Sewage treatment simulation regulation and control method and system based on Internet of Things

Abstract

The invention relates to the technical field of sewage treatment, in particular to a sewage treatment simulation regulation and control method and system based on the Internet of Things. The method comprises the following steps: detecting a sludge pump and a backflow pipeline of sludge backflow equipment through an Internet of Things sensor network; real-time operation parameter acquisition is carried out on a sludge pump and a backflow pipeline, the real-time operation parameters comprise sludge concentration data and backflow pipeline pressure data, and the sludge concentration data and the backflow pipeline pressure data are transmitted to a sewage treatment control center; monitoring the motor power and the impeller rotating speed of the sludge pump in a sewage treatment control center; and determining the pipeline inner diameter material of the backflow pipeline according to the pressure data of the backflow pipeline. According to the invention, through a real-time monitoring technology and a dynamic regulation and control technology of the Internet of Things sensor network, accurate monitoring and intelligent regulation and control of the sludge backflow equipment are realized, so that the operation efficiency and stability of the sewage treatment system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of sewage treatment, and in particular to a sewage treatment simulation control method and system based on the Internet of Things. Background Art

[0002] The core of wastewater treatment lies in sludge return equipment (such as sludge pumps and return piping), which is used to return sludge from sedimentation tanks to the bioreactor to maintain sludge concentration and microbial activity in the system. Traditional sludge return pumps mostly operate at a fixed speed, making it impossible to dynamically adjust the return ratio based on the real-time changes in sludge concentration and microbial activity in the bioreactor. This fixed mode cannot adapt to the fluctuating sludge concentration and changes in microbial metabolism during the wastewater treatment process, resulting in low return efficiency and difficulty in accurately maintaining the appropriate sludge concentration and microbial activity levels in the system. Furthermore, the viscosity and particle properties of sludge are the main causes of return pipe blockage. Increased sludge concentration, the aggregation of sludge particles, and increased sludge viscosity all increase the risk of pipe blockage. Furthermore, the inner diameter, material, and structural design of the return pipe also affect the degree of blockage. Summary of the Invention

[0003] Based on this, it is necessary to provide a sewage treatment simulation control method and system based on the Internet of Things to solve at least one of the above technical problems.

[0004] To achieve the above objectives, a sewage treatment simulation control method based on the Internet of Things is provided, the method comprising the following steps: Step S1: Detecting the sludge pump and return pipe of the sludge return equipment through the Internet of Things sensor network; collecting real-time operating parameters of the sludge pump and return pipe, wherein the real-time operating parameters include sludge concentration data and return pipe pressure data, and transmitting the sludge concentration data and return pipe pressure data to the sewage treatment control center; Step S2: At the sewage treatment control center, monitor the motor power and impeller speed of the sludge pump; determine the inner diameter and material of the return pipe based on the return pipe pressure data; detect the sludge particle size based on the sludge concentration data, and determine the sludge viscosity based on the sludge particle size; and assess the degree of sludge return blockage based on the pipe inner diameter and material and the sludge viscosity. Step S3: generating a motor control instruction for the sludge pump according to the degree of sludge reflux blockage and the motor power; generating an impeller control instruction for the sludge pump according to the degree of sludge reflux blockage and the impeller speed; generating a dredging operation instruction for the return pipe according to the degree of sludge reflux blockage and the inner diameter and material of the pipe; Step S4: Send the motor control instruction, impeller control instruction and dredging operation instruction to the sludge return equipment and record them as the sewage treatment control strategy; simulate the sewage treatment according to the sewage treatment control strategy to perform the sewage treatment operation.

[0005] The present invention collects real-time operating parameters of the sludge pump and return pipe of the sludge return equipment through the Internet of Things sensor network, can accurately obtain sludge concentration data and return pipe pressure data, and transmit these data to the sewage treatment control center in a timely manner. This real-time monitoring and data transmission mechanism ensures the continuity and accuracy of key parameters in the sewage treatment process, provides a reliable data basis for subsequent regulatory decisions, avoids regulatory errors caused by data delays or inaccuracies, and thus improves the operating efficiency and stability of the sewage treatment system. In the sewage treatment control center, by monitoring the motor power and impeller speed of the sludge pump, combining the return pipe pressure data to determine the inner diameter material of the pipeline, and detecting the sludge particle size and determining the sludge viscosity based on the sludge concentration data, the sludge return blockage degree is accurately assessed based on the inner diameter material of the pipeline and the sludge viscosity. This comprehensive parameter monitoring and integrated assessment approach provides a more accurate understanding of the operating status of the sludge return equipment. It generates precise motor control commands, impeller control commands, and dredging operation commands for different blockage levels and equipment operating conditions, enabling refined control of the sludge pump and return pipe. This effectively addresses issues such as low equipment efficiency, increased energy consumption, and poor sludge return flow caused by inaccurate control in traditional sewage treatment processes. The generated motor control commands, impeller control commands, and dredging operation commands are transmitted to the sludge return equipment and recorded as a sewage treatment control strategy. Simultaneously, sewage treatment is simulated and controlled according to the sewage treatment control strategy to execute sewage treatment operations. This process not only enables real-time control of sewage treatment operations but also verifies the effectiveness of the control strategy through simulated control. This allows potential issues to be identified and optimized before actual operation. This further improves the reliability and adaptability of the sewage treatment system, reduces the risk of sewage treatment accidents caused by improper control, ensures the continuous and stable operation of the sewage treatment process, and improves the overall quality and efficiency of sewage treatment.

[0006] Preferably, step S1 includes the following steps: Step S11: Sludge concentration sensors are placed at the inlet and outlet of the sludge pump. The sensors transmit a light beam of a specific wavelength through the sludge and detect the intensity of the transmitted light to collect sludge concentration data. The sensors collect data every 1 minute, and each collection lasts for 5 seconds to obtain the final sludge concentration data. Step S12: Multiple pressure sensors are arranged at the elbow, connecting flange, and middle section of the return pipe. Pressure is measured by detecting the resistance change caused by pressure changes in the pipe. Each sensor collects data every 30 seconds, and each collection lasts for 2 seconds. Finally, the return pipe pressure data is obtained. Step S13: encrypting the sludge concentration data and the return pipe pressure data, and transmitting the encrypted data to the sewage treatment control center.

[0007] This system deploys sludge concentration sensors at the inlet and outlet of the sludge pump to collect sludge concentration data every minute for 5 seconds. Pressure sensors are also deployed at key locations in the return pipe to collect return pipe pressure data every 30 seconds for 2 seconds. The collected sludge concentration and return pipe pressure data are encrypted and transmitted to the sewage treatment control center. This enables precise data collection, high-frequency monitoring, and secure transmission, providing reliable data support for subsequent sewage treatment control and improving the stability and safety of system operation.

[0008] Preferably, in step S2, monitoring the motor power and impeller speed of the sludge pump at the sewage treatment control center includes: Motor power data is collected from the input end, output end, and intermediate junction box of the sludge pump motor. The sensor at each location collects data every 1 second, and each collection lasts for 0.1 seconds. The impeller speed data is collected from different positions of the sludge pump impeller, such as the front end, middle section and rear end of the impeller shaft. The sensor at each position collects data every 0.5 seconds.

[0009] Preferably, determining the inner diameter and material of the return pipe according to the return pipe pressure data in step S2 includes: The return pipe pressure data is subjected to pressure gradient classification to obtain a pipe pressure gradient value; the pipe fluid flow rate is mapped according to the pipe pressure gradient value, and the pipe inner diameter is calculated according to the pipe fluid flow rate; Extract pipeline pressure time series of return pipeline pressure data; If the return pipe pressure data gradually decreases with the pipe pressure time series and the pipe inner diameter is stable, it is judged that the pipe material is corrosion-resistant; If the return pipe pressure data fluctuates continuously with the pipe pressure time series and the pipe inner diameter changes continuously, it is judged that the pipe material is a corrosive material.

[0010] The present invention collects motor power data from the input end, output end and intermediate junction box of the sludge pump motor respectively. The sensor at each position collects data every 1 second, and each collection lasts for 0.1 seconds. At the same time, the impeller speed data is collected from the front end, middle section and rear end of the sludge pump impeller respectively. The sensor at each position collects data every 0.5 seconds, thereby achieving high-precision and high-frequency monitoring of motor power and impeller speed. In addition, by performing pressure gradient classification on the return pipe pressure data, calculating the pipeline fluid flow rate and deriving the pipeline inner diameter, combined with the change characteristics of the pipeline pressure time series, the corrosion resistance of the pipeline material can be accurately judged. This monitoring and analysis method can grasp the equipment operation status and pipeline material characteristics in real time, providing a reliable basis for subsequent precise regulation, thereby improving the operating efficiency and reliability of the sewage treatment system.

[0011] Preferably, in step S2, detecting the sludge particle size according to the sludge concentration data, and determining the sludge viscosity by the sludge particle size includes: The sludge concentration data is divided into multiple data segments according to the time series, and each data segment contains 10 consecutive concentration values; each data segment is analyzed and the sludge concentration change rate is calculated to determine the degree of sludge concentration fluctuation; If the sludge concentration change rate is greater than the preset sludge concentration change rate and the sludge concentration fluctuates frequently, it is determined to be a small sludge particle size; If the sludge concentration change rate is less than the preset sludge concentration change rate and the sludge concentration fluctuates, it is determined to be large sludge particle size; If the sludge particle size is small, the sludge viscosity is determined to be high; if the sludge particle size is large, the sludge viscosity is determined to be low.

[0012] The present invention divides the sludge concentration data into multiple data segments according to the time series, each data segment contains 10 consecutive concentration values, and calculates the sludge concentration change rate for each data segment, thereby being able to quantitatively analyze the degree of fluctuation of the sludge concentration. Based on the comparison of the sludge concentration change rate with the preset threshold value, combined with the sludge concentration fluctuation frequency, the sludge particle size and the corresponding viscosity are accurately determined: when the sludge concentration change rate is greater than the preset value and fluctuates frequently, it is determined to be a sludge with small particle size and high viscosity; otherwise, it is determined to be a sludge with large particle size and low viscosity. This data-driven analysis method can accurately reflect the physical properties of the sludge, provide a scientific basis for subsequent sludge return flow control, thereby achieving refined management of the sewage treatment process and improving the operating efficiency and stability of the system.

[0013] Preferably, in step S3, generating a motor control instruction for the sludge pump according to the sludge backflow blockage degree and the motor power includes: The correlation analysis between the sludge return blockage degree and the motor power is carried out to obtain the sludge pump motor analysis results; if the motor power exceeds 90% of the rated power and the sludge return blockage degree is high, it is determined that the motor is in a high-load operation state; if the motor power is between 60% and 80% of the rated power and the sludge return blockage degree is low, it is determined that the motor is in a normal operation state; A motor control instruction is generated based on the sludge pump motor analysis results. If the motor is determined to be in a high-load operating state, a motor power reduction control instruction is generated; if the motor is determined to be in a normal operating state, a motor power maintenance control instruction is generated.

[0014] Preferably, in step S3, generating an impeller control instruction for the sludge pump according to the sludge backflow blockage degree and the impeller speed includes: The impeller operating state is divided according to the degree of sludge return blockage and the impeller speed. If the impeller speed is lower than 70% of the rated speed and the sludge return blockage is high, it is determined to be an impeller inefficient operating state; if the impeller speed is between 80% and 90% of the rated speed and the sludge return blockage is low, it is determined to be an impeller efficient operating state. Obtain the structural characteristics of the impeller, including the number of blades, blade angle, and impeller diameter. Determine the optimal operating parameters based on the impeller's structural characteristics. If the impeller has a large number of blades and a large blade angle, a high speed is required to maintain sludge conveying; if the impeller diameter is large, a low speed is required to avoid wear. A preliminary impeller control instruction is generated based on the impeller operating status and the impeller's optimal operating parameters. If the impeller is determined to be in an inefficient operating state, an impeller speed increase control instruction is generated; if the impeller is determined to be in an efficient operating state, a impeller speed maintenance control instruction is generated.

[0015] The present invention correlates sludge return blockage level with motor power, enabling precise determination of the sludge pump motor's operating status. When motor power exceeds 90% of rated power and sludge return blockage is high, the motor is deemed to be operating at high load, generating a control instruction to reduce motor power. When motor power is between 60% and 80% of rated power and sludge return blockage is low, the motor is deemed to be operating normally, generating a control instruction to maintain the current power. Furthermore, the impeller's operating status is classified based on the sludge return blockage level and impeller speed. Optimal operating parameters are determined based on impeller structural characteristics (such as the number of blades, blade angle, and impeller diameter), and impeller control instructions are generated. This control method, based on multi-parameter comprehensive analysis, precisely adjusts the motor and impeller operating status based on actual operating conditions, effectively preventing motor overload and impeller inefficiency, extending equipment life, and simultaneously improving sludge return efficiency, ensuring stable operation of the sewage treatment system.

[0016] Preferably, in step S3, generating a dredging operation instruction for the return pipe according to the degree of sludge return blockage and the inner diameter and material of the pipe includes: A comprehensive assessment is conducted based on the degree of sludge backflow blockage and the inner diameter and material of the pipeline. If the blockage degree is high and the pipeline material is corrosive, the pipeline is judged to be in a high-risk blockage state; if the blockage degree is low and the pipeline material is corrosion-resistant, the pipeline is judged to be in a low-risk blockage state. Generate dredging operation instructions based on the comprehensive assessment results; if the pipeline is determined to be in a high-risk blockage state, generate instructions to increase the dredging frequency and intensity; if the pipeline is determined to be in a low-risk blockage state, generate instructions to maintain the current dredging frequency; Dynamically adjust the dredging operation according to the dredging operation instructions. If the dredging frequency needs to be increased, the dredging interval time will be gradually shortened, with each adjustment range being 5 minutes and the adjustment interval being 30 minutes. If the current dredging frequency needs to be maintained, the dredging interval time will remain unchanged.

[0017] By comprehensively evaluating the degree of sludge backflow blockage and the inner diameter and material of the pipeline, the present invention can accurately determine the pipeline's blockage risk status. When the blockage level is high and the pipeline is made of a corrosive material, the pipeline is determined to be in a high-risk blockage state, and instructions are generated to increase the frequency and intensity of dredging. When the blockage level is low and the pipeline is made of a corrosion-resistant material, the pipeline is determined to be in a low-risk blockage state, and instructions are generated to maintain the current dredging frequency. The dredging operation is dynamically adjusted based on the dredging operation instructions. By gradually shortening the dredging interval (by 5 minutes each time, with an adjustment interval of 30 minutes) or maintaining the dredging interval unchanged, refined management of the pipeline dredging operation is achieved. This dynamic dredging strategy based on risk assessment can effectively reduce the risk of pipeline blockage and extend pipeline service life, while avoiding the waste of resources caused by excessive dredging and ensuring the stable operation of the sewage treatment system.

[0018] Preferably, step S4 includes the following steps: Step S41: In the sewage treatment control center, the motor control instructions, the impeller control instructions, and the dredging operation instructions are integrated to generate a comprehensive control instruction, which is then sent to the control system of the sludge return equipment via the Internet of Things network; Step S42: In the control system of the sludge return equipment, the comprehensive control instruction is received and confirmed for execution; the confirmation content includes motor power adjustment, impeller speed adjustment and initiation of dredging operation; if the confirmation is correct, the control operation is executed on the sewage treatment process; Step S43: Recording the comprehensive control instructions and their execution results in the database of the sewage treatment control center to form a sewage treatment control strategy. The record content includes the specific parameters of the control instructions, execution time, execution results, and feedback information; Step S44: During the sewage treatment operation, the operating status of the sludge return equipment is monitored in real time, including motor power, impeller speed, and return pipe pressure; and the monitoring data is fed back to the sewage treatment control center in real time; Step S45: Evaluate the control effect based on the real-time feedback monitoring data. If the monitoring data shows that the control effect does not reach the preset target, optimize the control strategy, including adjusting the motor power, impeller speed, and the frequency and intensity of the dredging operation.

[0019] The present invention achieves centralized control of sewage treatment equipment by integrating motor control instructions, impeller control instructions, and dredging operation instructions to generate comprehensive control instructions. These instructions are then sent to the sludge return equipment's control system via an Internet of Things (IoT) network. Within the sludge return equipment's control system, the execution of the comprehensive control instructions is confirmed. After ensuring that motor power adjustment, impeller speed adjustment, and the initiation of the dredging operation are correct, the control operation is executed. The control instructions and their execution results are recorded in the sewage treatment control center's database, forming a complete sewage treatment control strategy. The records include the specific parameters of the control instructions, execution time, execution results, and feedback information. During the control process, the operating status of the sludge return equipment, including motor power, impeller speed, and return pipe pressure, is monitored in real time, and the monitoring data is fed back to the control center. The control effect is evaluated based on the real-time feedback data. If the preset targets are not achieved, the control strategy is optimized to adjust the motor power, impeller speed, and the frequency and intensity of the dredging operation. This closed-loop control mechanism ensures precise execution and dynamic optimization of the sewage treatment process, improves system operating efficiency and stability, and provides data support and optimization basis for subsequent control.

[0020] In this specification, a sewage treatment simulation control system based on the Internet of Things is provided, which is used to execute the above-mentioned sewage treatment simulation control method based on the Internet of Things. The sewage treatment simulation control system based on the Internet of Things includes: The IoT sensor acquisition module is used to detect the sludge pump and return pipe of the sludge return equipment through the IoT sensor network; collect real-time operating parameters of the sludge pump and return pipe, where the real-time operating parameters include sludge concentration data and return pipe pressure data, and transmit the sludge concentration data and return pipe pressure data to the sewage treatment control center; The sewage quality assessment module is used in the sewage treatment control center to monitor the motor power and impeller speed of the sludge pump; determine the inner diameter and material of the return pipe based on the return pipe pressure data; detect the sludge particle size based on the sludge concentration data, and determine the sludge viscosity through the sludge particle size; and assess the degree of sludge return blockage based on the pipe inner diameter material and sludge viscosity. The sewage treatment control module is used to generate motor control instructions for the sludge pump based on the degree of sludge backflow blockage and motor power; generate impeller control instructions for the sludge pump based on the degree of sludge backflow blockage and impeller speed; and generate dredging operation instructions for the return pipe based on the degree of sludge backflow blockage and the inner diameter and material of the pipe; The sewage treatment operation execution module is used to send motor control instructions, impeller control instructions and dredging operation instructions to the sludge return equipment and record them as sewage treatment control strategies; simulate the sewage treatment according to the sewage treatment control strategies to execute sewage treatment operations.

[0021] The present invention obtains sludge concentration data and return pipe pressure data in real time through the Internet of Things sensor acquisition module, and transmits it to the sewage treatment control center. The sewage quality assessment module accurately assesses the degree of sludge return blockage based on these data and related monitoring parameters. The sewage treatment control module generates targeted motor and impeller control instructions and dredging operation instructions based on the assessment results. The sewage treatment operation execution module sends the instructions to the equipment and records the control strategy, and performs simulated control to optimize the sewage treatment operation. This systematic control method realizes real-time monitoring, accurate evaluation and dynamic optimization of the sewage treatment process, improves sewage treatment efficiency and system operation stability, reduces energy consumption and equipment loss, and ensures the continuity and reliability of sewage treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic diagram of the steps of a sewage treatment simulation control method based on the Internet of Things; Figure 2 for Figure 1 Detailed implementation steps of step S4 in FIG. The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0023] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0024] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0025] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0026] To achieve this, please refer to Figures 1 to 2 , a sewage treatment simulation control method based on the Internet of Things, the method comprising the following steps: Step S1: Detecting the sludge pump and return pipe of the sludge return equipment through the Internet of Things sensor network; collecting real-time operating parameters of the sludge pump and return pipe, wherein the real-time operating parameters include sludge concentration data and return pipe pressure data, and transmitting the sludge concentration data and return pipe pressure data to the sewage treatment control center; In this embodiment of the present invention, a sludge concentration sensor, model SC-1000, is installed at the sludge pump outlet of the sludge return system. This sensor uses an optical scattering method to measure sludge concentration. Its operating principle is to illuminate the sludge fluid with infrared light of a specific wavelength and calculate the sludge concentration by detecting the scattered light intensity. The sensor's measurement range is set to 0-50,000 mg / L, with an accuracy of ±50 mg / L. The sampling frequency is set to 10 times per second to ensure that it can capture even small changes in sludge concentration in real time. The sensor converts sludge concentration data into a standard current signal through a built-in 4-20 mA current output interface.

[0027] PS-200 pressure sensors were installed at key locations in the return pipe. Based on the piezoresistive principle, these sensors convert pressure changes within the pipe into electrical signals. The sensor's measurement range is set to 0-1.6 MPa, with an accuracy of ±0.005 MPa and a sampling frequency of 10 times per second. The pressure sensor outputs data via an RS485 communication interface, using the Modbus RTU protocol for data transmission.

[0028] To enable wireless data transmission, the sludge concentration sensor and pressure sensor are each connected to a WT-300 wireless communication module. This module supports the ZigBee protocol, operates at a frequency of 2.4 GHz, and has a communication range of up to 1000 meters (line-of-sight). The wireless communication module packages the sludge concentration and pressure data collected by the sensors into packets containing a device identifier, data type, data value, and timestamp. These packets are transmitted once per second via wireless signals to the receiving end of the sewage treatment control center. An RC-500 receiver is installed in the sewage treatment control center, which receives the data packets from the wireless communication module using the ZigBee protocol. The receiver decodes the received packets, extracts the sludge concentration and pressure data, and converts them into a format recognizable by the control center. The sludge concentration data is stored in the control center's database in a floating-point field named "SludgeConcentration." The pressure data is stored in a floating-point field named "PipelinePressure." The data acquisition timestamp is stored in a millisecond-level field named "Timestamp." The control center's database management system uses a relational database. A table named "SludgeFlowData" stores sludge concentration and pressure data. The table structure includes three fields: "SludgeConcentration," "PipelinePressure," and "Timestamp," with field types of FLOAT, FLOAT, and DATETIME, respectively. Whenever new data is received, the database management system automatically inserts it into the table and sorts it by timestamp for subsequent data analysis and processing.

[0029] Step S2: At the sewage treatment control center, monitor the motor power and impeller speed of the sludge pump; determine the inner diameter and material of the return pipe based on the return pipe pressure data; detect the sludge particle size based on the sludge concentration data, and determine the sludge viscosity based on the sludge particle size; and assess the degree of sludge return blockage based on the pipe inner diameter and material and the sludge viscosity. In this embodiment of the present invention, in step S2, the sewage treatment control center monitors the motor power and impeller speed in real time using a power sensor and a speed sensor installed on the sludge pump. The power sensor uses the electromagnetic induction principle to measure power by sensing changes in motor current and voltage, with a measurement range of 0-50 kW and an accuracy of ±0.5%. The speed sensor uses the magnetoelectric principle to measure speed by detecting magnetic markings on the impeller, with a measurement range of 0-3000 r / min and an accuracy of ±1 r / min. The control center's data acquisition system receives signals from the power and speed sensors once per second and stores the motor power data in the "Motor Power" field and the impeller speed data in the "Impeller Speed" field of the database. Based on the return pipe pressure data ("Return Pipe Pressure") transmitted in step S1, the control center determines the return pipe's inner diameter and material using a pre-set pressure-to-pipe parameter comparison table. This comparison table, stored in the control center's database, contains pipe inner diameter and material information corresponding to different pressure ranges. For example, when the "return pipe pressure" is 0.2-0.4 MPa, the comparison table shows the pipe's inner diameter is DN150 and the material is carbon steel; when the pressure is 0.4-0.6 MPa, the pipe's inner diameter is DN200 and the material is stainless steel. The control center's data processing system matches the pressure data with the ranges in the comparison table to determine the current return pipe's inner diameter and material, storing the results in the "Pipe Inner Diameter" and "Pipe Material" fields in the database. Simultaneously, the control center uses the sludge concentration data ("Sludge Concentration") collected in step S1 to detect sludge particle size. A high-resolution camera installed in the sludge flow channel captures images of the sludge sample with a resolution of 1920 × 1080 pixels and a capture frequency of once per minute. The image processing system processes the captured images, first converting them to grayscale, then using binarization to separate the sludge particles from the background. Finally, edge detection is used to extract the contours of the sludge particles and calculate their equivalent diameters. The sludge particle size test results are stored in the "Sludge Particle Size" field of the database in micrometers (μm). Based on the detected sludge particle size (Sludge Particle Size), the control center determines the sludge viscosity using a preset particle size-viscosity relationship table. This relationship table, established based on experimental data, lists the corresponding sludge viscosity values ​​for different particle sizes. The control center's data processing system matches the detected sludge particle size with the corresponding viscosity value by searching the relationship table and stores the sludge viscosity value in the "Sludge Viscosity" field of the database in Pascal-seconds (Pa·s). Finally, based on the determined pipe inner diameter and material (Pipeline Material) and the sludge viscosity (Sludge Viscosity), the control center uses a preset blockage assessment model to assess the degree of sludge backflow blockage. This model comprehensively considers the effects of pipe inner diameter and material on fluid resistance, as well as the effect of sludge viscosity on flow properties.The data processing system of the control center calculates the quantitative indicator of congestion degree "congestion degree" based on the model and stores the result in the "congestion degree" field of the database, ranging from 0 to 1, where 0 means no congestion and 1 means complete congestion.

[0030] Step S3: generating a motor control instruction for the sludge pump according to the degree of sludge reflux blockage and the motor power; generating an impeller control instruction for the sludge pump according to the degree of sludge reflux blockage and the impeller speed; generating a dredging operation instruction for the return pipe according to the degree of sludge reflux blockage and the inner diameter and material of the pipe; In this embodiment of the present invention, the sewage treatment control center generates motor control instructions for the sludge pump based on the "blockage degree" assessed in step S2 and the monitored "motor power." The control system in the control center invokes a preset motor power adjustment strategy based on the corresponding relationship between blockage degree and motor power. When the "blockage degree" is less than 0.3, the return pipe is unobstructed and the motor power is maintained at its current level. When the "blockage degree" is between 0.3 and 0.6, the motor power is appropriately increased by 10% of the current power to enhance the pump's delivery capacity. When the "blockage degree" is greater than 0.6, the motor power is increased by 20% to ensure smooth sludge delivery. Based on this strategy, the control system calculates the target motor power and converts it into a motor control instruction. This instruction is sent to the sludge pump's motor controller via the industrial control bus. The motor controller receives the instruction and adjusts the motor's operating power. Simultaneously, the control center generates an impeller control instruction based on the "blockage degree" and the monitored "impeller speed." The control system invokes an impeller speed adjustment strategy. When the "blockage degree" is less than 0.3, the impeller speed remains unchanged. When the "blockage degree" is between 0.3 and 0.6, the impeller speed is increased by 5% to improve sludge mixing and conveying. When the "blockage degree" is greater than 0.6, the impeller speed is increased by 10%. Based on the strategy, the control system calculates the target impeller speed, converts it into an impeller control command, and transmits it to the sludge pump's frequency converter via the industrial control bus. The frequency converter adjusts the impeller speed accordingly. Furthermore, the control center generates a clearing instruction for the return pipe based on the "blockage degree" and "pipeline inner diameter and material." For carbon steel pipes, clearing is not required when the "blockage degree" is less than 0.3. When the "blockage degree" is between 0.3 and 0.6, a low-frequency vibration device in the pipe is activated at a frequency of 30 Hz for 10 minutes to loosen the sludge. When the "blockage degree" is greater than 0.6, the vibration frequency is increased to 50 Hz and the duration is extended to 20 minutes. For stainless steel pipes, the vibration frequency and duration are set to 40 Hz and 15 minutes, and 60 Hz and 25 minutes, respectively. The control system generates corresponding dredging operation instructions based on the pipe material and the degree of blockage. These instructions are sent to the controller of the pipe vibration device via the industrial control bus. Upon receiving the instructions, the controller activates the vibration device and performs the dredging operation according to the set parameters.

[0031] Step S4: Send the motor control instruction, impeller control instruction and dredging operation instruction to the sludge return equipment and record them as the sewage treatment control strategy; simulate the sewage treatment according to the sewage treatment control strategy to perform the sewage treatment operation.

[0032] In this embodiment of the present invention, the sewage treatment control center transmits motor control instructions, impeller control instructions, and dredging operation instructions to the sludge return equipment via industrial Ethernet. The motor control instructions include target motor power parameters, the impeller control instructions include target impeller speed parameters, and the dredging operation instructions include vibration frequency and duration parameters for the vibrator. The control center's instruction transmission module uses the TCP / IP protocol for data transmission to ensure the accuracy and reliability of the instructions. While transmitting the instructions, the control center's data recording module packages and stores the detailed parameters of the control instructions, along with corresponding data such as "blockage level," "motor power," "impeller speed," and "pipeline inner diameter and material," as a sewage treatment control strategy. The control strategies are stored in a structured data format in the control center's strategy database. Each strategy record includes a timestamp, instruction type, instruction parameters, and related monitoring data for subsequent query and analysis. Subsequently, the control center activates the simulation control module to simulate the sewage treatment process according to the stored sewage treatment control strategy. The simulation control module invokes a preconfigured sewage treatment process model and, combined with the current control strategy parameters, uses numerical simulation methods to calculate changes in key parameters in the sewage treatment process, such as sludge flow rate and treatment efficiency. Based on the simulation results, the simulation control module determines whether the control strategy meets the preset sewage treatment target. If so, the control strategy is marked as valid and executed. If not, the system returns to step S2 to reevaluate and generate control instructions. During sewage treatment operations, the sludge return system's motor controller receives and interprets the motor control instructions, adjusting the motor's operating power to the target value. The frequency converter receives and interprets the impeller control instructions, adjusting the impeller speed to the target value. The pipe vibration device's controller receives and interprets the dredging operation instructions, activating the vibration device according to the set vibration frequency and duration. The entire execution process is monitored in real time through feedback signals from the equipment.

[0033] Preferably, step S1 includes the following steps: Step S11: Sludge concentration sensors are placed at the inlet and outlet of the sludge pump. The sensors transmit a light beam of a specific wavelength through the sludge and detect the intensity of the transmitted light to collect sludge concentration data. The sensors collect data every 1 minute, and each collection lasts for 5 seconds to obtain the final sludge concentration data. Step S12: Multiple pressure sensors are arranged at the elbow, connecting flange, and middle section of the return pipe. Pressure is measured by detecting the resistance change caused by pressure changes in the pipe. Each sensor collects data every 30 seconds, and each collection lasts for 2 seconds. Finally, the return pipe pressure data is obtained. Step S13: encrypting the sludge concentration data and the return pipe pressure data, and transmitting the encrypted data to the sewage treatment control center.

[0034] In this embodiment of the present invention, in step S11, SC-3000 sludge concentration sensors are installed at the inlet and outlet of the sludge pump. Based on the optical transmission principle, these sensors transmit an infrared beam with a wavelength of 850 nanometers through the sludge fluid and detect the intensity of the transmitted light to calculate the sludge concentration. The sensor has a measurement range of 0-10,000 mg / L with an accuracy of ±50 mg / L. The sensor is configured to initiate data acquisition every minute, with each acquisition lasting 5 seconds. During the acquisition process, the sensor's internal photodetector records changes in the transmitted light intensity and converts them into sludge concentration values ​​through its built-in signal processing circuitry. After acquisition, the sensor stores the sludge concentration data in a local buffer in floating-point format, in mg / L, and is named "sludge concentration data." In step S12, PS-500 pressure sensors are placed at the elbow, connecting flange, and mid-section of the return pipe. These sensors employ the piezoresistive principle, measuring pressure by detecting changes in resistance caused by pressure changes within the pipe. The sensor's measurement range is 0-1.6 MPa, with an accuracy of ±0.01 MPa. Each pressure sensor is set to initiate data acquisition every 30 seconds, with each acquisition lasting 2 seconds. During the acquisition process, the sensor's internal piezoresistor converts pressure changes into an electrical signal, which is then amplified and converted to analog-to-digital data. After acquisition, the sensor stores the pressure data in a local buffer in floating-point format, in MPa, and named "return pipe pressure data." In step S13, the sludge concentration sensor and pressure sensor transmit the "sludge concentration data" and "return pipe pressure data" stored in the local buffer via their respective communication interfaces (RS485 for the sludge concentration sensor and Modbus RTU for the pressure sensor) to a wireless communication module installed near the sludge pump. This wireless communication module uses ZigBee wireless communication technology, operating at a frequency of 2.4 GHz, with a communication range of up to 1000 meters. Before receiving the data, the communication module performs AES-128 encryption to ensure data security. The encrypted data is packaged according to a pre-set data packet format, which contains the device identifier, data type, encrypted data content, and a timestamp. Once packaged, the wireless communication module transmits the data packet to a receiving terminal in the sewage treatment control center via the ZigBee protocol. The receiving terminal in the control center decrypts the received data packet, extracts the "sludge concentration data" and "return pipe pressure data," and stores them in the control center's database.

[0035] Preferably, in step S2, monitoring the motor power and impeller speed of the sludge pump at the sewage treatment control center includes: Motor power data is collected from the input end, output end, and intermediate junction box of the sludge pump motor. The sensor at each location collects data every 1 second, and each collection lasts for 0.1 seconds. The impeller speed data is collected from different positions of the sludge pump impeller, such as the front end, middle section and rear end of the impeller shaft. The sensor at each position collects data every 0.5 seconds.

[0036] In this embodiment of the present invention, PM-200 power sensors are installed at the input, output, and intermediate junction box of the sludge pump motor to collect motor power data. These power sensors utilize the Hall effect principle to calculate power by detecting changes in motor current and voltage. Each sensor has a measurement range of 0-50 kW and an accuracy of ±0.5%. The sensors are configured to initiate data acquisition every 1 second, with each acquisition lasting 0.1 seconds. During the acquisition process, the Hall effect element within the sensor detects changes in the magnetic field generated by the passage of current, while the voltage sensor measures the voltage at the motor terminals. These two are combined to calculate the instantaneous power of the motor. After acquisition, the sensor stores the power data in a local buffer in floating-point format, in kW, and is named "motor power data." RS-300 speed sensors are installed at various locations on the sludge pump impeller, including the front, middle, and rear ends of the impeller shaft, to collect impeller speed data. These speed sensors utilize the principle of magnetoelectric induction to measure speed by detecting magnetic markings on the impeller. Each sensor has a measurement range of 0-3000 r / min with an accuracy of ±1 r / min. The sensors are set to initiate data acquisition every 0.5 seconds. During the acquisition process, the sensor's internal magnetoelectric sensing element detects the frequency of the magnetic field changes generated by the impeller's rotation, thereby calculating the impeller's rotational speed. After acquisition, the sensor stores the speed data in a local cache in integer format, in units of r / min, and is named "impeller speed data." The power sensor and speed sensor transmit the collected "motor power data" and "impeller speed data" to the wastewater treatment control center's data acquisition system via their respective communication interfaces (the power sensor uses the Modbus RTU protocol, and the speed sensor uses the RS485 interface). The data acquisition system parses and stores the received data, storing the "motor power data" in the "motor power" field and the "impeller speed data" in the "impeller speed" field of the database, while also recording the data acquisition timestamp.

[0037] Preferably, determining the inner diameter and material of the return pipe according to the return pipe pressure data in step S2 includes: The return pipe pressure data is subjected to pressure gradient classification to obtain a pipe pressure gradient value; the pipe fluid flow rate is mapped according to the pipe pressure gradient value, and the pipe inner diameter is calculated according to the pipe fluid flow rate; Extract pipeline pressure time series of return pipeline pressure data; If the return pipe pressure data gradually decreases with the pipe pressure time series and the pipe inner diameter is stable, it is judged that the pipe material is corrosion-resistant; If the return pipe pressure data fluctuates continuously with the pipe pressure time series and the pipe inner diameter changes continuously, it is judged that the pipe material is a corrosive material.

[0038] In an embodiment of the present invention, when processing return pipe pressure data, the pressure data is first classified by pressure gradient. The collected return pipe pressure data is classified according to preset classification standards through the data processing system of the sewage treatment control center. Pressure gradient classification is achieved by calculating the ratio of the pressure difference between adjacent pressure sensors to the distance between the sensors. For example, if the distance between two pressure sensors is 10 meters and the pressure difference is 0.1 MPa, the pressure gradient value is 0.01 MPa / m. The control center stores the calculated pressure gradient value in the "Pipeline Pressure Gradient Value" field of the database. The pipeline fluid flow rate is then mapped based on the pipeline pressure gradient value. The control center uses a pre-established pressure gradient-flow rate mapping table based on fluid mechanics principles and pipeline design parameters. For example, when the pressure gradient value is 0.01 MPa / m, the corresponding pipeline fluid flow rate is 2 m / s; when the pressure gradient value is 0.02 MPa / m, the flow rate is 3 m / s. The control center converts the pressure gradient value into pipeline fluid flow velocity by looking up a mapping table and stores the flow velocity data in the "Pipeline Fluid Flow Velocity" field of the database. Next, the pipeline inner diameter is calculated from the pipeline fluid flow velocity. Using the law of conservation of flow, the control center combines the known sludge flow rate and the calculated flow velocity to infer the pipeline inner diameter. For example, if the sludge flow rate is 0.1 m³ / s and the flow velocity is 2 m / s, the calculated pipeline inner diameter is 0.2236 meters. The result is stored in the "Pipeline Inner Diameter" field of the database. Simultaneously, the pipeline pressure time series is extracted. The control center's data analysis module reads the return pipeline pressure data from the database and arranges it in chronological order to form a pipeline pressure time series. The sampling frequency of the time series matches the pressure sensor's acquisition frequency, recording data every 30 seconds. Finally, the pipeline material is determined based on the changes in the pipeline pressure time series and the pipeline inner diameter. The control center's data analysis module performs trend analysis on the pipeline pressure time series. If the return pipe pressure data gradually decreases over time while the pipe inner diameter remains stable, the pipe material is determined to be corrosion-resistant and the result is stored in the "Pipe Material" field of the database, marked as "Corrosion-Resistant Material." If the return pipe pressure data continues to fluctuate over time and the pipe inner diameter continues to change, the pipe material is determined to be corrosive and the result is stored in the "Pipe Material" field, marked as "Corrosive Material."

[0039] Preferably, in step S2, detecting the sludge particle size according to the sludge concentration data, and determining the sludge viscosity by the sludge particle size includes: The sludge concentration data is divided into multiple data segments according to the time series, and each data segment contains 10 consecutive concentration values; each data segment is analyzed and the sludge concentration change rate is calculated to determine the degree of sludge concentration fluctuation; If the sludge concentration change rate is greater than the preset sludge concentration change rate and the sludge concentration fluctuates frequently, it is determined to be a small sludge particle size; If the sludge concentration change rate is less than the preset sludge concentration change rate and the sludge concentration fluctuates, it is determined to be large sludge particle size; If the sludge particle size is small, the sludge viscosity is determined to be high; if the sludge particle size is large, the sludge viscosity is determined to be low.

[0040] In an embodiment of the present invention, in the sewage treatment control center, the collected sludge concentration data is first subjected to time series segmentation processing. The data processing module of the control center divides the sludge concentration data into multiple data segments in chronological order, and each data segment contains 10 consecutive sludge concentration values. For example, if the sludge concentration data is collected once per minute, a data segment is formed every 10 minutes. Subsequently, the sludge concentration values ​​in each data segment are analyzed to calculate the rate of change of the sludge concentration. The specific operation is: for each data segment, the difference between the two adjacent concentration values ​​is calculated, and then the difference is divided by the previous concentration value to obtain the concentration change rate. For example, if the two adjacent concentration values ​​in a data segment are 1500 mg / L and 1600 mg / L, respectively, the concentration change rate is (1600-1500) / 1500 = 0.0667. The control center stores the concentration change rate for each data segment in the "Sludge Concentration Change Rate" field of the database and counts the number of times the absolute value of the concentration change rate within each data segment exceeds 0.05, using this as a quantitative indicator of the degree of sludge concentration fluctuation. The control center determines the degree of sludge concentration fluctuation based on a preset sludge concentration change rate threshold (for example, 0.05). If the concentration change rate within a data segment is greater than 0.05 and the number of concentration fluctuations exceeds five (i.e., frequent fluctuations), the sludge corresponding to that data segment is determined to have a small particle size and the result is stored in the "Sludge Particle Size" field of the database, labeled "Small Particle Size." If the concentration change rate within a data segment is less than 0.05 and the number of concentration fluctuations does not exceed five, the sludge corresponding to that data segment is determined to have a large particle size and the result is stored in the "Sludge Particle Size" field, labeled "Large Particle Size." Finally, the sludge viscosity is determined based on the sludge particle size determination. If the sludge particle size is small, the sludge is determined to be of high viscosity, and the result is stored in the "sludge viscosity" field of the database and marked as "high viscosity." If the sludge particle size is large, the sludge is determined to be of low viscosity, and the result is stored in the "sludge viscosity" field and marked as "low viscosity."

[0041] Preferably, in step S3, generating a motor control instruction for the sludge pump according to the sludge backflow blockage degree and the motor power includes: The correlation analysis between the sludge return blockage degree and the motor power is carried out to obtain the sludge pump motor analysis results; if the motor power exceeds 90% of the rated power and the sludge return blockage degree is high, it is determined that the motor is in a high-load operation state; if the motor power is between 60% and 80% of the rated power and the sludge return blockage degree is low, it is determined that the motor is in a normal operation state; A motor control instruction is generated based on the sludge pump motor analysis results. If the motor is determined to be in a high-load operating state, a motor power reduction control instruction is generated; if the motor is determined to be in a normal operating state, a motor power maintenance control instruction is generated.

[0042] In this embodiment of the present invention, the sewage treatment control center first performs a correlation analysis between the degree of sludge return flow blockage and motor power. The control center's data processing module reads "sludge return flow blockage degree" and "motor power" data from a database. Sludge return flow blockage degrees are categorized as "high blockage degree" or "low blockage degree," and motor power data is measured in kW.

[0043] The control center calculates the percentage of motor power relative to rated power. For example, if the rated power of a motor is 30 kW and the currently monitored motor power is 28 kW, the motor power percentage is 28 / 30 × 100% (approximately 93.33%). The control center analyzes the situation based on pre-set criteria: if the motor power exceeds 90% of the rated power and the sludge return flow blockage level is "high," the control center determines the motor is operating in a "high-load state." If the motor power is between 60% and 80% of the rated power and the sludge return flow blockage level is "low," the control center determines the motor is operating normally. The analysis results are stored in the "Motor Operating State" field in the database. Based on the "Motor Operating State" determination, the control center generates appropriate motor control instructions. If the motor is determined to be operating in a "high-load state," the control center generates a "motor power reduction control instruction" containing a target power value, such as reducing the motor power to 80% of the rated power. If the motor is determined to be operating normally, the control center generates a "maintain current power control instruction" to maintain the current power. The control instructions are sent to the motor controller of the sludge pump through the industrial control bus. After receiving the instructions, the motor controller adjusts the operating power of the motor to ensure that the motor operating status meets the control requirements.

[0044] Preferably, in step S3, generating an impeller control instruction for the sludge pump according to the sludge backflow blockage degree and the impeller speed includes: The impeller operating state is divided according to the degree of sludge return blockage and the impeller speed. If the impeller speed is lower than 70% of the rated speed and the sludge return blockage is high, it is determined to be an impeller inefficient operating state; if the impeller speed is between 80% and 90% of the rated speed and the sludge return blockage is low, it is determined to be an impeller efficient operating state. Obtain the structural characteristics of the impeller, including the number of blades, blade angle, and impeller diameter. Determine the optimal operating parameters based on the impeller's structural characteristics. If the impeller has a large number of blades and a large blade angle, a high speed is required to maintain sludge conveying; if the impeller diameter is large, a low speed is required to avoid wear. A preliminary impeller control instruction is generated based on the impeller operating status and the impeller's optimal operating parameters. If the impeller is determined to be in an inefficient operating state, an impeller speed increase control instruction is generated; if the impeller is determined to be in an efficient operating state, a impeller speed maintenance control instruction is generated.

[0045] In this embodiment of the present invention, the sewage treatment control center first analyzes the degree of sludge backflow blockage and impeller speed to determine the impeller's operating status. The control center's data processing module retrieves "sludge backflow blockage" and "impeller speed" data from a database. Impeller speed is measured in r / min, with a rated speed of 1500 r / min. If the impeller speed is less than 1050 r / min (i.e., 70% of the rated speed) and the sludge backflow blockage is "high," the impeller is considered to be operating in an "inefficient" state. If the impeller speed is between 1200 r / min and 1350 r / min (i.e., 80%-90% of the rated speed) and the sludge backflow blockage is "low," the impeller is considered to be operating in an "efficient" state. The results are stored in the "impeller operating status" field of the database. Subsequently, the impeller's structural characteristics, including the number of blades, blade angle, and impeller diameter, are obtained. These parameters are obtained from an impeller design parameter table pre-stored in the control center's database. For example, an impeller has six blades, a 30° blade angle, and a diameter of 0.5 meters. The optimal operating parameters for the impeller are determined based on its structural characteristics. If the impeller has a large number of blades (e.g., more than five) and a large blade angle (e.g., more than 25°), a higher speed (e.g., 1400 r / min) is required to maintain efficient sludge conveying. If the impeller diameter is large (e.g., more than 0.4 meters), a lower speed (e.g., 1200 r / min) is required to reduce wear. Preliminary impeller control instructions are generated based on the impeller's operating status and optimal operating parameters. If the impeller is determined to be operating in an inefficient state, an "increase impeller speed control instruction" is generated. The target speed is set based on the optimal operating parameters, for example, increasing the impeller speed from the current value to 1400 r / min. If the impeller is determined to be operating in an efficient state, a "maintain current speed control instruction" is generated to maintain the current speed. The control instruction is sent to the sludge pump's frequency converter via the industrial control bus. Upon receiving the instruction, the frequency converter adjusts the impeller's operating speed to ensure that the impeller meets the control requirements.

[0046] Preferably, in step S3, generating a dredging operation instruction for the return pipe according to the degree of sludge return blockage and the inner diameter and material of the pipe includes: A comprehensive assessment is conducted based on the degree of sludge backflow blockage and the inner diameter and material of the pipeline. If the blockage degree is high and the pipeline material is corrosive, the pipeline is judged to be in a high-risk blockage state; if the blockage degree is low and the pipeline material is corrosion-resistant, the pipeline is judged to be in a low-risk blockage state. Generate dredging operation instructions based on the comprehensive assessment results; if the pipeline is determined to be in a high-risk blockage state, generate instructions to increase the dredging frequency and intensity; if the pipeline is determined to be in a low-risk blockage state, generate instructions to maintain the current dredging frequency; Dynamically adjust the dredging operation according to the dredging operation instructions. If the dredging frequency needs to be increased, the dredging interval time will be gradually shortened, with each adjustment range being 5 minutes and the adjustment interval being 30 minutes. If the current dredging frequency needs to be maintained, the dredging interval time will remain unchanged.

[0047] In this embodiment of the present invention, the sewage treatment control center first conducts a comprehensive assessment of the degree of sludge backflow blockage and the inner diameter and material of the pipe. The control center's data processing module reads the "sludge backflow blockage degree" and "pipeline material" data from a database. If the "sludge backflow blockage degree" is "high" and the "pipeline material" is "corrosive material," the condition is determined to be a "high-risk pipeline blockage state." If the "sludge backflow blockage degree" is "low" and the "pipeline material" is "corrosive-resistant material," the condition is determined to be a "low-risk pipeline blockage state." The assessment results are stored in the "Pipeline Blockage Risk Status" field of the database. Based on the comprehensive assessment results, the control center generates unclogging instructions. If the condition is determined to be a "high-risk pipeline blockage state," an instruction to increase unclogging frequency and intensity is generated; if the condition is determined to be a "low-risk pipeline blockage state," an instruction to maintain the current unclogging frequency is generated. The unclogging instructions are transmitted to the controller of the pipe unclogging equipment via the industrial control bus. The unclogging equipment controller dynamically adjusts the unclogging operation based on the received instructions. If increased unclogging frequency is required, the unclogging interval is gradually shortened. The initial dredging interval is 120 minutes, and it can be adjusted in increments of 5 minutes at 30-minute intervals. For example, if the dredging interval is 115 minutes after the initial adjustment, it will be adjusted again to 110 minutes after 30 minutes, until the preset minimum dredging interval (e.g., 60 minutes) is reached. If you need to maintain the current dredging frequency, the dredging interval will remain unchanged and the dredging operation will continue at the currently set interval.

[0048] Of particular importance is that the evaluation of the degree of sludge backflow blockage based on the pipe inner diameter, material, and sludge viscosity in step S3 includes: According to the inner diameter material of the pipe and the viscosity of the sludge, the degree of sludge backflow blockage is evaluated to obtain the blockage assessment value; If the pipe is made of corrosive material and the sludge viscosity is high, the blockage assessment value will be increased by 20%; If the pipe is made of corrosion-resistant material and the sludge viscosity is low, the blockage assessment value will be reduced by 10%; If the inner diameter of the pipe is less than 300 mm, the blockage assessment value is increased by 15%; If the inner diameter of the pipe is greater than 300 mm, the blockage assessment value is reduced by 10%.

[0049] According to the blockage degree assessment value, the blockage risk is divided into low blockage degree of sludge return flow and high blockage degree of sludge return flow.

[0050] In this embodiment of the present invention, the sewage treatment control center first obtains data on "pipeline inner diameter material," "sludge viscosity," and initial "sludge return flow blockage" from a database. The control center's data processing module uses this data to comprehensively assess the sludge return flow blockage and calculate an estimated blockage level. The specific assessment process is as follows: First, the initial blockage level is adjusted based on the pipe inner diameter material and sludge viscosity. If the pipe material is "corrosive" and the sludge viscosity is "high," the initial blockage level is increased by 20%. For example, if the initial blockage level is 40%, the adjusted value is 40% x (1 + 20%) = 48%. If the pipe material is "corrosive-resistant" and the sludge viscosity is "low," the initial blockage level is reduced by 10%. For example, if the initial value is 40%, the adjusted value is 40% x (1 - 10%) = 36%. Next, the adjusted blockage level is further corrected based on the pipe inner diameter. If the pipe inner diameter is less than 300 mm, the value is increased by 15%. For example, if the adjusted assessment value is 48% and the inner diameter of the pipe is 250 mm, the final assessment value is 48%×(1+15%)=55.2%. If the inner diameter of the pipe is greater than 300 mm, the assessment value is reduced by 10%. For example, if the adjusted assessment value is 36% and the inner diameter of the pipe is 350 mm, the final assessment value is 36%×(1-10%)=32.4%. Finally, the blockage risk is divided into "low blockage degree of sludge return" and "high blockage degree of sludge return" based on the final blockage degree assessment value. The control center sets the threshold of the assessment value. For example, when the assessment value is less than or equal to 50%, it is judged as "low blockage degree of sludge return"; when the assessment value is greater than 50%, it is judged as "high blockage degree of sludge return". The judgment result is stored in the "Sludge Return Blockage Risk Level" field of the database.

[0051] As an example of the present invention, refer to Figure 2 As shown, in this example, step S4 includes: Step S41: In the sewage treatment control center, the motor control instructions, the impeller control instructions, and the dredging operation instructions are integrated to generate a comprehensive control instruction, which is then sent to the control system of the sludge return equipment via the Internet of Things network; Step S42: In the control system of the sludge return equipment, the comprehensive control instruction is received and confirmed for execution; the confirmation content includes motor power adjustment, impeller speed adjustment and initiation of dredging operation; if the confirmation is correct, the control operation is executed on the sewage treatment process; Step S43: Recording the comprehensive control instructions and their execution results in the database of the sewage treatment control center to form a sewage treatment control strategy. The record content includes the specific parameters of the control instructions, execution time, execution results, and feedback information; Step S44: During the sewage treatment operation, the operating status of the sludge return equipment is monitored in real time, including motor power, impeller speed, and return pipe pressure; and the monitoring data is fed back to the sewage treatment control center in real time; Step S45: Evaluate the control effect based on the real-time feedback monitoring data. If the monitoring data shows that the control effect does not reach the preset target, optimize the control strategy, including adjusting the motor power, impeller speed, and the frequency and intensity of the dredging operation.

[0052] In this embodiment of the present invention, in step S41, the data processing module of the sewage treatment control center integrates the generated "motor control instructions," "impeller control instructions," and "dredging operation instructions." The integrated control instructions include the motor power adjustment target value, the impeller speed adjustment target value, and specific parameters for the dredging operation (such as dredging frequency and intensity). The control center transmits the comprehensive control instructions to the sludge return equipment's control system via the Internet of Things (IoT) network using the MQTT protocol. During transmission, the data is encrypted using the AES-128 algorithm to ensure transmission security. In step S42, the sludge return equipment's control system receives the comprehensive control instructions through its communication module. The control system parses the received instructions and confirms their execution. The confirmation includes information about motor power adjustment, impeller speed adjustment, and the initiation of the dredging operation. The control system then feeds back confirmation information to the sewage treatment control center, including the command reception status and readiness for execution status. If confirmed, the sludge return equipment's control system executes control operations on the sewage treatment process according to the comprehensive control instructions, including adjusting motor power, changing impeller speed, and activating the dredging device. In step S43, the data recording module of the sewage treatment control center records the comprehensive control instructions and their execution results in a database. This record includes the specific parameters of the control instructions (such as motor power target, impeller speed target, dredging frequency and intensity), execution time, execution results, and feedback information. The execution results and feedback information are fed back to the control center in real time by the sludge return equipment's control system after executing the control operation. The control center stores this information in the "Sewage Treatment Control Strategy" table in the database for subsequent query and analysis. In step S44, during the sewage treatment operation, the sludge return equipment's control system monitors the operating status in real time using sensors installed on the equipment. These monitor data, including motor power, impeller speed, and return pipe pressure. The sensors collect data at a set frequency (e.g., once per second) and transmit this data in real time to the sewage treatment control center via the Internet of Things network. The feedback data uses the same encryption and transmission protocols to ensure data integrity and security. In step S45, the sewage treatment control center's data analysis module evaluates the control effectiveness based on the real-time feedback monitoring data. The analysis module compares monitoring data with preset target values. If the monitoring data indicates that the control effect does not meet the preset goals (for example, motor power does not reach the target value, impeller speed is not adjusted properly, or return pipe pressure is abnormal), the control strategy is optimized. This optimization includes adjusting the target motor power and impeller speed, as well as the frequency and intensity of dredging operations. The optimized control strategy regenerates comprehensive control instructions and sends them to the sludge return system's control system via the IoT network, achieving dynamic optimization and control of the sewage treatment process.

[0053] It is particularly important that step S451 includes the following steps: Step S451: In the sewage treatment control center, real-time data on the sludge pump's motor power, impeller speed, and return pipe pressure are collected. The frequency of the data collection is once per minute, and each collection lasts for 10 seconds. Step S452: Perform a preliminary evaluation of the control effect based on the collected monitoring data to obtain a preliminary evaluation result; Step S453: If the motor power is between 80% and 90% of the rated power, the impeller speed is between 80% and 90% of the rated speed, and the return pipe pressure is stable, it is preliminarily determined that the control effect is good; Step S454: If the motor power exceeds 90% or is less than 70% of the rated power, the impeller speed exceeds 90% or is less than 70% of the rated speed, or the return pipe pressure fluctuation exceeds ±0.1 bar, it is preliminarily determined that the control effect is poor; Step S455: Dynamically adjust the control strategy based on the preliminary evaluation results. If the control effect is not good, gradually adjust the motor power, with each adjustment amplitude of 5 watts and an adjustment interval of 10 minutes; synchronously adjust the impeller speed, with each adjustment amplitude of 10 rpm and an adjustment interval of 10 minutes; increase the frequency of dredging operations, with each increase amplitude of 1 time / hour and an adjustment interval of 30 minutes; increase the intensity of dredging operations, with each increase amplitude of 10% and an adjustment interval of 30 minutes.

[0054] In this embodiment of the present invention, in step S451, the data acquisition module of the sewage treatment control center collects real-time data on motor power, impeller speed, and return pipe pressure using sensors installed on the sludge pump and return pipe. The motor power sensor and impeller speed sensor are installed on the sludge pump motor and impeller, respectively, while the pressure sensor is installed at a key location in the return pipe. The acquisition frequency is set to once per minute, with each acquisition lasting 10 seconds. The collected data is transmitted to the control center's data processing module via Industrial Ethernet in floating-point format with units of kilowatts (kW), revolutions per minute (r / min), and bar. In step S452, the control center's data processing module performs a preliminary evaluation of the received monitoring data. The evaluation module analyzes the motor power, impeller speed, and return pipe pressure within preset parameter ranges to generate preliminary evaluation results. These preliminary evaluation results are stored in the "Preliminary Evaluation of Control Effect" field of the database. In step S453, if the motor power is between 80% and 90% of the rated power (for example, a rated power of 30 kW, i.e., 24-27 kW), the impeller speed is between 80% and 90% of the rated speed (for example, a rated speed of 1500 r / min, i.e., 1200-1350 r / min), and the return pipe pressure is stable (fluctuation range is less than ±0.1 bar), then the control effect is preliminarily determined to be good. The evaluation module marks the control effect as "good" and stores the result in the database. In step S454, if the motor power exceeds 90% of the rated power (for example, exceeds 27 kW) or falls below 70% (for example, falls below 21 kW), the impeller speed exceeds 90% of the rated speed (for example, exceeds 1350 r / min) or falls below 70% (for example, falls below 1050 r / min), or the return pipe pressure fluctuates by more than ±0.1 bar, then the control effect is preliminarily determined to be poor. The evaluation module marks the control effect as "poor" and stores the result in the database. In step S455, based on the preliminary evaluation results, the control module in the control center dynamically adjusts the control strategy. If the control effect is poor, the motor power is gradually adjusted by 5 watts at a 10-minute interval. For example, if the current motor power is 28 kW, it will be adjusted to 28.005 kW after 10 minutes. Simultaneously, the impeller speed is adjusted by 10 rpm at a 10-minute interval. For example, if the current impeller speed is 1400 rpm, it will be adjusted to 1410 rpm after 10 minutes. Furthermore, the frequency of dredging operations is increased by 1 per hour at a 30-minute interval. For example, if the current dredging frequency is 2 times / hour, it will be increased to 3 times / hour after 30 minutes. Simultaneously, the dredging intensity is increased by 10% at a 30-minute interval. For example, if the current dredging intensity is 50%, it will be increased to 55% after 30 minutes.The adjusted control instructions are sent to the control system of the sludge return equipment through the industrial control bus to achieve dynamic optimization and control of the sewage treatment process.

[0055] In this specification, a sewage treatment simulation control system based on the Internet of Things is provided, which is used to execute the above-mentioned sewage treatment simulation control method based on the Internet of Things. The sewage treatment simulation control system based on the Internet of Things includes: The IoT sensor acquisition module is used to detect the sludge pump and return pipe of the sludge return equipment through the IoT sensor network; collect real-time operating parameters of the sludge pump and return pipe, where the real-time operating parameters include sludge concentration data and return pipe pressure data, and transmit the sludge concentration data and return pipe pressure data to the sewage treatment control center; The sewage quality assessment module is used in the sewage treatment control center to monitor the motor power and impeller speed of the sludge pump; determine the inner diameter and material of the return pipe based on the return pipe pressure data; detect the sludge particle size based on the sludge concentration data, and determine the sludge viscosity through the sludge particle size; and assess the degree of sludge return blockage based on the pipe inner diameter material and sludge viscosity. The sewage treatment control module is used to generate motor control instructions for the sludge pump based on the degree of sludge backflow blockage and motor power; generate impeller control instructions for the sludge pump based on the degree of sludge backflow blockage and impeller speed; and generate dredging operation instructions for the return pipe based on the degree of sludge backflow blockage and the inner diameter and material of the pipe; The sewage treatment operation execution module is used to send motor control instructions, impeller control instructions and dredging operation instructions to the sludge return equipment and record them as sewage treatment control strategies; simulate the sewage treatment according to the sewage treatment control strategies to execute sewage treatment operations.

[0056] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0057] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A sewage treatment simulation control method based on the Internet of Things, characterized in that: The following steps are involved: Step S1: Detecting the sludge pump and return pipe of the sludge return equipment through the Internet of Things sensor network; Collect real-time operating parameters of the sludge pump and return pipe, including sludge concentration data and return pipe pressure data, and transmit the sludge concentration data and return pipe pressure data to the sewage treatment control center; Step S2: In the sewage treatment control center, the motor power and impeller speed of the sludge pump are monitored; the inner diameter and material of the return pipe are determined based on the return pipe pressure data; the sludge particle size is detected based on the sludge concentration data, and the sludge viscosity is determined by the sludge particle size; Evaluate the degree of sludge backflow blockage based on the pipe inner diameter material and sludge viscosity; Step S3: generating a motor control instruction for the sludge pump according to the degree of sludge reflux blockage and the motor power; generating an impeller control instruction for the sludge pump according to the degree of sludge reflux blockage and the impeller speed; generating a dredging operation instruction for the return pipe according to the degree of sludge reflux blockage and the inner diameter and material of the pipe; Step S4: sending the motor control instruction, the impeller control instruction and the dredging operation instruction to the sludge return equipment and recording them as the sewage treatment control strategy; The sewage treatment is simulated and regulated according to the sewage treatment regulation strategy to perform sewage treatment operations.

2. The sewage treatment simulation control method based on the Internet of Things according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Sludge concentration sensors are placed at the inlet and outlet of the sludge pump. The sensors transmit a light beam of a specific wavelength through the sludge and detect the intensity of the transmitted light to collect sludge concentration data. The sensors collect data every 1 minute, and each collection lasts for 5 seconds to obtain the final sludge concentration data. Step S12: Multiple pressure sensors are arranged at the elbow, connecting flange, and middle section of the return pipe. Pressure is measured by detecting the resistance change caused by pressure changes in the pipe. Each sensor collects data every 30 seconds, and each collection lasts for 2 seconds. Finally, the return pipe pressure data is obtained. Step S13: encrypting the sludge concentration data and the return pipe pressure data, and transmitting the encrypted data to the sewage treatment control center.

3. The sewage treatment simulation control method based on the Internet of Things according to claim 1 is characterized in that: In step S2, at the sewage treatment control center, monitoring the motor power and impeller speed of the sludge pump includes: Motor power data is collected from the input end, output end, and intermediate junction box of the sludge pump motor. The sensor at each location collects data every 1 second, and each collection lasts for 0.1 seconds. The impeller speed data is collected from different positions of the sludge pump impeller, such as the front end, middle section and rear end of the impeller shaft. The sensor at each position collects data every 0.5 seconds.

4. The sewage treatment simulation control method based on the Internet of Things according to claim 1 is characterized in that: Determining the inner diameter and material of the return pipe according to the return pipe pressure data in step S2 includes: The return pipe pressure data is subjected to pressure gradient classification to obtain a pipe pressure gradient value; the pipe fluid flow rate is mapped according to the pipe pressure gradient value, and the pipe inner diameter is calculated according to the pipe fluid flow rate; Extract pipeline pressure time series of return pipeline pressure data; If the return pipe pressure data gradually decreases with the pipe pressure time series and the pipe inner diameter is stable, it is judged that the pipe material is corrosion-resistant; If the return pipe pressure data fluctuates continuously with the pipe pressure time series and the pipe inner diameter changes continuously, it is judged that the pipe material is a corrosive material.

5. The sewage treatment simulation control method based on the Internet of Things according to claim 1 is characterized in that: In step S2, the sludge particle size is detected according to the sludge concentration data, and the sludge viscosity is determined by the sludge particle size, which includes: The sludge concentration data is divided into multiple data segments according to the time series, and each data segment contains 10 consecutive concentration values; each data segment is analyzed and the sludge concentration change rate is calculated to determine the degree of sludge concentration fluctuation; If the sludge concentration change rate is greater than the preset sludge concentration change rate and the sludge concentration fluctuates frequently, it is determined to be a small sludge particle size; If the sludge concentration change rate is less than the preset sludge concentration change rate and the sludge concentration fluctuates, it is determined to be large sludge particle size; If the sludge particle size is small, the sludge viscosity is determined to be high; if the sludge particle size is large, the sludge viscosity is determined to be low.

6. The method for simulating and controlling sewage treatment based on the Internet of Things according to claim 1, characterized in that: In step S3, generating a motor control instruction for the sludge pump according to the sludge backflow blockage degree and the motor power includes: The correlation analysis between the sludge return blockage degree and the motor power is carried out to obtain the sludge pump motor analysis results; if the motor power exceeds 90% of the rated power and the sludge return blockage degree is high, it is determined that the motor is in a high-load operation state; if the motor power is between 60% and 80% of the rated power and the sludge return blockage degree is low, it is determined that the motor is in a normal operating state; A motor control instruction is generated based on the sludge pump motor analysis results. If the motor is determined to be in a high-load operating state, a motor power reduction control instruction is generated; if the motor is determined to be in a normal operating state, a motor power maintenance control instruction is generated.

7. The sewage treatment simulation control method based on the Internet of Things according to claim 1 is characterized in that: In step S3, generating an impeller control instruction for the sludge pump according to the sludge backflow blockage degree and the impeller speed includes: The impeller operating state is divided according to the degree of sludge return blockage and the impeller speed. If the impeller speed is lower than 70% of the rated speed and the sludge return blockage is high, it is determined to be an impeller inefficient operating state; if the impeller speed is between 80% and 90% of the rated speed and the sludge return blockage is low, it is determined to be an impeller efficient operating state. Obtain the structural characteristics of the impeller, including the number of blades, blade angle, and impeller diameter. Determine the optimal operating parameters based on the impeller's structural characteristics. If the impeller has a large number of blades and a large blade angle, a high speed is required to maintain sludge conveying; if the impeller diameter is large, a low speed is required to avoid wear. A preliminary impeller control instruction is generated based on the impeller operating status and the impeller's optimal operating parameters. If the impeller is determined to be in an inefficient operating state, an impeller speed increase control instruction is generated; if the impeller is determined to be in an efficient operating state, a impeller speed maintenance control instruction is generated.

8. The method for simulating and controlling sewage treatment based on the Internet of Things according to claim 4, characterized in that: In step S3, generating a dredging operation instruction for the return pipe according to the degree of sludge return blockage and the inner diameter and material of the pipe includes: A comprehensive assessment is conducted based on the degree of sludge backflow blockage and the inner diameter and material of the pipeline. If the blockage degree is high and the pipeline material is corrosive, the pipeline is judged to be in a high-risk blockage state; if the blockage degree is low and the pipeline material is corrosion-resistant, the pipeline is judged to be in a low-risk blockage state. Generate dredging operation instructions based on the comprehensive assessment results; if the pipeline is determined to be in a high-risk blockage state, generate instructions to increase the dredging frequency and intensity; if the pipeline is determined to be in a low-risk blockage state, generate instructions to maintain the current dredging frequency; Dynamically adjust the dredging operation according to the dredging operation instructions. If the dredging frequency needs to be increased, the dredging interval time will be gradually shortened, with each adjustment range being 5 minutes and the adjustment interval being 30 minutes. If the current dredging frequency needs to be maintained, the dredging interval time will remain unchanged.

9. The sewage treatment simulation control method based on the Internet of Things according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: In the sewage treatment control center, the motor control instructions, the impeller control instructions, and the dredging operation instructions are integrated to generate a comprehensive control instruction, which is then sent to the control system of the sludge return equipment via the Internet of Things network; Step S42: In the control system of the sludge return equipment, the comprehensive control instruction is received and confirmed for execution; the confirmation content includes motor power adjustment, impeller speed adjustment and initiation of dredging operation; if the confirmation is correct, the control operation is executed on the sewage treatment process; Step S43: Recording the comprehensive control instructions and their execution results in the database of the sewage treatment control center to form a sewage treatment control strategy. The record content includes the specific parameters of the control instructions, execution time, execution results, and feedback information; Step S44: During the sewage treatment operation, the operating status of the sludge return equipment is monitored in real time, including motor power, impeller speed, and return pipe pressure; and the monitoring data is fed back to the sewage treatment control center in real time; Step S45: Evaluate the control effect based on the real-time feedback monitoring data. If the monitoring data shows that the control effect does not reach the preset target, optimize the control strategy, including adjusting the motor power, impeller speed, and the frequency and intensity of the dredging operation.

10. A sewage treatment simulation control system based on the Internet of Things, characterized in that: For executing the Internet of Things-based sewage treatment simulation control method according to claim 1, the Internet of Things-based sewage treatment simulation control system comprises: The IoT sensor acquisition module is used to detect the sludge pump and return pipe of the sludge return equipment through the IoT sensor network; collect real-time operating parameters of the sludge pump and return pipe, where the real-time operating parameters include sludge concentration data and return pipe pressure data, and transmit the sludge concentration data and return pipe pressure data to the sewage treatment control center; The sewage quality assessment module is used in the sewage treatment control center to monitor the motor power and impeller speed of the sludge pump; determine the inner diameter and material of the return pipe based on the return pipe pressure data; detect the sludge particle size based on the sludge concentration data, and determine the sludge viscosity through the sludge particle size; and assess the degree of sludge return blockage based on the pipe inner diameter material and sludge viscosity. The sewage treatment control module is used to generate motor control instructions for the sludge pump based on the degree of sludge backflow blockage and motor power; generate impeller control instructions for the sludge pump based on the degree of sludge backflow blockage and impeller speed; and generate dredging operation instructions for the return pipe based on the degree of sludge backflow blockage and the inner diameter and material of the pipe; The sewage treatment operation execution module is used to send motor control instructions, impeller control instructions and dredging operation instructions to the sludge return equipment and record them as sewage treatment control strategies; simulate the sewage treatment according to the sewage treatment control strategies to execute sewage treatment operations.

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