CN-121981501-A - Intelligent cleaning management and control system and method for food workshop based on digital factory

CN121981501ACN 121981501 ACN121981501 ACN 121981501ACN-121981501-A

Abstract

The invention relates to the technical field of data processing of production management, administrative supervision and resource prediction under the industrial Internet, and discloses an intelligent clean management and control system and method for a food workshop based on a digital factory, wherein the system comprises a feature extraction module, a control module and a control module, wherein the feature extraction module is used for obtaining process feature parameters of a work unit; the system comprises a target cleaning intensity parameter set, an instruction generation module, a logic intervention module, an authority release module and a logic coupling module, wherein the target cleaning intensity parameter set is used for generating a target cleaning intensity parameter set, the risk evaluation sub-module is used for identifying a residence time variable generated by stopping in an equipment running state flow so as to correct a washing time threshold value, the logic intervention module is used for generating nodes at a process switching point and locking equipment authorities, and the authority release module is used for acquiring execution parameters and comparing the execution parameters with the target parameter set.

Inventors

LI QIUSONG
ZHOU FUZHEN
WANG JIANLEI
SU TAIYU
YAO JINGJING
ZHANG XING
AI YAPING
LIN MIAOLI
LIU XUEPING
ZHONG XIN
XIE XIUZHEN

Assignees

福建耘福食品有限公司

Dates

Publication Date: 20260505
Application Date: 20260407

Claims (10)

1. Food workshop intelligence cleaning management and control system based on digital factory, its characterized in that, the system includes: the feature extraction module is used for acquiring process feature parameters of the work order to be executed from the production management flow, wherein the process feature parameters comprise physical properties of materials, sanitation compliance marks and process switching point time sequences; the instruction generation module is provided with a sanitary risk evaluation sub-module and is used for generating a target cleaning strength parameter set based on the process characteristic parameters and a preset sanitary evaluation model; The hygiene risk evaluation sub-module is used for monitoring the equipment operation state flow in the execution process of the work order to be executed, determining a static residence time variable based on an unplanned shutdown event in the equipment operation state flow, and calculating a material physical state change parameter by using the static residence time variable so as to correct a washing time threshold in the target cleaning intensity parameter set; The logic intervention module is used for generating a cleaning control node at a process switching point time sequence of the adjacent worksheets and locking the starting authority of downstream equipment according to the cleaning control node; And the permission release module is used for collecting the cleaning execution parameters, and carrying out consistency comparison on the cleaning execution parameters and the corrected target cleaning intensity parameter set, wherein when a result of the consistency comparison meets a preset compliance threshold, the permission release module outputs a release instruction to release the starting permission of downstream equipment.
2. The intelligent cleaning management and control system for the food workshop based on the digital factory according to claim 1, wherein the sanitary risk evaluation sub-module obtains power execution load deviation values of the production unit before and after an unplanned shutdown event when determining a material physical state change parameter, and the instruction generation module invokes a preset material adhesion mapping rule to determine a material residual thickness index of the inner wall of the pipeline according to the deviation degree of the power execution load deviation values relative to a process reference, and performs linear gain adjustment on a target cleaning intensity parameter set based on the material residual thickness index.
3. The intelligent cleaning management and control system for the food workshop based on the digital factory according to claim 1, further comprising a resource evaluation module for monitoring the real-time load state and the residual execution capacity of the clean resources shared by the whole factory, wherein the resource evaluation module is used for constructing a clean load prediction spectrogram of the whole factory by collecting pressure data and pump set frequency data of a central liquid supply system.
4. The intelligent cleaning and control system for a food plant based on a digital factory according to claim 3, further comprising a concurrent scheduling module for performing asymmetric scheduling of instruction release timing of the plurality of cleaning and control nodes based on the factory wide cleaning load prediction spectrum and the health risk weight in the process characteristic parameters when the plurality of cleaning and control nodes collide concurrently.
5. The intelligent cleaning management and control system for a food plant based on a digital factory according to claim 4, wherein the concurrent dispatch module performs peak-shifting scheduling on the corresponding cleaning management and control nodes in descending order of health risk weights when the real-time load status exceeds the remaining execution capacity.
6. The intelligent cleaning management and control system for the food workshop based on the digital factory according to claim 1, wherein the permission release module comprises a safety interface verification sub-module for acquiring execution residual parameters of a cleaning finishing stage, and the execution residual parameters comprise conductivity data of last rinsing water and pipeline back pressure stability data.
7. The intelligent cleaning and controlling system for the food workshop based on the digital factory according to claim 6, wherein the safety interface checking sub-module performs secondary logic locking on the starting authority of downstream equipment by using the execution residual parameter based on material compatibility grading of a to-be-processed sheet, and the safety interface checking sub-module triggers an optimized purging program in the cleaning and controlling node when the execution residual parameter exceeds the admission space threshold.
8. The intelligent cleaning management and control system for the food workshop based on the digital factory according to claim 2, wherein the sanitary risk evaluation sub-module acquires a power execution load deviation value by monitoring a current power spectrogram of a production pump motor in real time, and the material adhesion mapping rule defines a forward mapping relation between the power execution load deviation value and a pipeline inner diameter reduction ratio.
9. The intelligent cleaning management and control system for a food plant based on a digital factory according to claim 2, wherein the health risk assessment sub-module calculates a health risk correction coefficient The calculation formula is as follows: Wherein, the method comprises the steps of, In order to correct the coefficient of the health risk, For a preset scaling factor determined by the material's thermal sensitivity, In units of Is characterized in that the temperature of the inner cavity of the equipment is real-time, In units of A static dwell time variable of (2), a corrected wash time threshold and a sanitary risk correction factor In positive correlation, the logic intervention module defines the cleaning management and control node as a high-priority atomization task when the sanitary compliance identification between adjacent worksheets is identified to have attribute change, and suspends concurrent operation in the production management flow.
10. A method for intelligently cleaning and controlling a food workshop based on a digital factory, which is used for realizing the intelligent cleaning and controlling system for the food workshop based on the digital factory, and is characterized by comprising the following steps: step 1101, obtaining process characteristic parameters of a work order to be executed from a production management flow, wherein the process characteristic parameters comprise physical properties of materials, sanitation compliance identification and process switching point time sequence; Step 1102, generating a target cleaning intensity parameter set based on process characteristic parameters and a preset sanitation evaluation model; step 1103, monitoring the running state flow of the equipment in the execution process of the work order to be executed in real time, determining a static residence time variable based on an unplanned shutdown event in the running state flow of the equipment, and calculating a physical state change parameter of the material by using the static residence time variable so as to correct a washing time threshold in a target cleaning intensity parameter set; Step 1104, generating a cleaning control node at a process switching point time sequence of an adjacent work order, and locking the starting authority of downstream equipment according to the cleaning control node; step 1105, collecting cleaning execution parameters, and comparing the cleaning execution parameters with the corrected target cleaning intensity parameter set in a consistency manner; and 1106, outputting a release instruction to release the starting authority of the downstream equipment when the consistency comparison result meets a preset compliance threshold.

Description

Intelligent cleaning management and control system and method for food workshop based on digital factory Technical Field The invention relates to an intelligent clean management and control system and method for a food workshop based on a digital factory, and belongs to the technical field of data processing of production management, administrative supervision and resource prediction under an industrial Internet. Background The current food processing workshops generally adopt a manufacturing execution system to cooperate with automatic cleaning equipment, cleaning parameters are preset by extracting material attributes in a work order to be executed, automatic management of cleaning procedures is realized under continuous production working conditions, a current management mode mainly depends on static business data mapping, real-time checking of management logic and execution states under complex production environments is lacking, when a production plan fluctuates, supervision closed loop and accurate prediction of a management layer are difficult to realize by the system, and the management mode is based on static mapping of the work order data, so that the basic sanitary compliance requirements can be met under the premise of stable material characteristics. However, in the actual production process, unplanned shutdown is usually generated due to downstream faults or scheduling adjustment, so that materials are static and resident in the inner cavity of the equipment, and due to the fact that food materials have heat sensitivity and moisture migration characteristics, residues are subjected to physical evolution such as denaturation, dehydration and hardening along with residence time, so that the adhesive force is increased, the existing management logic ignores dynamic disturbance in the execution process, still gives an instruction according to the initial preset strength of a work order and releases production permission, the instruction strength is disjointed with the actual residual state, and systematic compliance audit risks are generated; aiming at the risks, a path of a high-precision visual or chemical detector is additionally arranged in a pipeline, the failure rate of a detection component is high and the maintenance cost is increased due to the influence of strong corrosion and easy scaling of a food processing environment, a large amount of redundant consumption of water resources, medicaments and energy consumption is generated by a mode of prolonging a cleaning time sequence through manual experience, effective stripping of a heavy wall hanging area cannot be ensured, the fundamental contradiction which cannot be solved by the traditional management architecture is that a decision logic capable of recycling production execution flow data to predict and compensate physical evolution of residues is lacked, for example, the Chinese patent application of an authorized bulletin number CN108121216B discloses an automatic workshop virtual debugging method based on a digital factory, an inter-vehicle management strategy and technological parameters are verified and optimized in advance through offline simulation, static motion simulation before side-put-on production is realized, the problems of layout optimization and logic feasibility are solved, in actual operation, the simulation architecture cannot deduce the physical evolution of residues in an inner cavity of equipment in real time, the unplanned parking deviation cannot be converted into dynamic cleaning strength compensation instructions, the management strategy and physical execution feedback real time are lacked, and the system faces to the multiple production lines and is concurrent, when the resources are occupied dynamically, the administrative resource game and task scheduling capability are lacked, and the large-scale collaborative process compliance audit closed loop is difficult to ensure. Therefore, how to utilize the energy efficiency fingerprint and the time stamp data in the production process to deduce the physical evolution trend of the residues in real time and establish a management decision closed loop covering the production authority locking and the resource allocation optimization, thereby realizing the deep supervision of the management logic on the physical execution process and the intelligent prediction of the resource scheduling in a data driving mode, and becoming the technical problem to be solved by the invention. Disclosure of Invention In order to solve the problems in the background technology, the technical scheme of the invention is as follows, a food workshop intelligent cleaning management and control system based on a digital factory, the system comprises: the feature extraction module is used for acquiring process feature parameters of the work order to be executed from the production management flow, wherein the process feature parameters comprise physical properties of materials, sanitation comp