1. Filtering technology
In high quality food metal detector, filtering technology is a basic and key signal processing technology. Due to the complex food production environment, the signal received by the detector contains a large number of interference components, such as electromagnetic noise generated by surrounding electronic equipment, vibration signals of production equipment, etc. Through filtering technology, these unwanted frequency components can be effectively removed, and the signals related to metal foreign bodies can be retained. Common filtering technologies include low-pass filtering, high-pass filtering and band-pass filtering. Low-pass filtering is used to remove high-frequency noise, high-pass filtering can eliminate low-frequency interference, and band-pass filtering is to extract or suppress signals in a specific frequency band. For example, in a food packaging workshop environment full of electromagnetic interference from motors, a suitable low-pass filter can filter out the high-frequency interference signal generated by the motor, so that the detector can focus on the processing of low-frequency or medium-frequency signals generated by metal foreign bodies, thereby improving the accuracy of detection.
2. Digital signal processing algorithm
With the development of digital technology, digital signal processing algorithms have been widely used in food metal detectors. Among them, fast Fourier transform (FFT) is an important algorithm. FFT can convert time domain signals into frequency domain signals, so that the detector can analyze the characteristics of the signal in the frequency domain. By analyzing the characteristics of the signal generated by metal foreign matter in the frequency domain, such as frequency, amplitude and phase, metal foreign matter can be identified more accurately. In addition, wavelet transform is also one of the commonly used algorithms. It has good localization characteristics in both time and frequency domains and can perform multi-scale analysis of signals. For the complex signals generated by metal foreign matter of different shapes, sizes and materials in food, wavelet transform can effectively decompose and reconstruct the signal, extract more valuable characteristic information, and thus improve the detector's ability to distinguish various metal foreign matter and reduce the possibility of misjudgment.
3. Signal amplification and compensation technology
In order to accurately detect tiny metal foreign matter, signal amplification technology is essential. In the detector, the preamplifier amplifies the weak signal received by the sensor so that the subsequent processing circuit can better identify and analyze these signals. However, simple amplification may introduce noise, so it is necessary to combine compensation technology. For example, temperature compensation technology can solve the problem of signal drift caused by changes in ambient temperature. In a food production workshop, the temperature may fluctuate greatly, which will affect the performance of the electronic components of the detector and thus affect the stability of the signal. By monitoring the ambient temperature through the temperature sensor and compensating for the signal changes caused by temperature during the signal processing, it can ensure that the detector can maintain stable detection performance under different temperature conditions and improve the reliability of signal processing.
4. Intelligent recognition and adaptive technology
High quality food metal detector also uses intelligent recognition and adaptive technology to process signals. Intelligent recognition technology uses machine learning algorithms to build a classification model by learning and training a large number of known metal foreign body signals and no foreign body signals. When a new signal is received, the detector can quickly determine whether the signal comes from a metal foreign body based on this model. Adaptive technology enables the detector to automatically adjust the signal processing parameters according to the food production environment and the characteristics of the food being detected. For example, when detecting food packaged in different materials or at different production speeds, the detector can automatically optimize the filtering parameters, amplification factors, etc. to adapt to new detection requirements, thereby improving the overall detection efficiency and accuracy.
