CIP self-cleaning cooperative control system and method for chemical diaphragm pump

Abstract

The invention provides a CIP self-cleaning cooperative control system and method for a chemical diaphragm pump, and relates to the technical field of diaphragm pump cleaning control, and the system comprises a parameter obtaining module which is used for obtaining an operation parameter sequence and a residue concentration sequence; the dynamic residue traceability analysis module is used for judging whether a self-cleaning process is triggered or not according to the dynamic residue traceability analysis result, and if yes, a preset cleaning process template is called; the cleaning process optimization module is used for optimizing a preset cleaning process template in combination with the dynamic residue traceability analysis result to obtain an optimized cleaning process; and the self-cleaning module is used for executing an optimized cleaning process through the embedded control unit and carrying out self-cleaning on the chemical diaphragm pump. By means of the chemical diaphragm pump cleaning method and device, the technical problem that in the prior art, cleaning efficiency is low due to the fact that cleaning control depends on a fixed period and deep analysis on a pollution mechanism is lacked can be solved, the cleaning process is optimized through dynamic traceability analysis, and the cleaning efficiency of the chemical diaphragm pump is improved.

Description

Technical Field

[0001] This application relates to the field of diaphragm pump cleaning control technology, and in particular to a chemical diaphragm pump CIP self-cleaning collaborative control system and method. Background Technology

[0002] After a period of use, chemical diaphragm pumps accumulate residual chemicals inside, which can clog the pump chamber or pipelines in severe cases. This is especially problematic for newer battery cell boron expansion processes, where crystallization and residue buildup can easily occur inside the pump. Failure to clean promptly will affect efficiency and product yield, and in severe cases, lead to pump or motor damage. Traditional diaphragm pump maintenance involves manual or semi-automatic cleaning every 10 to 15 days, which is labor-intensive. Because cleaning typically relies on manual labor or scheduled intervals, inaccurate timing of cleaning can lead to over-cleaning or delayed cleaning, wasting energy and potentially causing equipment contamination or damage, ultimately affecting the cleaning efficiency of the chemical diaphragm pump and the overall performance of the equipment.

[0003] In summary, existing technologies suffer from technical problems such as untimely cleaning or waste of resources due to the reliance on fixed cycles for cleaning control and the lack of real-time monitoring and analysis of contamination mechanisms and residual changes, which further affect cleaning efficiency. Summary of the Invention

[0004] The purpose of this application is to provide a chemical diaphragm pump CIP self-cleaning collaborative control system and method to solve the technical problems in the prior art where cleaning control depends on a fixed cycle and lacks real-time monitoring and analysis of contamination mechanisms and residual changes, resulting in untimely cleaning or waste of resources, which further affects cleaning efficiency.

[0005] In view of the above problems, this application provides a chemical diaphragm pump CIP self-cleaning collaborative control system and method.

[0006] In a first aspect, this application provides a chemical diaphragm pump (CIP) self-cleaning collaborative control system, wherein the CIP self-cleaning collaborative control system comprises: a parameter acquisition module, used to acquire the operating parameter sequence and residue concentration sequence of the diaphragm pump in the target process section; a dynamic residue tracing analysis module, used to perform dynamic residue tracing analysis based on the operating parameter sequence and the residue concentration sequence, and determine whether to trigger the self-cleaning process based on the dynamic residue tracing analysis result; if so, calling a preset cleaning process template; a cleaning process optimization module, used to optimize the preset cleaning process template in combination with the dynamic residue tracing analysis result to obtain an optimized cleaning process; and a self-cleaning module, used to execute the optimized cleaning process through an embedded control unit to perform self-cleaning of the chemical diaphragm pump.

[0007] Optionally, the abnormal operation identification unit is used to traverse the sequence of operating parameters to identify abnormal operation and obtain abnormal operation results; the trend identification unit is used to identify critical anomalies in residue concentration and identify the growth trend of residue concentration based on the residue concentration sequence, and obtain the critical anomaly identification results and the growth trend identification results of residue concentration; the source tracing analysis unit is used to use the abnormal operation results, the critical anomaly identification results, and the growth trend identification results of residue concentration as dynamic residue source tracing analysis results; and the template matching unit is used to trigger a self-cleaning process when the dynamic residue source tracing analysis results do not meet preset requirements, and to match the dynamic residue source tracing analysis results with the cleaning process template library to determine a preset cleaning process template.

[0008] Optionally, a data determination subunit is used to acquire multiple abnormal running results of samples and multiple running parameter sequences of samples as training data; a randomized perturbation subunit is used to randomly perturb the multiple abnormal running results of samples to obtain multiple perturbed abnormal running results; a recognizer acquisition subunit is used to use the multiple abnormal running results of samples as positive samples, the multiple perturbed abnormal running results as negative samples, and the multiple running parameter sequences of samples as input data to train a support vector machine until convergence, thereby obtaining an abnormal running recognizer; the abnormal running recognition uses the abnormal running recognizer to identify the running parameter sequences to obtain the abnormal running results.

[0009] Optionally, the concentration critical anomaly identification subunit is used to obtain a preset residue concentration threshold. When the residue concentration sequence contains residue concentrations greater than or equal to the preset residue concentration threshold, the residue exceeding the standard is taken as the residue concentration critical anomaly identification result. The multi-scale growth trend analysis subunit is used to perform multi-scale growth trend analysis on the residue concentration sequence to obtain a multi-scale residue concentration growth trend feature set. The concentration growth trend identification subunit is used to perform multi-scale interaction on the multi-scale residue concentration growth trend feature set to determine the residue concentration growth trend feature, and take the residue concentration growth trend feature as the residue concentration growth trend identification result.

[0010] Optionally, a random combination channel is used to randomly combine the multi-scale residue concentration growth trend feature set to obtain a random combination set, wherein the number of random combinations is greater than or equal to two-thirds of the number of features in the multi-scale residue concentration growth trend feature set; a similarity recognition channel is used to perform pairwise similarity recognition on the multi-scale residue concentration growth trend features in the random combination set, and normalize the recognition results to construct an interaction matrix set; a feature convolution channel is used to convolve the multi-scale residue concentration growth trend features of the corresponding random combinations in the random combination set using the interaction matrix set to obtain an interactive multi-scale residue concentration growth trend feature set; and a mean calculation channel is used to calculate the mean of the interactive multi-scale residue concentration growth trend feature set to obtain the residue concentration growth trend features.

[0011] Optionally, the similarity matching subunit is used to perform similarity matching between the dynamic residue tracing analysis results and the residue tracing analysis result template corresponding to each cleaning process template in the cleaning process template library, and to use the cleaning process template corresponding to the maximum matching value as the preset cleaning process template.

[0012] Optionally, the communication connection establishment unit is used to disconnect the communication between the host computer and the chemical diaphragm pump after closing the pipeline valve connecting the chemical diaphragm pump and the vacuum furnace via the host computer, and to send a TCP start signal to the cleaning device to establish a communication connection with the cleaning device, and to send a cleaning signal command to the cleaning device via the host computer; the cleaning control unit is used to control the opening of the water circuit electric valve and the cleaning purge electric valve according to the hot water cleaning stage operation steps in the optimized cleaning process via the embedded control unit of the cleaning device; the heating control unit is used to activate the heating unit to heat the pure water to a preset temperature, and start the chemical diaphragm pump to draw in hot water in a pulsating manner to dissolve and clean the crystalline dust in the pump chamber of the chemical diaphragm pump; the drying control unit is used to dry the pump chamber of the chemical diaphragm pump according to the hot nitrogen drying stage operation steps in the optimized cleaning process via the embedded control unit after the dissolution and cleaning is completed; the residual vapor venting control unit is used to control the execution of the residual vapor venting stage operation steps in the optimized cleaning process via the embedded control unit after the hot nitrogen drying is completed, to complete the self-cleaning of the chemical diaphragm pump.

[0013] Optionally, the drying and purging subunit is used to close the water circuit electric valve and open the nitrogen solenoid valve through the embedded control unit to supply hot nitrogen to the pump chamber for drying and purging; the hot nitrogen drying subunit is used to complete the hot nitrogen drying when the drying and purging time is greater than or equal to a preset time threshold.

[0014] Optionally, a completion reminder subunit is used to close the cleaning and purging electric valve and the nitrogen solenoid valve through the embedded control unit, stop the operation of the chemical diaphragm pump, and open the residual steam venting solenoid valve to vent residual steam. When the completion is completed, a signal is sent to the host computer to remind that the cleaning work is completed, and the host computer resumes communication with the chemical diaphragm pump.

[0015] Secondly, this application also provides a CIP self-cleaning collaborative control method for a chemical diaphragm pump, wherein the CIP self-cleaning collaborative control method for a chemical diaphragm pump includes: acquiring the operating parameter sequence and residue concentration sequence of the diaphragm pump in the target process section; performing dynamic residue source tracing analysis based on the operating parameter sequence and the residue concentration sequence, and determining whether a self-cleaning process is triggered based on the dynamic residue source tracing analysis results; if so, calling a preset cleaning process template; optimizing the preset cleaning process template in combination with the dynamic residue source tracing analysis results to obtain an optimized cleaning process; and executing the optimized cleaning process through an embedded control unit to perform self-cleaning on the chemical diaphragm pump.

[0016] One or more technical solutions provided in this application have at least the following technical effects or advantages: The system employs a parameter acquisition module to acquire the operating parameter sequence and residue concentration sequence of the diaphragm pump in the target process section. A dynamic residue source analysis module performs dynamic residue source analysis based on the operating parameter sequence and residue concentration sequence, and determines whether a self-cleaning process is triggered based on the analysis results. If triggered, a preset cleaning process template is invoked. A cleaning process optimization module optimizes the preset cleaning process template based on the dynamic residue source analysis results to obtain an optimized cleaning process. A self-cleaning module executes the optimized cleaning process through an embedded control unit to perform self-cleaning of the chemical diaphragm pump. In other words, by analyzing the operating parameter sequence and residue concentration sequence, performing dynamic residue source analysis, determining whether a self-cleaning process is triggered, and optimizing the preset cleaning process template based on the analysis results, the chemical diaphragm pump is self-cleaned, avoiding problems of untimely cleaning and resource waste, and improving the cleaning efficiency of the chemical diaphragm pump.

[0017] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the structure of a chemical diaphragm pump CIP self-cleaning collaborative control system according to this application.

[0020] Figure 2 This is a schematic diagram of the process for a chemical diaphragm pump CIP self-cleaning collaborative control method according to this application.

[0021] Figure labeling: Parameter acquisition module 11, Dynamic residue source analysis module 12, Cleaning process optimization module 13, Self-cleaning module 14. Detailed Implementation

[0022] This application provides a self-cleaning collaborative control system and method for chemical diaphragm pumps (CIP), solving the technical problems in existing technologies where cleaning control relies on fixed cycles and lacks real-time monitoring and analysis of contamination mechanisms and residue changes, leading to untimely cleaning or resource waste, further affecting cleaning efficiency. By analyzing the operating parameter sequence and residue concentration sequence, dynamic residue source analysis is performed to determine whether the self-cleaning process is triggered. Based on the results of the dynamic residue source analysis, the preset cleaning process template is optimized, thereby enabling the chemical diaphragm pump to self-clean, avoiding untimely cleaning and resource waste, and improving the cleaning efficiency of the chemical diaphragm pump.

[0023] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0024] Example 1, please refer to the appendix. Figure 1 This application provides a chemical diaphragm pump CIP self-cleaning collaborative control system, wherein the chemical diaphragm pump CIP self-cleaning collaborative control system is used to implement the steps of a chemical diaphragm pump CIP self-cleaning collaborative control method, and the chemical diaphragm pump CIP self-cleaning collaborative control system includes: The parameter acquisition module 11 is used to acquire the operating parameter sequence and residual concentration sequence of the diaphragm pump in the target process section.

[0025] Specifically, the system collects operational parameters (such as pump chamber pressure, flow rate, pulsation frequency, runtime, and chemical type) and internal residue detection data of the chemical diaphragm pump in the target process section, obtaining an operational parameter sequence and a residue concentration sequence. The target process section is a specific process link in the entire production process associated with the chemical diaphragm pump, such as the boron diffusion process for solar cells. This section involves the transport of corrosive chemicals, and the pump load and contamination are most significant in this area. The operational parameter sequence refers to various operational parameters continuously collected over time from the start to the end of pump operation. The sequence implies that these parameters are not values ​​at a single moment, but rather a set of data that changes over time. The residue concentration sequence refers to the change data of residue concentration inside the pump (usually near the diaphragm section or key sampling points) measured through specific methods (such as online sensors or periodic sampling analysis) during the operation of the target process section, also arranged in chronological order.

[0026] The sequence of operating parameters reflects the workload and operating mode of the chemical diaphragm pump. For example, high-frequency, high-pressure operation may more easily lead to the residue or crystallization of certain media; while low-frequency, low-pressure operation may correspond to different contamination risks. These parameters are usually measured by sensors (such as pressure sensors, flow meters, current transformers, frequency counters, etc.) on the pump body or process pipeline. The data is transmitted to the control system and stored by timestamp, forming a continuous parameter sequence. By installing optical turbidity sensors, conductivity sensors, or ultrasonic sensors at specific locations on the chemical diaphragm pump, the concentration changes of suspended particles or dissolved substances in the gas can be reflected in real time or near real time; or samples can be taken from the inside or outlet pipeline of the chemical diaphragm pump at preset time intervals (such as at the end of each shift) and sent to the laboratory for precise concentration determination using chemical analysis instruments (such as spectrophotometers, chromatographs, etc.) to obtain the trend of chemical residue concentration in the pump chamber over time. For example, the chemical diaphragm pump operates at a frequency of 45 Hz, with a flow rate of 3.0 m³ / h to 3.6 m³ / h, a pump chamber pressure stability of 42 kPa to 46 kPa, an operating temperature of 35°C to 38°C, an initial conductivity of 450 μS / cm (reference), which rises to 1680 μS / cm after 48 hours; turbidity increases from 5 NTU to 45 NTU; the cumulative operating time is approximately 28 hours; the motor current increases by an average of approximately 8%, and the power output increases from 150 W to 162 W. A residual accumulation rate of 25% / hour was identified, indicating potential contamination or crystallization in the pump chamber. A conductivity fluctuation rate exceeding three times the baseline indicates dynamic changes in contaminants within the pump chamber, suggesting possible severe residue buildup. A pump load increase rate greater than 10% indicates the equipment may be under significant load during operation, suggesting substantial crystallization or sticky deposits within the pump chamber. Based on the residual accumulation rate and load increase indicators, combined with conductivity fluctuations and turbidity changes, it was determined that crystallization or sticky deposits may have occurred within the pump chamber, automatically marking it as requiring cleaning. By acquiring continuous operating parameter sequences, the specific operating status and load changes of the pump in the target process section can be understood, and the acquired residual concentration sequence directly quantifies the real-time degree of contamination within the pump and its development trend.

[0027] The dynamic residue source tracing analysis module 12 is used to perform dynamic residue source tracing analysis based on the operating parameter sequence and the residue concentration sequence, and to determine whether to trigger the self-cleaning process based on the dynamic residue source tracing analysis results. If so, the preset cleaning process template is called.

[0028] Furthermore, the dynamic residue tracing analysis module 12 in the CIP self-cleaning collaborative control system of the chemical diaphragm pump is also used for: an abnormal operation identification unit, used to traverse the operating parameter sequence to identify abnormal operation and obtain abnormal operation results; a trend identification unit, used to identify critical anomalies in residue concentration and the growth trend of residue concentration based on the residue concentration sequence, and obtain critical anomaly identification results and growth trend identification results; a tracing analysis unit, used to use the abnormal operation results, critical anomaly identification results, and growth trend identification results as dynamic residue tracing analysis results; and a template matching unit, used to trigger the self-cleaning process when the dynamic residue tracing analysis results do not meet the preset requirements, and to match the dynamic residue tracing analysis results with the cleaning process template library to determine the preset cleaning process template.

[0029] Furthermore, the dynamic residue tracing analysis module 12 in the CIP self-cleaning collaborative control system of the chemical diaphragm pump is also used for: a data determination subunit, used to acquire multiple abnormal operating results of samples and multiple operating parameter sequences of samples as training data; a random derivative interference subunit, used to randomly generate interference on the multiple abnormal operating results of samples to obtain multiple derived interference abnormal operating results; an identifier acquisition subunit, used to use the multiple abnormal operating results of samples as positive samples, the multiple derived interference abnormal operating results as negative samples, and the multiple operating parameter sequences of samples as input data to train a support vector machine until convergence, thereby obtaining an abnormal operating identifier; and abnormal operating identification using the abnormal operating identifier to identify the operating parameter sequence to obtain the abnormal operating result.

[0030] Furthermore, the dynamic residue tracing analysis module 12 in the chemical diaphragm pump CIP self-cleaning collaborative control system is also used as a similarity matching subunit, which is used to perform similarity matching between the dynamic residue tracing analysis result and the residue tracing analysis result template corresponding to each cleaning process template in the cleaning process template library, and take the cleaning process template corresponding to the maximum matching value as the preset cleaning process template.

[0031] Specifically, this involves acquiring multiple sample abnormal operating results and multiple sample operating parameter sequences. The multiple sample abnormal operating results are collected from actual production records or simulations, representing operational status data or labels indicating that the chemical diaphragm pump experienced anomalies (such as decreased efficiency, abnormal pressure, or excessive residue) in the target process section. Examples include sudden pressure drops or rises, abnormal increases in conductivity, failure to reduce residual concentration after cleaning, and sudden changes or prolonged decreases in flow rate. The multiple sample operating parameter sequences are data sequences corresponding to the multiple sample abnormal operating results, showing the changes in diaphragm pump operating parameters over time at the time of the anomaly. These sequences describe the specific operating state of the chemical diaphragm pump when the anomaly occurred, such as pressure, temperature, flow rate, current, and conductivity.

[0032] Randomly derived perturbations are applied to multiple abnormal sample execution results. This involves introducing disturbances, misalignments, pruning, and noise into existing abnormal data to construct samples that do not actually represent anomalies but appear similar. These multiple derived perturbation abnormal execution results are used to enhance the model's discriminative ability. Randomly derived perturbations are a data augmentation technique that generates new data that are similar to the original abnormal patterns by randomly and slightly modifying or adding noise to the original abnormal sample execution result data. The aim is to increase the diversity and quantity of training data and prevent the model from overfitting (i.e., the model only remembers the specific details of the training data without learning general patterns).

[0033] Multiple original samples of abnormal execution results are used as positive samples, while newly generated derivative abnormal execution results are used as negative samples. Although the derivative samples are also based on anomalies, the interference makes them different from the original anomalies, and the model needs to learn to distinguish these differences. Positive samples represent the target category that the anomaly run identifier is expected to identify, while negative samples are used to enhance the robustness of the anomaly run identifier. Support Vector Machines (SVMs) are chosen as the classification algorithm. Multiple sample execution parameter sequences are used as input data, and the prepared positive and negative samples are fed into the SVM for training. The SVM learns how to distinguish between these two types of samples based on the input execution parameter sequences. During training, the SVM continuously adjusts its internal parameters (such as the position and orientation of the hyperplane) to maximize the margin between positive and negative samples. This process continues until the model's performance metrics (such as the loss function) no longer change significantly after multiple iterations, indicating convergence. At this point, the SVM has completed its training and can be used as an anomaly run identifier. The convergence state is when the model's learning process reaches a stable state, such as when the validation set error is less than 5%, the loss function value changes by less than 0.001, or the maximum number of training rounds (e.g., 1000 rounds) is reached.

[0034] The sequence of operating parameters is input into the abnormal operation identifier, which outputs a judgment result, i.e., the abnormal operation result, to indicate whether the current operating state of the chemical diaphragm pump belongs to abnormal operation (according to the definition during training, it is biased towards identifying serious abnormalities that are highly similar to the original abnormal pattern). The abnormal operation result is usually a two-factor judgment (such as abnormal or non-abnormal) or a probability value (indicating the likelihood of an abnormality).

[0035] Based on the residue concentration sequence, critical anomaly identification is performed to determine whether the current residue concentration has reached a preset threshold indicating a high level of pollution. The residue concentration growth trend is also identified based on the residue concentration sequence to determine the trend of change over a period of time, yielding the residue concentration growth trend identification results. Through multi-scale growth trend analysis of the residue concentration sequence and multi-scale interaction of the extracted residue concentration growth trend features at different scales, the interaction and influence between features at different time scales are considered, comprehensively understanding the complexity and dynamics of residue concentration changes, and obtaining the residue concentration growth trend characteristics.

[0036] The results of abnormal operation, critical anomaly identification of residue concentration, and residue concentration growth trend identification are used as dynamic residue source tracing analysis results. This integrates these results into a comprehensive judgment, not only describing the current state but also attempting to dynamically infer the possible causes or processes of residue generation or accumulation from the perspective of operational status and residue changes. When the dynamic residue source tracing analysis results do not meet preset requirements, a self-cleaning process is triggered. The dynamic residue source tracing analysis results are matched with the residue source tracing analysis result templates corresponding to each cleaning process template in the cleaning process template library. The cleaning process template with the highest matching value is used as the preset cleaning process template. This summarizes information such as the current operating status of the chemical diaphragm pump, residue concentration levels, and their changing trends, reflecting whether there are any concerning contamination or abnormalities within the chemical diaphragm pump.

[0037] The preset requirements are a set of conditions pre-defined to trigger the self-cleaning process, including one or more of the following: detecting operational abnormalities, residue concentration exceeding a threshold, or residue concentration showing a rapid upward trend. Cleaning is only initiated when the analysis results do not meet these preset conditions. The dynamic residue source tracing analysis results are compared with the preset requirements. When the dynamic residue source tracing analysis results do not meet the preset conditions, it indicates that the chemical diaphragm pump has a problem or a high risk of contamination, requiring the triggering of the self-cleaning process for intervention and cleaning.

[0038] The dynamic residue source tracing analysis results are matched with the residue source tracing analysis result templates corresponding to each cleaning process template in the cleaning process template library to select the cleaning process template most similar to the current situation. The cleaning process template library contains various cleaning process templates, each corresponding to a specific contamination condition or abnormal operating mode, and each template is associated with a residue source tracing analysis result template describing the typical contamination characteristics targeted in the cleaning process design (e.g., a template designed for high-concentration, rapidly growing contamination will describe these characteristics). The similarity between the current dynamic residue source tracing analysis results and the residue source tracing analysis result templates corresponding to each cleaning process template in the cleaning process template library is calculated (e.g., using a distance metric or matching algorithm) to assess which cleaning process template best matches the current actual condition of the chemical diaphragm pump. The cleaning process template with the highest similarity is selected as the preset cleaning process template. For example, template A targets minor operational anomalies and slight concentration exceedances, with corresponding template results showing current fluctuations <0.7A, concentrations of 40-60ppm, and increases <15ppm, resulting in a similarity of 0.6. Template B targets high concentrations and rapid increases, with corresponding template results showing concentrations >50ppm and 24-hour increases >20ppm, resulting in a similarity of 0.85. Template C targets severe operational anomalies and extremely high concentrations, with corresponding template results showing current fluctuations >1.0A and concentrations >80ppm, resulting in a similarity of 0.4. Template B has the highest similarity (0.85), therefore, template B is selected as the preset cleaning process template for this execution.

[0039] Cleaning is only triggered when an anomaly or contamination risk is detected (failure to meet preset requirements), avoiding blind cleaning based on fixed time intervals and reducing unnecessary resource consumption. By matching the current state with multiple predefined templates, the cleaning process that best matches the current contamination characteristics and pump status can be selected, rather than using a one-size-fits-all fixed process, thus significantly improving the cleaning effect.

[0040] Furthermore, the dynamic residue tracing analysis module 12 in the CIP self-cleaning collaborative control system of the chemical diaphragm pump is also used for: a concentration critical anomaly identification subunit, used to obtain a preset residue concentration threshold, and when there is a residue concentration greater than or equal to the preset residue concentration threshold in the residue concentration sequence, the residue exceeding the standard is taken as the residue concentration critical anomaly identification result; a multi-scale growth trend analysis subunit, used to perform multi-scale growth trend analysis on the residue concentration sequence to obtain a multi-scale residue concentration growth trend feature set; and a concentration growth trend identification subunit, used to perform multi-scale interaction on the multi-scale residue concentration growth trend feature set to determine the residue concentration growth trend feature, and take the residue concentration growth trend feature as the residue concentration growth trend identification result.

[0041] Furthermore, the dynamic residue tracing analysis module 12 in the CIP self-cleaning collaborative control system of the chemical diaphragm pump is also used for: a random combination channel, used to randomly combine the multi-scale residue concentration growth trend feature set to obtain a random combination set, wherein the number of random combinations is greater than or equal to two-thirds of the number of features in the multi-scale residue concentration growth trend feature set; a similarity recognition channel, used to perform pairwise similarity recognition on the multi-scale residue concentration growth trend features in the random combination set respectively, and normalize the recognition results to construct an interaction matrix set; a feature convolution channel, used to convolve the multi-scale residue concentration growth trend features of the corresponding random combinations in the random combination set using the interaction matrix set to obtain an interactive multi-scale residue concentration growth trend feature set; and a mean calculation channel, used to calculate the mean of the interactive multi-scale residue concentration growth trend feature set to obtain the residue concentration growth trend features.

[0042] Specifically, a preset residue concentration threshold is obtained. This threshold, set according to process requirements and the performance specifications of the chemical diaphragm pump, represents the upper limit of permissible chemical residue levels. It signifies that the residue concentration inside the chemical diaphragm pump within the target process section is considered to have reached a critical level requiring attention or action. For example, conductivity must not exceed 2000 μS / cm; exceeding this level may lead to crystallization and blockage. The real-time monitored residue concentration sequence is compared point-by-point with the preset residue concentration threshold. When a residue concentration greater than or equal to the preset threshold exists in the sequence, a clear judgment is immediately output: residue exceeds the limit. This residue exceeding the limit is used as a critical anomaly identification result. Exceeding the limit indicates that at least one time point in the monitored residue concentration sequence has reached or exceeded the preset residue concentration threshold, signifying that contamination within the chemical diaphragm pump has accumulated to a level requiring attention.

[0043] Multi-scale growth trend analysis of residue concentration sequences involves not only examining short-term instantaneous changes or long-term average changes, but also simultaneously considering the growth of data across different time scales. By using multiple time scales to analyze the growth trend of residue concentration sequences, the trend characteristics of concentration growth over time are extracted, forming a multi-scale residue concentration growth trend feature set. Each feature represents a quantitative representation of residue concentration changes at a specific time scale (e.g., short-term, medium-term, long-term), such as average growth rate, maximum growth rate, and growth stability indicators, encompassing concentration change information observed from different time dimensions.

[0044] The set of multi-scale residue concentration growth trend features is randomly combined to increase diversity and simulate multiple possible trend combinations. The number of random combinations is greater than or equal to two-thirds of the number of features in the multi-scale residue concentration growth trend feature set. For example, if the original set has 5 features (A, B, C, D, E), the random combination set may contain subsets such as {A, B, C}, {B, C, E}, {A, D, E}, etc., and the number of these subsets must be greater than or equal to two-thirds of the number of features in the original set (5), i.e., at least 2 subsets. The random combination set refers to the set of subsets formed by randomly selecting features from the multi-scale residue concentration growth trend feature set. Each subset contains several original features.

[0045] Pairwise similarity identification was performed on the multi-scale residual concentration growth trend features within a randomly combined subset. Within each random subset, the similarity between any two features was calculated using the cosine phase velocity. Similarity was represented numerically, between 0 and 1, with values ​​closer to 1 indicating greater similarity. All similarity values ​​were normalized to ensure they were between 0 and 1, eliminating the influence of differences in units or numerical ranges between different features and ensuring comparability of the similarity results. Based on the calculated similarities, interaction matrices were constructed. Each matrix represents the interaction relationship between features in a feature combination. The rows and columns of the matrix represent different features in the subset, and each element in the matrix represents the similarity value between two corresponding features after similarity identification and normalization. One random subset corresponds to one interaction matrix, and all these matrices constitute the set of interaction matrices.

[0046] For example, a multi-scale growth trend analysis was performed on the residue concentration sequence, and the resulting multi-scale residue concentration growth trend features include (simplified example): Feature A: average growth rate over the last 5 minutes: 0.2 ppm / min; Feature B: average growth rate over the last 30 minutes: 0.1 ppm / min; Feature C: average growth rate over the last 2 hours: 0.05 ppm / min; Feature D: maximum growth rate over the last 8 hours: 0.15 ppm / min. Features were randomly selected from these 5 features and combined to obtain three combinations. After calculating pairwise similarities, |AB|=0.1, |AC|=0.15, and |BC|=0.05. Assuming the normalization factor is 0.2 (maximum possible difference), the normalized similarity recognition results are Sim(A,B)=1-(0.1 / 0.2)=0.5, Sim(A,C)=1-(0.15 / 0.2)=0.25, Sim(B,C)=1-(0.05 / 0.2)=0.75, and the resulting interaction matrix set is (1.0,0.5,0.25),(0.5,1.0,0.75),(0.25,0.75,1.0).

[0047] The interaction matrix set is used to convolve the multi-scale residual concentration growth trend features of corresponding random combinations in the random combination set. This involves using the similarity information in the interaction matrix to weightedly integrate the features in the random combination subset. Specifically, high similarity values ​​in the interaction matrix mean that the corresponding two features have similar change patterns. The convolution operation makes these similar features have a greater common influence on the final result, which can be seen as extracting comprehensive information after the interaction between features. The convolution operation can be understood as fusing multiple feature information through weighted summation. Using the interaction matrix set, the original features within the subset (i.e., the multi-scale residual concentration growth trend features of corresponding random combinations in the random combination set) are weighted and integrated through convolution to obtain the interactive multi-scale residual concentration growth trend feature set. The interactive multi-scale residual concentration growth trend feature set refers to a comprehensive feature value or a new set of feature representations for each subset obtained after convolving each random combination subset and its corresponding interaction matrix. The new features have already incorporated the interrelationship information between features in the subset.

[0048] The mean of the set of interactive multi-scale residual concentration growth trend features is calculated to obtain the residual concentration growth trend features. This represents a more robust and comprehensive quantitative characterization of the overall growth trend of residual concentration after a series of complex processing steps, including multi-scale analysis, random combination, similarity interaction, and convolution. The residual concentration growth trend features are used as the result of residual concentration growth trend identification. If the trend features indicate a rapid increase, accelerated growth, or continuous change in concentration over a certain period, it is considered a significant pollution pattern. The residual concentration growth trend identification result is the final judgment result, used to indicate whether the residue is in a stable state, growing slowly, growing rapidly, or exhibiting other complex change patterns. By comprehensively considering information from multiple time scales and their interrelationships, the true trend of residual concentration change can be accurately identified, reducing misjudgments caused by incomplete information from a single time scale or noise interference.

[0049] The cleaning process optimization module 13 is used to optimize the preset cleaning process template by combining the dynamic residue traceability analysis results to obtain an optimized cleaning process.

[0050] Specifically, the selected preset cleaning process template is optimized based on the results of dynamic residue source tracing analysis. This includes adjusting the concentration and type of cleaning solution, changing the cleaning temperature or pressure, and adjusting the cleaning time or drying process. The optimized cleaning process can be personalized according to different contamination levels, improving cleaning effectiveness and reducing unnecessary resource consumption. Specific values ​​from the dynamic residue source tracing analysis results are used to dynamically adjust and optimize the preset cleaning process template. For example, if the analysis results indicate more severe contamination or higher risk, the upper limit of the template parameter range or a stronger, more thorough option is selected; conversely, if the contamination level is low or the risk is controllable, the lower limit of the parameter range or a milder option is selected. For instance, based on the concentration trend, if the concentration exceeds the standard significantly, the concentration of the cleaning solution is increased or a more potent cleaning agent is selected. If the concentration increases rapidly, the temperature of the cleaning solution is increased (e.g., from 60℃ to 80℃) to improve cleaning efficiency. Based on the analysis results and optimization strategies, the optimized cleaning process is finally generated. By combining dynamic residue source analysis results with preset cleaning process templates, and optimizing the cleaning process based on the current operating status of the equipment and the concentration of residues, the cleaning process becomes more precise and targeted.

[0051] The self-cleaning module 14 is used to perform the optimized cleaning process through the embedded control unit to self-clean the chemical diaphragm pump.

[0052] Furthermore, the self-cleaning module 14 in the aforementioned chemical diaphragm pump CIP self-cleaning collaborative control system is also used for: a communication connection establishment unit, used to disconnect the communication between the host computer and the chemical diaphragm pump after the host computer closes the pipeline valve connecting the chemical diaphragm pump and the vacuum furnace, and send a TCP start signal to the cleaning device to establish a communication connection with the cleaning device, and send a cleaning signal command to the cleaning device through the host computer; a cleaning control unit, used to control the water circuit electric valve and the cleaning purge electric valve to open according to the hot water cleaning stage operation steps in the optimized cleaning process through the embedded control unit of the cleaning device; a heating control unit, used to activate the heating unit to heat the pure water to a preset temperature, and start the chemical diaphragm pump to draw in hot water in a pulsating manner to dissolve and clean the crystalline dust in the pump chamber of the chemical diaphragm pump; a drying control unit, used to dry the pump chamber of the chemical diaphragm pump according to the hot nitrogen drying stage operation steps in the optimized cleaning process through the embedded control unit after the dissolution and cleaning is completed; and a residual vapor venting control unit, used to control the execution of the residual vapor venting stage operation steps in the optimized cleaning process through the embedded control unit after the hot nitrogen drying is completed, to complete the self-cleaning of the chemical diaphragm pump.

[0053] Furthermore, the self-cleaning module 14 in the chemical diaphragm pump CIP self-cleaning collaborative control system is also used for: a drying and purging subunit, which is used to close the water circuit electric valve and open the nitrogen solenoid valve through the embedded control unit to provide hot nitrogen to the pump chamber for drying and purging; and a hot nitrogen drying subunit, which is used to complete the hot nitrogen drying when the drying and purging time is greater than or equal to a preset time threshold.

[0054] Furthermore, the self-cleaning module 14 in the CIP self-cleaning collaborative control system of the chemical diaphragm pump is also used to: complete the reminder subunit, which is used to close the cleaning and purging electric valve and the nitrogen solenoid valve through the embedded control unit, stop the operation of the chemical diaphragm pump and open the residual steam venting solenoid valve to vent residual steam. When the cleaning is completed, a signal is sent to the host computer to remind that the cleaning work is completed, and the host computer resumes communication with the chemical diaphragm pump.

[0055] Specifically, the host computer refers to the upper-level control device located at the control level, responsible for monitoring, scheduling, and issuing commands for the overall process flow. It is typically a PLC (Programmable Logic Controller). The host computer communicates with the chemical diaphragm pump to ensure fully automated operation. After each process is completed, within a 40-minute downtime window, the valves connecting the chemical diaphragm pump to the vacuum furnace are closed, communication between the host computer and the chemical diaphragm pump is disconnected, and a signal is sent to start the online CIP cleaning device. The host computer sends a TCP start signal to the cleaning device, establishing a new communication connection. The TCP start signal is a specific instruction sent based on the TCP / IP protocol to start the cleaning device and establish a communication connection. Establishing a communication connection is a prerequisite for the host computer to send detailed cleaning instructions to the cleaning device, ensuring that data and control commands can be transmitted between the host computer and the cleaning device in real time and reliably. Once the communication connection is successfully established, the host computer begins sending specific cleaning signal instructions to the cleaning device, including which cleaning stage to start and setting cleaning parameters (such as temperature, time, and pressure). The host computer sends a command, which is equivalent to issuing a cleaning task order. After receiving the command, the embedded control unit of the cleaning device begins to coordinate the various components to perform the cleaning operation according to the command.

[0056] The embedded control unit inside the cleaning device, based on optimized cleaning process control logic, first opens the water circuit electric valve and the cleaning and purging electric valve. It then activates the heating unit, energizing it to heat the pure water to a preset temperature (e.g., around 80°C). The chemical diaphragm pump is then started, using low-frequency pulsating suction to draw the heated pure water into the pump chamber pipeline. The hot water dissolves the crystalline dust within the pump chamber, rinsing it off. The hot water circulates within the pump chamber, utilizing its dissolving power to gradually dissolve and wash away any crystalline dust adhering to the inner wall of the pump chamber and any that may have already formed.

[0057] After the dissolution and cleaning are complete, the embedded control unit closes the water circuit electric valve and opens the nitrogen solenoid valve, supplying hot nitrogen to dry and purge the pump chamber and pipelines. The preset time threshold is the drying time set, typically 1-3 minutes. Hot nitrogen drying is complete when the drying and purging time is greater than or equal to the preset time threshold. The hot nitrogen circulates and purges within the pump chamber, removing residual moisture and utilizing the inertness of nitrogen to prevent oxidation of metal components.

[0058] After the hot nitrogen drying is complete, the embedded control unit closes the cleaning and purging electric valve and the nitrogen solenoid valve, stopping the chemical diaphragm pump. Simultaneously, the residual vapor venting solenoid valve opens to vent residual vapor, safely discharging the gas from the chemical diaphragm pump. This process is typically set to 5-15 minutes to prevent hot nitrogen from accumulating in the cleaning device. Once the residual vapor venting is complete, the chemical diaphragm pump self-cleansing is finished, sending a signal to the host computer to indicate the cleaning is complete. The host computer then re-establishes communication with the chemical diaphragm pump and enters standby or the next operating cycle. The entire cleaning process, from start to finish, is fully automated by the host computer and the embedded control unit of the cleaning device, requiring no manual intervention, significantly saving costs and improving operational convenience. Cleaning is completed within the fixed time window of process downtime, without additional production time. Strict adherence to the optimized cleaning process ensures precise control of key parameters such as cleaning agent temperature, cleaning time, and drying temperature, improving cleaning effectiveness, especially in removing crystallized dust from the pump chamber.

[0059] In summary, the chemical diaphragm pump CIP self-cleaning collaborative control system provided in this application has the following technical effects: The system employs a parameter acquisition module to acquire the operating parameter sequence and residue concentration sequence of the diaphragm pump in the target process section. A dynamic residue source analysis module performs dynamic residue source analysis based on the operating parameter sequence and residue concentration sequence, and determines whether a self-cleaning process is triggered based on the analysis results. If triggered, a preset cleaning process template is invoked. A cleaning process optimization module optimizes the preset cleaning process template based on the dynamic residue source analysis results to obtain an optimized cleaning process. A self-cleaning module executes the optimized cleaning process through an embedded control unit to perform self-cleaning of the chemical diaphragm pump. In other words, by analyzing the operating parameter sequence and residue concentration sequence, performing dynamic residue source analysis, determining whether a self-cleaning process is triggered, and optimizing the preset cleaning process template based on the analysis results, the chemical diaphragm pump is self-cleaned, avoiding problems of untimely cleaning and resource waste, and improving the cleaning efficiency of the chemical diaphragm pump.

[0060] Example 2: Based on the same inventive concept as the chemical diaphragm pump CIP self-cleaning collaborative control system in Example 1, this application also provides a chemical diaphragm pump CIP self-cleaning collaborative control method. Please refer to the appendix. Figure 2 The aforementioned chemical diaphragm pump CIP self-cleaning collaborative control method includes: Obtain the operating parameter sequence and residue concentration sequence of the diaphragm pump in the target process section; perform dynamic residue source tracing analysis based on the operating parameter sequence and the residue concentration sequence, and determine whether the self-cleaning process is triggered based on the dynamic residue source tracing analysis results. If so, call the preset cleaning process template; optimize the preset cleaning process template based on the dynamic residue source tracing analysis results to obtain an optimized cleaning process; execute the optimized cleaning process through the embedded control unit to perform self-cleaning on the chemical diaphragm pump.

[0061] Furthermore, the CIP self-cleaning collaborative control method for a chemical diaphragm pump further includes: traversing the operating parameter sequence to identify abnormal operation and obtain abnormal operation results; identifying critical anomalies in residue concentration and identifying the growth trend of residue concentration based on the residue concentration sequence, and obtaining critical anomaly identification results and residue concentration growth trend identification results; using the abnormal operation results, critical anomaly identification results, and residue concentration growth trend identification results as dynamic residue source tracing analysis results; when the dynamic residue source tracing analysis results do not meet preset requirements, triggering the self-cleaning process, and matching the dynamic residue source tracing analysis results with the cleaning process template library to determine a preset cleaning process template.

[0062] Furthermore, the CIP self-cleaning collaborative control method for a chemical diaphragm pump further includes: acquiring multiple sample abnormal operation results and multiple sample operation parameter sequences as training data; randomly generating perturbations on the multiple sample abnormal operation results to obtain multiple derived perturbation abnormal operation results; using the multiple sample abnormal operation results as positive samples and the multiple derived perturbation abnormal operation results as negative samples, and combining them with the multiple sample operation parameter sequences as input data, training a support vector machine until convergence to obtain an abnormal operation identifier; and using the abnormal operation identifier to identify the operation parameter sequence to obtain the abnormal operation result.

[0063] Furthermore, the CIP self-cleaning collaborative control method for a chemical diaphragm pump further includes: obtaining a preset residue concentration threshold; when the residue concentration sequence contains residue concentrations greater than or equal to the preset residue concentration threshold, the residue exceeding the threshold is identified as a critical anomaly in residue concentration; performing multi-scale growth trend analysis on the residue concentration sequence to obtain a multi-scale residue concentration growth trend feature set; performing multi-scale interaction on the multi-scale residue concentration growth trend feature set to determine the residue concentration growth trend features, and using the residue concentration growth trend features as the residue concentration growth trend identification result.

[0064] Furthermore, the CIP self-cleaning collaborative control method for a chemical diaphragm pump further includes: randomly combining the multi-scale residue concentration growth trend feature set to obtain a random combination set, wherein the number of random combinations is greater than or equal to two-thirds of the number of features in the multi-scale residue concentration growth trend feature set; performing pairwise similarity identification on the multi-scale residue concentration growth trend features in the random combination set, and normalizing the identification results to construct an interaction matrix set; using the interaction matrix set to convolve the multi-scale residue concentration growth trend features of the corresponding random combinations in the random combination set to obtain an interactive multi-scale residue concentration growth trend feature set; and calculating the mean of the interactive multi-scale residue concentration growth trend feature set to obtain the residue concentration growth trend features.

[0065] Furthermore, the chemical diaphragm pump CIP self-cleaning collaborative control method further includes: performing similarity matching between the dynamic residue tracing analysis results and the residue tracing analysis result template corresponding to each cleaning process template in the cleaning process template library, and using the cleaning process template corresponding to the maximum matching value as the preset cleaning process template.

[0066] Furthermore, the CIP self-cleaning collaborative control method for a chemical diaphragm pump further includes: after closing the pipeline valve connecting the chemical diaphragm pump and the vacuum furnace via a host computer, disconnecting the communication between the host computer and the chemical diaphragm pump, and sending a TCP start signal to the cleaning device to establish a communication connection with the cleaning device; sending a cleaning signal command to the cleaning device via the host computer; controlling the opening of the water circuit electric valve and the cleaning purge electric valve via the embedded control unit of the cleaning device according to the hot water cleaning stage operation steps in the optimized cleaning process; activating the heating unit to heat the pure water to a preset temperature, and starting the chemical diaphragm pump to draw in hot water in a pulsating manner to dissolve and clean the crystalline dust in the pump chamber of the chemical diaphragm pump; after the dissolution and cleaning are completed, drying the pump chamber of the chemical diaphragm pump via the embedded control unit according to the hot nitrogen drying stage operation steps in the optimized cleaning process; after the hot nitrogen drying is completed, controlling the execution of the residual steam venting stage operation steps in the optimized cleaning process via the embedded control unit to complete the self-cleaning of the chemical diaphragm pump.

[0067] Furthermore, the chemical diaphragm pump CIP self-cleaning collaborative control method further includes: closing the water circuit electric valve and opening the nitrogen solenoid valve through the embedded control unit to provide hot nitrogen to the pump chamber for drying and purging; when the drying and purging time is greater than or equal to a preset time threshold, the hot nitrogen drying is completed.

[0068] Furthermore, the CIP self-cleaning collaborative control method for a chemical diaphragm pump further includes: closing the cleaning and purging electric valve and the nitrogen solenoid valve through an embedded control unit, stopping the operation of the chemical diaphragm pump, and opening the residual steam venting solenoid valve to vent residual steam. After completion, a signal is sent to the host computer to remind that the cleaning work is completed, and the host computer resumes communication with the chemical diaphragm pump.

[0069] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Figure 1 The chemical diaphragm pump CIP self-cleaning collaborative control system and specific examples in Example 1 are also applicable to the chemical diaphragm pump CIP self-cleaning collaborative control method in this embodiment. Through the foregoing detailed description of the chemical diaphragm pump CIP self-cleaning collaborative control system, those skilled in the art can clearly understand the chemical diaphragm pump CIP self-cleaning collaborative control method in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0070] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0071] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A chemical diaphragm pump CIP self-cleaning collaborative control system, characterized in that, include: The parameter acquisition module is used to acquire the operating parameter sequence and residual concentration sequence of the diaphragm pump in the target process section; The dynamic residue source tracing analysis module is used to perform dynamic residue source tracing analysis based on the operating parameter sequence and the residue concentration sequence, and to determine whether to trigger the self-cleaning process based on the dynamic residue source tracing analysis results. If so, the preset cleaning process template is called. The cleaning process optimization module is used to optimize the preset cleaning process template by combining the results of the dynamic residue tracing analysis to obtain an optimized cleaning process. The self-cleaning module is used to execute the optimized cleaning process through the embedded control unit to perform self-cleaning on the chemical diaphragm pump.

2. The chemical diaphragm pump CIP self-cleaning collaborative control system as described in claim 1, characterized in that, The dynamic residue tracing and analysis module includes: An abnormal operation identification unit is used to traverse the sequence of operation parameters to identify abnormal operation and obtain abnormal operation results. The trend recognition unit is used to identify critical anomalies in residue concentration and identify the growth trend of residue concentration based on the residue concentration sequence, and to obtain the results of critical anomaly recognition and growth trend recognition of residue concentration. The source tracing analysis unit is used to take the abnormal operation results, the critical abnormality identification results of residue concentration, and the residue concentration growth trend identification results as dynamic residue source tracing analysis results; The template matching unit is used to trigger a self-cleaning process when the dynamic residue source tracing analysis result does not meet the preset requirements, and to match the dynamic residue source tracing analysis result with the cleaning process template library to determine the preset cleaning process template.

3. The chemical diaphragm pump CIP self-cleaning collaborative control system as described in claim 2, characterized in that, The abnormal operation identification unit includes: The data determination subunit is used to obtain multiple abnormal running results of samples and multiple running parameter sequences of samples as training data; The randomized derivative interference subunit is used to randomly generate interference on the abnormal running results of the multiple samples to obtain multiple derived interference abnormal running results. The identifier acquisition subunit is used to take the multiple abnormal running results of the samples as positive samples, the multiple abnormal running results of the derived interference as negative samples, and combine the multiple sample running parameter sequences as input data to train the support vector machine until convergence, thereby obtaining the abnormal running identifier. Abnormal operation identification uses the abnormal operation identifier to identify the sequence of operating parameters and obtain the abnormal operation result.

4. The chemical diaphragm pump CIP self-cleaning collaborative control system as described in claim 3, characterized in that, The trend recognition unit includes: The concentration critical anomaly identification subunit is used to obtain a preset residue concentration threshold. When there is a residue concentration in the residue concentration sequence that is greater than or equal to the preset residue concentration threshold, the residue exceeding the standard is taken as the residue concentration critical anomaly identification result. The multi-scale growth trend analysis subunit is used to perform multi-scale growth trend analysis on the residue concentration sequence to obtain a set of multi-scale residue concentration growth trend features. The concentration growth trend identification subunit is used to perform multi-scale interaction on the multi-scale residue concentration growth trend feature set, determine the residue concentration growth trend features, and use the residue concentration growth trend features as the residue concentration growth trend identification result.

5. The chemical diaphragm pump CIP self-cleaning collaborative control system as described in claim 4, characterized in that, The concentration increase trend identification subunit includes: A random combination channel is used to randomly combine the multi-scale residue concentration growth trend feature set to obtain a random combination set, wherein the number of random combinations is greater than or equal to two-thirds of the number of features in the multi-scale residue concentration growth trend feature set. The similarity recognition channel is used to perform pairwise similarity recognition on the multi-scale residual concentration growth trend features within the random combination set, and to normalize the recognition results to construct an interaction matrix set. The feature convolution channel is used to convolve the multi-scale residual concentration growth trend features of the corresponding random combination in the random combination set using the interaction matrix set, so as to obtain the interactive multi-scale residual concentration growth trend feature set. The mean calculation channel is used to calculate the mean of the set of interactive multi-scale residual concentration growth trend features to obtain the residual concentration growth trend features.

6. The chemical diaphragm pump CIP self-cleaning collaborative control system as described in claim 2, characterized in that, The template matching unit includes a similarity matching subunit, which is used to perform similarity matching between the dynamic residue tracing analysis result and the residue tracing analysis result template corresponding to each cleaning process template in the cleaning process template library, and take the cleaning process template corresponding to the maximum matching value as the preset cleaning process template.

7. The chemical diaphragm pump CIP self-cleaning collaborative control system as described in claim 1, characterized in that, The self-cleaning module includes: The communication connection establishment unit is used to disconnect the communication between the host computer and the chemical diaphragm pump after the host computer closes the pipeline valve connecting the chemical diaphragm pump and the vacuum furnace, and to send a TCP start signal to the cleaning device to establish a communication connection with the cleaning device, and to send a cleaning signal command to the cleaning device through the host computer. A cleaning control unit is used to control the opening of the water circuit electric valve and the cleaning and purging electric valve according to the operation steps of the hot water cleaning stage in the optimized cleaning process through the embedded control unit of the cleaning device. The heating control unit is used to activate the heating unit, heat the pure water to the preset temperature, and start the chemical diaphragm pump to draw in hot water in a pulsating manner to dissolve and clean the crystallized dust in the pump chamber of the chemical diaphragm pump. The drying control unit is used to dry the chemical diaphragm pump chamber according to the hot nitrogen drying stage operation steps in the optimized cleaning process after the dissolution and cleaning are completed, through the embedded control unit. The residual vapor venting control unit is used to control the operation steps of the residual vapor venting stage in the optimized cleaning process after the hot nitrogen drying is completed, thereby completing the self-cleaning of the chemical diaphragm pump.

8. The chemical diaphragm pump CIP self-cleaning collaborative control system as described in claim 7, characterized in that, The drying control unit includes: The drying and purging subunit is used to close the water circuit electric valve and open the nitrogen solenoid valve through the embedded control unit to supply hot nitrogen to the pump chamber for drying and purging. The hot nitrogen drying subunit is used to complete the hot nitrogen drying when the drying and purging time is greater than or equal to a preset time threshold.

9. The chemical diaphragm pump CIP self-cleaning collaborative control system as described in claim 7, characterized in that, The residual steam venting control unit includes: The completion reminder subunit is used to close the cleaning and purging electric valve and the nitrogen solenoid valve through the embedded control unit, stop the operation of the chemical diaphragm pump, and open the residual steam venting solenoid valve to vent residual steam. When the completion is completed, a signal is sent to the host computer to remind that the cleaning work is completed, and the host computer resumes communication with the chemical diaphragm pump.

10. A method for coordinated control of CIP self-cleaning in a chemical diaphragm pump, characterized in that, The chemical diaphragm pump CIP self-cleaning collaborative control system, as described in any one of claims 1 to 9, comprises: Obtain the operating parameter sequence and residue concentration sequence of the diaphragm pump in the target process section; Dynamic residue source tracing analysis is performed based on the operating parameter sequence and the residue concentration sequence, and it is determined whether the self-cleaning process is triggered based on the results of the dynamic residue source tracing analysis. If so, the preset cleaning process template is called. The preset cleaning process template is optimized based on the results of the dynamic residue tracing analysis to obtain an optimized cleaning process; The optimized cleaning process is executed by an embedded control unit to perform self-cleaning of the chemical diaphragm pump.

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