Descaling control method based on Internet of Things

Abstract

The invention belongs to the technical field of scale treatment, and discloses a scale removal control method based on the Internet of Things. The method comprises the steps that a water supply pipe network is evenly divided into a plurality of water supply pipe sections, the scaling risk of each water supply pipe section is sequentially calculated, and the length of each water supply pipe section is dynamically adjusted; pipeline vibration signals of all the water supply pipe sections are collected, net resonance frequencies are extracted, and scaling pipe sections in the water supply pipe sections are accurately positioned; water quality standard indexes of all the scaling pipe sections are collected, the scale types of all the scaling pipe sections are determined, the sound wave frequency is automatically adjusted to generate directional resonance on all the scaling pipe sections, and the scale in all the scaling pipe sections is gradually stripped; the movement track of each stripped scale is monitored in real time, the arrival time of each stripped scale arriving at each collecting device is predicted, and the collecting devices are intelligently started to collect the stripped scales; intelligent sensing, accurate diagnosis and self-adaptive control of the scaling condition of the water supply pipe network can be achieved, and the operation continuity and the water quality stability of the water supply pipe network are guaranteed.

Description

Technical Field

[0001] This invention relates to the field of scale removal technology, and more specifically, to an Internet of Things-based scale removal control method. Background Technology

[0002] With the acceleration of urbanization and the continuous expansion of water supply systems, the problem of scale buildup in pipes is becoming increasingly serious, significantly impacting water supply safety, water quality stability, and system operational efficiency. Scale deposition in water supply networks not only reduces the effective diameter of pipes and increases water flow resistance, but also triggers a series of problems such as secondary water pollution and accelerated pipe corrosion. In severe cases, it can even lead to water supply interruptions, causing significant economic losses to people's livelihoods and industrial production. Especially in areas such as the renovation of old residential areas, the maintenance of water supply systems in industrial zones, and the supply of high-quality water sources, scale control has become a key bottleneck in water supply system management. With increasing water quality requirements and the promotion of energy conservation policies, traditional descaling methods are insufficient to meet the needs of efficient, precise, and environmentally friendly modern water supply system management.

[0003] Existing physical descaling methods mainly include mechanical cleaning and ultrasonic treatment. While ultrasonic descaling avoids chemical pollution, existing ultrasonic descaling technologies have several drawbacks: they typically employ a fixed frequency design, lacking the ability to adapt to different pipe materials and scale types, resulting in low descaling efficiency; they also cannot accurately locate scale-forming areas in the pipe network, leading to energy waste through indiscriminate full-coverage; more importantly, they lack effective monitoring and collection mechanisms for detached scale, causing free scale fragments to cause secondary blockages or water pollution downstream. In summary, existing ultrasonic descaling technologies struggle to achieve refined management of complex water supply systems, ultimately resulting in low descaling efficiency, poor accuracy, and insufficient intelligence, failing to meet the high standards required by modern water supply systems.

[0004] In view of this, the present invention proposes an Internet of Things-based descaling control method to solve the above problems. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art and achieve the above objectives, the present invention provides the following technical solution: a descaling control method based on the Internet of Things, comprising: Step S1: Divide the water supply network evenly into multiple water supply pipe sections, collect the pipe structure parameters of each water supply pipe section, calculate the scaling risk of each water supply pipe section in turn based on the pipe structure parameters, and dynamically adjust the length of each water supply pipe section based on the scaling risk. Step S2: Collect the pipe vibration signal of each water supply pipe section, extract the net resonant frequency of each water supply pipe section from the pipe vibration signal, and accurately locate the scaled pipe section in the water supply pipe section based on the net resonant frequency. Step S3: Collect water quality standard indicators for each scaled pipe section, determine the scale type for each scaled pipe section based on the water quality standard indicators, and combine the pipe structure parameters of each scaled pipe section to automatically adjust the sound wave frequency to generate directional resonance for each scaled pipe section, and gradually peel off the scale in each scaled pipe section. Step S4: Monitor the movement trajectory of each scale piece in real time and predict when each scale piece will arrive at its respective collection device. Based on the arrival time, intelligently activate the collection device to accurately collect each scale piece.

[0006] Furthermore, the steps for dynamically adjusting the length of each water supply pipe section include: Step S101: Calculate the total length of the pipeline network based on the length of each water supply pipe section; Step S102: Based on the scaling risk of each water supply pipe section, dynamically adjust the length of each water supply pipe section to obtain the dynamic length of each water supply pipe section. Step S103: Analyze the dynamic length of each water supply pipe section and identify the pipe sections exceeding the limit; Step S104: Based on the preset standard length, dynamically adjust the dynamic length of each over-limit pipe section, and calculate the total dynamic length of all water supply pipe sections. Step S105: Determine whether the convergence condition is met based on the total length of the pipeline network and the dynamic total length; if the convergence condition is not met, proceed to step S106; if the convergence condition is met, proceed to step S107. Step S106: Based on the total length of the pipeline network, dynamically adjust the dynamic length of all water supply pipe sections that are not over-limited, and return to step S103. Step S107: Use the dynamic length of each water supply pipe segment as the corresponding pipe segment length to complete the dynamic adjustment of the corresponding length of each water supply pipe segment.

[0007] Furthermore, in step S106, the method for dynamically adjusting the dynamic length of all water supply pipe sections that are not exceeding the limit includes: Calculate the total length exceeding the limit based on the dynamic length of each exceeding pipe segment; calculate the remaining total length based on the difference between the total length of the pipeline network and the total length exceeding the limit; mark all water supply pipe segments that are not exceeding the limit as regulating pipe segments, and calculate the total regulating length based on the dynamic length of each regulating pipe segment; calculate the ratio of the remaining total length to the total regulating length to obtain the regulating factor; calculate the product between the dynamic length of each regulating pipe segment and the regulating factor to obtain the regulating length of each regulating pipe segment, and dynamically adjust the dynamic length of each regulating pipe segment based on the regulating length of each regulating pipe segment.

[0008] Furthermore, methods for extracting the net resonant frequency of each water supply pipe section from the pipeline vibration signal include: Blind source separation is performed on the vibration signals of each water supply pipe section to obtain multiple independent vibration signals corresponding to each water supply pipe section. These multiple independent vibration signals are then input into a trained signal recognition model to predict the label set corresponding to each water supply pipe section. The label set includes the signal label corresponding to each independent vibration signal of the water supply pipe section, and the signal label includes intrinsic labels and interference labels. Independent vibration signals with intrinsic labels are used as the intrinsic vibration signals of the corresponding water supply pipe sections. Spectral analysis is then performed on the multiple intrinsic vibration signals corresponding to each water supply section sequentially to obtain multiple natural frequencies corresponding to each water supply section. Based on the natural frequencies of each water supply pipe section, the number of different natural frequencies under the same water supply pipe section is counted and marked as the occurrence count. The occurrence counts corresponding to the same water supply pipe section are compared, and the natural frequencies with the most occurrence counts are marked as candidate frequencies. If there is only one candidate frequency for a water supply pipe section, the candidate frequency is taken as the net resonant frequency of the corresponding water supply pipe section. If there are multiple candidate frequencies for a water supply pipe section, the amplitude of each candidate frequency in the corresponding natural vibration signal is obtained in turn, and the candidate frequency with the largest amplitude is taken as the net resonant frequency of the corresponding water supply pipe section.

[0009] Furthermore, methods for accurately locating scale-laden sections in water supply pipelines include: Calculate the running time and substitute it into the frequency attenuation function to obtain the frequency attenuation; obtain the reference frequency of each water supply pipe section and calculate the standard frequency of each water supply pipe section based on the frequency attenuation; calculate the frequency deviation of each water supply pipe section based on the standard frequency and the net resonant frequency of each water supply pipe section. A preset deviation threshold is set, and the frequency deviation of each water supply pipe section is compared with the deviation threshold. If the frequency deviation is greater than or equal to the deviation threshold, the corresponding water supply pipe section will be designated as the scaling pipe section. If the frequency deviation is less than the deviation threshold, the corresponding water supply pipe section will not be considered as a scaling pipe section.

[0010] Furthermore, methods for determining the type of scale buildup in each pipe section include: Multiple sets of key ion combinations are preset, and the ion ratios corresponding to each set of key ion combinations are calculated sequentially according to the water quality standard indicators of each scaled pipe section to obtain multiple ion ratios corresponding to each scaled pipe section. The ion ratios corresponding to each scaled pipe section are added to the corresponding water quality standard indicators, and multiple attribute sets are constructed for each data in the water quality standard indicators. The water quality standard indicators corresponding to each scaled pipe section are converted into membership degrees corresponding to each attribute set using attribute interval mapping technology. Attribute reasoning rules are defined. Each set of water quality standard indicators after attribute interval mapping is matched with the attribute reasoning rules, and attribute reasoning is performed to obtain the attribute reasoning results corresponding to each scaled pipe section. The attribute reasoning results include the membership degree of each scale type. The membership degree corresponding to each scaled pipe section is compared with a preset membership degree threshold. Scale types with membership degrees greater than the membership degree threshold are all taken as the scale type of the corresponding scaled pipe section.

[0011] Furthermore, methods for automatically adjusting the sound wave frequency to generate directional resonance in scaled pipe sections include: The scale type corresponding to the scaled pipe section is marked as a matching type. For each matching type of the scaled pipe section, the corresponding key ion combination is obtained and marked as an inference set. The ions in each inference set are marked as inference ions, and the concentration of each inference ion is obtained from the water quality standard index corresponding to the scaled pipe section. The concentration of each inference ion is converted into the corresponding molar concentration in turn. Based on the molar concentration of each inference ion corresponding to each matching type, the scale thickness corresponding to each matching type is calculated in turn. For different types of scale, appropriate frequency ranges are set sequentially, and the median value of each appropriate frequency range is used as the median frequency of the corresponding scale type. Based on the membership degree of each matching type, the median frequency of each matching type is weighted and averaged to obtain the base frequency. The scale thickness, base frequency, and pipe structure parameters corresponding to the scaled pipe section are used as the analysis set. The analysis set is input into the trained frequency determination model to predict the corresponding optimal frequency, and the sound wave frequency is automatically adjusted according to the optimal frequency.

[0012] Furthermore, methods for real-time monitoring of the movement trajectory of each scale piece being shed include: Along the water flow direction within the water supply network, sequentially assign incrementing numerical labels to the starting positions of each water supply pipe section, marking them as location labels; along the water flow direction within the water supply network, obtain the downstream locations of each scaled pipe section. A continuous water supply pipe section was established, serving as a tracking pipe section for the corresponding scaling pipe section; at the starting position of each tracking pipe section, the turbidity and real-time scattering spectrum of the water body were continuously collected, and the turbidity variable was continuously calculated. Establish a reference spectral library, which includes standard scattering spectra corresponding to different types of scale; compare each turbidity variable with a preset variable threshold. If the turbidity variable is greater than or equal to the variable threshold, the collection time and location label of the corresponding water turbidity are obtained, and the scale type of the corresponding scaled pipe section is marked as the analysis type; the real-time scattering spectrum belonging to the same starting position as the corresponding water turbidity is correlated with the standard scattering spectrum corresponding to each analysis type in the reference spectrum library, and the scale type corresponding to the standard scattering spectrum with the largest correlation coefficient is taken as the monitoring type; the obtained collection time and location label are used as the location vector of scale stripping corresponding to the monitoring type; When it exists When the group position vectors belong to the same scale removal scale, The combination of position vectors corresponds to the motion trajectory of the scale being peeled off.

[0013] Furthermore, methods for predicting the arrival time of scale stripping at each collection device include: Analyze the position labels in the movement trajectory corresponding to the scale removal to determine whether the scale removal passes through a branch pipe section. A branch pipe section refers to a water supply pipe section that connects multiple water supply pipe sections at the same time. If the scale removal process involves a branching pipe section, the scale removal should be divided into... Each individual scale deposit corresponds to a downstream pipe section, and is based on... Group position vectors are used to obtain the location of each individual scale. A set of independent vectors; where, This refers to the number of downstream pipe sections corresponding to the branching pipe section. The downstream pipe section refers to the water supply pipe section located downstream of the branching pipe section and connected to it. According to each individual scale The independent vectors are used to calculate the movement speed of each independent scale in turn; the arrival length between each collection device and each independent scale is obtained in turn, and based on the arrival length and movement speed, the arrival time of each independent scale to each collection device is calculated in turn. If the scale removal does not pass through the branch pipe section, then it should be based on the scale removal process. The system uses position vectors to calculate the speed of the scale removal process; it also obtains the distance between each collection device and the scale removal process, and calculates the arrival time of the scale removal process at each collection device based on the distance and speed.

[0014] Furthermore, the method for intelligently activating the collection device based on arrival time includes: Both the detached scale and the independent scale were labeled as analytical scale. The arrival times of each analytical scale were sorted from earliest to latest to generate a time sequence for each analytical scale. The start time was added to each time sequence, and the arrival time of the one following the start time in each time sequence was obtained and labeled as the matching time. The collection device corresponding to each matching time was used as the matching device for the corresponding analytical scale. Based on the matching time and the preset start time and start margin, the start time corresponding to each scale analysis is calculated sequentially; based on the start time of each scale analysis, the matching device corresponding to each scale analysis is intelligently started.

[0015] The technical effects and advantages of the descaling control method based on the Internet of Things of this invention are as follows: By dynamically adjusting the length of water supply pipe sections to adapt to the scaling risks of different sections, this system enables refined monitoring and management of complex water supply networks, improving the accuracy of local scaling control. Pipe vibration signal analysis technology is used to precisely locate scaling sections and automatically identify scale types based on water quality standards. The system then intelligently adjusts the acoustic frequency to perform directional resonance descaling on the target pipe section, enhancing descaling efficiency and targeting. Real-time monitoring of the scale's trajectory and prediction of its arrival time allows for precise collection of free scale, preventing secondary blockages and water pollution, and ensuring the long-term safe and stable operation of the water supply system. This embodiment integrates IoT technology, signal analysis, water chemistry, fluid mechanics, and other multidisciplinary knowledge to achieve intelligent perception, precise diagnosis, and adaptive control of scaling conditions in the water supply network. This effectively improves descaling efficiency, ensures the continuity of water supply network operation and water quality stability, and ultimately meets the high standards required by modern water supply systems. Attached Figure Description

[0016] Figure 1 This is a flowchart of an IoT-based descaling control method according to Embodiment 1 of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1 Please see Figure 1 As shown in this embodiment, a descaling control method based on the Internet of Things (IoT) includes: Step S1: Divide the water supply network evenly into multiple water supply pipe sections, collect the pipe structure parameters of each water supply pipe section, calculate the scaling risk of each water supply pipe section in turn based on the pipe structure parameters, and dynamically adjust the length of each water supply pipe section based on the scaling risk.

[0019] According to the preset initial length, the water supply network is evenly divided into multiple water supply pipe segments, that is, each water supply pipe segment has the same length; the initial length is preset by those skilled in the art based on industry standards (i.e., the technical requirements and recommended lengths for pipe segmentation in water conservancy projects and municipal water supply and drainage standards), engineering experience (i.e., practical knowledge accumulated from past projects, including experience in optimal pipe segmentation found in construction, maintenance and operation), pipe characteristics (i.e., the influence of pipe material, pipe wall thickness and other characteristics on the appropriate initial length).

[0020] Pipeline structural parameters include pipe material, pipe diameter, and pipe wall thickness; the pipeline structural parameters of each water supply pipe section are obtained through the design drawings of the water supply network (i.e., the construction blueprints drawn during the construction or renovation phase of the water supply network), technical specifications (i.e., national or industry standards), and the network management database (i.e., the digital system used by water supply companies to record and manage pipeline structural parameters). Pipe material refers to the type of material used in the corresponding water supply pipe section, such as steel pipe, cast iron pipe, PVC pipe, etc. Different pipe materials have differences in surface smoothness, corrosion resistance, and scaling tendency. For example, steel pipes are prone to rust and scale accumulation, while PVC pipes have a smooth surface and good corrosion resistance. In other words, pipe material affects flow friction and scaling risk. Therefore, when dividing pipe sections, materials with high scaling tendency usually need to be divided into shorter pipe sections for more precise monitoring and management of scaling, while materials with low scaling risk can be divided into longer pipe sections to optimize calculation and maintenance costs. Pipe diameter refers to the inner diameter of the pipe corresponding to a water supply section. The pipe diameter directly affects the flow velocity and sediment deposition characteristics. Under the same flow rate, a larger pipe diameter results in a lower flow velocity, making it easier for deposits and scale to form on the pipe wall. Conversely, a smaller pipe diameter results in a higher flow velocity and stronger flushing ability, leading to relatively less scale formation. Therefore, in pipe section division, large-diameter pipes usually need to be divided into shorter sections to accurately calculate the risk of scaling and control local deposition, while small-diameter pipes can be divided into longer sections. Pipe wall thickness refers to the thickness of the pipe wall corresponding to a water supply pipe section, which affects the pipe's pressure resistance and structural safety. Thicker-walled pipes can usually withstand higher water pressure, but may affect the water flow temperature distribution and deposit adhesion, thus indirectly affecting scale formation. Therefore, when dividing pipe sections, pipes with larger wall thickness need to be divided into shorter sections to allow for more precise monitoring and management of scale and implementation of maintenance measures, while pipes with smaller wall thickness can be divided into longer sections.

[0021] Methods for calculating the scaling risk of each water supply pipe section sequentially include: Different scaling tendencies are set for different pipe materials, and the range of scaling tendencies is as follows: Among them, the rougher the surface of the pipe material, the easier it is to oxidize, or the higher the chemical reactivity, the higher the scaling tendency, and vice versa; for example, steel pipes have a greater scaling tendency than cast iron pipes, and cast iron pipes have a greater scaling tendency than PVC pipes; the scaling tendency of each water supply pipe is obtained according to the pipe material of each water supply pipe section. The pipe diameter and wall thickness of each water supply pipe section are subjected to minimum-maximum normalization to obtain the standard diameter and standard wall thickness of each water supply pipe section; a preset weight set is set, which includes the weight coefficient corresponding to each parameter in the pipe structure parameters; the specific value of the weight coefficient is determined by those skilled in the art through analysis of historical scaling cases to determine the degree of influence of each parameter in the pipe structure parameters on scaling risk, and is determined based on the degree of influence; among them, the weight coefficient of pipe material is greater than the weight coefficient of pipe diameter, the weight coefficient of pipe diameter is greater than the weight coefficient of pipe wall thickness, and the sum of all weight coefficients is 1; Based on the weight set, the scaling tendency, standard diameter and standard wall thickness of each water supply pipe are weighted and summed to obtain the scaling risk of each water supply pipe.

[0022] The steps for dynamically adjusting the length of each water supply pipe section include: Step S101: Calculate the total length of the pipeline network based on the length of each water supply pipe section; Step S102: Based on the scaling risk of each water supply pipe section, dynamically adjust the length of each water supply pipe section to obtain the dynamic length of each water supply pipe section. Step S103: Analyze the dynamic length of each water supply pipe section and identify the pipe sections exceeding the limit; Step S104: Based on the preset standard length, dynamically adjust the dynamic length of each over-limit pipe section, and calculate the total dynamic length of all water supply pipe sections. Step S105: Determine whether the convergence condition is met based on the total length of the pipeline network and the dynamic total length; if the convergence condition is not met, proceed to step S106; if the convergence condition is met, proceed to step S107. Step S106: Based on the total length of the pipeline network, dynamically adjust the dynamic length of all water supply pipe sections that are not over-limited, and return to step S103. Step S107: Use the dynamic length of each water supply pipe segment as the corresponding pipe segment length to complete the dynamic adjustment of the corresponding length of each water supply pipe segment.

[0023] In step S101 above, the total length of the pipeline network is equal to the sum of the lengths of each water supply pipe segment.

[0024] In step S102 above, the method for obtaining the dynamic length of each water supply pipe section includes: A preset standard length is provided, which includes a maximum length and a minimum length. The maximum length refers to the maximum value of the corresponding length of the water supply pipe section, which is used to avoid the pipe section being too long, thereby ensuring the accuracy of scaling monitoring and control. The minimum length refers to the minimum value of the corresponding length of the water supply pipe section, which is used to avoid the pipe section being too short, thereby ensuring the stability and accuracy of subsequent calculations. The standard length is preset by those skilled in the art based on the actual situation of the water supply network. Calculate the difference between the maximum and minimum lengths to obtain the standard deviation; calculate the product of each scaling risk and the standard deviation to obtain the length variable of each water supply pipe segment; calculate the difference between the maximum length and each length variable to obtain the dynamic length of each water supply pipe segment.

[0025] In step S103 above, the method for identifying the over-limit pipe section includes: The dynamic length of each water supply pipe section is compared with the standard length. If the dynamic length of a water supply pipe section is greater than the maximum length or less than the minimum length, the corresponding water supply pipe section will be considered as an over-limit pipe section. If the dynamic length of a water supply pipe section is greater than or equal to the minimum length and less than or equal to the maximum length, then the corresponding water supply pipe section will not be considered an over-limit pipe section.

[0026] In step S104 above, the method for dynamically adjusting the dynamic length of each over-limit pipe segment includes: If the dynamic length of the pipe segment exceeding the limit is greater than the maximum length, the dynamic length of the pipe segment exceeding the limit will be dynamically adjusted according to the maximum length. If the dynamic length of the oversized pipe section is less than the minimum length, the dynamic length of the oversized pipe section will be dynamically adjusted according to the minimum length. The total dynamic length is equal to the sum of the dynamic lengths of each water supply pipe section.

[0027] In step S105 above, the method for determining whether the convergence condition is met includes: The absolute value of the difference between the total length of the pipeline network and the dynamic total length is calculated to obtain the length error, and the length error is compared with a preset error threshold; wherein, the error threshold is preset by those skilled in the art according to the actual situation; If the length error is greater than the error threshold, the convergence condition is not met. If the length error is less than or equal to the error threshold, then the convergence condition is met.

[0028] In step S106 above, the method for dynamically adjusting the dynamic length of all water supply pipe sections that are not exceeding the limit includes: Calculate the sum of the dynamic lengths of all over-limit pipe segments to obtain the total over-limit length; calculate the difference between the total length of the pipeline network and the total over-limit length to obtain the remaining total length; mark all water supply pipe segments that are not over-limit pipe segments as regulating pipe segments, and calculate the sum of the dynamic lengths of all regulating pipe segments to obtain the total regulating length; calculate the ratio of the remaining total length to the total regulating length to obtain the regulating factor. Calculate the product between the dynamic length of each regulating pipe section and the regulating factor to obtain the regulating length of each regulating pipe section, and dynamically adjust the dynamic length of each regulating pipe section according to the regulating length of each regulating pipe section.

[0029] It should be understood that the purpose of dynamically adjusting the length of each water supply pipe section is to: rationally allocate the length of each section based on its scaling risk, while ensuring that the total length of the entire water supply network remains unchanged; to make the sections with high scaling risk shorter for more precise monitoring and management, reducing the risk of local scaling; and to make the sections with low scaling risk longer to reduce calculation and maintenance costs, thereby achieving a balance between accuracy and efficiency and improving the overall safety and economy of the water supply network.

[0030] Step S2: Collect the pipe vibration signal of each water supply pipe section, extract the net resonant frequency of each water supply pipe section from the pipe vibration signal, and accurately locate the scaled pipe section in the water supply pipe section based on the net resonant frequency.

[0031] The pipeline vibration signal includes the unit vibration signal generated at different locations of the corresponding water supply pipeline section; the unit vibration signal refers to the dynamic response signal of the mechanical vibration generated by the pipe wall over time during the operation of the corresponding water supply pipeline section due to the combined effects of internal water flow impact, pressure pulsation, external environmental excitation and pipeline characteristics. Vibration signals of each water supply pipe section are obtained by multiple vibration sensors (such as piezoelectric accelerometers, MEMS accelerometers, etc.) evenly distributed on the outside of the corresponding pipe of each water supply pipe section.

[0032] Methods for extracting the net resonant frequency of each water supply pipe section from the pipeline vibration signal include: Blind source separation (e.g., ICA, SPBI) is performed on the vibration signals of each water supply pipe section to obtain multiple independent vibration signals corresponding to each water supply pipe section. These multiple independent vibration signals are then input into a trained signal recognition model to predict the label set corresponding to each water supply pipe section. The label set includes the signal label corresponding to each independent vibration signal of the water supply pipe section, and the signal label includes intrinsic labels and interference labels. Independent vibration signals with intrinsic labels are used as the intrinsic vibration signals of the corresponding water supply pipe section. Spectral analysis (e.g., FFT, power spectrum analysis, STFT) is then performed on the multiple intrinsic vibration signals corresponding to each water supply pipe section to obtain multiple natural frequencies corresponding to each water supply pipe section. Based on the natural frequencies of each water supply pipe section, the number of different natural frequencies under the same water supply pipe section is counted and marked as the occurrence count. The occurrence counts corresponding to the same water supply pipe section are compared, and the natural frequencies with the most occurrence counts are marked as candidate frequencies. If there is only one candidate frequency for a water supply pipe section, the candidate frequency is taken as the net resonant frequency of the corresponding water supply pipe section. If there are multiple candidate frequencies for a water supply pipe section, the amplitude of each candidate frequency in the corresponding natural vibration signal is obtained in turn, and the candidate frequency with the largest amplitude is taken as the net resonant frequency of the corresponding water supply pipe section.

[0033] It should be noted that the signal recognition model is a deep neural network model, which includes an input layer, hidden layers, and an output layer. Each hidden layer contains multiple neurons, and each neuron is connected to neurons in the next layer. These connections contain weights that determine the importance and influence of data transmitted within the neural network. An activation function is applied to each neuron between the hidden and output layers. This activation function introduces non-linearity, allowing the network to learn more complex patterns and features. The training process of the signal recognition model includes: Pre-collection Different sets of signals are grouped together, and a corresponding set of tags is sequentially assigned to each set of signals. The integer is greater than 1; where a signal set contains multiple independent vibration signals corresponding to a water supply pipe section; each signal set and its corresponding tag set are converted into a corresponding set of feature vectors; the tag set corresponding to the signal set is collected by those skilled in the art during the historical location of scaled pipe sections. Different signal sets are analyzed sequentially based on practical experience. A corresponding signal tag is assigned to each independent vibration signal within each signal set, thereby completing the process. Set the tag set for the group signal set; Each set of feature vectors is used as input to the signal recognition model, which outputs a set of predicted labels corresponding to each signal set and uses the set of actual labels corresponding to each signal set as the prediction target. The actual label set is the pre-set set of labels corresponding to the signal sets. The training objective is to minimize the sum of prediction errors for all signal sets. The formula for calculating the prediction error is as follows: ,in For prediction error, This represents the group number of the feature vector corresponding to the signal set. For the first The set of predicted labels corresponding to the set of group signals. For the first The actual label set corresponding to the set of signals; the signal recognition model is trained until the sum of prediction errors converges and training stops; since the label set is converted into the corresponding feature vector, that is, the label set is numerically represented, both the actual label set and the predicted label set are in numerical form and can be directly used in the calculation of prediction error.

[0034] Methods for accurately locating scale-contaminated sections in water supply pipelines include: Calculate the running time and substitute it into the frequency attenuation function to obtain the frequency attenuation; obtain the reference frequency of each water supply pipe section and calculate the difference between each reference frequency and the frequency attenuation in turn to obtain the standard frequency of each water supply pipe section; calculate the absolute value of the difference between the net resonant frequency and the standard frequency corresponding to the same water supply pipe section in turn to obtain the frequency deviation of each water supply pipe section. The reference frequency refers to the natural frequency of the corresponding pipe in a healthy state, that is, the natural vibration frequency of the pipe itself when there is no scale, abnormal sedimentation, or structural damage. The reference frequency is obtained by a person skilled in the art after the water supply network has been installed and operated stably for a period of time (usually 1-2 weeks). The operating time refers to the time interval between the current moment (i.e., the moment when the scaled pipe section is located) and the measurement moment (i.e., the moment when the reference frequency is measured). The frequency decay function is a function that monotonically decreases as the running time increases. It is used to quantify the influence of the time interval between the current moment and the measurement moment on the reference frequency. The specific form can be an exponential decay function, a linear decay function, etc., which can be pre-designed by those skilled in the art according to the actual situation. It should be noted that due to material fatigue, micro-corrosion, loose connections, etc., the reference frequency of each water supply pipe section will show a slow decreasing trend during the operation of the water supply network. A preset deviation threshold is set, and the frequency deviation of each water supply pipe section is compared with the deviation threshold. The deviation threshold is preset by a person skilled in the art based on the actual situation. If the frequency deviation is greater than or equal to the deviation threshold, the corresponding water supply pipe section will be designated as the scaling pipe section. If the frequency deviation is less than the deviation threshold, the corresponding water supply pipe section will not be considered as a scaling pipe section.

[0035] Step S3: Collect water quality standard indicators for each scaled pipe section, determine the scale type for each scaled pipe section based on the water quality standard indicators, and combine the pipe structure parameters of each scaled pipe section to automatically adjust the sound wave frequency to generate directional resonance for each scaled pipe section, and gradually peel off the scale in each scaled pipe section.

[0036] Water quality standard indicators refer to the concentration of various ions contained in the water sample in the pipe corresponding to the scaling section, such as calcium ions, magnesium ions, bicarbonate ions, sulfate ions, etc. The water quality standard indicators of each scaling section are obtained by ion selective electrode sensors or optical ion detection sensors installed in the pipe corresponding to each scaling section. Methods for determining the type of scale buildup in each pipe section include: Based on the chemical mechanism of scale formation, those skilled in the art can pre-determine multiple sets of key ion combinations for the analysis of scale types. Each set of key ion combinations includes two different ions, and each set of key ion combinations corresponds to a type of scale. For example, calcium ions and bicarbonate ions (used to determine whether calcium carbonate scale is formed), magnesium ions and hydroxide ions (used to determine whether magnesium hydroxide scale is formed), etc. Based on the water quality standards for each scaled pipe section, the ion ratios corresponding to each key ion combination are calculated sequentially to obtain multiple ion ratios for each scaled pipe section. The calculation process for the ion ratios is as follows: obtain the concentration of each ion in the key ion combination from the water quality standards; label the ion at the beginning of the key ion combination as the first ion and the ion at the end as the second ion; convert the concentrations of the first ion and the second ion into their corresponding molar concentrations, and calculate the ratio between the molar concentrations of the first ion and the second ion to obtain the ion ratios corresponding to the key ion combinations. The ion ratios corresponding to each scaled pipe section are added to the corresponding water quality standard indicators, and multiple attribute sets are constructed for each data in the water quality standard indicators. For example, the attribute set corresponding to calcium ion concentration is high concentration, medium concentration, low concentration, etc., and the attribute set corresponding to calcium bicarbonate ion ratio (i.e., the ion ratio between calcium ions and bicarbonate ions) is high proportion, medium proportion, low proportion, etc. The water quality standard indicators corresponding to each scaled pipe section are converted into membership degrees corresponding to each attribute set using attribute interval mapping technology. Attribute interval mapping technology is the process of converting precise numerical values ​​into membership degrees corresponding to attribute sets, such as triangular membership function, trapezoidal membership function, etc. For example, if the calcium ion concentration is high, it is inferred that in the attribute set corresponding to calcium ion concentration, the membership degree of high concentration is 0.9, the membership degree of medium concentration is 0.3, and the membership degree of low concentration is 0. Define attribute reasoning rules based on expert knowledge or relevant literature; for example, if the attribute set corresponding to the water quality standard index is high concentration of calcium ions, high concentration of bicarbonate ions, and medium ratio of calcium to bicarbonate ions, then it is inferred that the corresponding scale type belongs to calcium carbonate scale with a high degree of membership. Each set of water quality standard indicators after attribute interval mapping is matched with attribute inference rules, and attribute inference methods (such as Mamdani inference model, Sugeno inference model, etc.) are used to perform attribute inference to obtain the attribute inference results corresponding to each scaled pipe section. The attribute inference results include the membership degree of each scale type, such as calcium carbonate scale, magnesium hydroxide scale, etc. The membership degree corresponding to each scaled pipe section is compared with the preset membership degree threshold. Scale types with membership degrees greater than the membership degree threshold are all regarded as the scale type of the corresponding scaled pipe section. The membership degree threshold is preset by those skilled in the art according to the actual situation.

[0037] Methods for automatically adjusting the sound wave frequency to generate directional resonance in scaled pipe sections include: The scale type corresponding to the scaled pipe section is marked as a matching type. For each matching type of the scaled pipe section, the corresponding key ion combination is obtained and marked as an inference set. The ions in each inference set are marked as inference ions, and the concentration of each inference ion is obtained from the water quality standard index corresponding to the scaled pipe section. The concentration of each inference ion is converted into the corresponding molar concentration in turn. Based on the molar concentration of each inference ion corresponding to each matching type, the scale thickness corresponding to each matching type is calculated in turn. The expression for calculating scale thickness is: ; In the formula, For the thickness of scale, For type coefficients, , These represent the molar concentrations of the two corresponding inference ions. For the solubility product constant corresponding to the matching type, The scale growth index, Pipeline correction factor; Among them, the type coefficient reflects the material conversion rate of scale type from supersaturation to actual deposition thickness, and is related to scale crystallization rate and density; the solubility product constant characterizes the thermodynamic parameter of scale compound dissolution equilibrium in aqueous solution, and the smaller the value, the lower the solubility of the compound and the greater the tendency to form scale; the scale growth index is used to describe the degree of nonlinear relationship between scale growth rate and supersaturation, reflecting the kinetic characteristics of crystal growth, and is usually greater than 1; the pipeline correction factor is a correction coefficient that comprehensively considers the scale thickness deviation caused by actual pipeline operating conditions such as flow rate, temperature, and surface roughness, so that the calculated scale thickness is more in line with engineering reality; it should be noted that the type coefficient, solubility product constant, scale growth coefficient, and pipeline correction factor are all preset by those skilled in the art according to the actual situation; For different types of scale, appropriate frequency ranges are set sequentially, and the median value of each appropriate frequency range is used as the median frequency of the corresponding scale type. Based on the membership degree of each matching type, the median frequency of each matching type is weighted and averaged to obtain the base frequency. The scale thickness, base frequency, and pipe structure parameters corresponding to the scaled pipe section are used as the analysis set. The analysis set is input into the trained frequency determination model to predict the corresponding optimal frequency. The sound wave frequency of the ultrasonic vibrator is automatically adjusted according to the optimal frequency. The appropriate frequency range is set by those skilled in the art based on literature and practical experience.

[0038] It should be noted that the frequency determination model is a deep neural network model, and the specific training process is the same as that of the signal recognition model. The difference is that the input data of the frequency determination model is the analysis set, and the output data is the optimal frequency. Since the pipe material in the pipe scaling parameters is not numerical data, it is necessary to set different numerical labels for different pipe materials and replace the pipe material in the analysis set with the corresponding numerical labels to ensure that the frequency determination model can operate normally.

[0039] Step S4: Monitor the movement trajectory of each scale piece in real time and predict when each scale piece will arrive at its respective collection device. Based on the arrival time, intelligently activate the collection device to accurately collect each scale piece.

[0040] Methods for real-time monitoring of the movement trajectory of each scale stripped off include: Along the direction of water flow within the water supply network, sequentially assign incrementally increasing numerical labels to the starting positions of each water supply pipe segment, and mark them as location labels. For example, pipe segment A is upstream of the water supply network, and pipe segments A, B, and C are connected sequentially. Pipe segment C is also connected to pipe segments D and E, meaning the water supply network branches at pipe segment C. The location labels are as follows: 1 for pipe segment A, 2 for pipe segment B, 3 for pipe segment C, 3-1 for pipe segment D, and 3-2 for pipe segment E. Along the water flow direction within the water supply network, obtain the downstream of each scaled pipe section. A continuous water supply pipe section, which serves as a tracking section for the corresponding scaled pipe section. The value is an integer greater than 1. At the starting position of each tracking pipe section (i.e., the very front of the water flow entering the tracking pipe section), water turbidity and real-time scattering spectra are continuously collected, and the turbidity variable is continuously calculated. Water turbidity is obtained through turbidity sensors installed at each starting position, and the real-time scattering spectrum is obtained through photoelectric scattering sensors installed at each starting position. The turbidity variable is equal to the difference between water turbidity and baseline turbidity, where baseline turbidity refers to the water turbidity collected before the scale buildup in the pipe section is removed. A reference spectral library was established, which includes standard scattering spectra corresponding to different types of scale. This library was established by those skilled in the art after conducting experimental measurements on different types of scale samples. Each turbidity variable is compared with a preset variable threshold, which is preset by those skilled in the art based on the actual situation; If the turbidity variable is greater than or equal to the variable threshold, the acquisition time (i.e., the time of acquisition of water turbidity) and location label of the corresponding water turbidity are obtained, and the scale type of the corresponding scaling pipe section is marked as the analysis type; the real-time scattering spectrum belonging to the same starting position as the corresponding water turbidity is sequentially correlated with the standard scattering spectrum corresponding to each analysis type in the reference spectrum library, and the scale type corresponding to the standard scattering spectrum with the largest correlation coefficient is taken as the monitoring type; where the correlation coefficient is the Pearson correlation coefficient; the acquired acquisition time and location label are used as the location vector of the scale stripping corresponding to the monitoring type; If the turbidity variable is less than the variable threshold, no action is taken; When it exists When the group position vectors belong to the same scale removal scale, The combination of position vectors corresponds to the motion trajectory of the scale being peeled off. .

[0041] Methods for predicting when scale will arrive at each collection device include: Analyze the position labels in the movement trajectory corresponding to the scale removal to determine whether the scale removal passes through a branch pipe section; a branch pipe section refers to a water supply pipe section that connects to multiple water supply pipe sections at the same time, that is, pipe section C in the above example that is connected to both pipe section D and pipe section E at the same time. If the scale removal process involves a branching pipe section, the scale removal should be divided into... Each individual scale deposit corresponds to a downstream pipe section, and is based on... Group position vectors are used to obtain the location of each individual scale. A group of independent vectors ;in, This refers to the number of downstream pipe segments corresponding to the branching pipe segment. A downstream pipe segment refers to a water supply pipe segment located downstream of and connected to the branching pipe segment; for example, The position labels in the group position vector are 1, 2, 3, 3-1, and 3-2, respectively. Therefore, the two independent scales correspond to... The position labels in the independent vectors are 1, 2, 3, 3-1 and 1, 2, 3, 3-2, respectively. According to each individual scale The independent vectors are used to calculate the movement speed of each independent scale in turn; the arrival length between each collection device and each independent scale is obtained in turn, and based on the arrival length and movement speed, the arrival time of each independent scale to each collection device is calculated in turn. If the scale removal does not pass through the branch pipe section, then it should be based on the scale removal process. The system uses position vectors to calculate the speed of the scale removal process; it also obtains the distance between each collection device and the scale removal process, and calculates the arrival time of the scale removal process at each collection device based on the distance and speed.

[0042] Multiple collection devices are installed at the beginning of several water supply pipe sections. The entire collection device consists of a mechanical filter screen (or grid), an inclined guide channel, and a collection tank. When the water flow carries the scale through the pipe, it is first intercepted by the grid or screen. The scale particles are too large to pass through and are blocked at the top. Then, under the impact of the water flow and gravity, the scale slides along the inclined guide channel and enters the bottom of the collection tank to deposit. The water flow then bypasses the deposited scale and flows smoothly out of the pipe. This achieves efficient scale collection, prevents pipe blockage, ensures uninterrupted water supply, and prevents secondary pollution caused by scale.

[0043] Methods for calculating the movement speed of individual scale include: From the perspective of independent scale In each set of independent vectors, the independent vector corresponding to the water supply pipe segment downstream of the bifurcation segment is obtained and marked as the analysis vector. Each pair of adjacent analysis vectors is then treated as a vector set, and the time span corresponding to each vector set is calculated sequentially. The time span is equal to the difference between the acquisition time corresponding to the subsequent vector and the acquisition time corresponding to the preceding vector in the vector set. The preceding vector is the analysis vector corresponding to the upstream water supply pipe segment, and the subsequent vector is the analysis vector corresponding to the downstream water supply pipe segment. The pipe length of the water supply pipe segment corresponding to the preceding vector in each vector set is obtained and used as the movement length of the corresponding vector set. Calculate the ratio of the motion length to the time span corresponding to the same set of vectors to obtain the unit velocity of each set of vectors; calculate the mean of all unit velocities to obtain the motion velocity of individual scale particles. The method for calculating the speed of scale removal is the same as the method for calculating the speed of individual scale particles. The difference is that when calculating the speed of scale removal, the vector set used includes every two adjacent position vectors.

[0044] The distance between each collection device and each individual scale deposit is the distance between the location label and the location of each collection device. The sum of the lengths of all water supply pipe segments contained between the latest location labels in the group of independent vectors; The distance between each collection device and the scale removal point is the location label corresponding to each collection device and... The sum of the lengths of all water supply pipe segments included between the location labels with the latest acquisition time in the group location vector.

[0045] The method for calculating the arrival time is as follows: calculate the ratio between the arrival length and the movement speed to obtain the arrival time; add the arrival time to the real-time time (i.e., the time corresponding to each independent scale buildup). The latest acquisition time in the group of independent vectors or the corresponding time for scale removal The arrival time is obtained by taking the latest acquisition time from the group of location vectors.

[0046] Methods for intelligently activating the collection device based on arrival time include: A preset start-up time is defined, which refers to the time required from starting the collection device to the device entering normal operation. This time is preset by those skilled in the art based on the specific characteristics of the collection device. Both stripped scale and independent scale are labeled as analytical scale. The arrival times of each analytical scale are sorted from earliest to latest to generate a time sequence for each analytical scale. The start-up time is added to each time sequence, and the arrival time immediately following the start-up time in each sequence is obtained and labeled as the matching time. The collection device corresponding to each matching time is used as the matching device for the corresponding analytical scale. The start-up time and a preset start-up margin are subtracted sequentially from each matching time, and then the real-time time is added to obtain the start-up timing for each analytical scale. Based on the start-up timing of each analytical scale, the matching device corresponding to each analytical scale is intelligently activated; that is, when the real-time time reaches the start-up timing, the matching device corresponding to the analytical scale is intelligently activated. The start-up margin refers to the time buffer reserved to ensure that the collection device can start up before the scale analysis arrives. It is preset by those skilled in the art in combination with the response characteristics and operational stability of the collection device, in order to compensate for the impact of system delay or environmental fluctuations and improve the accuracy and reliability of the start-up timing.

[0047] This embodiment dynamically adjusts the length of water supply pipe sections to adapt to the scaling risks of different sections, achieving refined monitoring and management of complex water supply networks and improving the accuracy of local scaling control. It utilizes pipeline vibration signal analysis technology to accurately locate scaling sections and automatically identifies scale types based on water quality standards. Then, it intelligently adjusts the sound wave frequency to perform directional resonance descaling of the target pipe section, improving descaling efficiency and targeting. By monitoring the trajectory of the detached scale in real time and predicting its arrival time, it achieves precise collection of free scale, avoiding secondary blockages and water pollution, and ensuring the long-term safe and stable operation of the water supply system. This embodiment integrates IoT technology, signal analysis, water chemistry, fluid mechanics, and other multidisciplinary knowledge to achieve intelligent perception, accurate diagnosis, and adaptive control of scaling conditions in the water supply network, thereby effectively improving descaling efficiency, ensuring the continuity of water supply network operation and water quality stability, and ultimately meeting the high standards required by modern water supply systems.

[0048] Example 2 This application also provides an electronic device. The electronic device may include one or more processors and one or more memories. The memories store computer-readable code, which, when executed by the one or more processors, can perform an IoT-based descaling control method as described above.

[0049] The method or system according to the embodiments of this application can also be implemented using the architecture of the electronic device shown in this application. The electronic device may include a bus, one or more CPUs, ROM, RAM, a communication port connected to a network, input / output, a hard disk, etc. The storage device in the electronic device, such as a ROM or hard disk, may store the IoT-based descaling control method provided in this application. Furthermore, the electronic device may also include a user interface. Of course, the architecture shown in this application is merely exemplary; when implementing different devices, one or more components of the electronic device shown in this application may be omitted according to actual needs.

[0050] Example 3 Please refer to the accompanying drawings. One embodiment of this application discloses a computer-readable storage medium. The computer-readable storage medium stores computer-readable instructions. When the computer-readable instructions are executed by a processor, a descaling control method based on the Internet of Things (IoT) according to an embodiment of this application, as described above, can be performed. The storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0051] Furthermore, according to embodiments of this application, the processes described in the above-referenced flowcharts can be implemented as computer software programs. For example, this application provides a non-transitory machine-readable storage medium storing machine-readable instructions that can be executed by a processor to perform instructions corresponding to the method steps provided in this application, such as an Internet of Things-based descaling control method. When this computer program is executed by a central processing unit (CPU), it performs the functions defined in the method of this application.

[0052] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0053] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0054] In the description of this invention, it should be understood that the terms "first," "second," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0055] In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0056] In the description of this invention, "several" means one or more, and "a large number" means two or more.

[0057] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0058] All formulas in this manual are dimensionless and calculated numerically. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0059] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for controlling scale removal based on the Internet of Things, characterized by, include: Step S1: Divide the water supply network evenly into multiple water supply pipe sections, collect the pipe structure parameters of each water supply pipe section, calculate the scaling risk of each water supply pipe section in turn based on the pipe structure parameters, and dynamically adjust the length of each water supply pipe section based on the scaling risk. Step S2: Collect the pipe vibration signal of each water supply pipe section, extract the net resonant frequency of each water supply pipe section from the pipe vibration signal, and accurately locate the scaled pipe section in the water supply pipe section based on the net resonant frequency. Step S3: Collect water quality standard indicators for each scaled pipe section, determine the scale type for each scaled pipe section based on the water quality standard indicators, and combine the pipe structure parameters of each scaled pipe section to automatically adjust the sound wave frequency to generate directional resonance for each scaled pipe section, and gradually peel off the scale in each scaled pipe section. Step S4: Monitor the movement trajectory of each scale piece in real time and predict when each scale piece will arrive at its respective collection device. Based on the arrival time, intelligently activate the collection device to accurately collect each scale piece.

2. The water scale control method based on the Internet of Things according to claim 1, characterized in that, The steps for dynamically adjusting the length of each water supply pipe section include: Step S101: Calculate the total length of the pipeline network based on the length of each water supply pipe section; Step S102: Based on the scaling risk of each water supply pipe section, dynamically adjust the length of each water supply pipe section to obtain the dynamic length of each water supply pipe section. Step S103: Analyze the dynamic length of each water supply pipe section and identify the pipe sections exceeding the limit; Step S104: Based on the preset standard length, dynamically adjust the dynamic length of each over-limit pipe section, and calculate the total dynamic length of all water supply pipe sections. Step S105: Determine whether the convergence condition is met based on the total length of the pipeline network and the dynamic total length; if the convergence condition is not met, proceed to step S106; if the convergence condition is met, proceed to step S107. Step S106: Based on the total length of the pipeline network, dynamically adjust the dynamic length of all water supply pipe sections that are not over-limited, and return to step S103. Step S107: Use the dynamic length of each water supply pipe segment as the corresponding pipe segment length to complete the dynamic adjustment of the corresponding length of each water supply pipe segment.

3. The method of claim 2, wherein the method further comprises: In step S106, the method for dynamically adjusting the dynamic length of all water supply pipe sections that are not over-limit pipe sections includes: Calculate the total length exceeding the limit based on the dynamic length of each exceeding pipe segment; calculate the remaining total length based on the difference between the total length of the pipeline network and the total length exceeding the limit; mark all water supply pipe segments that are not exceeding the limit as regulating pipe segments, and calculate the total regulating length based on the dynamic length of each regulating pipe segment; calculate the ratio of the remaining total length to the total regulating length to obtain the regulating factor; calculate the product between the dynamic length of each regulating pipe segment and the regulating factor to obtain the regulating length of each regulating pipe segment, and dynamically adjust the dynamic length of each regulating pipe segment based on the regulating length of each regulating pipe segment.

4. The water scale control method based on the Internet of Things according to claim 3, characterized in that, Methods for extracting the net resonant frequency of each water supply pipe section from the pipeline vibration signal include: Blind source separation is performed on the vibration signals of each water supply pipe section to obtain multiple independent vibration signals corresponding to each water supply pipe section. These multiple independent vibration signals are then input into a trained signal recognition model to predict the label set corresponding to each water supply pipe section. The label set includes the signal label corresponding to each independent vibration signal of the water supply pipe section, and the signal label includes intrinsic labels and interference labels. Independent vibration signals with intrinsic labels are used as the intrinsic vibration signals of the corresponding water supply pipe sections. Spectral analysis is then performed on the multiple intrinsic vibration signals corresponding to each water supply section sequentially to obtain multiple natural frequencies corresponding to each water supply section. Based on the natural frequencies of each water supply pipe section, the number of different natural frequencies under the same water supply pipe section is counted and marked as the occurrence count. The occurrence counts corresponding to the same water supply pipe section are compared, and the natural frequencies with the most occurrence counts are marked as candidate frequencies. If there is only one candidate frequency for a water supply pipe section, the candidate frequency is taken as the net resonant frequency of the corresponding water supply pipe section. If there are multiple candidate frequencies for a water supply pipe section, the amplitude of each candidate frequency in the corresponding natural vibration signal is obtained in turn, and the candidate frequency with the largest amplitude is taken as the net resonant frequency of the corresponding water supply pipe section.

5. The method of claim 4, wherein the method further comprises: Methods for accurately locating scale-contaminated sections in water supply pipelines include: Calculate the running time and substitute it into the frequency attenuation function to obtain the frequency attenuation; obtain the reference frequency of each water supply pipe section and calculate the standard frequency of each water supply pipe section based on the frequency attenuation; calculate the frequency deviation of each water supply pipe section based on the standard frequency and the net resonant frequency of each water supply pipe section. A preset deviation threshold is set, and the frequency deviation of each water supply pipe section is compared with the deviation threshold. If the frequency deviation is greater than or equal to the deviation threshold, the corresponding water supply pipe section will be designated as the scaling pipe section. If the frequency deviation is less than the deviation threshold, the corresponding water supply pipe section will not be considered as a scaling pipe section.

6. The water scale control method based on the Internet of Things according to claim 5, characterized in that, Methods for determining the type of scale buildup in each pipe section include: Multiple sets of key ion combinations are preset, and the ion ratios corresponding to each set of key ion combinations are calculated sequentially according to the water quality standard indicators of each scaled pipe section to obtain multiple ion ratios corresponding to each scaled pipe section. The ion ratios corresponding to each scaled pipe section are added to the corresponding water quality standard indicators, and multiple attribute sets are constructed for each data in the water quality standard indicators. The water quality standard indicators corresponding to each scaled pipe section are converted into membership degrees corresponding to each attribute set using attribute interval mapping technology. Attribute reasoning rules are defined. Each set of water quality standard indicators after attribute interval mapping is matched with the attribute reasoning rules, and attribute reasoning is performed to obtain the attribute reasoning results corresponding to each scaled pipe section. The attribute reasoning results include the membership degree of each scale type. The membership degree corresponding to each scaled pipe section is compared with a preset membership degree threshold. Scale types with membership degrees greater than the membership degree threshold are all taken as the scale type of the corresponding scaled pipe section.

7. The water scale control method based on the Internet of Things according to claim 6, characterized in that, Methods for automatically adjusting the sound wave frequency to generate directional resonance in scaled pipe sections include: The scale type corresponding to the scaled pipe section is marked as a matching type. For each matching type of the scaled pipe section, the corresponding key ion combination is obtained and marked as an inference set. The ions in each inference set are marked as inference ions, and the concentration of each inference ion is obtained from the water quality standard index corresponding to the scaled pipe section. The concentration of each inference ion is converted into the corresponding molar concentration in turn. Based on the molar concentration of each inference ion corresponding to each matching type, the scale thickness corresponding to each matching type is calculated in turn. For different types of scale, appropriate frequency ranges are set sequentially, and the median value of each appropriate frequency range is used as the median frequency of the corresponding scale type. Based on the membership degree of each matching type, the median frequency of each matching type is weighted and averaged to obtain the base frequency. The scale thickness, base frequency, and pipe structure parameters corresponding to the scaled pipe section are used as the analysis set. The analysis set is input into the trained frequency determination model to predict the corresponding optimal frequency, and the sound wave frequency is automatically adjusted according to the optimal frequency.

8. The water scale control method based on the Internet of Things according to claim 7, characterized in that, Methods for real-time monitoring of the movement trajectory of each scale stripped off include: Along the water flow direction within the water supply network, sequentially assign incrementing numerical labels to the starting positions of each water supply pipe section, marking them as location labels; along the water flow direction within the water supply network, obtain the downstream locations of each scaled pipe section. A continuous water supply pipe section was established, serving as a tracking pipe section for the corresponding scaling pipe section; at the starting position of each tracking pipe section, the turbidity and real-time scattering spectrum of the water body were continuously collected, and the turbidity variable was continuously calculated. Establish a reference spectral library, which includes standard scattering spectra corresponding to different types of scale; compare each turbidity variable with a preset variable threshold. If the turbidity variable is greater than or equal to the variable threshold, the collection time and location label of the corresponding water turbidity are obtained, and the scale type of the corresponding scaled pipe section is marked as the analysis type; the real-time scattering spectrum belonging to the same starting position as the corresponding water turbidity is correlated with the standard scattering spectrum corresponding to each analysis type in the reference spectrum library, and the scale type corresponding to the standard scattering spectrum with the largest correlation coefficient is taken as the monitoring type; the obtained collection time and location label are used as the location vector of scale stripping corresponding to the monitoring type; When it exists When the group position vectors belong to the same scale removal scale, The combination of position vectors corresponds to the motion trajectory of the scale being peeled off.

9. The descaling control method based on the Internet of Things according to claim 8, characterized in that, Methods for predicting when scale will arrive at each collection device include: Analyze the position labels in the movement trajectory corresponding to the scale removal to determine whether the scale removal passes through a branch pipe section. A branch pipe section refers to a water supply pipe section that connects multiple water supply pipe sections at the same time. If the scale removal process involves a branching pipe section, the scale removal should be divided into... Each individual scale deposit corresponds to a downstream pipe section, and is based on... Group position vectors are used to obtain the location of each individual scale. A set of independent vectors; where, This refers to the number of downstream pipe sections corresponding to the branching pipe section. The downstream pipe section refers to the water supply pipe section located downstream of the branching pipe section and connected to it. According to each individual scale The independent vectors are used to calculate the movement speed of each independent scale in turn; the arrival length between each collection device and each independent scale is obtained in turn, and based on the arrival length and movement speed, the arrival time of each independent scale to each collection device is calculated in turn. If the scale removal does not pass through the branch pipe section, then it should be based on the scale removal process. The system uses position vectors to calculate the speed of the scale removal process; it also obtains the distance between each collection device and the scale removal process, and calculates the arrival time of the scale removal process at each collection device based on the distance and speed.

10. The descaling control method based on the Internet of Things according to claim 9, characterized in that, Methods for intelligently activating the collection device based on arrival time include: Both the detached scale and the independent scale were labeled as analytical scale. The arrival times of each analytical scale were sorted from earliest to latest to generate a time sequence for each analytical scale. The start time was added to each time sequence, and the arrival time of the one following the start time in each time sequence was obtained and labeled as the matching time. The collection device corresponding to each matching time was used as the matching device for the corresponding analytical scale. Based on the matching time and the preset start time and start margin, the start time corresponding to each scale analysis is calculated sequentially; based on the start time of each scale analysis, the matching device corresponding to each scale analysis is intelligently started.

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