Self-adaptive pulse flow CIP cleaning control and energy efficiency optimization system

Abstract

The invention relates to the technical field of CIP cleaning, and discloses an adaptive pulse flow CIP cleaning control and energy efficiency optimization system, which comprises a data acquisition module, an intelligent decision and decision generation module, an execution and control module, a feedback and optimization module and a maintenance and management module, through a multi-mode flow field intelligent control and cleaning object self-adaption identification technology, various scenes of CIP cleaning can be comprehensively covered, conventional cleaning efficiency indexes are concerned, elements such as pipe diameter adaptability, dirt type pertinence and flow field mode adaptability are also included, the cooperation process of pulse parameters and a flow field is deeply optimized, and the cleaning efficiency is improved. The cleaning effect can be more accurately matched with real requirements of different cleaning objects, and a more scientific basis is provided for high efficiency and pertinence of industrial cleaning.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of CIP cleaning, and more particularly discloses a self-adaptive pulse flow CIP cleaning control and energy efficiency optimization system. BACKGROUND

[0002] CIP cleaning is a technology that automatically cleans and sterilizes the inside of production equipment such as pipelines and tanks through a preset program without disassembling the equipment or interrupting the production line, which can effectively remove residues and kill microorganisms, significantly improve production efficiency and ensure product hygiene quality.

[0003] The patent document with the authorization announcement number CN118217816A discloses a "CIP cleaning method of tangential flow filtration system", which includes the following steps: opening the tangential flow filtration system membrane package backflow end valve, using the flushing liquid to flush the membrane package backflow end until reaching the set time T1; opening the tangential flow filtration system membrane package permeation end valve, adjusting the membrane package backflow end valve, using the flushing liquid to clean the membrane package permeation end until reaching the set time T2; opening the membrane package backflow end valve, using the disinfectant to flush the membrane package backflow end until reaching the set time T3; opening the membrane package permeation end valve, adjusting the membrane package backflow end valve, using the disinfectant to flush the membrane package permeation end until reaching the set time T4; compared with the traditional tangential flow filtration system cleaning method; The patent document with the authorization announcement number CN116967227A discloses a "CIP cleaning system", which includes two or more parallelly arranged cleaning liquid storage tanks, each of which is provided with a cleaning pipeline at the bottom and a recovery pipeline at the top that is in communication with the cleaning pipeline; it also includes one or more cleaning targets and a pipe gallery that is in communication with the cleaning targets, and each cleaning pipeline is also in communication with the pipe gallery; each cleaning liquid storage tank is provided with an overflow pipeline at the top and a sewage pipeline at the bottom; each overflow pipeline is connected through an auxiliary pipeline.

[0004] The prior art can shorten the cleaning liquid conveying length by arranging cleaning liquid storage tanks in parallel and canceling the shunt and the main pipeline, avoid the problem of too long waiting time caused by a single main pipeline, save cleaning liquid, cleaning water and time, and can also flush the membrane package backflow end and the permeation end in steps for the tangential flow filtration system, save process time and the amount of flushing liquid and disinfectant by controlling the flushing liquid flow rate, pressure and cleaning time, improve control accuracy and cleaning effect, and quickly restore system process performance; However, the prior art cannot intelligently switch the flow field mode (combination of laminar flow, turbulent flow and pulse flow) according to the cleaning stage, pipeline structure and dirt type, has insufficient cleaning ability for complex structures such as elbow pipes and valve cavities, and is prone to over-cleaning in some areas and insufficient cleaning in some areas. At the same time, different pipe diameters, structures and pollution types (such as oil and protein dirt) mostly use fixed values or simple manual adjustment, which is difficult to provide personalized adaptive parameters, is prone to incomplete cleaning or excessive resource consumption, lacks a closed-loop mechanism for energy efficiency management, cannot monitor resource consumption such as water, chemical reagents and heat energy in real time and dynamically optimize, cannot minimize resource consumption while ensuring cleaning effect, has serious resource waste, does not meet the green manufacturing trend, and finally cannot plan an optimal cleaning scheme in advance, lacks a device fault prediction mechanism, is prone to cleaning interruption or reduced effect due to pump, valve and other component failures, and has high manual operation and maintenance cost and delayed response. SUMMARY

[0005] The technical problem solved by the present application is to provide a self-adaptive pulse flow CIP cleaning control and energy efficiency optimization system that can solve the problems raised in the background art.

[0006] To solve the above technical problems, according to one aspect of the present application, more specifically a self-adaptive pulse flow CIP cleaning control and energy efficiency optimization system, comprising: a data acquisition module, an intelligent decision and decision generation module, an execution and control module, a feedback and optimization module, a maintenance and management module. The data acquisition module acquires feature data related to the cleaning object, real-time state data of the cleaning process, system energy efficiency consumption data, cleanliness data and equipment operating condition data; the intelligent decision and decision generation module receives the data transmitted by the data acquisition module, identifies the cleaning object and generates corresponding cleaning process decisions, and generates system full-cycle operation decisions in combination with energy efficiency data; the execution and control module receives the decision results, executes and controls the pulse parameters in the cleaning process, and switches and executes the cleaning flow field mode; the feedback and optimization module monitors the system energy efficiency consumption and the cleanliness after cleaning in real time, and feeds back and optimizes the monitored energy efficiency data and cleanliness data; the maintenance and management module manages the overall operation state of the system, monitors and analyzes the operating state of the key equipment in the system, predicts equipment failure and generates maintenance prompts.

[0007] Further, the data acquisition module comprises: a cleaning object feature acquisition module, a process state acquisition module, an energy efficiency and cleanliness acquisition module, and a working condition acquisition module. The cleaning object feature acquisition module adopts a pipe diameter sensor, a material sensor, and an industrial camera to acquire the pipe diameter, material, and pipe structure data of the cleaning object. The process state acquisition module adopts a flow sensor, a pressure sensor, and a temperature sensor to acquire the real-time flow, pressure, and temperature data of the cleaning liquid. The energy efficiency and cleanliness acquisition module adopts a flow meter, a chemical sensor, a heat meter, and an ATP biological fluorescence detector to acquire the water resource consumption, chemical reagent concentration, heat energy loss, and post-cleaning cleanliness data. The working condition acquisition module acquires the running speed of the pump and the vibration frequency data of the valve through a speed sensor and a vibration sensor.

[0008] Further, the intelligent decision-making and decision generation module comprises: a cleaning object recognition and flow field decision module, and an energy efficiency and full-cycle decision module. The cleaning object recognition and flow field decision module adopts fuzzy control and deep reinforcement learning technology to recognize the cleaning object feature data and generate cleaning process decisions for flow field mode switching and pulse parameter adjustment. The energy efficiency and full-cycle decision module adopts an "energy efficiency-cleanliness" Pareto optimization model to generate running optimization decisions for cleaning parameters and resource allocation in the system full cycle in combination with energy efficiency consumption data.

[0009] Further, the execution and control module comprises: a pulse execution module and a flow field mode execution module. The pulse execution module adopts a pulse parameter execution engine to receive cleaning process decisions and adjust the pulse frequency, pulse amplitude, and pulse duration of the cleaning liquid. The flow field mode execution module adopts a flow field switching valve to receive cleaning process decisions and switch the combined flow field mode of laminar flow, turbulent flow, and pulse flow.

[0010] Further, the feedback and optimization module comprises: an energy efficiency monitoring and feedback module, and a cleanliness detection and feedback module. The energy efficiency monitoring and feedback module adopts a data comparison algorithm to compare the real-time energy efficiency consumption data with the preset optimal energy efficiency threshold, generate an energy efficiency deviation signal, and feed it back to the intelligent decision-making and decision generation module. The cleanliness detection and feedback module adopts a standard judgment logic to compare the real-time cleanliness data with the cleanliness standard value, generate a cleanliness standard signal or an optimization signal, and feed it back to the intelligent decision-making and decision generation module.

[0011] Further, the maintenance and management module comprises a management module and a prediction and maintenance module. The management module: real-time display of cleaning process progress, energy consumption statistics, and visual data of equipment operation parameters. The prediction and maintenance module: using digital twin modeling technology, virtual modeling of equipment, monitoring of equipment wear degree and performance degradation trend, generation of fault warning information and maintenance time prompt.

[0012] Further, the sensors all use timestamp synchronization technology to ensure the time correlation of the cleaning object feature data, process state data, energy efficiency and cleanliness data, and working condition data.

[0013] The adaptive pulse flow CIP cleaning control and energy efficiency optimization system has the following beneficial effects: through multi-modal flow field intelligent control and cleaning object adaptive recognition technology, it can comprehensively cover various scenes of CIP cleaning, not only focusing on conventional cleaning efficiency indicators, but also including pipe diameter adaptability, dirt type specificity, and flow field mode adaptability, and deeply optimizing the cooperation process of pulse parameters and flow field, so that the cleaning effect more accurately matches the real needs of different cleaning objects, providing a more scientific basis for the efficiency and specificity of industrial cleaning. In addition, through the deep integration of the pulse parameter execution engine and the energy efficiency optimization controller, the effective linkage of cleaning execution and energy efficiency monitoring is realized, breaking the information barrier between parameter adjustment and resource consumption monitoring, improving the resource utilization efficiency and intelligent level of CIP cleaning, and improving the energy efficiency ratio and stability of the cleaning process. At the same time, through digital twin and real-time decision support technology, the expected effect and resource consumption change under different cleaning strategies can be clearly displayed, and the decision support system based on real-time cleaning data and multi-objective optimization model can quickly provide scientific and effective parameter adjustment suggestions during the cleaning process, greatly improving the energy efficiency management level of CIP cleaning and the ability to cope with complex cleaning scenes. BRIEF DESCRIPTION OF DRAWINGS

[0014] The application will be further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0015] Figure 1 The figure is a schematic diagram of the system module architecture. DETAILED DESCRIPTION

[0016] The application will be further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0017] According to one aspect of the application, as Figure 1As shown, an adaptive CIP cleaning control and energy efficiency optimization system is provided, comprising: a data acquisition module for acquiring feature data of a cleaning object, real-time state data of a cleaning process, system energy consumption data, cleanliness data, and equipment operating condition data. The module comprises: a cleaning object feature acquisition module: a pipe diameter sensor, a material sensor, and an industrial camera are used to acquire pipe diameter, material, and pipe structure data of the cleaning object; Specifically, the pipe diameter sensor captures the inner diameter information of different specifications of pipes in real time, ensuring the adaptation to the subsequent pulse parameter adjustment requirements, avoiding excessive or insufficient cleaning due to pipe diameter adaptation deviation, the material sensor accurately identifies the physical properties of the pipe material (such as stainless steel, plastic, etc.), providing a basis for selecting the appropriate cleaning fluid type, preventing chemical reactions between the cleaning fluid and the pipe material from causing equipment damage, and the industrial camera captures the internal structure details of the pipe clearly through high-definition image acquisition and recognition technology, accurately distinguishes different structure types such as straight pipes, elbow pipes, and valve cavities, and provides structural data support for the intelligent decision-making and decision-making module to judge the adaptability of the flow field mode; In addition, the pipe diameter, material, and pipe structure data collected are transmitted to the intelligent decision-making and decision-making module in real time as the core basic data for constructing a three-dimensional feature library of the cleaning object "pipe diameter-structure-pollution type", ensuring that the subsequent generated cleaning process decisions can accurately match the inherent characteristics of the cleaning object.

[0018] a process state acquisition module: a flow sensor, a pressure sensor, and a temperature sensor are used to acquire real-time flow, pressure, and temperature data of the cleaning fluid; Specifically, the flow sensor dynamically acquires the real-time flow changes of the cleaning fluid in the pipe, providing key data for judging the flow state (such as laminar flow, turbulent flow) of the cleaning fluid, assisting the intelligent decision-making module in judging whether the current flow field meets the cleaning stage requirements, the pressure sensor continuously monitors the pipe pressure of the cleaning fluid, avoiding pipe rupture due to excessive pressure or insufficient cleaning fluid delivery power due to low pressure, and providing pressure feedback basis for the adjustment of the amplitude in the pulse parameter, and the temperature sensor accurately captures the real-time temperature of the cleaning fluid, ensuring that the chemical cleaning reagent can exert the best cleaning activity within the appropriate temperature range, avoiding the influence of temperature abnormalities on cleaning efficiency or causing the reagent to fail; The simultaneously collected flow, pressure, and temperature data are fed back to the execution and control module, and when the data shows that the flow state does not match the current cleaning stage, the flow field mode execution module timely switches the flow field combination to ensure that the cleaning process is always in the optimal process state.

[0019] an energy efficiency and cleanliness acquisition module: a flowmeter, a chemical sensor, a calorimeter, and an ATP bioluminescence detector are used to acquire water resource consumption, chemical reagent concentration, heat loss, and cleanliness data after cleaning; Specifically, the flow meter accurately counts the real-time consumption of water resources in the whole cleaning process, providing accurate water consumption data support for subsequent energy efficiency optimization analysis, avoiding excessive consumption of water resources, and the chemical sensor monitors the concentration change of the chemical reagent in the cleaning liquid in real time, ensuring that the reagent concentration is maintained within the effective cleaning interval, avoiding both resource waste and environmental burden caused by excessive concentration, and preventing incomplete cleaning caused by low concentration, the heat meter captures the heat loss of the cleaning system in real time, including heat loss of heating equipment and pipeline heat dissipation, providing data basis for heat utilization efficiency optimization; the ATP bioluminescence detector quickly detects the microbial residue and organic dirt residue level in the cleaned pipeline through bioluminescence reaction, accurately determines whether the cleanliness meets the industry standard (such as the hygiene requirements of food and pharmaceutical industries); Among them, the energy efficiency consumption data of water resources, chemical reagents, heat energy and the like collected will be transmitted to the feedback and optimization module for energy efficiency deviation analysis, and the cleanliness data will be used as the core basis for determining whether the cleanliness meets the standard, both of which will provide real-time data input for the "energy efficiency-cleanliness" Pareto optimization model in the intelligent decision and decision generation module to support the dynamic adjustment of cleaning parameters by the model.

[0020] Working condition acquisition module: through speed sensor and vibration sensor, the running speed of pump and the vibration frequency data of valve are collected; Specifically, the speed sensor collects the running speed of the pump in the cleaning system in real time, accurately judges whether the delivery efficiency of the pump is stable, and when the speed appears abnormal fluctuation, the running state deviation of the pump (such as impeller wear, motor failure precursor) is reflected in time, the vibration sensor continuously captures the vibration frequency change of the valve, judges the wear degree of the internal parts (such as sealing element, valve core) of the valve by analyzing the vibration spectrum characteristics, and avoids valve leakage or switching failure caused by component aging; Among them, the working condition data of pump speed and valve vibration collected will be transmitted to the maintenance and management module in real time to provide real-time working condition data source for the construction of equipment digital twin model, so that the virtual model can accurately simulate the actual running state of the equipment, and ensure that the analysis of the model on the performance of the equipment is consistent with the actual situation.

[0021] The intelligent decision and decision generation module receives the data transmitted by the data acquisition module, identifies the cleaning object and generates corresponding cleaning process decision, and generates system full-cycle operation decision combined with energy efficiency data. The module includes: Cleaning object identification and flow field decision module: adopting fuzzy control + deep reinforcement learning technology, identifying cleaning object feature data, generating cleaning process decision of flow field mode switching and pulse parameter adjustment; Wherein the fuzzy control technology can effectively handle the uncertainty information of the cleaning object feature data, such as the slight deviation of the pipe diameter, the fuzzy description of the complexity of the pipe structure, etc., by establishing a "pipe diameter-structure-pollution type" fuzzy rule base, a preliminary flow field mode combination range and pulse parameter adjustment interval are quickly generated to avoid decision lag caused by data fluctuations; The deep reinforcement learning technology is based on historical cleaning data (such as the cleaning effect and parameter matching relationship of different pipe diameters, structures and dirt types) to train the model and continuously optimize the preliminary decision scheme, such as automatically strengthening the "high frequency low amplitude long duration" pulse parameter logic for grease type dirt, and optimizing the "low frequency high amplitude short duration" pulse strategy for protein type dirt, to ensure that the decision is highly adapted to the actual cleaning needs; At the same time, it will receive real-time updates of the cleaning object feature data and process state data from the data acquisition module. When the industrial camera recognizes complex structures such as elbow pipes and valve cavities, it can quickly adjust the flow field mode combination (such as increasing the proportion of turbulent flow to strengthen the scouring of complex areas), and when the flow sensor feedbacks that the flow state is close to the critical value of laminar flow, it can timely optimize the pulse parameters to maintain effective cleaning intensity and avoid the decline of cleaning effect caused by changes in structure or flow state; In addition, the generated cleaning process decision will clearly define the timing of flow field mode switching (such as the trigger conditions for switching from laminar flow and low-frequency weak pulse to turbulent flow and adaptive strong pulse when transitioning from the pre-cleaning stage to the main cleaning stage), and the coordinated adjustment relationship of pulse parameters (such as the reasonable range of amplitude matched when the pulse frequency is increased), to ensure that the instructions received by the execution and control module are operable. At the same time, the decision result will be stored in the system database in real time as a sample for subsequent model iteration training, further improving the decision accuracy.

[0022] Energy efficiency and whole cycle decision module: adopt "energy efficiency-cleaning degree" Pareto optimization model, combined with energy consumption data, to generate running optimization decisions of cleaning parameters and resource allocation in the whole cycle of the system; Among them, the "energy efficiency-cleaning degree" Pareto optimization model is based on the energy efficiency data such as water resource consumption, chemical reagent concentration and heat loss in the historical cleaning process, and the cleaning degree data detected by the ATP bioluminescence detector, to build a multi-objective optimization function. By solving the Pareto optimal solution set, it excludes invalid schemes such as "cleaning degree not up to standard but energy efficiency low" or "cleaning degree excessive but energy efficiency high", and selects the parameter combination with the lowest resource consumption under the premise of meeting the cleaning degree standard, to provide the core decision basis for single cleaning process; At the same time, real-time data transmitted by the energy efficiency and cleanliness collection module will be received in real time. When it is monitored that the water resource consumption of a certain cleaning stage (such as the rinsing stage) has exceeded the Pareto optimal threshold but the cleanliness has reached the standard, a parameter adjustment decision (such as reducing the pulse intensity, shortening the rinsing time) will be immediately generated. If the cleanliness has not reached the standard value, an optimization instruction (such as appropriately increasing the cleaning liquid temperature to enhance the activity of the reagent) will be generated within the energy efficiency allowable range, realizing the dynamic balance of "cleanliness-energy efficiency", and avoiding resource waste or incomplete cleaning; Finally, the energy efficiency trend data of multiple batches of cleaning and the production plan will be combined to generate a whole-cycle operation optimization decision. For example, according to the peak value of the power grid load in different production periods, the execution period of the high-energy-consumption cleaning process is adjusted (such as avoiding the energy consumption peak to reduce the comprehensive cost); according to the chemical reagent inventory data, the reagent concentration ratio of each batch of cleaning in the whole cycle is optimized to ensure balanced resource allocation. The decision result will be transmitted to the management module to display the whole-cycle energy efficiency optimization effect (such as the comparison of water resource consumption of each batch, the cleanliness standard reaching rate trend) in the form of visual charts, providing intuitive operation management reference for users.

[0023] The execution and control module receives the decision result, controls the execution of the pulse parameters in the cleaning process, and switches the execution of the cleaning flow field mode. The module includes: The pulse execution module adopts a pulse parameter execution engine to receive the cleaning process decision, adjust the pulse frequency, pulse amplitude, and pulse duration of the cleaning liquid; The pulse parameter execution engine interacts with the intelligent decision and decision generation module in real time. After receiving the personalized parameter instruction output by the cleaning object recognition and flow field decision module, the engine realizes parameter adjustment response through the built-in driving unit, avoiding fluctuations in cleaning effect caused by parameter adjustment delay; In addition, the engine will accurately execute differentiated adjustment logic according to the parameter adaptation needs of different pollution types: when the decision instruction is to handle grease dirt, the pulse frequency is increased, the pulse amplitude is reduced, and the duration is extended to enhance the emulsification effect of the reagent through high-frequency vibration. When handling protein dirt, the frequency is reduced, the amplitude is increased, and the duration is shortened to strip stubborn dirt with strong mechanical impact force, ensuring that the parameter adjustment is highly matched with the characteristics of the dirt; Finally, the engine will also receive the cleaning liquid pressure and flow data transmitted by the process state collection module in real time. If it is monitored that the pressure exceeds the safe range or the flow is abnormal after parameter execution, the pulse amplitude or duration will be adjusted to ensure the cleaning effect while avoiding equipment damage, realizing the safety and stability of parameter execution.

[0024] The flow field mode execution module adopts a flow field switching valve to receive the cleaning process decision and switch the combined flow field mode of laminar flow, turbulent flow, and pulse flow; The flow field switching valve accurately executes flow field combination switching according to the differentiated needs of the cleaning stages: in the pre-flushing stage, the switching valve opens the laminar flow channel and outputs low-frequency weak pulses in cooperation with the pulse execution module to form a "laminar flow + low-frequency weak pulse" combination, which quickly carries floating dust and light-attached dirt through the laminar flow, and reduces the initial sewage generation amount with the help of low-frequency pulses; When entering the main cleaning stage, the switching valve closes part of the laminar flow channel and increases the proportion of the turbulent flow channel, while cooperating with the adaptive strong pulse output of the pulse execution module to form a "turbulent flow + adaptive strong pulse" flow field, which utilizes turbulent flow to strengthen the mass transfer efficiency of the cleaning fluid and the pipe wall, and the strong pulse forms a directional impact on stubborn dirt in complex structures such as elbow pipes and valve cavities, solving the problem of insufficient cleaning of complex structures by traditional single flow field; During the rinsing stage, the switching valve adjusts the channel ratio again to form a "pulsed laminar flow" mode based on laminar flow and intermittent low-intensity pulses, which accurately rinses the residual chemical reagents on the inner wall of the pipeline through pulse action, avoids reagent residues or excessive water caused by laminar flow rinsing, and ensures that the flow rate and temperature of the cleaning fluid remain stable after flow field switching, without affecting the rinsing effect.

[0025] The feedback and optimization module monitors the energy efficiency consumption and cleanliness after cleaning in real time, and feeds back the monitored energy efficiency data and cleanliness data for optimization. The module includes: The energy efficiency monitoring and feedback module uses a data comparison algorithm to compare real-time energy efficiency consumption data with a preset optimal energy efficiency threshold, generates an energy efficiency deviation signal, and feeds it back to the intelligent decision-making and decision-making generation module; Specifically, the data comparison algorithm will first receive multi-dimensional real-time energy efficiency data (such as water resource consumption, chemical reagent concentration, and heat energy loss) transmitted by the energy efficiency and cleanliness collection module, and call the preset optimal energy efficiency threshold: the minimum resource consumption standard corresponding to the cleanliness standard under different cleaning scenarios (such as different pipe diameters and dirt types). The algorithm accurately locates the energy efficiency deviation direction and amplitude by comparing the real-time data and the threshold dimension by dimension, for example, when the heat energy loss exceeds the threshold, it automatically associates the temperature data of the process state collection module to preliminarily judge whether it is a pipe heat dissipation anomaly or a decrease in heating device efficiency, and when the water resource consumption exceeds the limit, it associates the pulse parameters or flow field mode operation data of the execution and control module; The energy efficiency deviation signal generated at the same time carries the deviation dimension and the preliminary analysis result of the deviation cause, and is transmitted to the intelligent decision-making and decision-making generation module in real time. After receiving the signal, the module quickly outputs an optimization scheme based on a deep reinforcement learning model, such as adjusting the reagent addition rate for a deviation of high chemical reagent concentration, or optimizing the heating device power or triggering the pipeline insulation state inspection for abnormal heat energy loss, forming an energy efficiency closed loop of “real-time monitoring-deviation identification-precise feedback-dynamic optimization” to ensure that resource consumption is always maintained in the optimal interval of cleanliness standard.

[0026] The cleanliness detection and feedback module uses a standard determination logic to compare real-time cleanliness data with cleanliness standard values, generates cleanliness standard signal or optimization signal and feeds back to the intelligent decision-making and decision-making generation module. Specifically, the standard determination logic takes the real-time data of the ATP bioluminescence detector in the energy efficiency and cleanliness collection module as the core, combines the cleanliness standard values of different industries (such as food industry microbial residue limit value, pharmaceutical industry organic dirt residue index), and establishes a grading determination rule: when the cleanliness data is lower than the standard value, a “standard signal” is generated, when it is between the standard values, a “to-be-optimized signal” is generated, and when it is higher than the standard value, a “non-standard signal” is generated. For non-standard or to-be-optimized conditions, the logic further matches the cleaning object feature data (such as pipeline structure, dirt type) of the data collection module and the running parameters (such as main cleaning time, pulse intensity) of the execution module to preliminarily locate the problem source, such as insufficient turbulent flow ratio when the cleanliness of the elbow area is not up to standard. In addition, real-time detection and feedback of the whole cleaning process are supported, rather than only detection after cleaning: for example, during the main cleaning stage, if stubborn dirt residues are detected to be not reduced to the expected level, the intelligent decision-making and decision-making generation module will be immediately fed back to adjust the pulse parameters (such as increasing the pulse amplitude) or extend the main cleaning time, avoiding secondary cleaning due to incomplete cleaning. At the same time, all cleanliness determination results and corresponding cleaning parameters are stored as training samples for the deep reinforcement learning model of the intelligent decision-making and decision-making generation module, continuously improving the cleanliness standard rate and parameter adaptation accuracy in different scenarios.

[0027] The maintenance and management module manages the overall operation state of the system, monitors and analyzes the running state of the key equipment in the system, predicts equipment failure and generates maintenance prompts. The module includes: The management module displays real-time cleaning process progress, energy consumption statistics, and visual data of equipment running parameters; Specifically, multi-dimensional data transmitted by the data acquisition module, the execution and control module, and the feedback and optimization module are received in real time through the data interface: cleaning process progress data comes from the pulse parameter execution engine of the execution and control module and the running state of the flow field switching valve, energy consumption statistical data comes from the flowmeter, chemical sensor, and calorimeter data of the energy efficiency and cleanliness acquisition module, and equipment running parameters come from the speed sensor and vibration sensor data of the working condition acquisition module. At the same time, these data are converted into visual forms, in which the cleaning process progress is displayed in the form of a progress bar + stage identification (for example, dynamic switching of "pre-rinse → main cleaning → rinse"), and the energy consumption statistics are presented in the form of a column chart (for example, comparison of multi-batch water resource / chemical reagent consumption) and a line chart (for example, real-time thermal energy loss change), and the equipment running parameters are displayed in the form of a dashboard showing the real-time values and normal range intervals of pump speed and valve vibration frequency. At the same time, the interface supports user time period filtering (for example, the last 1 hour, the last 24 hours) and equipment number inquiry (for example, a specific pump body, valve), which facilitates management personnel to quickly locate key information and realize transparent monitoring and data tracing of the entire cleaning process.

[0028] The prediction and maintenance module: using digital twin modeling technology, a virtual model of the equipment is established to monitor the degree of equipment wear and tear and performance degradation trend, and generate fault warning information and maintenance time prompt; The digital twin modeling technology will construct a virtual model for key equipment such as pumps, valves, and sensors in the system based on their physical parameters (such as material, structure size) and historical operation data (such as past speed, vibration, and fault records). The model receives pump running speed and valve vibration frequency data transmitted by the working condition acquisition module through a real-time data interface, and uses time stamp synchronization technology to ensure that the virtual model and the physical equipment are completely synchronized in terms of running state, avoiding monitoring deviations caused by data delays. At the same time, by comparing the real-time running data of the virtual model with the equipment performance baseline (such as the standard speed range of the pump and the normal vibration frequency interval of the valve), the performance degradation rate of the equipment is analyzed. For example, when the fluctuation amplitude of the pump speed gradually increases and the frequency of deviation from the standard range increases, it is determined that the impeller wear is intensifying. When the virtual model detects that the degree of equipment wear and tear exceeds the preset safety threshold (such as a 20% increase in valve vibration frequency beyond the normal range) or the performance degradation trend accelerates (such as a 5% monthly decrease in pump delivery efficiency), a fault warning message is generated. The warning content includes the fault equipment number, the preliminary judgment of the fault type (such as "pump impeller wear" and "valve seal aging"), and the current risk level (such as "general warning" and "emergency warning"). At the same time, combined with the mean time between failures (MTBF) of the equipment, historical maintenance records, and the current cleaning task schedule, a maintenance time prompt is generated, such as "it is recommended to check the pump impeller within 24 hours after the next cleaning task is completed", to avoid conflicts between maintenance operations and production cleaning tasks.

[0029] Of course, the above description is not a limitation on the present application, and the present application is not limited to the above examples. Changes, modifications, additions or substitutions made by those skilled in the art within the spirit and scope of the present application are also within the scope of the present application.

Claims

1. A self-adapting pulse flow (CIP) cleaning control and energy efficiency optimization system, characterized in that, Comprise: Data acquisition module, intelligent decision and decision generation module, execution and control module, feedback and optimization module, maintenance and management module. The data acquisition module collects cleaning object related feature data, cleaning process real-time state data, system energy efficiency consumption data, cleanliness data, equipment operating condition data; the intelligent decision and decision generation module receives the data transmitted by the data acquisition module, identifies the cleaning object and generates the corresponding cleaning process decision, and generates the system full cycle operation decision combined with the energy efficiency data; the execution and control module receives the decision result, executes the control of the pulse parameters in the cleaning process, and simultaneously switches and executes the cleaning flow field mode; the feedback and optimization module monitors the system energy efficiency consumption and the cleanliness after cleaning in real time, and feeds back the monitored energy efficiency data and cleanliness data for optimization; the maintenance and management module manages the overall operation state of the system, monitors and analyzes the operating state of the key equipment in the system, predicts equipment failure and generates maintenance prompts.

2. The adaptive impulse flow (CIP) wash control and energy efficiency optimization system of claim 1, wherein: The data acquisition module comprises: a cleaning object feature acquisition module, a process state acquisition module, an energy efficiency and cleanliness acquisition module, and an operating condition acquisition module; The cleaning object feature acquisition module: adopts a pipe diameter sensor, a material sensor, and an industrial camera to collect pipe diameter, material, and pipeline structure data of the cleaning object; The process state acquisition module: adopts a flow sensor, a pressure sensor, and a temperature sensor to collect real-time flow, pressure, and temperature data of the cleaning liquid; The energy efficiency and cleanliness acquisition module: adopts a flowmeter, a chemical sensor, a calorimeter, and an ATP biological fluorescence detector to collect water resource consumption, chemical reagent concentration, heat loss, and cleanliness data after cleaning; The operating condition acquisition module: collects pump operating speed and valve vibration frequency data through a speed sensor and a vibration sensor.

3. The adaptive impulse flow (CIP) wash control and energy efficiency optimization system of claim 1, wherein: The intelligent decision and decision generation module comprises: a cleaning object identification and flow field decision module, and an energy efficiency and full cycle decision module; The cleaning object identification and flow field decision module: adopts fuzzy control + deep reinforcement learning technology to identify cleaning object feature data and generate cleaning process decisions for flow field mode switching and pulse parameter adjustment; The energy efficiency and full cycle decision module: adopts an "energy efficiency-cleanliness" Pareto optimization model to generate running optimization decisions for cleaning parameters and resource allocation within the system full cycle in combination with energy efficiency consumption data.

4. The adaptive impulse flow (CIP) wash control and energy efficiency optimization system of claim 1, wherein: The execution and control module comprises: a pulse execution module and a flow field mode execution module; The pulse execution module: adopts a pulse parameter execution engine to receive cleaning process decisions and adjust pulse frequency, pulse amplitude, and pulse duration of the cleaning liquid; The flow field mode execution module: adopts a flow field switching valve to receive cleaning process decisions and switch combined flow field modes of laminar flow, turbulent flow, and pulse flow.

5. The adaptive impulse flow (CIP) wash control and energy efficiency optimization system of claim 1, wherein: The feedback and optimization module comprises: an energy efficiency monitoring and feedback module and a cleanliness detection and feedback module; The energy efficiency monitoring and feedback module: adopts a data comparison algorithm to compare real-time energy efficiency consumption data with a preset optimal energy efficiency threshold, generate an energy efficiency deviation signal, and feed back to the intelligent decision and decision generation module; The cleanliness detection and feedback module adopts a standard determination logic to compare real-time cleanliness data with cleanliness standard values, generate cleanliness standard signal or optimization signal and feedback to the intelligent decision and decision generation module.

6. The adaptive impulse flow (CIP) wash control and energy efficiency optimization system of claim 1, wherein: The maintenance and management module includes a management module and a prediction and maintenance module. The management module displays real-time cleaning process progress, energy consumption statistics, and visual data of equipment operation parameters. The prediction and maintenance module uses digital twin modeling technology to virtually model the equipment, monitor equipment wear and tear, performance degradation trends, generate fault warning information and maintenance time prompts.

7. The adaptive impulse flow (CIP) wash control and energy efficiency optimization system of claim 2, wherein: The sensors all use timestamp synchronization technology to ensure the time correlation of cleaning object feature data, process state data, energy efficiency and cleanliness data, and working condition data.

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