Pseudo-random number generator (PRNG) has been widely used in digital image encryption and secure communication. This paper reports a novel PRNG based on a generalized Sprott-A system that is conservative. To validate whether the system can produce high quality chaotic signals, we numerically investigate its conservative chaotic dynamics and the complexity based on the approximate entropy algorithm. In this PRNG, we first select an initial value as a key to generate conservative chaotic sequence, then a scrambling operation is introduced into the process to enhance the complexity of the sequence, which is quantified by the binary quantization method. The national institute of standards and technology statistical test suite is used to test the randomness of the scrambled sequence, and we also analyze its correlation, keyspace, key sensitivity, linear complexity, information entropy and histogram. The numerical results show that the binary random sequence produced by the PRNG algorithm has the advantages of the large keyspace, high sensitivity, and good randomness. Moreover, an improved finite precision period calculation (FPPC) algorithm is proposed to calculate the repetition rate of the sequence and further discuss the relationship between the repetition rate and fixed-point accuracy; the proposed FPPC algorithm can be used to set the fixed-point notation for the proposed PRNG and avoid the degradation of the chaotic system due to the data precision.

伪随机数发生器（PRNG）已广泛用于数字图像加密和安全通信中。本文报道了一种基于保守的广义Sprott-A系统的新颖PRNG。为了验证系统是否可以产生高质量的混沌信号，我们基于近似熵算法对它的保守混沌动力学及其复杂性进行了数值研究。在该PRNG中，我们首先选择一个初始值作为生成保守混沌序列的关键，然后将加扰操作引入该过程以提高序列的复杂性，并通过二进制量化方法对其进行量化。美国国家标准技术研究院统计测试套件用于测试加扰序列的随机性，我们还分析了其相关性，键空间，键敏感度，线性复杂度，信息熵和直方图。数值结果表明，PRNG算法产生的二进制随机序列具有密钥空间大，灵敏度高，随机性好的优点。此外，提出了一种改进的有限精度周期计算（FPPC）算法来计算序列的重复率，并进一步讨论了重复率与定点精度之间的关系。提出的FPPC算法可用于为提出的PRNG设置定点符号，并避免由于数据精度而导致的混沌系统降级。提出了一种改进的有限精度周期计算（FPPC）算法来计算序列的重复率，并进一步讨论了重复率与定点精度之间的关系。提出的FPPC算法可用于为提出的PRNG设置定点符号，并避免由于数据精度而导致的混沌系统降级。提出了一种改进的有限精度周期计算（FPPC）算法来计算序列的重复率，并进一步讨论了重复率与定点精度之间的关系。提出的FPPC算法可用于为提出的PRNG设置定点符号，并避免由于数据精度而导致的混沌系统降级。