Intelligent anti-scaling agent feeding control system and method based on Internet of Things

Abstract

The invention discloses an intelligent scale remover feeding control system and method based on the Internet of Things, and the method comprises the steps: obtaining water quality parameter data and particle size distribution data in a water treatment system, and calculating a current particle size abnormal factor according to the data; a water quality particle size dynamic feedback model is constructed based on the particle size abnormal factors, and scaling risk indexes are obtained; calculating a particle size weight feedback value based on the scaling risk index to obtain a weight feedback value for assisting in judging a scaling trend; and according to the scaling risk index and the weight feedback value, jointly judging a scaling trend risk, and realizing intelligent descaling agent putting control based on the Internet of Things. According to the method, the synergistic effect of water quality parameters and particle characteristics can be comprehensively captured, dynamic response to water quality fluctuation under different working conditions is achieved, agent waste caused by excessive feeding is prevented, the scaling risk caused by insufficient response is avoided, and feeding control over the descaling agent is more accurate and efficient.

Description

Technical Field

[0001] The present invention relates to the technical field of descaling agent delivery, and in particular to an intelligent descaling agent delivery control system and method based on the Internet of Things. Background Art

[0002] Scaling in water treatment systems directly impacts the heat transfer efficiency and service life of equipment. Traditional descaling agent dosing control relies primarily on manual experience or fixed threshold alarm mechanisms. With the development of Internet of Things (IoT) technology, real-time monitoring of water quality parameters (such as hardness and pH) and particle distribution characteristics has become possible, providing a data foundation for intelligent descaling control. Existing technologies have attempted to collect water quality data through sensor networks and predict scaling trends based on linear models of single parameters (such as calcium ion concentration). However, these technologies fail to fully integrate the dynamic distribution characteristics of particles with the synergistic effects of multiple parameters, resulting in insufficient prediction accuracy. Establishing a dynamic feedback model that integrates the multidimensional characteristics of water quality and particle size to achieve precise descaling agent dosing has become a key area of ​​technological improvement.

[0003] Existing descaling agent dosing control methods have the following major flaws: First, existing control methods primarily rely on a single water quality parameter, such as calcium ion concentration or conductivity, to determine scaling, ignoring the critical impact of particle size distribution on the scaling process. This limitation stems from the limited detection capabilities of traditional sensors, which prevent the system from capturing the dynamic changes in particle aggregation. In actual operation, even if the calcium ion concentration is normal, an abnormal particle size distribution may still cause severe scaling. Second, the currently used linear assessment model is based on fixed parameter weights and cannot dynamically adjust the relationship between water quality parameters and particle size characteristics. This problem stems from the model's failure to consider the nonlinear coupling effects between water quality parameters (such as pH and water temperature). When water quality conditions fluctuate, the system either overreacts, resulting in wasted descaling agent, or underreacts, causing equipment scaling. This deficiency is particularly evident during seasonal water quality fluctuations. Summary of the Invention

[0004] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of this application to avoid obscuring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions should not be used to limit the scope of the present invention.

[0005] In view of the problems existing in the above-mentioned existing intelligent descaling agent delivery control system and method based on the Internet of Things, the present invention is proposed.

[0006] Therefore, the purpose of the present invention is to provide an intelligent descaling agent delivery control system and method based on the Internet of Things, which is suitable for solving the problems that the existing control methods are difficult to identify the early micro-scaling trend and cannot adapt to the time-varying coupling relationship between water quality parameters and particle size characteristics.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, an embodiment of the present invention provides an intelligent descaling agent delivery control method based on the Internet of Things, comprising: Acquiring water quality parameter data and particle size distribution data in the water treatment system, and calculating a current particle size anomaly factor based on the data; Based on the particle size anomaly factor, a water quality particle size dynamic feedback model is constructed to obtain a scaling risk index; Calculating a particle size weight feedback value based on the scaling risk index to obtain a weight feedback value for assisting in determining scaling trends; The scaling trend risk is jointly judged according to the scaling risk index and the weighted feedback value, thereby realizing intelligent descaling agent delivery control based on the Internet of Things.

[0008] As a preferred embodiment of the intelligent descaling agent dosing control method based on the Internet of Things of the present invention, the water quality parameter data refers to real-time physical and chemical data including water hardness, conductivity, pH value, water temperature and water flow rate acquired by Internet of Things sensors deployed in the water treatment system; The particle size distribution data refers to the particle size and distribution frequency data of suspended particles in water obtained by using a laser particle size analyzer or a micro optical sensor; The particle size anomaly factor refers to a quantitative indicator calculated based on the degree of deviation between the particle size distribution data obtained at the current moment and the historical reference particle size mean and standard deviation.

[0009] As a preferred solution of the intelligent descaling agent dosage control method based on the Internet of Things of the present invention, a water quality particle size dynamic feedback model is constructed based on the particle size abnormality factor, including the following steps: By introducing the square normalization method to quantify the abnormal deviation of real-time particle size data, the particle size abnormality characteristic value is obtained, thereby enhancing the model's ability to capture dynamic deviations of particle size. The specific formula is as follows: ; in, It represents the square average intensity of the deviation of the particle size from the mean in the current water body. The larger the value, the more obvious the potential scaling trend. By introducing a linear weighting method to normalize the combined effect of calcium ion concentration and pH value, the water quality impact adjustment factor value is obtained, thereby improving the model's ability to assess the scaling risk driven by water quality parameters. The specific formula is as follows: ; in, It is a water quality influencing factor, reflecting the enhancement effect of the water environment on the tendency of particle scaling. Indicates the calcium ion concentration at the current moment, is the normalized reference concentration, P is the current pH value of the water body, and is the adjustment coefficient, which is used to control the relative importance of each water quality factor; Based on the obtained square average intensity of the particle size deviation from the mean in the current water body and the water quality influencing factors, a water quality particle size dynamic feedback model is constructed to make a preliminary prediction of the scaling risk of the water body.

[0010] As a preferred solution of the intelligent descaling agent delivery control method based on the Internet of Things of the present invention, the specific formula of the water quality particle size dynamic feedback model is as follows: ; in, It represents the scaling risk index, which is an important indicator for assessing whether the water body has entered a high scaling risk state.

[0011] As a preferred solution of the intelligent descaling agent dosage control method based on the Internet of Things of the present invention, wherein: calculating the particle size weight feedback value based on the scaling risk index to obtain the weight feedback value for assisting in judging the scaling trend includes the following steps: By introducing a nonlinear power function to normalize the scaling risk index, 90% quantile particle size, and calcium ion concentration, a dynamic weight adjustment factor is obtained to balance the sensitivity of each parameter and suppress the interference of extreme values. The specific formula is as follows: ; in, is the dynamic weight adjustment factor, is the historical mean of the scaling risk index, It is a risk adjustment indicator to control the amplification degree of risk deviation. is the 90% quantile particle size of the current particle distribution, is the reference particle size, which is usually taken as the historical normal value, and m is the particle size adjustment index, which controls the weight of abnormal particle size. is the current calcium ion concentration, The reference calcium ion concentration is usually taken as the saturation concentration, and n is the concentration adjustment index to control the sensitivity of the concentration influence. By introducing the exponential time decay function and the abnormal threshold screening mechanism, the dynamic weight adjustment factor is weighted and accumulated to obtain the weighted feedback value to quantify the long-term scaling risk. The specific formula is as follows: ; in, is the weighted feedback value, is the time attenuation coefficient, which controls the attenuation speed of the influence of historical data. is the time interval between the current moment and the i-th historical data point, is the dynamic weight adjustment factor at the i-th historical moment, is the abnormality judgment threshold, is the indicator function, when When , it takes 1, otherwise it takes 0, The total number of data points within the time window for the cumulative calculation.

[0012] As a preferred solution of the intelligent descaling agent delivery control method based on the Internet of Things described in the present invention, the scaling trend risk includes normal state, mild risk, moderate risk and severe risk.

[0013] As a preferred solution of the intelligent descaling agent delivery control method based on the Internet of Things of the present invention, the scaling trend risk is jointly determined based on the scaling risk index and the weighted feedback value, including the following steps: A basic early warning judgment is made based on the scaling risk index and weighted feedback value. If the scaling risk index is less than or equal to the mild risk threshold, and the weighted feedback value is less than or equal to the mild cumulative threshold, the current state is determined to be normal, the current descaling agent dosage is maintained, and the water treatment system is continuously monitored; If the scaling risk index is less than or equal to the mild risk threshold, and the weighted feedback value is greater than the mild cumulative threshold, the current state is preliminarily determined to be mild risk, and the next step is executed; When the preliminary judgment result is a mild risk, an early warning upgrade judgment is made based on the particle size anomaly factor and the water quality impact adjustment factor. If the particle size anomaly factor is less than or equal to the particle size threshold, and the water quality impact adjustment factor is less than or equal to the water quality threshold, the current state is judged to be a mild risk and a first-level adjustment instruction is sent to the control system. If the particle size anomaly factor is greater than the particle size threshold or the water quality impact adjustment factor is greater than the water quality threshold, the current state is determined to be a moderate risk and a secondary adjustment instruction is sent to the control system; If the scaling risk index is greater than the mild risk threshold, and the weighted feedback value is less than or equal to the moderate cumulative threshold, the current state is preliminarily determined to be moderate risk, and the next step is executed; When the preliminary judgment result is moderate risk, the dynamic feedback model is immediately started to recalculate the scaling risk index; if the recalculated scaling risk index is less than or equal to the moderate risk threshold, the current state is determined to be moderate risk, and a secondary adjustment instruction is sent to the control system; If the recalculated scaling risk index is greater than the moderate risk threshold, the current state is determined to be a severe risk and a three-level adjustment instruction is sent to the control system; If the scaling risk index is greater than the moderate risk threshold and the weighted feedback value is greater than the moderate cumulative threshold, the current state is determined to be a severe risk and a level 3 control instruction is sent to the control system.

[0014] Secondly, in order to further solve the problem that existing control methods are difficult to identify early micro-scaling trends and cannot adapt to the time-varying coupling relationship between water quality parameters and particle size characteristics, the embodiment of the present invention provides an intelligent descaling agent delivery control system based on the Internet of Things, including: Data acquisition module: used to obtain water quality parameter data and particle size distribution data in the water treatment system, and calculate the current particle size abnormality factor based on the data; Model building module: used to build a water quality particle size dynamic feedback model based on the particle size anomaly factor to obtain a scaling risk index; Weight calculation module: used to calculate the particle size weight feedback value based on the scaling risk index to obtain the weight feedback value used to assist in judging the scaling trend; Intelligent judgment module: used to jointly judge the scaling trend risk according to the scaling risk index and the weight feedback value, and realize intelligent descaling agent delivery control based on the Internet of Things.

[0015] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the intelligent descaling agent dosage control method based on the Internet of Things as described in the first aspect of the present invention is implemented.

[0016] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the intelligent descaling agent dosage control method based on the Internet of Things as described in the first aspect of the present invention is implemented.

[0017] The beneficial effects of the present invention are as follows: by simultaneously collecting water quality parameter data and particle size distribution data, and constructing a dynamic feedback model that integrates particle size anomaly characteristic values ​​and water quality impact adjustment factors, the single parameter dependence problem of the existing technology is effectively solved, and the synergistic effect of water quality parameters and particle characteristics can be fully captured, which significantly improves the accuracy of scaling risk prediction and avoids the misjudgment problem caused by ignoring the dynamic changes of particle size; a calculation method including nonlinear processing and time decay accumulation is adopted, combined with a four-level risk grading mechanism, which overcomes the defect of insufficient adaptability of the existing static model and realizes dynamic response to water quality fluctuations under different working conditions, which not only prevents the waste of reagents caused by excessive dosage, but also avoids the scaling risk caused by insufficient response, making the control of descaling agent dosage more accurate and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them: Figure 1 This is a schematic diagram of the overall process of the intelligent descaling agent delivery control method based on the Internet of Things proposed in the present invention; Figure 2 This is a logical diagram of scaling trend risk judgment of the intelligent descaling agent delivery control method based on the Internet of Things proposed in the present invention. DETAILED DESCRIPTION

[0019] 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.

[0020] 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.

[0021] 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.

[0022] Furthermore, the present invention is described in detail with reference to schematic diagrams. For ease of illustration, when describing the embodiments of the present invention, cross-sectional views illustrating device structures may be partially enlarged and not to scale. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of protection of the present invention. Furthermore, in actual production, the three-dimensional dimensions of length, width, and depth should be included.

[0023] Example 1 Reference Figure 1-Figure 2 , which is an embodiment of the present invention, provides an intelligent descaling agent delivery control method based on the Internet of Things.

[0024] Existing descaling agent dosing control methods have the following major flaws: First, existing control methods primarily rely on a single water quality parameter, such as calcium ion concentration or conductivity, to determine scaling, ignoring the critical impact of particle size distribution on the scaling process. This limitation stems from the limited detection capabilities of traditional sensors, which prevent the system from capturing the dynamic changes in particle aggregation. In actual operation, even if the calcium ion concentration is normal, an abnormal particle size distribution may still cause severe scaling. Second, the currently used linear assessment model is based on fixed parameter weights and cannot dynamically adjust the relationship between water quality parameters and particle size characteristics. This problem stems from the model's failure to consider the nonlinear coupling effects between water quality parameters (such as pH and water temperature). When water quality conditions fluctuate, the system either overreacts, resulting in wasted descaling agent, or underreacts, causing equipment scaling. This deficiency is particularly evident during seasonal water quality fluctuations.

[0025] The present application provides an effective solution to the above-mentioned problems. Next, multiple embodiments will be combined to explain in detail how to implement the intelligent descaling agent delivery control method based on the Internet of Things.

[0026] Figure 1 The overall flow chart of the intelligent descaling agent delivery control method based on the Internet of Things is shown, including: S1: Obtain water quality parameter data and particle size distribution data in the water treatment system, and calculate the current particle size anomaly factor based on the data.

[0027] Water quality parameter data refers to real-time physical and chemical data including water hardness, conductivity, pH value, water temperature and water flow rate obtained through IoT sensors deployed in the water treatment system; Particle size distribution data refers to the particle size and distribution frequency data of suspended particles in water obtained by laser particle size analyzer or micro optical sensor; The particle size anomaly factor refers to a quantitative indicator calculated based on the degree of deviation between the particle size distribution data obtained at the current moment and the historical reference particle size mean and standard deviation.

[0028] S2: Based on the particle size anomaly factor, a water quality particle size dynamic feedback model is constructed to obtain the scaling risk index.

[0029] The water quality particle size dynamic feedback model is constructed based on the particle size anomaly factor, including the following steps: By introducing the square normalization method to quantify the abnormal deviation of real-time particle size data, the particle size abnormality characteristic value is obtained, thereby enhancing the model's ability to capture dynamic deviations of particle size. The specific formula is as follows: ; in, It represents the square average intensity of the deviation of the particle size from the mean in the current water body. The larger the value, the more obvious the potential scaling trend. By introducing a linear weighting method to normalize the combined effect of calcium ion concentration and pH value, the water quality impact adjustment factor value is obtained, thereby improving the model's ability to assess the scaling risk driven by water quality parameters. The specific formula is as follows: ; in, It is a water quality influencing factor, reflecting the enhancement effect of the water environment on the tendency of particle scaling. Indicates the calcium ion concentration at the current moment, is the normalized reference concentration, pH is the current pH value of the water body, and is the adjustment coefficient, which is used to control the relative importance of each water quality factor; Based on the obtained square average intensity of the particle size deviation from the mean in the current water body and the water quality influencing factors, a water quality particle size dynamic feedback model is constructed to make a preliminary prediction of the scaling risk of the water body. The specific formula of the water quality particle size dynamic feedback model is as follows: ; in, It represents the scaling risk index, which is an important indicator for assessing whether the water body has entered a high scaling risk state.

[0030] In the examples of this application, the normalized reference concentration The methods for determining the calcium ion concentration include but are not limited to the following two: Normalized reference concentration , usually the 90th percentile of the historical calcium ion concentration of the water body is taken, and the normalized reference value of the pH value is 14 (the maximum value of the neutral pH value range); the values ​​of the adjustment coefficients b1 and b2 are determined according to the water quality type: in a hard water environment (calcium hardness is the main scaling factor), b1 is recommended to be 0.7-0.9, and b2 is recommended to be 0.1-0.3; in a water environment that is easily affected by pH fluctuations, b1 is recommended to be 0.4-0.6, and b2 is recommended to be 0.5-0.7. These parameters can be determined through the water quality test report or dynamically optimized through the system self-learning module.

[0031] It should be noted that the traditional water quality assessment model has two major flaws: first, it only uses fixed reference values ​​(such as Co = 200 mg / L), which cannot adapt to the differences in the characteristics of different water bodies; second, it uses equal weights (such as = =0.5), ignoring the actual impact differences of different water quality parameters, this embodiment dynamically determines value, and set it differently 、 The coefficient enables the model to accurately reflect the actual contribution of calcium ion concentration and pH value in a specific water body to the scaling risk. For example, when the water hardness is high, the weight of calcium ion concentration is automatically increased ( Increase); When pH fluctuates greatly, appropriately increase the weight of pH value ( This dynamic adjustment mechanism significantly improves the environmental adaptability of the water quality particle size dynamic feedback model. Tests have shown that it can increase the accuracy of scaling risk judgment by about 40%.

[0032] S3: Calculate the particle size weight feedback value based on the scaling risk index to obtain a weight feedback value for assisting in judging the scaling trend.

[0033] Calculating the particle size weighted feedback value based on the scaling risk index to obtain a weighted feedback value for assisting in judging scaling trends includes the following steps: By introducing a nonlinear power function to normalize the scaling risk index, 90% quantile particle size, and calcium ion concentration, a dynamic weight adjustment factor is obtained to balance the sensitivity of each parameter and suppress the interference of extreme values. The specific formula is as follows: ; in, is the dynamic weight adjustment factor, is the historical mean of the scaling risk index, It is a risk adjustment indicator to control the amplification degree of risk deviation. is the 90% quantile particle size of the current particle distribution, is the reference particle size, which is usually taken as the historical normal value, and m is the particle size adjustment index, which controls the weight of abnormal particle size. is the current calcium ion concentration, The reference calcium ion concentration is usually taken as the saturation concentration, and n is the concentration adjustment index to control the sensitivity of the concentration influence. By introducing the exponential time decay function and the abnormal threshold screening mechanism, the dynamic weight adjustment factor is weighted and accumulated to obtain the weighted feedback value to quantify the long-term scaling risk. The specific formula is as follows: ; in, is the weighted feedback value, is the time attenuation coefficient, which controls the attenuation speed of the influence of historical data. is the time interval between the current moment and the i-th historical data point, is the dynamic weight adjustment factor at the i-th historical moment, is the abnormality judgment threshold, is the indicator function, when When , it takes 1, otherwise it takes 0, The total number of data points within the time window for the cumulative calculation.

[0034] In an embodiment of the present application, the risk adjustment index k is optimized by analyzing the correlation between historical scaling event data and risk indicators to control the sensitivity of risk deviation; the particle size adjustment index m is set based on the actual impact of particles of different particle sizes on the scaling process, reflecting the weight of abnormal particle size; the concentration adjustment index n is dynamically adjusted according to the contribution of water quality parameters to the scaling trend, to balance the relative importance of each parameter; the time decay coefficient λ is optimized by analyzing the periodic characteristics of historical water quality data, so that the influence of historical data can reasonably decay over time; the abnormality judgment threshold Wth is dynamically adjusted based on the historical distribution characteristics of the weight feedback value to ensure that normal fluctuations can be effectively distinguished from real abnormalities; the time window size N is comprehensively determined based on the frequency of changes in water quality parameters and the system response requirements to ensure the timeliness and accuracy of the cumulative calculation.

[0035] It should be noted that traditional descaling control methods have two major limitations: first, they rely solely on current detection data, failing to fully utilize the trend information contained in historical data; second, they use fixed thresholds for anomaly determination, making them unable to adapt to dynamic changes in water quality conditions. This embodiment implements a dynamic assessment of scaling risk by introducing a weighted accumulation mechanism based on time decay. The risk adjustment index k enables the system to adjust its response intensity based on the actual risk level; the differentiated configuration of the particle size adjustment index m and the concentration adjustment index n accurately reflects the differential impact of different factors on the scaling process; the application of the time decay function enables the system to reasonably balance the influence of recent and historical data; and the setting of dynamic thresholds ensures that the system can adapt to natural fluctuations in water quality parameters. This design enables the system to both promptly capture sudden changes in water quality and identify slowly developing scaling trends, significantly improving the accuracy and timeliness of risk warnings.

[0036] S4: The scaling trend risk is jointly judged based on the scaling risk index and the weighted feedback value to realize the intelligent descaling agent delivery control based on the Internet of Things.

[0037] Preferably, the scaling tendency risk includes normal state, mild risk, moderate risk and severe risk.

[0038] Furthermore, a basic early warning judgment is made based on the scaling risk index and the weighted feedback value. If the scaling risk index is less than or equal to the mild risk threshold, and the weighted feedback value is less than or equal to the mild cumulative threshold, the current state is determined to be normal, the current descaling agent dosage is maintained, and the water treatment system is continuously monitored. If the scaling risk index is less than or equal to the mild risk threshold, and the weighted feedback value is greater than the mild cumulative threshold, the current state is preliminarily determined to be mild risk, and the next step is executed; When the preliminary judgment result is a mild risk, an early warning upgrade judgment is made based on the particle size anomaly factor and the water quality impact adjustment factor. If the particle size anomaly factor is less than or equal to the particle size threshold, and the water quality impact adjustment factor is less than or equal to the water quality threshold, the current state is judged to be a mild risk and a first-level adjustment instruction is sent to the control system. Furthermore, the specific operation process of sending a first-level adjustment instruction to the control system is as follows: The intelligent judgment module instructs the data acquisition module to obtain the latest water quality parameter data (including water hardness, conductivity, pH value, water temperature and water flow velocity) and particle size distribution data to confirm the stability of scaling risk indicators and weight feedback values; The intelligent judgment module calculates the amount of descaling agent required under mild risk conditions based on water quality parameter data and particle size distribution data, and sets it as a slight increment to the baseline amount; The intelligent judgment module encapsulates the determined descaling agent dosage and execution time window into a first-level adjustment instruction and sends it to the descaling agent dosage control system through the Internet of Things communication protocol; The control system drives the metering pump to release a specified amount of descaling agent, while continuously monitoring water quality parameter data and particle size distribution data through sensors, and feeding back to the data acquisition module to evaluate the effect.

[0039] If the particle size anomaly factor is greater than the particle size threshold or the water quality impact adjustment factor is greater than the water quality threshold, the current state is determined to be a moderate risk and a secondary adjustment instruction is sent to the control system; Furthermore, the specific operation process of sending the secondary adjustment instruction to the control system is as follows: The intelligent judgment module activates the water quality particle size dynamic feedback model, recalculates the scaling risk index based on the latest water quality parameter data and particle size distribution data, and confirms the moderate risk status; The intelligent judgment module determines the required descaling agent dosage and dosage frequency under moderate risk conditions based on water quality parameter data and particle size distribution data, and sets it as an appropriate increment to the baseline dosage; The intelligent judgment module encapsulates the adjusted descaling agent dosage, dosage frequency, and execution time window into a secondary adjustment instruction and sends it to the control system via the Internet of Things communication protocol; The control system adjusts the metering pump parameters to increase the amount of descaling agent added, optimizes the water flow rate to enhance the mixing effect, and monitors the water quality parameter data and particle size distribution data in real time through sensors to provide feedback on the effect.

[0040] If the scaling risk index is greater than the mild risk threshold, and the weighted feedback value is less than or equal to the moderate cumulative threshold, the current state is preliminarily determined to be moderate risk, and the next step is executed; When the preliminary judgment result is moderate risk, the dynamic feedback model is immediately started to recalculate the scaling risk index; if the recalculated scaling risk index is less than or equal to the moderate risk threshold, the current state is determined to be moderate risk, and a secondary adjustment instruction is sent to the control system; If the recalculated scaling risk index is greater than the moderate risk threshold, the current state is determined to be a severe risk and a three-level adjustment instruction is sent to the control system; Furthermore, the specific operation process of sending the three-level adjustment command to the control system is as follows: The intelligent judgment module combines historical data with the latest water quality parameter data and particle size distribution data to verify the cumulative effect of long-term scaling risks and confirm serious risk status; The intelligent judgment module determines the high-dose descaling agent dosage and frequent dosage plan under severe risk conditions based on water quality parameter data and particle size distribution data; The intelligent judgment module encapsulates the high-dose delivery amount, delivery plan, and execution time window into three-level adjustment instructions, along with pipeline flushing recommendations, and sends them to the control system via the IoT communication protocol. The control system drives the metering pump to release the descaling agent at maximum flow rate, adjusts the water pump power to accelerate diffusion, and the sensor collects water quality parameter data and particle size distribution data at high frequency, and feeds back to the system to dynamically track the effect.

[0041] If the scaling risk index is greater than the moderate risk threshold and the weighted feedback value is greater than the moderate cumulative threshold, the current state is determined to be a severe risk and a level 3 control instruction is sent to the control system.

[0042] For example, suppose that in an industrial circulating cooling water system, after a week of continuous operation of an intelligent descaling agent dosing control system, IoT sensors detected abnormal fluctuations in water hardness and particle size distribution data. Specifically, the system recorded a gradual increase in calcium ion concentration to 450 mg / L, approaching the normalized reference concentration of 500 mg / L. Simultaneously, the 90th percentile particle size increased from its historical normal value of 50 μm to 75 μm, and the pH fluctuated from 7.2 to 7.8. Calculations showed that the particle size anomaly factor reached 1.8 (above the particle size threshold A1=1.5), and the water quality impact adjustment factor was 0.85 (close to the water quality threshold B1=0.9). Based on the water quality particle size dynamic feedback model, the system calculated a scaling risk index S=2.3, slightly above the mild risk threshold F1=2.0. Furthermore, the weighted feedback value W, calculated using an exponential time decay function, was 1.2, exceeding the mild accumulation threshold W1=1.0. The system initially determined that it was in a mild risk state and immediately initiated further verification. Combined with the analysis of the particle size anomaly factor and the water quality impact adjustment factor, it was confirmed that the particle size anomaly factor was greater than the threshold A1, and the current state was determined to be moderate risk. The system sent a secondary adjustment instruction to the control center, automatically increasing the descaling agent dosage to 0.5 kg per hour, and continuously monitoring changes in water quality parameters and particle size distribution. After 48 hours, the system detected that the calcium ion concentration had dropped to 400 mg / L, the 90% quantile particle size had returned to 55 μm, and the scaling risk index had dropped to 1.8, returning to normal. This avoided the decrease in heat exchange efficiency and potential damage to equipment caused by pipe scaling, ensuring the stable operation of the cooling water system.

[0043] In the embodiment of the present application, for each threshold in the scaling risk assessment, the mild risk threshold F1 and the moderate risk threshold F2 are determined by statistical analysis of historical water quality data and scaling events, combined with pipeline material, operating conditions and chemical properties of the descaling agent, using the quantile method and scaling experimental data fitting; the mild cumulative threshold W1 and the moderate cumulative threshold W2 are based on the distribution characteristics of the weighted feedback values ​​in the long-term operation data, and are obtained through quantile analysis and simulated scaling scenarios; the particle size threshold A1 is determined by analyzing the normality and abnormal deviation characteristics of the particle size distribution, combined with the measurement accuracy of the laser particle size analyzer and the historical normal value range; the water quality threshold B1 is determined by nonlinear regression analysis of calcium ion concentration, pH value and scaling rate, combined with the dynamic characteristics of the water treatment system operating environment (such as water temperature and water flow rate). In addition, the above thresholds can be preliminarily set based on small-scale test data and dynamically optimized in actual operation by combining real-time monitoring data and machine learning algorithms (such as support vector machines or random forests) to adapt to water quality fluctuations in different seasons or working conditions, thereby improving the adaptability and prediction accuracy of the thresholds. This embodiment does not impose specific limitations on this.

[0044] It should be noted that traditional descaling agent dosing control systems typically rely on a single parameter (such as calcium ion concentration) or a fixed threshold to trigger dosing. This makes it difficult to capture the dynamic changes in particle size distribution and the nonlinear coupling effects of water quality parameters, easily leading to under- or overdosing. This embodiment introduces a dynamic feedback model for water quality and particle size and a multi-level risk grading mechanism, comprehensively considering particle size anomaly factors, water quality impact adjustment factors, and weighted feedback values ​​to construct a refined control system that includes normal status, mild risk, moderate risk, and severe risk. This system dynamically responds to the time-varying coupling relationship between water quality parameters and particle size characteristics, significantly improving the ability to identify early signs of minor scaling. It also optimizes descaling agent dosing through graded adjustment instructions, avoiding agent waste and reducing equipment efficiency and maintenance costs caused by scaling. The intelligent control method of this embodiment achieves three-dimensional, intelligent management of scaling risks in water treatment systems, providing efficient and precise technical support for the stable operation of industrial water treatment systems.

[0045] In summary, by simultaneously collecting water quality parameter data and particle size distribution data, and constructing a dynamic feedback model that integrates particle size anomaly characteristic values ​​and water quality impact adjustment factors, the single parameter dependence problem of existing technologies is effectively solved, and the synergistic effect of water quality parameters and particle characteristics can be fully captured, which significantly improves the accuracy of scaling risk prediction and avoids the misjudgment problem caused by ignoring the dynamic changes of particle size. The calculation method including nonlinear processing and time decay accumulation is combined with a four-level risk grading mechanism to overcome the defect of insufficient adaptability of existing static models and realize dynamic response to water quality fluctuations under different working conditions, which not only prevents the waste of chemicals caused by excessive dosage, but also avoids the scaling risk caused by insufficient response, making the control of descaling agent dosage more accurate and efficient.

[0046] Example 2 is an embodiment of the present invention. This embodiment provides an intelligent descaling agent delivery control system based on the Internet of Things, including: Data acquisition module: used to obtain water quality parameter data and particle size distribution data in the water treatment system, and calculate the current particle size abnormality factor based on the data; Model building module: used to build a water quality particle size dynamic feedback model based on the particle size anomaly factor to obtain scaling risk indicators; Weight calculation module: used to calculate the particle size weight feedback value based on the scaling risk index, and obtain the weight feedback value used to assist in judging the scaling trend; Intelligent judgment module: used to jointly judge the scaling trend risk based on the scaling risk index and weight feedback value, and realize intelligent descaling agent delivery control based on the Internet of Things.

[0047] Example 3 is an embodiment of the present invention, which is different from the previous embodiment in that: If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0048] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0049] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting, or processing it in another suitable manner as necessary, and then storing it in a computer memory.

[0050] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0051] 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 descaling agent delivery control method based on the Internet of Things, characterized in that: include: Acquiring water quality parameter data and particle size distribution data in the water treatment system, and calculating a current particle size anomaly factor based on the data; Based on the particle size anomaly factor, a water quality particle size dynamic feedback model is constructed to obtain a scaling risk index; Calculating a particle size weight feedback value based on the scaling risk index to obtain a weight feedback value for assisting in determining scaling trends; The scaling trend risk is jointly judged according to the scaling risk index and the weighted feedback value, thereby realizing intelligent descaling agent delivery control based on the Internet of Things.

2. The method for controlling the delivery of an intelligent descaling agent based on the Internet of Things according to claim 1, wherein: The water quality parameter data refers to real-time physical and chemical data including water hardness, conductivity, pH value, water temperature and water flow rate obtained through IoT sensors deployed in the water treatment system; The particle size distribution data refers to the particle size and distribution frequency data of suspended particles in water obtained by using a laser particle size analyzer or a micro optical sensor; The particle size anomaly factor refers to a quantitative indicator calculated based on the degree of deviation between the particle size distribution data obtained at the current moment and the historical reference particle size mean and standard deviation.

3. The method for controlling the dispensing of an intelligent descaling agent based on the Internet of Things according to claim 1, characterized in that: Constructing a water quality particle size dynamic feedback model based on the particle size anomaly factor includes the following steps: By introducing the square normalization method to quantify the abnormal deviation of real-time particle size data, the particle size abnormality characteristic value is obtained, thereby enhancing the model's ability to capture dynamic deviations of particle size. By introducing a linear weighting method to normalize the combined effects of calcium ion concentration and pH value, the water quality impact adjustment factor value is obtained, thereby improving the model's ability to assess the scaling risk driven by water quality parameters; Based on the obtained square average intensity of the particle size deviation from the mean in the current water body and the water quality influencing factors, a water quality particle size dynamic feedback model is constructed to make a preliminary prediction of the scaling risk of the water body.

4. The method for controlling the dispensing of an intelligent descaling agent based on the Internet of Things according to claim 3 is characterized in that: The specific formula of the water quality particle size dynamic feedback model is as follows: ； in, represents the scaling risk index, It represents the square average intensity of the deviation of particle size from the mean in the current water body. It is a factor affecting water quality.

5. The method for controlling the dispensing of an intelligent descaling agent based on the Internet of Things according to claim 4, characterized in that: Calculating a particle size weight feedback value based on the scaling risk index to obtain a weight feedback value for assisting in determining scaling trends includes the following steps: By introducing a nonlinear power function to normalize the scaling risk index, 90% quantile particle size and calcium ion concentration, a dynamic weight adjustment factor is obtained to balance the sensitivity of each parameter and suppress the interference of extreme values. By introducing an exponential time decay function and an abnormal threshold screening mechanism, the dynamic weight adjustment factor is weightedly accumulated to obtain a weighted feedback value to quantify the long-term scaling risk.

6. The method for controlling the dispensing of an intelligent descaling agent based on the Internet of Things according to claim 1, characterized in that: The scaling trend risk includes normal state, mild risk, moderate risk and severe risk.

7. The method for controlling the delivery of an intelligent descaling agent based on the Internet of Things according to claim 6, characterized in that: The scaling trend risk is determined based on the scaling risk index and the weighted feedback value, including the following steps: A basic early warning judgment is made based on the scaling risk index and weighted feedback value. If the scaling risk index is less than or equal to the mild risk threshold F1, and the weighted feedback value is less than or equal to the mild cumulative threshold W1, the current state is determined to be normal, the current descaling agent dosage is maintained, and the water treatment system is continuously monitored; If the scaling risk index is less than or equal to the mild risk threshold F1, and the weighted feedback value is greater than the mild cumulative threshold W1, the current state is preliminarily determined to be mild risk, and the next step is executed; If the preliminary determination result is a mild risk, an upgrade warning is made based on the particle size anomaly factor and the water quality impact adjustment factor. If the particle size anomaly factor is less than or equal to the particle size threshold A1, and the water quality impact adjustment factor is less than or equal to the water quality threshold B1, the current state is determined to be a mild risk, and a first-level adjustment instruction is sent to the control system. If the particle size anomaly factor is greater than the particle size threshold A1 or the water quality impact adjustment factor is greater than the water quality threshold B1, the current state is determined to be a moderate risk and a secondary adjustment instruction is sent to the control system; If the scaling risk index is greater than the mild risk threshold F1, and the weighted feedback value is less than or equal to the moderate cumulative threshold W2, the current state is preliminarily determined to be moderate risk, and the next step is executed; When the preliminary judgment result is moderate risk, the dynamic feedback model is immediately started to recalculate the scaling risk index; if the recalculated scaling risk index is less than or equal to the moderate risk threshold F2, the current state is determined to be moderate risk, and a secondary adjustment instruction is sent to the control system; If the recalculated scaling risk index is greater than the moderate risk threshold F2, the current state is determined to be a severe risk and a level 3 adjustment instruction is sent to the control system; If the scaling risk index is greater than the moderate risk threshold F2 and the weighted feedback value is greater than the moderate cumulative threshold W2, the current state is determined to be a severe risk and a level 3 control instruction is sent to the control system.

8. An intelligent descaling agent delivery control system based on the Internet of Things, based on the control method according to any one of claims 1 to 7, characterized in that: include: Data acquisition module: used to obtain water quality parameter data and particle size distribution data in the water treatment system, and calculate the current particle size abnormality factor based on the data; Model building module: used to build a water quality particle size dynamic feedback model based on the particle size anomaly factor to obtain a scaling risk index; Weight calculation module: used to calculate the particle size weight feedback value based on the scaling risk index to obtain the weight feedback value used to assist in judging the scaling trend; Intelligent judgment module: used to jointly judge the scaling trend risk according to the scaling risk index and the weight feedback value, and realize intelligent descaling agent delivery control based on the Internet of Things.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the intelligent descaling agent delivery control method based on the Internet of Things according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent descaling agent delivery control method based on the Internet of Things according to any one of claims 1 to 7 are implemented.

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