How to merge two datasets based on different time interval conditions?

I have two different datasets and would like to join Dataset2 to Dataset1. In Dataset1, there are different CBs in each day, and there are IV 1 to 3 in each CB. In Dataset2, there are different time intervals, for example every 1 hour, 15 min, or 5 minute. I am looking to add conditions in left_join or fuzzy_left_join or any other way to join these two datasets as below,

IF time interval in Dataset2 is 1 hour Then add Value_y to each row of related CB in Dataset1 as is.
IF time interval in Dataset2 is 15min or 5min, then based on the duration of each CB add the average of Value_y. So, if the duration of CB1 in 2020-01-01 is 20 minutes, then add average of Value_y from those 5-minute Value_y which are fallen into the CB1 time range.

Sorry I have tried so hard to simplify this but please let me know if I need to explain it better. Thanks

Dataset1:

    Exp            DateTime IV CB Value
 1:   1 2020-01-01 10:00:00  1  1    22
 2:   1 2020-01-01 10:10:00  2  1    21
 3:   1 2020-01-01 10:20:00  3  1    33
 4:   1 2020-01-01 17:00:00  1  8    21
 5:   1 2020-01-01 17:20:00  3  8    78
 6:   1 2020-01-01 17:30:00  1  9    NA
 7:   1 2020-01-02 09:00:00  1  1    54
 8:   1 2020-01-02 09:10:00  2  1    43
 9:   1 2020-01-02 09:20:00  3  1    33
10:   1 2020-01-02 10:01:00  1  3    21
11:   1 2020-01-02 10:11:00  2  3     9
12:   1 2020-01-02 10:21:00  3  3    87
13:   2 2020-04-10 00:00:00  1  1    67
14:   2 2020-04-10 00:10:00  2  1    75
15:   2 2020-04-10 00:20:00  3  1    32
16:   2 2020-04-10 18:00:00  1 11    12
17:   2 2020-04-10 18:10:00  2 11   345
18:   2 2020-04-10 18:20:00  3 11    32
19:   2 2020-04-11 09:00:00  1  2    12
20:   2 2020-04-11 09:10:00  2  2    98
21:   2 2020-04-11 09:20:00  3  2    90
22:   2 2020-04-11 02:01:00  1  7    76
23:   2 2020-04-11 02:11:00  2  7    33
24:   2 2020-04-11 02:21:00  3  7    11

Dataset2:

             Timestamp Value_y
 1: 2020-01-01 10:01:00      33
 2: 2020-01-01 11:01:00      12
 3: 2020-01-01 12:01:00      56
 4: 2020-01-01 13:01:00      32
 5: 2020-01-01 14:01:00      16
 6: 2020-01-01 15:01:00      57
 7: 2020-01-01 16:01:00      53
 8: 2020-01-01 17:01:00      21
 9: 2020-01-01 18:01:00      NA
10: 2020-01-01 19:01:00      55
11: 2020-01-01 20:00:59      53
12: 2020-01-01 21:00:59      24
13: 2020-01-02 09:00:59      33
14: 2020-01-02 10:00:59      22
15: 2020-01-02 11:00:59      22
16: 2020-01-02 12:00:59      35
17: 2020-01-02 13:00:59      35
18: 2020-01-02 14:00:59      67
19: 2020-01-02 15:00:59      23
20: 2020-01-02 16:00:59      46
21: 2020-01-02 17:00:59      36
22: 2020-01-02 18:00:59      32
23: 2020-04-10 00:00:00       4
24: 2020-04-10 00:15:00       3
25: 2020-04-10 00:29:59       4
26: 2020-04-10 00:44:59       5
27: 2020-04-10 00:59:59       6
28: 2020-04-10 01:14:59     777
29: 2020-04-10 01:29:59     554
30: 2020-04-10 17:49:59      22
31: 2020-04-10 17:54:59      11
32: 2020-04-10 17:59:59      NA
33: 2020-04-10 18:04:59      87
34: 2020-04-10 18:09:59      65
35: 2020-04-10 18:14:59     334
36: 2020-04-10 18:19:59     554
37: 2020-04-10 18:24:59      33
38: 2020-04-10 18:29:59      NA
39: 2020-04-10 18:34:59      NA
40: 2020-04-10 18:39:59      NA
41: 2020-04-10 18:44:59      NA
42: 2020-04-10 18:49:59      NA
43: 2020-04-10 18:54:59      NA
44: 2020-04-11 01:40:17      33
45: 2020-04-11 01:55:17      NA
46: 2020-04-11 02:10:17      NA
47: 2020-04-11 02:25:17      NA
48: 2020-04-11 02:40:17      NA
49: 2020-04-11 02:55:17      33
50: 2020-04-11 03:10:17      21
51: 2020-04-11 03:25:17      34
52: 2020-04-11 03:40:17      67
53: 2020-04-11 03:55:17      54
54: 2020-04-11 04:10:17     668
55: 2020-04-11 04:25:17      44
56: 2020-04-11 08:40:14      22
57: 2020-04-11 08:55:14     345
58: 2020-04-11 09:10:14      NA
59: 2020-04-11 09:25:15     555
60: 2020-04-11 09:40:15      NA
61: 2020-04-11 09:55:15      NA

Note: Just wanted to add that each day devided into several CBs. In each CB, I have three IVs. But some of the IVs could be removed due to outlier removal. For example, CB 9 in 2020-01-01 has only one IV.

Edit: The output of dput(Dataset1) and dput(Dataset2):

Dataset1:

structure(list(Exp = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2), DateTime = structure(c(1577872800, 
1577873400, 1577874000, 1577898000, 1577899200, 1577899800, 1577955600, 
1577956200, 1577956800, 1577959260, 1577959860, 1577960460, 1586476800, 
1586477400, 1586478000, 1586541600, 1586542200, 1586542800, 1586595600, 
1586596200, 1586596800, 1586570460, 1586571060, 1586571660), class = c("POSIXct", 
"POSIXt"), tzone = "UTC"), IV = c(1, 2, 3, 1, 3, 1, 1, 2, 3, 
1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3), CB = c(1, 1, 1, 
8, 8, 9, 1, 1, 1, 3, 3, 3, 1, 1, 1, 11, 11, 11, 2, 2, 2, 7, 7, 
7), Value = c(22, 21, 33, 21, 78, NA, 54, 43, 33, 21, 9, 87, 
67, 75, 32, 12, 345, 32, 12, 98, 90, 76, 33, 11)), row.names = c(NA, 
-24L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x000001ceefb31fd0>)

Dataset2:

structure(list(Timestamp = structure(c(1577872860, 1577876460, 
1577880060, 1577883660, 1577887260, 1577890860, 1577894460, 1577898060, 
1577901660, 1577905260, 1577908859.995, 1577912459.995, 1577955659.995, 
1577959259.995, 1577962859.995, 1577966459.995, 1577970059.995, 
1577973659.995, 1577977259.995, 1577980859.995, 1577984459.995, 
1577988059.995, 1586476800, 1586477700, 1586478599.99, 1586479499.985, 
1586480399.98, 1586481299.975, 1586482199.97, 1586540999.905, 
1586541299.9, 1586541599.895, 1586541899.89, 1586542199.885, 
1586542499.88, 1586542799.875, 1586543099.87, 1586543399.865, 
1586543699.86, 1586543999.855, 1586544299.85, 1586544599.845, 
1586544899.84, 1586569217, 1586570117.005, 1586571017.01, 1586571917.015, 
1586572817.02, 1586573717.025, 1586574617.03, 1586575517.035, 
1586576417.04, 1586577317.045, 1586578217.05, 1586579117.055, 
1586594414.275, 1586595314.615, 1586596214.955, 1586597115.295, 
1586598015.635, 1586598915.975), class = c("POSIXct", "POSIXt"
), tzone = "UTC"), Value_y = c(33, 12, 56, 32, 16, 57, 53, 21, 
NA, 55, 53, 24, 33, 22, 22, 35, 35, 67, 23, 46, 36, 32, 4, 3, 
4, 5, 6, 777, 554, 22, 11, NA, 87, 65, 334, 554, 33, NA, NA, 
NA, NA, NA, NA, 33, NA, NA, NA, NA, 33, 21, 34, 67, 54, 668, 
44, 22, 345, NA, 555, NA, NA)), row.names = c(NA, -61L), class = c("data.table", 
"data.frame"), .internal.selfref = <pointer: 0x000001ceefb31fd0>)

Solution

Note:

First I grouped the Dataset1 by CB, then filtered the datasets by a condition; with this condition, I can find the rows of Dataset2 that are in the range of start (min()) and end time (max()) of each CB in Dataset2. Therefore, if the interval in Dataset2 is 1 hour, I will have a mean of only one row, and in a case the interval is 15 minutes then I will have a mean of 4 or 5 rows.
In my real datasets I had to group the Dataset1 by other columns as well as CB, which can be added into group_by().
Also, I have different columns in Dataset2 which I used list(avg-values), and then did unnest them, but this can be easily modified for only one column, like Value_y of Dataset2 in the example provided for the question.

Code:

Dataset_merged <- Dataset1 %>%
  rowwise() %>%
  group_by(CB) %>%   
  mutate(
    avg_values = list(
      Dataset2 %>%
        filter(Timestamp >= min(DateTime) & Timestamp < (max(DateTime))) %>%
        summarise(across(everything(), ~ mean(.x, na.rm = TRUE))))) %>%
  unnest_wider(avg_values) %>%  
  ungroup() %>%
  data.table()