Liquid inlet automatic backwashing control method and system for filter station

Abstract

The invention provides a liquid inlet automatic backwashing control method and system for a filter station, and relates to the technical field of backwashing control, and the method comprises the following steps: interactively obtaining liquid characteristic parameters and filtration requirement parameters of a to-be-filtered liquid; filtering medium matching is carried out, after filtering medium type selection is obtained, filtering element configuration of a filtering station is carried out, and a target filtering element is obtained; target structure features are obtained, filtering condition initialization is carried out, and initial filtering control constraints are output; the initial filtering control constraint is used as an optimization condition, liquid inlet preparation speed optimization is executed, and the liquid inlet control speed is positioned; carrying out blockage trend analysis, and executing backwashing control optimization to obtain backwashing control parameters; and carrying out intermittent automatic backwashing by adopting the backwashing control parameters. According to the invention, the technical problem of resource waste or influence on the filtering effect caused by backwashing when the filtering element is not completely blocked or is severely blocked because the backwashing control in the prior art is usually carried out at a fixed time or a preset period is solved.

Description

Technical Field

[0001] The invention relates to the technical field of backwashing control, and in particular to an automatic liquid inlet backwashing control method and system for a filtering station. Background Art

[0002] During the operation of the filter element in the filter station, as impurities continue to accumulate, the surface of the filter element is prone to clogging. Most existing backwashing systems use timed or preset cycles for backwashing. Since the backwashing cycle is preset, backwashing in advance when the filter element is not completely blocked or the optimal backwashing time has not been reached will lead to a waste of resources. On the contrary, if the backwashing is delayed, the filter element has been seriously blocked, which will further affect the filtering effect. In addition, the backwashing control of traditional filtration systems is mostly based on fixed parameters, such as liquid inlet speed, pressure or cycle. This single control strategy cannot be dynamically adjusted according to the actual content of impurities, the blockage of the filter element, and the filtration accuracy requirements, resulting in the filtration task cannot be completed efficiently within the specified time. Summary of the invention

[0003] The present application provides an automatic backwash control method and system for liquid inlet in a filtration station, aiming to solve the technical problem that the backwash control in the prior art is usually carried out at a timed or preset cycle, and backwashing is often carried out when the filter element is not completely blocked or is already severely blocked, resulting in waste of resources or affecting the filtering effect.

[0004] The first aspect disclosed in the present application provides an automatic backwashing control method for a filter station, the method comprising: interactively obtaining liquid characteristic parameters and filtration requirement parameters of a liquid to be filtered, wherein the liquid characteristic parameters include impurity component information and filtration volume information, and the filtration requirement parameters include filtration time constraints and filtration accuracy constraints; matching filter media according to the impurity component information and filtration accuracy constraints, and after obtaining the filter medium selection, obtaining a target filter element by configuring the filter element of the filter station based on the filter medium selection; interactively obtaining a target structural feature of the target filter element, and according to the target structural feature and the filter medium selection, obtaining a target filter element; Initialize the filtering conditions and output the initial filtering control constraints, wherein the initial filtering control constraints include a filtering pressure difference threshold and a maximum allowable blockage rate; use the initial filtering control constraints as optimization conditions, perform liquid inlet speed optimization according to the filtering volume information and filtering time constraints, and locate the liquid inlet control speed; perform a blockage trend analysis of the target filter element according to the liquid inlet control speed, and perform backwashing control optimization based on the analysis results to obtain backwashing control parameters; during the process of performing inlet filtration of the liquid to be filtered at the filtering station at the liquid inlet control speed, use the backwashing control parameters to perform intermittent automatic backwashing of the target filter element.

[0005] The second aspect disclosed in the present application provides an automatic backwashing control system for liquid inlet of a filter station, the system being used for the above-mentioned automatic backwashing control method for liquid inlet of a filter station, the system comprising: a liquid parameter acquisition module, the liquid parameter acquisition module being used to interactively obtain liquid characteristic parameters and filtration requirement parameters of a liquid to be filtered, wherein the liquid characteristic parameters include impurity component information and filtration volume information, and the filtration requirement parameters include filtration time constraints and filtration accuracy constraints; a filter element configuration module, the filter element configuration module being used to match filter media according to the impurity component information and filtration accuracy constraints, and after obtaining the filter medium selection, obtaining a target filter element by configuring the filter element of the filter station based on the filter medium selection; a condition initialization module, the condition initialization module being used to interactively obtain target structural features of the target filter element, and Initialize the filtering conditions according to the target structural characteristics and the filter medium selection, and output the initial filtering control constraints, wherein the initial filtering control constraints include a filtering pressure difference threshold and a maximum allowable blockage rate; a liquid inlet speed optimization module, wherein the liquid inlet speed optimization module is used to use the initial filtering control constraints as optimization conditions, perform liquid inlet speed optimization according to the filtering volume information and the filtering time constraints, and locate the liquid inlet control speed; a control optimization module, wherein the control optimization module is used to perform a blockage trend analysis of the target filter element according to the liquid inlet control speed, and perform backwashing control optimization based on the analysis results to obtain backwashing control parameters; an automatic backwashing module, wherein the automatic backwashing module is used to perform intermittent automatic backwashing of the target filter element using the backwashing control parameters during the liquid inlet filtration process of the liquid to be filtered at the filtering station at the liquid inlet control speed.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: Interactively obtain the liquid characteristic parameters and filtration requirement parameters of the liquid to be filtered, where the liquid characteristic parameters include impurity component information and filtration volume information, and the filtration requirement parameters include filtration time constraints and filtration accuracy constraints. These data provide an important basis for subsequent filtration operations and effectively ensure the filtration quality; match the filter media according to the impurity component information and filtration accuracy constraints, select appropriate filter media and configure filter elements. Through precise medium selection and filter element configuration, it is ensured that the system can intercept impurities of different particle sizes. The matching of multi-layer filter media ensures effective interception of large particles to tiny particles, improves the accuracy of filtration, and at the same time, different filter layer structures can be designed according to different impurity components to flexibly respond to complex filtration requirements; according to the structural characteristics of the target filter element and the selection of filter media, initialize the filtration conditions, output the filtration pressure difference threshold and the maximum allowable blockage rate. These control constraints can ensure that the system is maintained within the service life of the filter element. Maintain its efficient operation; optimize the liquid inlet speed by filtering volume information and filtering time constraints, so that the filter station can automatically adjust the liquid inlet speed according to the needs of the task, ensure that the task is completed efficiently within the specified time, avoid time waste and unnecessary energy consumption, and at the same time, the optimized speed helps to reduce the fluctuation of pressure difference and the clogging speed of the filter element; analyze the clogging trend of the filter element through the liquid inlet speed, and optimize the backwashing control parameters. This step ensures that the backwashing is carried out at the best time by real-time monitoring of the clogging situation and pressure difference changes, while reducing unnecessary resource consumption and improving the overall energy efficiency of the system; finally, the intermittent automatic backwashing of the filter station is realized through the backwashing control parameters, and the filter element is automatically cleaned without affecting the filtration efficiency, ensuring the stability and continuity of the filter station in long-term operation. Since the backwashing process is automated, the need for human intervention is reduced, the risk of operating errors is reduced, and the reliability of the system is improved.

[0007] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A schematic flow chart of an automatic backwash control method for liquid inlet in a filtration station provided in an embodiment of the present application.

[0009] Figure 2 A schematic diagram of the structure of an automatic backwash control system for liquid inlet used in a filtration station provided in an embodiment of the present application.

[0010] Explanation of reference numerals: liquid parameter acquisition module 10 , filter element configuration module 20 , condition initialization module 30 , liquid inlet speed optimization module 40 , control optimization module 50 , automatic backwashing module 60 . DETAILED DESCRIPTION

[0011] The embodiment of the present application solves the technical problem that the backwash control in the prior art is usually performed at a time or preset cycle, and backwashing is often performed when the filter element is not completely blocked or is severely blocked, resulting in waste of resources or affecting the filtering effect, by providing an automatic backwash control method for the liquid inlet of the filter station.

[0012] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0013] like Figure 1 As shown, the embodiment of the present application provides an automatic backwash control method for liquid inlet of a filtration station, the method comprising: Interactively obtain liquid characteristic parameters and filtration requirement parameters of the liquid to be filtered, wherein the liquid characteristic parameters include impurity component information and filtration volume information, and the filtration requirement parameters include filtration time constraints and filtration accuracy constraints.

[0014] Parameter information is obtained from multiple sources, such as users, sensors, and process monitoring systems. The liquid characteristic parameters are about the physical and chemical properties of the liquid to be filtered, including impurity component information and filtration volume information. The impurity component information refers to the type, particle size, shape, and concentration of impurities contained in the liquid to be filtered. Different types of impurities have a direct impact on the selection of filtration media and the design of filtration processes. They can be obtained through laboratory analysis or online sensor equipment detection, such as using a laser particle size analyzer to measure particle size distribution; filtration volume information refers to the total amount of liquid to be filtered, which can be obtained through a liquid flow meter or other fluid monitoring equipment.

[0015] The filtration requirement parameters define the performance standards of the entire filtration process, which are determined by the specific requirements of the filtration task, including filtration time constraints and filtration accuracy constraints. The filtration time constraint is the requirement that the filtration task must be completed within a specified time. It defines how long the filtration station must take to complete the required filtration task. It is set by the user or process engineer based on actual production needs; the filtration accuracy constraint is the minimum particle size or minimum particle concentration that the system can effectively filter impurities, which is also set by the user or process engineer based on actual production needs.

[0016] The filter medium is matched according to the impurity component information and the filter accuracy constraint. After the filter medium selection is obtained, the filter element of the filter station is configured based on the filter medium selection to obtain the target filter element.

[0017] Filter media matching is to select suitable filter materials and structures according to the impurity components and filtration accuracy requirements of the liquid. Specifically, different types of impurities, such as sediment, suspended matter, grease, organic matter, etc., require different filter materials. For example, grease impurities require hydrophobic media, and suspended solid particles require hydrophilic media; filtration accuracy indicates the minimum particle size or concentration of impurities to be removed. According to the filtration accuracy constraint, the pore size or layer setting of the filter medium can be determined. For example, if the filtration accuracy is required to be 10 microns, the selected filter medium must be able to remove particles of 10 microns and above. By analyzing the impurity type and filtration particle size, select the appropriate filter medium, including medium material, pore size, and filter layer structure. After selecting the filter medium, assemble the medium. The multi-layer filter elements can be arranged in order from large to small particle size to gradually filter different impurities in the liquid.

[0018] After completing the media selection, the filter elements of the filter station are configured. The filter elements include filter elements, filter bags, filter screens, etc. The filter element configuration includes the size configuration of the filter element, the number configuration of the filter element, and the arrangement configuration of the filter element. Specifically, according to the design specifications of the filter station, select the appropriate filter element size, including length, diameter, etc. These dimensions affect the filtration area, thereby affecting the filtration efficiency and service life; according to the filtration volume and filtration time requirements, calculate the number of filter elements to ensure sufficient processing capacity. For example, if the filtration task requires processing a large amount of liquid, multiple groups of filter elements are required to work in parallel. Through the configuration process, the target filter element that matches the impurity components and filtration accuracy requirements is obtained to ensure that the filter station can work efficiently while meeting the accuracy requirements, and maintain the filtration performance through subsequent backwashing.

[0019] Furthermore, according to the impurity component information and the filtration accuracy constraint, filter medium matching is performed to obtain the filter medium selection, and the method includes: Based on the impurity type, the impurity component information is disassembled to obtain multiple impurity particle size distribution characteristics of multiple impurity types; the multiple impurity particle size distribution characteristics are updated according to the filtration accuracy constraint to obtain multiple filter particle size distribution characteristics; the multiple filter particle size distribution characteristics are integrated to obtain a global particle size distribution characteristic; based on the global particle size distribution characteristic, multi-stage filter media matching is performed to obtain a multi-layer filter element, wherein the multi-layer filter element has multiple filter scales, filter layer thicknesses and filter layer material identifiers; based on the multiple filter scales, multi-layer filter elements are serially assembled to obtain the filter medium selection.

[0020] Impurity component information includes various types of impurities in the liquid to be filtered, such as solid particles, suspended matter, organic matter, colloids, etc. Different impurity types have different physical and chemical properties, and these impurities will have different effects on the filter medium during the filtration process. By analyzing the impurity component information, the impurities in the liquid are broken down by category. Common classification methods include classification based on physical form (such as particulate matter, colloids, etc.), chemical properties (organic matter, inorganic matter, etc.) or size (micron level, nanometer level). The particle size range of each impurity determines their ability to pass through different porous media. Through experimental analysis or online monitoring equipment, the particle size distribution characteristics of different types of impurities are obtained. Among them, solid particles may have a larger particle size, such as more than 10 microns, which are easily captured by the primary filter medium; the particle size of colloids is smaller, usually between 0.1 and 1 micron, and requires a finer filter medium; organic matter and dissolved substances usually have extremely small particle sizes, even at the nanometer level, requiring special treatment or deep filtration.

[0021] Not all impurities with extremely small particle sizes need to be completely removed. For example, in some applications, some very small particles are allowed to exist, so the filtration accuracy can be appropriately relaxed. According to the filtration accuracy constraint, the impurity particle size distribution characteristics are updated, that is, those extremely low particle size impurities that do not need to be removed are ignored, and the overall particle size distribution is adjusted to meet the filtration requirements. For example, assuming that the filtration accuracy requirement is 5 microns, impurities below 5 microns will not be filtered even if they exist, and only impurities of 5 microns and above need to be removed. In this way, it is ensured that the design of the filter medium can meet the actual needs of the filtration task.

[0022] By superimposing or statistically processing the particle size distribution characteristics of each impurity type, a comprehensive global particle size distribution characteristic is formed. The global particle size distribution characteristic represents the overall distribution of impurity particles in the liquid to be filtered within all particle size ranges, providing a basis for subsequent filtration media selection and design.

[0023] In order to handle impurities of different particle size ranges, it is necessary to design a multi-layer filtration structure. Each layer of filter medium is responsible for filtering impurities of a specific particle size range. The multi-layer filter element has multiple filtration scales, filter layer thicknesses and filter layer material identifiers. Among them, the filtration scale refers to the size range of particles that can be intercepted by each layer of filter medium; the filter layer thickness refers to the thickness of each layer of filter material, which affects the depth and durability of filtration; the filter layer material identifier is based on the physical properties of different materials, such as corrosion resistance, mechanical strength, etc., to select appropriate materials for multi-layer design, such as metal mesh, fiber filter layer, ceramic material, etc. For example, the coarse filter layer is used to capture larger particles, such as 10 microns and above, to reduce the burden on subsequent filter layers; the intermediate filter layer is used to capture medium-sized particles, such as 1-10 microns; the fine filter layer is used to remove smaller particles, such as 0.1-1 microns.

[0024] Arrange multiple filter media layer by layer according to particle size to form a filter structure from coarse to fine. The accuracy of each layer of filter elements is gradually improved to ensure that impurities are intercepted step by step. Specifically, the outermost layer is the coarse filter layer, which captures larger particles. The filtration accuracy increases as you go deeper into the inner layer until the finest filter layer. The final filter media selection ensures that the system can efficiently remove impurities, while extending the life of the filter elements and reducing frequent maintenance and backwashing.

[0025] The target structural characteristics of the target filter element are interactively obtained, and the filter conditions are initialized according to the target structural characteristics and the filter medium selection, and the initial filter control constraints are output, wherein the initial filter control constraints include a filter pressure difference threshold and a maximum allowable blockage rate.

[0026] The structural characteristics of the target filter element refer to the physical and geometric properties of the filter element. These characteristics will affect key performance indicators such as filtration efficiency, pressure difference, and clogging tendency. The target structural characteristics include the geometric dimensions of the filter element, such as length, diameter, thickness, and material porosity. These characteristics determine the flow path and area of ​​the liquid when passing through the filter element.

[0027] Initialize the filtering conditions according to the target structural characteristics and the filter medium selection, and output the filter pressure difference threshold and the maximum allowable blockage rate. Specifically, the filter pressure difference threshold is the maximum allowable difference in liquid pressure before and after the filter element, and is an indicator to measure whether the filter element is close to blockage. The pressure difference can be calculated through fluid mechanics simulation, combined with the standard liquid inlet speed and the resistance coefficient of the filter element; the maximum allowable blockage rate is the maximum impurity coverage or blockage ratio that the filter element can withstand. When the element surface is blocked beyond this ratio, the filtration efficiency is significantly reduced, or the liquid cannot pass smoothly. The maximum allowable blockage rate can be set according to the material properties of the filter element, the impurity particle size distribution and the fluid dynamics simulation. It should be understood that when the blockage rate is the largest, that is, when the pressure difference is the largest, that is, when backwashing is required.

[0028] Further, the target structural features of the target filter element are interactively obtained, and the filter conditions are initialized according to the target structural features and the filter medium selection, and the initial filter control constraints are output. The method includes: Interactively obtain multiple element material information of the multi-layer filter element; calculate the filter area according to the target structural characteristics to obtain the effective filter area; perform network data call according to the multiple element material information and the effective filter area, and after obtaining multiple filter simulation models, obtain the filter element model by fusing the multiple filter simulation models; after performing local data call on the filter station to obtain the standard liquid inlet speed, use the standard liquid inlet speed to perform fluid mechanics simulation of the filter element model, output the structural pressure difference threshold, use the standard liquid inlet speed to perform fluid mechanics simulation of the multiple filter simulation models, and output multiple monomer pressure difference thresholds; solve the intersection of the structural pressure difference threshold and the multiple monomer pressure difference thresholds to obtain the filtration pressure difference threshold; use the maximum value of the filtration pressure difference threshold as a constraint to perform fluid mechanics simulation of the filter element model, and obtain the maximum allowable blockage rate in the filter element model call based on the simulation result.

[0029] The material of the multi-layer filter element determines the performance of the filter medium, such as corrosion resistance, mechanical strength, high temperature resistance, hydrophilicity or hydrophobicity, etc. These characteristics directly affect the performance and life of the filter element in different working environments. By interacting with suppliers, designers or material databases, specific material information of the filter element can be obtained. Each layer of the filter element may use different materials, depending on the requirements of the filter medium and the working environment. The material types include metal, ceramic, fiber, polymer, etc.

[0030] The effective filtration area is the surface area of ​​the filter element actually used for liquid filtration and is a key factor affecting filtration efficiency. The target structural features include the geometric dimensions of the filter element, such as length, diameter, thickness, and material porosity. The effective filtration area is obtained through geometric calculation or simulation tools based on the target structural features of the filter element. The larger the effective filtration area, the faster the liquid flows through the filter element, which helps to improve the filtration effect and reduce the pressure difference and blockage rate.

[0031] According to the element material information and effective filtration area of ​​the filter element, call the online material database or simulation tool to obtain the filtration simulation models related to these materials. These models can simulate the performance of the filter element under different conditions, such as impurity interception capacity, pressure difference change, blockage rate change, etc. Through data call, multiple filtration simulation models are obtained. These models cover different materials, different filtration areas and performance under different operating conditions. Different filtration simulation models are integrated to form a comprehensive filter element model, which can provide comprehensive filtration performance prediction, including filtration efficiency, pressure difference change, blockage trend, etc., to provide a reference for the optimization and control of the filtration system. The standard liquid inlet speed is the default liquid inlet speed set by the system. This speed is the rate at which the fluid flows into the filter element, which is usually set according to the design and historical data of the filter station. In the filter element model, the standard liquid inlet speed is used for fluid mechanics simulation to simulate the flow path and velocity distribution of the liquid in the filter element, and predict the pressure difference generated when the liquid passes through the element. The simulation result outputs the structural pressure difference threshold, which indicates the maximum pressure difference that the filter element can withstand in its overall structure. If this pressure difference threshold is exceeded, the filter element may be damaged or the efficiency will be significantly reduced. For multiple filtration simulation models, standard liquid inlet speed is used for fluid mechanics simulation. Each model represents a filter layer with different materials, different pore sizes and different filtration scales. Each simulation model outputs a corresponding single pressure difference threshold, which indicates the maximum pressure difference that the filter element of this layer can withstand at the standard liquid inlet speed.

[0032] By comparing the structural pressure difference threshold and multiple single-unit pressure difference thresholds, their common limit value is found as the filtration pressure difference threshold. The filtration pressure difference threshold is the maximum pressure difference that the system can withstand in actual operation. If this threshold is exceeded, a part of the system may fail or the filtration performance may be seriously degraded.

[0033] Using the maximum value of the filter pressure difference threshold as a constraint, further fluid mechanics simulation is performed to predict how the pressure difference changes when the filter element is gradually blocked, and the changes in the blockage rate of the filter element in different time periods. The blockage rate refers to the proportion of the effective filter area of ​​the filter element reduced due to the accumulation of impurities. As impurities are deposited on the filter element, the flow channel of the liquid becomes narrower, resulting in an increase in the pressure difference. The blockage rate is equal to the difference between the initial filter area and the effective filter area divided by the initial filter area. When the blockage rate reaches the maximum value, the pressure difference of the filter element will reach the filter pressure difference threshold, which is the moment when the system needs to be backwashed. At this time, the filter element can no longer effectively filter more liquid, and the sediment must be removed by backwashing to restore the filtration performance. Through fluid mechanics simulation, it is possible to predict the pressure change of the filter element under different blockage rates, and finally determine the maximum allowable blockage rate. This value corresponds to the degree of blockage when the filter element reaches its pressure difference limit, reflecting the maximum amount of impurity accumulation that the filter element can withstand without affecting the filtration efficiency. It should be understood that when the blockage rate is the largest, that is, when the pressure difference is the largest, that is, when backwashing is required.

[0034] The initial filtration control constraint is used as an optimization condition, and the liquid inlet speed optimization is performed according to the filtration volume information and the filtration time constraint to locate the liquid inlet control speed.

[0035] The set filtration pressure difference threshold and maximum allowable blockage rate are used as optimization conditions for the liquid inlet process to provide restrictions and references for the subsequent liquid inlet speed optimization. The liquid inlet speed optimization is performed according to the filtration volume information and filtration time constraints. The purpose is to find a suitable liquid inlet speed to maximize the filtration efficiency and minimize the clogging risk under the premise of meeting the filtration volume and filtration time constraints. Specifically, 1 / K of the standard liquid inlet speed is used as the speed change scale, and the liquid inlet speed is gradually adjusted to test the impact of different speeds on the filtration performance. The filtration efficiency is predicted by the model, and the filtration time corresponding to each liquid inlet speed is calculated in combination with the filtration volume information to ensure that the filtration task can be completed within the specified time. By continuously adjusting the liquid inlet speed and performing simulation analysis, the appropriate liquid inlet control speed is finally determined.

[0036] The clogging trend of the target filter element is analyzed according to the liquid inlet control speed, and the backwash control optimization is performed based on the analysis result to obtain the backwash control parameters.

[0037] After determining the liquid inlet control speed, the clogging trend of the target filter element is monitored in real time. For example, the pressure difference before and after the filter element is monitored to predict the clogging trend of the filter element. When the pressure difference increases to a certain threshold, it means that the filter element is being clogged. Combined with the structural characteristics of the filter element and the real-time monitoring data, the clogging rate of the filter element is calculated. The higher the clogging rate, the more obvious the decline in filtration performance.

[0038] By comparing the blockage rate and the pressure difference time limit, the backwash cycle is determined, that is, how often the backwash is started. Generally, when the blockage rate or the pressure difference is close to the maximum allowable value, the backwash needs to be started. The fitness evaluation function is used, which takes into account energy consumption, life loss, water consumption, etc. According to the historical backwash records and simulation data, the backwash parameters are optimized to obtain the backwash control parameters. The backwash control parameters include backwash water flow rate, backwash water duration, backwash gas flow rate, backwash gas duration, and comprehensive flushing duration.

[0039] During the process of the filtration station performing the inlet filtration of the liquid to be filtered at the inlet control speed, the backwashing control parameter is used to perform intermittent automatic backwashing of the target filter element.

[0040] During the normal operation of the filter station, the liquid to be filtered is controlled to enter the filter system according to the determined liquid inlet control speed. At this time, the filter element performs conventional filtering operations to gradually remove impurities in the liquid. In order to avoid the filter element from being blocked, the backwashing operation is performed intermittently. Specifically, the backwashing is automatically started according to the backwashing cycle. During backwashing, the impurities on the surface of the filter element are first cleaned by reverse water flow, and then the cleaning effect is improved by gas flushing. The backwashing is intermittent and will not be continuously paralleled with the filtering process to ensure the overall efficiency of the filter station. After the backwashing is completed, the permeability of the filter element is restored, and the filtering operation continues at the set liquid inlet speed, ensuring that the filter station can efficiently complete the filtering task while maintaining the performance of the filter element through the optimized backwashing process, reducing the operating cost of the system.

[0041] Furthermore, the initial filtration control constraint is used as an optimization condition, and the liquid inlet speed optimization is performed according to the filtration volume information and the filtration time constraint to locate the liquid inlet control speed. The method includes: Pre-constructing a liquid inlet correlation prediction model; using 1 / K of the standard liquid inlet speed as a speed change scale to update the data of the standard liquid inlet speed to obtain a first standby liquid inlet speed; inputting the first standby liquid inlet speed into the liquid inlet correlation prediction model to obtain a first blockage rate time limit, a first filtration pressure difference time limit and a first predicted filtration efficiency; using the filtration volume information and the first predicted filtration efficiency to calculate and obtain a first predicted filtration time, if the first predicted filtration time satisfies the filtration time constraint, retaining the first standby liquid inlet speed, and using the first difference between the first blockage rate time limit and the first filtration pressure difference time limit as the first control reliability coefficient of the first standby liquid inlet speed; and so on, by continuously updating the standard liquid inlet speed, and using the liquid inlet correlation prediction model and the filtration time constraint to evaluate the update results, obtaining multiple standby liquid inlet speeds and multiple control reliability coefficients that meet the preset update frequency; serializing the multiple control reliability coefficients, and locating the liquid inlet control speed from the multiple standby liquid inlet speeds based on the sorting results.

[0042] The liquid inlet correlation prediction model is built based on the back propagation neural network. Its purpose is to predict the clogging trend, filtration pressure difference and filtration efficiency of the filter element at different liquid inlet speeds through training and learning of historical data. The model is used to evaluate the performance of the system at various liquid inlet speeds and help select the optimal liquid inlet speed.

[0043] The standard liquid inlet speed is the initial setting liquid inlet speed according to the system design and filtering task, which can be set based on design specifications, historical data or the requirements of the filtering task. The speed change scale is a reference value for adjustment based on the standard liquid inlet speed. Specifically, 1 / K of the standard liquid inlet speed is used as the basis for adjustment to increase or decrease the speed. K is an integer, which indicates the degree of subdivision of the adjustment range. If K is small, such as 2, the adjustment range is large and the change in liquid inlet speed is more extreme; if K is large, such as 10, the adjustment range is small and the change in liquid inlet speed is more subtle. Adjust the standard liquid inlet speed according to the determined speed change scale to obtain the first backup liquid inlet speed.

[0044] The obtained first backup liquid inlet speed is input into the constructed liquid inlet association prediction model. The model analyzes the input speed according to the patterns and features learned during the training process, and outputs the corresponding first blockage rate time limit, first filtration pressure difference time limit and first predicted filtration efficiency, wherein the first blockage rate time limit is the length of time for the filter element to reach the maximum allowable blockage rate at the liquid inlet speed. The length of this time limit determines the time the filter element can work continuously and also affects the backwashing cycle; the first filtration pressure difference time limit is the length of time for the filter element to reach the maximum pressure difference threshold at the liquid inlet speed, which indicates the pressure loss trend of the filter element over time; the first predicted filtration efficiency indicates the volume of liquid that the filtration system can handle per unit time at the first backup liquid inlet speed.

[0045] The filtration time is calculated using the filtration volume information (i.e., the total volume of the liquid to be filtered) and the first predicted filtration efficiency (the volume of liquid that the filter element can process per unit time). That is, the filtration volume is divided by the first predicted filtration efficiency to obtain the first predicted filtration time. The predicted filtration time is a measure to ensure that the filtration task can be completed within the specified time. If the first predicted filtration time meets, that is, is less than or equal to, the time constraint, the current first backup liquid inlet speed can be retained; otherwise, it means that the current liquid inlet speed is inappropriate and needs to be readjusted.

[0046] While retaining the first backup liquid inlet speed, the first difference between the first blockage rate time limit and the first filtration pressure difference time limit is calculated. This difference reflects the time difference between the filter element reaching the blockage rate and the pressure difference threshold. The smaller the difference, the closer the system is to the ideal state. The first difference is used as the first control reliability coefficient of the speed. The smaller the control reliability coefficient, the better the liquid inlet speed and the more stable the system operation.

[0047] On the basis of the first backup liquid inlet speed, continue to adjust the liquid inlet speed, fine-tune the standard liquid inlet speed each time, and gradually increase or decrease the liquid inlet speed. After each speed adjustment, input the new backup liquid inlet speed into the liquid inlet association prediction model, repeat the previous process, obtain the blockage rate time limit, filtration pressure difference time limit, and predicted filtration efficiency, and calculate the predicted filtration time to determine whether the filtration time constraint is met. If not, continue to adjust the speed. If satisfied, retain the speed and calculate the corresponding control reliability coefficient until the preset update frequency is met. The preset update frequency is set according to actual needs, and finally multiple backup liquid inlet speeds that meet the filtration time constraint are obtained. For each backup liquid inlet speed, the corresponding control reliability coefficient is calculated.

[0048] All the obtained control reliability coefficients are arranged in ascending order. The smaller the control reliability coefficient, the better the performance of the filtration system at this speed. Therefore, the alternative liquid inlet speed with a smaller control reliability coefficient will be given priority. According to the sorting results, the selection starts from the speed with the smallest control reliability coefficient, and the liquid inlet speed corresponding to the smallest control reliability coefficient is located as the final liquid inlet control speed.

[0049] Further, a liquid inlet correlation prediction model is pre-built, and the method includes: The filter medium selection and impurity component information are used as historical data call constraints to obtain historical liquid inlet related data, wherein the historical liquid inlet related data includes multiple sample liquid inlet speeds, multiple sample filtration efficiencies, multiple blockage rate change speeds and multiple filtration pressure difference change speeds; multiple filtration pressure difference change time limits are calculated based on the filtration pressure difference threshold and multiple filtration pressure difference change speeds; multiple blockage rate change time limits are calculated based on the maximum allowable blockage rate and multiple blockage rate change speeds; the multiple sample liquid inlet speeds, multiple sample filtration efficiencies, multiple filtration pressure difference time limits and multiple blockage rate change time limits are used as training constructions to train the liquid inlet related prediction model constructed based on the back propagation neural network.

[0050] Taking the filter medium selection and impurity component information as constraints, the historical liquid inflow data related to it is called. These data can come from previous filtration operation records, laboratory test results or simulation data. Among them, the sample liquid inflow speed is the liquid inflow speed used in the historical operation (each speed corresponds to a filtration process), and these speeds are the liquid inflow flow rates selected under different circumstances; the sample filtration efficiency is the liquid volume processed by the filter element per unit time at different liquid inflow speeds, representing the performance of the filtration system; the blockage rate change rate represents the growth rate of the filter element blockage rate at different liquid inflow speeds for each sample, reflecting the speed at which impurities accumulate on the surface or inside of the filter element at a given liquid inflow speed; the filter pressure difference change rate represents the speed at which the pressure difference before and after the filter element changes with time at each liquid inflow speed. The increase in pressure difference means that the filter element is gradually blocked and the liquid flow is obstructed. The filter pressure difference change time limit is the maximum time allowed for the system to reach the filter pressure difference threshold from the current pressure difference at the current pressure difference change speed. The filter pressure difference change time limit is obtained by calculating the difference between the filter pressure difference threshold and the current pressure difference, and then dividing the difference by the filter pressure difference change speed.

[0051] The congestion rate change time limit represents the time required to reach the maximum allowable congestion rate from the current congestion rate at the current congestion rate change speed. The congestion rate change time limit is obtained by calculating the difference between the maximum allowable congestion rate and the current congestion rate, and then dividing the difference by the congestion rate change speed.

[0052] The obtained sample data, including liquid inlet speed, filtration efficiency, pressure difference time limit, and blockage rate change time limit, are used as training data for the model. These data are used to establish the association between liquid inlet speed and system performance (blockage rate, pressure difference, filtration efficiency). The back propagation neural network is a common multi-layer neural network, which is trained using the error back propagation algorithm. The network inputs historical data, and after multiple iterations and learning, adjusts the weight parameters to make the model's prediction results as consistent as possible with the actual situation. The specific training process is as follows: the sample liquid inlet speed, filtration efficiency, blockage rate time limit, and pressure difference time limit are input into the neural network, and after nonlinear mapping of the hidden layer, the predicted value is output. The difference between the predicted value output by the model and the actual historical data is compared, and the error is calculated. According to the error, the gradient descent algorithm is used to adjust the weight parameters of the neural network to gradually reduce the error. The forward propagation and back propagation processes are repeated until the model error converges to the preset threshold range to obtain the liquid inlet association prediction model. The trained liquid inlet association prediction model can predict the blockage rate time limit, the filtration pressure difference time limit, and the filtration efficiency according to the input liquid inlet speed.

[0053] Further, the clogging trend analysis of the target filter element is performed according to the liquid inlet control speed, and the backwash control optimization is performed based on the analysis result to obtain the backwash control parameters. The method includes: Direction data is called for the liquid inlet control speed to obtain a control blockage rate time limit and a control filter pressure difference time limit; the smaller value is called by comparing the control blockage rate time limit and the control filter pressure difference time limit as a backwash cycle; a fitness weight configuration is predefined, and a fitness evaluation function is constructed based on the fitness weight configuration, wherein the fitness weight configuration includes energy consumption weight, life loss weight and water consumption weight; the initial filtration control constraint is used as a data call constraint to filter and call historical backwash records to obtain multiple sample backwash records, wherein each of the multiple sample backwash records includes a sample control parameter and a sample flushing consumption; the fitness evaluation function is used to evaluate the fitness of the multiple sample backwash records, and the initial control parameters are located based on the evaluation results, wherein the initial control parameters are composed of backwash water flow rate, backwash water flushing time, backwash gas flow rate, backwash gas flushing time and comprehensive flushing time; the backwash cycle is added to the initial control parameters to obtain the backwash control parameters.

[0054] For the liquid inlet control speed, the system's operation records at the current liquid inlet speed are called through historical data, simulation models or real-time monitoring data to obtain two time limit data: the control blockage rate time limit and the control filtration pressure difference time limit. Among them, the control blockage rate time limit indicates when the filter element reaches the set blockage rate threshold as the blockage rate gradually increases; the control filtration pressure difference time limit indicates when the pressure difference before and after the filter element gradually increases with the progress of the filtration process, and when it reaches the maximum allowable pressure difference.

[0055] Compare the time limit for controlling the blockage rate and the time limit for controlling the filtration pressure difference, and select the smaller of the two time limits as the backwash cycle. This is because no matter whether the blockage rate exceeds the threshold or the pressure difference exceeds the limit, the filter element can no longer continue to work efficiently. Therefore, it is necessary to start backwashing when the smaller time limit is reached to prevent a serious decline in filtration performance.

[0056] In order to optimize the backwashing process, it is necessary to comprehensively consider multiple influencing factors and assign a weight to each factor. Specifically, energy consumption includes the electrical energy or other energy consumed in the backwashing process. The lower the energy consumption, the lower the operating cost of the system. Therefore, energy consumption is an important weight; the backwashing process will cause certain wear and tear on the filter element, affecting its service life. In order to extend the service life of the element, the life loss is included in the fitness evaluation; the backwashing process requires a certain amount of water, and water consumption is a key consideration. The size of these three weights is predefined by the user or system engineer according to the needs of the application scenario. For example, in situations where energy saving is prioritized, the energy consumption weight can be set to a higher value, while in scenarios where the equipment maintenance cost is high, the weight of life loss may be higher.

[0057] Based on these weights, a fitness evaluation function is constructed to evaluate the comprehensive effects of different backwash parameter configurations. The goal of fitness evaluation is to find the best balance between energy consumption, life loss, and water consumption, so that backwashing can effectively remove impurities and minimize resource consumption and equipment loss. The output of the fitness function is a numerical value. The larger the value, the better the balance between energy consumption, life loss, and water consumption, and the better the backwash process.

[0058] Taking the initial filtration control constraint as the screening condition, backwash records similar to the current filtration task and filter element are screened out from the historical operation to obtain multiple sample backwash records. Each record can effectively eliminate the congestion condition described by the initial filtration control constraint. Each sample backwash record includes sample control parameters and sample flushing consumption. The sample control parameters record the specific parameters of the historical backwash operation, including backwash water flow rate, air flow rate, flushing time, etc. The sample flushing consumption represents the resources consumed during the backwashing process, including water consumption, energy consumption, and wear of the filter element.

[0059] The constructed fitness evaluation function is used to evaluate multiple sample backwash records. The fitness evaluation function calculates the fitness value of each sample based on its energy consumption, life loss, water consumption and other indicators to represent its comprehensive performance. The larger the fitness value, the better the backwash scheme is in terms of the balance between energy consumption, life loss and water consumption. According to the fitness evaluation results, the sample backwash record with the largest fitness is selected, and its corresponding control parameters are extracted as the initial control parameters. The initial control parameters are composed of backwash water flow rate, backwash water flushing time, backwash gas flow rate, backwash gas flushing time and comprehensive flushing time. The backwash water flow rate is the water flow rate used to flush the filter element, which usually affects the cleaning effect and water consumption; the backwash water flushing duration is the duration of the water flow during the backwash process, which determines the length of the cleaning process; the backwash gas flow rate is the gas flow rate used for auxiliary flushing, which is usually used to accelerate the removal of impurities; the backwash gas flushing duration is the duration of the gas flow, which cooperates with the water flow flushing; comprehensive flushing is the duration of the gas flow and water flow to flush the filter element synchronously using the water flow rate and the gas flow rate.

[0060] After combining the initial control parameters with the backwash cycle, the final backwash control parameters are generated to guide the backwash process.

[0061] Furthermore, the sample control parameters are composed of sample water flow rate, sample water flushing time, sample gas flow rate, sample gas flushing time, and sample comprehensive flushing time.

[0062] The sample water flow rate is the water flow rate used to flush the filter element, which usually affects the cleaning effect and water consumption; the sample water flushing time is the duration of the water flow during the backwashing process, which determines the length of the cleaning process; the sample gas flow rate is the gas flow rate used for auxiliary flushing, which is usually used to accelerate the removal of impurities; the sample gas flushing time is the duration of the gas flow, which cooperates with the water flushing; the sample comprehensive flushing is the duration of the gas flow and water flow to flush the filter element synchronously using the sample water flow rate and the sample gas flow rate.

[0063] Furthermore, the sample flushing consumption is composed of sample backwashing energy consumption, sample element life loss and sample water consumption.

[0064] During the backwashing operation, certain resources will be consumed, including energy consumption, life loss of filter elements, and water resource consumption. Among them, the sample backwashing energy consumption refers to the electrical energy or other forms of energy consumed during the backwashing process, and the amount of energy consumption directly affects the economy of backwashing; the life loss of the sample element refers to the wear and tear of the filter element caused by repeated flushing during each backwashing process, which shortens its service life. The lower the life shortening rate of the filter element, the higher the adaptability; the sample water consumption refers to the amount of water consumed during the backwashing process. Reducing water consumption is an important goal of optimizing the backwashing operation.

[0065] In summary, the automatic backwash control method for the inlet of the filter station provided in the embodiment of the present application has the following technical effects: Interactively obtain the liquid characteristic parameters and filtration requirement parameters of the liquid to be filtered, where the liquid characteristic parameters include impurity component information and filtration volume information, and the filtration requirement parameters include filtration time constraints and filtration accuracy constraints. These data provide an important basis for subsequent filtration operations and effectively ensure the filtration quality; match the filter media according to the impurity component information and filtration accuracy constraints, select appropriate filter media and configure filter elements. Through precise medium selection and filter element configuration, it is ensured that the system can intercept impurities of different particle sizes. The matching of multi-layer filter media ensures effective interception of large particles to tiny particles, improves the accuracy of filtration, and at the same time, different filter layer structures can be designed according to different impurity components to flexibly respond to complex filtration requirements; according to the structural characteristics of the target filter element and the selection of filter media, initialize the filtration conditions, output the filtration pressure difference threshold and the maximum allowable blockage rate. These control constraints can ensure that the system is maintained within the service life of the filter element. Maintain its efficient operation; optimize the liquid inlet speed by filtering volume information and filtering time constraints, so that the filter station can automatically adjust the liquid inlet speed according to the needs of the task, ensure that the task is completed efficiently within the specified time, avoid time waste and unnecessary energy consumption, and at the same time, the optimized speed helps to reduce the fluctuation of pressure difference and the clogging speed of the filter element; analyze the clogging trend of the filter element through the liquid inlet speed, and optimize the backwashing control parameters. This step ensures that the backwashing is carried out at the best time by real-time monitoring of the clogging situation and pressure difference changes, while reducing unnecessary resource consumption and improving the overall energy efficiency of the system; finally, the intermittent automatic backwashing of the filter station is realized through the backwashing control parameters, and the filter element is automatically cleaned without affecting the filtration efficiency, ensuring the stability and continuity of the filter station in long-term operation. Since the backwashing process is automated, the need for human intervention is reduced, the risk of operating errors is reduced, and the reliability of the system is improved.

[0066] Based on the same inventive concept as the automatic backwashing control method for the filter station in the above-mentioned embodiment, Figure 2 As shown, an embodiment of the present application provides an automatic backwashing control system for a filter station, the system comprising: A liquid parameter acquisition module 10, the liquid parameter acquisition module 10 is used to interactively obtain liquid characteristic parameters and filtration requirement parameters of the liquid to be filtered, wherein the liquid characteristic parameters include impurity component information and filtration volume information, and the filtration requirement parameters include filtration time constraints and filtration accuracy constraints; a filter element configuration module 20, the filter element configuration module 20 is used to match the filter medium according to the impurity component information and filtration accuracy constraints, and after obtaining the filter medium selection, the filter element configuration of the filter station is performed based on the filter medium selection to obtain the target filter element; a condition initialization module 30, the condition initialization module 30 is used to interactively obtain the target structural characteristics of the target filter element, and initialize the filtration conditions according to the target structural characteristics and the filter medium selection, and output the initial filter Control constraints, wherein the initial filtration control constraints include a filtration pressure difference threshold and a maximum allowable blockage rate; a liquid inlet speed optimization module 40, wherein the liquid inlet speed optimization module 40 is used to use the initial filtration control constraints as optimization conditions, perform liquid inlet speed optimization according to the filtration volume information and the filtration time constraints, and locate the liquid inlet control speed; a control optimization module 50, wherein the control optimization module 50 is used to perform a blockage trend analysis of the target filter element according to the liquid inlet control speed, and perform backwash control optimization based on the analysis results to obtain backwash control parameters; an automatic backwash module 60, wherein the automatic backwash module 60 is used to use the backwash control parameters to perform intermittent automatic backwashing of the target filter element during the liquid inlet filtration process of the liquid to be filtered at the liquid inlet control speed at the filtration station.

[0067] Furthermore, the system further includes a filter medium selection acquisition module to perform the following operation steps: Based on the impurity type, the impurity component information is disassembled to obtain multiple impurity particle size distribution characteristics of multiple impurity types; the multiple impurity particle size distribution characteristics are updated according to the filtration accuracy constraint to obtain multiple filter particle size distribution characteristics; the multiple filter particle size distribution characteristics are integrated to obtain a global particle size distribution characteristic; based on the global particle size distribution characteristic, multi-stage filter media matching is performed to obtain a multi-layer filter element, wherein the multi-layer filter element has multiple filter scales, filter layer thicknesses and filter layer material identifiers; based on the multiple filter scales, multi-layer filter elements are serially assembled to obtain the filter medium selection.

[0068] Furthermore, the system further includes a maximum allowable congestion rate acquisition module to perform the following operation steps: Interactively obtain multiple element material information of the multi-layer filter element; calculate the filter area according to the target structural characteristics to obtain the effective filter area; perform network data call according to the multiple element material information and the effective filter area, and after obtaining multiple filter simulation models, obtain the filter element model by fusing the multiple filter simulation models; after performing local data call on the filter station to obtain the standard liquid inlet speed, use the standard liquid inlet speed to perform fluid mechanics simulation of the filter element model, output the structural pressure difference threshold, use the standard liquid inlet speed to perform fluid mechanics simulation of the multiple filter simulation models, and output multiple monomer pressure difference thresholds; solve the intersection of the structural pressure difference threshold and the multiple monomer pressure difference thresholds to obtain the filtration pressure difference threshold; use the maximum value of the filtration pressure difference threshold as a constraint to perform fluid mechanics simulation of the filter element model, and obtain the maximum allowable blockage rate in the filter element model call based on the simulation result.

[0069] Furthermore, the system further includes a liquid inlet control speed positioning module to perform the following operation steps: Pre-constructing a liquid inlet correlation prediction model; using 1 / K of the standard liquid inlet speed as a speed change scale to update the data of the standard liquid inlet speed to obtain a first standby liquid inlet speed; inputting the first standby liquid inlet speed into the liquid inlet correlation prediction model to obtain a first blockage rate time limit, a first filtration pressure difference time limit and a first predicted filtration efficiency; using the filtration volume information and the first predicted filtration efficiency to calculate and obtain a first predicted filtration time, if the first predicted filtration time satisfies the filtration time constraint, retaining the first standby liquid inlet speed, and using the first difference between the first blockage rate time limit and the first filtration pressure difference time limit as the first control reliability coefficient of the first standby liquid inlet speed; and so on, by continuously updating the standard liquid inlet speed, and using the liquid inlet correlation prediction model and the filtration time constraint to evaluate the update results, obtaining multiple standby liquid inlet speeds and multiple control reliability coefficients that meet the preset update frequency; serializing the multiple control reliability coefficients, and locating the liquid inlet control speed from the multiple standby liquid inlet speeds based on the sorting results.

[0070] Furthermore, the system also includes a liquid inlet correlation prediction model building module to perform the following operation steps: The filter medium selection and impurity component information are used as historical data call constraints to obtain historical liquid inlet related data, wherein the historical liquid inlet related data includes multiple sample liquid inlet speeds, multiple sample filtration efficiencies, multiple blockage rate change speeds and multiple filtration pressure difference change speeds; multiple filtration pressure difference change time limits are calculated based on the filtration pressure difference threshold and multiple filtration pressure difference change speeds; multiple blockage rate change time limits are calculated based on the maximum allowable blockage rate and multiple blockage rate change speeds; the multiple sample liquid inlet speeds, multiple sample filtration efficiencies, multiple filtration pressure difference time limits and multiple blockage rate change time limits are used as training constructions to train the liquid inlet related prediction model constructed based on the back propagation neural network.

[0071] Furthermore, the system further includes a backwash control parameter acquisition module to perform the following operation steps: Direction data is called for the liquid inlet control speed to obtain a control blockage rate time limit and a control filter pressure difference time limit; the smaller value is called by comparing the control blockage rate time limit and the control filter pressure difference time limit as a backwash cycle; a fitness weight configuration is predefined, and a fitness evaluation function is constructed based on the fitness weight configuration, wherein the fitness weight configuration includes energy consumption weight, life loss weight and water consumption weight; the initial filtration control constraint is used as a data call constraint to filter and call historical backwash records to obtain multiple sample backwash records, wherein each of the multiple sample backwash records includes a sample control parameter and a sample flushing consumption; the fitness evaluation function is used to evaluate the fitness of the multiple sample backwash records, and the initial control parameters are located based on the evaluation results, wherein the initial control parameters are composed of backwash water flow rate, backwash water flushing time, backwash gas flow rate, backwash gas flushing time and comprehensive flushing time; the backwash cycle is added to the initial control parameters to obtain the backwash control parameters.

[0072] Furthermore, the sample control parameters are composed of sample water flow rate, sample water flushing time, sample gas flow rate, sample gas flushing time, and sample comprehensive flushing time.

[0073] Furthermore, the sample flushing consumption is composed of sample backwashing energy consumption, sample element life loss and sample water consumption.

[0074] Through the above detailed description of the automatic backwashing control method for the inlet of the filter station, those skilled in the art can clearly understand the automatic backwashing control system for the inlet of the filter station in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description. The above description of the disclosed embodiments enables professionals and technicians in this field to implement or use the present application. Various modifications to these embodiments will be obvious to professionals and technicians in this field, and the general principles defined in this article can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown in this article, but will conform to the widest range consistent with the principles and novel features disclosed in this article.

Claims

1. A method for controlling the automatic backwashing of liquid inlet in a filtration station, characterized in that: The method comprises: Interactively obtaining liquid characteristic parameters and filtration requirement parameters of the liquid to be filtered, wherein the liquid characteristic parameters include impurity component information and filtration volume information, and the filtration requirement parameters include filtration time constraints and filtration accuracy constraints; Matching the filter medium according to the impurity component information and the filter accuracy constraint, after obtaining the filter medium selection, configuring the filter element of the filter station based on the filter medium selection to obtain the target filter element; Interactively obtaining the target structural characteristics of the target filter element, and initializing the filter conditions according to the target structural characteristics and the filter medium selection, and outputting the initial filter control constraints, wherein the initial filter control constraints include a filter pressure difference threshold and a maximum allowable blockage rate; Taking the initial filtration control constraint as an optimization condition, performing liquid inlet speed optimization according to the filtration volume information and the filtration time constraint, and locating the liquid inlet control speed; Performing a clogging trend analysis of the target filter element according to the liquid inlet control speed, and performing backwash control optimization based on the analysis result to obtain backwash control parameters; During the process of the filtration station performing the inlet filtration of the liquid to be filtered at the inlet control speed, the backwashing control parameter is used to perform intermittent automatic backwashing of the target filter element.

2. The automatic backwashing control method for a filter station according to claim 1, characterized in that: According to the impurity component information and the filtration accuracy constraint, filter medium matching is performed to obtain filter medium selection, and the method includes: Decomposing the impurity component information based on the impurity type to obtain multiple impurity particle size distribution characteristics of multiple impurity types; Update the plurality of impurity particle size distribution characteristics according to the filtration accuracy constraint to obtain a plurality of filtration particle size distribution characteristics; Fusion of the plurality of filtered particle size distribution features to obtain a global particle size distribution feature; Performing multi-stage filter media matching based on the global particle size distribution characteristics to obtain a multi-layer filter element, wherein the multi-layer filter element has multiple filter scales, filter layer thicknesses, and filter layer material identifiers; The multi-layer filter elements are serially assembled based on the multiple filter dimensions to obtain the filter medium selection.

3. The automatic backwashing control method for a filter station according to claim 2, characterized in that: Interactively obtaining the target structural features of the target filter element, initializing the filter conditions according to the target structural features and the filter medium selection, and outputting the initial filter control constraints, the method comprising: interactively obtaining multiple element material information of the multi-layer filter element; Calculate the filtration area according to the target structural characteristics to obtain an effective filtration area; After acquiring multiple filtration simulation models through online data call based on the multiple element material information and effective filtration areas, a filtration element model is acquired by fusing the multiple filtration simulation models; After local data of the filter station is called to obtain a standard liquid inlet speed, the standard liquid inlet speed is used to perform a fluid mechanics simulation of the filter element model, and a structural pressure difference threshold is output; the standard liquid inlet speed is used to perform a fluid mechanics simulation of the multiple filter simulation models, and multiple monomer pressure difference thresholds are output; Solving the intersection of the structural pressure difference threshold and multiple monomer pressure difference thresholds to obtain the filtering pressure difference threshold; Taking the maximum value of the filter pressure difference threshold as a constraint, a fluid mechanics simulation of the filter element model is performed, and based on the simulation result, the maximum allowable blockage rate is obtained by calling the filter element model.

4. The automatic backwashing control method for a filter station according to claim 3, characterized in that: The initial filtration control constraint is used as an optimization condition, and the liquid inlet speed optimization is performed according to the filtration volume information and the filtration time constraint to locate the liquid inlet control speed. The method includes: Pre-built liquid inlet correlation prediction model; Using 1 / K of the standard liquid inlet speed as a speed change scale to update the data of the standard liquid inlet speed, and obtaining a first standby liquid inlet speed; Inputting the first standby liquid inlet speed into a liquid inlet correlation prediction model to obtain a first blockage rate time limit, a first filtration pressure difference time limit and a first predicted filtration efficiency; The first predicted filtration time is calculated by using the filtration volume information and the first predicted filtration efficiency. If the first predicted filtration time satisfies the filtration time constraint, the first standby liquid inlet speed is retained, and a first difference between the first blockage rate time limit and the first filtration pressure difference time limit is used as a first control reliability coefficient of the first standby liquid inlet speed. By analogy, by continuously updating the standard liquid inlet speed and using the liquid inlet correlation prediction model and the filtering time constraint to evaluate the update result, a plurality of backup liquid inlet speeds and a plurality of control reliability coefficients that meet the preset update frequency are obtained; The plurality of control reliability coefficients are sequenced, and the fluid intake control speed is located from the plurality of backup fluid intake speeds based on the sequencing result.

5. The automatic backwashing control method for a filter station according to claim 4, characterized in that: Pre-constructing a liquid inlet correlation prediction model, the method comprising: The filter medium selection and impurity component information are used as historical data call constraints to obtain historical liquid inlet associated data, wherein the historical liquid inlet associated data includes multiple sample liquid inlet speeds, multiple sample filtration efficiencies, multiple blockage rate change speeds, and multiple filter pressure difference change speeds; Calculate and obtain multiple filter pressure difference change time limits according to the filter pressure difference threshold and multiple filter pressure difference change speeds; Calculate and obtain multiple congestion rate change time limits according to the maximum allowable congestion rate and multiple congestion rate change speeds; The multiple sample liquid inlet speeds, multiple sample filtration efficiencies, multiple filtration pressure difference time limits and multiple blockage rate change time limits are used as training structures to train the liquid inlet association prediction model constructed based on a back propagation neural network.

6. The automatic backwashing control method for a filter station according to claim 4, characterized in that: The clogging trend of the target filter element is analyzed according to the liquid inlet control speed, and backwash control optimization is performed based on the analysis result to obtain backwash control parameters. The method includes: Direction data of the liquid inlet control speed is called to obtain a control blockage rate time limit and a control filtration pressure difference time limit; By comparing the control blockage rate time limit and the control filtration pressure difference time limit, a smaller value is called as the backwashing cycle; Predefine a fitness weight configuration, and construct a fitness evaluation function based on the fitness weight configuration, wherein the fitness weight configuration includes an energy consumption weight, a life loss weight, and a water consumption weight; Using the initial filtering control constraint as a data calling constraint to perform screening calling of historical backwashing records, a plurality of sample backwashing records are obtained, wherein each of the plurality of sample backwashing records includes a sample control parameter and a sample flushing consumption; Using the fitness evaluation function to perform fitness evaluation on the plurality of sample backwash records, and positioning initial control parameters based on the evaluation results, wherein the initial control parameters are composed of backwash water flow rate, backwash water flushing time, backwash gas flow rate, backwash gas flushing time and comprehensive flushing time; The backwashing period is added to the initial control parameters to obtain the backwashing control parameters.

7. The automatic backwashing control method for a filter station according to claim 6, characterized in that: The sample control parameters are composed of sample water flow rate, sample water flushing time, sample gas flow rate, sample gas flushing time, and sample comprehensive flushing time.

8. The automatic backwashing control method for a filter station according to claim 7, characterized in that: The sample flushing consumption is composed of sample backwashing energy consumption, sample element life loss and sample water consumption.

9. Automatic backwashing control system for liquid inlet in the filtration station, characterized in that: The system is used to implement the automatic backwashing control method for liquid inlet of a filtration station according to any one of claims 1 to 8, the system comprising: A liquid parameter acquisition module, the liquid parameter acquisition module is used to interactively obtain liquid characteristic parameters and filtration requirement parameters of the liquid to be filtered, wherein the liquid characteristic parameters include impurity component information and filtration volume information, and the filtration requirement parameters include filtration time constraints and filtration accuracy constraints; A filter element configuration module, the filter element configuration module is used to match the filter medium according to the impurity component information and the filter accuracy constraint, and after obtaining the filter medium selection, obtain the target filter element by configuring the filter element of the filter station based on the filter medium selection; A condition initialization module, the condition initialization module is used to interactively obtain the target structural characteristics of the target filter element, and initialize the filter conditions according to the target structural characteristics and the filter medium selection, and output the initial filter control constraints, wherein the initial filter control constraints include a filter pressure difference threshold and a maximum allowable blockage rate; A liquid inlet speed optimization module, wherein the liquid inlet speed optimization module is used to use the initial filtration control constraint as an optimization condition, perform liquid inlet speed optimization according to the filtration volume information and the filtration time constraint, and locate the liquid inlet control speed; A control optimization module, the control optimization module is used to analyze the clogging trend of the target filter element according to the liquid inlet control speed, and perform backwash control optimization based on the analysis result to obtain backwash control parameters; an automatic backwash module, the automatic backwash module is used to use the backwash control parameters to perform intermittent automatic backwashing of the target filter element during the liquid inlet filtration process of the liquid to be filtered at the liquid inlet control speed at the filtration station.

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