Gypsum rotational flow station anti-blocking system and method based on intelligent regulation and control and structure optimization

Abstract

The invention relates to the technical field of intelligent regulation and control, and discloses a gypsum cyclone station anti-blocking system and method based on intelligent regulation and control and structure optimization, and the system comprises a data cleaning module, a crystal state judgment module, a key parameter extraction module, a risk index calculation module, a regulation and control strategy generation module and a closed-loop control module. Performing data cleaning on the real-time operation data to obtain standard operation data; judging the gypsum crystal state of the gypsum rotational flow station; extracting the oxidation-reduction potential and the pH value of the gypsum slurry in the standard operation data; performing weighted averaging on the gypsum crystal state, the redox potential and the pH value of the gypsum slurry to obtain a blockage risk index; comparing the blockage risk index with a blockage threshold value, and generating a self-adaptive regulation and control strategy; real-time response data of the self-adaptive regulation and control strategy is responded and monitored, the real-time response data is fed back to the intelligent decision-making center, parameter optimization is completed, and closed-loop control is achieved; the anti-blocking efficiency of the gypsum cyclone station can be improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, and in particular to an anti-clogging system and method for gypsum hydrocyclone stations based on intelligent control and structural optimization. Background Technology

[0002] The clogging problem of gypsum hydrocyclones severely restricts their operational efficiency and stability, and existing technologies have significant shortcomings in data processing. The lack of standardized procedures for processing real-time operational data from gypsum hydrocyclones fails to effectively resolve issues such as inconsistent data formats, missing data, and logical contradictions. This leads to deviations in the extraction of key parameters and the assessment of gypsum crystal states, making it difficult to accurately predict potential clogging.

[0003] Existing anti-clogging solutions lack scientific risk assessment and dynamic control mechanisms. They fail to conduct comprehensive weighted analysis of multiple key factors influencing clogging, and control strategies are mostly fixed patterns, unable to adapt to changes in different operating conditions. Furthermore, the lack of a closed-loop feedback mechanism prevents the optimization of control parameters based on real-time response data, resulting in insufficient targeting of anti-clogging measures, poor overall anti-clogging effectiveness, frequent equipment blockages, increased maintenance costs, and the possibility of operational interruptions. Therefore, improving the efficiency of anti-clogging measures in gypsum hydrocyclone stations has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides an anti-clogging system and method for gypsum hydrocyclone stations based on intelligent control and structural optimization, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, this invention provides an anti-clogging system for a gypsum hydrocyclone station based on intelligent control and structural optimization. The system comprises a data cleaning module, a crystal state judgment module, a key parameter extraction module, a risk index calculation module, a control strategy generation module, and a closed-loop control module, wherein: The data cleaning module is used to clean the real-time operating data of the gypsum hydrocyclone station to obtain the standard operating data of the gypsum hydrocyclone station; The crystal state judgment module is used to judge the state of gypsum crystals at the outlet of the gypsum discharge pump in the gypsum hydrocyclone station based on the gypsum crystal image in the standard operating data. The key parameter extraction module is used to extract the redox potential and pH of gypsum slurry from the standard operating data; The risk index calculation module is used to calculate the blockage risk index of the gypsum hydrocyclone station by weighted averaging of gypsum crystal state, redox potential and gypsum slurry pH. The control strategy generation module is used to compare the blockage risk indicators with the blockage threshold in the intelligent decision-making center of the gypsum hydrocyclone station to generate an adaptive control strategy for the gypsum hydrocyclone station. The closed-loop control module is used to monitor the real-time response data of the adaptive control strategy and feed the real-time response data back to the intelligent decision center to complete parameter optimization and achieve closed-loop control.

[0006] In a preferred embodiment, when the data cleaning module performs data cleaning on the real-time operating data of the gypsum hydrocyclone station to obtain the standard operating data of the gypsum hydrocyclone station, it is specifically used for: The real-time operating data of the gypsum hydrocyclone station is checked for format consistency to obtain the preliminary formatted data of the gypsum hydrocyclone station; Fill in the missing data segments of the preliminary formatted data to generate a complete data sequence for the gypsum hydrocyclone station; Remove logically contradictory data points from the complete data sequence to generate standard operating data for the gypsum hydrocyclone station.

[0007] In a preferred embodiment, when the crystal state determination module determines the gypsum crystal state at the outlet of the gypsum discharge pump in the gypsum hydrocyclone station based on the gypsum crystal image in the standard operating data, it is specifically used for: Enhance the contrast and clarity of gypsum crystal images to obtain enhanced images based on standard operating data; By using the enhanced image through edge detection, the contour information of the gypsum crystals is identified and segmented to obtain the segmented region of the gypsum crystals; Based on the segmented regions, the crystal size distribution and crystal shape regularity of gypsum crystals are extracted to obtain the morphological characteristics of gypsum crystals; The gypsum crystal state of the gypsum hydrocyclone station is obtained by matching and judging the morphological characteristics with the preset crystal state classification rules.

[0008] In a preferred embodiment, when the crystal state determination module performs the extraction of crystal size distribution and crystal shape regularity of gypsum crystals based on the segmented region to obtain the morphological characteristics of gypsum crystals, it is specifically used for: Analyze the connected components of the segmented region to identify independent crystal regions within the connected components; The circumscribed contour of the independent crystal region is defined to obtain the length and width values ​​of the independent crystal, thus obtaining the crystal projection size data of the independent crystal region. The crystal size distribution of gypsum crystals is obtained by statistically analyzing the projected crystal size data. The shape regularity parameter of gypsum crystals is obtained by evaluating the degree of similarity between the outline shape of independent crystalline regions and regular geometric shapes. By integrating the shape regularity parameters, the crystal shape regularity of gypsum crystals is obtained; The distribution of crystal size and the regularity of crystal shape are considered as morphological characteristics of gypsum crystals.

[0009] In a preferred embodiment, when the key parameter extraction module performs a weighted average of the gypsum crystal state, redox potential, and gypsum slurry pH to obtain the blockage risk index of the gypsum hydrocyclone station, it is specifically used for: Extract the weight parameter set of the intelligent decision-making center. The weight parameter set includes the weight of gypsum crystal state, the weight of redox potential, and the weight of gypsum slurry pH. Based on the preset crystal state classification rules, the gypsum crystal state is mapped to a state index value; Use redox potential as the potential input value; Use the pH value of the gypsum slurry as the pH input value; The state index value, potential input value, and pH input value are weighted and fused using a set of weighted parameters to obtain the blockage risk index of the gypsum hydrocyclone station.

[0010] In a preferred embodiment, the formula for calculating the congestion risk index is as follows: ; In the formula, To block risk indicators, The weighting parameters are the weights of the gypsum crystal states. This is the state index value. For potential input value, The optimal redox potential reference value is set within the weighted parameter set. The redox potential weights are set as the weight parameters. Input the pH value. The optimal pH baseline value is set within the weighted parameter set. The weighting parameters are the acidity and alkalinity weights of the gypsum grout. The preset state index benchmark value, It is a natural exponential function. It is a logarithmic function.

[0011] In a preferred embodiment, when the control strategy generation module compares the blockage risk index with the blockage threshold in the intelligent decision-making center of the gypsum hydrocyclone station to generate an adaptive control strategy for the gypsum hydrocyclone station, it is specifically used for: Obtain the congestion threshold from historical operational data in the intelligent decision-making center, and classify the congestion threshold into early warning threshold and alarm threshold according to the severity of risk; By comparing the blockage risk indicators with the blockage threshold in sequence, the risk level of the gypsum hydrocyclone station is determined. Based on the risk level assessment results, control strategies from the control strategy set of the intelligent decision-making center are selected and matched to obtain the process parameter optimization strategy of the intelligent decision-making center. An adaptive control strategy for the gypsum hydrocyclone station is generated based on the control strategy and the standard operating data.

[0012] In a preferred embodiment, when the control strategy generation module performs the process parameter optimization strategy of the intelligent decision-making center by filtering and matching the control strategies in the control strategy set based on the risk level judgment result, it is specifically used for: The intelligent decision-making center obtains the control strategies and centralizes the control strategy types, which include flow adjustment strategy, oxidation component adjustment strategy, slurry acid-base adjustment strategy, pressure adjustment strategy, and process parameter optimization strategy. Match the risk level assessment results with the corresponding regulatory strategy type; Based on the control strategy, the current operating parameters of the gypsum hydrocyclone station are adjusted to obtain the process parameter optimization strategy for the gypsum hydrocyclone station.

[0013] In a preferred embodiment, when the closed-loop control module executes the real-time response data of the adaptive control strategy and feeds the real-time response data back to the intelligent decision center to complete parameter optimization and achieve closed-loop control, it is specifically used for: Real-time response data is collected by sensing units deployed at key process nodes. The real-time response data includes data on changes in inlet pressure of the cyclone station, changes in gypsum slurry flow rate, and changes in the state of gypsum crystals. Real-time response data is transmitted to the intelligent decision-making center via a data interface; In the intelligent decision-making center, real-time response data is compared and analyzed with the expected control effects to generate control effect evaluation results; Based on the evaluation results of the regulation effect, the weight parameters and blocking thresholds in the intelligent decision-making center are dynamically adjusted to complete parameter optimization; The optimized parameters are updated in the intelligent decision center to form a closed-loop control circuit.

[0014] To address the aforementioned problems, this invention also provides a method for preventing clogging in gypsum hydrocyclones based on intelligent control and structural optimization, the method comprising: S1. Perform data cleaning on the real-time operating data of the gypsum hydrocyclone station to obtain the standard operating data of the gypsum hydrocyclone station; S2. Determine the state of gypsum crystals at the outlet of the gypsum discharge pump in the gypsum hydrocyclone station based on the gypsum crystal image in the standard operating data. S3. Extract the redox potential and pH of gypsum slurry from the standard operating data; S4. The weighted average of the gypsum crystal state, redox potential and gypsum slurry pH is used to obtain the blockage risk index of the gypsum hydrocyclone station. S5. Compare the blockage risk indicators with the blockage threshold in the intelligent decision-making center of the gypsum hydrocyclone station to generate an adaptive control strategy for the gypsum hydrocyclone station. S6. Response monitoring and adaptive control strategy real-time response data, and feed the real-time response data back to the intelligent decision center to complete parameter optimization and achieve closed-loop control.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention significantly improves the accuracy and reliability of anti-clogging measures in gypsum hydrocyclones through multi-module collaborative operation. The data cleaning module performs format consistency checks, fills in missing data, and removes logically contradictory data from real-time operating data to ensure high-quality standard operating data. The crystal state judgment module accurately identifies the size distribution and shape regularity of gypsum crystals through image enhancement, edge detection, and morphological feature extraction. Combined with the redox potential and gypsum slurry pH obtained by the key parameter extraction module, and then weighted and fused by the risk index calculation module, a comprehensive and accurate clogging risk index can be generated, providing precise data for anti-clogging control and effectively improving the accuracy of clogging risk prediction.

[0016] 2. This invention, relying on adaptive regulation and closed-loop control mechanisms, significantly improves the efficiency and stability of anti-clogging in gypsum hydrocyclones. The regulation strategy generation module selects and matches suitable process parameter optimization strategies based on the comparison results of clogging risk indicators and preset thresholds, ensuring the targeted nature of the regulation measures. The closed-loop control module collects real-time response data after regulation, feeds it back to the intelligent decision center for effect evaluation, and dynamically adjusts weight parameters and clogging thresholds to achieve continuous parameter optimization. This closed-loop mechanism can adapt to changes in equipment operating conditions in real time, ensuring the timeliness and effectiveness of anti-clogging regulation, significantly reducing the clogging rate, and ensuring the long-term stable operation of the gypsum hydrocyclone. Attached Figure Description

[0017] Figure 1 A system architecture diagram of an anti-clogging system for a gypsum hydrocyclone station based on intelligent control and structural optimization is provided in an embodiment of the present invention. Figure 2 This is a flowchart illustrating an anti-clogging method for a gypsum hydrocyclone station based on intelligent control and structural optimization, as provided in an embodiment of the present invention.

[0018] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments belong to some, but not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “said” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0021] Depending on the context, the word "if" or "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0022] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0023] In practice, the server-side equipment deployed in a gypsum vortex station anti-clogging system based on intelligent control and structural optimization may consist of one or more devices. This system can be implemented as a business instance, a virtual machine, or hardware equipment. For example, it can be implemented as a business instance deployed on one or more devices in a cloud node. Simply put, it can be understood as software deployed on a cloud node, providing a gypsum vortex station anti-clogging system based on intelligent control and structural optimization to various user terminals. Alternatively, it can also be implemented as a virtual machine deployed on one or more devices in a cloud node, with application software installed to manage various user terminals. Alternatively, this anti-clogging system for gypsum hydrocyclone stations based on intelligent control and structural optimization can also be implemented as a server consisting of numerous identical or different types of hardware devices, with one or more hardware devices set up to provide each user terminal with an anti-clogging system for gypsum hydrocyclone stations based on intelligent control and structural optimization.

[0024] In terms of implementation, the anti-clogging system for gypsum hydrocyclone stations based on intelligent control and structural optimization is mutually adaptable to the user terminal. Specifically, if the anti-clogging system is implemented as an application installed on a cloud service platform, the user terminal acts as a client establishing a communication connection with that application; or if the system is implemented as a website, the user terminal acts as a webpage; or if the system is implemented as a cloud service platform, the user terminal acts as a mini-program within an instant messaging application.

[0025] like Figure 1 The figure shown is a system architecture diagram of an anti-clogging system for a gypsum hydrocyclone station based on intelligent control and structural optimization, provided by an embodiment of the present invention.

[0026] The anti-clogging system 100 for a gypsum hydrocyclone station based on intelligent control and structural optimization described in this invention can be installed on a cloud server. In terms of implementation, it can be used as one or more service devices, or as an application installed on the cloud (e.g., a mobile service operator's server, server cluster, etc.), or it can be developed into a website. Depending on the functions implemented, the anti-clogging system 100 for a gypsum hydrocyclone station based on intelligent control and structural optimization may include a data cleaning module 101, a crystal state judgment module 102, a key parameter extraction module 103, a risk indicator calculation module 104, a control strategy generation module 105, and a closed-loop control module 106. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0027] In this embodiment of the invention, in a gypsum hydrocyclone anti-clogging system based on intelligent control and structural optimization, each of the above-mentioned modules can be implemented independently and can be called upon with other modules. Here, "calling upon" can be understood as one module connecting to multiple modules of another type and providing corresponding services to those connected modules. The gypsum hydrocyclone anti-clogging system based on intelligent control and structural optimization provided by this embodiment of the invention allows for adjustment of the applicable scope of the system architecture without modifying the program code. This is achieved by adding modules and directly calling them, enabling cluster-based horizontal expansion and flexibly expanding the gypsum hydrocyclone anti-clogging system. In practical applications, the above modules can be set in the same device or different devices, or they can be set in a virtual device, such as a service instance in a cloud server.

[0028] The following describes, with reference to specific embodiments, each component and its specific workflow of an anti-clogging system for gypsum hydrocyclones based on intelligent control and structural optimization: The data cleaning module 101 is used to clean the real-time operating data of the gypsum hydrocyclone station to obtain the standard operating data of the gypsum hydrocyclone station. In this embodiment of the invention, when the data cleaning module performs data cleaning on the real-time operating data of the gypsum hydrocyclone station to obtain the standard operating data of the gypsum hydrocyclone station, it is specifically used for: The real-time operating data of the gypsum hydrocyclone station is checked for format consistency to obtain the preliminary formatted data of the gypsum hydrocyclone station; Fill in the missing data segments of the preliminary formatted data to generate a complete data sequence for the gypsum hydrocyclone station; Remove logically contradictory data points from the complete data sequence to generate standard operating data for the gypsum hydrocyclone station.

[0029] When checking the format consistency of real-time operating data from a gypsum hydrocyclone station, the preset standard format is first defined. This includes ensuring the data's time stamp format is uniformly set to "year-month-day hour:minute:second," and that all parameters have consistent units and data types. Each record in the real-time operating data is checked line by line. If the time stamp format does not conform to the standard, it is adjusted to "year-month-day hour:minute:second." If there are inconsistencies in units, they are converted according to the preset unit conversion relationships. If data type errors are found, such as numerical parameters existing in text format, they are converted to the corresponding numerical form. After the above processing, all the obtained data conforms to the preset format standard, which constitutes the initial formatted data of the gypsum hydrocyclone station.

[0030] When filling in missing data segments in the initially formatted data, the data is first sorted chronologically to identify segments where there is a time interval between two consecutive valid data points and no data records are found; these are identified as missing data segments. For each missing data segment, the nearest and nearest valid data points before and after the segment are found, and their values ​​are averaged. This average value is used as the filling value to fill in the missing data segment at each corresponding time point, ensuring that data records are present in the originally missing time period. After filling in all missing data segments, the resulting sequence containing the complete time series and corresponding data is the complete data sequence of the gypsum hydrocyclone station.

[0031] When removing logically contradictory data points from a complete data sequence, first clarify the reasonable logical relationships and ranges of various parameters during the operation of the gypsum hydrocyclone station. For example, the inlet pressure should be within a preset reasonable pressure range, the underflow concentration should show an upward trend when the feed concentration increases, and the underflow concentration should always be higher than the overflow concentration. Check each data point in the complete data sequence one by one. If the parameter value of a data point exceeds the preset reasonable range, or if its parameter changes with adjacent data points violate the above logical relationships, then that data point is determined to be a logically contradictory data point. Delete all identified logically contradictory data points directly from the complete data sequence. The sequence of remaining data points constitutes the standard operating data of the gypsum hydrocyclone station.

[0032] The beneficial effects are as follows: By checking the format consistency of real-time operating data of the gypsum hydrocyclone station, it is ensured that all data conforms to preset standards in terms of time stamp format, parameter units, and data types, thus obtaining preliminary formatted data with a unified format, providing a standardized foundation for subsequent data processing. By filling in missing data segments in the preliminary formatted data, the time series with data gaps is made complete, generating a complete data series containing data corresponding to continuous time points, avoiding the impact of data gaps on the complete analysis of the gypsum hydrocyclone station's operating status. By removing logically contradictory data points in the complete data series and eliminating data that does not conform to the reasonable logical relationship and range of the gypsum hydrocyclone station's operation, standard operating data that can truly reflect the normal operating status of the gypsum hydrocyclone station is generated, providing a reliable basis for subsequent operation analysis and control.

[0033] The crystal state judgment module 102 is used to judge the gypsum crystal state at the outlet of the gypsum discharge pump in the gypsum hydrocyclone station based on the gypsum crystal image in the standard operating data. In this embodiment of the invention, when the crystal state determination module determines the gypsum crystal state at the outlet of the gypsum discharge pump in the gypsum hydrocyclone station based on the gypsum crystal image in the standard operating data, it is specifically used for: Enhance the contrast and clarity of gypsum crystal images to obtain enhanced images based on standard operating data; By using the enhanced image through edge detection, the contour information of the gypsum crystals is identified and segmented to obtain the segmented region of the gypsum crystals; Based on the segmented regions, the crystal size distribution and crystal shape regularity of gypsum crystals are extracted to obtain the morphological characteristics of gypsum crystals; The gypsum crystal state of the gypsum hydrocyclone station is obtained by matching and judging the morphological characteristics with the preset crystal state classification rules.

[0034] The crystal state determination module, when performing the extraction of crystal size distribution and crystal shape regularity of gypsum crystals based on the segmented region to obtain the morphological characteristics of gypsum crystals, is specifically used for: Analyze the connected components of the segmented region to identify independent crystal regions within the connected components; The circumscribed contour of the independent crystal region is defined to obtain the length and width values ​​of the independent crystal, thus obtaining the crystal projection size data of the independent crystal region. The crystal size distribution of gypsum crystals is obtained by statistically analyzing the projected crystal size data. The shape regularity parameter of gypsum crystals is obtained by evaluating the degree of similarity between the outline shape of independent crystalline regions and regular geometric shapes. By integrating the shape regularity parameters, the crystal shape regularity of gypsum crystals is obtained; The distribution of crystal size and the regularity of crystal shape are considered as morphological characteristics of gypsum crystals.

[0035] To enhance the contrast and clarity of a gypsum crystal image, first observe the difference in brightness between the gypsum crystal and the background. Adjust the brightness and contrast of the image, appropriately increasing the brightness of the darker areas of the crystal and appropriately decreasing the brightness of the brighter areas of the background. This makes the boundary between the light and dark areas of the crystal and the background more obvious, while maintaining the visibility of the internal details of the crystal. After such adjustments, the outline and details of the gypsum crystal in the image become clearer and more distinguishable, resulting in the enhanced image of the standard operating data.

[0036] When identifying and segmenting the contour information of gypsum crystals using the enhanced image through edge detection, each pixel in the enhanced image is scanned row by row and column by column. The brightness value of each pixel is compared with that of its neighboring pixels. When there is a significant difference in the brightness value of neighboring pixels, the pixel is marked as an edge point. The scanning continues and all consecutive edge points are connected to form a closed contour line around the gypsum crystal. Based on these closed contour lines, the area inside the contour lines is defined as the area where the gypsum crystal is located, clearly separated from the background area outside the contour lines. The resulting area surrounded by the contour lines is the segmented area of ​​the gypsum crystal.

[0037] When extracting the crystal size distribution and crystal shape regularity of gypsum crystals based on the segmented regions, the longest and shortest diameters of each segmented region are measured, and the longest diameter values ​​of all segmented regions are recorded. The proportion of regions within different length intervals to the total number of regions is then calculated to obtain the crystal size distribution. Simultaneously, the outline of each segmented region is compared with a preset standard regular shape to observe the degree of curvature and symmetry of the outline. The closer the outline is to the standard regular shape, the fewer the curvatures and the higher the symmetry, the higher the crystal shape regularity. Integrating the obtained crystal size distribution and crystal shape regularity together yields the morphological characteristics of gypsum crystals.

[0038] When matching and judging based on morphological features and preset crystal state classification rules, the preset crystal state classification rules include the crystal size distribution range and crystal shape regularity range corresponding to different crystal states. For example, the size distribution of a certain state is concentrated in a narrow range and has a high degree of regularity, while the size distribution of another state is scattered and has a low degree of regularity. The crystal size distribution and crystal shape regularity in the extracted morphological features are compared with the corresponding ranges in the classification rules. If both meet the rule range of a certain crystal state, the gypsum crystal is determined to belong to that state. The result is the gypsum crystal state of the gypsum cyclone station.

[0039] Analyzing the connected components of the segmented region and identifying independent crystal regions within them, all pixels within the segmented region are scanned line by line. When a pixel belonging to the segmented region is encountered, its four adjacent pixels in the four directions (up, down, left, and right) are checked to see if they also belong to the segmented region. If adjacent pixels also belong to the same region, they are classified into the same connected component, and the check continues until all connected pixels are included in the connected component. If a pixel is not found to belong to an identified connected component, it is used as the starting point of a new connected component, and the process of checking adjacent pixels is repeated. In this way, the segmented region is divided into multiple unconnected connected components, each of which is an independent crystal region.

[0040] When defining the circumscribed contour of an independent crystal region and obtaining its length and width values, to obtain the crystal projection size data of the independent crystal region, for each independent crystal region, find the leftmost, rightmost, topmost, and bottommost pixels in that region. Draw a minimum rectangle that can completely enclose the independent crystal region using these four points as the boundary. The horizontal side length of this rectangle is the width value of the independent crystal, and the vertical side length is the length value of the independent crystal. Record the length and width values ​​corresponding to each independent crystal region. These recorded values ​​are the crystal projection size data of the independent crystal region.

[0041] When obtaining the crystal size distribution of gypsum crystals by statistically analyzing the crystal projection size data, the length values ​​in the crystal projection size data are divided into preset continuous intervals. The length value of each independent crystal region is checked one by one to determine its interval. The number of independent crystal regions contained in each interval is counted, and the proportion of the number of each interval to the total number of all independent crystal regions is calculated. The interval ranges and their corresponding proportions are organized into an ordered list, which is the crystal size distribution of gypsum crystals.

[0042] To assess the similarity between the outline shape of an independent crystal region and a regular geometric shape, and to obtain the shape regularity parameter of the gypsum crystal, a circle is selected as the regular geometric shape. Multiple points are evenly selected along the outline of the independent crystal region, and the distance from each point to the geometric center of the region is measured. The degree of difference between these distances is calculated. The smaller the difference, the closer the outline shape is to a circle. At the same time, the curvature of the outline is observed. The fewer the number of curvatures, the more regular the shape. A value representing the degree of similarity is determined based on the degree of distance difference and the number of curvatures. This value is the shape regularity parameter of the gypsum crystal.

[0043] When integrating shape regularity parameters to obtain the crystal shape regularity of gypsum crystals, shape regularity parameters of all independent crystal regions are collected, and the average value of these parameters is calculated. This average value is used as an indicator to measure the overall regularity of the gypsum crystal shape. The higher the average value, the closer the shape of most crystals is to a regular geometric shape. This average value is the crystal shape regularity of gypsum crystals.

[0044] When compiling crystal size distribution and crystal shape regularity as morphological characteristics of gypsum crystals, the obtained crystal size distribution list and the average crystal shape regularity are integrated into the same dataset. This dataset contains both the size distribution of gypsum crystals and information on the regularity of their shapes. This integrated dataset is the morphological characteristics of gypsum crystals.

[0045] The beneficial effects are that by enhancing the contrast and clarity of the gypsum crystal image, the light and dark boundaries between the gypsum crystal and the background become more distinct, and the outline and details of the crystal are easier to identify. The enhanced image provides a clear and reliable foundation for subsequent edge detection and contour recognition. By performing edge detection on the enhanced image, the edge points of the gypsum crystal are accurately identified and connected to form closed contours, thereby segmenting the region of the gypsum crystal and effectively separating the crystal from the background. This lays a clear regional foundation for the subsequent accurate extraction of the crystal's morphological features.

[0046] Based on the crystal size distribution and shape regularity of gypsum crystals extracted from segmented regions, the resulting morphological features comprehensively reflect the size distribution and shape regularity of the crystals. These features serve as key criteria for judging the crystal state, ensuring the relevance and accuracy of subsequent judgments. Matching the morphological features with pre-defined crystal state classification rules, and comparing the crystal size distribution and shape regularity with the corresponding ranges of each state in the classification rules, the gypsum crystal state at the gypsum discharge pump outlet in the gypsum hydrocyclone station can be clearly determined, providing a direct and reliable judgment result for understanding the operating status of the gypsum hydrocyclone station.

[0047] By analyzing the connected components of the segmented region to identify independent crystal regions, it is possible to accurately distinguish between individual, unconnected crystals, avoiding mutual interference between different crystal regions. This provides a clear and independent analytical object for the subsequent targeted extraction of the size and shape features of each crystal. Defining the circumscribed contour of each independent crystal region and obtaining its length and width values ​​yields crystal projection size data that directly reflects the size characteristics of each individual crystal. This specific data forms the basis for subsequent statistical analysis of crystal size distribution, ensuring the objectivity and accuracy of the size analysis. The crystal size distribution obtained from the statistical analysis of the crystal projection size data clearly presents the proportion of crystals in different size ranges, comprehensively reflecting the overall size distribution pattern of gypsum crystals and providing key information about size characteristics for determining the crystal state.

[0048] The shape regularity parameters, obtained by assessing the similarity between the outlines of individual crystal regions and regular geometric shapes, quantify the regularity of crystal shapes. Each parameter corresponds to the shape characteristics of a single crystal, providing a concrete basis for subsequent integration to obtain the overall shape regularity. The crystal shape regularity obtained by integrating the shape regularity parameters combines the shape information of all individual crystals, objectively reflecting the overall shape regularity of gypsum crystals, and is an important component of morphological features reflecting crystal shape characteristics. The morphological features of gypsum crystals, formed by combining crystal size distribution and crystal shape regularity, comprehensively include key information on both size distribution and shape regularity, providing a comprehensive and systematic basis for subsequent judgment of crystal state based on morphological features.

[0049] Key parameter extraction module 103 is used to extract redox potential and gypsum slurry pH from standard operating data; In this embodiment of the invention, the data organization structure is clearly defined from the database or data file storing standard operating data. The standard operating data is stored according to parameter type, and each parameter corresponds to a unique identifier. The identifier for oxidation-reduction potential is "Standard Operation - Oxidation-Reduction Potential", and the identifier for gypsum slurry pH is "Standard Operation - Gypsum Slurry pH". The specific location of these two parameters in the storage system is determined by consulting the data storage directory index or classification list, for example, in the "Standard Parameter Summary Table" of the database, or in a specific classification section of the data file.

[0050] Based on the identified identification information and storage location, a search is conducted in the standard operating data. If it is stored in a database, the search term "standard operation - redox potential" is used to locate the corresponding record in the "standard parameter summary table." The content of the "parameter value" field in this record is the redox potential in the standard operating data. At the same time, the search term "standard operation - gypsum slurry pH" is used to find the corresponding record, and the content of the "parameter value" field in this record is extracted, which is the pH of the gypsum slurry in the standard operating data. If it is stored in a data file, the search term is followed according to the path of a specific category chapter to find the numerical content after "standard operation - redox potential:" and "standard operation - gypsum slurry pH:".

[0051] The extracted redox potential and gypsum slurry pH values ​​are formatted and verified to ensure they conform to the format requirements specified in the standard operating data, including whether the correct units are included and whether the presentation format is consistent with other parameters in the same set of standard operating data. If any format deviation is found, the identification information and storage location used during retrieval are rechecked, and the extraction operation is performed again until the correct format values ​​are obtained, ensuring that the extracted redox potential and gypsum slurry pH accurately reflect the parameters under standard operating conditions.

[0052] The beneficial effects are as follows: By clearly defining the organizational structure of standard operating data and determining the unique identifiers and storage locations of redox potential and gypsum slurry pH, clear guidance is provided for subsequent extraction work, avoiding parameter positioning errors caused by data storage chaos and ensuring the accuracy of the extracted targets. Retrieving and extracting from the standard operating data based on the determined identifiers and storage locations allows direct identification and acquisition of the numerical values ​​of redox potential and gypsum slurry pH, reducing interference from irrelevant data and ensuring that the extracted parameters completely correspond to the target parameters in the standard operating data, thus improving extraction efficiency. Format verification of the extracted redox potential and gypsum slurry pH allows for timely detection and correction of format deviations, ensuring that the values ​​of these two parameters meet the format requirements of the standard operating data. This provides a standardized and reliable data foundation for subsequent analysis or control based on these parameters, avoiding the impact of format issues on the accuracy of subsequent work.

[0053] The risk index calculation module 104 is used to perform a weighted average of the gypsum crystal state, redox potential and gypsum slurry pH to obtain the blockage risk index of the gypsum hydrocyclone station. In this embodiment of the invention, the key parameter extraction module, when performing a weighted average of the gypsum crystal state, redox potential, and gypsum slurry pH to obtain the blockage risk index of the gypsum hydrocyclone station, is specifically used for: Extract the weight parameter set of the intelligent decision-making center. The weight parameter set includes the weight of gypsum crystal state, the weight of redox potential, and the weight of gypsum slurry pH. Based on the preset crystal state classification rules, the gypsum crystal state is mapped to a state index value; Use redox potential as the potential input value; Use the pH value of the gypsum slurry as the pH input value; The state index value, potential input value, and pH input value are weighted and fused using a set of weighted parameters to obtain the blockage risk index of the gypsum hydrocyclone station.

[0054] The formula for calculating the congestion risk indicator is as follows: ; In the formula, To block risk indicators, The weighting parameters are the weights of the gypsum crystal states. This is the state index value. For potential input value, The optimal redox potential reference value is set within the weighted parameter set. The redox potential weights are set as the weight parameters. Input the pH value. The optimal pH baseline value is set within the weighted parameter set. The weighting parameters are the acidity and alkalinity weights of the gypsum grout. The preset state index benchmark value, It is a natural exponential function. It is a logarithmic function.

[0055] When extracting the weight parameter set of the intelligent decision-making center, the pre-set weight parameters are retrieved from the preset storage location of the intelligent decision-making center. These parameters clearly include three parts: the weight of gypsum crystal state used to measure the degree of influence of gypsum crystal state, the weight of oxidation-reduction potential used to measure the degree of influence of oxidation-reduction potential, and the weight of gypsum slurry acidity and alkalinity used to measure the degree of influence of gypsum slurry acidity and alkalinity. The set formed by integrating these three weights is the weight parameter set.

[0056] Based on the preset crystal state classification rules, when mapping the gypsum crystal state to a state index value, the preset crystal state classification rules have clearly defined the fixed index value corresponding to each gypsum crystal state. For example, a certain crystal state corresponds to a specific index value, and another crystal state corresponds to another specific index value. According to the actual obtained gypsum crystal state, the corresponding index value is found in the classification rules, and this index value is the state index value.

[0057] When using redox potential as the potential input value, the specific value of the redox potential at the relevant location of the gypsum hydrocyclone station, obtained by the detection equipment, is directly taken without any conversion or modification, and this value is determined as the potential input value.

[0058] When using the pH value of gypsum slurry as the pH input value, the specific pH value of the gypsum slurry in the gypsum hydrocyclone station, measured by the testing equipment, is directly taken without any conversion or modification, and this value is determined as the pH input value.

[0059] When using a set of weighted parameters to perform weighted fusion processing on the state index value, potential input value, and pH input value to obtain the blockage risk index of the gypsum hydrocyclone station, the following steps are taken: first, the gypsum crystal state weight is extracted from the set of weighted parameters and combined with the state index value; then, the oxidation-reduction potential weight is extracted and combined with the potential input value; next, the pH weight of the gypsum slurry is extracted and combined with the pH input value. Finally, the results of these three combinations are summarized to form a comprehensive value, which is the blockage risk index of the gypsum hydrocyclone station.

[0060] The weights of gypsum crystal states are derived from a set of weight parameters. To determine these weights, blockage case data under different gypsum crystal states are first collected. The influence of the morphology, size, and aggregation degree of gypsum crystals on the risk of blockage is analyzed. This influence is quantified using statistical methods, and the quantification results are assigned specific values. These values ​​are the weights of gypsum crystal states and are included in the set of weight parameters.

[0061] The state index value is calculated by detecting the physicochemical state of gypsum slurry. During the test, a gypsum slurry sample is first collected, and indicators such as the concentration, particle size distribution, and solubility of gypsum crystals in the sample are measured. These indicators are converted into scores of a uniform magnitude according to preset scoring rules, and all scores are weighted and summed to obtain the state index value.

[0062] The potential input value is obtained directly through professional testing equipment. A redox potential detector is used to contact the gypsum slurry, converting the redox potential signal into a readable numerical value, which is the potential input value. The optimal redox potential benchmark value is derived from a weighted parameter set. This is determined through a series of comparative experiments, setting different redox potential gradients, observing the clogging of the gypsum slurry at each gradient, and selecting the redox potential value corresponding to the lowest clogging probability. This value is then incorporated into the weighted parameter set as the optimal redox potential benchmark value.

[0063] The redox potential (OPP) weights are derived from a weighting parameter set. During determination, historical OPP data and corresponding blockage events are correlated. The correlation between the deviation of the OPP from the optimal baseline value and the blockage risk is analyzed. Specific values ​​are assigned based on the strength of the correlation; these values ​​are the OPP weights and are included in the weighting parameter set. The pH input value is obtained directly through pH measurement equipment. A pH meter is inserted into the gypsum slurry, and the meter provides real-time feedback on the slurry's pH value; this value is the pH input value.

[0064] The optimal pH baseline value is derived from a weighted parameter set. Determining this value involves conducting multiple experiments, adjusting the pH of the gypsum slurry to different values, and monitoring the stability and clogging trend of the slurry at each value. The pH value with the lowest clogging risk is then determined and included in the weighted parameter set as the optimal pH baseline value. The pH weights for the gypsum slurry are also derived from the weighted parameter set. Determining these weights involves analyzing the correlation between historical pH data and clogging events, statistically analyzing the contribution ratio of pH deviation from the optimal baseline value to clogging risk, and assigning specific values ​​based on this contribution ratio. These values ​​are the pH weights for the gypsum slurry and are included in the weighted parameter set. The preset state index baseline value is a fixed value set in advance. When setting this value, the range of state indices for gypsum slurry under normal operating conditions is referenced, combined with equipment safety standards and historical stable operating data. Technical personnel directly determine a value that represents the normal state; this value is the preset state index baseline value.

[0065] The natural exponential function is a mathematical function used to calculate an exponent with the natural constant as the base. The calculation first determines the value of the exponent part, and then uses mathematical operations to calculate the power of that value of the natural constant to obtain the result of the natural exponential function.

[0066] The logarithmic function is a mathematical function used to calculate the natural logarithm. The calculation first determines the value for which the logarithm needs to be calculated, then uses mathematical operations to obtain the logarithm of that value with the natural constant as the base, yielding the logarithmic function result. The blockage risk index is a quantitative indicator measuring the likelihood of blockage in a gypsum slurry system; its value directly reflects the level of blockage risk. Multiplying the gypsum crystal state weight by the state index value yields a result reflecting the basic contribution of the gypsum crystal state to the blockage risk. The larger the state index value, the more likely the gypsum crystal state is to cause blockage, and the higher the contribution of this part. The absolute value of the difference between the potential input value and the optimal redox potential reference value is divided by the optimal redox potential reference value to obtain the potential deviation rate. The natural exponential function acts on this deviation rate, exponentially amplifying the impact of the potential deviation from the optimal reference value. The larger the deviation, the larger the calculation result of this part. Multiplying this by the product of the gypsum crystal state weight and the state index value comprehensively reflects the combined impact of the gypsum crystal state and potential deviation on the blockage risk.

[0067] The absolute value of the difference between the pH input value and the optimal pH benchmark value is divided by the optimal pH benchmark value to obtain the pH deviation rate. This deviation rate is multiplied by the redox potential weight to reflect the impact of pH deviation from the optimal benchmark value on the blockage risk. The larger the deviation rate, the higher the contribution of this part. The ratio of the state index value to the preset state index benchmark value is increased by 1, and then transformed using a logarithmic function to make the impact of the state index value deviating from the benchmark value more consistent with the actual risk change pattern. This is then multiplied by the pH weight of the gypsum slurry to reflect the impact of the state index deviating from the normal benchmark on the blockage risk. The larger the deviation, the larger the calculation result of this part. The sum of the above three calculation results is the blockage risk index. The larger this value, the more significant the combined impact of factors such as gypsum crystal state, redox potential, pH, and state index on system blockage, and the higher the probability of blockage. The smaller the value, the less significant the impact of each factor, and the lower the blockage risk, providing a quantitative reference for system operation adjustment and blockage prevention.

[0068] The beneficial effect is that the set of weight parameters extracted from the intelligent decision-making center includes the weight of gypsum crystal state, the weight of redox potential, and the weight of gypsum slurry pH. This provides a clear weight basis for subsequent weighted fusion of various influencing factors, ensuring that the degree of influence of each factor can be accurately quantified and considered.

[0069] Based on the preset crystal state classification rules, the gypsum crystal state is mapped to the state index value, realizing the quantitative transformation of qualitative crystal state, and making the crystal state information, which was originally difficult to participate in the calculation, into standardized data that can be used for weighted fusion.

[0070] Using redox potential directly as the potential input value preserves the original measurement information of this parameter, avoids errors that may be caused by data conversion, and provides real and reliable potential basis data for weighted fusion.

[0071] Using the pH value of the gypsum slurry directly as the pH input ensures the integrity and accuracy of the measurement results. The fact that it does not require additional processing allows it to be directly used in subsequent calculations, guaranteeing the original reliability of the input data.

[0072] The state index value, potential input value, and pH input value are weighted and integrated using a set of weighted parameters. The three key influencing factors are integrated into a unified blockage risk index according to their corresponding weights. This index comprehensively reflects the combined effect of each factor on the blockage risk of the gypsum hydrocyclone station, providing an intuitive and comprehensive basis for judging the blockage risk.

[0073] The control strategy generation module 105 is used to compare the blockage risk index with the blockage threshold in the intelligent decision-making center of the gypsum hydrocyclone station to generate an adaptive control strategy for the gypsum hydrocyclone station. In this embodiment of the invention, when the control strategy generation module compares the blockage risk index with the blockage threshold in the intelligent decision-making center of the gypsum hydrocyclone station to generate an adaptive control strategy for the gypsum hydrocyclone station, it is specifically used for: Obtain the congestion threshold from historical operational data in the intelligent decision-making center, and classify the congestion threshold into early warning threshold and alarm threshold according to the severity of risk; By comparing the blockage risk indicators with the blockage threshold in sequence, the risk level of the gypsum hydrocyclone station is determined. Based on the risk level assessment results, control strategies from the control strategy set of the intelligent decision-making center are selected and matched to obtain the process parameter optimization strategy of the intelligent decision-making center. An adaptive control strategy for the gypsum hydrocyclone station is generated based on the control strategy and the standard operating data.

[0074] All threshold data related to blockage of the gypsum hydrocyclone station are retrieved from the historical operation database of the intelligent decision-making center. These data are based on the critical state before blockage and the state record of minor blockage during the past operation of the hydrocyclone station. Then, these thresholds are divided according to the severity of the blockage risk. The threshold range with low risk and only requiring early attention is defined as the warning threshold, and the threshold range with high risk and possible imminent significant blockage is defined as the alarm threshold. This ensures that there is a clear boundary between the warning threshold and the alarm threshold and covers all possible risk ranges.

[0075] The blockage risk index of the gypsum hydrocyclone station, calculated in real time, is first compared with the warning threshold. If the blockage risk index is lower than the warning threshold, it is judged as no risk level; if the blockage risk index reaches or exceeds the warning threshold but does not reach the alarm threshold, it is judged as a warning risk level; if the blockage risk index reaches or exceeds the alarm threshold, it is judged as an alarm risk level. Through this sequential comparison, the current risk level of the gypsum hydrocyclone station is finally determined.

[0076] The intelligent decision-making center stores control strategies that correspond one-to-one with different risk levels. Among them, the no-risk level corresponds to the strategy of maintaining the current operating parameters, the early warning risk level corresponds to the strategy of making small adjustments to the key operating parameters, and the alarm risk level corresponds to the strategy of making larger adjustments to the key operating parameters. Based on the previously obtained risk level judgment results, the control strategy that completely corresponds to it is found from the control strategy set. This control strategy is the process parameter optimization strategy of the intelligent decision-making center.

[0077] The current standard operating data are the baseline values ​​of various operating parameters of the gypsum hydrocyclone station under normal, non-clogging conditions. The adjustment requirements of each parameter in the selected process parameter optimization strategy are combined with the corresponding parameter values ​​in the current standard operating data to clarify the specific direction and magnitude of adjustment for each parameter. For example, if the strategy requires an increase in a certain parameter, the specific increase is determined by combining the standard value. At the same time, the implementation sequence and operation steps of these adjustments are clarified. The final solution, which includes specific parameter adjustment values ​​and implementation steps, is the adaptive control strategy of the gypsum hydrocyclone station.

[0078] All control strategy types were retrieved from the control strategy set storage area of ​​the intelligent decision center. These types were clearly categorized by function during storage. Among them, the flow rate adjustment strategy is used to change the flow rate of the gypsum hydrocyclone station's feed or discharge; the oxidation component adjustment strategy is used to adjust the amount of oxidizing substances participating in the slurry reaction; the slurry acid-base adjustment strategy is used to change the acidity or alkalinity of the slurry; the pressure adjustment strategy is used to adjust the operating pressure inside the hydrocyclone station; and the process parameter optimization strategy is used to comprehensively optimize multiple process-related parameters. During the retrieval process, each type was checked to ensure that all five types were completely obtained and that the functional description of each type was accurate.

[0079] The risk level assessment results include three specific levels: no risk, early warning, and alarm. The intelligent decision-making center has a pre-set correspondence table between risk levels and control strategy types. The table clearly states that the no-risk level corresponds to the basic strategy type that does not require adjustment, the early warning level corresponds to the flow adjustment strategy or pressure adjustment strategy, and the alarm level corresponds to a combination of oxidation component adjustment strategy, slurry acid-base adjustment strategy, and process parameter optimization strategy. The current risk level assessment result is compared with the correspondence table to find the control strategy type in the table that completely corresponds to the current risk level.

[0080] Based on the matched control strategy type, specific adjustments are made to the corresponding parameters in the current operating conditions of the gypsum hydrocyclone station. If a flow rate adjustment strategy is matched, the feed flow rate is changed by operating the opening and closing degree of the feed valve, or the discharge flow rate is changed by adjusting the operating power of the discharge pump. If an oxidation component adjustment strategy is matched, the amount of oxidant entering the hydrocyclone station per unit time is adjusted by controlling the opening and closing degree of the control valve on the oxidant supply pipeline. If a slurry acid-base adjustment strategy is matched, acidic or alkaline regulators are added to the slurry, and the acid-base changes of the slurry are observed until the target acid-base state is reached. If a pressure adjustment strategy is matched, the internal pressure is changed by adjusting the opening degree of the hydrocyclone station outlet valve. The specific scheme formed by integrating these adjusted parameters is the process parameter optimization strategy for the gypsum hydrocyclone station.

[0081] The beneficial effects are as follows: By acquiring historical operational data from the intelligent decision-making center regarding blockage thresholds and categorizing them into warning and alarm thresholds based on risk severity, the boundaries between different risk levels can be clearly defined. This provides a clear and reliable standard for subsequent risk level assessment, ensuring the standardization and consistency of risk assessment. By sequentially comparing blockage risk indicators with blockage thresholds, the current risk level (no risk, warning, or alarm) of the gypsum hydrocyclone station can be accurately determined, providing a direct and precise basis for selecting subsequent control strategies and avoiding inappropriate control due to ambiguous risk assessments. Based on the risk level assessment results, the control strategies selected and matched from the intelligent decision-making center's control strategy set can quickly locate the process parameter optimization strategy corresponding to the current risk level, ensuring the relevance and applicability of the control strategy and reducing the time spent selecting ineffective strategies. By generating adaptive control strategies for the gypsum hydrocyclone station based on the control strategies and current standard operational data, abstract strategies can be transformed into concrete and operable parameter adjustment schemes, making control measures fit the actual operating state and effectively improving the flexibility and effectiveness of the gypsum hydrocyclone station in responding to blockage risks.

[0082] By acquiring control strategy types from the intelligent decision-making center, including flow adjustment strategies, oxidation component adjustment strategies, slurry acid-base adjustment strategies, pressure adjustment strategies, and process parameter optimization strategies, the functions and applicable scenarios of each strategy can be clearly defined. This provides a clear and comprehensive foundation for matching risk levels with strategies, ensuring that all potentially usable strategy types are covered and avoiding incomplete control due to missing strategy types. Matching risk level judgment results with control strategy types, using a pre-set correspondence table, can quickly locate the strategy type suitable for the current risk level, reducing blind spots in the matching process and ensuring the accuracy of the matching results. This ensures that the selected strategy type accurately corresponds to the risk level, providing the correct direction for subsequent parameter adjustments. Adjusting the current operating parameters of the gypsum hydrocyclone station according to the control strategies, and transforming abstract strategies into actual parameter adjustment actions through specific operational methods, and then integrating the adjusted parameters to form a process parameter optimization strategy, makes the strategy operable and can be directly used to guide the operation and adjustment of the hydrocyclone station, effectively improving the pertinence and effectiveness in dealing with different risk levels.

[0083] The closed-loop control module 106 is used to monitor the real-time response data of the adaptive control strategy and feed the real-time response data back to the intelligent decision center to complete parameter optimization and realize closed-loop control.

[0084] In this embodiment of the invention, when the closed-loop control module executes the real-time response data of the response monitoring and adaptive control strategy and feeds the real-time response data back to the intelligent decision center to complete parameter optimization and achieve closed-loop control, it is specifically used for: Real-time response data is collected by sensing units deployed at key process nodes. The real-time response data includes data on changes in inlet pressure of the cyclone station, changes in gypsum slurry flow rate, and changes in the state of gypsum crystals. Real-time response data is transmitted to the intelligent decision-making center via a data interface; In the intelligent decision-making center, real-time response data is compared and analyzed with the expected control effects to generate control effect evaluation results; Based on the evaluation results of the regulation effect, the weight parameters and blocking thresholds in the intelligent decision-making center are dynamically adjusted to complete parameter optimization; The optimized parameters are sent to the intelligent decision-making center to form a closed-loop control circuit.

[0085] A pressure sensor is deployed at the inlet pipe of the gypsum hydrocyclone station as a sensing unit to monitor the pressure fluctuations inside the pipe in real time, record the pressure rise or fall process, and form the inlet pressure change data of the hydrocyclone station. A flow meter is installed on the gypsum slurry delivery pipe as a sensing unit to continuously track the changes in slurry flow velocity and record the increase or decrease in flow rate per unit time, forming the gypsum slurry flow rate change data. An image sensor is installed at the crystal observation window of the hydrocyclone station as a sensing unit to periodically photograph the morphology, size, and distribution of gypsum crystals. By comparing the differences in images at different time points, the changes in crystal state are recorded, forming the gypsum crystal state change data. All sensing units work synchronously to ensure that the collected real-time response data is complete and continuous.

[0086] A dedicated industrial data bus interface is used as the data transmission channel. This interface is pre-connected to the deployed sensing units and intelligent decision-making center. The sensing units collect data on changes in inlet pressure of the cyclone station, changes in gypsum slurry flow rate, and changes in gypsum crystal state, and organize them according to a preset format. Each data type corresponds to a unique identifier. Then, the organized data is continuously sent to the intelligent decision-making center through the data interface. The receiving module of the intelligent decision-making center verifies the transmitted data. After confirming that the data is not lost or erroneous, it stores it in the designated data buffer.

[0087] The intelligent decision-making center pre-stores expected control effects based on adaptive control strategies, including the stable range of the inlet pressure of the cyclone station, the range of gypsum slurry flow rate, and the normal morphology of gypsum crystals. The real-time response data stored in the data buffer is compared item by item with these expected control effects. If the cyclone station inlet pressure change data is within a stable range, the gypsum slurry flow rate change data is within the acceptable range, and the gypsum crystal state change data conforms to the normal morphology, then the control effect is judged to be satisfactory. If some data meet the requirements and some do not, then the control effect is judged to be partially satisfactory. If all data do not meet the requirements, then the control effect is judged to be unsatisfactory. Based on the above judgments, a clear control effect evaluation result is formed.

[0088] In the intelligent decision-making center, weight parameters are used to measure the importance of different real-time response data in risk assessment. The blockage threshold is the critical standard for judging the level of blockage risk. When the assessment result of the control effect is satisfactory, the current weight parameters and blockage threshold remain unchanged. When the assessment result is partially satisfactory, the proportion of the corresponding weight parameters is increased for data items that do not meet the standard. For example, if the gypsum slurry flow rate change data does not meet the standard, the proportion of flow-related parameters in the weight is increased, while the blockage threshold remains unchanged. When the assessment result is unsatisfactory, the weight parameters and blockage threshold are adjusted simultaneously. Depending on the specific circumstances of the unsatisfactory result, the blockage threshold is appropriately lowered or raised to make the threshold more consistent with the current operating state. After the adjustment is completed, it is confirmed that the new weight parameters and blockage threshold conform to the process operation logic, and the parameter optimization is completed.

[0089] The optimized weight parameters and congestion thresholds are transmitted to the core database storing parameters through the data transmission channel inside the intelligent decision center, overwriting the original parameter records. The new parameters take effect immediately and are used in the next adaptive control strategy generation process. At this time, from the collection of real-time response data from the sensing unit to the transmission to the intelligent decision center, after evaluation and optimization of parameters, the optimized parameters are applied to the generation of new control strategies, forming a complete and continuously operating closed-loop control loop, ensuring that the operating status of the gypsum hydrocyclone station can be continuously optimized based on real-time feedback.

[0090] The beneficial effects are as follows: By acquiring the blockage threshold from historical operating data in the intelligent decision-making center and classifying it into early warning thresholds and alarm thresholds, a clear and practical standard for classifying blockage risk is provided, ensuring a reliable basis for subsequent risk level assessments. The blockage risk indicators are compared sequentially with the blockage thresholds, and a progressive comparison process is used to accurately define the risk level, avoiding potential misjudgments that might result from a single threshold. The resulting risk level assessment accurately reflects the blockage risk status of the gypsum hydrocyclone station. Based on the risk level assessment results, matching control strategies are selected. A strategy set categorized by risk level is used to quickly locate suitable strategies, and those incompatible with the current operating scenario are eliminated, ensuring the targeted and applicable optimization strategies for process parameters. Adaptive control strategies are generated based on the control strategies and current standard operating data, transforming abstract adjustment directions into specific adjustment schemes that combine actual operating values. This makes the control strategies more aligned with the current state of the equipment, providing a directly executable basis for the precise control of the gypsum hydrocyclone station.

[0091] Reference Figure 2 The diagram shown is a flowchart illustrating an anti-clogging method for a gypsum hydrocyclone station based on intelligent control and structural optimization, according to an embodiment of the present invention. In this embodiment, the anti-clogging method for a gypsum hydrocyclone station based on intelligent control and structural optimization includes: S1. Perform data cleaning on the real-time operating data of the gypsum hydrocyclone station to obtain the standard operating data of the gypsum hydrocyclone station; S2. Determine the state of gypsum crystals at the outlet of the gypsum discharge pump in the gypsum hydrocyclone station based on the gypsum crystal image in the standard operating data. S3. Extract the redox potential and pH of gypsum slurry from the standard operating data; S4. The weighted average of the gypsum crystal state, redox potential and gypsum slurry pH is used to obtain the blockage risk index of the gypsum hydrocyclone station. S5. Compare the blockage risk indicators with the blockage threshold in the intelligent decision-making center of the gypsum hydrocyclone station to generate an adaptive control strategy for the gypsum hydrocyclone station. S6. Response monitoring and adaptive control strategy real-time response data, and feed the real-time response data back to the intelligent decision center to complete parameter optimization and achieve closed-loop control.

[0092] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0093] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0094] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A gypsum hydrocyclone station anti-clogging system based on intelligent control and structural optimization, characterized in that, The system includes a data cleaning module, a crystal state judgment module, a key parameter extraction module, a risk indicator calculation module, a control strategy generation module, and a closed-loop control module, wherein: The data cleaning module is used to clean the real-time operating data of the gypsum hydrocyclone station to obtain the standard operating data of the gypsum hydrocyclone station; The crystal state judgment module is used to judge the state of gypsum crystals at the outlet of the gypsum discharge pump in the gypsum hydrocyclone station based on the gypsum crystal image in the standard operating data. The key parameter extraction module is used to extract the redox potential and pH of gypsum slurry from the standard operating data; The risk index calculation module is used to calculate the blockage risk index of the gypsum hydrocyclone station by weighted averaging of gypsum crystal state, redox potential and gypsum slurry pH. The control strategy generation module is used to compare the blockage risk indicators with the blockage threshold in the intelligent decision-making center of the gypsum hydrocyclone station to generate an adaptive control strategy for the gypsum hydrocyclone station. The closed-loop control module is used to monitor the real-time response data of the adaptive control strategy and feed the real-time response data back to the intelligent decision center to complete parameter optimization and achieve closed-loop control.

2. The anti-clogging system for a gypsum hydrocyclone station based on intelligent control and structural optimization as described in claim 1, characterized in that, When the data cleaning module performs data cleaning on the real-time operating data of the gypsum hydrocyclone station to obtain the standard operating data of the gypsum hydrocyclone station, it is specifically used for: The real-time operating data of the gypsum hydrocyclone station is checked for format consistency to obtain the preliminary formatted data of the gypsum hydrocyclone station; Fill in the missing data segments of the preliminary formatted data to generate a complete data sequence for the gypsum hydrocyclone station; Remove logically contradictory data points from the complete data sequence to generate standard operating data for the gypsum hydrocyclone station.

3. The anti-clogging system for a gypsum hydrocyclone station based on intelligent control and structural optimization as described in claim 1, characterized in that, When the crystal state determination module determines the gypsum crystal state at the outlet of the gypsum discharge pump in the gypsum hydrocyclone station based on the gypsum crystal image in the standard operating data, it is specifically used for: Enhance the contrast and clarity of gypsum crystal images to obtain enhanced images based on standard operating data; By using the enhanced image through edge detection, the contour information of the gypsum crystals is identified and segmented to obtain the segmented region of the gypsum crystals; Based on the segmented regions, the crystal size distribution and crystal shape regularity of gypsum crystals are extracted to obtain the morphological characteristics of gypsum crystals; The gypsum crystal state of the gypsum hydrocyclone station is obtained by matching and judging the morphological characteristics with the preset crystal state classification rules.

4. The anti-clogging system for a gypsum hydrocyclone station based on intelligent control and structural optimization as described in claim 3, characterized in that, The crystal state determination module, when performing the extraction of crystal size distribution and crystal shape regularity of gypsum crystals based on the segmented region to obtain the morphological characteristics of gypsum crystals, is specifically used for: Analyze the connected components of the segmented region to identify independent crystal regions within the connected components; The circumscribed contour of the independent crystal region is defined to obtain the length and width values ​​of the independent crystal, thus obtaining the crystal projection size data of the independent crystal region. The crystal size distribution of gypsum crystals is obtained by statistically analyzing the projected crystal size data. The shape regularity parameter of gypsum crystals is obtained by evaluating the degree of similarity between the outline shape of independent crystalline regions and regular geometric shapes. By integrating the shape regularity parameters, the crystal shape regularity of gypsum crystals is obtained; The distribution of crystal size and the regularity of crystal shape are considered as morphological characteristics of gypsum crystals.

5. The anti-clogging system for a gypsum hydrocyclone station based on intelligent control and structural optimization as described in claim 1, characterized in that, The key parameter extraction module, when performing a weighted average of gypsum crystal state, redox potential, and gypsum slurry pH to obtain the blockage risk index of the gypsum hydrocyclone station, is specifically used for: Extract the weight parameter set of the intelligent decision-making center. The weight parameter set includes the weight of gypsum crystal state, the weight of redox potential, and the weight of gypsum slurry pH. Based on the preset crystal state classification rules, the gypsum crystal state is mapped to a state index value; Use redox potential as the potential input value; Use the pH value of the gypsum slurry as the pH input value; The state index value, potential input value, and pH input value are weighted and fused using a set of weighted parameters to obtain the blockage risk index of the gypsum hydrocyclone station.

6. The anti-clogging system for a gypsum hydrocyclone station based on intelligent control and structural optimization as described in claim 5, characterized in that, The formula for calculating the congestion risk indicator is as follows: ； In the formula, To block risk indicators, The weighting parameters are the weights of the gypsum crystal states. This is the state index value. For potential input value, The optimal redox potential reference value is set within the weighted parameter set. The redox potential weights are set as the weight parameters. Input the pH value. The optimal pH baseline value is set within the weighted parameter set. The weighting parameters are the acidity and alkalinity weights of the gypsum grout. The preset state index benchmark value, It is a natural exponential function. It is a logarithmic function.

7. The anti-clogging system for a gypsum hydrocyclone station based on intelligent control and structural optimization as described in claim 1, characterized in that, When the control strategy generation module compares the blockage risk index with the blockage threshold in the intelligent decision-making center of the gypsum hydrocyclone station to generate an adaptive control strategy for the gypsum hydrocyclone station, it is specifically used for: Obtain the congestion threshold from historical operational data in the intelligent decision-making center, and classify the congestion threshold into early warning threshold and alarm threshold according to the severity of risk; By comparing the blockage risk indicators with the blockage threshold in sequence, the risk level of the gypsum hydrocyclone station is determined. Based on the risk level assessment results, control strategies from the control strategy set of the intelligent decision-making center are selected and matched to obtain the process parameter optimization strategy of the intelligent decision-making center. An adaptive control strategy for the gypsum hydrocyclone station is generated based on the control strategy and current standard operating data.

8. The anti-clogging system for a gypsum hydrocyclone station based on intelligent control and structural optimization as described in claim 7, characterized in that, When the control strategy generation module executes the process parameter optimization strategy of the intelligent decision-making center by filtering and matching the control strategies in the control strategy set based on the risk level judgment result, it is specifically used for: The intelligent decision-making center obtains the control strategies and centralizes the control strategy types, which include flow adjustment strategy, oxidation component adjustment strategy, slurry acid-base adjustment strategy, pressure adjustment strategy, and process parameter optimization strategy. Match the risk level assessment results with the corresponding regulatory strategy type; Based on the control strategy, the current operating parameters of the gypsum hydrocyclone station are adjusted to obtain the process parameter optimization strategy for the gypsum hydrocyclone station.

9. The anti-clogging system for a gypsum hydrocyclone station based on intelligent control and structural optimization as described in claim 1, characterized in that, When the closed-loop control module executes the response monitoring and adaptive control strategy based on real-time response data and feeds the real-time response data back to the intelligent decision center to complete parameter optimization and achieve closed-loop control, it is specifically used for: Real-time response data is collected by sensing units deployed at key process nodes. The real-time response data includes data on changes in inlet pressure of the cyclone station, changes in gypsum slurry flow rate, and changes in the state of gypsum crystals. Real-time response data is transmitted to the intelligent decision-making center via a data interface; In the intelligent decision-making center, real-time response data is compared and analyzed with the expected control effects to generate control effect evaluation results; Based on the evaluation results of the regulation effect, the weight parameters and blocking thresholds in the intelligent decision-making center are dynamically adjusted to complete parameter optimization; The optimized parameters are sent to the intelligent decision-making center to form a closed-loop control circuit.

10. A method for preventing clogging in a gypsum hydrocyclone station based on intelligent control and structural optimization, characterized in that, The method is used for employing the anti-clogging system for a gypsum hydrocyclone station based on intelligent control and structural optimization as described in claim 1. S1. Perform data cleaning on the real-time operating data of the gypsum hydrocyclone station to obtain the standard operating data of the gypsum hydrocyclone station; S2. Determine the state of gypsum crystals at the outlet of the gypsum discharge pump in the gypsum hydrocyclone station based on the gypsum crystal image in the standard operating data. S3. Extract the redox potential and pH of gypsum slurry from the standard operating data; S4. The weighted average of the gypsum crystal state, redox potential and gypsum slurry pH is used to obtain the blockage risk index of the gypsum hydrocyclone station. S5. Compare the blockage risk indicators with the blockage threshold in the intelligent decision-making center of the gypsum hydrocyclone station to generate an adaptive control strategy for the gypsum hydrocyclone station. S6. Response monitoring monitors the real-time response data of the adaptive control strategy and feeds the real-time response data back to the intelligent decision center to complete parameter optimization and achieve closed-loop control.

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