Water balance management system for water-saving process

Abstract

The invention relates to the field of water-saving processes, and discloses a water-saving process water balance management system which comprises a central water supply module which is linked with a pipe network pressure sensor through an adjustable pump station, predicts a water demand based on an LSTM algorithm according to an analysis result of an intelligent control system, and dynamically adjusts the power of the pump station; the regional water supply module is provided with industrial, agricultural and urban life special pipe networks, each pipe network is provided with an electromagnetic flowmeter and an electric control valve, transmission energy consumption is reduced through a pipe network pressure gradient control technology, and real-time data of each pipe network is fed back to the intelligent control system. The water demand is predicted through the central water supply module based on the LSTM algorithm, the pump station power is dynamically adjusted in combination with a pipe network pressure sensor, meanwhile, the regional water supply module utilizes the pipe network pressure gradient control technology and real-time data feedback, the problems of water supply amount and uneven water pressure distribution are effectively solved, and the water resource utilization rate is increased.

Description

Technical Field

[0001] The present invention relates to the field of water-saving processes, and specifically to a water balance management system for water-saving processes. Background Art

[0002] As an important strategic resource for the development of human society, the efficient utilization of water resources is crucial for the sustainable development of the economy and society. In the fields of agricultural irrigation, industrial production, and urban water supply, the exploration and practice of water-saving processes have become the key path to ensure the sustainable supply of water resources.

[0003] Currently, a series of relatively mature basic water-saving measures have been formed in the industry. In agricultural irrigation, the water volume is accurately controlled through hierarchical metering, and differential irrigation is carried out according to the water demand characteristics of different crops; in industrial production, end-of-pipe reuse technology re-injects the treated wastewater into the production process to achieve the recycling of water resources; the urban water supply system uses hierarchical metering of intelligent water meters to monitor the water usage of each area in real time. These measures have alleviated the problem of water resource waste to a certain extent and become an important support for water-saving work.

[0004] However, the existing water-saving measures still have many limitations. Due to the lack of systematic planning, the water-saving measures in each link operate independently, resulting in poor system coordination and the inability to form a joint force for water conservation. In the face of changes in water demand, the dynamic response mechanism lags behind, making it difficult to quickly adjust the water supply strategy. As a result, the water supply volume and water pressure are unevenly distributed, with over-supply in some areas and under-supply in some areas, and at the same time, the problem of imbalance of acid-base ions in the water body occurs frequently, affecting the water quality and the service life of equipment, and restricting the overall efficiency improvement of water-saving work. Summary of the Invention

[0005] Aiming at the deficiencies of the existing technology, the present invention provides a water balance management system for water-saving processes, which solves the problems of lack of systematic planning, independent operation of water-saving measures in each link, resulting in poor system coordination and the inability to form a joint force for water conservation.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: A water balance management system for water-saving processes, comprising: Central water supply module: Linked with the pipeline network pressure sensor through an adjustable pump station, the central water supply module predicts the water demand based on the LSTM algorithm according to the analysis results of the intelligent control system, and dynamically adjusts the pump station power; Regional water supply module: separate industrial, agricultural and urban life dedicated pipe networks, each pipe network is equipped with an electromagnetic flowmeter and an electric regulating valve, the pipe network pressure gradient control technology is used to reduce transmission energy consumption, and the real-time data of each pipe network is fed back to the intelligent control system; Circulation purification module: The circulation purification module receives industrial and urban sewage in the regional water supply module, and the treated reclaimed water can be added to the central water supply module or the regional water supply module for water resource recycling. The industrial / urban sewage quality-based treatment process is adopted, and special treatment units and centralized treatment units are set up to achieve the reuse of qualified reclaimed water. The special treatment unit is equipped with a grille, a grit chamber and an automatic dosing device. The centralized treatment unit adopts a modified activated sludge method coupled with a biofilm method; Intelligent control system: Integrates multi-source sensor data to trigger leakage location, water quality warning and valve linkage control in real time.

[0007] Through the above scheme: relying on the coordinated operation of multiple modules, the dynamic management of the whole process of water-saving process is realized. The central water supply module uses the LSTM algorithm to predict demand and dynamically adjust the linkage equipment; the regional water supply module uses the dedicated pipe network and pressure gradient control technology to finely manage the water use of each region and solve the problem of uneven water supply and water pressure; the circulation purification module realizes the reuse of reclaimed water through quality-based treatment and improves the utilization rate of water resources; ensures the efficient and stable operation of the system, thus realizing the dynamic management of the water balance of the whole process of water-saving process.

[0008] Preferably, the central water supply module performs data preprocessing, collects historical water usage data for at least the past year, including hourly, daily and monthly water consumption, weather data including temperature and rainfall, and holiday information, normalizes these data to unify their numerical ranges, and predicts water demand through an LSTM algorithm.

[0009] Preferably, in the regional water supply module, an LSTM neural network is constructed, and the network includes an input layer, multiple LSTM hidden layers and an output layer. The input layer receives preprocessed data, the hidden layer processes data through memory cells and a gating mechanism, and the output layer outputs water demand forecast values ​​for different future time periods including the next 1 hour, 6 hours and 12 hours. By minimizing the mean square error between the predicted value and the actual water consumption, the parameters of the LSTM network including weights and biases are adjusted to optimize the model.

[0010] Preferably, in the central water supply module, the current data collected in real time, including the current time and real-time weather, are input into the trained LSTM model to obtain the future water demand forecast value. The adjustable pump station dynamically adjusts the pump station power according to the predicted water demand and the real-time pressure information fed back by the pipe network pressure sensor.

[0011] Preferably, in the central water supply module, first, according to the terrain height data of each dedicated pipe network coverage area, the pressure demand data of water-using equipment, and combining with historical water flow data, a pipe network pressure distribution model is established. The pipe network pressure distribution model first collects and preprocesses data, covering terrain height, water-using equipment pressure demand, and historical water flow data for at least the past year, and normalizes these data. Secondly, a model is constructed: P_node = P_pumping station - ΔP_friction - ΔP_local - ΔP_gravity, where P_node is the pressure at the node, P_pumping station is the pressure at the outlet of the pumping station, ΔP_friction is the frictional pressure loss along the way, ΔP_local is the local pressure loss, and ΔP_gravity is the pressure change caused by gravitational potential energy. The pressure of each node in the pipe network can be calculated. The real-time collected data is input into the trained model to predict the pressure distribution of each point in the pipe network. During the operation of the system, the electromagnetic flowmeter monitors the pipe network flow in real time, and the electric control valve adjusts according to the pipe network pressure distribution model and real-time flow data.

[0012] Preferably, in the regional water supply module, the electromagnetic flowmeters and electric control valves of each dedicated pipe network are connected to the intelligent control system through industrial Ethernet, and the ModbusTCP / IP communication protocol is used for data transmission. The electromagnetic flowmeter packs and sends the collected flow data to the intelligent control system according to the protocol format. After the intelligent control system analyzes the data, according to the pipe network pressure gradient control strategy, it generates the control command of the electric control valve and then sends it to the electric control valve through industrial Ethernet in the same protocol format for remote precise control.

[0013] Preferably, in the centralized treatment unit of the circulating purification module, the sectional aeration process of the improved activated sludge process divides the aeration tank into multiple stages, and different dissolved oxygen concentrations and sludge loads are set in each stage. The first stage maintains a relatively high dissolved oxygen concentration of 4 - 6 mg / L and a sludge load of 0.3 - 0.5 kgBOD5 / kgMLSS·d; in the subsequent stages, the dissolved oxygen concentration gradually decreases to 2 - 4 mg / L and the sludge load is 0.1 - 0.3 kgBOD5 / kgMLSS·d.

[0014] Preferably, in the circulating purification module, the fixed-bed biofilm reactor used in the biofilm method is internally filled with a large amount of biofilm carriers including polyurethane fillers. When the sewage flows in the reactor, microorganisms attach and grow on the surface of the carrier to form a biofilm. The microorganisms in the biofilm adsorb and degrade to remove organic matter, nitrogen, and phosphorus pollutants in the sewage. When the improved activated sludge method and the biofilm method are operated in a coupled manner, they cooperate with each other. The sewage treated by the activated sludge method enters the biofilm reactor for further advanced treatment, and the shed biofilm generated by the biofilm reactor flows back to the activated sludge method system.

[0015] Preferably, in the circulating purification module, the PLC control system of the automatic dosing device incorporates a PID control algorithm. The water quality sensor continuously monitors key sewage indicators including pH value, COD, and ammonia nitrogen, and transmits the data to the PLC control system. The control system calculates the type and dosage of the chemicals to be added currently based on the preset water quality standards and the PID algorithm.

[0016] Preferably, the PID control algorithm collects pH value, COD, and ammonia nitrogen data through the water quality sensor, compares the deviation with the standard value, and then calculates through proportional, integral, and differential operations to comprehensively obtain the control quantity and precisely regulate the dosing pump.

[0017] The present invention provides a water balance management system for water-saving processes, with the following beneficial effects: 1. Through the central water supply module, the present invention predicts water demand based on the LSTM algorithm, dynamically adjusts the pump station power in combination with the pipeline network pressure sensor. At the same time, the regional water supply module uses the pipeline network pressure gradient control technology and real-time data feedback, changing the traditional mode of only controlling water conservation for a single link, realizing the dynamic management of the water balance throughout the entire water-saving process, effectively solving the problems of uneven water supply and water pressure distribution, and improving the utilization rate of water resources.

[0018] 2. The present invention separately sets up dedicated pipe networks for industry, agriculture, and urban life, and is equipped with electromagnetic flow meters and electric control valves. By using the pipeline network pressure gradient control technology, compared with traditional water-saving processes, it can accurately adjust according to the actual needs of each pipe network, avoiding unnecessary energy waste, reducing transmission energy consumption, and improving the energy utilization efficiency of the water-saving process.

[0019] 3. The circulating purification module of the present invention adopts an industrial / municipal sewage separate treatment process, with special treatment units and centralized treatment units set up. Among them, the improved activated sludge method is coupled with the biofilm method, and with the precise control of the automatic dosing device, it can effectively treat industrial and municipal sewage, realize the reuse of up-to-standard reclaimed water, and supplement it to the central or regional water supply module, improving the recycling rate of water resources and alleviating the problem of water resource shortage.

[0020] 4. The intelligent control system integrates multi-source sensor data, can trigger leakage location, water quality early warning, and valve linkage control in real time, changes the situation of poor system coordination and lagging dynamic response in traditional water-saving processes, realizes the intelligent collaborative operation of each link, and ensures the stable and efficient operation of the entire water-saving system. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram of the architecture of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0022] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] Please see attached Figure 1 The embodiment of the present invention provides a water balance management system for a water-saving process, including: Central water supply module: Through the linkage between the adjustable pump station and the pipe network pressure sensor, the water demand is predicted based on the LSTM algorithm, and the pump station power is dynamically adjusted; Regional water supply module: separate dedicated pipe networks for industry, agriculture, and urban life. Each pipe network is equipped with an electromagnetic flow meter and an electric regulating valve. The transmission energy consumption is reduced through the pipe network pressure gradient control technology. Circulation purification module: adopts industrial / urban sewage separation treatment process, sets up special treatment unit and centralized treatment unit, realizes qualified water reuse, special treatment unit is equipped with grille, sand settling tank and automatic dosing device, centralized treatment unit adopts improved activated sludge method coupled with biofilm method; Intelligent control system: Integrates multi-source sensor data to trigger leakage location, water quality warning and valve linkage control in real time.

[0024] Specifically, the central water supply module forms a close linkage with the pipe network pressure sensor through the adjustable pump station, and uses the time series prediction capability of the LSTM algorithm to accurately predict water demand. In the prediction process, a variety of factors are taken into account, such as historical water use patterns, weather changes, and special circumstances such as holidays, so as to provide a basis for the dynamic adjustment of the pump station power, ensure that the water supply can accurately match the actual demand, and avoid energy waste.

[0025] The regional water supply module divides the water supply network into industrial, agricultural, and urban life dedicated pipe networks according to different water demand. Each pipe network is equipped with an electromagnetic flow meter and an electric regulating valve. With the help of pipe network pressure gradient control technology, the water flow speed and pressure are adjusted according to the water use characteristics and water pressure requirements of different regions, effectively reducing the energy consumption of water during transmission and improving the transmission efficiency of water resources.

[0026] The circulation purification module adopts innovative industrial / urban sewage separation treatment technology, and sets up special treatment units and centralized treatment units. The special treatment unit performs preliminary treatment on sewage to remove large particle impurities and some pollutants; the centralized treatment unit uses the improved activated sludge method and biofilm method coupling technology to deeply purify sewage, achieve qualified reclaimed water reuse, and improve the recycling rate of water resources.

[0027] The intelligent control system integrates multi-source sensor data, covering various aspects of information such as flow rate, pressure, and water quality. Based on the GBDT algorithm, a multi-objective optimization model for energy consumption - water quality - water volume is constructed, comprehensively considering multiple factors such as energy consumption, water quality compliance, and water volume supply, and triggering functions such as leakage location, water quality early warning, and valve linkage control in real time to ensure the operation of the entire system in a safe and energy-saving state.

[0028] Please refer to the appendix Figure 1 , in the central water supply module, data preprocessing is carried out, collecting historical water usage data for at least the past year, including hourly, daily, and monthly water consumption, weather data including temperature and rainfall, and holiday information. These data are normalized to make their numerical ranges unified, and the water demand is predicted through the LSTM algorithm.

[0029] Specifically, in the central water supply module, first, historical water usage data for at least the past year are comprehensively collected. These data not only include the detailed hourly, daily, and monthly water consumption but also comprehensively consider weather factors such as temperature and rainfall, as well as holiday information. The collected data have different dimensions and numerical ranges. To enable subsequent algorithms to better process these data, the system adopts a normalization processing method. Normalization maps all data to a unified numerical interval, such as [0, 1] or [-1, 1], eliminating the influence of different dimensions on the algorithm and improving the convergence speed and prediction accuracy of the algorithm. The data after normalization processing are input into the LSTM algorithm for water demand prediction. The LSTM algorithm, through its unique memory cells and gating mechanisms, can effectively capture long-term dependencies and complex patterns in the data. During the training process, the algorithm continuously adjusts its internal parameters, learns the patterns in the historical water usage data, and thus establishes an accurate water demand prediction model.

[0030] Please refer to the appendix Figure 1 , in the regional water supply module, an LSTM neural network is built. The network includes an input layer, multiple LSTM hidden layers, and an output layer. The input layer receives the preprocessed data, the hidden layers process the data through memory cells and gating mechanisms, and the output layer outputs the predicted values of water demand for different future time periods, including 1 hour, 6 hours, and 12 hours in the future. By minimizing the mean square error between the predicted values and the actual water consumption, the parameters of the LSTM network, including weights and biases, are adjusted to optimize the model.

[0031] Specifically, the LSTM neural network built in the regional water supply module is a deep learning model, which consists of an input layer, multiple LSTM hidden layers, and an output layer. The input layer is responsible for receiving the preprocessed data, which contains various factors affecting water demand, such as time, weather, holidays, etc. The LSTM hidden layer is the core part of the model, which processes the input data through memory cells and gating mechanisms. Memory cells can store and transmit historical information, while gating mechanisms (including input gates, forget gates, and output gates) control the flow of information. The input gate determines which new information can enter the memory cell, the forget gate determines which old information needs to be forgotten, and the output gate controls how the information in the memory cell is output to the next layer or the final output layer. The output layer outputs the predicted values of water demand for different future time periods based on the processing results of the hidden layer, such as the water consumption in the next 1 hour, 6 hours, and 12 hours. To ensure the accuracy of the prediction model, the system uses the method of minimizing the mean square error between the predicted value and the actual water consumption to adjust the parameters of the LSTM network, including weights and biases. Through continuous iterative optimization, the model can more accurately predict future water demand and provide strong support for the reasonable regulation of the regional water supply module.

[0032] Please refer to the attached Figure 1 , in the central water supply module, the currently collected data including the current time and real-time weather are input into the trained LSTM model to obtain the predicted values of future water demand. The adjustable pump station dynamically adjusts the pump station power according to the predicted water demand and the real-time pressure information feedback by the pipe network pressure sensor.

[0033] Specifically, during the actual operation of the central water supply module, the system will collect current data in real time, including the current time and real-time weather information. These data are input into the trained LSTM model, and the model quickly and accurately predicts the future water demand based on the learned historical rules and current input information. The adjustable pump station, as the power output of the water supply system, dynamically adjusts according to the predicted water demand and the real-time pressure information feedback by the pipe network pressure sensor. When it is predicted that the future water demand will increase, the pump station will increase the power in advance and increase the water supply to ensure the stability of the pipe network pressure and meet the water demand; when it is predicted that the water demand will decrease, the pump station will appropriately reduce the power and reduce the water supply to avoid water resource waste and energy consumption caused by overwater supply. This dynamic adjustment mechanism enables the water supply system to more flexibly respond to changes in water demand and improve the water supply efficiency.

[0034] Please refer to the attached Figure 1, in the central water supply module, first, according to the terrain height data of the coverage areas of each dedicated pipe network, the pressure demand data of water-using equipment, and in combination with the historical water flow data, a pipe network pressure distribution model is established. The pipe network pressure distribution model first collects and preprocesses data, covering terrain height, the pressure demand of water-using equipment, and at least the historical water flow data of the past year, and normalizes this data. Secondly, a model is constructed: P node = P pump station - ΔP along the way - ΔP local - ΔP gravity, where P node is the pressure at the node, P pump station is the pressure at the outlet of the pump station, ΔP along the way is the pressure loss along the way, ΔP local is the local pressure loss, and ΔP gravity is the pressure change caused by gravitational potential energy. The pressure of each node in the pipe network can be calculated. The real-time collected data is input into the trained model to predict the pressure distribution of each point in the pipe network. During the operation of the system, the electromagnetic flowmeter monitors the pipe network flow in real time, and the electric control valve adjusts according to the pipe network pressure distribution model and the real-time flow data.

[0035] Specifically, in the central water supply module, in order to achieve precise control of the pressure of each dedicated pipe network, it is first necessary to establish a pipe network pressure distribution model according to the terrain height data of the coverage areas of each dedicated pipe network, the pressure demand data of water-using equipment, and in combination with the historical water flow data. The terrain height affects the gravitational potential energy of water, which in turn affects the pipe network pressure; different water-using equipment has different pressure requirements. For example, the water supply pressure requirement for high-rise buildings is usually higher; the historical water flow data reflects the water usage patterns in different time periods and different regions. During the operation of the system, the electromagnetic flowmeter monitors the pipe network flow in real time and transmits the flow data to the intelligent control system. The electric control valve adjusts according to the pipe network pressure distribution model and the real-time flow data. When the pipe network flow changes, the intelligent control system calculates the required pressure of each pipe section at present according to the pressure distribution model, and then sends a control instruction to the electric control valve to adjust the opening of the valve, thereby changing the water flow velocity and pressure in the pipe network to ensure that the water pressure in each area always remains within a reasonable range and guarantee the stability and reliability of water supply.

[0036] Please refer to the appendix Figure 1 , in the regional water supply module, the electromagnetic flowmeters and electric control valves of each dedicated pipe network are connected to the intelligent control system through industrial Ethernet, and the ModbusTCP / IP communication protocol is used for data transmission. The electromagnetic flowmeter packs and sends the collected flow data to the intelligent control system according to the protocol format. After the intelligent control system analyzes the data, according to the pipe network pressure gradient control strategy, it generates a control instruction for the electric control valve and then sends it to the electric control valve through industrial Ethernet in the same protocol format for remote precise control.

[0037] Specifically, in the regional water supply module, the electromagnetic flowmeters and electric control valves of each dedicated pipe network are efficiently connected to the intelligent control system through industrial Ethernet, and the ModbusTCP / IP communication protocol is used for data transmission. The ModbusTCP / IP protocol has the advantages of stable communication, reliable data transmission, and strong compatibility, and can meet the requirements for real-time performance and accuracy in the industrial environment. As a data acquisition device, the electromagnetic flowmeter packages the collected flow data according to the protocol format. During the packaging process, the flowmeter performs operations such as data encoding and verification to ensure the integrity and accuracy of the data. The packaged data is sent to the intelligent control system through industrial Ethernet. After receiving the data, the intelligent control system first performs a parsing operation to extract the flow data. Then, according to the preset control strategy for the pipe network pressure gradient, the flow data is analyzed and processed to generate a control instruction for the electric control valve. The control instruction is also packaged according to the ModbusTCP / IP protocol format and sent to the electric control valve through industrial Ethernet. After receiving the instruction, the electric control valve performs decoding and execution operations to precisely adjust the valve opening, achieving remote and precise control of the pipe network flow. This communication and control method improves the automation level and response speed of the system, enabling the regional water supply module to more flexibly respond to changes in water demand.

[0038] Please refer to the appendix Figure 1 , in the centralized processing unit of the circulating purification module, the sectional aeration process of the improved activated sludge method divides the aeration tank into multiple stages, and different dissolved oxygen concentrations and sludge loads are set for each stage. The first stage maintains a relatively high dissolved oxygen concentration of 4 - 6 mg / L and a sludge load of 0.3 - 0.5 kgBOD5 / kgMLSS·d; in subsequent stages, the dissolved oxygen concentration gradually decreases to 2 - 4 mg / L, and the sludge load is 0.1 - 0.3 kgBOD5 / kgMLSS·d.

[0039] Specifically, in the centralized processing unit of the cyclic purification module, the improved activated sludge process adopts a stepped aeration process. The aeration tank is divided into multiple stages, and different dissolved oxygen concentrations and sludge loads are set for each stage to meet the oxygen demand and treatment requirements of microorganisms in different stages. In the first stage, since the sewage contains a large amount of easily degradable organic matter, microorganisms require a relatively high dissolved oxygen concentration to maintain their metabolic activities. Therefore, a relatively high dissolved oxygen concentration of 4-6 mg / L is maintained, and a relatively high sludge load of 0.3-0.5 kgBOD5 / kgMLSS·d is set, enabling microorganisms to quickly decompose organic matter and reduce the BOD5 (biochemical oxygen demand in five days) content in the sewage. As the sewage flows in the aeration tank, it enters the subsequent stages. At this time, most of the easily degradable organic matter in the sewage has been decomposed, and the remaining organic matter is relatively difficult to degrade. To reduce energy consumption and improve treatment efficiency, the dissolved oxygen concentration is gradually reduced to 2-4 mg / L, and the sludge load is reduced to 0.1-0.3 kgBOD5 / kgMLSS·d. This stepped aeration process can reasonably allocate oxygen resources according to the changes in sewage quality, improving the treatment effect and energy utilization rate of the activated sludge process. In the cyclic purification module, the fixed-bed biofilm reactor used in the biofilm process is filled with a large amount of biofilm carriers including polyurethane fillers. When the sewage flows in the reactor, microorganisms attach and grow on the surface of the carriers to form a biofilm. The microorganisms in the biofilm adsorb and degrade to remove organic matter, nitrogen, and phosphorus pollutants in the sewage. When the improved activated sludge process and the biofilm process operate in a coupled manner, they cooperate with each other. The sewage treated by the activated sludge process enters the biofilm reactor for further advanced treatment, and the shed biofilm generated by the biofilm reactor flows back to the activated sludge process system.

[0040] Please refer to the attached Figure 1 In the cyclic purification module, the fixed-bed biofilm reactor used in the biofilm process is filled with a large amount of biofilm carriers including polyurethane fillers. When the sewage flows in the reactor, microorganisms attach and grow on the surface of the carriers to form a biofilm. The microorganisms in the biofilm adsorb and degrade to remove organic matter, nitrogen, and phosphorus pollutants in the sewage. When the improved activated sludge process and the biofilm process operate in a coupled manner, they cooperate with each other. The sewage treated by the activated sludge process enters the biofilm reactor for further advanced treatment, and the shed biofilm generated by the biofilm reactor flows back to the activated sludge process system.

[0041] Specifically, in the cyclic purification module, the fixed-bed biofilm reactor used in the biofilm method is filled with a large amount of biofilm carriers, such as polyurethane fillers. These fillers have a large specific surface area and good hydrophilicity, providing a place for the growth and attachment of microorganisms. When the sewage flows through the reactor, the microorganisms attach and grow on the surface of the carrier, gradually forming a biofilm. The biofilm can be divided into an aerobic layer, a facultative layer, and an anaerobic layer from the outside to the inside. In the aerobic layer, the microorganisms decompose the organic matter in the sewage into carbon dioxide and water through aerobic respiration; the microorganisms in the facultative layer can carry out both aerobic respiration and anaerobic respiration to further decompose the organic matter; the anaerobic layer mainly conducts anaerobic reactions such as denitrification to remove nitrogen elements from the sewage. When the improved activated sludge method and the biofilm method are operated in a coupled manner, they cooperate with each other and give full play to their respective advantages. The sewage treated by the activated sludge method enters the biofilm reactor for further advanced treatment. The microorganisms in the biofilm can adsorb and degrade pollutants such as organic matter, nitrogen, and phosphorus that the activated sludge method fails to completely remove, improving the sewage treatment effect. At the same time, the sloughed biofilm generated by the biofilm reactor flows back to the activated sludge method system, supplementing microbial strains for the activated sludge method and enhancing the impact load resistance and stability of the system.

[0042] Please refer to the attached Figure 1 , in the cyclic purification module, the PLC control system of the automatic dosing device incorporates a PID control algorithm. The water quality sensor continuously monitors the key indicators of the sewage, including pH value, COD, and ammonia nitrogen, and transmits the data to the PLC control system. The control system calculates the type and dosage of the chemicals to be added currently according to the preset water quality standards and the PID algorithm.

[0043] Specifically, in the cyclic purification module, the PLC control system of the automatic dosing device incorporates a PID control algorithm to achieve precise control of chemical dosing. The water quality sensor continuously monitors the key indicators of the sewage, such as pH value, COD (chemical oxygen demand), and ammonia nitrogen, and transmits this data to the PLC control system. After receiving the data, the PLC control system first compares the monitored value with the preset water quality standards to calculate the deviation between the current water quality indicators and the standard values. Then, according to the PID control algorithm, considering the three factors of the proportion, integral, and differential of the deviation comprehensively, it calculates the type and dosage of the chemicals to be added currently. For example, when the pH value of the sewage deviates from the standard value, the PLC control system will calculate the amount of acid or alkali to be added through the PID algorithm according to the magnitude and trend of the deviation, and control the rotation speed and running time of the dosing pump to accurately add the chemicals into the sewage, quickly restoring the pH value of the sewage to the normal range. Similarly, for indicators such as COD and ammonia nitrogen, the system will also perform precise regulation according to the real-time monitoring data and the PID algorithm to ensure that the effluent water quality always meets the treatment requirements.

[0044] Please refer to the attachedFigure 1 , the PID control algorithm collects pH value, COD and ammonia nitrogen data through a water quality sensor, compares them with the standard values to obtain the deviation, and then calculates through proportional, integral and differential operations to comprehensively obtain the control quantity and accurately regulate the dosing pump.

[0045] Specifically, the PID control algorithm plays a core role in the automatic dosing device of the circulating purification module. It realizes the accurate regulation of the dosing pump through the comprehensive calculation of three key links - proportional (P), integral (I), and differential (D). Proportional link (P): When the pH value, COD or ammonia nitrogen data collected by the water quality sensor deviates from the standard value, the proportional link will immediately generate a control quantity according to the size of the deviation. The larger the deviation, the larger the control quantity generated by the proportional link, and the corresponding adjustment range of the dosing pump will also increase. For example, if the pH value of the sewage deviates significantly from the standard value, the proportional link will quickly adjust the rotation speed of the dosing pump to increase or decrease the dosage of the chemical agent to quickly reduce the deviation. Integral link (I): The integral link is mainly used to eliminate the steady-state error of the system. During actual operation, due to the influence of various factors, the water quality indicators may not reach the standard value immediately, and there will be a certain steady-state error. The integral link will perform integral operation on the deviation. As time goes by, the value of the integral term will continue to accumulate, thereby gradually increasing the control quantity and driving the water quality indicators closer to the standard value until the steady-state error is eliminated. Differential link (D): The differential link can predict the change trend of the deviation. It generates a control quantity according to the change rate of the deviation. When the deviation has an increasing trend, the differential link will generate a reverse control quantity in advance to inhibit the further increase of the deviation; when the deviation has a decreasing trend, the differential link will appropriately reduce the control quantity to avoid overshoot of the system. Through the role of the differential link, the adjustment of the dosing pump is more stable and fast, improving the response speed and stability of the system.

[0046] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A water balance management system for water-saving processes, characterized in that: include: Central water supply module: Through the linkage between the adjustable pump station and the pipe network pressure sensor, the central water supply module predicts water demand based on the LSTM algorithm according to the analysis results of the intelligent control system and dynamically adjusts the pump station power; Regional water supply module: separate dedicated pipe networks for industry, agriculture, and urban life. Each pipe network is equipped with an electromagnetic flow meter and an electric regulating valve. The pipe network pressure gradient control technology is used to reduce transmission energy consumption, and the real-time data of each pipe network is fed back to the intelligent control system. Circulation purification module: The circulation purification module receives industrial and urban sewage from the regional water supply module. The treated reclaimed water can be added to the central water supply module or the regional water supply module for water resource recycling. The industrial / urban sewage separation treatment process is adopted, and a special treatment unit and a centralized treatment unit are set up to achieve the reuse of qualified reclaimed water. The special treatment unit is equipped with a grid, a grit chamber and an automatic dosing device. The centralized treatment unit adopts a modified activated sludge method coupled with a biofilm method; Intelligent control system: Integrates multi-source sensor data to trigger leakage location, water quality warning and valve linkage control in real time.

2. A water balance management system for water-saving processes according to claim 1, characterized in that: The central water supply module performs data preprocessing, collects historical water use data for at least the past year, including hourly, daily and monthly water consumption, weather data including temperature and rainfall, and holiday information, normalizes these data to unify their numerical ranges, and predicts water demand through the LSTM algorithm.

3. A water balance management system for water-saving processes according to claim 2, characterized in that: In the regional water supply module, an LSTM neural network is built, and the network includes an input layer, multiple LSTM hidden layers and an output layer. The input layer receives preprocessed data, the hidden layer processes data through memory cells and a gating mechanism, and the output layer outputs water demand forecast values ​​for different future time periods including the next 1 hour, 6 hours and 12 hours. By minimizing the mean square error between the predicted value and the actual water consumption, the parameters of the LSTM network including weights and biases are adjusted to optimize the model.

4. A water balance management system for water-saving processes according to claim 3, characterized in that: In the central water supply module, the current data collected in real time, including the current time and real-time weather, are input into the trained LSTM model to obtain the future water demand forecast value. The adjustable pump station dynamically adjusts the pump station power according to the predicted water demand and the real-time pressure information fed back by the pipe network pressure sensor.

5. A water balance management system for water-saving processes according to claim 4, characterized in that: In the central water supply module, firstly, a network pressure distribution model is established according to the terrain height data of the area covered by each dedicated pipeline network, the pressure demand data of the water-using equipment, and the historical water flow data. The network pressure distribution model first collects and preprocesses data, covering the terrain height, the pressure demand of the water-using equipment and the historical water flow data of at least the past year, and normalizes these data. Secondly, a model is constructed, P node = P pump station - ΔP along the way - ΔP local - ΔP gravity, wherein P node is the pressure at the node, P pump station is the pressure at the outlet of the pump station, ΔP along the way is the pressure loss along the way, ΔP local is the local pressure loss, and ΔP gravity is the pressure change caused by gravitational potential energy. The pressure of each node in the pipeline network can be calculated, and the real-time collected data is input into the trained model to predict the pressure distribution of each point in the pipeline network. During the operation of the system, the electromagnetic flowmeter monitors the pipeline network flow in real time, and the electric control valve is adjusted according to the pipeline network pressure distribution model and the real-time flow data.

6. A water balance management system for water-saving processes according to claim 1, characterized in that: In the regional water supply module, the electromagnetic flowmeter and electric regulating valve of each dedicated pipeline network are connected to the intelligent control system through industrial Ethernet, and the ModbusTCP / IP communication protocol is used for data transmission. The electromagnetic flowmeter packages the collected flow data according to the protocol format and sends it to the intelligent control system. After the intelligent control system parses the data, it generates control instructions for the electric regulating valve according to the pipeline pressure gradient control strategy, and then sends them to the electric regulating valve through industrial Ethernet in the same protocol format for remote and precise control.

7. The water balance management system for water-saving process according to claim 1, characterized in that: In the centralized treatment unit of the circulating purification module, the staged aeration process of the improved activated sludge method divides the aeration tank into multiple stages, and different dissolved oxygen concentrations and sludge loads are set in each stage. In the first stage, a relatively high dissolved oxygen concentration of 4-6 mg / L and a sludge load of 0.3-0.5 kgBOD5 / kgMLSS·d are maintained; in subsequent stages, the dissolved oxygen concentration is gradually reduced to 2-4 mg / L, and the sludge load is 0.1-0.3 kgBOD5 / kgMLSS·d.

8. The water balance management system for water-saving process according to claim 1, characterized in that: In the circulating purification module, a fixed-bed biofilm reactor used in the biofilm method is filled with a large number of biofilm carriers including polyurethane fillers. When the sewage flows in the reactor, microorganisms attach and grow on the surface of the carrier to form a biofilm. The microorganisms in the biofilm adsorb and degrade organic matter, nitrogen and phosphorus pollutants in the sewage. When the improved activated sludge method and the biofilm method are coupled and operated, the two work together. The sewage treated by the activated sludge method enters the biofilm reactor for further deep treatment, and the detached biofilm produced by the biofilm reactor flows back to the activated sludge method system.

9. The water balance management system for water-saving process according to claim 1, characterized in that: In the circulation purification module, the PLC control system of the automatic dosing device has a built-in PID control algorithm. The water quality sensor monitors the key indicators of the sewage in real time, including pH value, COD and ammonia nitrogen, and transmits the data to the PLC control system. The control system calculates the type and dosage of the agent currently required to be added based on the preset water quality standards and PID algorithm.

10. A water balance management system for water-saving processes according to claim 9, characterized in that: The PID control algorithm collects pH, COD and ammonia nitrogen data from water quality sensors, compares them with standard values ​​to obtain deviations, and then uses proportional, integral and differential calculations to comprehensively obtain the control amount and accurately regulate the dosing pump.

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