CN-121979313-A - Food evacuation control system based on intelligent chip

CN121979313ACN 121979313 ACN121979313 ACN 121979313ACN-121979313-A

Abstract

The invention relates to the technical field of intelligent control and food packaging, in particular to a food vacuumizing control system based on an intelligent chip, which comprises a data acquisition module, a benchmark modeling module, a simulation and residual error calculation module, a strategy decision module and a strategy decision module, wherein the data acquisition module acquires an instantaneous air pressure of an air path and a load current sequence of a motor, the benchmark modeling module generates an ideal benchmark data sequence based on fluid dynamics, the simulation and residual error calculation module generates a simulation data sequence by injecting abnormal working condition parameters, the first residual error data sequence and the second residual error data sequence of a benchmark are respectively subjected to calculation reality and simulation, the strategy decision module carries out similarity matching on the residual error sequences and outputs a control strategy signal according to the matching degree.

Inventors

YAN WEI
LIANG SHUIGEN
DIAO JIAN
PENG JIE

Assignees

广州市新鲜世界电器有限公司

Dates

Publication Date: 20260505
Application Date: 20260201

Claims (7)

1. The food vacuumizing control system based on the intelligent chip is characterized by comprising a data processing chip serving as a computing core, wherein the data processing chip is in communication connection with a data acquisition module, a reference modeling module, a simulation and residual error computing module and a strategy decision module; the data acquisition module is used for acquiring and outputting a time sequence data set representing the operation state of the vacuumizing system, and the time sequence data set at least comprises an air path instantaneous air pressure sequence and a motor load current sequence; The reference modeling module is used for executing modeling calculation based on a preset fluid dynamics principle and system ideal parameters, and generating and outputting an ideal reference data sequence representing an ideal vacuumizing process; The simulation and residual calculation module is used for injecting at least one group of preset abnormal working condition parameters into the model used by the reference modeling module, executing simulation calculation and generating at least one abnormal working condition simulation data sequence; calculating a first residual data sequence between the time series data set and the ideal reference data sequence; calculating a second residual error data sequence between the at least one abnormal working condition simulation data sequence and the ideal reference data sequence; The strategy decision module is used for carrying out similarity matching calculation based on waveform morphological characteristics or time sequence characteristics on the first residual data sequence and the second residual data sequence to obtain at least one matching degree value, executing predefined decision logic according to the comparison result of the at least one matching degree value and a preset threshold value, and outputting a corresponding vacuumizing working condition judgment result and a control strategy signal.
2. The smart chip-based food evacuation control system of claim 1, wherein the process of the data acquisition module acquiring the time series data set comprises: Receiving an air pressure sampling value stream from an air pressure sensor through a preset data interface, and formatting the air pressure sampling value stream into the air path instantaneous air pressure sequence; And synchronously receiving the current sampling value flow from the current detection unit through a preset data interface, and formatting the current sampling value flow into the motor load current sequence.
3. The smart chip based food evacuation control system of claim 1, wherein the process of generating the ideal reference data sequence by the reference modeling module comprises: Calling stored initial system parameters, wherein the parameters at least comprise the nominal volume of the air extraction container, the nominal sectional area of the flow passage and the nominal power of the vacuum pump; And carrying out iterative computation based on an ideal gas state equation and a fluid motion equation according to the initial system parameters, and generating a theoretical air pressure value sequence and a theoretical motor load value sequence which change along with time to jointly form the ideal reference data sequence.
4. The smart chip-based food evacuation control system of claim 1, wherein the process of generating the abnormal condition simulation data sequence by the simulation and residual calculation module comprises: Acquiring a first group of abnormal working condition parameters, wherein the first group of parameters are used for simulating the reduction of the sectional area of a flow channel, correcting model parameters in the reference modeling module based on the first group of parameters, and then executing first simulation calculation to generate a first simulation data sequence; and acquiring a second group of abnormal working condition parameters, wherein the second group of parameters are used for simulating the nonlinear change relation of the volume of the container along with the air pressure, correcting the model in the reference modeling module based on the nonlinear change relation, and then executing second simulation calculation to generate a second simulation data sequence.
5. The smart chip-based food evacuation control system of claim 4, wherein the process of calculating the first residual data sequence and the second residual data sequence by the simulation and residual calculation module is a point-by-point subtraction operation or a least squares fit difference operation performed in the time-series data domain.
6. The smart chip-based food evacuation control system of claim 5, wherein the process of similarity matching calculation by the policy decision module comprises: calculating a cross-correlation coefficient or a dynamic time warping distance between the first residual data sequence and a second residual data sequence corresponding to the first simulation data sequence as a first matching degree; and calculating a cross-correlation coefficient or a dynamic time warping distance between the first residual data sequence and a second residual data sequence corresponding to the second simulation data sequence as a second matching degree.
7. The smart chip-based food evacuation control system of claim 6, wherein the process of outputting the control strategy signal by the strategy decision module is configured to perform a logic closed loop of: if the first matching degree is larger than a first preset threshold value, outputting a working condition judgment result representing air passage blocking and a first control strategy signal corresponding to starting a blowback program; Outputting a working condition judgment result representing the deformation of the flexible package and a second control strategy signal corresponding to the early termination of air suction and sealing if the first matching degree is not greater than the first preset threshold value and the second matching degree is greater than the second preset threshold value; If the first matching degree and the second matching degree are both lower than a third preset threshold value and the last state value of the gas circuit instantaneous gas pressure sequence meets a preset vacuum degree condition, outputting a working condition judgment result representing vacuum achievement and a third control strategy signal corresponding to executing standard sealing; if any one of the conditions is not met, outputting a signal for maintaining the current running state; the third preset threshold value is smaller than the first preset threshold value and the second preset threshold value.

Description

Food evacuation control system based on intelligent chip Technical Field The invention relates to the technical field of intelligent control and food packaging, in particular to a food vacuumizing control system based on an intelligent chip. Background Along with the wide application of intelligent control technology in the food preservation field, a vacuum packaging system becomes an important device for prolonging the shelf life of food, and in order to realize efficient and nondestructive preservation effect, modeling analysis and accurate control on the vacuumizing process become particularly important; In the actual scene, such as suction nozzle blockage caused by package bag adsorption or volume collapse caused by soft package deformation, the air pressure change curve of the air pressure change curve shows nonlinearity and similarity, and particularly the soft package volume can shrink nonlinearly with pressure reduction, so that the effective air suction volume is dynamically reduced, and the air pressure reduction rate characteristic which is remarkably different from that of a rigid container is generated; Therefore, how to carry out advanced treatment on the collected time sequence data, a mathematical model conforming to a fluid operation mechanism is constructed, different abnormal working conditions are accurately identified through the difference between actual operation data and ideal model data, and a corresponding self-adaptive control strategy is constructed, so that the intelligent and reliability of the food vacuumizing system are very important. Disclosure of Invention In order to solve the technical problems, the invention provides a food vacuumizing control system based on an intelligent chip, and specifically, the technical scheme of the invention comprises the following steps: The data processing chip is used as a computing core and is in communication connection with a data acquisition module, a reference modeling module, a simulation and residual error computing module and a strategy decision module; the data acquisition module is used for acquiring and outputting a time sequence data set representing the operation state of the vacuumizing system, and the time sequence data set at least comprises an air path instantaneous air pressure sequence and a motor load current sequence; The reference modeling module is used for executing modeling calculation based on a preset fluid dynamics principle and system ideal parameters, and generating and outputting an ideal reference data sequence representing an ideal vacuumizing process; The simulation and residual calculation module is used for injecting at least one group of preset abnormal working condition parameters into the model used by the reference modeling module, executing simulation calculation and generating at least one abnormal working condition simulation data sequence; calculating a first residual data sequence between the time series data set and the ideal reference data sequence; calculating a second residual error data sequence between the at least one abnormal working condition simulation data sequence and the ideal reference data sequence; The strategy decision module is used for carrying out similarity matching calculation based on waveform morphological characteristics or time sequence characteristics on the first residual data sequence and the second residual data sequence to obtain at least one matching degree value, executing predefined decision logic according to the comparison result of the at least one matching degree value and a preset threshold value, and outputting a corresponding vacuumizing working condition judgment result and a control strategy signal. Preferably, the process of acquiring the time series data set by the data acquisition module includes: Receiving an air pressure sampling value stream from an air pressure sensor through a preset data interface, and formatting the air pressure sampling value stream into the air path instantaneous air pressure sequence; And synchronously receiving the current sampling value flow from the current detection unit through a preset data interface, and formatting the current sampling value flow into the motor load current sequence. Preferably, the process of generating the ideal reference data sequence by the reference modeling module comprises: Calling stored initial system parameters, wherein the parameters at least comprise the nominal volume of the air extraction container, the nominal sectional area of the flow passage and the nominal power of the vacuum pump; And carrying out iterative computation based on an ideal gas state equation and a fluid motion equation according to the initial system parameters, and generating a theoretical air pressure value sequence and a theoretical motor load value sequence which change along with time to jointly form the ideal reference data sequence. Preferably, the process of generating the abnormal condition simulation data se