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Traditional particle electrostatic induction instruments, which rely on multi-stage analog amplification as their core technology, have the following characteristics: They amplify signals within a selected bandwidth without distinction, using these amplified signals as the basis for the instrument’s final output; as a result, the instrument is unable to distinguish between various electromagnetic interferences, nor can it detect probe failures such as contamination. Limited signal bandwidth: For devices with AC coupling, the typical range for signal processing is in the low-frequency range of 1Hz to 10Hz. The instrument isolates useful DC signals and only monitors the weakest signals within that frequency range, which are most susceptible to turbulence effects. For devices with DC coupling, the typical processing bandwidth is from DC up to 4Hz. While the instrument can detect DC signals, it is unable to process information from higher frequencies. Effective electrical isolation cannot be achieved; the probe, the front-end circuitry, and adjacent pipes cannot form an independent potential reference system. As a result, the instrument is prone to interference from grounding circuits, such as those from large electrical equipment nearby, as well as interference coupled through signal lines and power lines. This is also one of the main reasons why such instruments exhibit unstable performance under different operating conditions and in various environments. There is no specific analysis of signal characteristics, so it is not possible to determine the flow conditions, and thus no necessary corrections can be made to the signals. This means that the output is affected by the flow conditions, leading to inaccurate measurement results. Considering the features and limitations of particle electrostatic induction instruments based on multi-stage analog amplification, these instruments are suitable as sensitive devices for detecting dust leaks in most situations, but it is difficult for them to meet the standards required for precise quantitative measurements. Technological progress: With the development of microprocessors and digital signal processing technologies, particle electrostatic induction technology has also entered the digital era. Just as with radar, a technology invented during World War II, digital signal processing has enabled a fundamental improvement in the amount of information that radar can acquire, as well as in its resistance to interference, accuracy, and stability – progressing from the simple echo amplification methods of the early days to modern phased array technologies. At the beginning of this century, with the widespread use of high-precision, high-speed analog-to-digital converters, the application of fluid dynamics models for dusty gas flow, the development of statistical models and signal processing algorithms, as well as algorithm optimization and low-cost computing hardware (DSPs, FPGAs), it became possible to carry out \"bao li\" calculations – that is, using optimized algorithms to perform iterative computations on over-sampled data in order to obtain comprehensive results. A new generation of particle electrostatic induction instruments, based on digital signal processing technology, has also matured as their use in field applications increases. The characteristics of particle electrostatic induction instruments based on digital signal processing are as follows: A completely independent front-end measurement circuit, separated from the rest of the device through electrical isolation, allowing the probe, the front-end measurement circuit, and the surrounding pipes to form an independent reference system, thereby eliminating interference from power supplies, signal lines, and grounding circuits. Continuous drift tracking and deviation correction techniques in the front-end measurement circuit, which effectively eliminate the effects of amplifier drift within a reasonable temperature range (industrial grade: -40°C to 85°C). The analog circuit section does not process the signals in any way; it merely buffers or slightly amplifies the raw signals, preserving enough of the original signal bandwidth so that more information about the fluid can be available for subsequent algorithms. Within the signal chain, the signals are converted to digital form as early as possible, after which statistical and digital algorithms are used to process them. DC signals and AC signals are processed separately, with a reference value determined based on operating conditions; typically, the intensity of either DC or AC signals is used as the reference. Flow velocity is estimated by analyzing the characteristic frequencies of AC signals or the correlation between signals from probes located upstream and downstream, thereby compensating for the effects of changes in flow velocity on the reference signal and ensuring that the output signal reflects only the particle concentration. By analyzing the correlation between multiple signals, electromagnetic interference at any frequency, especially spatial electromagnetic interference, can be eliminated. “Bao li” calculations are utilized to analyze over-sampled data, thereby reducing the impact of random noise caused by factors such as turbulence. By comparing the characteristics of DC and AC signals, faults such as probe contamination can be detected. When the sampling frequency exceeds the range of the “spatial filtering effect,” it becomes possible to process complete information regarding particle flow. By processing thousands of data points in a sample, digital signal processors can perform various statistical and spectral analyses on signals, and cross-correlation algorithms can also be used to measure particle flow velocities with precision. Various models and algorithms have been validated in laboratories and in the field, and offline data analysis software is used when necessary to tune them for optimal performance under specific operating conditions. Therefore, particle electrostatic induction instruments based on digital signal processing technology exhibit better stability and repeatability, which is precisely the foundation for the calibrability of quantitative measurements. Compared to traditional devices, devices based on digital signal processing technology