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CN-121994825-A - System and method for vegetable detection

CN121994825ACN 121994825 ACN121994825 ACN 121994825ACN-121994825-A

Abstract

The invention discloses a system and a method for vegetable detection, which relate to the technical field of agricultural product quality detection and comprise the following steps of collecting electromagnetic change signals, sensing line noise signals and power supply fluctuation signals on a vegetable detection site, drawing an interference change curve according to the collection result, and recording the time positions of sudden rise and return to zero of detection signals; and identifying an interference source according to the interference change curve, analyzing the propagation direction and the coupling path of the electromagnetic wave, extracting an interference frequency section and a duration section, and forming a signal anomaly comparison table. The vegetable detection system realizes real-time sensing and self-adaptive avoidance of electromagnetic interference through multi-source interference signal acquisition and characteristic identification, ensures the stability of vegetable detection signals, plans the detection rhythm based on an interference risk list, dynamically adjusts the sampling and illumination period, effectively inhibits signal drift and fluctuation, realizes continuous and accurate detection in a complex environment, and improves detection reliability and data consistency.

Inventors

  • WU WEIWEI
  • JIANG JINGYI
  • JIANG YUNHUI

Assignees

  • 潍坊丸和食品有限公司

Dates

Publication Date
20260508
Application Date
20260227

Claims (10)

  1. 1. The vegetable detection method is characterized by comprising the following steps of: Collecting electromagnetic change signals, sensing line noise signals and power supply fluctuation signals on a vegetable detection site, drawing an interference change curve according to the collection result, and recording the time positions of sudden rise and return to zero of the detection signals; identifying an interference source according to the interference change curve, analyzing the propagation direction and the coupling path of electromagnetic waves, extracting an interference frequency section and a duration time section, and forming a signal anomaly comparison table; performing backtracking analysis on each batch of vegetable detection data according to the signal anomaly comparison table, searching for time dislocation, batch crossing and sensor saturation phenomena of the detection data, determining trigger points and continuous ranges of anomaly signals, and generating an interference risk list; Performing rhythm planning on the detection process based on the interference risk list, and determining a sampling sequence, exposure time and a power supply stationary stage to form an interference suppression scheme; And (3) implementing self-adaptive detection adjustment according to an interference suppression scheme in an interference risk period, setting a reverse silence sampling period, an intermittent sampling period and a delay sampling window, dynamically coordinating sampling time and illumination period, balancing signal energy fluctuation, and stabilizing an output signal in a vegetable detection process.
  2. 2. The method for vegetable detection according to claim 1, wherein the process of drawing the disturbance change curve is as follows: Arranging a signal acquisition unit on a vegetable detection site, respectively acquiring an electromagnetic change signal, a sensing line noise signal and a power supply fluctuation signal, establishing a reference potential standard, and determining a sampling frequency, a sampling period and a signal channel response threshold; performing time sequence alignment and amplitude standardization processing according to the collected electromagnetic change signals, sensing line noise signals and power supply fluctuation signals, and unifying the signals on the same time coordinate axis to form a time sequence frame; Drawing an interference change curve according to a time sequence frame, expressing an electromagnetic change signal, a sensing line noise signal and a power supply fluctuation signal in an independent curve form, and marking an interference concentration interval; recording the time positions of the sudden rise and the return to zero of the detection signal on the basis of the interference change curve, forming an interference time table and correlating the real-time amplitude data of each signal to obtain basic data for interference analysis.
  3. 3. The method for detecting vegetables according to claim 2, wherein when an interference change curve is drawn, the sequence of interference conduction is determined by comparing the correspondence between the electromagnetic change signal, the noise signal of the sensing line and the peaks, the troughs and the abrupt points of the power supply fluctuation signal, and the time positions are synchronously recorded when the signal amplitude is suddenly raised and zeroed so as to distinguish the propagation direction and the duration of the interference energy, and an interference distribution map is established for subsequent interference analysis.
  4. 4. The method for vegetable detection according to claim 2, wherein the signal abnormality comparison table is formed as follows: Dividing intervals of electromagnetic change signals, sensing line noise signals and power supply fluctuation signals according to an interference change curve, and performing layering comparison on the same time axis to determine an interference energy concentration time period and an initial sequence; analyzing the amplitude change synchronicity of the electromagnetic change signal and the sensing line noise signal according to a time interval table of the interference change curve so as to identify the propagation direction of electromagnetic waves and determine a coupling path; Dividing the frequency components of the interference change curve into intervals by combining the propagation direction and the coupling path, and extracting an interference frequency section and a duration section; And forming a signal anomaly comparison table according to analysis results of the propagation direction, the coupling path, the frequency band and the duration period, and recording information of the time position, the propagation direction, the coupling path and the frequency band of the interference.
  5. 5. The method for detecting vegetables according to claim 4, wherein when the signal abnormality comparison table is formed, a start time, an end time, a propagation direction, a coupling path, and a frequency band of occurrence of interference are associated with time series of the electromagnetic change signal, the sensor line noise signal, and the power supply fluctuation signal, and an interference energy concentration section is marked in the comparison table.
  6. 6. The method for vegetable inspection according to claim 4, wherein the interference risk list generation step is as follows: performing time sequence matching on original detection data of each batch of vegetable detection by taking the signal abnormal comparison table as an index basis, and establishing a mapping relation between batch data and an interference period; after the time sequence matching is completed, performing time dislocation analysis and batch cross analysis on each batch of detection data, judging an interference action interval according to an interference frequency segment, and recording the interference action interval in a time correlation table; According to the time correlation table, carrying out saturation phenomenon analysis on the amplitude change of the sensor output signal in each batch of detection data, and determining an interference trigger point and an interference continuous range; after time dislocation, batch crossing and saturation phenomenon analysis are completed, the analysis results are summarized to generate an interference risk list, and the triggering time, duration and affected batch and sensing channel information of an interference event are recorded.
  7. 7. The method for vegetable inspection according to claim 6, wherein the interference suppression scheme is formed as follows: Performing time sequence matching on original detection data of each batch of vegetable detection by taking the signal anomaly comparison table as an index basis, establishing a mapping relation between batch data and an interference period, and forming a batch time index table; After the time series matching is completed, performing time dislocation analysis and batch cross analysis on each batch of detection data, and recording the recognition results of the time dislocation and batch cross in a time correlation table; according to the time correlation table, carrying out saturation phenomenon analysis on the amplitude change of the sensor output signal in each batch of detection data, determining an interference trigger point and an interference continuous range, and marking a saturation interval; After time dislocation, batch intersection and saturation phenomenon analysis are completed, the analysis results are summarized to generate an interference risk list, and the triggering time, duration time, affected batch and sensing channel information of an interference event are recorded.
  8. 8. The method for detecting vegetables according to claim 7, wherein the interference events in the interference risk list are arranged according to the trigger time sequence, and are identified in a grading manner according to the duration of the interference and the affected range of the sensing channel, the interference risk list simultaneously records the detection stage information when the interference occurs, and the periodic characteristics of the interference source are determined by the repetition interval of the interference events.
  9. 9. The method for vegetable detection according to claim 7, wherein the step of performing the dynamic coordination of the sampling time and the illumination period by performing the adaptive detection adjustment according to the interference suppression scheme in the interference risk period, setting the reverse silence sampling, the intermittent sampling period, and the delay sampling window is as follows: Setting a reverse silence sampling stage according to an interference suppression scheme in a high interference interval marked by an interference risk list, and realizing sampling time misplacement by setting a sampling trigger point in an attenuation interval behind an interference peak value; after finishing the reverse silence sampling phase, constructing an intermittent sampling period according to the risk level distribution in the interference suppression scheme, and realizing time avoidance of sampling time and interference fluctuation period by adjusting the sampling interval; After the intermittent sampling period is completed, a delay sampling window is established, and sampling action is delayed to be executed after the interference peak value is ended so as to utilize a signal stabilizing interval of an interference attenuation stage; And after the reverse silence sampling, the intermittent sampling period and the delay sampling window are constructed, the sampling time and the illumination period are dynamically coordinated and executed, and the synchronous balance of the sampling and the illumination is realized in a time matching mode so as to stabilize the output of the detection signal.
  10. 10. A vegetable detection system for implementing a vegetable detection method as claimed in any one of claims 1 to 9, and comprising an interference sensing module, a feature analysis module, an anomaly diagnosis module, a rhythm planning module and an adaptive control module: The interference sensing module is used for collecting electromagnetic change signals, sensing line noise signals and power supply fluctuation signals on a vegetable detection site, drawing an interference change curve according to the collection result, and recording the time positions of sudden rise and zero return of the detection signals; The characteristic analysis module is used for identifying an interference source according to the interference change curve, analyzing the propagation direction and the coupling path of electromagnetic waves, extracting an interference frequency section and a duration time section, and forming a signal anomaly comparison table; The anomaly diagnosis module performs backtracking analysis on the vegetable detection batch data according to the signal anomaly comparison table, searches for time dislocation, batch crossing and sensor saturation phenomena of the detection data, determines trigger points and continuous ranges of anomaly signals, and generates an interference risk list; the rhythm planning module performs rhythm planning on the detection process based on the interference risk list, and determines a sampling sequence, exposure time and a power supply rest stage to form an interference suppression scheme; The self-adaptive control module is used for implementing self-adaptive detection adjustment according to an interference suppression scheme in an interference risk period, setting a reverse silence sampling period, an intermittent sampling period and a delay sampling window, dynamically coordinating sampling time and illumination period, balancing signal energy fluctuation and stabilizing output signals in a vegetable detection process.

Description

System and method for vegetable detection Technical Field The invention belongs to the technical field of agricultural product quality detection, and particularly relates to a system and a method for detecting vegetables. Background The vegetable detection comprises the steps of identifying and evaluating key indexes such as quality, safety and freshness of vegetables in production, circulation and sales links by utilizing a multi-source sensing and data analysis technology, acquiring information such as color, smell, hardness, moisture, temperature and surface reflection spectrum of the vegetables in real time by an intelligent sensor, inputting the acquired multidimensional data into an analysis system, comprehensively judging appearance characteristics, nutritional ingredients and pesticide residues of the vegetables by combining a signal processing and pattern recognition technology, and realizing automatic and intelligent detection of the quality of the vegetables. In the prior art, when a vegetable detection device runs near high-power detection equipment, a sensing circuit is extremely susceptible to electromagnetic interference, as the high-power equipment can generate strong electromagnetic fields and instantaneous pulse signals in the working process, the interference signals can enter the sensing circuit through space radiation or power supply coupling, so that the sensor output signals can have severe fluctuation in extremely short time, the fluctuation can lead to sudden rising or instantaneous zeroing of detection values, a system is easy to misjudge that the vegetable quality is abnormal, and further, recognition result deviation is caused, and especially in a multi-sensor parallel detection environment, the interference superposition effect is more likely to cause data distortion, and the overall detection precision and stability are affected. Disclosure of Invention The system and the method for vegetable detection are used for solving the technical problems in the background technology, realizing real-time sensing and self-adaptive avoidance of electromagnetic interference through multi-source interference signal acquisition and feature recognition, guaranteeing the stability of vegetable detection signals, planning the detection rhythm based on an interference risk list, dynamically adjusting the sampling and illumination period, effectively inhibiting signal drift and fluctuation, realizing continuous and accurate detection in a complex environment, and improving the detection reliability and data consistency. In order to solve the technical problems, the invention provides the following technical scheme: A method for vegetable detection, comprising the steps of: Collecting electromagnetic change signals, sensing line noise signals and power supply fluctuation signals on a vegetable detection site, drawing an interference change curve according to the collection result, and recording time positions of sudden rise and return to zero of detection signals to obtain basic data for interference analysis; Identifying an interference source according to the interference change curve, analyzing the propagation direction and the coupling path of electromagnetic waves, extracting an interference frequency section and a duration time section, forming a signal anomaly comparison table, and providing a basis for retrospective analysis of detection data; Performing backtracking analysis on the vegetable detection batch data according to the signal anomaly comparison table, searching for time dislocation, batch crossing and sensor saturation phenomena of the detection data, determining trigger points and continuous ranges of anomaly signals, generating an interference risk list, and providing reference for detection rhythm planning; Performing rhythm planning on the detection process based on the interference risk list, determining a sampling sequence, exposure time and a power supply static stage, and forming an interference suppression scheme for guiding dynamic adjustment of a subsequent detection process; And (3) implementing self-adaptive detection adjustment according to an interference suppression scheme in an interference risk period, setting a reverse silence sampling period, an intermittent sampling period and a delay sampling window, dynamically coordinating sampling time and illumination period, balancing signal energy fluctuation, and stabilizing an output signal in a vegetable detection process. The following is a further optimization of the above technical solution according to the present invention: The interference change curve is drawn as follows: Arranging a signal acquisition unit on a vegetable detection site, respectively acquiring an electromagnetic change signal, a sensing line noise signal and a power supply fluctuation signal, establishing a reference potential standard, and determining a sampling frequency, a sampling period and a signal channel response threshold; performing