in the last 24 hours, $3876 in the last 7 days, $11286 in the last 30 days, and $123,456 for the entire period. For this operation, the bot processes data from the table (calculates occupancy in percentage - the number of "ones" (1 - occupied) in the specified period. Then it calculates using the formula - the power of the column (150 kW for example) multiplied by 24 (the number of hours in a day) multiplied by the tariff ($0.55 - per kW/hour) and by the occupancy percentage. Next, depending on the time period, we multiply by - one day, 7 days, 30 days, and for the entire period (the number of days since the start of measurements (data collection in the table).
25 addresses - the list will be provided - a maximum of one week
