Intelligent cleaning control method and system for emulsion pump

Abstract

The invention discloses an intelligent cleaning control method and system for an emulsion pump, and relates to the technical field of intelligent cleaning, and the method comprises the steps: calibrating a relative state curve of the emulsion pump, extracting data from a cleaning record database, carrying out the clustering segmentation and resampling of the data, and generating k working condition recombination sample sets; based on the recombination sample data and the relative pump state curve, k pollution rate mapping models are trained, and the accumulated pollution state of the pump is predicted; the operation parameters of the pump are monitored in real time for pollution state evaluation, and the accumulated pollution state and the monitored pump state are fused for cleaning decision making and cleaning control. The technical problem that due to the fact that an existing emulsion pump cleaning method lacks accurate pollution state evaluation and dynamic adaptability, cleaning operation is excessive or insufficient is solved, and the technical effects that through real-time monitoring and pollution state evaluation, cleaning operation is dynamically adjusted, accurate cleaning control is achieved, and excessive or insufficient cleaning operation is avoided are achieved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent cleaning technology, and in particular to an intelligent cleaning control method and system for an emulsion pump. Background Art

[0002] As one of the common equipment in industrial production, emulsion pumps are widely used in various fluid delivery and circulation systems. However, during long-term operation, the performance of emulsion pumps will gradually decline due to contamination, affecting the working efficiency and service life of the pump. Traditional cleaning methods usually rely on fixed cleaning cycles or human judgment, which makes it difficult to accurately grasp the contamination status of the pump, resulting in excessive or insufficient cleaning operations, which in turn affects production efficiency and equipment maintenance costs. Summary of the invention

[0003] The present application provides an intelligent cleaning control method and system for an emulsion pump, which is used to solve the technical problem that the existing emulsion pump cleaning method lacks accurate pollution status assessment and dynamic adaptability, resulting in excessive or insufficient cleaning operations.

[0004] The first aspect of the present application provides an intelligent cleaning control method for an emulsion pump, the method comprising: combining the target scene requirements and the target emulsion pump, calibrating a relative pump state curve, wherein the relative pump state represents the pollution progress of the target emulsion pump; obtaining unique identification information of the target emulsion pump, and extracting data from a cleaning record database based on the unique identification information; clustering and resampling the data extraction results to obtain reconstructed sample data, wherein the data extraction results include sample pump control parameters and sample pump state parameters, and the reconstructed sample data includes k working condition reconstructed samples set; based on the reorganized sample data and the relative pump state curve, construct and train k relative pollution rate mapping models, wherein the relative pollution rate mapping model takes the pump control parameter as input; extract the current cycle operation log of the target emulsion pump, and predict the cumulative pump state of the target emulsion pump according to the current cycle operation log and the k relative pollution rate mapping models; obtain the pump operation parameter information in real time, and perform direct pollution state evaluation based on the relative pump state curve to obtain the monitoring pump state; integrate the cumulative pump state with the monitoring pump state to make cleaning decisions and cleaning control.

[0005] The second aspect of the present application provides an intelligent cleaning control system for an emulsion pump, the system comprising: a relative pump state curve calibration module, the relative pump state curve calibration module is used to combine the target scene requirements and the target emulsion pump to calibrate the relative pump state curve, wherein the relative pump state represents the pollution progress of the target emulsion pump; a data extraction module, the data extraction module is used to obtain the unique identification information of the target emulsion pump, and extract data from the cleaning record database based on the unique identification information; a clustering segmentation and resampling module, the clustering segmentation and resampling module is used to cluster and resample the data extraction results to obtain reorganized sample data, wherein the data extraction results include sample pump control parameters and sample pump state parameters, and the reorganized sample data includes k working condition reorganization sample sets; a rate mapping model construction module The rate mapping model construction module is used to construct and train k relative pollution rate mapping models based on the recombined sample data and the relative pump state curve, wherein the relative pollution rate mapping model takes the pump control parameter as input; the cumulative pump state prediction module, the cumulative pump state prediction module is used to extract the current cycle operation log of the target emulsion pump, and predict the cumulative pump state of the target emulsion pump based on the current cycle operation log and the k relative pollution rate mapping models; the direct pollution state evaluation module, the direct pollution state evaluation module is used to obtain the pump operation parameter information in real time, and perform direct pollution state evaluation based on the relative pump state curve to obtain the monitoring pump state; the cleaning decision control module, the cleaning decision control module is used to integrate the cumulative pump state and the monitoring pump state to perform cleaning decision and cleaning control.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: The present application provides an intelligent cleaning control method and system for an emulsion pump, which relates to the field of intelligent cleaning technology. The method and system calibrate the relative pump state curve, extract cleaning record data in combination with unique identification information, cluster and resample the data, train a pollution rate mapping model to predict the cumulative pollution state of the pump, monitor the pump operating parameters in real time, evaluate pollution based on the state curve, integrate the accumulation and monitoring states, and achieve accurate cleaning decision-making and control. The method solves the technical problem that the existing emulsion pump cleaning method lacks accurate pollution state evaluation and dynamic adaptability, resulting in excessive or insufficient cleaning operations. The method achieves the technical effect of dynamically adjusting the cleaning operation through real-time monitoring and pollution state evaluation, realizing accurate cleaning control, avoiding excessive or insufficient cleaning operations, and thus improving the operating efficiency of the pump and extending the life of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0008] Figure 1 A schematic flow chart of an intelligent cleaning control method for an emulsion pump provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of an intelligent cleaning control system for an emulsion pump provided in an embodiment of the present application.

[0009] Explanation of the accompanying drawings: relative pump state curve calibration module 11, data extraction module 12, clustering segmentation and resampling module 13, rate mapping model construction module 14, cumulative pump state prediction module 15, direct pollution state evaluation module 16, cleaning decision control module 17. DETAILED DESCRIPTION

[0010] The present application provides an intelligent cleaning control method and system for an emulsion pump, which is used to solve the technical problem that the existing emulsion pump cleaning method lacks accurate pollution status assessment and dynamic adaptability, resulting in excessive or insufficient cleaning operations.

[0011] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0012] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0013] Embodiment 1, as Figure 1 As shown, the present application provides an intelligent cleaning control method for an emulsion pump, the method comprising: P10: Combine the target scenario requirements with the target emulsion pump and calibrate the relative pump status curve, where the relative pump status represents the contamination progress of the target emulsion pump.

[0014] Furthermore, step P10 of the embodiment of the present application further includes: P11: Interact with the target emulsion pump to obtain the key status indicator set of the pump; P12: Determine the evaluation weights and performance control limits of the key status indicator set based on the target scenario requirements; P13: Define a relative performance evaluation function based on the evaluation weights and the key status indicator set, and calibrate the relative pump status curve of the target emulsion pump in combination with the performance control limits.

[0015] It should be understood that, in combination with the target scenario requirements and the actual situation of the target emulsion pump, calibrating the relative pump state curve is a key step to ensure that the emulsion pump can operate stably under different pollution conditions. The relative pump state indicates the pollution progress of the emulsion pump, which reflects the changes in pump performance and efficiency through a series of key state indicators.

[0016] Specifically, first, interact with the target emulsion pump to obtain the key status indicator set of the pump. Key status indicators are important parameters for evaluating the performance and pollution degree of the emulsion pump. These indicators may include the flow rate, pressure, energy efficiency ratio, temperature change, etc. of the pump, which can directly affect the operating efficiency and pollution status of the pump. By monitoring these key indicators in real time, it is possible to reflect the changes of the pump during operation as the pollution progresses.

[0017] Next, according to the specific application scenarios and requirements, these key status indicators are weighted and the performance control limits of each indicator are determined. The performance control limit indicates the maximum performance degradation that the pump can tolerate during operation. For example, assuming that the ideal performance value of an indicator is 100%, the performance control limit may be 90%, that is, the tolerance for performance degradation is 10%. This step ensures that in the case of pump contamination, the acceptable performance degradation range of the pump can be clearly defined to evaluate the health of the pump.

[0018] Furthermore, after obtaining the weights and control limits of each indicator, the next step is to define a relative performance evaluation function. This function converts the performance data of multiple indicators into a comprehensive evaluation value. The value of this function is not strictly required to be between 0 and 1. Its function is to convert it into a single evaluation value based on multiple performance aspects of the pump, so as to facilitate a comprehensive evaluation of the overall status of the pump. For example, when all key performance indicators of the pump reach the ideal level, the function value is 1; when the pump status is close to the worst, the function value may be close to 0. In this way, the contamination process of the pump can be reflected through a comprehensive function value.

[0019] Combining the performance evaluation function and the control limits, the relative pump state curve of the target emulsion pump can be calibrated. This curve is similar to the SOC (State of Charge) curve of a battery, showing how the contamination process of the pump is related to its overall performance changes. The 100% point of the curve represents the state of the pump at ideal performance, while the 0% point represents the pump at 90% of the ideal performance (set according to the performance control limit). The calibration of this curve helps to accurately evaluate the performance of the pump at different stages of contamination and decide when to clean it.

[0020] Through this calibration method, the relationship between the contamination process of the emulsion pump and the pump performance is quantified and visualized, providing important data support and decision-making basis for subsequent intelligent cleaning control.

[0021] P20: Acquire unique identification information of the target emulsion pump, and extract data from the cleaning record database based on the unique identification information.

[0022] Optionally, by obtaining the unique identification information of the emulsion pump, data can be further extracted from the cleaning record database to provide data support for subsequent intelligent cleaning control. This process involves equipment identification and data management, which is the basis for achieving precise operation of the intelligent cleaning control system.

[0023] First, the target emulsion pump is identified by the unique identification information of the system or device. Usually, the emulsion pump is given a unique identification code (such as a QR code, RFID tag or serial number), which can uniquely identify the pump in the device and system. In practical applications, this unique identification information is used to accurately distinguish different emulsion pumps to prevent the system from mistakenly classifying data to the wrong device. Therefore, the accuracy of operation and data analysis is ensured by unique identification information.

[0024] The unique identification information is used to access the historical cleaning records and operation data related to the pump. Specifically, the system locates the relevant cleaning records of the target pump from the data set stored in the cleaning record database based on the unique identification information. These data usually include the historical cleaning time, cleaning frequency, cleaning method, and performance evaluation after cleaning of the pump. These historical records provide valuable information for judging the current contamination status of the pump and predicting future cleaning needs.

[0025] The cleaning record database is a comprehensive management system that usually contains a large amount of historical operation data. Through effective management and retrieval of the database, accurate data extraction can be achieved. In this step, the database management capability of the system is crucial, which can support efficient data access and processing. Generally speaking, the data in the database can be accurately extracted through methods such as SQL query language to ensure rapid response.

[0026] After data extraction, the extracted historical cleaning records are further analyzed. These data will serve as the basis for subsequent analysis to help the system better understand the cleaning cycle and contamination process of the pump. For example, by analyzing historical cleaning records, the system can determine whether the cleaning interval of the target pump is normal, whether the degree of contamination of the pump has reached the standard that requires cleaning, and then decide whether to start the next cleaning operation based on these data.

[0027] Through this step, accurate data acquisition of the target emulsion pump is achieved, which can provide strong data support when dealing with pollution problems in complex environments. This precise data extraction mechanism not only enhances the reliability of the system, but also makes the management of the pump more intelligent and automated.

[0028] P30: Clustering and resampling the data extraction results to obtain reorganized sample data, wherein the data extraction results include sample pump control parameters and sample pump state parameters, and the reorganized sample data includes k working condition reorganized sample sets.

[0029] Furthermore, step P30 of the embodiment of the present application further includes: P31: Determine the number of clusters k based on the elbow method, and perform density clustering analysis on the data extraction results based on the sample pump control parameters; P32: Define k standard operating condition intervals based on the k clusters in the cluster analysis results; P33: Use the k standard operating condition intervals as segmentation constraints to segment the data extraction results, and output the segmentation results as k reorganized data sets; P34: Based on preset confidence constraints, perform confidence screening on the k reorganized data sets to obtain k operating condition reorganization sample sets.

[0030] Specifically, the data extraction results are clustered and resampled to obtain reorganized sample data, providing strong support for subsequent cleaning control.

[0031] First, based on the data extraction results, the elbow method is used to determine the number of clusters k. The elbow method is a common clustering algorithm optimization method, which mainly determines the optimal number of clusters by drawing a relationship curve between the number of different clusters k and the sum of squares of clustering errors. In this step, the system uses the elbow method to analyze the sample pump control parameters (such as flow, pressure, energy efficiency ratio, etc.) to determine the most suitable number of clusters. This method can effectively divide data samples with similar characteristics and provide a clear classification basis for subsequent data processing and model training.

[0032] After determining the number of clusters k, k clusters are divided according to the cluster analysis results, and k standard operating ranges are further defined. The standard operating range is based on the sample characteristics in each cluster, and the operating range that meets the characteristics of the cluster samples is formulated. These operating ranges will reflect the performance of the pump under different operating conditions, helping the system to monitor and control the status of the pump more carefully in future operations. For example, if a cluster represents the high-load operating state of the pump, then the operating range corresponding to the cluster will be defined as the high-load operating range of the pump.

[0033] Next, the data extraction results are segmented using k standard operating condition intervals as segmentation constraints. Specifically, the system segments the extracted data according to the standard operating condition intervals, and the data corresponding to each interval is divided into an independent reorganized data set. In this way, the system can perform targeted analysis and clean control decisions based on the different operating conditions of the pump. Each reorganized data set represents the pump state under a specific operating condition, which facilitates subsequent pollution state assessment and model training.

[0034] Finally, the k reorganized data sets are screened based on preset confidence constraints (such as confidence intervals). Confidence screening refers to selecting credible data samples based on certain statistical standards (such as a 95% confidence level) to remove noise and unreliable data, thereby improving the accuracy of model prediction. This step ensures that the final selected data set is more accurate and stable, thereby generating k operating condition reorganized sample sets. These operating condition reorganized sample sets will serve as the basic data for training the pollution rate mapping model in subsequent steps.

[0035] Through the above processing steps, the original extracted data is transformed into more meaningful working condition reorganization sample data through clustering, segmentation, screening and other processing. These data will provide an accurate basis for subsequent pollution status assessment, cleaning decision-making and other links, ensuring that the cleaning control of the emulsion pump can be dynamically adjusted according to the actual working conditions, so as to achieve the goal of optimizing pump performance and extending service life.

[0036] P40: Based on the reorganized sample data and the relative pump state curve, k relative contamination rate mapping models are constructed and trained, wherein the relative contamination rate mapping model uses pump control parameters as input.

[0037] Furthermore, step P40 of the embodiment of the present application also includes: P41: Sample pump state parameters based on the reorganized sample data; P42: Combining the relative pump state curve with the sample pump state parameters, relative state marking is performed on the reorganized sample data to obtain marked sample data; P43: With the relative state marking result as supervision, k relative pollution rate mapping models are constructed and trained based on the marked sample data, wherein the k relative pollution rate mapping models correspond one-to-one to the k standard operating conditions.

[0038] It should be understood that based on the reorganized sample data and the relative pump state curve, k relative contamination rate mapping models are constructed and trained. These models can predict the pump contamination rate and provide real-time contamination state assessment by inputting pump control parameters, providing accurate decision support for intelligent cleaning control.

[0039] First, the sample pump status parameters in the reorganized sample data are used. These parameters usually include key performance indicators such as pump flow, pressure, and energy efficiency ratio. These status parameters are the basic information for evaluating the working status of the pump and can reflect the current operating status of the pump. By analyzing these sample pump status parameters, the system can identify the performance of the pump under different working conditions and provide data support for the construction of the subsequent pollution rate model.

[0040] Next, the reorganized sample data is labeled by combining the relative pump state curve with the sample pump state parameters. The relative pump state curve reflects the performance changes of the pump during the pollution process. Through this curve, the system can associate each sample pump state with its corresponding pollution degree. Specifically, the system will calibrate the pollution progress of each sample according to the pump state parameters and the relative pump state curve, and assign a label value to each sample. These label values ​​represent the pollution state of the pump at a specific point in time, thus forming labeled sample data. The labeled sample data provides supervised samples for subsequent model training, which can improve the accuracy and reliability of the model.

[0041] Next, based on the labeled sample data, the system uses the relative state labeling results as supervision to build and train k relative contamination rate mapping models. Each mapping model takes the control parameters of the pump (such as flow, pressure, etc.) as input to predict the contamination rate of the pump under different operating conditions. The k mapping models correspond to k standard operating intervals, and each operating interval represents the performance of the pump under different loads or operating conditions. By inputting the labeled sample data into these models, the system can train a set of models that can reflect the relationship between the pump contamination rate and the control parameters. These models will evaluate the contamination rate of the pump in real time in actual applications, helping the system to perform effective cleaning control.

[0042] The construction process of the relative pollution rate mapping model can use supervised learning methods. By using labeled sample data, these models can learn the mapping relationship between pump control parameters and pollution rates. Regression algorithms (such as linear regression, decision tree regression, random forest regression, etc.) are used to build these models, and training data is used to continuously optimize parameters and improve prediction accuracy.

[0043] In addition, the definition of k standard operating conditions used during model training ensures that each mapping model focuses on the pollution rate prediction under specific operating conditions. Through this operating condition distinction, the system can provide more accurate pollution rate assessment for different operating modes. Improve the accuracy of pump contamination assessment and provide an important basis for the dynamic adjustment of the intelligent cleaning system.

[0044] P50: extracting the current cycle operation log of the target emulsion pump, and predicting the cumulative pump state of the target emulsion pump according to the current cycle operation log and the k relative pollution rate mapping models.

[0045] Furthermore, step P50 of the embodiment of the present application also includes: P51: Obtain the current cycle operation log of the target pump, and extract the operating condition setting sequence from the current cycle operation log; P52: Map and transform the operating condition setting sequence based on k relative pollution rate mapping models to obtain the relative pollution rate sequence; P53: Cumulatively analyze the relative pollution rate sequence to obtain the cumulative pump state.

[0046] Optionally, the current cycle operation logs of the target emulsion pump are extracted, and the cumulative pump state of the target emulsion pump is predicted using these logs and the k trained relative pollution rate mapping models.

[0047] First, obtain the operation log of the target emulsion pump in this cycle. The operation log records the various operating parameters of the pump within a certain period of time, including flow, pressure, energy efficiency ratio, temperature and other information. By extracting the operating condition setting sequence from these logs, the system can understand the specific operating status of the pump in this cycle. These operating condition setting sequences reflect the specific working mode of the pump in different time periods. For example, some time periods may indicate a high-load operating state, while other time periods may indicate a low-load or standby state. Extracting these operating condition sequences helps to analyze the operation of the pump in more detail.

[0048] Next, the extracted operating condition setting sequence is input into k relative pollution rate mapping models for mapping conversion. Each model corresponds to a specific operating condition interval one by one. In this way, the system can convert each operating condition state into a corresponding relative pollution rate. The relative pollution rate sequence is the speed or progress of pump pollution under different operating conditions. The mapping conversion process essentially associates the operating parameters of the pump with the pollution process, reflecting the pollution changes of the pump under different operating conditions. These relative pollution rate sequences provide a basis for further cumulative analysis.

[0049] Finally, the relative contamination rate sequence is cumulatively analyzed to calculate the contamination progress of the target emulsion pump throughout the cycle. The cumulative analysis process is to sum up the contamination rate of each period in chronological order to obtain the overall contamination status of the pump. The key to this process is to accurately evaluate the contamination progress of the pump throughout the operating cycle. Cumulative analysis can provide a more comprehensive understanding of the cumulative contamination of the pump. The system generates a cumulative pump state through the cumulative results, which can reflect the degree of contamination of the pump throughout the cycle and provide a reliable basis for subsequent cleaning control decisions.

[0050] In addition, the implementation of cumulative analysis requires a weighted accumulation mechanism to consider the impact of different time periods on the pump pollution state. For example, long-term high-load operation may lead to an acceleration of the pollution rate, and such a time period will contribute more to the cumulative pollution state. Therefore, cumulative analysis is not just a simple summation, but may also involve weighted treatment of the pollution rate under different working conditions.

[0051] Through the above steps, the cumulative pump state of the pump can be accurately predicted by using the current cycle operation log of the target emulsion pump and the relative pollution rate mapping model, providing the intelligent cleaning control system with a pollution state assessment based on actual operation data, helping the system to identify pollution problems in a timely manner and take corresponding cleaning measures, thereby improving the operation efficiency of the pump and extending the service life of the equipment.

[0052] P60: Acquire the operating parameter information of the pump in real time, and perform direct pollution status evaluation based on the relative pump status curve to acquire the monitoring pump status.

[0053] Specifically, the pump status is obtained by acquiring the pump operating parameter information in real time and combining it with the relative pump status curve to evaluate the pollution status in real time. This process can monitor the operation of the pump in real time, reflect the progress of the pump pollution in a timely manner, and provide data support for cleaning decisions.

[0054] First, monitor the operating parameters of the target emulsion pump in real time, such as flow, pressure, temperature, energy efficiency ratio, etc. These operating parameters are an important basis for evaluating the current working status of the pump. For example, flow and pressure can reflect the workload of the pump, while temperature and energy efficiency ratio can indicate the operating efficiency and potential abnormal status of the pump. By collecting these data in real time, the system can dynamically track the status of the pump and provide timely basic data for subsequent pollution status assessment.

[0055] After obtaining the real-time operating parameters, they are combined with the relative pump status curve to perform direct pollution status evaluation. The relative pump status curve is a curve obtained through calibration, which reflects the relationship between the pump's operating parameters (such as flow, pressure, etc.) and the pump's pollution progress. By inputting the real-time operating parameters into the relative pump status curve, the system can evaluate the pump's pollution status in real time. For example, the pump's flow and pressure parameters may change during the pollution process, and this curve can map the current degree of pollution.

[0056] The core of this evaluation method is to quickly evaluate the pump contamination status by matching the real-time operation data with the calibrated status curve. This method does not rely on too much historical data, but performs contamination detection based on the current real-time operation status, so that it can respond to the pump contamination in a timely manner.

[0057] After the pollution status is evaluated, the status of the monitoring pump is generated, for example, expressed as a numerical value of the pollution degree. This monitoring pump status can be expressed as a quantitative indicator, such as a percentage of pollution progress, or a relative performance degradation value. These monitoring results can reflect the current pollution process of the pump and provide a basis for subsequent cleaning control. If the monitoring pump status reaches a certain pollution threshold, the cleaning program can be automatically started to ensure that the pump operates in an ideal working state.

[0058] P70: Integrate the accumulated pump status and the monitoring pump status to make cleaning decisions and perform cleaning control.

[0059] For example, cleaning decisions and cleaning control are made by integrating the accumulated pump status and the monitored pump status. The purpose is to integrate the pump's pollution process and current pollution situation to make more accurate and effective cleaning decisions, thereby ensuring the continuous optimization of the emulsion pump's performance and the long-term stable operation of the equipment.

[0060] First, the cumulative pump status is integrated with the monitoring pump status. The cumulative pump status is obtained by cumulatively analyzing the pump's operating data over a period of time, reflecting the pump's pollution progress throughout the entire cycle. The monitoring pump status is based on the real-time acquisition of pump operating parameters and the pollution status evaluated by the relative pump status curve. The integration of the two aims to combine the historical pollution progress of the pump with the current pollution level to form a comprehensive pollution status assessment.

[0061] Specifically, the cumulative pump status provides the pollution trend of the pump throughout the cycle, while the monitoring pump status reflects the pollution level of the pump at the current moment. Through the combined analysis of the two, the system is able to take into account the pollution accumulation in long-term operation and the current pollution rate, providing more comprehensive information for cleaning decisions. For example, if the cumulative pollution status is high but the monitoring status is low, it may mean that the pollution accumulation of the pump has not yet seriously affected the current performance, but preventive cleaning is required.

[0062] Furthermore, based on the fusion results, cleaning decisions are made. The goal of cleaning decisions is to ensure that the emulsion pump operates in the best working condition. If after comprehensive evaluation it is found that the contamination state of the pump reaches the set threshold (for example, the cumulative contamination exceeds a certain percentage, or the monitoring state exceeds a certain contamination level), the system will decide to start the cleaning process. Cleaning decisions do not rely solely on a single contamination indicator, but combine historical data with real-time data, consider factors such as the pump's operating efficiency, cleaning frequency, and operating costs, and intelligently determine whether cleaning is needed.

[0063] When a cleaning decision is made, the system initiates the corresponding cleaning control operation. The specific implementation process of the cleaning control varies according to the pollution status of the pump and the cleaning requirements. The system can select different cleaning methods or cleaning intensities. For example, for a lightly polluted pump, only a simple flushing or adjustment may be required, while for a severely polluted pump, more thorough cleaning work such as disassembly or deep cleaning may be required. At this time, the system's intelligent control algorithm automatically adjusts the parameters of the cleaning process according to the cleaning strategy and the current pollution status to ensure that the cleaning effect achieves the expected goal. Through intelligent cleaning decisions and controls, the service life of the emulsion pump can be effectively extended, its operating efficiency can be optimized, and the waste of resources caused by excessive cleaning can be reduced.

[0064] Furthermore, the embodiment of the present application further includes step P80, and step P80 further includes: P81: Record and store cleaning control records; P82: If the deviation between the accumulated pump state and the monitored pump state is greater than a preset value, feedback correction is performed on the k relative contamination rate mapping models based on the cleaning control records.

[0065] Optionally, the feedback mechanism of the intelligent cleaning control process can be further enhanced by recording the cleaning control records and correcting the contamination rate mapping model based on the state deviation to optimize the cleaning decision and control strategy of the pump to ensure the long-term effectiveness and accuracy of the cleaning operation.

[0066] Specifically, after each cleaning operation, the cleaning process is recorded in detail and the relevant data is stored in the cleaning control record. These records include the recovery of pump performance after cleaning, the specific details of the cleaning operation (such as cleaning intensity, cleaning method, etc.), and the change in the state of the pump after cleaning. The performance recovery after cleaning usually reflects the degree of functional recovery of the pump after the cleaning operation, for example, whether the efficiency, energy consumption, pressure, etc. of the pump have returned to a near ideal state. These cleaning control records not only provide data support for subsequent decision-making, but also serve as an important basis for evaluating the cleaning effect.

[0067] By storing cleaning control records, the system can track over a long period of time and accumulate rich cleaning data. These records provide valuable information for subsequent model optimization and pollution rate mapping correction. For example, if the pump performance after cleaning fails to return to the expected level, the system can analyze the possible reasons and provide improved strategies to ensure that future cleaning operations are more accurate and effective.

[0068] Furthermore, the model is fed back and corrected based on the deviation between the accumulated pump status and the monitored pump status. If the deviation between the two is greater than the preset threshold, the system will consider that there is a deviation between the prediction of the existing model and the actual effect after cleaning, and thus start the feedback correction mechanism. The deviation is usually caused by the error between the actual operation of the pump and the model prediction, which may be due to certain characteristics of the pump not being fully captured by the model, or the state recovery after cleaning not fully meeting expectations.

[0069] Analysis of cleaning control records can identify potential model deficiencies. For example, if pump performance recovery after cleaning is incomplete, it may indicate that the contamination rate mapping model is not accurate in its predictions under certain operating conditions, resulting in suboptimal cleaning decisions. In this case, the system will feedback and correct the k relative contamination rate mapping models based on the recorded data, optimizing the parameters or structure of the model so that it can more accurately predict the pump's contamination rate and recovery after cleaning. Through this dynamic optimization, the system can continuously improve the management and maintenance of emulsion pumps, improve the operating efficiency of the equipment and extend its service life.

[0070] Furthermore, the embodiment of the present application further includes step P90, and step P90 further includes: P91: Based on the length of the operating condition interval, define k transition intervals of the standard operating condition interval; P92: Define continuity constraints, and according to the transition intervals and the continuity constraints, fuse the k relative pollution rate mapping models to obtain a relative pollution rate integrated mapping model; P93: According to the current cycle operation log and the relative pollution rate integrated mapping model, perform predictive analysis on the current cycle operation log to obtain the accumulated pump status.

[0071] In a possible embodiment of the present application, by defining transition intervals and continuity constraints, k standard operating intervals are fused to construct a relative pollution rate integrated mapping model, and a prediction analysis is performed based on the current cycle operation log to obtain the cumulative pump state. This process is to achieve a smooth transition between different operating intervals and improve the accuracy and robustness of the pollution rate mapping model, thereby providing a more accurate basis for subsequent cleaning decisions.

[0072] First, the transition interval is defined based on the length of each standard operating interval. Each standard operating interval represents the state of the pump under different operating conditions, and there may be a certain degree of transition between different intervals, that is, the continuity and complexity of the state change of the pump during the transition. In order to achieve a smooth transition between different operating intervals, the system will define transition intervals between these intervals.

[0073] Exemplarily, for cases of low complexity and a small number of segments, weighted fusion or continuity constraints can be used to achieve the connection of transition intervals. This method can ensure the smoothness and continuity of the transition interval by weighted averaging multiple relative pollution rate mapping models. If a smooth connection with higher precision is required, mathematical methods such as basis function method or spline interpolation can be used. These methods can generate continuous and smooth transition curves to accurately connect different standard operating conditions. For example, spline interpolation can fit the curves between standard operating conditions through polynomial functions, thereby achieving a more accurate transition. In some cases, if the target predicted by the model needs to be further optimized, the system can also consider adding regularization constraints. Regularization constraints help prevent model overfitting, ensure that the connection between transition intervals is not only smooth but also stable, and improve the generalization ability of the model in practical applications.

[0074] Furthermore, according to the defined transition interval and continuity constraints, k relative pollution rate mapping models are fused to generate a relative pollution rate integrated mapping model. The continuity constraint ensures that the transition process between different standard operating conditions is not only smooth but also has consistent physical meaning. For example, the system can set constraints so that the pollution rate change trend between adjacent operating conditions remains consistent without sudden changes or unreasonable jumps.

[0075] Through these constraints and fusion processes, an integrated pollution rate mapping model is generated, which can cover all operating ranges and achieve smooth transitions between each range. This integrated model not only improves the accuracy of prediction, but also can adapt to the changing operating environment, making the evaluation of pollution rate more accurate and stable.

[0076] After the integrated mapping model is completed, the operation log of this cycle is used with the relative contamination rate integrated mapping model for prediction analysis. This cycle operation log records the specific operation data of the pump in the current cycle, including flow, pressure, temperature and other information. By inputting these data into the integrated mapping model, the contamination rate of the pump in the current cycle can be predicted, and the cumulative pump state of the pump can be further calculated.

[0077] The cumulative pump status is a cumulative calculation of the degree of contamination based on the predicted contamination rate of this cycle. This status indicates the progress of the pump's contamination throughout the cycle and provides a basis for subsequent cleaning decisions. If the cumulative pump status reaches a certain preset threshold, the system will trigger cleaning control to ensure that the pump is always in the best operating state. Through this process, the system can better adapt to the changing operating environment and improve the management efficiency of the pump and the accuracy of cleaning control.

[0078] In summary, the embodiments of the present application have at least the following technical effects: This application calibrates the relative state curve of the emulsion pump, extracts data from the cleaning record database in combination with unique identification information, and clusters and resamples the data to generate k working condition reorganization sample sets. Based on the reorganized sample data and the relative pump state curve, k pollution rate mapping models are trained to predict the cumulative pollution state of the pump, monitor the operating parameters of the pump in real time to evaluate the pollution state, and integrate the cumulative pollution state with the monitored pump state to ultimately achieve accurate cleaning decision-making and control.

[0079] The technical effect of achieving precise cleaning control by dynamically adjusting cleaning operations through real-time monitoring and pollution status assessment, avoiding excessive or insufficient cleaning operations, and thus improving the operating efficiency of the pump and extending the life of the equipment has been achieved.

[0080] Embodiment 2 is based on the same inventive concept as the intelligent cleaning control method of an emulsion pump in the above embodiment. Figure 2 As shown, the present application provides an intelligent cleaning control system for an emulsion pump, and the system and method embodiments in the present application are based on the same inventive concept. The system includes: The relative pump state curve calibration module 11 is used to calibrate the relative pump state curve in combination with the target scene requirements and the target emulsion pump, wherein the relative pump state represents the pollution progress of the target emulsion pump.

[0081] The data extraction module 12 is used to obtain the unique identification information of the target emulsion pump and extract data from the cleaning record database based on the unique identification information.

[0082] The cluster segmentation and resampling module 13 is used to perform cluster segmentation and resampling on the data extraction results to obtain reorganized sample data, wherein the data extraction results include sample pump control parameters and sample pump state parameters, and the reorganized sample data includes k working condition reorganized sample sets.

[0083] The rate mapping model construction module 14 is used to construct and train k relative contamination rate mapping models based on the reorganized sample data and the relative pump state curve, wherein the relative contamination rate mapping model takes the pump control parameter as input.

[0084] The cumulative pump state prediction module 15 is used to extract the current cycle operation log of the target emulsion pump, and predict the cumulative pump state of the target emulsion pump according to the current cycle operation log and the k relative pollution rate mapping models.

[0085] The direct contamination state evaluation module 16 is used to obtain the operating parameter information of the pump in real time, and to perform direct contamination state evaluation based on the relative pump state curve to obtain the monitoring pump state.

[0086] The cleaning decision control module 17 is used to integrate the accumulated pump state and the monitoring pump state to perform cleaning decision and cleaning control.

[0087] Furthermore, the relative pump state curve calibration module 11 is further used to perform the following steps: Interactive target emulsion pump, obtain the key status indicator set of the pump; based on the target scenario requirements, determine the evaluation weight and performance control limit of the key status indicator set; define a relative performance evaluation function according to the evaluation weight and the key status indicator set, and calibrate the relative pump status curve of the target emulsion pump in combination with the performance control limit.

[0088] Furthermore, the cluster segmentation and resampling module 13 is further configured to perform the following steps: The number of clusters k is determined based on the elbow method, and a density clustering analysis based on the sample pump control parameters is performed on the data extraction results; according to the k clusters in the cluster analysis results, k standard operating condition intervals are defined; with the k standard operating condition intervals as segmentation constraints, the data extraction results are segmented, and the segmented segmentation results are output as k reorganized data sets; based on preset confidence constraints, the k reorganized data sets are confidence screened to obtain k operating condition reorganization sample sets.

[0089] Furthermore, the rate mapping model building module 14 is also used to perform the following steps: Based on the sample pump state parameters of the reorganized sample data; combining the relative pump state curve and the sample pump state parameters, the reorganized sample data is relatively state marked to obtain marked sample data; with the relative state marking result as supervision, k relative pollution rate mapping models are constructed and trained based on the marked sample data, wherein the k relative pollution rate mapping models correspond one-to-one to the k standard operating conditions.

[0090] Furthermore, the cumulative pump state prediction module 15 is also used to perform the following steps: The operation log of the target pump in this cycle is obtained, and the operating condition setting sequence is extracted from the operation log of the current cycle; the operating condition setting sequence is mapped and converted based on k relative pollution rate mapping models to obtain a relative pollution rate sequence; the relative pollution rate sequence is cumulatively analyzed to obtain the cumulative pump state.

[0091] Furthermore, the system further comprises a feedback correction module, and the feedback correction module is further configured to perform the following steps: Recording and storing cleaning control records; if the deviation between the accumulated pump state and the monitored pump state is greater than a preset value, performing feedback correction on the k relative contamination rate mapping models based on the cleaning control records.

[0092] Furthermore, the system further includes an operation log prediction and analysis module, and the operation log prediction and analysis module is further used to perform the following steps: Based on the length of the operating condition interval, k transition intervals of the standard operating condition interval are defined; continuity constraints are defined, and according to the transition intervals and the continuity constraints, k relative pollution rate mapping models are integrated to obtain a relative pollution rate integrated mapping model; according to the current cycle operation log and the relative pollution rate integrated mapping model, the current cycle operation log is predicted and analyzed to obtain the accumulated pump state.

[0093] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0094] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0095] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. An intelligent cleaning control method for an emulsion pump, characterized in that: The method comprises: Combine the target scenario requirements with the target emulsion pump to calibrate the relative pump state curve, where the relative pump state represents the pollution progress of the target emulsion pump; Acquire unique identification information of a target emulsion pump, and extract data from a cleaning record database based on the unique identification information; Perform clustering and resampling on the data extraction results to obtain reorganized sample data, wherein the data extraction results include sample pump control parameters and sample pump state parameters, and the reorganized sample data includes k working condition reorganized sample sets; Based on the reorganized sample data and the relative pump state curve, k relative contamination rate mapping models are constructed and trained, wherein the relative contamination rate mapping model takes a pump control parameter as input; Extracting the current cycle operation log of the target emulsion pump, and predicting the cumulative pump state of the target emulsion pump according to the current cycle operation log and the k relative pollution rate mapping models; Acquire the operating parameter information of the pump in real time, and perform direct pollution status evaluation based on the relative pump status curve to acquire the monitoring pump status; The accumulated pump status and the monitored pump status are integrated to perform cleaning decision and cleaning control.

2. The intelligent cleaning control method of an emulsion pump according to claim 1, characterized in that: Combine the target scenario requirements with the target emulsion pump to calibrate the relative pump status curve, including: Interact with the target emulsion pump to obtain the key status indicator set of the pump; Based on the target scenario requirements, determine the evaluation weights and performance control limits of the key status indicator set; A relative performance evaluation function is defined according to the evaluation weight and the key status indicator set, and the relative pump status curve of the target emulsion pump is calibrated in combination with the performance control limit.

3. The intelligent cleaning control method of an emulsion pump according to claim 2, characterized in that: The data extraction results are clustered and resampled to obtain reorganized sample data, wherein the data extraction results include sample pump control parameters and sample pump state parameters, and the reorganized sample data includes k working condition reorganized sample sets, including: Determine the number of clusters k based on the elbow method, and perform density cluster analysis based on the sample pump control parameters on the data extraction results; According to the k clusters in the cluster analysis results, k standard operating condition intervals are defined; Taking the k standard operating condition intervals as segmentation constraints, segmenting the data extraction results, and outputting the segmentation results as k reorganized data sets; Based on the preset confidence constraints, confidence screening is performed on the k reorganized data sets to obtain k operating condition reorganized sample sets.

4. The intelligent cleaning control method of an emulsion pump according to claim 3, characterized in that: Based on the reorganized sample data and the relative pump state curve, k relative contamination rate mapping models are constructed and trained, wherein the relative contamination rate mapping model takes the pump control parameter as input, and includes: a sample pump state parameter based on the reorganized sample data; Combining the relative pump state curve with the sample pump state parameter, relative state marking is performed on the recombined sample data to obtain marked sample data; With the relative state labeling result as supervision, k relative pollution rate mapping models are constructed and trained based on the labeled sample data, wherein the k relative pollution rate mapping models correspond one-to-one to the k standard operating condition intervals.

5. The intelligent cleaning control method of an emulsion pump according to claim 4, characterized in that: Extracting the current cycle operation log of the target emulsion pump, and predicting the cumulative pump state of the target emulsion pump according to the current cycle operation log and the k relative pollution rate mapping models, including: Obtaining the current cycle operation log of the target pump, and extracting the working condition setting sequence from the current cycle operation log; Mapping and transforming the operating condition setting sequence based on the k relative pollution rate mapping models to obtain a relative pollution rate sequence; The relative pollution rate sequence is cumulatively analyzed to obtain the cumulative pump state.

6. The intelligent cleaning control method of an emulsion pump according to claim 1, characterized in that: The method further comprises: Record and store cleaning control records; If the deviation between the accumulated pump state and the monitored pump state is greater than a preset value, feedback correction is performed on the k relative contamination rate mapping models based on the cleaning control record.

7. The intelligent cleaning control method of an emulsion pump according to claim 4, characterized in that: The method further comprises: Based on the length of the operating condition interval, defining k transition intervals of the standard operating condition interval; Defining a continuity constraint, and fusing k relative pollution rate mapping models according to the transition interval and the continuity constraint to obtain a relative pollution rate integrated mapping model; According to the operation log of the current cycle and the relative pollution rate integrated mapping model, the operation log of the current cycle is predicted and analyzed to obtain the cumulative pump state.

8. An intelligent cleaning control system for an emulsion pump, characterized in that: The system comprises: A relative pump state curve calibration module, wherein the relative pump state curve calibration module is used to calibrate a relative pump state curve in combination with target scene requirements and a target emulsion pump, wherein the relative pump state indicates the pollution progress of the target emulsion pump; A data extraction module, the data extraction module is used to obtain unique identification information of a target emulsion pump, and extract data from a cleaning record database based on the unique identification information; A clustering segmentation and resampling module, wherein the clustering segmentation and resampling module is used to perform clustering segmentation and resampling on the data extraction results to obtain reorganized sample data, wherein the data extraction results include sample pump control parameters and sample pump state parameters, and the reorganized sample data includes k working condition reorganized sample sets; A rate mapping model construction module, the rate mapping model construction module is used to construct and train k relative contamination rate mapping models based on the recombined sample data and the relative pump state curve, wherein the relative contamination rate mapping model takes the pump control parameter as input; A cumulative pump state prediction module, the cumulative pump state prediction module is used to extract the current cycle operation log of the target emulsion pump, and predict the cumulative pump state of the target emulsion pump according to the current cycle operation log and the k relative pollution rate mapping models; A direct contamination state evaluation module, which is used to obtain the operating parameter information of the pump in real time, and to perform direct contamination state evaluation based on the relative pump state curve to obtain the monitoring pump state; A cleaning decision control module is used to integrate the accumulated pump state and the monitoring pump state to perform cleaning decision and cleaning control.

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