Intelligent electromagnetic scale removal and prevention method and scale removal and prevention device based on hill climbing method

Abstract

The invention discloses an intelligent electromagnetic scale removal and prevention method based on a hill climbing method and a scale removal and prevention device, and relates to the technical field of electromagnetic scale removal and prevention. Based on step length self-adaptive adjustment and direction switching of a conductivity change trend, electromagnetic field frequency tracks water quality fluctuation in real time, and sudden reduction of scale inhibition efficiency caused by resonance mismatch is avoided; guiding multi-coil phase difference iterative adjustment through a field intensity distribution data table pre-stored by finite element simulation; in addition, the dynamic weight distribution algorithm adjusts the fusion weight in real time according to the deviation degree of sensor data, the misjudgment risk in an oil-containing / high-turbidity scene is inhibited, and errors are reduced. Dynamic frequency adjustment and phase cooperative control reduce an invalid electromagnetic field coverage area, and redundant energy consumption is reduced in combination with a closed-loop updating mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field of electromagnetic descaling and antiscaling, and in particular to an intelligent electromagnetic descaling and antiscaling method and a descaling and antiscaling device based on a hill climbing method. Background Art

[0002] Scale removal and prevention technology is widely used in industrial circulating cooling systems, central air conditioning and heat exchange equipment. Its core principle is to change the crystal form of scale-forming ions such as calcium and magnesium in the water body through high-frequency electromagnetic fields, so that they change from a dense structure that is easy to adhere to a loose structure, thereby inhibiting scale formation on the inner wall of the pipe.

[0003] Traditional systems are mostly based on preset frequencies and fixed power output electromagnetic fields, and rely on feedback from water quality sensors for adjustment. However, since the scale inhibition efficiency of the electromagnetic field is strongly correlated with parameters such as the ion concentration and temperature of the water body, when external water replenishment or process changes cause sudden changes in water quality, such as a sudden increase in ion concentration, the originally set resonant frequency will not match the new water quality conditions, and the electromagnetic energy will not be able to effectively couple to the target ions, resulting in a decrease in scale inhibition efficiency. At this time, the system can only trigger manual intervention through a delayed threshold alarm, or rely on a violent output mode with wide-band coverage to maintain the effect, resulting in a surge in energy consumption and equipment loss.

[0004] To alleviate the above problems, some traditional solutions use a lookup table method with dual-parameter feedback of conductivity and temperature to match the approximate frequency based on historical data; some solutions use a multi-frequency alternating scanning mode, but such methods are limited by static models and discrete adjustment mechanisms, making it difficult to achieve dynamic and accurate tracking of high-frequency electromagnetic fields, and are prone to oscillation or overshoot problems in complex water quality fluctuations. Summary of the Invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] The present invention provides an intelligent electromagnetic scale removal and prevention method and scale removal and prevention device based on the hill climbing method to solve the problem that existing solutions are mostly based on preset frequency and fixed power output electromagnetic field, rely on water quality sensor feedback for adjustment, and the effect deteriorates when external water replenishment is introduced or process changes cause sudden changes in water quality.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides an intelligent electromagnetic descaling and antiscaling method based on a hill climbing method, which comprises:

[0009] Step S1, obtaining the conductivity data of the water in the pipeline in real time, and establishing a conductivity-electromagnetic field frequency mapping relationship model based on historical operation data;

[0010] Step S2, extracting the initial electromagnetic field frequency from the mapping relationship model according to the current conductivity data, and controlling the electromagnetic field generation module to output a high-frequency electromagnetic field of the corresponding frequency;

[0011] Step S3, periodically fine-tuning the electromagnetic field frequency with a preset step size, including forward fine-tuning and reverse fine-tuning, and simultaneously monitoring the conductivity change trend;

[0012] Step S4: If the conductivity change trend indicates that the scale inhibition efficiency has improved, the frequency is continuously adjusted along the current fine-tuning direction; if the efficiency has decreased, the fine-tuning direction is switched until the conductivity fluctuation rate is less than the set threshold;

[0013] Step S5: dynamically controlling the phase difference of multiple sets of electromagnetic coils according to the optimized electromagnetic field frequency, and adjusting the excitation phase of each coil based on a pre-stored pipeline cross-section field intensity distribution data table to improve field intensity uniformity;

[0014] In step S6, the real-time data of the conductivity sensor, the optical turbidity sensor, and the capacitance sensor are integrated to generate a water quality status estimation value through a dynamic weight distribution algorithm, and the estimated value is fed back to the frequency mapping model in step S2 for updating.

[0015] As a preferred solution of the intelligent electromagnetic descaling and antiscaling method based on the hill climbing method described in the present invention, in step S3, the preset step size is dynamically adjusted according to the conductivity change rate, including:

[0016] When the conductivity change rate exceeds the first threshold, a large step size fine-tuning is adopted;

[0017] When the conductivity change rate is lower than the second threshold, the fine adjustment is switched to small step size.

[0018] As a preferred solution of the intelligent electromagnetic scale removal and prevention method based on the hill climbing method described in the present invention, in step S3, the method of periodically fine-tuning the electromagnetic field frequency with a preset step size is:

[0019] Combined with the rate of change of water conductivity, dynamic step size and fine-tuning period are introduced to define the electromagnetic field frequency update formula as follows: f n+1 =f n +d, where f n+1 Indicates the electromagnetic field frequency in the n+1th cycle, f n represents the electromagnetic field frequency in the nth cycle, d represents the fine-tuning direction, a value of +1 represents positive fine-tuning, -1 represents negative fine-tuning, and δ represents the fine-tuning step size used in this cycle;

[0020] The step size adaptation strategy is set according to the water conductivity change rate g, and the actual step size is determined by the conditional function. The formula is:

[0021] If |g|>β1, then δ=δ L , if |g|<β2, then δ=δ S , in other cases δ=δ0,

[0022] Among them, g represents the rate of change of water conductivity, β1 represents the threshold value triggered by large step length, β2 represents the threshold value triggered by small step length, δ L Indicates the large step size used when the conductivity changes at a large rate, δ S Indicates the small step size used when the conductivity change rate is small, δ0 indicates the intermediate step size used when the conductivity is between two thresholds, the small step size is 1-5% of the fundamental frequency, and the large step size is 10-20% of the first preset step size, which can be adjusted as needed;

[0023] The relationship between the set period T and the conductivity change rate is:

[0024]

[0025] Where T represents the fine-tuning period, κ represents the adjustment constant used for scale conversion, g represents the rate of change of water conductivity, and ∈ represents a small positive number to prevent the denominator from being zero.

[0026] As a preferred solution of the intelligent electromagnetic descaling and antiscaling method based on the hill climbing method described in the present invention, in step S5, the phase difference adjustment of the multiple sets of electromagnetic coils includes:

[0027] Based on the pipeline geometric parameters and the pre-stored field intensity distribution data table, the phase difference between adjacent coils is used as the optimization variable, and the field intensity uniformity is used as the optimization target. The phase difference is iteratively adjusted in the gradient direction until the field intensity standard deviation is less than the set value.

[0028] As a preferred solution of the intelligent electromagnetic descaling and antiscaling method based on the hill climbing method described in the present invention, in step S5, the pre-stored field intensity distribution data table is generated as follows:

[0029] The pre-stored field intensity distribution data table is generated in advance through finite element simulation. During the finite element simulation process, the phase difference of each coil is discretely valued, and the standard deviation of the field intensity distribution on the corresponding pipeline cross section is calculated. The data table construction process is expressed as follows:

[0030] Among them, S j represents the jth group of simulation data records, represents the phase difference between the first pair of adjacent coils in the jth group, represents the phase difference between the second pair of adjacent coils in the jth group, represents the phase difference between the mth pair of adjacent coils in the jth group, where m represents the total number of adjacent coil pairs, σ j represents the standard deviation of the field intensity distribution in the pipeline section corresponding to the jth group of simulation data;

[0031] The preset phase difference range is [φ min ,φ max ], where φ min Represents the lower limit of the phase difference, φ max Indicates the upper limit of the phase difference;

[0032] The calling rules require that, first, it is determined whether the phase difference of the current coil falls within the above-mentioned interval. If no direct match is found, the field strength distribution data corresponding to the similar combination is estimated by interpolation method, and the validity of the data table is required to be maintained within the system calibration cycle. After the cycle is exceeded, data update or online correction will be triggered.

[0033] As a preferred solution of the intelligent electromagnetic descaling and antiscaling method based on the hill climbing method described in the present invention, the dynamic weight allocation algorithm in step S6 specifically includes:

[0034] The confidence weight is calculated based on the deviation of each sensor data from the historical mean, where the sensor with lower deviation has higher weight, and the total weight sum is 1.

[0035] As a preferred solution of the intelligent electromagnetic scale removal and prevention method based on the hill climbing method described in the present invention, in step S6, the method of calculating the confidence weight according to the deviation between each sensor data and the historical mean is:

[0036] Calculate the deviation between the current data of each sensor and the historical mean. The calculation formula is:

[0037] d i =|x i -μ i |, where d i Indicates the deviation of the i-th sensor, x i represents the real-time data of the i-th sensor, μ i represents the historical mean of the i-th sensor;

[0038] The confidence weight is constructed through the exponential decay function, and the weight calculation formula is:

[0039]

[0040] Among them, w i represents the confidence weight of the i-th sensor, λ represents the scale factor of weight sensitivity, N represents the total number of sensors, j is the sensor index, and its value range is 1, 2, ..., N;

[0041] As a result, sensors with lower deviations obtain higher confidence weights, while the sum of all weights is equal to 1, providing a basis for real-time estimation of water quality status.

[0042] In a second aspect, the present invention provides an intelligent electromagnetic descaling and antiscaling device based on a hill climbing method, comprising:

[0043] Main control module, including:

[0044] Dynamic frequency adjustment submodule, used to perform frequency fine-tuning and direction switching according to the conductivity change trend;

[0045] Phase coordination control submodule, used to generate multi-coil phase difference control instructions based on a pre-stored field intensity distribution data table;

[0046] The weight dynamic allocation submodule is used to calculate the fusion weight according to the deviation of sensor data;

[0047] An electromagnetic field generation module, comprising multiple sets of electromagnetic coils with independently adjustable frequency and phase;

[0048] Sensor modules, including conductivity sensors, optical turbidity sensors, and capacitance sensors, are used to collect water quality data in pipelines;

[0049] The data storage module pre-stores the pipeline cross-section field intensity distribution data table and the conductivity-frequency mapping relationship model.

[0050] As a preferred solution of the intelligent electromagnetic descaling and antiscaling device based on the hill climbing method described in the present invention, the phase coordinated control submodule further includes:

[0051] The field intensity uniformity evaluation unit is used to calculate the current field intensity distribution standard deviation based on the real-time phase difference and the pre-stored data table, and feed it back to the phase difference adjustment instruction generation unit.

[0052] As a preferred solution of the intelligent electromagnetic descaling and antiscaling device based on the hill climbing method described in the present invention, wherein: in the field strength uniformity evaluation unit:

[0053] The matching error between the real-time measured field intensity distribution and the pre-stored finite element simulation data is compared with each set of simulation data in the pre-stored data table, and the root mean square error is calculated. The calculation formula is:

[0054]

[0055] Among them, ε j represents the RMSE error corresponding to the jth group of pre-stored data, n represents the total number of measurement points, It represents the field strength measured in real time at the i-th measurement point, Indicates the field strength of the jth group of simulation data at the i-th measurement point in the pre-stored data table, where i represents the measurement point index and j represents the index of the pre-stored data group;

[0056] Select the smallest error among all groups:

[0057]

[0058] Among them, ε min represents the minimum RMSE error among all pre-stored data groups corresponding to the real-time measurement data, and j represents the index of the pre-stored data group;

[0059] Then define the field intensity uniformity quantification index U as U = exp(-γε min ), where U represents the quantitative index of field strength uniformity, ranging from 0 to 1, γ represents the proportional factor used to adjust the uniformity sensitivity, and ε min Indicates the minimum RMSE error;

[0060] Through the above calculation process, the real-time field strength measurement value is matched with the simulation data in the pre-stored data table to form a unified quantitative index, which provides a reference for the judgment of field strength uniformity and subsequent phase adjustment.

[0061] The beneficial effects of the present invention are as follows: the present invention adaptively adjusts the step size and switches the direction based on the conductivity change trend, so that the electromagnetic field frequency tracks the water quality fluctuation in real time, avoids the sudden drop in scale inhibition efficiency caused by resonance mismatch, and guides the iterative adjustment of the multi-coil phase difference through the pre-stored field strength distribution data table of finite element simulation; in addition, the dynamic weight allocation algorithm adjusts the fusion weight in real time according to the deviation of sensor data, suppresses the risk of misjudgment in oil-containing / high turbidity scenarios, and reduces errors; dynamic frequency adjustment and phase coordinated control reduce the invalid electromagnetic field coverage area, and combine with the closed-loop update mechanism to reduce redundant energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0063] Figure 1 This is a flow chart of the intelligent electromagnetic descaling and antiscaling method based on the hill climbing method in Example 1.

[0064] Figure 2 This is a schematic diagram of the framework of the intelligent electromagnetic descaling and antiscaling device based on the hill climbing method in Example 1. DETAILED DESCRIPTION

[0065] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0066] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0067] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0068] Example 1, with reference to Figure 1 and Figure 2 This embodiment provides an intelligent electromagnetic descaling and antiscaling method based on a hill climbing method, comprising the following steps:

[0069] Step S1, obtaining the conductivity data of the water in the pipeline in real time, and establishing a conductivity-electromagnetic field frequency mapping relationship model based on historical operation data;

[0070] Step S2: extracting the initial electromagnetic field frequency from the mapping relationship model according to the current conductivity data, and controlling the electromagnetic field generation module to output a high-frequency electromagnetic field of the corresponding frequency;

[0071] Step S3, periodically fine-tuning the electromagnetic field frequency with a preset step size, including forward fine-tuning and reverse fine-tuning, and simultaneously monitoring the conductivity change trend;

[0072] In step S3, the preset step size is dynamically adjusted according to the conductivity change rate, including:

[0073] When the conductivity change rate exceeds the first threshold, a large step size fine-tuning is adopted;

[0074] When the conductivity change rate is lower than the second threshold, it switches to small step fine-tuning;

[0075] In step S3, the method of periodically fine-tuning the electromagnetic field frequency with a preset step size is as follows:

[0076] Combined with the rate of change of water conductivity, dynamic step size and fine-tuning period are introduced to define the electromagnetic field frequency update formula as follows: f n+1 =f n +d, where f n+1 Indicates the electromagnetic field frequency in the n+1th cycle, f n represents the electromagnetic field frequency in the nth cycle, d represents the fine-tuning direction, a value of +1 represents positive fine-tuning, -1 represents negative fine-tuning, and δ represents the fine-tuning step size used in this cycle;

[0077] The step size adaptation strategy is set according to the water conductivity change rate g, and the actual step size is determined by the conditional function. The formula is:

[0078] If |g|>β1, then δ=δ L , if |g|<β2, then δ=δ S , in other cases δ=δ0,

[0079] Among them, g represents the rate of change of water conductivity, β1 represents the threshold value triggered by large step length, β2 represents the threshold value triggered by small step length, δ L Indicates the large step size used when the conductivity changes at a large rate, δ S Indicates the small step size used when the conductivity change rate is small, δ0 indicates the intermediate step size used when the conductivity is between two thresholds, the small step size is 1-5% of the fundamental frequency, and the large step size is 10-20% of the first preset step size, which can be adjusted as needed;

[0080] The relationship between the set period T and the conductivity change rate is:

[0081]

[0082] Where T represents the fine-tuning period, κ represents the adjustment constant used for scale conversion, g represents the rate of change of water conductivity, and ∈ represents a small positive number to prevent the denominator from being zero;

[0083] Specifically, a continuously updated frequency fine-tuning mechanism is constructed to achieve dynamic adjustment of the electromagnetic field frequency. A dynamic step-size mechanism is adopted to enable the system to respond quickly when water quality changes drastically, while making fine adjustments in a stable state, thereby helping to maintain efficient descaling and anti-scaling effects. The inverse relationship between the fine-tuning period and the rate of change of conductivity ensures that the frequency adjustment action is synchronized with the actual water quality status in real time, providing high robustness in responding to sudden changes.

[0084] Step S4: If the conductivity change trend indicates that the scale inhibition efficiency has improved, the frequency is continuously adjusted along the current fine-tuning direction; if the efficiency has decreased, the fine-tuning direction is switched until the conductivity fluctuation rate is less than the set threshold;

[0085] Step S5: dynamically controlling the phase difference of multiple sets of electromagnetic coils according to the optimized electromagnetic field frequency, and adjusting the excitation phase of each coil based on a pre-stored pipeline cross-section field intensity distribution data table to improve field intensity uniformity;

[0086] In step S5, the phase difference adjustment of the multiple sets of electromagnetic coils includes:

[0087] Based on the pipeline geometry parameters and the pre-stored field intensity distribution data table, the phase difference between adjacent coils is used as the optimization variable, and the field intensity uniformity is used as the optimization target. The phase difference is iteratively adjusted in the gradient direction until the field intensity standard deviation is less than the set value.

[0088] In step S5, the pre-stored field intensity distribution data table is generated in the following manner:

[0089] The pre-stored field intensity distribution data table is generated in advance through finite element simulation. During the finite element simulation process, the phase difference of each coil is discretely valued, and the standard deviation of the field intensity distribution on the corresponding pipeline cross section is calculated. The data table construction process is expressed as follows:

[0090] Among them, S j represents the jth group of simulation data records, represents the phase difference between the first pair of adjacent coils in the jth group, represents the phase difference between the second pair of adjacent coils in the jth group, represents the phase difference between the mth pair of adjacent coils in the jth group, where m represents the total number of adjacent coil pairs, σ j represents the standard deviation of the field intensity distribution in the pipeline section corresponding to the jth group of simulation data;

[0091] The preset phase difference range is [φ min ,φ max ], where φ min Represents the lower limit of the phase difference, φ max Indicates the upper limit of the phase difference;

[0092] The call rule requires that the phase difference of the current coil be determined to be within the above range. If a direct match is not found, the field intensity distribution data corresponding to the close combination is estimated through interpolation. The data table must be valid within the system calibration period. If the period is exceeded, a data update or online correction will be triggered.

[0093] Specifically, the data table generated by finite element simulation provides an offline calculation basis for online phase coordinated control. The discrete phase difference combination and the corresponding field intensity distribution record enable the system to quickly find the corresponding indicators during real-time optimization, thereby guiding the iterative adjustment of the phase difference in the gradient direction to minimize the standard deviation of the field intensity distribution. The preset phase difference interval and data interpolation method ensure the continuity and reliability of numerical acquisition. Setting data call rules and limiting the calibration cycle not only improves the real-time nature of the data, but also prevents deviations introduced by old data. The overall solution builds a solid numerical bridge between offline simulation data and online control, effectively improving the overall collaborative efficiency of the system.

[0094] Step S6: Integrate the real-time data from the conductivity sensor, optical turbidity sensor, and capacitance sensor to generate a water quality status estimate using a dynamic weight allocation algorithm, and feed it back to the frequency mapping model in step S2 for updating;

[0095] The dynamic weight allocation algorithm in step S6 specifically includes:

[0096] The confidence weight is calculated based on the deviation of each sensor data from the historical mean, where the sensor with lower deviation has higher weight, and the total weight sum is 1;

[0097] In step S6, the confidence weight is calculated based on the deviation of each sensor data from the historical mean as follows:

[0098] Calculate the deviation between the current data of each sensor and the historical mean. The calculation formula is:

[0099] d i =|x i -μ i |, where d i Indicates the deviation of the i-th sensor, x i represents the real-time data of the i-th sensor, μ i represents the historical mean of the i-th sensor;

[0100] The confidence weight is constructed through the exponential decay function, and the weight calculation formula is:

[0101]

[0102] Among them, w i represents the confidence weight of the i-th sensor, λ represents the scale factor of weight sensitivity, N represents the total number of sensors, j is the sensor index, and its value range is 1, 2, ..., N;

[0103] As a result, sensors with lower deviations are given higher confidence weights, and the sum of all weights is equal to 1, providing a basis for real-time estimation of water quality status.

[0104] Specifically, a two-level calculation process is adopted. First, the absolute difference between the real-time sensor data and the historical mean is calculated to quantify the deviation of each sensor reading. Then, an exponential decay function is used to convert the deviation into a confidence weight. The smaller the deviation, the higher the weight. The denominator is used to normalize the weights of all sensors to ensure that the sum is 1, meeting the requirements of dynamic data fusion. This method automatically gives a higher proportion to stable data when the data fluctuation is small, and reduces its weight when the data is abnormal, thereby enhancing the robustness of water quality state estimation.

[0105] This embodiment also provides an intelligent electromagnetic descaling and antiscaling device based on the hill climbing method, comprising:

[0106] Main control module, including:

[0107] Dynamic frequency adjustment submodule, used to perform frequency fine-tuning and direction switching according to the conductivity change trend;

[0108] Phase coordination control submodule, used to generate multi-coil phase difference control instructions based on a pre-stored field intensity distribution data table;

[0109] The weight dynamic allocation submodule is used to calculate the fusion weight according to the deviation of sensor data;

[0110] An electromagnetic field generation module, comprising multiple sets of electromagnetic coils with independently adjustable frequency and phase;

[0111] Sensor modules, including conductivity sensors, optical turbidity sensors, and capacitance sensors, are used to collect water quality data in pipelines;

[0112] Data storage module, pre-stores the pipeline cross-section field intensity distribution data table and the conductivity-frequency mapping relationship model;

[0113] The phase coordination control submodule also includes:

[0114] A field intensity uniformity evaluation unit is used to calculate the current field intensity distribution standard deviation based on the real-time phase difference and the pre-stored data table, and feed it back to the phase difference adjustment instruction generation unit;

[0115] In the field strength uniformity evaluation unit:

[0116] The matching error between the real-time measured field intensity distribution and the pre-stored finite element simulation data is compared with each set of simulation data in the pre-stored data table, and the root mean square error is calculated. The calculation formula is:

[0117]

[0118] Among them, ε j represents the RMSE error corresponding to the jth group of pre-stored data, n represents the total number of measurement points, It represents the field strength measured in real time at the i-th measurement point, Indicates the field strength of the jth group of simulation data at the i-th measurement point in the pre-stored data table, where i represents the measurement point index and j represents the index of the pre-stored data group;

[0119] Select the smallest error among all groups:

[0120]

[0121] Among them, ε min represents the minimum RMSE error among all pre-stored data groups corresponding to the real-time measurement data, and j represents the index of the pre-stored data group;

[0122] Then define the field intensity uniformity quantification index U as U = exp(-γε min ), where U represents the quantitative index of field strength uniformity, ranging from 0 to 1, γ represents the proportional factor used to adjust the uniformity sensitivity, and ε min Indicates the minimum RMSE error;

[0123] Through the above calculation process, the real-time field strength measurement value is matched with the simulation data in the pre-stored data table to form a unified quantitative index, which provides a reference for judging the field strength uniformity and subsequent phase adjustment;

[0124] Specifically, this step constructs a field strength uniformity evaluation mechanism based on RMSE error. The error value is calculated by comparing the real-time field strength distribution data with the pre-stored finite element simulation data, and the minimum error is selected as the optimal matching standard. The minimum error is mapped to a uniformity index using an exponential function. The closer the index value is to 1, the higher the uniformity, and vice versa. This method makes full use of the simulation results of the offline pre-stored data table and plays a bridging role in real-time monitoring and control.

[0125] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent electromagnetic descaling and antiscaling method based on the hill climbing method, characterized by: include, Step S1, obtaining the conductivity data of the water in the pipeline in real time, and establishing a conductivity-electromagnetic field frequency mapping relationship model based on historical operation data; Step S2, extracting the initial electromagnetic field frequency from the mapping relationship model according to the current conductivity data, and controlling the electromagnetic field generation module to output a high-frequency electromagnetic field of the corresponding frequency; Step S3, periodically fine-tuning the electromagnetic field frequency with a preset step size, including forward fine-tuning and reverse fine-tuning, and simultaneously monitoring the conductivity change trend; Step S4: If the conductivity change trend indicates that the scale inhibition efficiency has improved, the frequency is continuously adjusted along the current fine-tuning direction; if the efficiency has decreased, the fine-tuning direction is switched until the conductivity fluctuation rate is less than the set threshold; Step S5, dynamically controlling the phase difference of the multiple sets of electromagnetic coils according to the optimized electromagnetic field frequency, and adjusting the excitation phase of each coil based on a pre-stored pipeline cross-section field intensity distribution data table; In step S6, the real-time data of the conductivity sensor, the optical turbidity sensor, and the capacitance sensor are integrated to generate a water quality status estimation value through a dynamic weight distribution algorithm, and the estimated value is fed back to the frequency mapping model in step S2 for updating.

2. The intelligent electromagnetic descaling and antiscaling method based on the hill climbing method according to claim 1, characterized in that: In step S3, the preset step size is dynamically adjusted according to the conductivity change rate, including: When the conductivity change rate exceeds the first threshold, a large step size fine-tuning is adopted; When the conductivity change rate is lower than the second threshold, the fine adjustment is switched to small step size.

3. The intelligent electromagnetic descaling and antiscaling method based on the hill climbing method according to claim 2, characterized in that: In step S3, the method of periodically fine-tuning the electromagnetic field frequency with a preset step size is: Combined with the rate of change of water conductivity, dynamic step size and fine-tuning period are introduced to define the electromagnetic field frequency update formula as follows: f n+1 =f n +d, where f n+1 Indicates the electromagnetic field frequency in the n+1th cycle, f n represents the electromagnetic field frequency in the nth cycle, d represents the fine-tuning direction, a value of +1 represents positive fine-tuning, -1 represents negative fine-tuning, and δ represents the fine-tuning step size used in this cycle; The step size adaptation strategy is set according to the water conductivity change rate g, and the actual step size is determined by the conditional function. The formula is: If |g|>β1, then δ=δ L , if |g|<β2, then δ=δ S , in other cases δ=δ0, Among them, g represents the rate of change of water conductivity, β1 represents the threshold value triggered by large step length, β2 represents the threshold value triggered by small step length, δ L Indicates the large step size used when the conductivity changes at a large rate, δ S It represents the small step size used when the conductivity change rate is small, and δ0 represents the intermediate step size used when the conductivity is between two thresholds. The relationship between the set period T and the conductivity change rate is: Where T represents the fine-tuning period, κ represents the adjustment constant used for scale conversion, g represents the rate of change of water conductivity, and ∈ represents a small positive number to prevent the denominator from being zero.

4. The intelligent electromagnetic descaling and antiscaling method based on the hill climbing method according to claim 1, characterized in that: In step S5, the phase difference adjustment of the multiple sets of electromagnetic coils includes: Based on the pipeline geometric parameters and the pre-stored field intensity distribution data table, the phase difference between adjacent coils is used as the optimization variable, and the field intensity uniformity is used as the optimization target. The phase difference is iteratively adjusted in the gradient direction until the field intensity standard deviation is less than the set value.

5. The intelligent electromagnetic descaling and antiscaling method based on the hill climbing method according to claim 4, characterized in that: In step S5, the pre-stored field intensity distribution data table is generated in the following manner: The pre-stored field intensity distribution data table is generated in advance through finite element simulation. During the finite element simulation process, the phase difference of each coil is discretely valued, and the standard deviation of the field intensity distribution on the corresponding pipeline cross section is calculated. The data table construction process is expressed as follows: Among them, S j represents the jth group of simulation data records, represents the phase difference between the first pair of adjacent coils in the jth group, represents the phase difference between the second pair of adjacent coils in the jth group, represents the phase difference between the mth pair of adjacent coils in the jth group, where m represents the total number of adjacent coil pairs, σ j represents the standard deviation of the field intensity distribution in the pipeline section corresponding to the jth group of simulation data; The preset phase difference range is [φ min ,φ max ], where φ min Represents the lower limit of the phase difference, φ max Indicates the upper limit of the phase difference; The calling rules require that, first, it is determined whether the phase difference of the current coil falls within the above range. If no direct match is found, the field strength distribution data corresponding to the similar combination is estimated through interpolation method, and the validity of the data table is required to be maintained within the system calibration period. After the period is exceeded, data update or online correction will be triggered.

6. The intelligent electromagnetic descaling and antiscaling method based on the hill climbing method according to claim 1, characterized in that: The dynamic weight allocation algorithm in step S6 specifically includes: The confidence weight is calculated based on the deviation of each sensor data from the historical mean, where the sensor with lower deviation has higher weight, and the total weight sum is 1.

7. The intelligent electromagnetic descaling and antiscaling method based on the hill climbing method according to claim 6, characterized in that: In step S6, the confidence weight is calculated based on the deviation between each sensor data and the historical mean value as follows: Calculate the deviation between the current data of each sensor and the historical mean. The calculation formula is: d i =|x i -μ i |, where d i Indicates the deviation of the i-th sensor, x i represents the real-time data of the i-th sensor, μ i represents the historical mean of the i-th sensor; The confidence weight is constructed through the exponential decay function, and the weight calculation formula is: Among them, w i represents the confidence weight of the i-th sensor, λ represents the scale factor of the weight sensitivity, N represents the total number of sensors, and j is the sensor index, which ranges from 1, 2, …, N.

8. An intelligent electromagnetic descaling and antiscaling device based on the hill climbing method, based on the intelligent electromagnetic descaling and antiscaling method based on the hill climbing method according to any one of claims 1 to 7, characterized in that: include: Main control module, including: Dynamic frequency adjustment submodule, used to perform frequency fine-tuning and direction switching according to the conductivity change trend; Phase coordination control submodule, used to generate multi-coil phase difference control instructions based on a pre-stored field intensity distribution data table; The weight dynamic allocation submodule is used to calculate the fusion weight according to the deviation of sensor data; An electromagnetic field generation module, comprising multiple sets of electromagnetic coils with independently adjustable frequency and phase; Sensor modules, including conductivity sensors, optical turbidity sensors, and capacitance sensors, are used to collect water quality data in pipelines; The data storage module pre-stores the pipeline cross-section field intensity distribution data table and the conductivity-frequency mapping relationship model.

9. The intelligent electromagnetic descaling and antiscaling device based on the hill climbing method according to claim 8, characterized in that: The phase coordinated control submodule also includes: The field intensity uniformity evaluation unit is used to calculate the current field intensity distribution standard deviation based on the real-time phase difference and the pre-stored data table, and feed it back to the phase difference adjustment instruction generation unit.

10. The intelligent electromagnetic descaling and antiscaling device based on the hill climbing method according to claim 9, characterized in that: In the field intensity uniformity evaluation unit: The matching error between the real-time measured field intensity distribution and the pre-stored finite element simulation data is compared with each set of simulation data in the pre-stored data table, and the root mean square error is calculated. The calculation formula is: Among them, ε j represents the RMSE error corresponding to the jth group of pre-stored data, n represents the total number of measurement points, It represents the field strength measured in real time at the i-th measurement point, Indicates the field strength of the jth group of simulation data at the i-th measurement point in the pre-stored data table, where i represents the measurement point index and j represents the index of the pre-stored data group; Select the smallest error among all groups: Among them, ε min represents the minimum RMSE error among all pre-stored data groups corresponding to the real-time measurement data, and j represents the index of the pre-stored data group; Then define the field intensity uniformity quantification index U as U = exp(-γε min ), where U represents the quantitative index of field strength uniformity, ranging from 0 to 1, γ represents the proportional factor used to adjust the uniformity sensitivity, and ε min Indicates the minimum RMSE error.

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