The strategy of generating endless garbled sequences can be implemented in a variety of ways. The following is a method based on pseudo-random number generation.： ### Strategy overview： 1. **Use a pseudo-random number generator**: Choose a pseudo-random number generation algorithm (such as a linear remainder generator, Mason rotation algorithm, etc.) to generate random numbers. 2. **Map to character set**: Map the generated random number to a pre-defined character set (such as ASCII characters, Unicode characters, etc.). 3. **Loop generation**: Random numbers are continuously generated through loops and mapped to characters, thereby generating endless garbled sequences. ### Detailed steps： 1. **Select a pseudo-random number generator**： -For example, using a linear remainder generator (LCG), the formula is： \[ X_{n+1} = (a \cdot X_n + c) \mod m \] Where \(X_n\) is the current state, and \(a\), \(c\), and \(m\) are constant. 2. **Define character set**： -For example, select the printable characters (32 to 126) in the ASCII character set, for a total of 95 characters. 3. **Initialize the seed**： -Select an initial seed\(X_0\), which can be based on the current time or other variables. 4. **Generate garbled sequences**： -Use a pseudo-random number generator to generate a random number\(X_n\). -Map \(X_n\) to a character in the character set： \[ \text {character} = \text {character set} [X_n \mod \text {character set length}] \] - Output the character. - Repeat the above steps to generate endless garbled sequences. ### Sample code (Python)： ```python import time def lcg(seed, a=1664525, c=1013904223, m=2**32): while True: seed = (a * seed + c) % m yield seed def generate_gibberish(seed=None): if seed is None: seed = int(time. time()) random_gen = lcg(seed) charset = ''. join([chr(i) for i in range(32, 127)]) charset_length = len(charset) while True: random_num = next(random_gen) char = charset[random_num % charset_length] print(char, end='') # Generate endless garbled code generate_gibberish() ``` ### Precautions： -**Randomness**: The choice of pseudo-random number generator will affect the randomness of garbled code. High-quality pseudo-random number generators (such as Mason's rotation algorithm) can generate more “random” garbled code. -**Character set**: The character set can be adjusted as needed, for example, only letters, numbers, special characters, etc. can be used. -**Seed**: The choice of seed will affect the starting point of the garbled sequence. The timestamp is usually used as the seed to ensure that the sequence generated is different each time. In this way, you can generate an endless and seemingly random garbled sequence.