Intelligent core replacement control method and device based on filter core life analysis

Abstract

The invention relates to the technical field of filter elements, and discloses an intelligent element replacement control method and device based on filter element life analysis, the method comprises the following steps: determining first multi-dimensional use information of a target filter element, the first multi-dimensional use information comprising at least two of use cycle information, water quality information, use pressure information and standing cycle information; analyzing service life information of the target filter element according to the first multi-dimensional use information; judging whether the target filter element meets a preset aging state replacement condition or not according to the service life information; when it is judged that the target filter element meets the preset aging state replacement condition, an aging factor of the target filter element is determined, and the aging factor is used for representing a factor enabling the target filter element to meet the preset aging state replacement condition; and according to the aging factor, generating a core changing control parameter of the target filter core. Therefore, the accuracy and timeliness of core changing control can be improved by improving the comprehensiveness and accuracy of core changing control analysis.

Description

Technical Field

[0001] This invention relates to the field of filter technology, and in particular to an intelligent filter replacement control method and device based on filter life analysis. Background Technology

[0002] In the field of filter cartridge life management, traditional cartridge replacement control methods mainly rely on operating time thresholds as the basis for replacement judgment. Specifically, when the actual cumulative operating time of the filter cartridge reaches the preset fixed replacement cycle, a cartridge replacement reminder is triggered or a replacement operation is performed. However, in practice, it has been found that even when the actual cumulative operating time of the filter cartridge has not reached the preset fixed replacement cycle, there are still situations where the filter cartridge's filtration effect is poor and it needs to be replaced. In this case, if the user cannot visually detect the poor water quality filtered by the cartridge in time and continues to use the cartridge that is about to be replaced, it will pose a threat to the user's health.

[0003] Therefore, it is particularly important to propose a technical solution that improves the accuracy and timeliness of core replacement control by enhancing the comprehensiveness and accuracy of core replacement control analysis. Summary of the Invention

[0004] This invention provides an intelligent filter replacement control method and device based on filter life analysis, which can improve the accuracy and timeliness of filter replacement control by improving the comprehensiveness and accuracy of filter replacement control analysis.

[0005] To address the aforementioned technical problems, the first aspect of this invention discloses an intelligent filter replacement control method based on filter life analysis, the method comprising: Determine the first multi-dimensional usage information of the target filter element, which includes at least two of the following: usage cycle information, water quality information, usage pressure information, and settling cycle information. Based on the first multi-dimensional usage information, analyze the lifespan information of the target filter element; Based on the lifespan information, determine whether the target filter element meets the preset aging condition replacement conditions; When it is determined that the target filter element meets the preset aging condition replacement conditions, the aging factor of the target filter element is determined. The aging factor is used to represent the factors that make the target filter element meet the preset aging condition replacement conditions. Based on the aging factor, the replacement control parameters for the target filter element are generated.

[0006] As an optional implementation, in a first aspect of the present invention, the target filter element includes functional components, the functional components including at least a sealing component and a filtering component, and the step of analyzing the lifespan information of the target filter element based on the first multi-dimensional usage information includes: For each of the functional components, a second multi-dimensional usage information corresponding to the functional component is determined. The second multi-dimensional usage information includes at least two of the following: deformation degree information, elastic decay state information, deposit density information, deposit type information, deposit corrosion information, and micropore expansion state information. The first multi-dimensional usage information is used to determine the main dimension field; The second multi-dimensional usage information is identified as a sub-dimensional field; By aligning the collection nodes of the main dimension field and the sub-dimensional field with the timestamps, a dimension fusion matrix is ​​generated; The dimensional fusion matrix is ​​fused to obtain the coupling analysis results; Based on the coupling analysis results, a lifespan decay model is constructed, and the lifespan information of the target filter element is output.

[0007] As an optional implementation, in the first aspect of the present invention, the step of performing fusion calculation on the dimensional fusion matrix to obtain the coupling analysis result includes: Based on the main dimension field, the main dimension attenuation increment is calculated, which includes at least the main dimension chemical erosion attenuation increment and the main dimension mechanical stress attenuation increment. Based on the sub-dimension field, the sub-dimension coupling contribution increment is calculated, and the sub-dimension coupling contribution increment includes at least the sealing elasticity attenuation increment and the filter structure degradation increment. Based on the main dimension field and the sub-dimension field, the dimension cross-influence compensation increment is calculated. The dimension cross-influence compensation increment is used to represent the attenuation of the combined effect of the cross-effect region of the main dimension field and the sub-dimension field. The coupling analysis results are generated based on the main dimension decay increment, the sub-dimension coupling contribution increment, and the dimension cross-influence compensation increment.

[0008] As an optional implementation, in the first aspect of the present invention, the step of constructing a lifetime decay model based on the coupling analysis results and outputting the lifetime information of the target filter element includes: Based on the coupling analysis results, a formula framework for the life decay function is defined, which is used to represent the mathematical structure of filter performance degradation over time. Based on the formula framework, a decay trajectory simulation is performed to obtain the decay trajectory simulation results. The decay trajectory simulation is used to predict the future change path of filter performance, specifically including the decay rate and the failure critical point. Based on the attenuation trajectory simulation results, the lifespan information of the target filter element is generated. The lifespan information is used to represent the overall lifespan status of the target filter element, and the lifespan information includes the remaining predicted lifespan information and the failure risk level.

[0009] As an optional implementation, in the first aspect of the present invention, determining whether the target filter element meets the preset aging condition replacement conditions based on the lifespan information includes: Based on the lifetime information, the lifetime consumption threshold and failure risk threshold are analyzed; The remaining predicted lifetime information is dynamically compared with the lifetime consumption threshold to generate a lifetime consumption status. The failure risk level is mapped and matched with the failure risk threshold to generate a risk trigger state; Based on the logical combination of the lifespan consumption state and the risk triggering state, determine whether the target filter element meets the preset aging state replacement conditions; Wherein, the life consumption threshold is used to represent the failure boundary of the remaining life of the filter element, the failure risk threshold is used to represent the upper limit of the failure probability of the filter element, and the logical combination relationship is used to define the joint judgment rule of life consumption and risk triggering.

[0010] As an optional implementation, in the first aspect of the present invention, determining the aging factor of the target filter element includes: Extract the dominant dimension field that triggers the aging state replacement condition. The dominant dimension field is used to represent the dimension category that contributes the most to the lifespan decay. Based on the main dimension attenuation increment, sub-dimension coupling contribution increment, and dimension cross-influence compensation increment in the coupling analysis results, the aging action path is located. Based on the aging pathway, the dominant aging factor and the synergistic aging factor are separated, and the dominant aging factor and the synergistic aging factor are the aging factors of the target filter element. Wherein, the dominant aging factor is used to represent the factor that directly leads to the triggering of the lifespan threshold, and the dominant aging factor includes at least one of the dominant chemical erosion factor, the dominant mechanical stress factor, and the dominant microstructure deterioration factor; the synergistic aging factor is used to represent the auxiliary factor that accelerates the effect of the dominant factor, and the synergistic aging factor includes at least one of the synergistic water quality fluctuation factor and the synergistic pressure transient factor.

[0011] As an optional implementation, in the first aspect of the present invention, generating the replacement control parameters for the target filter element based on the aging factor includes: Based on the type of the dominant aging factor, a matching time control parameter generation rule is established; Calculate the compensation coefficient for the operating control parameters based on the strength of the synergistic aging factor; Based on the time control parameter generation rules and operation control parameter compensation coefficients, the replacement control parameters for the target filter element are generated. The time control parameter generation rule is used to define the execution time window of the core replacement operation, and the time control parameter generation rule includes at least one of emergency core replacement time threshold and gradual core replacement time gradient; the operation control parameter compensation coefficient is used to define the core replacement execution mode, and the operation control parameter compensation coefficient includes at least one of pressure adjustment amplitude and flow control curve.

[0012] A second aspect of this invention discloses an intelligent filter replacement control device based on filter life analysis, the device comprising: The determination module is used to determine the first multi-dimensional usage information of the target filter element, wherein the first multi-dimensional usage information includes at least two of the following: usage cycle information, water quality information, usage pressure information, and settling cycle information. The analysis module is used to analyze the lifespan information of the target filter element based on the first multi-dimensional usage information; The judgment module is used to determine whether the target filter element meets the preset aging condition replacement conditions based on the lifespan information. The determining module is further configured to determine the aging factor of the target filter element when the judging module determines that the target filter element meets the preset aging state replacement conditions. The aging factor is used to represent the factors that make the target filter element meet the preset aging state replacement conditions. The generation module is used to generate the replacement control parameters for the target filter element based on the aging factor.

[0013] As an optional implementation, in a second aspect of the present invention, the target filter element includes functional components, the functional components including at least a sealing component and a filtering component, and the specific method by which the analysis module analyzes the lifespan information of the target filter element based on the first multi-dimensional usage information includes: For each of the functional components, a second multi-dimensional usage information corresponding to the functional component is determined. The second multi-dimensional usage information includes at least two of the following: deformation degree information, elastic decay state information, deposit density information, deposit type information, deposit corrosion information, and micropore expansion state information. The first multi-dimensional usage information is used to determine the main dimension field; The second multi-dimensional usage information is identified as a sub-dimensional field; By aligning the collection nodes of the main dimension field and the sub-dimensional field with the timestamps, a dimension fusion matrix is ​​generated; The dimensional fusion matrix is ​​fused to obtain the coupling analysis results; Based on the coupling analysis results, a lifespan decay model is constructed, and the lifespan information of the target filter element is output.

[0014] As an optional implementation, in the second aspect of the present invention, the specific method by which the analysis module performs fusion calculation on the dimensional fusion matrix to obtain the coupling analysis result includes: Based on the main dimension field, the main dimension attenuation increment is calculated, which includes at least the main dimension chemical erosion attenuation increment and the main dimension mechanical stress attenuation increment. Based on the sub-dimension field, the sub-dimension coupling contribution increment is calculated, and the sub-dimension coupling contribution increment includes at least the sealing elasticity attenuation increment and the filter structure degradation increment. Based on the main dimension field and the sub-dimension field, the dimension cross-influence compensation increment is calculated. The dimension cross-influence compensation increment is used to represent the attenuation of the combined effect of the cross-effect region of the main dimension field and the sub-dimension field. The coupling analysis results are generated based on the main dimension decay increment, the sub-dimension coupling contribution increment, and the dimension cross-influence compensation increment.

[0015] As an optional implementation, in the second aspect of the present invention, the analysis module constructs a lifespan decay model based on the coupling analysis results and outputs the lifespan information of the target filter element in the following specific ways: Based on the coupling analysis results, a formula framework for the life decay function is defined, which is used to represent the mathematical structure of filter performance degradation over time. Based on the formula framework, a decay trajectory simulation is performed to obtain the decay trajectory simulation results. The decay trajectory simulation is used to predict the future change path of filter performance, specifically including the decay rate and the failure critical point. Based on the attenuation trajectory simulation results, the lifespan information of the target filter element is generated. The lifespan information is used to represent the overall lifespan status of the target filter element, and the lifespan information includes the remaining predicted lifespan information and the failure risk level.

[0016] As an optional implementation, in the second aspect of the present invention, the specific method by which the determining module determines whether the target filter element meets the preset aging state replacement conditions based on the lifespan information includes: Based on the lifetime information, the lifetime consumption threshold and failure risk threshold are analyzed; The remaining predicted lifetime information is dynamically compared with the lifetime consumption threshold to generate a lifetime consumption status. The failure risk level is mapped and matched with the failure risk threshold to generate a risk trigger state; Based on the logical combination of the lifespan consumption state and the risk triggering state, determine whether the target filter element meets the preset aging state replacement conditions; Wherein, the life consumption threshold is used to represent the failure boundary of the remaining life of the filter element, the failure risk threshold is used to represent the upper limit of the failure probability of the filter element, and the logical combination relationship is used to define the joint judgment rule of life consumption and risk triggering.

[0017] As an optional implementation, in a second aspect of the present invention, the determining module determines the aging factor of the target filter element in the following specific manner: Extract the dominant dimension field that triggers the aging state replacement condition. The dominant dimension field is used to represent the dimension category that contributes the most to the lifespan decay. Based on the main dimension attenuation increment, sub-dimension coupling contribution increment, and dimension cross-influence compensation increment in the coupling analysis results, the aging action path is located. Based on the aging pathway, the dominant aging factor and the synergistic aging factor are separated, and the dominant aging factor and the synergistic aging factor are the aging factors of the target filter element. Wherein, the dominant aging factor is used to represent the factor that directly leads to the triggering of the lifespan threshold, and the dominant aging factor includes at least one of the dominant chemical erosion factor, the dominant mechanical stress factor, and the dominant microstructure deterioration factor; the synergistic aging factor is used to represent the auxiliary factor that accelerates the effect of the dominant factor, and the synergistic aging factor includes at least one of the synergistic water quality fluctuation factor and the synergistic pressure transient factor.

[0018] As an optional implementation, in the second aspect of the present invention, the specific method by which the generation module generates the replacement control parameters of the target filter element based on the aging factor includes: Based on the type of the dominant aging factor, a matching time control parameter generation rule is established; Calculate the compensation coefficient for the operating control parameters based on the strength of the synergistic aging factor; Based on the time control parameter generation rules and operation control parameter compensation coefficients, the replacement control parameters for the target filter element are generated. The time control parameter generation rule is used to define the execution time window of the core replacement operation, and the time control parameter generation rule includes at least one of emergency core replacement time threshold and gradual core replacement time gradient; the operation control parameter compensation coefficient is used to define the core replacement execution mode, and the operation control parameter compensation coefficient includes at least one of pressure adjustment amplitude and flow control curve.

[0019] A third aspect of this invention discloses another intelligent filter replacement control device based on filter life analysis, the device comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the intelligent filter replacement control method based on filter life analysis disclosed in the first aspect of the present invention.

[0020] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute the intelligent filter replacement control method based on filter life analysis disclosed in the first aspect of the present invention.

[0021] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: In this embodiment of the invention, a first multi-dimensional usage information of the target filter element is determined. The first multi-dimensional usage information includes at least two of the following: usage cycle information, water quality information, usage pressure information, and settling cycle information. Based on the first multi-dimensional usage information, the lifespan information of the target filter element is analyzed. Based on the lifespan information, it is determined whether the target filter element meets the preset aging state replacement conditions. When it is determined that the target filter element meets the preset aging state replacement conditions, the aging factor of the target filter element is determined. The aging factor is used to represent the factors that cause the target filter element to meet the preset aging state replacement conditions. Based on the aging factor, the replacement control parameters of the target filter element are generated. It is evident that implementing this invention can cover multiple causes of filter element aging, such as time decay, chemical corrosion, and mechanical fatigue, by collecting multi-dimensional usage information. This improves the comprehensiveness of lifespan assessment, helps avoid misjudgments caused by a single time dimension, and enables multi-factor collaborative monitoring of filter element status. By locating aging factors and generating replacement parameters, specific failure causes (such as sudden changes in water quality or pressure shocks) can be identified, thereby improving the targeting of replacement decisions. This facilitates dynamic adjustment of control strategies based on the main causes of aging, achieving closed-loop control from failure diagnosis to strategy execution. This enhances the comprehensiveness and accuracy of replacement control analysis, and improves the accuracy and timeliness of replacement control. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of an intelligent filter replacement control scenario based on filter life analysis disclosed in an embodiment of the present invention; Figure 2 This is a flowchart illustrating an intelligent filter replacement control method based on filter life analysis disclosed in an embodiment of the present invention. Figure 3This is a flowchart illustrating another intelligent filter replacement control method based on filter life analysis disclosed in an embodiment of the present invention. Figure 4 This is a schematic diagram of the structure of an intelligent filter replacement control device based on filter life analysis disclosed in an embodiment of the present invention; Figure 5 This is a schematic diagram of another intelligent filter replacement control device based on filter life analysis disclosed in an embodiment of the present invention. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present invention, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0026] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0027] This invention discloses an intelligent filter cartridge replacement control method and device based on filter cartridge lifespan analysis. By collecting multi-dimensional usage information, it can cover various causes of filter cartridge aging, including time decay, chemical corrosion, and mechanical fatigue, thereby improving the comprehensiveness of lifespan assessment and avoiding misjudgments caused by a single time dimension. It enables multi-factor collaborative monitoring of filter cartridge status. Through aging factor localization and filter cartridge replacement parameter generation, it can identify specific failure causes (such as sudden water quality changes or pressure shocks), thereby improving the targeting of filter cartridge replacement decisions. This facilitates dynamic adjustment of control strategies based on the main causes of aging, achieving closed-loop control from failure diagnosis to strategy execution, thus improving the comprehensiveness and accuracy of filter cartridge replacement control analysis, and enhancing the accuracy and timeliness of filter cartridge replacement control. Detailed descriptions follow.

[0028] To better understand the intelligent filter replacement control method and device based on filter life analysis described in this invention, the applicable scenario for the intelligent filter replacement control method based on filter life analysis is first described. Specifically, the schematic diagram of this scenario can be shown as follows: Figure 1 As shown, the scenario includes a water purifier containing a filter cartridge and a mobile terminal. The water purifier's visual interface displays "Filter cartridge life monitoring in progress," while the mobile terminal's visual interface displays a cartridge replacement reminder. Figure 1 As shown, during the lifespan monitoring of filter cartridges in a water purifier, the following steps can be taken: First multi-dimensional usage information of the target filter cartridge can be determined. This first multi-dimensional usage information includes at least two of the following: usage cycle information, water quality information, usage pressure information, and settling cycle information. Based on this first multi-dimensional usage information, the lifespan information of the target filter cartridge is analyzed. Based on the lifespan information, it is determined whether the target filter cartridge meets the preset aging state replacement conditions. When it is determined that the target filter cartridge meets the preset aging state replacement conditions, the aging factor of the target filter cartridge is determined. The aging factor represents the factors that cause the target filter cartridge to meet the preset aging state replacement conditions. Based on the aging factor, the replacement control parameters of the target filter cartridge are generated. It should be noted that, Figure 1 The schematic diagram shown is only intended to illustrate a scenario where an intelligent filter replacement control method based on filter life analysis is applicable. The water purifiers and mobile terminals involved are also only for illustrative purposes. Figure 1 The scenario diagram shown is not limited to this. The following is a detailed description of an intelligent filter replacement control method and device based on filter life analysis.

[0029] Example 1 Please see Figure 2 , Figure 2 This is a flowchart illustrating an intelligent filter replacement control method based on filter life analysis disclosed in an embodiment of the present invention. Figure 2The described intelligent filter replacement control method based on filter life analysis can be applied to filter elements, and also to intelligent devices associated with the filter elements. These intelligent devices include, but are not limited to, one or more of smart home devices, battery devices, cloud devices, edge computing devices, relay devices, base station devices, urban management devices, and intelligent connected devices. This invention does not limit the scope of these applications. Figure 2 As shown, this intelligent filter replacement control method based on filter life analysis can include the following operations: 101. Determine the first multi-dimensional usage information of the target filter element, which includes at least two of the following: usage cycle information, water quality information, usage pressure information, and settling cycle information; In this embodiment of the invention, optionally, the first multi-dimensional usage information is determined: Usage cycle information: The cumulative running time of the filter element (in hours) is recorded by a timer, indicating the degree of aging over time.

[0030] Water quality information: The concentration of pollutants in the source water (such as TDS value and turbidity), pH value, and heavy metal ion content are collected by water quality sensors to indicate the intensity of chemical erosion.

[0031] Pressure information: The pressure sensor monitors the pressure fluctuation range and frequency at the filter element inlet, indicating the mechanical stress load.

[0032] Settlement period information: The number of consecutive days the filter element remains unused is recorded by the shutdown detection module, indicating the degree of natural aging of the material. Execution logic: At least two types of data mentioned above are periodically collected and stored as a time-series dataset.

[0033] Furthermore, optionally, the first multi-dimensional usage information may also include: Temperature field information characterizes the distribution of thermal stress inside the filter element; acquisition method: three sets of thermocouples (inlet / middle / outlet) are arranged axially in the filter element shell; monitoring temperature gradient ΔT (°C / cm) and number of thermal cycles; function: high temperature zone (>60°C) → accelerates the oxidation and embrittlement of polymer materials, temperature fluctuation (ΔT>15°C) → induces thermal fatigue cracks; Fluid dynamics information characterizes the scouring / deposition effect of water flow on the filter element; acquisition method: ultrasonic flow meter acquires instantaneous flow fluctuation variance, particle image velocimeter (PIV) captures local velocity distribution; function: high flow variance → accelerates fretting wear of sealing components, low-velocity dead zone → early warning of pollutant deposition; Ion load information quantifies the chemical attack intensity of dissolved substances on filter media; Acquisition method: periodic detection of ion concentration (ppm) by ion chromatograph, and real-time monitoring of the total ion change rate (μS / cm·h) by conductivity sensor; Effects: High calcium and magnesium ions → lead to fouling and pore blockage of filter membrane, excessive chloride ions → induce stress corrosion of stainless steel components. Further, optional, multi-dimensional collaborative analysis logic, such as cross-dimensional failure scenario mapping: Filter cartridge bursting: Pressure spectrum + temperature field + deformation degree, high-frequency pressure amplitude > threshold ∩ temperature > 70℃ ∩ deformation rate > 15% → triggers brittle fracture warning; Biofouling: Settling period + humidity + type of deposits. Settling > 7 days ∩ Humidity > 75% ∩ Extracellular polymeric substances (EPS) detected → Activate biofilm cleaning command; 102. Based on the first multi-dimensional usage information, analyze the lifespan information of the target filter element; Optionally, multi-dimensional data can be input into the life assessment engine, and a composite life decay index can be calculated using a weighted fusion algorithm (such as the entropy weight method) to quantify the overall performance degradation of the filter element.

[0034] In this embodiment of the invention, as an optional implementation, the target filter element includes functional components, which at least include a sealing component and a filtering component. Based on the first multi-dimensional usage information, the lifespan information of the target filter element is analyzed, including: For each functional component, determine the second multi-dimensional usage information corresponding to that functional component. The second multi-dimensional usage information includes at least two of the following: deformation degree information, elastic decay state information, deposit density information, deposit type information, deposit corrosion information, and micropore expansion state information. The first multi-dimensional information is used to determine the primary dimension field; The second multi-dimensional information is identified as a sub-dimensional field; By aligning the collection nodes of the main dimension field and the sub-dimensional field with the timestamp, a dimension fusion matrix is ​​generated; The dimensional fusion matrix is ​​fused and calculated to obtain the coupling analysis results; Based on the coupling analysis results, a lifespan decay model is constructed, and the lifespan information of the target filter element is output.

[0035] In this embodiment of the invention, optionally, for determining the second multi-dimensional usage information: Sealing component level: Deformation information: The radial compression deformation rate (%) of the sealing ring is measured using a laser rangefinder.

[0036] Elastic decay status information: The elastic modulus decay rate (%) is obtained by stress relaxation tester.

[0037] At the filter component level: Adhesion density information: The contaminant coverage (%) on the filter membrane surface is calculated using X-ray imaging.

[0038] Information on microscopic pore expansion: Analysis of the average pore size expansion rate (%) using electron microscope images.

[0039] For the construction of the dimension fusion matrix: Align the main dimension (such as water quality information) with the sub-dimension (such as attachment density) collection time points: When the water quality TDS value is collected at time t1, the attachment density data at time t1 is extracted synchronously.

[0040] Generate matrix rows: timestamp, main dimension field, sub-dimension field.

[0041] For the fusion calculation and lifetime decay model: Principal component analysis (PCA) is performed on the matrix to extract key decay eigenvectors.

[0042] Construct a multiple regression model: Remaining lifetime rate = f(eigenvector1, eigenvector2). Output: Remaining lifetime percentage and failure probability value.

[0043] As can be seen, implementing this optional embodiment can distinguish the independent degradation characteristics of sealing components and filtration components through detailed monitoring of functional components, thereby improving the precision of lifetime analysis, which is conducive to locating component-level failure foci and achieving a penetrating assessment from overall lifetime to local performance. Through the fusion of principal-sub-dimension fields, it is possible to establish the correlation between macroscopic operating conditions (such as water quality) and microscopic states (such as pore expansion), thereby enhancing the interpretability of the degradation mechanism, which is conducive to revealing the causal chain in the complex aging process and supporting cross-scale lifetime degradation modeling.

[0044] In this optional embodiment, as an optional implementation, the above-mentioned fusion calculation of the dimension fusion matrix to obtain the coupling analysis result includes: Based on the main dimension field, calculate the main dimension attenuation increment, which includes at least the main dimension chemical erosion attenuation increment and the main dimension mechanical stress attenuation increment. Based on the sub-dimension fields, calculate the sub-dimension coupling contribution increment, which includes at least the sealing elasticity attenuation increment and the filter structure degradation increment. Based on the main dimension field and the sub-dimension field, the dimension cross-influence compensation increment is calculated. The dimension cross-influence compensation increment is used to represent the combined effect attenuation of the cross-effect region of the main dimension field and the sub-dimension field. The coupling analysis results are generated based on the main dimension decay increment, the sub-dimension coupling contribution increment, and the dimension cross-influence compensation increment.

[0045] In this embodiment of the invention, optionally, the calculation of the decay increment for the main dimension is as follows: Chemical corrosion attenuation increment: Based on water pH and heavy metal content, the increase in rust thickness (μm / hour) of metal parts is calculated according to the corrosion rate formula.

[0046] Mechanical stress attenuation increment: The crack propagation rate (millimeters per hour) of the sealing component is calculated based on the pressure fluctuation amplitude and the fatigue cumulative damage model.

[0047] For the incremental contribution of sub-dimensional coupling: Seal elastic decay increment: Input the deformation degree and elastic decay state data into the Hooke's law correction model, and output the seal failure rate coefficient.

[0048] Filter structure degradation increment: Combining the data on deposit density and pore expansion, the filtration efficiency decay rate (% / hour) is calculated using Darcy's law.

[0049] For the incremental compensation for the cross-dimensional effect: Calculate the principal-sub-dimension interaction: for example, high pressure (principal dimension) accelerates pore expansion (sub-dimension), and quantify the acceleration factor through covariance analysis.

[0050] Regarding the generation of coupling analysis results: Output triples: chemical erosion increment, mechanical stress increment, and cross-compensation value.

[0051] As can be seen, implementing this optional embodiment can decouple independent contributions and interactions in complex attenuation scenarios by using three types of incremental separation calculations: the main dimension attenuation increment to quantify the effect of environmental stress, the sub-dimensional coupling contribution increment to quantify the component's own degradation, and the dimension cross-influence compensation increment to quantify the synergistic effect of multiple factors. This reduces the evaluation bias caused by multi-source interference and achieves accurate attribution of attenuation drivers.

[0052] In this optional embodiment, as another optional implementation, the above-mentioned construction of a lifespan decay model based on the coupling analysis results, and output of the target filter element's lifespan information, includes: Based on the results of the coupling analysis, a formula framework for the life decay function is defined, which is used to represent the mathematical structure of filter performance degradation over time. Based on the formula framework, the attenuation trajectory simulation is performed to obtain the attenuation trajectory simulation results. The attenuation trajectory simulation is used to predict the future change path of filter performance, specifically including the attenuation rate and the failure critical point. Based on the attenuation trajectory simulation results, the lifespan information of the target filter element is generated. The lifespan information is used to represent the overall lifespan status of the target filter element, including the remaining predicted lifespan information and the failure risk level.

[0053] In this embodiment of the invention, optionally, the formula framework may specifically include a decay basis function and a dynamic weighting coefficient; the decay basis function is used to represent the inherent decay characteristics of the filter element and is generated through an exponential decay model or a linear combination model; the dynamic weighting coefficient is used to represent the relative importance of multi-dimensional influences and is obtained through principal component analysis or feature weighting algorithm, with the input being the principal dimension decay increment, sub-dimensional coupling contribution increment, and dimension cross-influence compensation increment in the coupling analysis results. Further optional, the decay rate is used to represent the performance degradation trend and is obtained by time derivative calculation; the failure critical point is used to represent the estimated time when the performance degrades to an unusable state and is calculated by numerical integration or iterative approximation algorithm, with the output of the formula framework as the input. Further optionally, the remaining predicted lifespan information is used to quantify the available lifespan of the target filter element, obtained through normalization; the failure risk level is used to identify the probability of sudden failure, calculated through a probability distribution model. Further optional, for defining the lifetime decay function framework: We adopt the Weibull distribution function framework: R(t)=exp(-(t / η)^β), where η is the characteristic lifetime parameter and β is the failure mode parameter.

[0054] For decay trajectory simulation: Monte Carlo simulation steps: Based on the current coupling analysis results, set the initial values ​​for η and β.

[0055] Inject random disturbances (simulate water quality fluctuations and pressure shocks).

[0056] Iteratively calculate the performance degradation curves at future time points. Output: Predicted failure time points and 95% confidence intervals.

[0057] For generating lifetime information: Remaining predicted lifetime = predicted failure time - current time.

[0058] Failure risk level: classified according to confidence interval width (interval width > 50 hours → low risk, < 10 hours → high risk).

[0059] As can be seen, implementing this optional embodiment can predict performance degradation paths (such as accelerated failure thresholds) through attenuation trajectory simulation, thereby extending the life warning time window, which is conducive to planning maintenance resources in advance and changing passive replacement to proactive life management; by outputting synthetic life indicators, the remaining predicted life can quantify the available time, and the failure risk level can quantify the probability of sudden failure, which can take into account the dual risks of gradual aging and sudden failure, thereby improving the robustness of replacement condition judgment.

[0060] 103. Based on the lifespan information, determine whether the target filter element meets the preset aging condition replacement conditions; In this embodiment of the invention, optionally, the composite lifespan decay index is compared with a preset threshold: if the index ≥ the threshold, it is determined that the replacement condition is met. Logic: The threshold is calibrated based on the anti-aging test of the filter material.

[0061] In this embodiment of the invention, as another optional implementation, the above-mentioned determination of whether the target filter element meets the preset aging condition replacement conditions based on the lifespan information includes: Based on lifetime information, analyze lifetime consumption threshold and failure risk threshold; The remaining predicted lifetime information is dynamically compared with the lifetime consumption threshold to generate lifetime consumption status. The failure risk level is mapped and matched with the failure risk threshold to generate a risk trigger status; Based on the logical combination of lifespan consumption status and risk trigger status, determine whether the target filter element meets the preset aging status replacement conditions. Among them, the life consumption threshold is used to represent the failure boundary of the remaining life of the filter element, the failure risk threshold is used to represent the upper limit of the failure probability of the filter element, and the logical combination relationship is used to define the joint judgment rule of life consumption and risk triggering.

[0062] In this embodiment of the invention, optionally, for threshold parsing: Lifetime consumption threshold = Material safety margin × Initial design life (e.g., 0.2 × 8000 hours).

[0063] Failure risk threshold = the maximum allowable failure probability of the system (e.g., 5%).

[0064] For dynamic alignment and mapping: Lifetime Expiration Status = Remaining Predicted Lifetime / Lifetime Expiration Threshold (triggered if the ratio is ≤1).

[0065] Risk trigger status = failure risk level mapped to probability value (e.g., "high risk" → 15%), compared with failure risk threshold.

[0066] For logical combination determination: Using OR logic: Lifetime depletion state trigger or risk trigger state trigger → Replacement conditions are met.

[0067] As can be seen, implementing this optional embodiment can control progressive failure through a dual-threshold dynamic comparison mechanism, a lifetime consumption threshold to control sudden failure, and a failure risk threshold to control sudden failure. It can distinguish the judgment logic of different failure modes, thereby avoiding false alarms / missed alarms caused by a single threshold and realizing replacement condition triggering in different scenarios. Through the design of logical combination relationships, it can be compatible with strategies such as "OR" (replacement upon either trigger) or "AND" (replacement only upon both triggers), thereby adapting to the application requirements of different security levels and helping to balance security and economy.

[0068] 104. When it is determined that the target filter element meets the preset aging condition replacement conditions, the aging factor of the target filter element is determined. The aging factor is used to represent the factors that make the target filter element meet the preset aging condition replacement conditions. In this embodiment of the invention, optionally, to determine the aging factor, the main cause leading to the index exceeding the limit can be identified: if the water quality information shows the largest change, then "water quality deterioration" is marked as the aging factor. Function: to locate the root cause of failure.

[0069] 105. Based on the aging factor, generate the replacement control parameters for the target filter element.

[0070] In this embodiment of the invention, optionally, for generating filter replacement control parameters, if the aging factor is water quality deterioration, the parameters can be generated as follows: {Filter replacement delay time = 0 hours, flushing mode = strong flushing}. Logic: Parameter types are mapped and bound to aging factor types.

[0071] As can be seen, implementing the embodiments of the present invention can cover multiple causes of filter element aging, such as time decay, chemical corrosion, and mechanical fatigue, by collecting multi-dimensional usage information. This improves the comprehensiveness of life assessment and helps avoid misjudgments caused by a single time dimension, enabling multi-factor collaborative monitoring of filter element status. By locating aging factors and generating replacement parameters, specific failure causes (such as sudden changes in water quality or pressure shocks) can be identified, thereby improving the targeting of replacement decisions. This facilitates dynamic adjustment of control strategies based on the main causes of aging, achieving closed-loop control from failure diagnosis to strategy execution, thus improving the comprehensiveness and accuracy of replacement control analysis, and enhancing the accuracy and timeliness of replacement control.

[0072] Example 2 Please see Figure 3 , Figure 3 This is a flowchart illustrating another intelligent filter replacement control method based on filter life analysis disclosed in an embodiment of the present invention. Figure 3The described intelligent filter replacement control method based on filter life analysis can be applied to filter elements, and also to intelligent devices associated with the filter elements. These intelligent devices include, but are not limited to, one or more of smart home devices, battery devices, cloud devices, edge computing devices, relay devices, base station devices, urban management devices, and intelligent connected devices. This invention does not limit the scope of these applications. Figure 3 As shown, this intelligent filter replacement control method based on filter life analysis can include the following operations: 201. Determine the first multi-dimensional usage information of the target filter element, which includes at least two of the following: usage cycle information, water quality information, usage pressure information, and settling cycle information; 202. Based on the first multi-dimensional usage information, analyze the lifespan information of the target filter element; 203. Based on the lifespan information, determine whether the target filter element meets the preset aging condition replacement conditions; 204. When it is determined that the target filter element meets the preset aging state replacement conditions, the dominant dimension field that triggers the aging state replacement conditions is extracted. The dominant dimension field is used to represent the dimension category that contributes the most to the lifespan decay. 205. Based on the main dimension attenuation increment, sub-dimension coupling contribution increment, and dimension cross-influence compensation increment in the coupling analysis results, locate the aging action path; 206. Based on the aging action path, the dominant aging factor and the synergistic aging factor are separated. The dominant aging factor and the synergistic aging factor are the aging factors of the target filter element. The aging factor is used to represent the factors that make the target filter element meet the preset aging state replacement conditions. Among them, the dominant aging factor is used to represent the factor that directly leads to the triggering of the lifespan threshold. The dominant aging factor includes at least one of the dominant chemical erosion factor, the dominant mechanical stress factor, and the dominant microstructure deterioration factor. The synergistic aging factor is used to represent the auxiliary factor that accelerates the effect of the dominant factor. The synergistic aging factor includes at least one of the synergistic water quality fluctuation factor and the synergistic pressure transient factor. 207. Based on the aging factor, generate the replacement control parameters for the target filter element.

[0073] In this embodiment of the invention, optionally, for extracting the dominant dimension field: Calculate the contribution rate of each dimension to lifespan decay: Contribution rate = (Dimensional increment value / Total increment value) × 100%.

[0074] Dimensions with a contribution rate greater than 50% are selected as the dominant dimensions (e.g., water quality information with a contribution rate of 60%).

[0075] Regarding the location of aging pathways: Incremental correlation in the results of tracing coupling analysis: If the increment of chemical erosion in the main dimension is high and strongly correlated with the increment of the deposit density, the path is marked as "chemical deposit degradation".

[0076] For separating aging factors: Dominant aging factors: Extract direct causative factors along the action pathway (such as "excessive heavy metal ions").

[0077] Synergistic aging factors: auxiliary variables in the extraction path (such as "pH value < 5.0" accelerates heavy metal corrosion).

[0078] As can be seen, implementing the embodiments of the present invention can separate aging factors in layers, identify the root cause by focusing on the dominant aging factor, and identify the accelerating conditions by focusing on the synergistic aging factor. This can reveal the primary and secondary contradictions in the aging process, thereby guiding targeted maintenance (such as prioritizing the treatment of the dominant factor) and improving the efficiency of maintenance resource allocation. By tracing the aging action path, the transmission chain of "macro input → micro response → lifespan decay" can be reconstructed, thereby forming a reusable failure analysis knowledge base, which is beneficial for the lifespan prediction and optimization of similar filter elements.

[0079] In this embodiment of the invention, as an optional implementation, the above-mentioned generation of filter replacement control parameters based on aging factors includes: Based on the type of dominant aging factor, the time control parameter generation rules are matched; Calculate the compensation coefficient for the operating control parameters based on the intensity of the synergistic aging factor; Based on the time control parameter generation rules and the operation control parameter compensation coefficient, the replacement control parameters of the target filter element are generated. Among them, the time control parameter generation rule is used to define the execution time window of the core replacement operation, and the time control parameter generation rule includes at least one of the emergency core replacement time threshold and the gradual core replacement time gradient; the operation control parameter compensation coefficient is used to define the core replacement execution mode, and the operation control parameter compensation coefficient includes at least one of the pressure adjustment amplitude and the flow control curve.

[0080] In this embodiment of the invention, optionally, the matching time control parameter rules are as follows: If the dominant factor is mechanical stress: Matching rule {emergency core replacement time threshold = 24 hours} (to avoid pressure shock causing rupture).

[0081] If the dominant factor is chemical erosion: Matching rule {progressive core replacement time gradient = [72h, 48h, 24h]} (staged early warning).

[0082] For calculating the compensation coefficients of the operating control parameters: Synergy factor strength quantification: Water quality fluctuation synergy factor strength = (Water quality fluctuation variance / Benchmark variance).

[0083] The compensation coefficient K = 1 + strength value (e.g., strength = 0.8 → K = 1.8).

[0084] For generating core-swapping control parameters: Combination of time parameter and operation parameter: Example: {Time window: Asymptotic gradient [72h, 48h, 24h], Operation mode: Flow control curve = standard curve × K} Execution logic: When entering the 48h window, reduce the system flow to 55% of the calibrated value by K=1.8.

[0085] As can be seen, implementing this optional embodiment can generate time parameters and operation parameters by decoupling them. The time control parameter controls the timing of strategy execution, while the operation control parameter controls the execution method of the strategy. This can separate the time dimension and operation dimension of core replacement decision, thereby adapting to the flexible execution requirements under complex working conditions. By dynamically adjusting the compensation coefficient, the acceleration effect of synergistic factors (such as the amplitude of water quality fluctuations) can be quantified, thereby scaling the operation intensity according to the risk level. This is beneficial to reduce maintenance costs while ensuring safety, and to achieve a dynamic balance between economy and reliability.

[0086] Example 3 Please see Figure 4 , Figure 4 This is a schematic diagram of an intelligent filter replacement control device based on filter life analysis, as disclosed in an embodiment of the present invention. This intelligent filter replacement control device can be applied to filters, and also to intelligent devices associated with filters. These intelligent devices include, but are not limited to, one or more of smart home devices, battery devices, cloud devices, edge computing devices, relay devices, base station devices, urban management devices, and smart connected devices; the present invention does not limit the application to these devices. Figure 4 As shown, the intelligent filter replacement control device based on filter life analysis may include: The determination module 301 is used to determine the first multi-dimensional usage information of the target filter element. The first multi-dimensional usage information includes at least two of the following: usage cycle information, water quality information, usage pressure information, and settling cycle information. Analysis module 302 is used to analyze the lifespan information of the target filter element based on the first multi-dimensional usage information; The judgment module 303 is used to determine whether the target filter element meets the preset aging state replacement conditions based on the lifespan information. The determining module 301 is also used to determine the aging factor of the target filter element when the judging module 303 determines that the target filter element meets the preset aging state replacement conditions. The aging factor is used to represent the factors that make the target filter element meet the preset aging state replacement conditions. The generation module 304 is used to generate the replacement control parameters for the target filter element based on the aging factor.

[0087] As can be seen, implementing the embodiments of the present invention can cover multiple causes of filter element aging, such as time decay, chemical corrosion, and mechanical fatigue, by collecting multi-dimensional usage information. This improves the comprehensiveness of life assessment and helps avoid misjudgments caused by a single time dimension, enabling multi-factor collaborative monitoring of filter element status. By locating aging factors and generating replacement parameters, specific failure causes (such as sudden changes in water quality or pressure shocks) can be identified, thereby improving the targeting of replacement decisions. This facilitates dynamic adjustment of control strategies based on the main causes of aging, achieving closed-loop control from failure diagnosis to strategy execution, thus improving the comprehensiveness and accuracy of replacement control analysis, and enhancing the accuracy and timeliness of replacement control.

[0088] In this embodiment of the invention, as an optional implementation, the target filter element includes functional components, which at least include a sealing component and a filtering component. The specific method by which the analysis module 302 analyzes the lifespan information of the target filter element based on the first multi-dimensional usage information includes: For each functional component, determine the second multi-dimensional usage information corresponding to that functional component. The second multi-dimensional usage information includes at least two of the following: deformation degree information, elastic decay state information, deposit density information, deposit type information, deposit corrosion information, and micropore expansion state information. The first multi-dimensional information is used to determine the primary dimension field; The second multi-dimensional information is identified as a sub-dimensional field; By aligning the collection nodes of the main dimension field and the sub-dimensional field with the timestamp, a dimension fusion matrix is ​​generated; The dimensional fusion matrix is ​​fused and calculated to obtain the coupling analysis results; Based on the coupling analysis results, a lifespan decay model is constructed, and the lifespan information of the target filter element is output.

[0089] As can be seen, implementing this optional embodiment can distinguish the independent degradation characteristics of sealing components and filtration components through detailed monitoring of functional components, thereby improving the precision of lifetime analysis, which is conducive to locating component-level failure foci and achieving a penetrating assessment from overall lifetime to local performance. Through the fusion of principal-sub-dimension fields, it is possible to establish the correlation between macroscopic operating conditions (such as water quality) and microscopic states (such as pore expansion), thereby enhancing the interpretability of the degradation mechanism, which is conducive to revealing the causal chain in the complex aging process and supporting cross-scale lifetime degradation modeling.

[0090] In this optional embodiment, as an optional implementation method, the analysis module 302 performs fusion calculation on the dimension fusion matrix to obtain the coupling analysis result in the following specific ways: Based on the main dimension field, calculate the main dimension attenuation increment, which includes at least the main dimension chemical erosion attenuation increment and the main dimension mechanical stress attenuation increment. Based on the sub-dimension fields, calculate the sub-dimension coupling contribution increment, which includes at least the sealing elasticity attenuation increment and the filter structure degradation increment. Based on the main dimension field and the sub-dimension field, the dimension cross-influence compensation increment is calculated. The dimension cross-influence compensation increment is used to represent the combined effect attenuation of the cross-effect region of the main dimension field and the sub-dimension field. The coupling analysis results are generated based on the main dimension decay increment, the sub-dimension coupling contribution increment, and the dimension cross-influence compensation increment.

[0091] As can be seen, implementing this optional embodiment can decouple independent contributions and interactions in complex attenuation scenarios by using three types of incremental separation calculations: the main dimension attenuation increment to quantify the effect of environmental stress, the sub-dimensional coupling contribution increment to quantify the component's own degradation, and the dimension cross-influence compensation increment to quantify the synergistic effect of multiple factors. This reduces the evaluation bias caused by multi-source interference and achieves accurate attribution of attenuation drivers.

[0092] In this optional embodiment, as another optional implementation, the analysis module 302 constructs a lifespan decay model based on the coupling analysis results and outputs the lifespan information of the target filter element in the following specific ways: Based on the results of the coupling analysis, a formula framework for the life decay function is defined, which is used to represent the mathematical structure of filter performance degradation over time. Based on the formula framework, the attenuation trajectory simulation is performed to obtain the attenuation trajectory simulation results. The attenuation trajectory simulation is used to predict the future change path of filter performance, specifically including the attenuation rate and the failure critical point. Based on the attenuation trajectory simulation results, the lifespan information of the target filter element is generated. The lifespan information is used to represent the overall lifespan status of the target filter element, including the remaining predicted lifespan information and the failure risk level.

[0093] As can be seen, implementing this optional embodiment can predict performance degradation paths (such as accelerated failure thresholds) through attenuation trajectory simulation, thereby extending the life warning time window, which is conducive to planning maintenance resources in advance and changing passive replacement to proactive life management; by outputting synthetic life indicators, the remaining predicted life can quantify the available time, and the failure risk level can quantify the probability of sudden failure, which can take into account the dual risks of gradual aging and sudden failure, thereby improving the robustness of replacement condition judgment.

[0094] In an optional embodiment, the specific method by which the determination module 303 determines whether the target filter element meets the preset aging condition replacement conditions based on the lifespan information includes: Based on lifetime information, analyze lifetime consumption threshold and failure risk threshold; The remaining predicted lifetime information is dynamically compared with the lifetime consumption threshold to generate lifetime consumption status. The failure risk level is mapped and matched with the failure risk threshold to generate a risk trigger status; Based on the logical combination of lifespan consumption status and risk trigger status, determine whether the target filter element meets the preset aging status replacement conditions. Among them, the life consumption threshold is used to represent the failure boundary of the remaining life of the filter element, the failure risk threshold is used to represent the upper limit of the failure probability of the filter element, and the logical combination relationship is used to define the joint judgment rule of life consumption and risk triggering.

[0095] As can be seen, implementing this optional embodiment can control progressive failure through a dual-threshold dynamic comparison mechanism, a lifetime consumption threshold to control sudden failure, and a failure risk threshold to control sudden failure. It can distinguish the judgment logic of different failure modes, thereby avoiding false alarms / missed alarms caused by a single threshold and realizing replacement condition triggering in different scenarios. Through the design of logical combination relationships, it can be compatible with strategies such as "OR" (replacement upon either trigger) or "AND" (replacement only upon both triggers), thereby adapting to the application requirements of different security levels and helping to balance security and economy.

[0096] In another optional embodiment, the specific method by which the determining module 301 determines the aging factor of the target filter element includes: Extract the dominant dimension field that triggers the aging state replacement condition. The dominant dimension field is used to represent the dimension category that contributes the most to the lifespan decay. Based on the main dimension attenuation increment, sub-dimension coupling contribution increment, and dimension cross-influence compensation increment in the coupling analysis results, the aging action path is located. Based on the aging process pathway, the dominant aging factor and the synergistic aging factor are separated, and the dominant aging factor and the synergistic aging factor are the aging factors of the target filter element. Among them, the dominant aging factor is used to represent the factor that directly leads to the triggering of the lifespan threshold. The dominant aging factor includes at least one of the chemical erosion dominant factor, the mechanical stress dominant factor, and the microstructure deterioration dominant factor. The synergistic aging factor is used to represent the auxiliary factor that accelerates the effect of the dominant factor. The synergistic aging factor includes at least one of the water quality fluctuation synergistic factor and the pressure transient synergistic factor.

[0097] As can be seen, implementing the embodiments of the present invention can separate aging factors in layers, identify the root cause by focusing on the dominant aging factor, and identify the accelerating conditions by focusing on the synergistic aging factor. This can reveal the primary and secondary contradictions in the aging process, thereby guiding targeted maintenance (such as prioritizing the treatment of the dominant factor) and improving the efficiency of maintenance resource allocation. By tracing the aging action path, the transmission chain of "macro input → micro response → lifespan decay" can be reconstructed, thereby forming a reusable failure analysis knowledge base, which is beneficial for the lifespan prediction and optimization of similar filter elements.

[0098] In yet another optional embodiment, the specific method by which the generation module 304 generates the replacement control parameters of the target filter element based on the aging factor includes: Based on the type of dominant aging factor, the time control parameter generation rules are matched; Calculate the compensation coefficient for the operating control parameters based on the intensity of the synergistic aging factor; Based on the time control parameter generation rules and the operation control parameter compensation coefficient, the replacement control parameters of the target filter element are generated. Among them, the time control parameter generation rule is used to define the execution time window of the core replacement operation, and the time control parameter generation rule includes at least one of the emergency core replacement time threshold and the gradual core replacement time gradient; the operation control parameter compensation coefficient is used to define the core replacement execution mode, and the operation control parameter compensation coefficient includes at least one of the pressure adjustment amplitude and the flow control curve.

[0099] As can be seen, implementing this optional embodiment can generate time parameters and operation parameters by decoupling them. The time control parameter controls the timing of strategy execution, while the operation control parameter controls the execution method of the strategy. This can separate the time dimension and operation dimension of core replacement decision, thereby adapting to the flexible execution requirements under complex working conditions. By dynamically adjusting the compensation coefficient, the acceleration effect of synergistic factors (such as the amplitude of water quality fluctuations) can be quantified, thereby scaling the operation intensity according to the risk level. This is beneficial to reduce maintenance costs while ensuring safety, and to achieve a dynamic balance between economy and reliability.

[0100] Example 4 Please see Figure 5 , Figure 5 This is a schematic diagram of another intelligent filter replacement control device based on filter life analysis disclosed in an embodiment of the present invention. This intelligent filter replacement control device based on filter life analysis can be applied to filter elements, and also to intelligent devices associated with the filter elements. These intelligent devices include, but are not limited to, one or more of smart home devices, battery devices, cloud devices, edge computing devices, relay devices, base station devices, urban management devices, and smart connected devices; the embodiments of the present invention do not impose limitations. Figure 5 As shown, the intelligent filter replacement control device based on filter life analysis may include: Memory 401 that stores executable program code.

[0101] Processor 402 coupled to memory 401.

[0102] The processor 402 calls the executable program code stored in the memory 401 to execute the steps in the intelligent filter replacement control method based on filter life analysis described in Embodiment 1 or Embodiment 2 of the present invention.

[0103] Example 5 This invention discloses a computer storage medium storing computer instructions. When these computer instructions are invoked, they are used to execute the steps in the intelligent filter replacement control method based on filter life analysis described in Embodiment 1 or Embodiment 2 of this invention.

[0104] Example 6 This invention discloses a computer program product, which includes a non-transitory computer storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps in the intelligent filter replacement control method based on filter life analysis described in Embodiment 1 or Embodiment 2.

[0105] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0106] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0107] Finally, it should be noted that the intelligent filter replacement control method and device based on filter life analysis disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent filter replacement control method based on filter life analysis, characterized in that, The method includes: Determine the first multi-dimensional usage information of the target filter element, which includes at least two of the following: usage cycle information, water quality information, usage pressure information, and settling cycle information. Based on the first multi-dimensional usage information, analyze the lifespan information of the target filter element; Based on the lifespan information, determine whether the target filter element meets the preset aging condition replacement conditions; When it is determined that the target filter element meets the preset aging condition replacement conditions, the aging factor of the target filter element is determined. The aging factor is used to represent the factors that make the target filter element meet the preset aging condition replacement conditions. Based on the aging factor, the replacement control parameters for the target filter element are generated.

2. The intelligent filter replacement control method based on filter life analysis according to claim 1, characterized in that, The target filter element includes functional components, which at least include a sealing component and a filtering component. Analyzing the lifespan information of the target filter element based on the first multi-dimensional usage information includes: For each of the functional components, a second multi-dimensional usage information corresponding to the functional component is determined. The second multi-dimensional usage information includes at least two of the following: deformation degree information, elastic decay state information, deposit density information, deposit type information, deposit corrosion information, and micropore expansion state information. The first multi-dimensional usage information is used to determine the main dimension field; The second multi-dimensional usage information is identified as a sub-dimensional field; By aligning the collection nodes of the main dimension field and the sub-dimensional field with the timestamps, a dimension fusion matrix is ​​generated; The dimensional fusion matrix is ​​fused to obtain the coupling analysis results; Based on the coupling analysis results, a lifespan decay model is constructed, and the lifespan information of the target filter element is output.

3. The intelligent filter replacement control method based on filter life analysis according to claim 2, characterized in that, The process of performing fusion calculations on the dimensional fusion matrix to obtain coupling analysis results includes: Based on the main dimension field, the main dimension attenuation increment is calculated, which includes at least the main dimension chemical erosion attenuation increment and the main dimension mechanical stress attenuation increment. Based on the sub-dimension field, the sub-dimension coupling contribution increment is calculated, and the sub-dimension coupling contribution increment includes at least the sealing elasticity attenuation increment and the filter structure degradation increment. Based on the main dimension field and the sub-dimension field, the dimension cross-influence compensation increment is calculated. The dimension cross-influence compensation increment is used to represent the attenuation of the combined effect of the cross-effect region of the main dimension field and the sub-dimension field. The coupling analysis results are generated based on the main dimension decay increment, the sub-dimension coupling contribution increment, and the dimension cross-influence compensation increment.

4. The intelligent filter replacement control method based on filter life analysis according to claim 3, characterized in that, Based on the coupling analysis results, a lifespan decay model is constructed, and the lifespan information of the target filter element is output, including: Based on the coupling analysis results, a formula framework for the life decay function is defined, which is used to represent the mathematical structure of filter performance degradation over time. Based on the formula framework, a decay trajectory simulation is performed to obtain the decay trajectory simulation results. The decay trajectory simulation is used to predict the future change path of filter performance, specifically including the decay rate and the failure critical point. Based on the attenuation trajectory simulation results, the lifespan information of the target filter element is generated. The lifespan information is used to represent the overall lifespan status of the target filter element, and the lifespan information includes the remaining predicted lifespan information and the failure risk level.

5. The intelligent filter replacement control method based on filter life analysis according to claim 4, characterized in that, The step of determining whether the target filter element meets the preset aging condition replacement conditions based on the lifespan information includes: Based on the lifetime information, the lifetime consumption threshold and failure risk threshold are analyzed; The remaining predicted lifetime information is dynamically compared with the lifetime consumption threshold to generate a lifetime consumption status. The failure risk level is mapped and matched with the failure risk threshold to generate a risk trigger state; Based on the logical combination of the lifespan consumption state and the risk triggering state, determine whether the target filter element meets the preset aging state replacement conditions; The lifespan consumption threshold is used to represent the failure boundary of the remaining lifespan of the filter element, the failure risk threshold is used to represent the upper limit of the failure probability of the filter element, and the logical combination relationship is used to define the joint judgment rule of lifespan consumption and risk triggering.

6. The intelligent filter replacement control method based on filter life analysis according to any one of claims 2-5, characterized in that, Determining the aging factor of the target filter element includes: Extract the dominant dimension field that triggers the aging state replacement condition. The dominant dimension field is used to represent the dimension category that contributes the most to the lifespan decay. Based on the main dimension attenuation increment, sub-dimension coupling contribution increment, and dimension cross-influence compensation increment in the coupling analysis results, the aging action path is located. Based on the aging pathway, the dominant aging factor and the synergistic aging factor are separated, and the dominant aging factor and the synergistic aging factor are the aging factors of the target filter element. Wherein, the dominant aging factor is used to represent the factor that directly leads to the triggering of the lifespan threshold, and the dominant aging factor includes at least one of the dominant chemical erosion factor, the dominant mechanical stress factor, and the dominant microstructure deterioration factor; the synergistic aging factor is used to represent the auxiliary factor that accelerates the effect of the dominant factor, and the synergistic aging factor includes at least one of the synergistic water quality fluctuation factor and the synergistic pressure transient factor.

7. The intelligent filter replacement control method based on filter life analysis according to claim 6, characterized in that, The step of generating the replacement control parameters for the target filter element based on the aging factor includes: Based on the type of the dominant aging factor, a matching time control parameter generation rule is established; Calculate the compensation coefficient for the operating control parameters based on the strength of the synergistic aging factor; Based on the time control parameter generation rules and operation control parameter compensation coefficients, the replacement control parameters for the target filter element are generated. The time control parameter generation rule is used to define the execution time window of the core replacement operation, and the time control parameter generation rule includes at least one of emergency core replacement time threshold and gradual core replacement time gradient; the operation control parameter compensation coefficient is used to define the core replacement execution mode, and the operation control parameter compensation coefficient includes at least one of pressure adjustment amplitude and flow control curve.

8. An intelligent filter replacement control device based on filter life analysis, characterized in that, The device includes: The determination module is used to determine the first multi-dimensional usage information of the target filter element, wherein the first multi-dimensional usage information includes at least two of the following: usage cycle information, water quality information, usage pressure information, and settling cycle information. The analysis module is used to analyze the lifespan information of the target filter element based on the first multi-dimensional usage information; The judgment module is used to determine whether the target filter element meets the preset aging condition replacement conditions based on the lifespan information. The determining module is further configured to determine the aging factor of the target filter element when the judging module determines that the target filter element meets the preset aging state replacement conditions. The aging factor is used to represent the factors that make the target filter element meet the preset aging state replacement conditions. The generation module is used to generate the replacement control parameters for the target filter element based on the aging factor.

9. An intelligent filter replacement control device based on filter life analysis, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the intelligent filter replacement control method based on filter life analysis as described in any one of claims 1-7.

10. A computer storage medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked, are used to execute the intelligent filter replacement control method based on filter life analysis as described in any one of claims 1-7.

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