A sewage treatment simulation regulation method and system based on the Internet of Things

Abstract

The present application relates to sewage treatment technical field, especially to a kind of sewage treatment simulation regulation and control method and system based on internet of things.The method includes the following steps: by internet of things sensor network detection sludge pump and reflux pipeline of sludge reflux equipment;Real-time operating parameter acquisition is carried out to sludge pump and reflux pipeline, wherein real-time operating parameter includes sludge concentration data and reflux pipeline pressure data, sludge concentration data and reflux pipeline pressure data are transmitted to sewage treatment control center;In sewage treatment control center, motor power and impeller speed of sludge pump are monitored;The pipe diameter material of reflux pipeline is determined according to reflux pipeline pressure data.The present application is monitored by internet of things sensor network real-time monitoring technology, dynamic regulation and control technology, to realize the accurate monitoring and intelligent regulation and control of sludge reflux equipment, to improve the operation efficiency and stability of sewage treatment system.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, and in particular to a wastewater treatment simulation and control method and system based on the Internet of Things. Background Technology

[0002] The core of wastewater treatment is sludge return equipment (such as sludge pumps and return pipes), which is used to return sludge from sedimentation tanks to biological reactors to maintain sludge concentration and microbial activity in the system. On the one hand, traditional sludge return pumps mostly operate at a fixed speed, unable to dynamically adjust the return ratio based on real-time changes in sludge concentration and microbial activity in the biological reactor. This fixed mode cannot adapt to the fluctuations in sludge concentration and changes in microbial metabolism during wastewater treatment, resulting in low return efficiency and difficulty in accurately maintaining suitable sludge concentration and microbial activity levels in the system. On the other hand, the viscosity and particulate characteristics of sludge are the main causes of blockage in return pipes. Increased sludge concentration, sludge particle aggregation, and increased sludge viscosity all exacerbate the risk of pipe blockage. Furthermore, the inner diameter, material, and structural design of the return pipes also affect the degree of blockage. Summary of the Invention

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

[0004] To achieve the above objectives, a wastewater treatment simulation and control method based on the Internet of Things (IoT) is provided, the method comprising the following steps: Step S1: Detect the sludge pump and return pipeline of the sludge return equipment through the Internet of Things sensor network; collect real-time operating parameters of the sludge pump and return pipeline, including sludge concentration data and return pipeline pressure data, and transmit the sludge concentration data and return pipeline pressure data to the wastewater treatment control center. Step S2: At the wastewater 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; assess the degree of sludge return blockage based on the pipe inner diameter, material, and sludge viscosity. Step S3: Generate motor control commands for the sludge pump based on the degree of sludge return blockage and motor power; generate impeller control commands for the sludge pump based on the degree of sludge return blockage and impeller speed; generate unblocking operation commands for the return pipe based on the degree of sludge return blockage and pipe inner diameter and material. Step S4: Send the motor control command, impeller control command, and dredging operation command to the sludge return equipment and record them as the sewage treatment control strategy; simulate and control the sewage treatment according to the sewage treatment control strategy to execute the sewage treatment operation.

[0005] This invention utilizes an Internet of Things (IoT) sensor network to collect real-time operating parameters of the sludge pump and return pipeline of the sludge return equipment. It accurately acquires sludge concentration data and return pipeline pressure data, and transmits this data promptly to the wastewater treatment control center. This real-time monitoring and data transmission mechanism ensures the continuity and accuracy of key parameters during wastewater treatment, providing a reliable data foundation for subsequent control decisions and avoiding control errors caused by data delays or inaccuracies, thereby improving the operational efficiency and stability of the wastewater treatment system. At the wastewater treatment control center, by monitoring the motor power and impeller speed of the sludge pump, combined with return pipeline pressure data to determine the pipeline's inner diameter and material, and by detecting sludge particle size and determining sludge viscosity based on sludge concentration data, the degree of sludge return blockage can be accurately assessed based on the pipeline's inner diameter, material, and sludge viscosity. This comprehensive parameter monitoring and evaluation method enables a more precise understanding of the sludge return equipment's operational status. It generates precise motor control commands, impeller control commands, and unblocking operation commands based on different levels of blockage and equipment operating conditions. This achieves refined control of the sludge pump and return pipeline, effectively solving problems such as low equipment efficiency, increased energy consumption, and poor sludge return caused by inaccurate control in traditional wastewater treatment processes. The generated motor control commands, impeller control commands, and unblocking operation commands are sent to the sludge return equipment and recorded as wastewater treatment control strategies. Simultaneously, wastewater treatment is simulated and controlled according to these strategies to execute wastewater treatment operations. This process not only achieves real-time control of wastewater treatment operations but also verifies the effectiveness of the control strategy in advance through simulation. Potential problems can be identified and optimized before actual operation, further improving the reliability and adaptability of the wastewater treatment system, reducing the risk of wastewater treatment accidents caused by improper control, ensuring the continuous and stable operation of the wastewater treatment process, and improving the overall quality and efficiency of wastewater treatment.

[0006] Preferably, step S1 includes the following steps: Step S11: Install sludge concentration sensors at the inlet and outlet of the sludge pump. By emitting a light beam of a specific wavelength to penetrate the sludge and detecting the intensity of the transmitted light, the sludge concentration is collected. The sensor collects data once every 1 minute, and each collection lasts for 5 seconds, and finally obtains the sludge concentration data. Step S12: Multiple pressure sensors are arranged at the elbows, connecting flanges, and middle sections of the return pipe. The pressure is measured by detecting the resistance change caused by the pressure change in the pipe. Each sensor collects data once every 30 seconds, and each collection lasts for 2 seconds, finally obtaining the pressure data of the return pipe. Step S13: Encrypt the sludge concentration data and return pipeline pressure data, and transmit the encrypted data to the wastewater treatment control center.

[0007] This invention utilizes sludge concentration sensors installed at the inlet and outlet of the sludge pump to collect sludge concentration data at a frequency of once per minute for 5 seconds each time. Pressure sensors are also installed at key locations in the return pipeline to collect pressure data at a frequency of once every 30 seconds for 2 seconds each time. Simultaneously, the collected sludge concentration and return pipeline pressure data are encrypted before being transmitted to the wastewater treatment control center. This achieves accurate data collection, high-frequency monitoring, and secure transmission, providing reliable data support for subsequent wastewater treatment control and improving the stability and safety of the system operation.

[0008] Preferably, in step S2, monitoring the motor power and impeller speed of the sludge pump at the wastewater 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 once every 1 second, and each collection lasts for 0.1 seconds. Impeller speed data were collected from different locations on the sludge pump impeller, such as the front, middle and rear ends of the impeller shaft, with the sensor at each location collecting data every 0.5 seconds.

[0009] Preferably, in step S2, determining the inner diameter and material of the return pipe based on the return pipe pressure data includes: The pressure data of the return pipeline is classified into pressure gradients to obtain the pipeline pressure gradient value; the pipeline fluid velocity is mapped based on the pipeline pressure gradient value, and the pipeline inner diameter is calculated based on the pipeline fluid velocity; Extract the time series of pressure data from the return pipeline; If the pressure data of the return pipe gradually decreases with the time series of pipe pressure and the inner diameter of the pipe remains stable, then the pipe material is determined to be a corrosion-resistant material. If the pressure data of the return pipeline fluctuates continuously with the pipeline pressure time series and the inner diameter of the pipeline changes continuously, then the pipeline material is determined to be a corrosive material.

[0010] This invention achieves high-precision, high-frequency monitoring of motor power and impeller speed by collecting motor power data from the input, output, and intermediate junction boxes of the sludge pump motor. Sensors at each location collect data every 1 second, with each data collection lasting 0.1 seconds. Simultaneously, impeller speed data is collected from the front, middle, and rear ends of the sludge pump impeller, with sensors at each location collecting data every 0.5 seconds. Furthermore, by classifying the return pipe pressure data into pressure gradients, calculating the pipe fluid velocity, and deriving the pipe's inner diameter, combined with the characteristics of the pipe pressure time series changes, the corrosion resistance of the pipe material can be accurately determined. This monitoring and analysis method allows for real-time monitoring of equipment operating status and pipe material characteristics, providing a reliable basis for subsequent precise control, thereby improving the operating efficiency and reliability of the wastewater treatment system.

[0011] Preferably, in step S2, the sludge particle size is detected based on the sludge concentration data, and the degree of sludge viscosity is determined by the sludge particle size, including: The sludge concentration data was divided into multiple data segments according to the time series, with each segment containing 10 consecutive concentration values. Each data segment was analyzed to calculate the sludge concentration change rate in order 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 sludge with small 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 sludge with large particle size. If the sludge particles are small, the degree of sludge viscosity is determined; if the sludge particles are large, the degree of sludge viscosity is determined.

[0012] This invention divides sludge concentration data into multiple data segments according to a time series, with each segment containing 10 consecutive concentration values. The sludge concentration change rate is calculated for each segment, enabling quantitative analysis of sludge concentration fluctuations. Based on a comparison of the sludge concentration change rate with a preset threshold, combined with the frequency of sludge concentration fluctuations, the sludge particle size and 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 small-particle, high-viscosity sludge; conversely, it is determined to be large-particle, low-viscosity sludge. This data-driven analysis method accurately reflects the physical characteristics of sludge, providing a scientific basis for subsequent sludge recirculation control, thereby achieving refined management of the wastewater treatment process and improving system operating efficiency and stability.

[0013] Preferably, step S3, which generates motor control commands for the sludge pump based on the degree of sludge return blockage and motor power, includes: A correlation analysis was conducted between the degree of sludge return blockage and motor power to obtain the analysis results of the sludge pump motor. If the motor power exceeds 90% of the rated power and the degree of sludge return blockage 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 degree of sludge return blockage is low, it is determined that the motor is in a normal operation state. Based on the analysis results of the sludge pump motor, motor control commands are generated. If the motor is determined to be in a high-load operating state, a command to reduce motor power is generated; if the motor is determined to be in a normal operating state, a command to maintain the current power control is generated.

[0014] Preferably, in step S3, generating impeller control commands for the sludge pump based on the degree of sludge return blockage and impeller speed includes: The impeller operating status is determined based on the degree of sludge return clogging and the impeller speed. If the impeller speed is lower than 70% of the rated speed and the sludge return clogging is high, it is judged as an inefficient impeller operating status. If the impeller speed is between 80% and 90% of the rated speed and the sludge return clogging is low, it is judged as an efficient impeller operating status. Obtain the impeller structural characteristics, including the number of blades, blade angle, and impeller diameter; determine the optimal operating parameters of the impeller based on the impeller structural characteristics; if the impeller has a large number of blades and a large blade angle, a high speed is required to maintain sludge transport; if the impeller diameter is large, a low speed is required to avoid wear. Based on the impeller's operating status and optimal operating parameters, preliminary impeller control commands are generated. If the impeller is determined to be in an inefficient operating state, a command to increase the impeller speed is generated; if the impeller is determined to be in a high-efficiency operating state, a command to maintain the current speed is generated.

[0015] This invention performs a correlation analysis between the degree of sludge return clogging and motor power, enabling precise determination of the sludge pump motor's operating status. When the motor power exceeds 90% of its rated power and the sludge return is in a high-clogging state, it is determined to be a high-load operating state, generating a control command to reduce the motor power. When the motor power is between 60% and 80% of its rated power and the sludge return is in a low-clogging state, it is determined to be a normal operating state, generating a control command to maintain the current power. Furthermore, the impeller operating state is classified based on the degree of sludge return clogging and impeller speed, and optimal operating parameters are determined according to impeller structural characteristics (such as the number of blades, blade angle, and impeller diameter), thereby generating impeller control commands. This control method based on multi-parameter comprehensive analysis can accurately adjust the operating status of the motor and impeller according to actual operating conditions, effectively avoiding motor overload and impeller inefficient operation, extending equipment lifespan, improving sludge return efficiency, and ensuring the stable operation of the wastewater treatment system.

[0016] Preferably, step S3, which generates a dredging operation command for the return pipe based on the degree of sludge backflow blockage and the pipe's inner diameter and material, includes: A comprehensive assessment is conducted based on the degree of sludge return blockage and the pipe's inner diameter and material. If the blockage degree is high and the pipe material is easily corroded, the pipe is classified as a high-risk blockage state; if the blockage degree is low and the pipe material is corrosion-resistant, the pipe is classified as a low-risk blockage state. Based on the comprehensive assessment results, a dredging operation command is generated; if the pipeline is determined to be in a high-risk blockage state, a command to increase the dredging frequency and intensity is generated; if the pipeline is determined to be in a low-risk blockage state, a command to maintain the current dredging frequency is generated. The dredging operation is dynamically adjusted according to the dredging operation instructions. If it is necessary to increase the dredging frequency, the dredging interval time is gradually shortened, with each adjustment increment being 5 minutes and the adjustment interval being 30 minutes. If it is necessary to maintain the current dredging frequency, the dredging interval time is kept unchanged.

[0017] This invention accurately determines the blockage risk status of pipelines by comprehensively assessing the degree of sludge backflow blockage and the inner diameter and material of the pipes. When the blockage degree is high and the pipe material is easily corroded, it is determined to be a high-risk blockage state, generating instructions to increase the frequency and intensity of unblocking. When the blockage degree is low and the pipe material is corrosion-resistant, it is determined to be a low-risk blockage state, generating instructions to maintain the current unblocking frequency. Unblocking operations are dynamically adjusted based on these instructions, achieving refined management of pipeline unblocking operations by gradually shortening the unblocking interval (each adjustment increment is 5 minutes, with an interval of 30 minutes) or keeping the interval constant. This risk-assessment-based dynamic unblocking strategy effectively reduces the risk of pipeline blockage, extends pipeline lifespan, avoids resource waste caused by over-unblocking, and ensures the stable operation of the wastewater treatment system.

[0018] Preferably, step S4 includes the following steps: Step S41: In the wastewater treatment control center, the motor control command, impeller control command and dredging operation command are integrated to generate a comprehensive control command, which is then sent to the control system of the sludge return equipment through the Internet of Things network. Step S42: In the control system of the sludge return equipment, receive the comprehensive control command and confirm its execution; the confirmation includes motor power adjustment, impeller speed adjustment and start of dredging operation; if the confirmation is correct, perform the control operation on the sewage treatment process. Step S43: Record the comprehensive control instructions and their execution results in the database of the wastewater treatment control center to form a wastewater treatment control strategy. The recorded content includes the specific parameters of the control instructions, execution time, execution results, and feedback information. Step S44: During the wastewater treatment operation, monitor the operating status of the sludge return equipment in real time, including motor power, impeller speed and return pipeline pressure; and feed the monitoring data back to the wastewater 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 has not reached the preset target, optimize the control strategy. The optimization includes adjusting the motor power, impeller speed, and the frequency and intensity of the unblocking operation.

[0019] This invention integrates motor control commands, impeller control commands, and unblocking operation commands to generate a comprehensive control command, which is then sent to the control system of the sludge return equipment via an Internet of Things (IoT) network, achieving centralized control of the wastewater treatment equipment. In the control system of the sludge return equipment, the comprehensive control command is executed and confirmed. After ensuring that the motor power adjustment, impeller speed adjustment, and unblocking operation are initiated correctly, the control operation is executed. The control commands and their execution results are recorded in the database of the wastewater treatment control center, forming a complete wastewater treatment control strategy. The record includes the specific parameters of the control command, 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 pipeline 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 target is not achieved, the control strategy is optimized, adjusting the frequency and intensity of the motor power, impeller speed, and unblocking operation. This closed-loop control mechanism ensures precise execution and dynamic optimization of the wastewater treatment process, improves system operating efficiency and stability, and provides data support and optimization basis for subsequent control.

[0020] This specification provides an Internet of Things (IoT)-based wastewater treatment simulation and control system for executing the aforementioned IoT-based wastewater treatment simulation and control method. The IoT-based wastewater treatment simulation and control system includes: The IoT sensor acquisition module is used to detect the sludge pump and return pipeline of the sludge return equipment through the IoT sensor network; to collect real-time operating parameters of the sludge pump and return pipeline, including sludge concentration data and return pipeline pressure data, and to transmit the sludge concentration data and return pipeline pressure data to the wastewater treatment control center. The wastewater quality assessment module is used in the wastewater 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 sludge particle size based on sludge concentration data and determine the degree of sludge viscosity; and assess the degree of sludge return blockage based on the pipe inner diameter, material, and sludge viscosity. The wastewater treatment control module is used to generate motor control commands for the sludge pump based on the degree of sludge return blockage and motor power; generate impeller control commands for the sludge pump based on the degree of sludge return blockage and impeller speed; and generate unblocking operation commands for the return pipe based on the degree of sludge return blockage and pipe inner diameter and material. The wastewater treatment operation execution module is used to send motor control commands, impeller control commands, and dredging operation commands to the sludge return equipment and record them as wastewater treatment control strategies; and to simulate and control wastewater treatment according to the wastewater treatment control strategies in order to execute wastewater treatment operations.

[0021] This invention acquires real-time sludge concentration and return pipe pressure data via an IoT sensor acquisition module and transmits this data to the wastewater treatment control center. Based on this data and relevant monitoring parameters, a wastewater quality assessment module accurately evaluates the degree of sludge return blockage. A wastewater treatment control module generates targeted motor and impeller control commands and unblocking instructions based on the assessment results. A wastewater treatment operation execution module sends the commands to the equipment, records the control strategy, and performs simulated control to optimize wastewater treatment operations. This systematic control method enables real-time monitoring, accurate assessment, and dynamic optimization of the wastewater treatment process, improving wastewater treatment efficiency and system operational stability, reducing energy consumption and equipment wear, and ensuring the continuity and reliability of wastewater treatment. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the steps in a wastewater treatment simulation and control method based on the Internet of Things. Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S4. The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0023] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0024] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

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

[0026] To achieve the above objectives, please refer to Figures 1 to 2 A wastewater treatment simulation and control method based on the Internet of Things, the method comprising the following steps: Step S1: Detect the sludge pump and return pipeline of the sludge return equipment through the Internet of Things sensor network; collect real-time operating parameters of the sludge pump and return pipeline, including sludge concentration data and return pipeline pressure data, and transmit the sludge concentration data and return pipeline pressure data to the wastewater treatment control center. In this embodiment of the invention, an SC-1000 sludge concentration sensor is installed at the sludge pump outlet of the sludge return equipment. This sensor uses an optical scattering method to measure sludge concentration. Its working principle involves irradiating the sludge fluid with infrared light of a specific wavelength and calculating the sludge concentration by detecting the intensity of the scattered light. The sensor's measurement range is set to 0-50000 mg / L, with an accuracy of ±50 mg / L, and a sampling frequency of 10 times per second to ensure real-time capture of minute changes in sludge concentration. The sensor converts the sludge concentration data into a standard current signal through a built-in 4-20 mA current output interface.

[0027] Meanwhile, PS-200 pressure sensors are installed at key locations in the return pipeline. These sensors, based on the piezoresistive principle, convert pressure changes within the pipeline 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 achieve wireless data transmission, the sludge concentration sensor and pressure sensor are 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 distance of up to 1000 meters (line-of-sight). The wireless communication module packages the sludge concentration and pressure data collected by the sensors, with the data packet format including device identifier, data type, data value, and timestamp. The data packet is transmitted wirelessly to the receiving end at the wastewater treatment control center once per second. At the wastewater treatment control center, an RC-500 receiving device is installed, which receives data packets from the wireless communication module via the ZigBee protocol. The receiving device decodes the received data 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 field named "SludgeConcentration" with a floating-point data type; the pressure data is stored in a field named "PipelinePressure" with the same floating-point data type. Simultaneously, the timestamp of the data acquisition is stored in a field named "Timestamp" with millisecond precision. The control center's database management system uses a relational database with a table named "SludgeFlowData" used to store sludge concentration and pressure data. The table structure includes three fields: "SludgeConcentration," "PipelinePressure," and "Timestamp," with data types FLOAT, FLOAT, and DATETIME, respectively. Each time 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 wastewater 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; assess the degree of sludge return blockage based on the pipe inner diameter, material, and sludge viscosity. In this embodiment of the invention, in step S2, the wastewater 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 principle of electromagnetic induction, measuring 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 principle of magnetoelectricity, measuring speed by detecting magnetic marks 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 sensor and speed sensor at a frequency of once per second, storing the motor power data in the "Motor Power" field of the database and the impeller speed data in the "Impeller Speed" field. Based on the return pipe pressure data "Return Pipe Pressure" transmitted in step S1, the control center determines the inner diameter and material of the return pipe using a pre-set pressure-to-pipe parameter lookup table. This lookup table is stored in the control center's database and contains information on the pipe inner diameter and material corresponding to different pressure ranges. For example, when the "return pipe pressure" is 0.2-0.4 MPa, the lookup table shows the pipe inner diameter as DN150 and the material as carbon steel; when the pressure is 0.4-0.6 MPa, the pipe inner diameter is DN200 and the material is stainless steel. The control center's data processing system determines the current return pipe's inner diameter and material by matching the pressure data with the range in the lookup table, and stores the results in the "pipe inner diameter" and "pipe material" fields of 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 samples. The camera resolution is 1920×1080 pixels, and the shooting frequency is once per minute. The image processing system processes the acquired images, first converting them to grayscale, then separating the sludge particles from the background using binarization, and finally extracting the contour information of the sludge particles through edge detection and calculating the equivalent diameter of the particles. The sludge particle size detection results are stored in the "Sludge Particle Size" field of the database, in micrometers (μm). Based on the detected sludge particle size, the control center determines the sludge viscosity using a pre-defined particle size-viscosity relationship table. This table, established based on experimental data, lists the sludge viscosity values ​​corresponding to different particle sizes. The control center's data processing system matches the detected sludge particle size with the corresponding viscosity value by looking up 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 ("Pipe Material") and the sludge viscosity ("Sludge Viscosity"), the control center assesses the degree of sludge backflow blockage using a pre-defined blockage assessment model. This model comprehensively considers the influence of pipe inner diameter and material on fluid resistance, as well as the influence of sludge viscosity on flow performance.The control center's data processing system calculates the quantitative index of congestion level, "congestion level," based on the model and stores the result in the "congestion level" field of the database, ranging from 0 to 1, where 0 represents no congestion and 1 represents complete congestion.

[0030] Step S3: Generate motor control commands for the sludge pump based on the degree of sludge return blockage and motor power; generate impeller control commands for the sludge pump based on the degree of sludge return blockage and impeller speed; generate unblocking operation commands for the return pipe based on the degree of sludge return blockage and pipe inner diameter and material. In this embodiment of the invention, the wastewater treatment control center generates motor control commands for the sludge pump based on the "blockage level" assessed in step S2 and the monitored "motor power". The control center's control system invokes a preset motor power adjustment strategy, which is based on the correspondence between blockage level and motor power. When the "blockage level" is less than 0.3, it indicates that the return pipe is unobstructed, and the motor power is maintained at the current level; when the "blockage level" is between 0.3 and 0.6, the motor power is appropriately increased, specifically by 10% of the current power, to enhance the pump's conveying capacity; when the "blockage level" is greater than 0.6, the motor power is increased by 20% to ensure smooth sludge conveying. The control system calculates the target motor power according to the above strategy and converts it into motor control commands, which are sent to the sludge pump's motor controller via the industrial control bus. After receiving the commands, the motor controller adjusts the motor's operating power. Simultaneously, the control center generates impeller control commands based on the "blockage level" and the monitored "impeller speed". The control system employs an impeller speed adjustment strategy. When the "blockage degree" is less than 0.3, the impeller speed remains constant; 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%. The control system calculates the target impeller speed based on the strategy, converts it into an impeller control command, and sends it to the frequency converter of the sludge pump via the industrial control bus. The frequency converter adjusts the impeller speed according to the command. In addition, the control center generates a dredging operation command for the return pipe based on the "blockage degree" and the "pipe inner diameter and material." For carbon steel pipes, no dredging operation is 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 inside the pipe is activated, with a vibration frequency set to 30 Hz and a duration of 10 minutes to loosen the sludge inside the pipe; 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 unblocking operation commands based on the pipe material and degree of blockage, and sends them to the controller of the pipe vibration device via the industrial control bus. Upon receiving the commands, the controller starts the vibration device and performs the unblocking operation according to the set parameters.

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

[0032] In this embodiment of the invention, the wastewater treatment control center sends motor control commands, impeller control commands, and unblocking operation commands to the sludge return equipment via industrial Ethernet. The motor control commands include target motor power parameters, the impeller control commands include target impeller speed parameters, and the unblocking operation commands include vibration frequency and duration parameters of the vibration device. The control center's command sending module uses the TCP / IP protocol for data transmission to ensure the accuracy and reliability of the commands. Simultaneously with sending the commands, the control center's data recording module packages and stores the detailed parameters of the aforementioned control commands, along with corresponding data such as "blockage degree," "motor power," "impeller speed," and "pipe inner diameter and material," into a wastewater treatment control strategy. The control strategy is stored in the control center's strategy database in a structured data format. Each strategy record includes a timestamp, command type, command parameters, and related monitoring data for subsequent querying and analysis. Subsequently, the control center activates the simulation control module to simulate and control the wastewater treatment process according to the stored wastewater treatment control strategy. The simulation control module calls a pre-configured wastewater treatment process model and, combined with the current control strategy parameters, calculates the changes in key parameters during the wastewater treatment process, such as sludge flow rate and treatment efficiency, using numerical simulation methods. The simulation control module determines whether the control strategy meets the preset wastewater treatment target based on the simulation results. If it does, the control strategy is marked as valid and executed; otherwise, it returns to step S2 to re-evaluate and generate control instructions. During wastewater treatment operations, the motor controller of the sludge return equipment receives and parses the motor control instructions, adjusting the motor's operating power to the target value; the frequency converter receives and parses the impeller control instructions, adjusting the impeller speed to the target value; and the controller of the pipeline vibration device receives and parses the unblocking operation instructions, starting the vibration device according to the set vibration frequency and duration. The entire execution process is monitored in real time through the equipment's feedback signals.

[0033] Preferably, step S1 includes the following steps: Step S11: Install sludge concentration sensors at the inlet and outlet of the sludge pump. By emitting a light beam of a specific wavelength to penetrate the sludge and detecting the intensity of the transmitted light, the sludge concentration is collected. The sensor collects data once every 1 minute, and each collection lasts for 5 seconds, and finally obtains the sludge concentration data. Step S12: Multiple pressure sensors are arranged at the elbows, connecting flanges, and middle sections of the return pipe. The pressure is measured by detecting the resistance change caused by the pressure change in the pipe. Each sensor collects data once every 30 seconds, and each collection lasts for 2 seconds, finally obtaining the pressure data of the return pipe. Step S13: Encrypt the sludge concentration data and return pipeline pressure data, and transmit the encrypted data to the wastewater treatment control center.

[0034] In this embodiment of the invention, in step S11, SC-3000 sludge concentration sensors are installed at the inlet and outlet of the sludge pump, respectively. These sensors are based on the principle of optical transmission, calculating the sludge concentration by emitting an infrared beam with a wavelength of 850 nanometers that penetrates the sludge fluid and detecting the intensity of the transmitted light. The sensor's measurement range is 0-10000 mg / L, with an accuracy of ±50 mg / L. The sensor is set to initiate data acquisition every 1 minute, with each acquisition lasting 5 seconds. During the acquisition process, the photodetector inside the sensor records the change in transmitted light intensity and converts it into a sludge concentration value through a built-in signal processing circuit. After acquisition, the sensor stores the sludge concentration data in a local cache in floating-point format, with units of mg / L, and names it "sludge concentration data". In step S12, PS-500 pressure sensors are installed at the bends, connecting flanges, and mid-sections 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 acquisition, the pressure-sensitive resistor inside the sensor converts pressure changes into electrical signals, which are amplified and converted from analog to digital to obtain pressure 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 the pressure sensor transmit the "Sludge Concentration Data" and "Return Pipe Pressure Data" from their local buffers to a wireless communication module installed near the sludge pump via their respective communication interfaces (the sludge concentration sensor uses an RS485 interface, and the pressure sensor uses the Modbus RTU protocol). This wireless communication module uses ZigBee wireless communication technology, operates at a frequency of 2.4 GHz, and has a communication distance of up to 1000 meters. Before receiving data, the communication module first performs AES-128-bit encryption to ensure data transmission security. The encrypted data is encapsulated according to a preset data packet format. The data packet contains the device identifier, data type, encrypted data content, and timestamp. After encapsulation, the wireless communication module sends the data packet to the receiving terminal at the wastewater treatment control center via the ZigBee protocol. The receiving terminal at 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 wastewater 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 once every 1 second, and each collection lasts for 0.1 seconds. Impeller speed data were collected from different locations on the sludge pump impeller, such as the front, middle and rear ends of the impeller shaft, with the sensor at each location collecting data every 0.5 seconds.

[0036] In this embodiment of the 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, calculating 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 set to initiate data acquisition every 1 second, with each acquisition lasting 0.1 seconds. During acquisition, the Hall element inside the sensor detects changes in the magnetic field generated when current passes through, while the voltage sensor measures the motor terminal voltage. The instantaneous power of the motor is calculated by combining these two measurements. After acquisition, the sensors store the power data in a local cache in floating-point format, in kW units, and name it "Motor Power Data". RS-300 speed sensors are installed at different 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 magnetoelectric induction principle, measuring speed by detecting magnetic marks on the impeller. Each sensor has a measurement range of 0-3000 r / min and an accuracy of ±1 r / min. The sensors are set to initiate data acquisition every 0.5 seconds. During acquisition, the magnetoelectric induction element inside the sensor detects the frequency of magnetic field changes generated by the impeller's rotation, thereby calculating the impeller's rotational speed. After acquisition, the sensor stores the rotational speed data in a local cache as an integer in r / min, named "Impeller Rotational Speed ​​Data". The power sensor and rotational speed sensor transmit the acquired "Motor Power Data" and "Impeller Rotational 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 rotational speed sensor uses an RS485 interface). The data acquisition system parses and stores the received data, storing the "Motor Power Data" in the "Motor Power" field of the database and the "Impeller Rotational Speed ​​Data" in the "Impeller Rotational Speed" field, while also recording the data acquisition timestamp.

[0037] Preferably, in step S2, determining the inner diameter and material of the return pipe based on the return pipe pressure data includes: The pressure data of the return pipeline is classified into pressure gradients to obtain the pipeline pressure gradient value; the pipeline fluid velocity is mapped based on the pipeline pressure gradient value, and the pipeline inner diameter is calculated based on the pipeline fluid velocity; Extract the time series of pressure data from the return pipeline; If the pressure data of the return pipe gradually decreases with the time series of pipe pressure and the inner diameter of the pipe remains stable, then the pipe material is determined to be a corrosion-resistant material. If the pressure data of the return pipeline fluctuates continuously with the pipeline pressure time series and the inner diameter of the pipeline changes continuously, then the pipeline material is determined to be a corrosive material.

[0038] In this embodiment of the invention, when processing the pressure data of the return pipeline, the pressure data is first classified according to a pressure gradient. The data processing system of the wastewater treatment control center classifies the collected return pipeline pressure data according to a preset classification standard. Pressure gradient classification is achieved by calculating the ratio of the pressure difference between adjacent pressure sensors to the sensor spacing. For example, if the spacing 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. Subsequently, the pipeline fluid velocity is mapped based on the pipeline pressure gradient value. The control center calls a pre-established mapping table between pressure gradient and velocity, which is 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 velocity is 2 m / s; when the pressure gradient value is 0.02 MPa / m, the velocity is 3 m / s. The control center converts pressure gradient values ​​into pipe fluid velocity by looking up a mapping table and stores the velocity data in the "Pipe Fluid Velocity" field of the database. Next, the pipe inner diameter is calculated using the pipe fluid velocity. The control center uses the law of conservation of flow rate, combined with the known sludge flow rate and the calculated velocity, to deduce the pipe inner diameter. For example, if the known sludge flow rate is 0.1 m³ / s and the velocity is 2 m / s, the calculated pipe inner diameter is 0.2236 meters. The calculation result is stored in the "Pipe Inner Diameter" field of the database. Simultaneously, the pipe pressure time series of the return pipe pressure data is extracted. The control center's data analysis module reads the return pipe pressure data from the database and arranges it in chronological order to form a pipe pressure time series. The sampling frequency of the time series is consistent with the pressure sensor's acquisition frequency, i.e., data is recorded every 30 seconds. Finally, the pipe material is determined based on the changes in the pipe pressure time series and the pipe inner diameter. The control center's data analysis module performs trend analysis on the pipe 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 fluctuates continuously over time while the pipe inner diameter changes continuously, the pipe material is determined to be easily corroded, and the result is stored in the "Pipe Material" field, marked as "easily corroded material".

[0039] Preferably, in step S2, the sludge particle size is detected based on the sludge concentration data, and the degree of sludge viscosity is determined by the sludge particle size, including: The sludge concentration data was divided into multiple data segments according to the time series, with each segment containing 10 consecutive concentration values. Each data segment was analyzed to calculate the sludge concentration change rate in order 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 sludge with small 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 sludge with large particle size. If the sludge particles are small, the degree of sludge viscosity is determined; if the sludge particles are large, the degree of sludge viscosity is determined.

[0040] In this embodiment of the invention, at the wastewater treatment control center, the collected sludge concentration data is first processed by time series segmentation. The data processing module of the control center divides the sludge concentration data into multiple data segments according to time sequence, with each data segment containing 10 consecutive sludge concentration values. For example, if the sludge concentration data is collected once per minute, then a data segment is formed every 10 minutes. Subsequently, the sludge concentration values ​​within each data segment are analyzed to calculate the rate of change of sludge concentration. Specifically, for each data segment, the difference between two adjacent concentration values ​​is calculated, and then this difference is divided by the previous concentration value to obtain the rate of change of concentration. For example, if two adjacent concentration values ​​within a data segment are 1500 mg / L and 1600 mg / L, then the rate of change of concentration is (1600-1500) / 1500 = 0.0667. The control center stores the concentration change rate of 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 is greater than 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 (e.g., 0.05). If the concentration change rate within a data segment is greater than 0.05 and the number of concentration fluctuations exceeds 5 (i.e., frequent fluctuations), the sludge corresponding to that data segment is determined to be of small particle size, and the result is stored in the "Sludge Particle Size" field of the database, marked as "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 5, the sludge corresponding to that data segment is determined to be of large particle size, and the result is stored in the "Sludge Particle Size" field, marked as "Large Particle Size". Finally, the sludge viscosity is determined based on the sludge particle size determination results. If the sludge particles are 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 particles are 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, step S3, which generates motor control commands for the sludge pump based on the degree of sludge return blockage and motor power, includes: A correlation analysis was conducted between the degree of sludge return blockage and motor power to obtain the analysis results of the sludge pump motor. If the motor power exceeds 90% of the rated power and the degree of sludge return blockage 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 degree of sludge return blockage is low, it is determined that the motor is in a normal operation state. Based on the analysis results of the sludge pump motor, motor control commands are generated. If the motor is determined to be in a high-load operating state, a command to reduce motor power is generated; if the motor is determined to be in a normal operating state, a command to maintain the current power control is generated.

[0042] In this embodiment of the invention, at the wastewater treatment control center, a correlation analysis is first performed on the degree of sludge return blockage and motor power. The data processing module of the control center reads the "degree of sludge return blockage" and "motor power" data from the database. The degree of sludge return blockage is divided into "high blockage" and "low blockage," and the motor power data is in kW.

[0043] The control center calculates the percentage of motor power to rated power. For example, if the motor's rated power is 30 kW and the currently monitored motor power is 28 kW, then the motor power percentage is 28 / 30 × 100% ≈ 93.33%. The control center analyzes the data according to preset criteria: if the motor power exceeds 90% of the rated power and the sludge return flow blockage is classified as "high blockage," it is determined to be in a "high-load motor operation state"; if the motor power is between 60% and 80% of the rated power and the sludge return flow blockage is classified as "low blockage," it is determined to be in a "normal motor operation state." The analysis results are stored in the "Motor Operation Status" field of the database. Based on the "Motor Operation Status" determination result, the control center generates corresponding motor control commands. If it is determined to be in a "high-load motor operation state," a "reduce motor power control command" is generated, which includes the target power value, such as reducing the motor power to 80% of the rated power. If it is determined to be in a "normal motor operation state," a "maintain current power control command" is generated, which maintains the current power unchanged. Control commands are sent to the motor controller of the sludge pump via the industrial control bus. After receiving the commands, the motor controller adjusts the motor's operating power to ensure that the motor's operating status meets the control requirements.

[0044] Preferably, in step S3, generating impeller control commands for the sludge pump based on the degree of sludge return blockage and impeller speed includes: The impeller operating status is determined based on the degree of sludge return clogging and the impeller speed. If the impeller speed is lower than 70% of the rated speed and the sludge return clogging is high, it is judged as an inefficient impeller operating status. If the impeller speed is between 80% and 90% of the rated speed and the sludge return clogging is low, it is judged as an efficient impeller operating status. Obtain the impeller structural characteristics, including the number of blades, blade angle, and impeller diameter; determine the optimal operating parameters of the impeller based on the impeller structural characteristics; if the impeller has a large number of blades and a large blade angle, a high speed is required to maintain sludge transport; if the impeller diameter is large, a low speed is required to avoid wear. Based on the impeller's operating status and optimal operating parameters, preliminary impeller control commands are generated. If the impeller is determined to be in an inefficient operating state, a command to increase the impeller speed is generated; if the impeller is determined to be in a high-efficiency operating state, a command to maintain the current speed is generated.

[0045] In this embodiment of the invention, at the wastewater treatment control center, the degree of sludge return clogging and impeller speed are first analyzed to classify the impeller operating state. The data processing module of the control center reads the "sludge return clogging degree" and "impeller speed" data from the database. The impeller speed is in r / min, and the rated speed is 1500 r / min. If the impeller speed is below 1050 r / min (i.e., 70% of the rated speed) and the sludge return clogging degree is "high clogging degree", it is determined to be "impeller inefficient operating 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 return clogging degree is "low clogging degree", it is determined to be "impeller efficient operating state". The determination result is stored in the "impeller operating state" field of the database. Subsequently, the structural characteristics of the impeller are obtained, including the number of blades, blade angle, and impeller diameter. These parameters are obtained through the impeller design parameter table pre-stored in the control center database. For example, the impeller has 6 blades with a blade angle of 30° and a diameter of 0.5 meters. The optimal operating parameters of the impeller are determined based on its structural characteristics: if the number of blades is high (e.g., more than 5) and the blade angle is large (e.g., more than 25°), a higher rotational speed (e.g., 1400 r / min) is required to maintain effective sludge transport; if the impeller diameter is large (e.g., more than 0.4 meters), a lower rotational speed (e.g., 1200 r / min) is required to reduce wear. Preliminary impeller control commands are generated based on the impeller's operating status and optimal operating parameters. If the impeller is determined to be in an "inefficient operating state," an "increase impeller speed control command" is generated, with the target speed set according to 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 in an "efficient operating state," a "maintain current speed control command" is generated, keeping the current speed unchanged. The control commands are sent to the frequency converter of the sludge pump via the industrial control bus. After receiving the commands, the frequency converter adjusts the impeller's operating speed to ensure that the impeller's operating status meets the control requirements.

[0046] Preferably, step S3, which generates a dredging operation command for the return pipe based on the degree of sludge backflow blockage and the pipe's inner diameter and material, includes: A comprehensive assessment is conducted based on the degree of sludge return blockage and the pipe's inner diameter and material. If the blockage degree is high and the pipe material is easily corroded, the pipe is classified as a high-risk blockage state; if the blockage degree is low and the pipe material is corrosion-resistant, the pipe is classified as a low-risk blockage state. Based on the comprehensive assessment results, a dredging operation command is generated; if the pipeline is determined to be in a high-risk blockage state, a command to increase the dredging frequency and intensity is generated; if the pipeline is determined to be in a low-risk blockage state, a command to maintain the current dredging frequency is generated. The dredging operation is dynamically adjusted according to the dredging operation instructions. If it is necessary to increase the dredging frequency, the dredging interval time is gradually shortened, with each adjustment increment being 5 minutes and the adjustment interval being 30 minutes. If it is necessary to maintain the current dredging frequency, the dredging interval time is kept unchanged.

[0047] In this embodiment of the invention, at the wastewater treatment control center, a comprehensive assessment is first conducted on the degree of sludge backflow blockage and the inner diameter and material of the pipes. The data processing module of the control center reads the "degree of sludge backflow blockage" and "pipe material" data from the database. If the "degree of sludge backflow blockage" is "high" and the "pipe material" is "corrosion-prone material," it is determined to be a "high-risk blockage state"; if the "degree of sludge backflow blockage" is "low" and the "pipe material" is "corrosion-resistant material," it is determined to be a "low-risk blockage state." The assessment results are stored in the "pipe blockage risk status" field of the database. Based on the comprehensive assessment results, the control center generates a dredging operation command. If it is determined to be a "high-risk blockage state," a command to "increase the dredging frequency and intensity" is generated; if it is determined to be a "low-risk blockage state," a command to "maintain the current dredging frequency" is generated. The dredging operation command is sent to the controller of the pipe dredging equipment via the industrial control bus. The controller of the dredging equipment dynamically adjusts the dredging operation according to the received command. If it is necessary to increase the dredging frequency, the dredging interval time is gradually shortened. The initial dredging interval is 120 minutes, with adjustments made in increments of 5 minutes at 30-minute intervals. For example, if the initial dredging interval is 115 minutes, it will be adjusted again to 110 minutes after 30 minutes, until the preset minimum dredging interval (e.g., 60 minutes) is reached. To maintain the current dredging frequency, the dredging interval will remain unchanged, and dredging operations will continue according to the currently set interval.

[0048] Of particular importance, step S3, which assesses the degree of sludge backflow blockage based on the pipe's inner diameter, material, and sludge viscosity, includes: The degree of sludge backflow blockage is assessed based on the pipe's inner diameter, material, and sludge viscosity, resulting in a blockage assessment value. If the pipe material is corrosive and the sludge has high viscosity, the clogging level assessment value increases by 20%. If the pipe material is corrosion-resistant and the sludge viscosity is low, the clogging level assessment value is reduced by 10%. If the pipe's inner diameter is less than 300 mm, the assessment value for the degree of blockage increases by 15%. If the pipe's inner diameter is greater than 300 mm, the assessment value for the degree of blockage is reduced by 10%.

[0049] Based on the clogging level assessment value, the clogging risk is divided into low-level clogging and high-level clogging of sludge return.

[0050] In this embodiment of the invention, at the wastewater treatment control center, the "pipe inner diameter and material," "sludge viscosity," and initial "sludge return blockage degree" data are first obtained from the database. The control center's data processing module comprehensively evaluates the sludge return blockage degree based on this data and calculates a blockage degree assessment value. The specific evaluation process is as follows: First, the initial blockage degree is adjusted based on the pipe inner diameter and material and the sludge viscosity. If the pipe material is a "corrosion-prone material" and the sludge viscosity is "high," the initial blockage degree assessment value is increased by 20%. For example, if the initial blockage degree assessment value is 40%, the adjusted assessment value is 40% × (1 + 20%) = 48%. If the pipe material is a "corrosion-resistant material" and the sludge viscosity is "low," the initial blockage degree assessment value is decreased by 10%. For example, if the initial value is 40%, the adjusted assessment value is 40% × (1 - 10%) = 36%. Next, the adjusted blockage degree assessment value is further corrected based on the pipe inner diameter. If the pipe inner diameter is less than 300 mm, the assessment value is increased by 15%. For example, if the adjusted assessment value is 48% and the pipe inner diameter is 250 mm, the final assessment value is 48% × (1 + 15%) = 55.2%. If the pipe inner diameter is greater than 300 mm, the assessment value is reduced by 10%. For example, if the adjusted assessment value is 36% and the pipe inner diameter is 350 mm, the final assessment value is 36% × (1 - 10%) = 32.4%. Finally, based on the final clogging assessment value, the clogging risk is divided into "low clogging level of sludge return" and "high clogging level of sludge return". The control center sets thresholds for the assessment value; for example, when the assessment value is less than or equal to 50%, it is judged as "low clogging level of sludge return"; when the assessment value is greater than 50%, it is judged as "high clogging level of sludge return". The judgment result is stored in the "sludge return clogging risk level" field of the database.

[0051] As an example of the present invention, reference is made to Figure 2 As shown, step S4 in this example includes: Step S41: In the wastewater treatment control center, the motor control command, impeller control command and dredging operation command are integrated to generate a comprehensive control command, which is then sent to the control system of the sludge return equipment through the Internet of Things network. Step S42: In the control system of the sludge return equipment, receive the comprehensive control command and confirm its execution; the confirmation includes motor power adjustment, impeller speed adjustment and start of dredging operation; if the confirmation is correct, perform the control operation on the sewage treatment process. Step S43: Record the comprehensive control instructions and their execution results in the database of the wastewater treatment control center to form a wastewater treatment control strategy. The recorded content includes the specific parameters of the control instructions, execution time, execution results, and feedback information. Step S44: During the wastewater treatment operation, monitor the operating status of the sludge return equipment in real time, including motor power, impeller speed and return pipeline pressure; and feed the monitoring data back to the wastewater 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 has not reached the preset target, optimize the control strategy. The optimization includes adjusting the motor power, impeller speed, and the frequency and intensity of the unblocking operation.

[0052] In this embodiment of the invention, in step S41, the data processing module of the wastewater treatment control center integrates the generated "motor control command," "impeller control command," and "dredging operation command." The integrated control command includes the target value for motor power adjustment, the target value for impeller speed adjustment, and specific parameters for the dredging operation (such as dredging frequency and intensity). The control center sends the integrated control command to the control system of the sludge return equipment via the Internet of Things network using the MQTT protocol. During transmission, the data is encrypted using the AES-128 encryption algorithm to ensure transmission security. In step S42, the control system of the sludge return equipment receives the integrated control command through its communication module. The control system parses the received command and confirms its execution. The confirmation includes motor power adjustment, impeller speed adjustment, and initiation of the dredging operation. The control system feeds back the confirmation information to the wastewater treatment control center, which includes the command reception status and the readiness to execute status. If the confirmation is correct, the control system of the sludge return equipment executes control operations on the wastewater treatment process according to the integrated control command, including adjusting the motor power, changing the impeller speed, and activating the dredging device. In step S43, the data recording module of the wastewater treatment control center records the comprehensive control commands and their execution results in the database. The recorded content includes the specific parameters of the control commands (such as target motor power, target impeller speed, 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 the control operation is performed. The control center stores this information in the "Wastewater Treatment Control Strategy" table in the database for subsequent querying and analysis. In step S44, the sludge return equipment's control system monitors the operating status in real time during wastewater treatment operations using sensors installed on the equipment. The monitoring content includes motor power, impeller speed, and return pipe pressure. The sensors collect data at a set frequency (e.g., once per second) and feed the monitoring data back to the wastewater treatment control center in real time via the Internet of Things (IoT) network. The feedback data uses the same encryption and transmission protocol to ensure data integrity and security. In step S45, the data analysis module of the wastewater treatment control center evaluates the control effect based on the real-time feedback monitoring data. The analysis module compares the monitored data with preset target values. If the monitoring data shows that the control effect has not reached the preset target (e.g., motor power has not reached the target value, impeller speed has not been adjusted properly, or return pipe pressure is abnormal), the control strategy is optimized. Optimization includes adjusting the target values ​​for motor power and impeller speed, as well as the frequency and intensity of unblocking operations. The optimized control strategy regenerates comprehensive control commands and sends them to the control system of the sludge return equipment via the Internet of Things (IoT) network to achieve dynamic optimization and control of the wastewater treatment process.

[0053] Of particular importance, step S451 includes the following steps: Step S451: At the wastewater treatment control center, collect real-time data on the sludge pump's motor power, impeller speed, and return pipeline pressure. The data collection frequency is once per minute, and each collection session lasts 10 seconds. Step S452: Conduct a preliminary assessment of the control effect based on the collected monitoring data to obtain preliminary assessment results; Step S453: If the motor power is 80%-90% of the rated power, the impeller speed is 80%-90% of the rated speed, and the return pipe pressure is stable, then the control effect is preliminarily determined to be good. Step S454: If the motor power exceeds 90% or falls below 70% of the rated power, the impeller speed exceeds 90% or falls below 70% of the rated speed, or the pressure fluctuation in the return pipe exceeds ±0.1 bar, it is preliminarily determined that the control effect is not good. Step S455: Dynamically adjust the control strategy based on the preliminary assessment results. If the control effect is not good, gradually adjust the motor power by 5 watts each time, with an adjustment interval of 10 minutes; simultaneously adjust the impeller speed by 10 revolutions per minute each time, with an adjustment interval of 10 minutes; increase the frequency of unblocking operations by 1 time per hour each time, with an adjustment interval of 30 minutes; increase the intensity of unblocking operations by 10% each time, with an adjustment interval of 30 minutes.

[0054] In this embodiment of the invention, in step S451, the data acquisition module of the wastewater treatment control center collects real-time data on motor power, impeller speed, and return pipe pressure using sensors installed on the sludge pump and the return pipe. The motor power sensor and impeller speed sensor are installed on the motor and impeller of the sludge pump, 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 acquired data is transmitted to the data processing module of the control center via an industrial Ethernet network. The data format is floating-point, with units of kilowatts (kW), revolutions per minute (r / min), and bar (bar). In step S452, the data processing module of the control center performs a preliminary evaluation of the received monitoring data. The evaluation module analyzes the motor power, impeller speed, and return pipe pressure according to a preset parameter range to obtain preliminary evaluation results. The 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 (e.g., 24-27 kW for a rated power of 30 kW), the impeller speed is between 80% and 90% of the rated speed (e.g., 1200-1350 r / min for a rated speed of 1500 r / min), and the return pipe pressure is stable (fluctuation range less than ±0.1 bar), the initial assessment is that the control effect is 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 (e.g., exceeds 27 kW) or is below 70% (e.g., below 21 kW), the impeller speed exceeds 90% of the rated speed (e.g., exceeds 1350 r / min) or is below 70% (e.g., below 1050 r / min), or the return pipe pressure fluctuates more than ±0.1 bar, the initial assessment is that the control effect is 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 center's control module dynamically adjusts the control strategy. If the control effect is unsatisfactory, the motor power is gradually adjusted in increments of 5 watts, with an adjustment interval of 10 minutes. 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 synchronously in increments of 10 revolutions per minute, with an adjustment interval of 10 minutes. For example, if the current impeller speed is 1400 r / min, it will be adjusted to 1410 r / min after 10 minutes. Furthermore, the frequency of unblocking operations is increased, with an increase of 1 operation per hour, with an adjustment interval of 30 minutes. For example, if the current unblocking frequency is 2 operations per hour, it will be adjusted to 3 operations per hour after 30 minutes. Simultaneously, the intensity of unblocking operations is increased, with an increase of 10%, with an adjustment interval of 30 minutes. For example, if the current unblocking intensity is 50%, it will be adjusted to 55% after 30 minutes.The adjusted control commands are sent to the control system of the sludge return equipment via the industrial control bus to achieve dynamic optimization and control of the wastewater treatment process.

[0055] This specification provides an Internet of Things (IoT)-based wastewater treatment simulation and control system for executing the aforementioned IoT-based wastewater treatment simulation and control method. The IoT-based wastewater treatment simulation and control system includes: The IoT sensor acquisition module is used to detect the sludge pump and return pipeline of the sludge return equipment through the IoT sensor network; to collect real-time operating parameters of the sludge pump and return pipeline, including sludge concentration data and return pipeline pressure data, and to transmit the sludge concentration data and return pipeline pressure data to the wastewater treatment control center. The wastewater quality assessment module is used in the wastewater 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 sludge particle size based on sludge concentration data and determine the degree of sludge viscosity; and assess the degree of sludge return blockage based on the pipe inner diameter, material, and sludge viscosity. The wastewater treatment control module is used to generate motor control commands for the sludge pump based on the degree of sludge return blockage and motor power; generate impeller control commands for the sludge pump based on the degree of sludge return blockage and impeller speed; and generate unblocking operation commands for the return pipe based on the degree of sludge return blockage and pipe inner diameter and material. The wastewater treatment operation execution module is used to send motor control commands, impeller control commands, and dredging operation commands to the sludge return equipment and record them as wastewater treatment control strategies; and to simulate and control wastewater treatment according to the wastewater treatment control strategies in order to execute wastewater treatment operations.

[0056] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0057] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the 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 invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A wastewater treatment simulation and control method based on the Internet of Things, characterized in that, Includes the following steps: Step S1: Detect the sludge pump and return pipeline of the sludge return equipment through an IoT sensor network; Real-time operating parameters of the sludge pump and return pipeline are collected, including sludge concentration data and return pipeline pressure data. The sludge concentration data and return pipeline pressure data are then transmitted to the wastewater treatment control center. Step S2: At the wastewater 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 through the sludge particle size. Assess the degree of sludge backflow blockage based on the pipe's inner diameter, material, and sludge viscosity; In step S2, the sludge particle size is detected based on the sludge concentration data, and the degree of sludge viscosity is determined by the sludge particle size, including: The sludge concentration data was divided into multiple data segments according to the time series, with each segment containing 10 consecutive concentration values. Each data segment was analyzed to calculate the sludge concentration change rate in order 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 sludge with small 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 sludge with large particle size. If the sludge particles are small, the degree of sludge viscosity is determined; if the sludge particles are large, the degree of sludge viscosity is determined. The assessment of sludge backflow blockage based on pipe inner diameter, material, and sludge viscosity includes: The degree of sludge backflow blockage is assessed based on the pipe's inner diameter, material, and sludge viscosity, resulting in a blockage assessment value. If the pipe material is corrosive and the sludge has a high viscosity, the clogging level assessment value increases by 20%. If the pipe material is corrosion-resistant and the sludge viscosity is low, the clogging level assessment value is reduced by 10%. If the pipe's inner diameter is less than 300 mm, the assessment value for the degree of blockage increases by 15%. If the pipe's inner diameter is greater than 300 mm, the assessment value for the degree of blockage is reduced by 10%. Based on the clogging severity assessment value, the clogging risk is divided into low-level clogging and high-level clogging of sludge return flow. Step S3: Generate motor control commands for the sludge pump based on the degree of sludge return blockage and motor power; generate impeller control commands for the sludge pump based on the degree of sludge return blockage and impeller speed; generate unblocking operation commands for the return pipe based on the degree of sludge return blockage and pipe inner diameter and material. In step S3, generating impeller control commands for the sludge pump based on the degree of sludge return blockage and impeller speed includes: The impeller operating status is determined based on the degree of sludge return clogging and the impeller speed. If the impeller speed is lower than 70% of the rated speed and the sludge return clogging is high, it is judged as an inefficient impeller operating status. If the impeller speed is between 80% and 90% of the rated speed and the sludge return clogging is low, it is judged as an efficient impeller operating status. Obtain the impeller structural characteristics, including the number of blades, blade angle, and impeller diameter; determine the optimal operating parameters of the impeller based on the impeller structural characteristics; if the impeller has a large number of blades and a large blade angle, a high speed is required to maintain sludge transport; if the impeller diameter is large, a low speed is required to avoid wear. Based on the impeller operating status and optimal impeller operating parameters, preliminary impeller control commands are generated. If the impeller is determined to be in an inefficient operating state, a command to increase the impeller speed is generated; if the impeller is determined to be in an efficient operating state, a command to maintain the current speed is generated. Step S4: Send motor control commands, impeller control commands, and dredging operation commands to the sludge return equipment and record them as wastewater treatment control strategies; simulate and control wastewater treatment according to the wastewater treatment control strategies to execute wastewater treatment operations.

2. The wastewater treatment simulation and control method based on the Internet of Things according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Install sludge concentration sensors at the inlet and outlet of the sludge pump. By emitting a light beam of a specific wavelength to penetrate the sludge and detecting the intensity of the transmitted light, the sludge concentration is collected. The sensor collects data once every 1 minute, and each collection lasts for 5 seconds, and finally obtains the sludge concentration data. Step S12: Multiple pressure sensors are arranged at the elbows, connecting flanges, and middle sections of the return pipe. The pressure is measured by detecting the resistance change caused by the pressure change in the pipe. Each sensor collects data every 30 seconds, and each collection lasts for 2 seconds, finally obtaining the pressure data of the return pipe. Step S13: Encrypt the sludge concentration data and return pipeline pressure data, and transmit the encrypted data to the wastewater treatment control center.

3. The wastewater treatment simulation and control method based on the Internet of Things according to claim 1, characterized in that, In step S2, at the wastewater 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 once every 1 second, and each collection lasts for 0.1 seconds. Impeller speed data were collected from different locations on the sludge pump impeller, such as the front, middle and rear ends of the impeller shaft, with the sensor at each location collecting data every 0.5 seconds.

4. The wastewater treatment simulation and control method based on the Internet of Things according to claim 1, characterized in that, In step S2, the inner diameter and material of the return pipe are determined based on the return pipe pressure data, including: The pressure data of the return pipeline is classified into pressure gradients to obtain the pipeline pressure gradient value; the pipeline fluid velocity is mapped based on the pipeline pressure gradient value, and the pipeline inner diameter is calculated based on the pipeline fluid velocity; Extract the time series of pressure data from the return pipeline; If the pressure data of the return pipe gradually decreases with the time series of pipe pressure and the inner diameter of the pipe remains stable, then the pipe material is determined to be a corrosion-resistant material. If the pressure data of the return pipeline fluctuates continuously with the pipeline pressure time series and the inner diameter of the pipeline changes continuously, then the pipeline material is determined to be a corrosive material. If the sludge particles are small, the degree of sludge viscosity is determined; if the sludge particles are large, the degree of sludge viscosity is determined.

5. The wastewater treatment simulation and control method based on the Internet of Things according to claim 1, characterized in that, Step S3 involves generating motor control commands for the sludge pump based on the degree of sludge return blockage and motor power, including: A correlation analysis was conducted between the degree of sludge return blockage and motor power to obtain the analysis results of the sludge pump motor. If the motor power exceeds 90% of the rated power and the degree of sludge return blockage is high, it is determined that the motor is operating under high load. If the motor power is between 60% and 80% of the rated power and the degree of sludge return blockage is low, it is determined that the motor is operating normally. Based on the analysis results of the sludge pump motor, motor control commands are generated. If the motor is determined to be in a high-load operating state, a command to reduce motor power is generated; if the motor is determined to be in a normal operating state, a command to maintain the current power control is generated.

6. The wastewater treatment simulation and control method based on the Internet of Things according to claim 4, characterized in that, Step S3 generates unblocking instructions for the return pipe based on the degree of sludge backflow blockage and the pipe's inner diameter and material, including: A comprehensive assessment is conducted based on the degree of sludge return blockage and the pipe's inner diameter and material. If the blockage degree is high and the pipe material is easily corroded, the pipe is classified as a high-risk blockage state; if the blockage degree is low and the pipe material is corrosion-resistant, the pipe is classified as a low-risk blockage state. Based on the comprehensive assessment results, a dredging operation command is generated; if the pipeline is determined to be in a high-risk blockage state, a command to increase the dredging frequency and intensity is generated; if the pipeline is determined to be in a low-risk blockage state, a command to maintain the current dredging frequency is generated. The dredging operation is dynamically adjusted according to the dredging operation instructions. If it is necessary to increase the dredging frequency, the dredging interval time is gradually shortened, with each adjustment increment being 5 minutes and the adjustment interval being 30 minutes. If it is necessary to maintain the current dredging frequency, the dredging interval time is kept unchanged.

7. The wastewater treatment simulation and control method based on the Internet of Things according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: In the wastewater treatment control center, the motor control command, impeller control command and dredging operation command are integrated to generate a comprehensive control command, which is then sent to the control system of the sludge return equipment through the Internet of Things network. Step S42: In the control system of the sludge return equipment, receive the comprehensive control command and confirm its execution; the confirmation includes motor power adjustment, impeller speed adjustment and start of dredging operation; if the confirmation is correct, perform the control operation on the sewage treatment process. Step S43: Record the comprehensive control instructions and their execution results in the database of the wastewater treatment control center to form a wastewater treatment control strategy. The recorded content includes the specific parameters of the control instructions, execution time, execution results, and feedback information. Step S44: During the wastewater treatment operation, monitor the operating status of the sludge return equipment in real time, including motor power, impeller speed and return pipeline pressure; and feed the monitoring data back to the wastewater 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 has not reached the preset target, optimize the control strategy. The optimization includes adjusting the motor power, impeller speed, and the frequency and intensity of the unblocking operation.

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

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