Intelligent control system of electromagnetic wave water processor

Abstract

This invention relates to an intelligent control system for an electromagnetic wave water processor, belonging to the field of water treatment technology. It includes a multi-parameter detection module, an electromagnetic wave generation module, a frequency sweep optimization module, a fuzzy self-tuning control module, and a mode-coordinated controller. The multi-parameter detection module collects water temperature, conductivity, pH value, and flow velocity in real time; the frequency sweep optimization module scans and determines the optimal operating frequency for the current water quality; the fuzzy self-tuning control module generates frequency adjustment amounts based on real-time changes in water quality parameters for closed-loop regulation; and the mode-coordinated controller realizes a dynamic coordinated closed loop of frequency sweeping, tracking, and re-sweeping. This invention, through deep coordination of frequency sweep optimization and fuzzy self-tuning control, combined with pre-compensation and adaptive frequency sweeping stepping, has the advantages of strong water quality adaptability, stable treatment effect, and optimized energy consumption.

Description

Technical Field

[0001] This invention relates to the field of water treatment technology, and in particular to an intelligent control system for an electromagnetic wave water processor. Background Technology

[0002] Electromagnetic wave water treatment technology is an environmentally friendly water treatment technology based on physical fields. It is widely used in industrial circulating cooling water, central air conditioning, boilers, heat exchangers and other scenarios. It uses high-frequency electromagnetic fields to act on water bodies to achieve functions such as scale prevention and removal, sterilization and algae removal, corrosion prevention and rust removal without the need to add chemical agents.

[0003] Among related technologies, the core mechanism of electromagnetic wave water treatment technology is that high-frequency electromagnetic signals act on the water body through coils wound on the pipes, causing the crystallization habits of scale-forming ions such as calcium and magnesium ions dissolved in the water to change from a hard calcite structure to a soft aragonite structure, which is discharged with the water flow and does not adhere to the pipe wall.

[0004] However, existing electromagnetic wave water treatment control systems still suffer from the following technical problems in terms of intelligence and adaptability: the treatment effect is highly dependent on water parameters and lacks an adaptive mechanism. The effectiveness of electromagnetic wave water treatment is influenced by a combination of factors, including water temperature, conductivity, pH value, flow rate, and mineral content. The resonant frequencies of water molecular clusters differ under different water quality conditions; optimal treatment results can only be achieved when the electromagnetic wave frequency matches the water's resonant frequency. Summary of the Invention

[0005] To address the aforementioned issues, this application provides an intelligent control system for an electromagnetic wave water processor. Based on a dynamic synergy of frequency sweep optimization and fuzzy self-tuning PID control, this intelligent control system achieves precise tracking of the electromagnetic wave output frequency to the water body's resonant frequency and dynamic optimization of output power through dual-mode adaptive switching, physical model-based pre-compensation, adaptive frequency sweep interval adjustment, and deep synergy of multi-parameter fusion. This significantly improves water quality adaptability and the stability of treatment effects.

[0006] The intelligent control system for an electromagnetic wave water processor provided in this application adopts the following technical solution: An intelligent control system for an electromagnetic wave water processor includes a multi-parameter detection module, an electromagnetic wave generation module, a frequency sweep optimization module, a fuzzy self-tuning control module, and a mode coordination controller. The multi-parameter detection module is installed on the water treatment pipeline to collect multiple water quality parameters in real time, including temperature, conductivity, pH value, and flow rate. The electromagnetic wave generation module generates high-frequency electromagnetic wave signals and applies them to the water. The frequency sweep optimization module is connected to the electromagnetic wave generation module and scans the output frequency in a stepwise manner within a preset frequency range, determining the optimal operating frequency under the current water quality conditions based on the treatment effect parameters fed back by the multi-parameter detection module. The fuzzy self-tuning control module and the frequency sweep optimization module... The module is connected to the electromagnetic wave generation module and is used to generate frequency adjustment amounts based on the optimal working frequency determined by the frequency sweep optimization module and the real-time changes of multiple water quality parameters to perform closed-loop regulation of the output frequency of the electromagnetic wave generation module. The mode coordination controller is connected to the frequency sweep optimization module and the fuzzy self-tuning control module respectively. After the frequency sweep optimization module completes the determination of the optimal working frequency, it switches the system to the closed-loop tracking mode dominated by the fuzzy self-tuning control module. When the frequency adjustment amount output by the fuzzy self-tuning control module accumulates in the same direction for multiple consecutive times and exceeds the preset threshold, it re-triggers the frequency sweep optimization module to perform frequency verification, forming a dynamic coordinated closed loop of frequency sweep-tracking-re-frequency sweep.

[0007] By adopting the above technical solution, the system achieves deep dual-mode fusion of frequency sweep optimization and fuzzy self-tuning control. The frequency sweep optimization module finds the optimal resonant frequency for the current water quality through full-band scanning when the system starts up or when significant changes occur in water quality. In tracking mode, the fuzzy self-tuning control module fine-tunes the frequency in real time based on multi-dimensional changes in temperature, conductivity, pH, and flow velocity. The mode-coordinated controller establishes a re-frequency sweep trigger mechanism; when the fuzzy control output accumulates continuously in the same direction, it indicates a significant drift in water quality, automatically triggering a re-frequency sweep to avoid long-term deviation from the optimal state. These three components form a closed loop of "optimization-tracking-re-optimization," solving the problem of the separation between frequency sweeping and tracking in existing technologies.

[0008] Furthermore, it also includes an adaptive sweep frequency step adjustment module, which is connected to the sweep frequency optimization module, and is used to dynamically adjust the sweep frequency step interval according to the water quality parameter change rate fed back by the multi-parameter detection module: when the water quality parameter change rate exceeds the first preset threshold, a fine step interval is used for fine scanning; when the water quality parameter change rate is lower than the second preset threshold, a coarse step interval is used for fast scanning.

[0009] By adopting the above technical solution, the adaptive sweep frequency step adjustment module automatically matches the sweep frequency resolution according to the severity of water quality changes. When water quality changes drastically, a fine step (such as 1kHz) is used to ensure that the precise resonance point is found; when water quality is stable, a coarse step (such as 5kHz) is used to shorten the sweep frequency time and reduce interference with the normal treatment process.

[0010] Furthermore, it also includes a pre-compensation module based on a physical model. The pre-compensation module is connected to the multi-parameter detection module and the electromagnetic wave generation module, respectively. It is used to pre-calculate the compensation amount based on the physical model of "electromagnetic wave energy accumulation - action time" according to the influence of water flow velocity changes on the effective action time of electromagnetic waves. When the flow velocity increases, the output power is increased in advance and / or the frequency is adjusted, and when the flow velocity decreases, the output power is decreased in advance. The physical model is: E_eff=P×t_contact×η(v,σ), where E_eff is the effective action energy, P is the output power, t_contact is the effective action time of water flowing through the coil, and η is the energy coupling efficiency function related to the flow velocity v and conductivity σ.

[0011] By adopting the above technical solution, the pre-compensation module uses a physical model to predict the impact of flow velocity changes on the treatment effect. When the flow velocity increases, the time it takes for water to flow through the coil is shortened, and the effective energy decreases, so the system increases its output power in advance to compensate; when the flow velocity decreases, the system reduces its output power to save energy. This feedforward control overcomes the lag of feedback control and improves dynamic response capability.

[0012] Furthermore, in the multi-parameter detection module, the introduction of pH value is used to correct the temperature-conductivity reference model of the water body resonant frequency: f_res=f_0(T,σ)+k_pH×(pH-pH_0), where f_0(T,σ) is the reference resonant frequency determined by temperature T and conductivity σ, k_pH is the pH value influence coefficient, and pH_0 is the neutral reference value; the fuzzy self-tuning control module uses pH value as an independent input variable to participate in the fuzzy inference of the frequency adjustment amount.

[0013] By adopting the above technical solution and incorporating pH into the resonant frequency calculation model, the shortcomings of considering only temperature and conductivity are corrected. Changes in the molecular cluster structure in acidic or alkaline water bodies cause shifts in the resonant frequency; the introduction of pH makes frequency prediction more accurate. In fuzzy control, pH is used as an independent input, improving control precision.

[0014] Furthermore, it also includes a self-diagnosis and sensor redundancy verification module, which is connected to the multi-parameter detection module. This module compares the detection values ​​of the temperature sensor, conductivity sensor, and pH sensor with preset physical correlation constraints. The physical correlation constraints include: the temperature compensation change rate of conductivity should be within a preset range when the temperature rises, and the empirical correlation range between pH and conductivity. When the detection values ​​of any two sensors violate the physical correlation constraints, the corresponding sensor is determined to be faulty, and the fault type and fault location signal are output.

[0015] By adopting the above technical solution, the physical correlation between water quality parameters (such as the positive correlation between temperature and conductivity, and the empirical range of pH value and conductivity) is used for sensor redundancy verification. When the reading of a certain sensor is inconsistent with the readings of other sensors, the system can quickly locate the faulty sensor and avoid control errors caused by sensor failure.

[0016] Furthermore, the mode coordination controller also includes a frequency sweep maintenance timing unit, which records the running time since the last frequency sweep verification. When the running time exceeds a preset time threshold, the frequency sweep optimization module is automatically triggered to perform frequency verification in order to eliminate the drift of the water body resonance frequency caused by the slow change of water quality.

[0017] By adopting the above technical solution, even if the rate of change of water quality parameters does not exceed the threshold, slow drift may still occur after long-term operation. The timing unit periodically triggers a frequency rescan to ensure that the system always operates near the optimal frequency.

[0018] This invention also provides an intelligent control method for an electromagnetic wave water processor based on the above system, comprising the following steps: Step A: The temperature, conductivity, pH value and flow rate of the water body are collected in real time through the multi-parameter detection module and transmitted to the mode coordination controller; Step B: The mode coordination controller determines whether the frequency sweep optimization triggering conditions are met. The frequency sweep optimization triggering conditions include the initial startup of the system, the rate of change of water quality parameters exceeding a preset threshold, the frequency adjustment amount output by the fuzzy self-tuning control module accumulating in the same direction multiple times to exceed a preset threshold, or the time since the last frequency sweep verification exceeding a preset time threshold. Step C: When the frequency sweep optimization trigger condition is met, the frequency sweep optimization module is started. The frequency sweep step interval is determined by the adaptive frequency sweep step adjustment module according to the current water quality parameter change rate. The output frequency is scanned in a step manner within the preset frequency range. The treatment effect parameters at each frequency point are detected, and the frequency point with the best treatment effect is selected as the optimal working frequency under the current water quality conditions. Step D: Based on the optimal operating frequency, start the fuzzy self-tuning control module, using temperature, conductivity, pH value, and flow rate as multi-dimensional input variables, and use fuzzy inference to generate frequency adjustment amount to perform closed-loop adjustment of the output frequency of the electromagnetic wave generation module. Step E: The pre-compensation module based on the physical model pre-calculates the compensation amount according to the change in flow velocity and the "electromagnetic wave energy accumulation - action time" physical model, and dynamically adjusts the output power. Step F: During the closed-loop adjustment process, the mode co-controller monitors the cumulative direction of the frequency adjustment output by the fuzzy self-tuning control module. When the cumulative frequency adjustment exceeds the preset threshold multiple times in the same direction, it returns to step C to trigger frequency verification.

[0019] By adopting the above technical solutions, the method organically integrates frequency sweep optimization, fuzzy self-tuning closed loop, pre-compensation, and re-frequency sweep triggering mechanism to form a complete intelligent control process. The system can adapt to changes in water quality, optimizing energy consumption while ensuring treatment effectiveness.

[0020] Furthermore, the specific method for determining the optimal operating frequency in step C is as follows: at each frequency step point, record the rate of change of water conductivity and the time required for stabilization, construct a comprehensive effect evaluation function J=w1·Δσ / Δt+w2·(1 / T_stable), and select the frequency point with the largest J value as the optimal operating frequency, where Δσ / Δt is the rate of change of conductivity, T_stable is the stabilization time, and w1 and w2 are weighting coefficients.

[0021] By adopting the above technical solution, the rate of change in conductivity reflects the effect of electromagnetic waves on scale-forming ions, and the settling time reflects the system response speed. The comprehensive evaluation function takes into account both the treatment effect and the response speed, making the selected optimal frequency more practical.

[0022] Furthermore, the fuzzy inference of the fuzzy self-tuning control module in step D includes: mapping the temperature deviation, conductivity deviation, pH value deviation, and flow rate deviation to corresponding fuzzy subsets, performing inference based on a preset multidimensional fuzzy rule table, outputting the fuzzy value of the frequency adjustment amount, and obtaining the accurate frequency adjustment amount after defuzzification; in the multidimensional fuzzy rule table, the pH value deviation is used to correct the frequency adjustment amount corresponding to the temperature-conductivity deviation combination.

[0023] By adopting the above technical solution, four-dimensional fuzzy control fully considers the coupling relationship between various water quality parameters, and pH value as a correction term improves the accuracy of frequency adjustment.

[0024] Furthermore, the calculation of the compensation amount of the pre-compensation module in step E includes: when the flow velocity detection value v is greater than the reference flow velocity v0, the effective action time reduction ratio Δt / t0=(v0 / v-1) is calculated, and the output power P is adjusted to P0×(1+λ·Δt / t0) according to the physical model, where λ is the compensation intensity coefficient and P0 is the reference power; when the flow velocity decreases, the output power is reduced in the same way to save energy consumption.

[0025] By adopting the above technical solution, the compensation intensity coefficient λ can be adjusted according to the actual water quality and treatment requirements (typical value 0.5~2.0), achieving energy saving while ensuring treatment effect.

[0026] In summary, the present invention has at least one of the following beneficial effects: 1. It adopts a dynamic collaborative architecture of frequency sweep optimization and fuzzy self-tuning control, and realizes the closed loop of "optimization-tracking-re-optimization" through the mode collaborative controller, which solves the problem of the separation of frequency sweep and tracking functions, and does not deviate from the optimal operating point during long-term operation; 2. A pre-compensation module based on a physical model is introduced to adjust the output power in advance according to the change in flow velocity, which overcomes the lag of feedback control and improves the dynamic response capability. 3. Through the adaptive sweep frequency step adjustment module, the sweep frequency resolution is automatically matched according to the degree of water quality change, which takes into account both sweep frequency accuracy and sweep frequency efficiency. 4. Incorporating pH value into the resonant frequency calculation model and the multi-dimensional input of fuzzy control improves the accuracy of frequency prediction and control; 5. By using the self-diagnosis and sensor redundancy verification module, the system improves its reliability by utilizing the physical correlation between water quality parameters to locate faults. Attached Figure Description

[0027] Figure 1 This is a system structure block diagram according to an embodiment of the present invention; Figure 2 This is a flowchart of the control method according to an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the pre-compensation principle of an embodiment of the present invention; Figure 4 This is a schematic diagram of the frequency sweep step adaptive adjustment according to an embodiment of the present invention.

[0028] In the diagram: 1. Multi-parameter detection module; 2. Electromagnetic wave generation module; 3. Frequency sweep optimization module; 4. Fuzzy self-tuning control module; 5. Mode coordination controller; 51. Frequency sweep sustaining timing unit; 6. Adaptive frequency sweep step adjustment module; 7. Pre-compensation module; 8. Self-diagnosis and sensor redundancy verification module. Detailed Implementation

[0029] The following is in conjunction with the appendix Figure 1-4 The present invention will be described in further detail below.

[0030] This invention discloses an intelligent control system and control method for an electromagnetic wave water processor. (Refer to...) Figure 1 The system includes a multi-parameter detection module 1, an electromagnetic wave generation module 2, a frequency sweep optimization module 3, a fuzzy self-tuning control module 4, a mode coordination controller 5, an adaptive frequency sweep step adjustment module 6, a pre-compensation module 7, and a self-diagnosis and sensor redundancy verification module 8.

[0031] The multi-parameter detection module 1 is installed on the water treatment pipeline, approximately 0.5 meters upstream of the coil of the electromagnetic wave generating module 2. It is used to collect real-time data on water temperature, conductivity, pH value, and flow rate. Specifically, the temperature sensor is a PT100 platinum resistance thermometer with a range of 0-100℃ and an accuracy of ±0.2℃; the conductivity sensor is a four-electrode type with a range of 0-20000μS / cm and an accuracy of ±1%; the pH sensor is a glass electrode with a range of 0-14 and an accuracy of ±0.1%; and the flow rate sensor is an ultrasonic time-of-flight type with a range of 0-5m / s and an accuracy of ±0.01m / s. Each sensor outputs a 4-20mA standard analog signal, which is isolated, transmitted, and then connected to the controller.

[0032] Electromagnetic wave generation module 2 includes a DDS frequency synthesizer, a power amplifier, and a coupling coil. The DDS frequency synthesizer (such as AD9850) generates a sine wave signal with a frequency range of 1kHz-10MHz and adjustable steps under the controller command; the power amplifier amplifies the signal to an adjustable 10-200W; the coupling coil is a multi-turn copper wire wound on the outer wall of the pipe, used to couple electromagnetic wave energy to the water.

[0033] The frequency sweep optimization module 3 is connected to the electromagnetic wave generation module 2 and is implemented by software within the controller. The frequency sweep optimization module 3 scans the output frequency in a stepwise manner within a preset frequency range (e.g., 10kHz-500kHz). At each frequency step point, the frequency sweep optimization module 3 records the rate of change of water conductivity Δσ / Δt and the stabilization time T_stable through the multi-parameter detection module 1, constructing a comprehensive effect evaluation function J=w1·Δσ / Δt+w2·(1 / T_stable), where w1=0.6 and w2=0.4. The frequency point with the largest J value is selected as the optimal operating frequency under the current water quality conditions.

[0034] The adaptive sweep frequency step adjustment module 6 is connected to the sweep frequency optimization module 3 and is used to dynamically adjust the sweep frequency step interval. The controller calculates the water quality parameter change rate R=|dT / dt| / T_range+|dσ / dt| / σ_range+|dpH / dt| / pH_range in real time. When R>0.1 / s (first preset threshold), a fine step interval of 1kHz is used for fine scanning; when R<0.02 / s (second preset threshold), a coarse step interval of 5kHz is used for fast scanning; when it is between the two, an intermediate step of 3kHz is used.

[0035] The fuzzy self-tuning control module 4 uses the optimal operating frequency f_opt determined by the frequency sweep optimization module 3 as a reference, and generates a frequency adjustment amount Δf based on the real-time feedback of temperature, conductivity, pH value, and flow rate from the multi-parameter detection module 1. The input variables of the fuzzy self-tuning control module 4 are: temperature deviation e_T (the difference between the actual temperature and the reference temperature of 20℃), conductivity deviation e_σ (the difference between the actual conductivity and the reference conductivity of 500μS / cm), pH deviation e_pH (the difference between the actual pH value and the neutral value of 7), and flow rate deviation e_v (the difference between the actual flow rate and the reference flow rate of 0.5m / s). Each input variable is mapped to five fuzzy subsets: NB (negative large), NS (negative small), Z (zero), PS (positive small), and PB (positive large). The fuzzy rule table is a multi-dimensional matrix with a total of 5^4 = 625 rules. For example: IFe_T=NBANDe_σ=NBANDe_pH=ZANDe_v=ZTHENΔf=PB (low temperature and low conductivity, the frequency should be increased significantly). The output quantity Δf is defuzzified (centroid method) to obtain the precise frequency adjustment amount, which controls the DDS frequency synthesizer to adjust the output frequency. The frequency adjustment step size is limited to no more than 1kHz each time to prevent sudden changes from causing system instability.

[0036] The mode coordination controller 5 is connected to both the frequency sweep optimization module 3 and the fuzzy self-tuning control module 4. The mode coordination controller 5 internally includes a frequency sweep sustain timing unit 51. The system workflow is as follows: -Initial state: The mode co-controller 5 triggers the frequency sweep optimization module 3 to perform the first frequency sweep and obtain f_opt.

[0037] - Tracking status: After the frequency sweep is completed, the mode co-controller 5 will switch the system to the closed-loop tracking mode dominated by the fuzzy self-tuning control module 4.

[0038] - Re-scan frequency trigger: The mode coordination controller 5 monitors the direction of Δf output by the fuzzy self-tuning control module 4. When Δf is positive (or negative) for 5 consecutive times and the absolute value of each time is greater than 100Hz, it is determined to be "continuous accumulation in the same direction", and the frequency sweep optimization module 3 is re-triggered to perform frequency verification. At the same time, the frequency sweep maintenance timing unit 51 records the running time since the last frequency sweep verification. When it exceeds 24 hours, the frequency sweep is automatically triggered.

[0039] The pre-compensation module 7 is connected to both the multi-parameter detection module 1 and the electromagnetic wave generation module 2. The pre-compensation module 7 has a built-in physical model: E_eff = P × t_contact × η(v,σ). Where t_contact = L / v, L is the effective operating length of the coil (fixed value 0.5m), and v is the flow velocity; η(v,σ) = η0 × (σ / σ0) / (1 + v / v0), and η0 is the reference coupling efficiency. When the flow velocity v increases from the reference v0 = 0.5m / s, the pre-compensation module 7 calculates the effective operating time reduction ratio Δt / t0 = (v0 / v - 1), and adjusts the output power P to P0 × (1 + λ·Δt / t0), where λ is 1.2. For example, when v = 1.0m / s, Δt / t0 = -0.5 (time reduced by 50%), and the power increases to P0 × 1.6. When the flow velocity decreases, the power decreases accordingly to save energy.

[0040] The self-diagnosis and sensor redundancy verification module 8 is connected to the multi-parameter detection module 1. The self-diagnosis and sensor redundancy verification module 8 has preset physical correlation constraints: for every 1°C increase in temperature, conductivity increases by approximately 2% (temperature compensation coefficient 1.02); when the pH value is in the range of 6.5-8.5, conductivity and the total amount of dissolved solids show a linear relationship. When the temperature sensor displays 30°C but the conductivity sensor reading does not increase proportionally, it is determined that the conductivity sensor may be scaled; when the pH value displays 5.0 but the conductivity is abnormally low, it is determined that the pH sensor may be malfunctioning. When a fault occurs, the system outputs the fault type and fault location signal to the human-machine interface and automatically switches to conservative control mode (fixed frequency output).

[0041] Reference Figure 2 The flowchart of the control method of the present invention is as follows: Step A: The multi-parameter detection module 1 collects temperature, conductivity, pH value and flow rate in real time and transmits them to the mode co-controller 5.

[0042] Step B: The mode coordination controller 5 determines whether the frequency sweep optimization trigger conditions are met (initial start-up, water quality parameter change rate > 0.05 / s, 5 consecutive unidirectional cumulative sweeps, or > 24 hours since the last frequency sweep).

[0043] Step C: When the conditions are met, the adaptive frequency sweep step adjustment module 6 determines the step interval based on the water quality change rate, and the frequency sweep optimization module 3 performs frequency sweep to determine the optimal frequency f_opt.

[0044] Step D: Using f_opt as the reference, start the fuzzy self-tuning control module 4, generate Δf through four-dimensional fuzzy inference, and adjust the output frequency in a closed loop.

[0045] Step E: The pre-compensation module 7 calculates the power compensation amount according to the physical model based on the flow rate change and dynamically adjusts the output power.

[0046] Step F: Monitor the direction of Δf accumulation. If the continuous accumulation in the same direction exceeds the threshold, return to step C.

[0047] Reference Figure 3 The principle of pre-compensation: the horizontal axis represents the flow velocity v, and the vertical axis represents the output power P. The curve shows that when the flow velocity increases from 0.3 m / s to 1.2 m / s, the power increases linearly from 80% to 180% of the reference power, with a compensation intensity coefficient λ = 1.2. The shaded area represents the compensation range.

[0048] Reference Figure 4 Frequency sweep step adaptive adjustment: The horizontal axis represents the rate of change of water quality parameters R, and the vertical axis represents the frequency sweep step interval Δf_step. When R < 0.02 / s, Δf_step = 5kHz (coarse step); when 0.02 ≤ R ≤ 0.1, Δf_step is linearly interpolated; when R > 0.1, Δf_step = 1kHz (fine step).

[0049] The implementation principle of this embodiment is as follows: During system operation, the multi-parameter detection module 1 continuously monitors water quality. The mode coordination controller 5 decides whether to trigger frequency sweep optimization based on water quality changes and operating time. During frequency sweep, the adaptive stepping module selects the scanning resolution based on the degree of water quality fluctuation and finds the optimal frequency. Subsequently, the system enters fuzzy self-tuning closed-loop tracking, finely adjusting the frequency in real time based on deviations in temperature, conductivity, pH value, and flow velocity. The pre-compensation module adjusts the power in advance based on changes in flow velocity. When the closed-loop tracking output continuously adjusts in the same direction, it indicates that the optimal frequency has drifted, and re-sweeping is automatically triggered. At the same time, the self-diagnosis module uses the physical correlation between parameters to verify the sensor status. Through the above mechanism, the system achieves accurate tracking of the electromagnetic wave frequency to the water body resonant frequency and dynamic optimization of the output power.

[0050] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An intelligent control system for an electromagnetic wave water processor, characterized in that, include: A multi-parameter detection module (1) is installed on a water treatment pipeline to collect multiple water quality parameters of the water body in real time, including temperature, conductivity, pH value and flow rate; Electromagnetic wave generating module (2) is used to generate high-frequency electromagnetic wave signals and apply them to the water body; The frequency sweep optimization module (3) is connected to the electromagnetic wave generation module (2) and is used to scan the output frequency in a step manner within a preset frequency range, and determine the optimal working frequency under the current water quality conditions based on the processing effect parameters fed back by the multi-parameter detection module (1). The fuzzy self-tuning control module (4) is connected to the frequency sweep optimization module (3) and the electromagnetic wave generation module (2). It is used to generate a frequency adjustment amount based on the real-time changes of the multiple water quality parameters, using the optimal working frequency determined by the frequency sweep optimization module (3) as a reference, and to perform closed-loop adjustment of the output frequency of the electromagnetic wave generation module (2). The mode coordination controller (5) is connected to the frequency sweep optimization module (3) and the fuzzy self-tuning control module (4) respectively. After the frequency sweep optimization module (3) completes the determination of the optimal working frequency, it switches the system to the closed-loop tracking mode dominated by the fuzzy self-tuning control module (4). When the frequency adjustment amount output by the fuzzy self-tuning control module (4) accumulates in the same direction for multiple consecutive times and exceeds the preset threshold, it re-triggers the frequency sweep optimization module (3) to perform frequency verification, forming a dynamic coordinated closed loop of frequency sweep-tracking-re-frequency sweep.

2. The intelligent control system for the electromagnetic wave water processor according to claim 1, characterized in that, It also includes an adaptive sweep frequency step adjustment module (6), which is connected to the sweep frequency optimization module (3) and is used to dynamically adjust the sweep frequency step interval according to the water quality parameter change rate fed back by the multi-parameter detection module (1): when the water quality parameter change rate exceeds the first preset threshold, fine step interval is used for fine scanning; when the water quality parameter change rate is lower than the second preset threshold, coarse step interval is used for fast scanning; the sweep frequency optimization module (3) automatically matches the sweep frequency resolution according to the current water quality parameter change rate.

3. The intelligent control system for the electromagnetic wave water processor according to claim 1, characterized in that, It also includes a pre-compensation module (7) based on a physical model. The pre-compensation module (7) is connected to the multi-parameter detection module (1) and the electromagnetic wave generation module (2) respectively. It is used to pre-calculate the compensation amount based on the "electromagnetic wave energy accumulation-action time" physical model according to the influence of water flow velocity change on the effective action time of electromagnetic waves. It also increases the output power and / or adjusts the frequency in advance when the flow velocity increases, and decreases the output power in advance when the flow velocity decreases. The physical model is: E_eff=P×t_contact×η(v,σ), where E_eff is the effective action energy, P is the output power, t_contact is the effective action time of water flowing through the coil, and η is the energy coupling efficiency function related to the flow velocity v and conductivity σ.

4. The intelligent control system for the electromagnetic wave water processor according to claim 1, characterized in that, In the multi-parameter detection module (1), the introduction of pH value is used to correct the temperature-conductivity reference model of the water body resonance frequency: f_res=f_0(T,σ)+k_pH×(pH-pH_0), where f_0(T,σ) is the reference resonance frequency determined by temperature T and conductivity σ, k_pH is the pH value influence coefficient, and pH_0 is the neutral reference value; the fuzzy self-tuning control module (4) uses pH value as an independent input variable to participate in the fuzzy inference of frequency adjustment amount.

5. The intelligent control system for the electromagnetic wave water processor according to claim 1, characterized in that, It also includes a self-diagnosis and sensor redundancy verification module (8), which is connected to the multi-parameter detection module (1) and is used to compare the detection values ​​of the temperature sensor, conductivity sensor, and pH sensor with preset physical correlation constraints. The physical correlation constraints include: the temperature compensation change rate of conductivity should be within a preset range when the temperature rises, and the empirical correlation range between pH and conductivity. When the detection values ​​of any two sensors violate the physical correlation constraints, the corresponding sensor is determined to be faulty and the fault type and fault location signal are output.

6. The intelligent control system for the electromagnetic wave water processor according to claim 1, characterized in that, The mode coordination controller (5) also includes a frequency sweep maintenance timing unit (51) for recording the running time since the last frequency sweep verification. When the running time exceeds the preset time threshold, the frequency sweep optimization module (3) is automatically triggered to perform frequency verification in order to eliminate the drift of the water body resonance frequency caused by the slow change of water quality.

7. A smart control method for an electromagnetic wave water processor based on the system described in any one of claims 1 to 6, characterized in that, Includes the following steps: Step A: The temperature, conductivity, pH value and flow rate of the water body are collected in real time by the multi-parameter detection module (1) and transmitted to the mode co-controller (5); Step B: The mode coordination controller (5) determines whether the frequency sweep optimization triggering conditions are met. The frequency sweep optimization triggering conditions include the initial startup of the system, the rate of change of water quality parameters exceeding the preset threshold, the frequency adjustment amount output by the fuzzy self-tuning control module (4) accumulating in the same direction for multiple consecutive times exceeding the preset threshold, or the time since the last frequency sweep verification exceeding the preset time threshold. Step C: When the frequency sweep optimization trigger condition is met, start the frequency sweep optimization module (3), determine the frequency sweep step interval by the adaptive frequency sweep step adjustment module (6) according to the current water quality parameter change rate, scan the output frequency in a step manner within the preset frequency range, detect the treatment effect parameters at each frequency point, and select the frequency point with the best treatment effect as the optimal working frequency under the current water quality conditions. Step D: Based on the optimal working frequency, start the fuzzy self-tuning control module (4), use temperature, conductivity, pH value and flow rate as multi-dimensional input variables, use fuzzy inference to generate frequency adjustment amount, and perform closed-loop adjustment of the output frequency of the electromagnetic wave generation module (2). Step E: The pre-compensation module (7) based on the physical model calculates the compensation amount in advance according to the "electromagnetic wave energy accumulation-action time" physical model based on the change in flow velocity, and dynamically adjusts the output power; Step F: During the closed-loop adjustment process, the mode coordination controller (5) monitors the cumulative direction of the frequency adjustment output by the fuzzy self-tuning control module (4). When the cumulative frequency adjustment exceeds the preset threshold multiple times in the same direction, it returns to step C to trigger frequency verification.

8. The intelligent control method for the electromagnetic wave water processor according to claim 7, characterized in that, The specific method for determining the optimal operating frequency in step C is as follows: at each frequency step point, record the rate of change of water conductivity and the time required for stabilization, construct a comprehensive effect evaluation function J=w1·Δσ / Δt+w2·(1 / T_stable), and select the frequency point with the largest J value as the optimal operating frequency, where Δσ / Δt is the rate of change of conductivity, T_stable is the stabilization time, and w1 and w2 are weighting coefficients.

9. The intelligent control method for the electromagnetic wave water processor according to claim 7, characterized in that, The fuzzy inference of the fuzzy self-tuning control module (4) in step D includes: mapping the temperature deviation, conductivity deviation, pH value deviation, and flow rate deviation to corresponding fuzzy subsets, performing inference based on a preset multidimensional fuzzy rule table, outputting the fuzzy value of the frequency adjustment amount, and obtaining the accurate frequency adjustment amount after defuzzification; in the multidimensional fuzzy rule table, the pH value deviation is used to correct the frequency adjustment amount corresponding to the temperature-conductivity deviation combination.

10. The intelligent control method for the electromagnetic wave water processor according to claim 7, characterized in that, The calculation of the compensation amount of the pre-compensation module (7) in step E includes: when the flow velocity detection value v is greater than the reference flow velocity v0, the effective action time shortening ratio Δt / t0=(v0 / v-1) is calculated, and the output power P is adjusted to P0×(1+λ·Δt / t0) according to the physical model, where λ is the compensation intensity coefficient and P0 is the reference power; when the flow velocity decreases, the output power is reduced in the same way to save energy consumption.

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