A high-precision intelligent large-throughput filtering method and system based on real-time monitoring

Abstract

The present invention discloses a high-precision intelligent large-throughput filtering method and system based on real-time monitoring. The method includes: performing real-time monitoring on a fluid to obtain a real-time data stream; analyzing the real-time data stream by using a dynamic interaction comprehensive analysis algorithm to obtain an analysis result, and generating filtering optimization parameters based on the analysis result; preliminarily separating particulate matter in the fluid based on the filtering optimization parameters to obtain a clean fluid and separated particulate matter, and performing real-time monitoring on the obtained clean fluid to obtain state parameters of the clean fluid; updating and optimizing the filtering optimization parameters based on the state parameters to obtain updated and optimized filtering parameters; and optimizing the updated and optimized filtering parameters in combination with historical data of the clean fluid to obtain final filtering parameters. The present invention ensures the precise optimization of filtering parameters, reduces unnecessary adjustments and redundant processing, generates optimization parameters through efficient calculation, and improves the operation efficiency of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of high-throughput filtration, and particularly to a high-precision intelligent high-throughput filtration method and system based on real-time monitoring. Background Art

[0002] In the field of filtration technology, traditional filtration systems mainly rely on fixed parameter design and manual adjustment, with limited operating efficiency and adaptability. Especially when dealing with complex fluid components or dynamically changing working environments, there are significant deficiencies. Specifically, in the prior art, key parameters such as the pore size, pressure, and flow rate of filters are usually set by fixed empirical values and cannot be adjusted in real time according to the actual fluid characteristics, resulting in unstable filtration accuracy. In addition, when improving the filtration accuracy in traditional systems, the flux often decreases significantly, making it difficult to meet the requirements of large-scale industrial filtration. At the same time, the prior art lacks a real-time monitoring and optimization feedback mechanism for the filtration state. The system cannot timely capture the change characteristics of the fluid. When problems such as blockage and efficiency decline occur, manual intervention is often required, leading to low operating efficiency. That is to say, in intelligent high-throughput filtration, there are technical problems that the fluid state data cannot be accurately analyzed, resulting in the inability to timely adjust the filtration parameters and inaccurate adjustment of the filtration parameters. Summary of the Invention

[0003] Embodiments of the present invention provide a high-precision intelligent high-throughput filtration method and system based on real-time monitoring to solve the above technical problems in the prior art.

[0004] To provide a basic understanding of some aspects of the disclosed embodiments, a simple summary is given below. This summary part is not a general review, nor is it intended to identify key / important constituent elements or delineate the protection scope of these embodiments. Its sole purpose is to present some concepts in a simple form as a prelude to the subsequent detailed description.

[0005] According to the first aspect of the embodiments of the present invention, a high-precision intelligent high-throughput filtration method based on real-time monitoring is provided.

[0006] In one embodiment, the high-precision intelligent high-throughput filtration method based on real-time monitoring includes:

[0007] Performing real-time monitoring on the fluid to be filtered to obtain monitoring data, and converting the monitoring data into continuous electronic signals and outputting them as a real-time data stream;

[0008] Analyzing the real-time data stream by using a dynamic interaction comprehensive analysis algorithm to obtain an analysis result, and generating filtration optimization parameters based on the analysis result;

[0009] Preliminarily separate particulate matter in the fluid based on filtration optimization parameters to obtain clean fluid and separated particulate matter, and perform real-time monitoring on the obtained clean fluid to obtain the state parameters of the clean fluid;

[0010] Update and optimize the filtration optimization parameters under the condition of balancing flux and filtration accuracy based on the state parameters to obtain the updated and optimized filtration parameters; and optimize the updated and optimized filtration parameters in combination with the historical data of the clean fluid to obtain the final filtration parameters, and perform filtration adjustment based on the final filtration parameters.

[0011] In one embodiment, perform real-time monitoring on the fluid to be filtered to obtain monitoring data, and convert the monitoring data into continuous electronic signals and output them as real-time data streams, including:

[0012] Use pre-configured sensors to perform real-time monitoring on the fluid to be filtered to obtain monitoring data; wherein, the monitoring data includes particulate matter concentration, particle size distribution, and chemical composition;

[0013] Amplify the monitoring data through a signal amplification circuit and then perform analog-to-digital conversion to obtain continuous electronic signals as real-time data streams.

[0014] In one embodiment, the filtration optimization parameters include filtration aperture, filtration pressure, and filtration flow rate; and use a dynamic interaction comprehensive analysis algorithm to analyze the real-time data stream to obtain an analysis result, and generate filtration optimization parameters based on the analysis result, including:

[0015] Through dynamic feature distribution modeling, convert the multi-dimensional characteristics of the real-time data stream into a feature distribution function;

[0016] Based on the feature distribution function, construct a dynamic interaction mapping function, and perform comprehensive evolution analysis on the dynamic interaction mapping function to generate the time evolution quantity of the feature;

[0017] Based on the time evolution quantity of the feature, construct a recursive optimization function, and generate filtration optimization parameters in real time according to the recursive optimization function.

[0018] In one embodiment, the state parameters include: the particulate matter residual concentration of the clean fluid, the fluid flow rate, and the pressure difference across the filter; and update and optimize the filtration optimization parameters under the condition of balancing flux and filtration accuracy based on the state parameters to obtain the updated and optimized filtration parameters, including:

[0019] Compare the particulate matter residual concentration of the clean fluid with a pre-configured target concentration. If the comparison result shows that the particulate matter residual concentration of the clean fluid is higher than the target concentration, reduce the filtration aperture; otherwise, maintain or increase the filtration aperture;

[0020] Based on the adjusted filtration aperture, determine the filtration area of the fluid, and adjust the fluid flow rate according to the filtration area; adjust the filtration pressure according to the pressure difference across the filter and a predetermined pressure difference adjustment coefficient.

[0021] In one embodiment, optimize the updated and optimized filtration parameters in combination with the historical data of the cleaning fluid to obtain the final filtration parameters, including:

[0022] Use a time series algorithm to perform trend analysis on the historical data of the cleaning fluid to obtain trend optimization sequence values; and optimize the updated and optimized filtration parameters based on the trend optimization sequence values to obtain the final filtration parameters.

[0023] According to the second aspect of the embodiments of the present invention, there is provided a high-precision intelligent large-throughput filtration system based on real-time monitoring.

[0024] In one embodiment, the high-precision intelligent large-throughput filtration system based on real-time monitoring includes:

[0025] A real-time monitoring module for performing real-time monitoring on the fluid to be filtered to obtain monitoring data, converting the monitoring data into continuous electronic signals and outputting them as a real-time data stream;

[0026] An intelligent analysis module for analyzing the real-time data stream using a dynamic interaction comprehensive analysis algorithm to obtain an analysis result, and generating filtration optimization parameters based on the analysis result;

[0027] An adaptive filtration module for preliminarily separating particulate matter in the fluid based on the filtration optimization parameters to obtain a cleaning fluid and separated particulate matter, and performing real-time monitoring on the obtained cleaning fluid to obtain the state parameters of the cleaning fluid;

[0028] A balance control module for updating and optimizing the filtration optimization parameters in the case of balancing the flux and filtration accuracy based on the state parameters to obtain updated and optimized filtration parameters;

[0029] An intelligent regulation module for optimizing the updated and optimized filtration parameters in combination with the historical data of the cleaning fluid to obtain the final filtration parameters, and performing filtration adjustment based on the final filtration parameters.

[0030] In one embodiment, when the real-time monitoring module performs real-time monitoring on the fluid to be filtered to obtain monitoring data, converts the monitoring data into continuous electronic signals and outputs them as a real-time data stream, it uses a pre-configured sensor to perform real-time monitoring on the fluid to be filtered to obtain monitoring data; wherein, the monitoring data includes particulate matter concentration, particle size distribution, and chemical composition; the monitoring data is amplified by a signal amplification circuit and then subjected to analog-to-digital conversion to obtain continuous electronic signals as the real-time data stream.

[0031] In one embodiment, the filtration optimization parameters include filtration aperture, filtration pressure, and filtration flow rate; moreover, when the intelligent analysis module analyzes the real-time data stream by using the dynamic interaction comprehensive analysis algorithm, obtains an analysis result, and generates filtration optimization parameters based on the analysis result, it converts the multi-dimensional characteristics of the real-time data stream into a characteristic distribution function through dynamic feature distribution modeling; based on the characteristic distribution function, constructs a dynamic interaction mapping function, and performs comprehensive evolution analysis on the dynamic interaction mapping function to generate the time evolution quantity of the feature; based on the time evolution quantity of the feature, constructs a recursive optimization function, and generates filtration optimization parameters in real time according to the recursive optimization function.

[0032] In one embodiment, the state parameters include: the particulate matter residual concentration of the cleaning fluid, the fluid flow rate, and the pressure difference across the filter; moreover, when the balance control module updates and optimizes the filtration optimization parameters under the condition of balancing the flux and filtration accuracy to obtain the updated and optimized filtration parameters, it compares the particulate matter residual concentration of the cleaning fluid with a pre-configured target concentration. In the case where the comparison result is that the particulate matter residual concentration of the cleaning fluid is higher than the target concentration, it reduces the filtration aperture; otherwise, it maintains or increases the filtration aperture; based on the adjusted filtration aperture, determines the filtration area of the fluid, and adjusts the fluid flow rate according to the filtration area; adjusts the filtration pressure according to the pressure difference across the filter and a predetermined pressure difference adjustment coefficient.

[0033] In one embodiment, when the intelligent regulation module optimizes the updated and optimized filtration parameters in combination with the historical data of the cleaning fluid to obtain the final filtration parameters, it uses a time series algorithm to perform trend analysis on the historical data of the cleaning fluid to obtain a trend optimization sequence value; and optimizes the updated and optimized filtration parameters based on the trend optimization sequence value to obtain the final filtration parameters.

[0034] According to the third aspect of the embodiments of the present invention, a computer device is provided.

[0035] In some embodiments, the computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the above method are implemented.

[0036] According to the fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided.

[0037] In one embodiment, a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0038] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0039] 1. Through the dynamic interaction comprehensive analysis algorithm, combined with real-time monitoring data (such as particulate matter concentration, particle size distribution, chemical composition, etc.), a dynamic characteristic distribution function and a dynamic interaction mapping function are constructed to capture the complex change rules of fluid characteristics, ensure the precise optimization of filtration parameters, reduce unnecessary adjustments and redundant processing, generate optimized parameters through efficient calculation, and improve the operation efficiency of the system.

[0040] 2. Through the dynamic balance optimization of flow rate and pressure, high-throughput fluid processing is achieved while ensuring high precision, meeting the large-scale filtration requirements of industrial scale.

[0041] 3. Using the recursive optimization function and time series analysis algorithm, based on the adjustment of filtration parameters, trend optimization values are generated through the trend analysis of historical data to further optimize the filtration parameters, and an intelligent optimization closed-loop is constructed.

[0042] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present invention. Brief Description of the Drawings

[0043] The drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present invention, and are used together with the specification to explain the principles of the present invention.

[0044] Figure 1 It is a schematic flow chart of a high-precision intelligent large-throughput filtration method based on real-time monitoring shown according to an exemplary embodiment;

[0045] Figure 2 It is a schematic structural diagram of a high-precision intelligent large-throughput filtration system based on real-time monitoring shown according to an exemplary embodiment;

[0046] Figure 3 It is a schematic structural diagram of a computer device shown according to an exemplary embodiment. Detailed Description of the Invention

[0047] The following description and the accompanying drawings fully disclose specific embodiments herein, enabling those skilled in the art to practice them. Some parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments herein includes the entire scope of the claims and all available equivalents of the claims. In this document, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a structure, device or equipment comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such structure, device or equipment. Without further limitation, an element defined by the statement "comprising one..." does not exclude the presence of additional identical elements in the structure, device or equipment comprising the said element. The embodiments herein are described in a progressive manner, with each embodiment highlighting the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other.

[0048] In this document, the orientation or positional relationships indicated by the terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. are based on the orientation or positional relationships shown in the drawings, and are only for the convenience of describing this document and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be construed as a limitation on the present invention. In the description of this document, unless otherwise specified and defined, the terms "mounted", "connected", "coupled" shall be understood in a broad sense. For example, it can be a mechanical connection or an electrical connection, or can also be the communication inside two elements. It can be directly connected or indirectly connected through an intermediate medium. For those of ordinary skill in the art, the specific meanings of the above terms can be understood according to specific circumstances.

[0049] In this document, unless otherwise stated, the term "plurality" means two or more.

[0050] In this document, the character " / " indicates that the objects before and after are in an "or" relationship. For example, A / B means: A or B.

[0051] In this document, the term "and / or" is a description of the associated relationship of an object, indicating that three relationships can exist. For example, A and / or B means: A or B, or, A and B these three relationships.

[0052] It should be understood that although the steps in the flowchart are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise clearly stated in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the figure may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0053] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, and their combination. The above-mentioned modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each of the above modules.

[0054] Without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0055] Figure 1 An embodiment of a high-precision intelligent large-throughput filtering method based on real-time monitoring according to the present invention is shown.

[0056] In this alternative embodiment, the high-precision intelligent large-throughput filtering method based on real-time monitoring includes:

[0057] Step S101, performing real-time monitoring on the fluid to be filtered to obtain monitoring data, and converting the monitoring data into continuous electronic signals and outputting them as real-time data streams;

[0058] Step S103, analyzing the real-time data stream by using a dynamic interaction comprehensive analysis algorithm to obtain an analysis result, and generating filtering optimization parameters based on the analysis result;

[0059] Step S105, preliminarily separating the particulate matter in the fluid based on the filtering optimization parameters to obtain a clean fluid and the separated particulate matter, and performing real-time monitoring on the obtained clean fluid to obtain the state parameters of the clean fluid;

[0060] Step S107, updating and optimizing the filtering optimization parameters based on the state parameters under the condition of balancing the flux and the filtering accuracy to obtain updated and optimized filtering parameters; and optimizing the updated and optimized filtering parameters by combining the historical data of the clean fluid to obtain the final filtering parameters, and performing filtering adjustment based on the final filtering parameters.

[0061] Figure 2An embodiment of a high-precision intelligent large-throughput filtration system based on real-time monitoring according to the present invention is shown.

[0062] In this alternative embodiment, the high-precision intelligent large-throughput filtration system based on real-time monitoring includes:

[0063] A real-time monitoring module 201 for performing real-time monitoring on the fluid to be filtered, obtaining monitoring data, and converting the monitoring data into continuous electronic signals and outputting them as a real-time data stream;

[0064] An intelligent analysis module 203 for analyzing the real-time data stream using a dynamic interactive comprehensive analysis algorithm, obtaining an analysis result, and generating filtration optimization parameters based on the analysis result;

[0065] An adaptive filtration module 205 for preliminarily separating particulate matter in the fluid based on the filtration optimization parameters, obtaining a clean fluid and separated particulate matter, and performing real-time monitoring on the obtained clean fluid to obtain state parameters of the clean fluid;

[0066] A balance control module 207 for updating and optimizing the filtration optimization parameters in the case of balancing the flux and filtration accuracy based on the state parameters to obtain updated and optimized filtration parameters;

[0067] An intelligent regulation module 209 for optimizing the updated and optimized filtration parameters by combining historical data of the clean fluid to obtain final filtration parameters, and performing filtration adjustment based on the final filtration parameters.

[0068] In specific applications, when performing real-time monitoring on the fluid to be filtered, obtaining monitoring data, and converting the monitoring data into continuous electronic signals and outputting them as a real-time data stream, a pre-configured sensor is used to perform real-time monitoring on the fluid to be filtered to obtain monitoring data; the monitoring data is amplified by a signal amplification circuit and then subjected to analog-to-digital conversion to obtain continuous electronic signals as the real-time data stream.

[0069] Among them, the sensor includes: a laser particle counter, an optical sensor of a spectral analyzer, and an electrochemical sensor; the monitoring data includes particulate matter concentration, particle size distribution, and chemical composition;

[0070] In addition, in the above embodiments, the filtration optimization parameters include the filtration pore size, filtration pressure, and filtration flow rate; when using the dynamic interaction comprehensive analysis algorithm to analyze the real-time data stream, obtain the analysis result, and generate the filtration optimization parameters based on the analysis result, the multi-dimensional characteristics of the real-time data stream can be transformed into a characteristic distribution function through dynamic feature distribution modeling; based on the characteristic distribution function, a dynamic interaction mapping function is constructed, and a comprehensive evolution analysis is performed on the dynamic interaction mapping function to generate the time evolution quantity of the feature; based on the time evolution quantity of the feature, a recursive optimization function is constructed, and the filtration optimization parameters are generated in real time according to the recursive optimization function.

[0071] Specifically, the dynamic interaction comprehensive analysis algorithm aims to comprehensively optimize the high-precision filtration parameters by dynamically modeling the real-time data and multi-dimensional feature interaction, combined with the sensitivity analysis of the optimization function and the parameter dynamic update mechanism. The specific implementation process is as follows:

[0072] First, based on the real-time data stream , where represents the particulate matter concentration; is the time function of the pollutant concentration; represents the particle size distribution, which is the time distribution of the particle diameter; represents the vector of chemical components, and each is the th chemical property at time . The formula is:

[0073] ;

[0074] where, is the characteristic distribution function, which describes the dynamic change of the comprehensive action of the particulate matter concentration, particle size distribution, and chemical properties of the fluid within the time range; represents time, which is a continuous variable used to describe the time change range; is the time weight factor used to adjust the contribution degree of the time point in the overall characteristic distribution, and the value range is ; are discrete time sampling points, which are a set of finite time segments selected on the continuous time ; is the particulate matter concentration, which describes the particulate matter concentration in the fluid at time ; [[ID=�0]] is the particle size distribution, which describes the average particle size of the particulate matter in the fluid at time ; is the mean value of the particle size distribution, reflecting the entire time range The central value of the particle size distribution of the internal fluid; is the standard deviation of the particle size distribution, indicating the degree of dispersion of the particle size distribution of the fluid; is the th chemical property, describing the particle at time the th chemical property, such as metal ion concentration, dissolved organic matter content, etc.; is the total time range, indicating the time range for fluid property modeling; is the total number of discrete time points, indicating the number of sampling times or discrete time points within the time range; is the total number of chemical property dimensions, indicating the number of types of chemical properties of the particles.

[0075] Based on the feature distribution function construct a dynamic interaction mapping function to capture the deep associations and temporal correlations among various features. The formula is:

[0076] ;

[0077] where, is the dynamic interaction mapping function, which is the result of the feature distribution function after non - linear and time - periodic mapping, reflecting the complex interactions and temporal evolution relationships of different features; is the mapping weight factor, indicating the influence degree of a specific power and time period on the overall mapping result; is the mapping bias, indicating the basic offset of the mapping result, used to adjust non - zero properties or eliminate errors; is the th power of the feature distribution function indicating the non - linear expansion of the feature distribution; is the time - period function, reflecting the periodic change of the feature distribution over time; is the integration variable; is the maximum value of the polynomial power, indicating the highest order of non - linear expansion of the feature distribution function ; is the maximum value of the number of time periods, indicating the resolution control of the time - period function .

[0078] After completing the dynamic interaction mapping, perform a comprehensive evolution analysis on the dynamic interaction mapping function to generate the temporal evolution quantity of the feature. The formula is:

[0079] ;

[0080] Among them, is the characteristic time evolution quantity on time , representing the comprehensive dynamic change trend of feature interaction at the current moment; is the dynamic interaction mapping function with respect to the change rate of time , representing the immediate change trend of feature interaction in the time dimension; is the cumulative weight factor, used to adjust the influence degree of the historical time cumulative term; is the time interval for the cumulative amount of the square value of the dynamic interaction mapping function, representing the cumulative intensity of interaction features in the historical time dimension; is the exponential adjustment factor, used to control the amplitude of the exponential suppression term; is the exponential decay term of the dynamic interaction feature, representing the non - linear suppression of the dynamic interaction mapping function on the evolution trend; is the exponential decay adjustment parameter, representing the influence range of the dynamic interaction mapping function .

[0081] Based on the characteristic time evolution quantity , a recursive optimization function is constructed to generate optimization parameters in real - time. The recursive optimization formula is:

[0082] ;

[0083] Among them, is the recursive optimization function, representing the dynamic optimization result of the filtering parameter. Its value is used to guide the generation of the filtering aperture, filtering pressure and flow rate; is the weight in the recursive optimization function, representing the influence weight of the -th layer recursion on the optimization result; is the adjustment factor of the recursive optimization, controlling the influence degree of the characteristic time evolution quantity on the optimization result in the -th layer recursion; is the identifier of the recursive layer, used to represent the contribution of the current calculation in the recursive optimization function for the -th layer recursion; is the total number of layers of the recursive optimization function, representing the complexity of the recursive optimization; is the regularization factor, used to prevent over - fitting in the optimization process and ensure the robustness of the optimization result; is the time interval for the cumulative amount of the square value of the characteristic time evolution quantity.

[0084] Recursive optimization function based on filtering parameters , generating filtering optimization parameters, including filtering aperture , filtering pressure and filtering flow rate . The specific formula is as follows:

[0085] Filtering aperture:

[0086] ;

[0087] Wherein, is the filtering aperture, which is the actual working aperture of the filter under the current conditions; is the minimum allowable aperture of the filter; is the recursive optimization function for the standard deviation of particle size distribution; of sensitivity; is the sensitivity adjustment factor, which is used to balance the influence of the partial derivative term on the adjustment of the filtering aperture.

[0088] Filtering pressure:

[0089] ;

[0090] Wherein, is the filtering pressure, which is used to drive the fluid for filtering; is the dynamic viscosity of the fluid, which describes the internal friction of the fluid and is obtained through a correlation function related to the chemical composition and concentration distribution of the particulate matter, and the correlation function is obtained according to the empirical method; is the effective filtering area during filtering, which is obtained through a correlation function related to the particle size distribution and concentration distribution of the particulate matter, and the correlation function is obtained according to the empirical method; is the weight factor, which describes the sensitivity of the change of the effective filtering area during filtering to the optimized pressure; is the partial derivative of the recursive optimization function with respect to the effective filtering area during filtering, which is composed of the partial derivatives with respect to the concentration distribution and particle size distribution, indicating the sensitivity of the optimized state to the change of the filtering area; is the weight factor, which describes the sensitivity of the change of the dynamic viscosity of the fluid to the optimized pressure; is the partial derivative of the recursive optimization function with respect to the dynamic viscosity of the fluid, which is composed of the partial derivatives with respect to the concentration distribution and chemical composition, indicating the sensitivity of the optimized state to the change of the fluid viscosity.

[0091] Filtering flow rate:

[0092] ;

[0093] Among them, is the filtration flow rate; is the target fluid flow rate, representing the maximum value of the fluid volume flow rate that the filtration system needs to process; is the density of the fluid, describing the mass-to-volume ratio of the fluid; is the adjustment coefficient of the partial derivative of the flow rate with respect to the density sensitivity; is the adjustment coefficient of the partial derivative of the flow rate with respect to the viscosity sensitivity; is the partial derivative of the recursive optimization function with respect to the fluid density of.

[0094] When initially separating particulate matter in the fluid based on the filtration optimization parameters to obtain clean fluid and separated particulate matter, the filtration module can be selected according to the required filtration parameters, such as filtration devices customized or directly purchased from companies like Mott, Micronics Engineering Filtration Group, Jiangsu Younaite, etc. for filtration treatment.

[0095] In addition, in the above embodiments, the state parameters include: the particulate matter residual concentration of the clean fluid, the fluid flow rate, and the pressure difference across the filter; furthermore, the state parameters can be compared with the target state preset according to the specific scenario using the empirical method. If all meet the preset target state, there is no need to perform update optimization and subsequent filtration parameter optimization using historical data.

[0096] When updating and optimizing the filtration optimization parameters based on the state parameters to balance the flux and filtration accuracy and obtaining the updated and optimized filtration parameters, the particulate matter residual concentration of the clean fluid is compared with the pre-configured target concentration. If the comparison result shows that the particulate matter residual concentration of the clean fluid is higher than the target concentration, the filtration aperture is reduced; otherwise, the filtration aperture is maintained or increased; based on the adjusted filtration aperture, the filtration area of the fluid is determined, and the fluid flow rate is adjusted according to the filtration area; the filtration pressure is adjusted according to the pressure difference across the filter and the predetermined pressure difference adjustment coefficient.

[0097] Specifically, based on the state parameters including the particulate matter residual concentration of the clean fluid , the pressure difference across the filter , and the fluid flow rate ; by comparing the particulate matter residual concentration with the target concentration , it is judged whether the filtration accuracy meets the requirements, and the evaluation of the clean fluid filtration effect is realized. The derivation logic is as follows: If the particulate matter concentration is higher than the target value, it means that the current filtration aperture is too large and needs to be reduced; otherwise, the aperture is maintained or appropriately relaxed. The update formula for the filtration aperture is:

[0098] ;

[0099] Among them, is the updated and optimized filtration aperture, representing the pore size of the filtration medium, and is used to control the particle retention ability when the fluid passes through the filter; is the aperture adjustment coefficient, which is used to control the sensitivity of the filtration aperture adjustment to the concentration deviation, and is determined according to the specific scenario, with the value range .

[0100] Adjust the flow rate according to the filter area , and at the same time ensure that the filtration flux meets the target requirements. The logic of the flow rate adjustment is based on the flow formula:

[0101] ;

[0102] Among them, is the updated and optimized filtration flow rate, representing the operating speed of the fluid during filtration after a new round of parameter adjustment; is the adjustment coefficient of the flux adjustment, which controls the sensitivity of the filtration flux deviation to the flow rate adjustment, and is determined according to the empirical method, with the value range of ; is the target filtration flux, representing the filtration flow rate value that is expected to be achieved during the operation of the system;[[ID=3)]] is the current filtration flux, representing the actual volume flow rate of the fluid passing through the filter monitored in real time; is the effective filtration area of the filter, representing the current working area during filtration; is the adjustment coefficient of the particle concentration adjustment, which controls the sensitivity of the particle concentration deviation to the flow rate adjustment, and is determined according to the empirical method, with the value range of .

[0103] Further adjust the filtration pressure in real time to balance the filtration resistance and the fluid flux. The logic of the pressure adjustment is based on the pressure difference at both ends of the current filter. If the pressure difference is too large, it will cause the filter to become blocked; if the pressure difference is too small, the filtration efficiency may be insufficient. The pressure adjustment formula is:

[0104] ; ]>]

[0105] Among them, is the updated and optimized filtration pressure, which is the pressure that should be achieved after this optimization adjustment during filtration; is the pressure difference adjustment coefficient, which is used to determine the influence degree of the current pressure difference on the filtration pressure adjustment, and is determined according to the specific scenario, with the value range of ; is the flux adjustment coefficient, which is used to determine the contribution weight of the filtration flux deviation to the pressure adjustment, and is determined according to the empirical method, with the value range of .

[0106] When obtaining the updated and optimized filtration parameters, the particulate matter in the fluid can also be secondarily separated based on the updated and optimized filtration parameters, so as to obtain the optimized clean fluid, and the monitoring parameters of the obtained clean fluid are stored and analyzed to form historical data, including historical data records and trend analysis.

[0107] When optimizing the updated and optimized filtration parameters by combining the historical data of the clean fluid to obtain the final filtration parameters, the time series algorithm can be used to perform trend analysis on the historical data of the clean fluid to obtain the trend optimization sequence value; and based on the trend optimization sequence value, the updated and optimized filtration parameters are optimized to obtain the final filtration parameters.

[0108] Specifically, when performing trend analysis on the historical data of the clean fluid using the time series algorithm, the obtained trend optimization sequence value is ; then, based on the trend optimization sequence value, the updated and optimized filtration parameters are optimized, and the obtained final filtration parameters are:

[0109] ;

[0110] ;

[0111] ;

[0112] Among them, 、 、 are the final filtration parameters; is the trend optimization value of the pore size change; is the trend optimization value of the flow rate change; is the trend optimization value of the pressure change; is the proportionality factor for pore size adjustment, determined by the empirical method; is the proportionality factor for flow rate adjustment, determined by the empirical method; is the proportionality factor for pressure adjustment, determined by the empirical method.

[0113] In addition, during the process of performing filtration adjustment using the final filtration parameters, the final state parameters at this time are obtained, and the final state parameters are compared with the target state preset according to the specific scenario using the empirical method. If both meet the preset target state, no update and optimization are required; if the preset target state is not met, further processing is performed according to the update and optimization process of the filtration parameters until the preset target state is met.

[0114] Figure 3An embodiment of a computer device according to the present invention is shown. The computer device may be a server, and the computer device includes a processor, a memory, and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0115] Those skilled in the art can understand that Figure 3 the structure shown in [the figure] is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different component layout.

[0116] In addition, the present invention also provides a computer device, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiment are implemented.

[0117] Furthermore, the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0118] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided by the present invention can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0119] The present invention is not limited to the structures already described and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.

Claims

1. A high-precision intelligent large-throughput filtering method based on real-time monitoring, characterized in that, Comprising: Performing real-time monitoring on the fluid to be filtered to obtain monitoring data, converting the monitoring data into continuous electronic signals and outputting them as a real-time data stream; Analyzing the real-time data stream using a dynamic interactive comprehensive analysis algorithm to obtain an analysis result, and generating filtration optimization parameters based on the analysis result; Performing preliminary separation of particulate matter in the fluid based on the filtration optimization parameters to obtain a clean fluid and separated particulate matter, and performing real-time monitoring on the obtained clean fluid to obtain state parameters of the clean fluid; Updating and optimizing the filtration optimization parameters under the condition of balancing flux and filtration accuracy based on the state parameters to obtain updated and optimized filtration parameters; and optimizing the updated and optimized filtration parameters in combination with historical data of the clean fluid to obtain final filtration parameters, and performing filtration adjustment based on the final filtration parameters; Among them, analyzing the real-time data stream using a dynamic interactive comprehensive analysis algorithm to obtain an analysis result, and generating filtration optimization parameters based on the analysis result includes: Converting the multi-dimensional characteristics of the real-time data stream into a characteristic distribution function through dynamic feature distribution modeling; Based on the characteristic distribution function, constructing a dynamic interactive mapping function, and performing comprehensive evolution analysis on the dynamic interactive mapping function to generate a time evolution quantity of the feature; Based on the time evolution quantity of the feature, constructing a recursive optimization function, and generating filtration optimization parameters in real time according to the recursive optimization function.

2. The high-precision intelligent large-throughput filtering method based on real-time monitoring according to claim 1, wherein Performing real-time monitoring on the fluid to be filtered to obtain monitoring data, converting the monitoring data into continuous electronic signals and outputting them as a real-time data stream includes: Using a pre-configured sensor to perform real-time monitoring on the fluid to be filtered to obtain monitoring data; wherein, the monitoring data includes particulate matter concentration, particle size distribution, and chemical composition; Amplifying the monitoring data through a signal amplification circuit and then performing analog-to-digital conversion to obtain continuous electronic signals as the real-time data stream.

3. The high-precision intelligent large-flux filtering method based on real-time monitoring according to claim 1, characterized in that, The filtration optimization parameters include filtration aperture, filtration pressure, and filtration flow rate.

4. The high-precision intelligent large-throughput filtering method based on real-time monitoring according to claim 1, wherein, The state parameters include: particulate matter residual concentration of the clean fluid, fluid flow rate, and pressure difference across the filter; And, updating and optimizing the filtration optimization parameters under the condition of balancing flux and filtration accuracy based on the state parameters to obtain updated and optimized filtration parameters includes: Comparing the particulate matter residual concentration of the clean fluid with a pre-configured target concentration, and reducing the filtration aperture when the comparison result shows that the particulate matter residual concentration of the clean fluid is higher than the target concentration; otherwise, maintaining or increasing the filtration aperture; Based on the adjusted filtration aperture, determining the filtration area of the fluid, and adjusting the fluid flow rate according to the filtration area; adjusting the filtration pressure according to the pressure difference across the filter and a predetermined pressure difference adjustment coefficient.

5. The high-precision intelligent large-throughput filtering method based on real-time monitoring according to claim 1, wherein Optimizing the updated and optimized filtration parameters in combination with historical data of the clean fluid to obtain final filtration parameters includes: Performing trend analysis on the historical data of the clean fluid using a time series algorithm to obtain a trend optimization sequence value; and optimizing the updated and optimized filtration parameters based on the trend optimization sequence value to obtain final filtration parameters.

6. A high-precision intelligent large-throughput filtration system based on real-time monitoring, characterized in that, Comprising: A real-time monitoring module for real-time monitoring of the fluid to be filtered, obtaining monitoring data, converting the monitoring data into continuous electronic signals and outputting them as a real-time data stream; An intelligent analysis module for analyzing the real-time data stream using a dynamic interaction comprehensive analysis algorithm to obtain an analysis result, and generating filtration optimization parameters based on the analysis result; An adaptive filtration module for preliminarily separating particulate matter in the fluid based on the filtration optimization parameters to obtain a clean fluid and separated particulate matter, and performing real-time monitoring on the obtained clean fluid to obtain the state parameters of the clean fluid; A balance control module for updating and optimizing the filtration optimization parameters under the condition of balancing flux and filtration accuracy based on the state parameters to obtain updated and optimized filtration parameters; An intelligent regulation module for optimizing the updated and optimized filtration parameters by combining historical data of the clean fluid to obtain final filtration parameters, and performing filtration adjustment based on the final filtration parameters; Among them, when the intelligent analysis module analyzes the real-time data stream using a dynamic interaction comprehensive analysis algorithm to obtain an analysis result and generates filtration optimization parameters based on the analysis result, it transforms the multi-dimensional characteristics of the real-time data stream into a characteristic distribution function through dynamic feature distribution modeling; based on the characteristic distribution function, constructs a dynamic interaction mapping function, and conducts comprehensive evolution analysis on the dynamic interaction mapping function to generate the time evolution quantity of the characteristics; based on the time evolution quantity of the characteristics, constructs a recursive optimization function, and generates filtration optimization parameters in real time according to the recursive optimization function.

7. The high-precision intelligent large-throughput filtration system based on real-time monitoring according to claim 6, wherein When the real-time monitoring module performs real-time monitoring on the fluid to be filtered, obtains monitoring data, and converts the monitoring data into continuous electronic signals and outputs them as a real-time data stream, it uses a pre-configured sensor to perform real-time monitoring on the fluid to be filtered to obtain monitoring data; among them, the monitoring data includes particulate matter concentration, particle size distribution and chemical composition; the monitoring data is amplified by a signal amplification circuit and then subjected to analog-to-digital conversion to obtain continuous electronic signals as the real-time data stream.

8. The high-precision intelligent large-throughput filtration system based on real-time monitoring according to claim 6, characterized in that The filtration optimization parameters include filtration aperture, filtration pressure and filtration flow rate.

9. The high-precision intelligent large-throughput filtration system based on real-time monitoring according to claim 6, wherein The state parameters include: the particulate matter residual concentration of the clean fluid, the fluid flow rate and the pressure difference across the filter; Moreover, when the balance control module updates and optimizes the filtration optimization parameters under the condition of balancing flux and filtration accuracy based on the state parameters to obtain updated and optimized filtration parameters, it compares the particulate matter residual concentration of the clean fluid with a pre-configured target concentration. In the case where the comparison result is that the particulate matter residual concentration of the clean fluid is higher than the target concentration, it reduces the filtration aperture; otherwise, it maintains or increases the filtration aperture; based on the adjusted filtration aperture, determines the filtration area of the fluid, and adjusts the fluid flow rate according to the filtration area; adjusts the filtration pressure according to the pressure difference across the filter and a predetermined pressure difference adjustment coefficient.

10. The high-precision intelligent large-throughput filtration system based on real-time monitoring according to claim 6, wherein, When the intelligent regulation module optimizes the updated and optimized filtration parameters by combining historical data of the clean fluid to obtain final filtration parameters, it uses a time series algorithm to perform trend analysis on the historical data of the clean fluid to obtain trend optimization sequence values; And optimize the updated optimized filtering parameters based on the trend-optimized sequence values to obtain the final filtering parameters.

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