Food workshop intelligent cleaning management and control system and method based on digital factory

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 a food workshop intelligent cleaning management and control system and method based on a digital factory, and the system comprises a feature extraction module which obtains work order process feature parameters; the instruction generation module is used for generating a target cleaning intensity parameter set, is provided with a risk evaluation sub-module and is used for identifying a residence duration variable generated by shutdown in the equipment operation state flow so as to correct a washing time threshold value; the logic intervention module is used for generating a node at the process switching point and locking the equipment authority; according to the method, the problem of misalignment caused by residue evolution due to the fact that an instruction cannot sense disturbance is solved, a cleaning process is determined through logic coupling to serve as a production preposed constraint, and the accuracy and compliance of a control decision are improved.

Description

Technical Field

[0001] This invention relates to an intelligent cleaning control system and method for food workshops based on digital factories, belonging to the field of data processing technology for production management, administrative supervision and resource prediction under the Industrial Internet. Background Technology

[0002] Currently, food processing workshops typically use Manufacturing Execution Systems (MES) in conjunction with automated cleaning equipment. By extracting material attributes from work orders to be executed and presetting cleaning parameters, the cleaning process can be automated under continuous production conditions. The current management model mainly relies on static business data mapping and lacks real-time verification of management logic and execution status in complex production environments. As a result, when production plans fluctuate, the system struggles to achieve closed-loop supervision and accurate prediction at the management level. This management method, based on static mapping of work order data, can meet basic hygiene compliance requirements under the premise that material characteristics are stable.

[0003] However, unplanned downtime often occurs during actual production due to downstream failures or scheduling adjustments, causing materials to remain stationary within the equipment cavity. Because food materials are heat-sensitive and prone to moisture migration, residues undergo physical changes such as denaturation, dehydration, and hardening over time, increasing adhesion. Current management logic ignores dynamic disturbances during execution, issuing instructions and releasing production permissions based on the initial preset intensity of the work order. This leads to a disconnect between the instruction intensity and the actual residue state, creating systemic compliance audit risks. To address these risks, adding high-precision visual or chemical detectors to the pipeline is problematic. The strong corrosiveness and scaling in the food processing environment result in high failure rates for the detector components and increased maintenance costs. Extending cleaning intervals based on manual experience leads to redundant consumption of water, chemicals, and energy, and cannot guarantee effective cleaning of heavily adhered areas. The fundamental contradiction that the existing management architecture fails to address lies in the lack of a decision-making logic that can reuse production execution flow data to predict and compensate for the physical evolution of residues. For example, Chinese invention patent CN108121216B discloses a virtual debugging method for automated workshops based on digital factories. It uses offline simulation to pre-verify and optimize workshop control strategies and process parameters, focusing on static motion simulation before production to solve layout optimization and logical feasibility issues. However, in actual operation, the above simulation architecture cannot predict the evolution of the physical properties of residues inside the equipment cavity in real time, nor can it convert unplanned downtime deviations into dynamic cleaning intensity compensation instructions. The management strategy and physical execution feedback are disconnected in real time. When the system faces multiple production lines running concurrently and dynamic resource occupancy, it lacks the ability to negotiate administrative resources and schedule tasks, making it difficult to ensure a closed loop for compliance auditing of large-scale collaborative processes.

[0004] Therefore, the technical problem to be solved by this invention is how to use energy efficiency fingerprints and timestamp data in the production process to predict the physical evolution trend of residues in real time, and to establish a management decision-making closed loop that covers production access control and resource allocation optimization, so as to realize the deep supervision of the physical execution process and intelligent prediction of resource scheduling by management logic through data-driven approach. Summary of the Invention

[0005] To address the problems mentioned in the background art, the technical solution of the present invention is as follows: A smart cleaning and control system for a food processing workshop based on a digital factory, the system comprising: The feature extraction module is used to obtain the process feature parameters of the work orders to be executed from the production management flow. The process feature parameters include material physical properties, hygiene compliance identification, and process changeover timing. The instruction generation module has a hygiene risk assessment sub-module. The instruction generation module is used to generate a target cleanliness intensity parameter set based on process characteristic parameters and a preset hygiene assessment model. The hygiene risk assessment submodule is used to monitor the equipment operation status flow during the execution of work orders to be executed, and to determine the static dwell time variable based on unplanned downtime events in the equipment operation status flow. The static dwell time variable is used to calculate the material physical state change parameters to correct the washing time threshold in the target cleaning intensity parameter set. The logic intervention module is used to generate cleaning control nodes at the process switching points of adjacent work orders and lock the start permissions of downstream equipment based on the cleaning control nodes. The permission release module is used to collect cleaning execution parameters and compare the cleaning execution parameters with the corrected target cleaning intensity parameter set. When the result of the consistency comparison meets the preset compliance threshold, the permission release module outputs a release command to release the startup permission of the downstream device.

[0006] Preferably, when determining the parameters of material physical state change, the hygiene risk assessment submodule obtains the power execution load deviation value of the production unit before and after the unplanned shutdown event; the instruction generation module calls the preset material adhesion mapping rules to determine the material residue thickness index of the pipeline inner wall based on the deviation of the power execution load deviation value from the process reference, and performs linear gain adjustment on the target cleaning intensity parameter set based on the material residue thickness index.

[0007] Preferably, the system also includes a resource assessment module, which is used to monitor the real-time load status and remaining execution capacity of shared cleaning resources throughout the plant; the resource assessment module constructs a plant-wide cleaning load prediction spectrum by collecting pressure data from the central liquid supply system and pump frequency data.

[0008] Preferably, the system also includes a concurrent scheduling module, which is used to perform asymmetric scheduling on the instruction release sequence of multiple cleaning control nodes when concurrent conflicts occur at multiple cleaning control nodes, based on the plant-wide cleaning load prediction spectrum and the hygiene risk weight in the process characteristic parameters.

[0009] Preferably, when the real-time load exceeds the remaining execution capacity, the concurrent scheduling module performs staggered scheduling on the corresponding cleaning control nodes according to the descending order of hygiene risk weights.

[0010] Preferably, the permission release module includes a security interface verification submodule, used to obtain the execution residual parameters at the end of the cleaning stage; the execution residual parameters include the conductivity data of the last rinse water and the pipeline backpressure stability data.

[0011] Preferably, the safety interface verification submodule uses the material compatibility classification of the order to be processed to perform a secondary logic lock on the start permission of downstream equipment using the execution residual parameters; when the execution residual parameters exceed the access space threshold, the safety interface verification submodule triggers the optimized purging procedure in the cleaning control node.

[0012] Preferably, the hygiene risk assessment submodule obtains the power execution load deviation value by real-time monitoring of the current power spectrum of the production pump motor; the material adhesion mapping rule defines a positive mapping relationship between the power execution load deviation value and the pipeline inner diameter reduction ratio.

[0013] Preferably, the health risk assessment submodule calculates the health risk correction coefficient. The calculation formula is: ;in, This is a health risk correction factor. This is a preset proportionality coefficient determined by the material's heat sensitivity. For the unit The real-time temperature of the equipment's internal cavity. For the unit The static dwell time variable; the corrected washing time threshold and the hygiene risk correction coefficient. The correlation is positive. When the logic intervention module detects a change in the attribute of the hygiene compliance identifier between adjacent work orders, it defines the cleaning control node as a high-priority atomic task and suspends concurrent operations in the production management flow.

[0014] A method for intelligent cleaning and control of a food processing workshop based on a digital factory includes the following steps: Step 1101: Obtain the process characteristic parameters of the work order to be executed from the production management flow. The process characteristic parameters include material physical properties, hygiene compliance identification, and process changeover timing. Step 1102: Generate a target cleaning intensity parameter set based on process characteristic parameters and a preset hygiene evaluation model; Step 1103: Monitor the equipment operation status flow during the execution of the work order to be executed in real time, and determine the static dwell time variable based on the unplanned downtime events in the equipment operation status flow. Use the static dwell time variable to calculate the material physical state change parameters to correct the washing time threshold in the target cleaning intensity parameter set. Step 1104: Generate a cleaning control node at the process switching point of the adjacent work order, and lock the start-up permission of the downstream equipment based on the cleaning control node. Step 1105: Collect cleaning execution parameters and compare the cleaning execution parameters with the corrected target cleaning intensity parameter set for consistency. Step 1106: When the consistency comparison result meets the preset compliance threshold, output a release command to release the startup permission of the downstream device.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. In intelligent cleaning of food workshops, the physical properties of materials and hygiene compliance marks of work orders are obtained from the production management flow through the feature extraction module. The instruction generation module converts the physical properties of materials and compliance marks into a target cleaning intensity parameter set. Combined with the logic instrumentation module, a cleaning control node is generated at the process switching node and the start-up permission of downstream equipment is locked. This enables deep coupling between production management instructions and the underlying execution layer operation logic, establishes the status of the cleaning process as a pre-constraint condition of the production process, and eliminates the problems of time sequence disconnect and unclear responsibilities between production and hygiene control in the management model.

[0016] 2. The system monitors the offset of the power execution unit's efficiency parameters and the static dwell time caused by unplanned shutdowns in real time, calls the preset material physical evolution model to calculate the hygiene load correction index, and uses this index to perform dynamic compensation on the cleaning intensity vector. This ensures that the cleaning command intensity can adapt to the changes in residue adhesion caused by production process disturbances, solves the problem that static management logic cannot perceive the changes in the physical state of materials inside the equipment cavity, and improves the accuracy of control decisions under non-steady-state conditions.

[0017] 3. The resource assessment module monitors the real-time load status and remaining execution capacity of the plant's shared cleaning resources. When multiple cleaning control anchor points experience concurrent conflicts, the management team uses a data game arbitration mechanism to perform asymmetric scheduling of the release sequence of cleaning instructions based on the hygiene risk weights in the process characteristic parameters of each production line. The cleaning duration is dynamically adjusted according to the resource load difference. The management team's data game arbitration resolves the risk of decreased fluid physical scouring force caused by multi-line parallelism, achieving administrative management logic guarantee and optimal resource allocation for the certainty of cleaning quality in large-scale collaborative processes. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the intelligent cleaning control execution process of the present invention, which integrates dynamic parameter correction and permission verification. Figure 2 This is a system hardware topology diagram of the integrated manufacturing execution system and field control unit of the present invention. Detailed Implementation

[0019] The present invention will be further described in detail below with reference to the accompanying drawings. The following embodiments are intended to explain the present invention and are not intended to limit the scope of protection of the present invention.

[0020] This invention provides an intelligent cleaning control system and method for food processing workshops based on digital factories, including a feature extraction module, an instruction generation module, a logic intervention module, a resource assessment module, and a permission release module. The feature extraction module retrieves work orders to be executed from the manufacturing execution system through a standardized data interface, and extracts material physical properties, hygiene compliance identifiers, and process changeover timing from them. The material physical properties include the material viscosity coefficient. Allergen labeling and processing temperature gradient The hygiene compliance identifier is used to define the material compatibility level between the current batch and subsequent batches. The system encapsulates these attribute fields into a hygiene load feature dimension vector for the current production cycle. The instruction generation module has a hygiene risk assessment submodule, which is used to monitor the equipment operation status stream and determine the static dwell time variable based on the motor current zeroing event in the stream data. The system collects the current power spectrum of the production pump motor in real time. If the current deviates from the reference value by more than [a certain amount], [the system will detect the problem]. And the duration exceeds If s, it is determined to be an unplanned shutdown, and a sanitary entropy increase timer is started simultaneously to obtain the static residence time variable. The system calls the material evolution function to determine the hygiene risk correction factor. The corrected formula is as follows: ,in This is a health risk correction factor. The proportionality coefficient is determined by the heat sensitivity of the material. This refers to the real-time temperature inside the equipment cavity, in units of... , The static dwell time variable is in seconds. The material adhesion mapping rule built into the instruction generation module defines a positive linear mapping relationship between the power execution load deviation value and the pipe inner diameter reduction ratio. The processor monitors the deviation of the effective current value of the production pump motor in real time and calculates the attenuation rate of the effective flow cross-sectional area of ​​the pipe. Based on the preset fluid dynamics model, it determines the coverage thickness of the residue on the inner wall of the pipe and uses the inner diameter reduction ratio as an independent variable to input the washing intensity calculation formula to generate the corrected washing time threshold.

[0021] Determine the mapping logic between the power load deviation value and the residual material thickness on the inner wall of the pipeline, and perform benchmark calibration during the deployment phase to ensure that the production pump motor operates at a preset pump set frequency within the clean pipeline. Run and collect reference current A known thickness was implanted in stages inside the tube. Material samples were collected to obtain the measured current. Calculate the measured current With reference current Inter-deviation Establish characteristic mapping curves and material thermal sensitivity ratio coefficients. Real-time temperature of the device cavity is preset Test different static dwell time variables The minimum scouring momentum required for subsequent material stripping is obtained, and its slope over time is defined as a proportionality constant. It is stored in the work order feature database, so that the instruction generation module can determine the actual deviation based on the measured deviation. and residence duration variable The corrected washing time threshold is determined; the logic intervention module performs logic instrumentation at the process switching point timing, and when it detects a change in the material attributes or hygiene compliance marks between adjacent work orders, it generates a cleaning control node in the production management flow and sends a start permission lock command to the underlying device through the industrial Ethernet protocol, so that the start register of the downstream device is in a logically blocked state; the logic intervention module implements logic instrumentation by embedding an interrupt flag bit in the instruction frame of the industrial Ethernet communication protocol, the processor parses the process switching point timing in the work order to be executed, and rewrites the permission status bit at the control word address offset of the instruction frame, so that the programmable logic controller of the downstream device will preferentially jump to the interrupt service routine corresponding to the cleaning control node during the scan cycle, thereby realizing non-intrusive logical suspension of the production instruction flow.

[0022] The logic intervention module controls the start-up permissions of downstream equipment. This is achieved by the programmable logic controller (PLC) in the field control cabinet through a logic interlock register. The intelligent management and control data processing server identifies process switching points and writes a locking vector to the control word address. The controller reads the address status at the start of the main logic. If the locking vector is detected, the start-up signal output contact of the downstream equipment is blocked, and the program is forced to jump to the small loop corresponding to the cleaning management node. This continues until the permission release module compares the measured execution residual parameters with the target cleaning intensity parameter set and finds that they meet the preset threshold. The server then sends a reset command to clear the vector, remove the start-up output contact restriction, and restore the physical start-up capability. The resource assessment module collects pressure data from the central liquid supply system and... Pump frequency data is used to construct a plant-wide cleaning load prediction spectrum, defining the current physical execution capacity. When the concurrent scheduling module detects multiple cleaning control nodes scheduled to trigger within the same time window and the predicted load exceeds the remaining execution capacity, the system allocates execution timing based on the hygiene risk weights in the process characteristic parameters of each production line. The permission release module executes a consistency comparison procedure based on execution residual parameters. The safety interface verification submodule obtains the conductivity data of the final rinse water at the end of the cleaning phase and the pipeline back pressure stability characteristics, and performs a secondary logic lock on the start permission based on the material compatibility classification of the pending work order. A preset threshold matrix for the ion concentration of the rinse liquid and the back pressure fluctuation of the purge is established, and the measured conductivity is less than... S / cm and the variance of pipeline backpressure fluctuation is continuous Less than within each sampling period When the pressure reaches MPa, a release command is output to release the startup permission of the downstream device.

[0023] Example 1: In a digital sauce filling workshop, the production plan switched from processing high-viscosity sauces containing peanut components to jam products. Filling pump motor current monitoring data indicated that during the peanut sauce filling cycle, [the following occurred]. Unplanned shutdown of s, real-time temperature inside the equipment cavity Maintain at This causes high-viscosity materials to harden on the filling pipeline wall due to thermal evolution during the static residence of the material inside the pipeline.

[0024] This embodiment is a specific operational example of the general technical solution described above in a specific scenario. The feature extraction module retrieves work order attributes from the manufacturing execution system and identifies the current material viscosity coefficient. for mPa·s, hygiene compliance label Marked as high-risk, the hygiene risk assessment submodule of the instruction generation module detected that the filling pump motor current deviated from the baseline value. And the duration exceeds s, is determined to be an unplanned shutdown and the static dwell time variable is recorded. for s, call the material evolution function and according to the formula The health risk correction coefficient was calculated. for , This is a health risk correction factor. The proportionality coefficient is determined by the material's thermal sensitivity; in this embodiment, it is taken as a value of [value missing]. , This refers to the real-time temperature inside the equipment cavity, in units of... , The static residence time variable is expressed in seconds (s). This correction coefficient represents the increase in sanitary load due to residence time. The instruction generation module adjusts the washing time in the target cleaning intensity parameter set based on this coefficient. s is corrected to s, to achieve logical adaptation between the cleaning instruction intensity and the changes in residue adhesion caused by production disturbances.

[0025] Example 2: The experimental platform is constructed based on a fluid processing physics experimental system. The data source is an automatic force monitoring unit and sensor array, and its current acquisition resolution is [missing information]. A, the accuracy of conductivity measurement is S / cm, sampling period The settings are limited by the data processing load and the accuracy of transient feature capture, and the frequency bandwidth of the monitored motor start-up fluctuation is within a certain range. Hz to In the Hz range, to avoid signal aliasing and control response delay, the current sampling period is... Determined as ms; To simulate a food processing environment, a signal-to-noise ratio of is injected into the acquisition link. dB Gaussian white noise, viscosity coefficient of materials in the experimental group for mPa·s, real-time temperature inside the equipment cavity for The experimental design included three experimental groups with time gradients, a control group without correction logic, and a set of static dwell time variables. for The control group of s is out of range, which is used to characterize the contribution of material physical state change parameters to cleaning load. The logic intervention module locks the start-up permission of downstream equipment at the process switching point. The instruction generation module receives the correction parameters generated by the hygiene risk assessment submodule. See Table 1 for specific data.

[0026] Table 1: Quantitative Comparison Data Table

[0027] In the comparison sample, due to the lack of time compensation, the static washing time threshold could not peel off the hardened material, causing the residual parameter to exceed the admission space threshold; this was due to the static residence time variable. Depend on s increases to s, the washing time for system reconfiguration is determined by s linear gain to s, the conductivity measurement of the final rinse water is consistently within the compliant range; when the static residence time variable achieve When the limit overload zone of s is reached, the washing time is extended to The conductivity still rebounds, at which point the mapping relationship between the adhesion of material residue and washing time enters the saturation region.

[0028] Example 3: This example combines Figures 1 to 2 This document describes an intelligent cleaning control system and method for a food processing workshop based on a digital factory, such as... Figure 1 As shown, the system obtains process characteristic parameters of the work orders to be executed from the production management flow. These parameters include material physical properties, hygiene compliance identifiers, and process switching point timing. Based on the process characteristic parameters and a preset hygiene evaluation model, a target cleaning intensity parameter set is generated. In subsequent steps, the system monitors the equipment operation status flow during the execution of the work orders in real time and determines the static dwell time variable based on unplanned downtime events in the equipment operation status flow. The static dwell time variable is used to calculate material physical state change parameters to correct the washing time threshold in the target cleaning intensity parameter set. Then, a cleaning control node is generated at the process switching point timing of adjacent work orders to lock the start permission of downstream equipment. After that, cleaning execution parameters are collected and compared with the corrected target cleaning intensity parameter set. Finally, when the result of the consistency comparison meets the preset compliance threshold, a release command is output to release the start permission of downstream equipment.

[0029] like Figure 2As shown, the system hardware architecture includes a production management server and an intelligent control data processing server. The production management server is equipped with a Manufacturing Execution System (MES) to handle work order issuance or compliance records and stores a work order feature database. It maintains data synchronization with the intelligent control data processing server, which is equipped with an intelligent cleaning control system for the food workshop. It includes a feature extraction and instruction generation module, a resource assessment and concurrent scheduling module, and an access control verification module. All of the above servers are connected to an industrial Ethernet data bus to achieve data interaction and instruction issuance. The field control cabinet at the bottom layer integrates a programmable logic controller (PLC), which runs logic intervention or access control programs and interrupt service routines. The field control cabinet is connected to a power execution unit containing production pump motors and current or power monitoring functions via I / O control, an online sensor array consisting of conductivity meters or pressure transmitters and temperature sensors via analog signal acquisition, and public works facilities including a central liquid supply system and variable frequency pump sets via frequency conversion control.

[0030] Example 4: In a digital dairy workshop where the filling lines share a central liquid supply system, when the process switching timelines of production lines A and B are within the same sampling period as recorded in the production management flow record, the simultaneous activation of the high-pressure flushing program across multiple lines due to the system's total rated flow limit will cause the terminal spray pressure to increase. Below the preset MPa threshold, resulting in the risk of residue stripping due to insufficient fluid physical scouring force; system implements hygiene risk weighting. The dynamic mapping procedure extracts allergen identifiers from the material physical properties issued by the manufacturing execution system. Compatibility factor with hygiene compliance labels The priority weight of the current task is calculated based on linear weighted logic, using the following formula: ,in As a health risk weight, Allergen identification score, This is the material compatibility coefficient. As a proportional allocation factor, it was identified by comparing historical audit data. The concurrent scheduling module initiates priority arbitration after determining that a resource concurrency conflict has occurred. The system obtains the corresponding feature from the feature extraction module. Health risk weights Corresponding to production line B Health risk weights Production line A is defined as a high-priority atomization task; at this time, the logic intervention module sends a start permission lock command to production line B and calculates the physical capacity occupancy time required for production line A to execute the cleaning procedure. When the residual parameters of the final rinse of production line A meet the consistency comparison requirements and the plant-wide cleaning load prediction spectrum shows the current remaining execution capacity. When the flow rate requirement exceeds the rated flow rate requirement of production line B, the system releases the execution privileges of production line B.

[0031] In a food processing workshop containing multiple batches of materials with varying properties, a proportional coefficient is applied to a material physical evolution model. The calibration procedure; the flow testing system integrates an online viscometer and pressure sensor and has temperature control capabilities, collecting viscosity coefficients of different materials. Corresponding sample material data, recording the real-time temperature of the internal cavity of different equipment. static dwell time variable under certain conditions Numerical correlation between parameters and changes in the physical state of materials, and determination of proportionality coefficients through numerical fitting. The mapping relationship between the material's heat sensitivity level and the resulting value is used as the correction factor for calculating the health risk. The input benchmark is used to determine the power load benchmark calibration procedure before the production unit starts operation for the first time. The power spectrum of the production pump motor current corresponding to different flow rates and material physical properties under standard process conditions is collected to determine the benchmark distribution of the efficiency parameters during steady-state operation. The determination threshold for unplanned shutdown events is determined based on the variance of the distribution. The conductivity data of the last rinse water at the end of the cleaning stage and the pipeline back pressure stability data are acquired simultaneously. The measured residual parameters are used to construct the initial benchmark of the access space threshold matrix, so that the consistency comparison standard of the permission release module is adapted to the physical characteristics of the specific equipment.

[0032] Example 5: In a digital filling deployment scenario involving high-frequency process switching, the startup permission lockout state of downstream equipment is limited by the fluid back pressure stability deviation caused by pipeline physical wear. This leads to the execution residual parameters collected by the safety interface verification submodule deviating from the initial preset benchmark, resulting in inaccurate consistency comparison and production scheduling interruptions due to false alarms. This example provides a standard pre-calibration and weight matrix optimization procedure for the aforementioned instruction generation module and safety interface verification submodule. By constructing a load benchmark distribution model, it provides a reference for system operation, performs system self-checks under no-load cyclic conditions, and collects no-load pressure data of the main pipe of the liquid supply system. With pump frequency The baseline curve is obtained, and the load baseline distribution model is generated by fitting the second-order least squares method.

[0033] For materials with different levels of heat sensitivity, the system performs offline material adhesion mapping experiments and records the variable of specific static residence time. The deviation of the dynamic load under the given conditions from the process reference is determined, and this deviation is converted into an index of the residual material thickness on the inner wall of the pipeline, thereby determining the proportionality coefficient. Dynamic gain lookup table; technicians will use the conductivity data of the final rinse water and continuous The variance of pipeline backpressure fluctuation within each sampling period is written into the admission space threshold matrix, which serves as an atomic judgment benchmark for the release of startup permissions by the logic intervention module.

[0034] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0035] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A smart cleaning and control system for a food processing workshop based on a digital factory, characterized in that, The system includes: The feature extraction module is used to obtain the process feature parameters of the work orders to be executed from the production management flow. The process feature parameters include material physical properties, hygiene compliance identifiers, and process changeover timing. The instruction generation module has a hygiene risk assessment sub-module. The instruction generation module is used to generate a target cleanliness intensity parameter set based on process characteristic parameters and a preset hygiene assessment model. The hygiene risk assessment submodule is used to monitor the equipment operation status flow during the execution of work orders to be executed, and to determine the static dwell time variable based on unplanned downtime events in the equipment operation status flow. The static dwell time variable is used to calculate the material physical state change parameters to correct the washing time threshold in the target cleaning intensity parameter set. The logic intervention module is used to generate cleaning control nodes at the process switching points of adjacent work orders and lock the start permissions of downstream equipment based on the cleaning control nodes. The permission release module is used to collect cleaning execution parameters and compare the cleaning execution parameters with the corrected target cleaning intensity parameter set. When the result of the consistency comparison meets the preset compliance threshold, the permission release module outputs a release command to release the startup permission of the downstream device.

2. The intelligent cleaning and control system for a food processing workshop based on a digital factory, as described in claim 1, is characterized in that... When determining the parameters of material physical state change, the hygiene risk assessment submodule obtains the power execution load deviation value of the production unit before and after the unplanned shutdown event; the instruction generation module determines the material residue thickness index of the inner wall of the pipeline by calling the preset material adhesion mapping rules according to the deviation of the power execution load deviation value from the process reference, and performs linear gain adjustment on the target cleaning intensity parameter set based on the material residue thickness index.

3. The intelligent cleaning and control system for a food processing workshop based on a digital factory, as described in claim 1, is characterized in that... The system also includes a resource assessment module, which monitors the real-time load status and remaining execution capacity of shared cleaning resources throughout the plant. The resource assessment module constructs a plant-wide cleaning load prediction spectrum by collecting pressure data from the central liquid supply system and pump frequency data.

4. The intelligent cleaning and control system for a food processing workshop based on a digital factory, as described in claim 3, is characterized in that... The system also includes a concurrent scheduling module, which is used to perform asymmetric scheduling on the instruction release sequence of multiple clean management nodes when concurrent conflicts occur, based on the plant-wide clean load prediction spectrum and the hygiene risk weight in the process characteristic parameters.

5. The intelligent cleaning and control system for a food processing workshop based on a digital factory, as described in claim 4, is characterized in that... When the real-time load exceeds the remaining execution capacity, the concurrent scheduling module performs staggered scheduling on the corresponding cleaning control nodes according to the descending order of hygiene risk weight.

6. The intelligent cleaning and control system for a food processing workshop based on a digital factory, as described in claim 1, is characterized in that... The permission release module includes a security interface verification submodule, which is used to obtain the execution residual parameters at the end of the cleaning stage; the execution residual parameters include the conductivity data of the last rinse water and the pipeline backpressure stability data.

7. The intelligent cleaning and control system for a food processing workshop based on a digital factory, as described in claim 6, is characterized in that... The safety interface verification submodule uses the material compatibility classification of the order to be processed and performs a secondary logic lock on the start permission of downstream equipment using the execution residual parameters. When the execution residual parameters exceed the access space threshold, the safety interface verification submodule triggers the optimized purging procedure in the cleaning control node.

8. The intelligent cleaning and control system for a food processing workshop based on a digital factory, as described in claim 2, is characterized in that... The hygiene risk assessment submodule obtains the power execution load deviation value by monitoring the current power spectrum of the production pump motor in real time; the material adhesion mapping rule defines the positive mapping relationship between the power execution load deviation value and the pipeline inner diameter reduction ratio.

9. The intelligent cleaning and control system for a food processing workshop based on a digital factory, as described in claim 2, is characterized in that... The health risk assessment submodule calculates the health risk correction coefficient. The calculation formula is: ;in, This is a health risk correction factor. This is a preset proportionality coefficient determined by the material's heat sensitivity. For the unit The real-time temperature of the equipment's internal cavity. For the unit The static dwell time variable; the corrected washing time threshold and the hygiene risk correction coefficient. The correlation is positive. When the logic intervention module detects a change in the attribute of the hygiene compliance identifier between adjacent work orders, it defines the cleaning control node as a high-priority atomic task and suspends concurrent operations in the production management flow.

10. A method for intelligent cleaning and control of a food processing workshop based on a digital factory, used to implement the intelligent cleaning and control system for a food processing workshop based on a digital factory as described in claim 1, characterized in that, Includes the following steps: Step 1101: Obtain the process characteristic parameters of the work order to be executed from the production management flow. The process characteristic parameters include material physical properties, hygiene compliance identification, and process changeover timing. Step 1102: Generate a target cleaning intensity parameter set based on process characteristic parameters and a preset hygiene evaluation model; Step 1103: Monitor the equipment operation status flow during the execution of the work order to be executed in real time, and determine the static dwell time variable based on the unplanned downtime events in the equipment operation status flow. Use the static dwell time variable to calculate the material physical state change parameters to correct the washing time threshold in the target cleaning intensity parameter set. Step 1104: Generate a cleaning control node at the process switching point of the adjacent work order, and lock the start-up permission of the downstream equipment based on the cleaning control node. Step 1105: Collect cleaning execution parameters and compare the cleaning execution parameters with the corrected target cleaning intensity parameter set for consistency. Step 1106: When the consistency comparison result meets the preset compliance threshold, output a release command to release the startup permission of the downstream device.

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