Port sewage treatment intelligent control and water resource efficient utilization system and method

Abstract

The invention discloses a port sewage treatment intelligent control and water resource efficient utilization system and method, belongs to the technical field of water conservancy projects, and collects multi-source water quality, water quantity, equipment operation state, pipe network and environment data in real time by deploying an environment data sensing module in a port area. And the multi-source data fusion and analysis module cleans, analyzes and predicts the data and generates an optimization decision basis. The flow decision control module generates an intelligent control instruction based on the flow decision control module, and controls sewage treatment facilities and water resource scheduling. And the intelligent execution and feedback module executes the instruction and visually presents the instruction. Intelligentization and energy consumption optimization of port sewage treatment are effectively realized, and through linkage of dust concentration and reclaimed water dust suppression, intelligent allocation of multi-source water bodies and the like, the utilization efficiency of water resources is greatly improved, the operation cost is reduced, dependence on municipal water is reduced, and sustainable development of port water affairs is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of water conservancy engineering, and in particular relates to a system and method for intelligent control of port sewage treatment and efficient utilization of water resources. Background Art

[0002] As important economic hubs, ports face increasingly severe challenges in wastewater treatment and water resource management. Traditional port wastewater treatment systems typically rely on a combination of physical, chemical, and biological processes, and are managed through preset operating parameters and regular manual inspections. Water resource utilization typically relies primarily on municipal fresh water, with some port areas conducting preliminary recycled water reuse, such as for toilet flushing or landscaping irrigation. However, this scheduling is often static, lacking comprehensive consideration and dynamic optimization of multiple water resources. With the expansion of port scale and increasingly stringent environmental protection standards, higher requirements are being placed on the refined management of water systems and efficient resource utilization, prompting the industry to seek more intelligent and green solutions.

[0003] However, existing technologies still have many shortcomings in port water management. Current systems often lack the ability to perceive real-time, multi-dimensional data on the entire port water process. For example, they are unable to obtain detailed operating status of the water supply and drainage network, dust concentration, or linkage data with meteorological conditions in real time. The fragmentation of data collection and insufficient analysis capabilities make it difficult for the system to conduct comprehensive operating condition assessments, trend forecasts, and anomaly detection. As a result, the operation and control of sewage treatment facilities are mostly passive or empirical, making it difficult to optimize energy consumption and chemical consumption. At the same time, water resource scheduling lacks intelligent overall considerations and fails to fully utilize non-traditional water sources such as treated recycled water and rainwater. In particular, in terms of meeting dynamically changing water demand, it fails to achieve intelligent coordination and efficient allocation of multiple water sources, resulting in waste of water resources, increased operating costs, and limiting the sustainable development of port water. Summary of the Invention

[0004] In response to the above-mentioned problems existing in the prior art, the purpose of the present invention is to provide a port sewage treatment intelligent control and water resource efficient utilization system, including an environmental data perception module, which is arranged in the port area and is used to collect water quality parameters, water quantity data and operating status parameters of related equipment in the port sewage treatment process in real time, and synchronously collect water pressure and flow data of the port area water supply and drainage network; the water quantity data includes sewage inlet flow, outlet flow and liquid levels of various water tanks; the operating status parameters include the start and stop status of the boost pump and the operating frequency of the dosing pump.

[0005] The multi-source data fusion and analysis module is in communication with the environmental data perception module and is used to clean, standardize, store and analyze the real-time data collected by the environmental data perception module. The analysis includes data statistics, trend prediction, anomaly detection and operating condition evaluation; and based on the analysis results, generates an optimization decision basis by running the sewage treatment operation control algorithm and the water resource scheduling optimization algorithm.

[0006] The process decision control module is communicatively connected to the multi-source data fusion and analysis module, and is used to generate intelligent control instructions for the port sewage treatment facilities and water resource scheduling based on the optimization decision basis; wherein the port sewage treatment facilities include lift pumps and dosing systems; and the water resource scheduling includes spray systems and water supply valves.

[0007] The intelligent execution and feedback module is in communication with the process decision control module, and is used to receive and execute the intelligent control instructions, directly control the port sewage treatment facilities and related water-using equipment through the industrial control interface, and provide real-time feedback of the execution results to the multi-source data fusion and analysis module.

[0008] Furthermore, the environmental data perception module further includes: Water quality sensors, flow meters, liquid level meters, dust concentration sensors, meteorological sensors and operation status sensors of various equipment are set up at multiple monitoring points in the port area to collect the water quality parameters, the water volume data, the operation status parameters, as well as dust concentration and meteorological data.

[0009] Furthermore, the process decision control module further includes: a sewage lift pump intelligent start and stop control unit, a coagulation dosing intelligent control unit, and a water supply and drainage network leakage assessment and positioning unit, which are used to optimize the start and stop control of the lift pump according to real-time monitoring data, accurately adjust the dosing amount, and assess and locate the leakage points of the water supply and drainage network.

[0010] Furthermore, the intelligent execution and feedback module provides visualization data and supports user interaction functions, and the visualization data and interactive functions include presenting real-time monitoring data, historical data query, report generation, abnormal alarm, system configuration to users, and allowing users to perform related operations.

[0011] A method for intelligent control of port sewage treatment and efficient utilization of water resources comprises the following steps: Step S1: By deploying sensors at various key monitoring points in the port area, water quality parameters, water volume data, operating status parameters of related equipment, and water pressure and flow data of the port area's water supply and drainage network during the port sewage treatment process are obtained in real time, and the data are transmitted to the data processing platform.

[0012] Step S2, cleaning, standardizing, storing and deeply analyzing the port environmental factor data collected in real time, wherein the deep analysis is based on big data processing, including data statistics, trend prediction, anomaly detection and operation condition evaluation; and based on the analysis results, by running the sewage treatment operation control algorithm and the water resource scheduling optimization algorithm, dynamically generating intelligent optimization instructions for the port sewage treatment facilities and water resource scheduling, and the generation of the instructions is adjusted based on the comparison of real-time data with preset judgment criteria.

[0013] Step S3, receiving and executing the intelligent optimization instructions, directly adjusting the operating status of the port sewage treatment facilities through the automated control system, the operating status including intelligent start and stop of the lifting pump and optimization of the coagulation and dosing process; allocating water resources through the intelligent optimization instructions, including the linkage between dust monitoring and recycled water supply.

[0014] Furthermore, the port environmental factor data collected in real time in step S1 include: water quality parameters, including turbidity, total dissolved solids concentration, pH value, ammonia nitrogen, chemical oxygen demand, total nitrogen, total phosphorus and total iron; water quantity data, including sewage inlet flow, outlet flow and liquid levels of various water pools; equipment operating status parameters, including the start and stop status of the lifting pump and the operating frequency of the dosing pump; and water pressure and flow data of the port area water supply and drainage network.

[0015] Furthermore, the step S2 includes: Step S201: Clean, standardize and store the collected port environmental factor data.

[0016] Step S202 , performing statistical analysis, trend prediction, and anomaly detection on the cleaned data to obtain the real-time status of various environmental factors.

[0017] Step S203: Run the sewage treatment operation control algorithm, compare the real-time status with the preset water quality target, water quantity target or equipment operating condition threshold, and dynamically generate intelligent optimization instructions for the sewage treatment facility based on the comparison results. The intelligent optimization instructions are one or more of equipment start and stop instructions, operation frequency adjustment instructions or process parameter adjustment instructions.

[0018] Step S204, running the water resource scheduling optimization algorithm, by comparing the real-time status with the preset water demand standard, water quality requirements or available water indicators, dynamically generating intelligent optimization instructions for water resource scheduling based on the comparison results, the intelligent optimization instructions are one or more of multi-source water allocation instructions, water supply path optimization instructions or recycled water utilization priority adjustment instructions.

[0019] Furthermore, the functional relationship of the generation of the intelligent optimization instruction is expressed as: ,in, Represents the intelligent optimization instruction vector, which contains all specific instructions for sewage treatment facilities and water resource scheduling; Represents the intelligent optimization decision function, which learns and optimizes based on historical operation data, system model and preset control strategy, and selects the sub-algorithm optimization strategy; A parameter vector representing the element data to be collected; Represents the preset target and threshold parameter vector; Represents input parameters and decision context information, including patterns learned from historical data, predicted values ​​of real-time conditions, or intermediate variables generated during algorithm execution.

[0020] Furthermore, the sub-algorithm optimization strategy determines the type of algorithm actually executed according to the type of input parameters and / or decision context of the sewage treatment operation control algorithm or water resource scheduling optimization algorithm being executed.

[0021] Furthermore, the comprehensive allocation of water resources in step S3 includes: Step S301, real-time monitoring of the dust concentration in the port yard and surrounding areas; intelligent calculation of the dust suppression water required in the yard at the current moment based on the dust concentration, meteorological conditions and yard operation conditions, and the calculation is based on the dust suppression water demand calculation.

[0022] In step S302, the water demand includes water for production operations, domestic water, landscaping water, road washing water, and dust suppression water; and the available water quantity and water quality of the treated recycled water are evaluated.

[0023] Step S303, based on the water demand, the recycled water supply capacity, meteorological conditions and the liquid level of the water storage facility, the municipal water, rainwater and recycled water are intelligently allocated through the optimization scheduling algorithm.

[0024] Step S304: Send the calculated dust suppression water demand to the water resources comprehensive scheduling optimization module in real time, and trigger the intelligent scheduling of recycled water supply.

[0025] Compared with the prior art, the present invention has the following beneficial effects: This invention significantly improves the intelligence level of port sewage treatment and the efficiency of water resource utilization. Through the extensive deployment of multiple types of sensors, the system perceives multi-source data such as port water quality, water quantity, equipment operating status, water supply and drainage network, dust and meteorology in real time, realizing refined perception of the entire port water chain. These multi-source heterogeneous data are deeply integrated with big data analysis, combined with trend prediction, anomaly detection and other algorithms to accurately evaluate the operating conditions of the system. More importantly, the system can run sewage treatment and water resource scheduling optimization algorithms based on the dynamic comparison of real-time data with preset standards, and generate forward-looking intelligent decision-making basis. This realizes the automated and refined management of sewage treatment facilities and water resource scheduling, significantly reduces manual intervention, and improves the response speed and accuracy of the overall operation.

[0026] The present invention has outstanding advantages in the efficient use of water resources, breaking through the limitations of single treatment or simple reuse. By comprehensively evaluating the multiple water needs of port production, life, greening, road washing and dust suppression, and real-time evaluation of the availability of recycled water, the system can run an optimized scheduling algorithm and intelligently allocate municipal water, rainwater, recycled water and other multi-source water bodies. In particular, the system can intelligently calculate the water consumption for dust suppression based on real-time dust concentration, meteorological conditions and yard operations, and trigger intelligent scheduling of recycled water, maximize the utilization of recycled water, and significantly reduce dependence on municipal new water. The water supply and drainage network leakage assessment and positioning function effectively avoids the waste of water resources and enhances the economic and environmental benefits of port water management. Intelligent start-stop lifting pumps and optimized coagulation and dosing processes effectively reduce energy consumption and chemical consumption, ensure that sewage is discharged in compliance with standards, and provide a solid guarantee for the sustainable development of the port. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a schematic diagram of the system module structure of the present invention.

[0028] Figure 2 The figure is a flowchart of an exemplary step of the control method of the present invention.

[0029] Figure 3 A flow chart of an exemplary process for generating self-decision making instructions according to the present invention.

[0030] Figure 4 The figure is a flow chart of an exemplary step of scheduling according to instructions of the present invention. DETAILED DESCRIPTION

[0031] The present invention will be further described below with reference to specific embodiments.

[0032] like Figure 1The figure shows a schematic block diagram of the system module composition of the present invention. This embodiment provides a port sewage treatment intelligent control and water resource efficient utilization system, including an environmental data perception module, which is arranged in the port area and is used to collect water quality parameters, water quantity data and operating status parameters of related equipment in the port sewage treatment process in real time, and simultaneously collect water pressure and flow data of the port area water supply and drainage network; water quantity data includes sewage inlet flow, outlet flow and liquid levels of various water tanks; operating status parameters include the start and stop status of the lift pump and the operating frequency of the dosing pump.

[0033] Specifically, the environmental data sensing module is equipped with a wireless sensor network, such as LoRaWAN or NB-IoT, to achieve wide-area coverage and low-power data transmission. Water quality parameters can be further refined into dissolved oxygen, chemical oxygen demand, biochemical oxygen demand, and other parameters. Water quantity data may be specific to initial rainwater flow and peak inflow flow. Data from the water supply and drainage network is collected in real time using pressure sensors and electromagnetic flowmeters installed at key nodes.

[0034] The multi-source data fusion and analysis module communicates with the environmental data perception module and cleans, standardizes, stores, and analyzes the real-time data collected by the module. This analysis includes data statistics, trend prediction, anomaly detection, and operational condition assessment. Based on the analysis results, it runs the sewage treatment operation control algorithm and the water resource scheduling optimization algorithm to generate a basis for optimization decisions. For example, the cleaning process may include removing data noise and filling missing values, while standardization may use Z-score or Min-Max normalization methods.

[0035] The process decision control module is connected to the multi-source data fusion and analysis module to generate intelligent control instructions for the port sewage treatment facilities and water resource scheduling based on the optimization decision basis; among them, the port sewage treatment facilities include lift pumps and dosing systems; water resource scheduling includes spray systems and water supply valves.

[0036] The intelligent execution and feedback module is connected to the process decision control module to receive and execute intelligent control instructions, directly control the port sewage treatment facilities and related water-using equipment through the industrial control interface, and provide real-time feedback of execution results to the multi-source data fusion and analysis module.

[0037] The environmental data perception module further includes: Water quality sensors, flow meters, liquid level meters, dust concentration sensors, meteorological sensors and operation status sensors of various equipment installed at multiple monitoring points in the port area are used to collect water quality parameters, water volume data, operation status parameters, as well as dust concentration and meteorological data.

[0038] The process decision control module further includes: an intelligent start-stop control unit for the sewage lift pump, an intelligent coagulation and dosing control unit, and a water supply and drainage network leakage assessment and positioning unit, which are used to optimize the start-stop control of the lift pump based on real-time monitoring data, accurately adjust the dosing amount, and assess and locate leakage points in the water supply and drainage network.

[0039] The intelligent execution and feedback module provides data visualization and supports user interaction functions. The data visualization and interactive functions include presenting real-time monitoring data, historical data query, report generation, abnormal alarm, system configuration to users, and allowing users to perform related operations.

[0040] like Figure 2 As shown, this embodiment provides a method for intelligent control of port sewage treatment and efficient utilization of water resources, including the following steps: In step S1, sensors are deployed at key monitoring points within the port area to obtain real-time water quality parameters, water volume data, operating status parameters of related equipment, and water pressure and flow data of the port water supply and drainage network during the port sewage treatment process, and transmit the data to the data processing platform.

[0041] For example, multi-parameter water quality probes, electromagnetic flow meters, ultrasonic level gauges, and pressure transmitters can be deployed. Data transmission can be achieved through 5G, optical fiber, or industrial Ethernet, and real-time data can be aggregated to a central data processing platform located in the port control center or in the cloud.

[0042] Step S2: Clean, standardize, store and deeply analyze the port environmental factor data collected in real time. The deep analysis is based on big data processing, including data statistics, trend prediction, anomaly detection and operation condition evaluation. Based on the analysis results, by running the sewage treatment operation control algorithm and the water resource scheduling optimization algorithm, intelligent optimization instructions for the port sewage treatment facilities and water resource scheduling are dynamically generated. The generation of instructions is adjusted based on the comparison of real-time data and preset judgment criteria.

[0043] Step S3, receiving and executing intelligent optimization instructions, and directly adjusting the operating status of the port sewage treatment facilities through the automated control system. The operating status includes intelligent start and stop of the booster pump and optimization of the coagulation and dosing process; water resources are allocated through intelligent optimization instructions, and water resource allocation includes the linkage between dust monitoring and recycled water supply.

[0044] For example, booster pump start-up and shutdown can be optimized through a pump group patrol strategy based on real-time inflow and liquid level predictions to achieve energy consumption optimization. Coagulation dosing processes can utilize an online Zeta potential meter or turbidity meter, combined with machine learning models, to adjust the dosage in real time to achieve optimal flocculation results. Specifically, the linkage between dust monitoring and reclaimed water supply can intelligently adjust the water volume and frequency of the reclaimed water spray system based on real-time dust concentration data, thereby achieving precise dust suppression and water conservation.

[0045] The port environmental data collected in real time during step S1 includes: water quality parameters, including turbidity, total dissolved solids concentration, pH, ammonia nitrogen, chemical oxygen demand, total nitrogen, total phosphorus, and total iron; water volume data, including sewage inlet and outlet flow rates, and the liquid levels of various water tanks; equipment operating status parameters, including the start and stop status of lift pumps and the operating frequency of dosing pumps; and water pressure and flow data for the port's water supply and drainage network. Specifically, these data points are collected every five minutes or less and uploaded via industrial Ethernet or wireless transmission to ensure the real-time and integrity of the data chain.

[0046] like Figure 3 The figure shows an exemplary flow chart of steps for generating a self-energy decision instruction in step S2 of this embodiment, including: Step S201 cleans, standardizes, and stores the collected port environmental factor data. For example, the cleaning process may identify and remove abnormal peaks or valleys caused by sensor failures. Standardization unifies data of different dimensions into comparable ranges, such as mapping pH and turbidity to a range of 0-1. Storage may utilize a distributed file system such as HDFS or cloud storage services to ensure data security and scalability.

[0047] Step S202 , performing statistical analysis, trend prediction, and anomaly detection on the cleaned data to obtain the real-time status of various environmental factors.

[0048] Step S203: Run the sewage treatment operation control algorithm, compare the real-time status with the preset water quality target, water quantity target or equipment operating condition threshold, and dynamically generate intelligent optimization instructions for the sewage treatment facility based on the comparison results. The intelligent optimization instructions are one or more of equipment start and stop instructions, operation frequency adjustment instructions or process parameter adjustment instructions.

[0049] For example, preset water quality targets might be COD < 30 mg / L, ammonia nitrogen < 5 mg / L, and effluent turbidity < 5 NTU. A water volume target might be to achieve a treatment capacity exceeding 80% of the design load. Equipment operating condition thresholds might include pump current not exceeding 1.2 times the rated value and dosing pump frequency between 10 and 50 Hz. Specific instructions might include "Stop pump #3 at pump station," "Adjust blower frequency to 40 Hz," or "Adjust reflux ratio to 120%."

[0050] Step S204: Run the water resource scheduling optimization algorithm, compare the real-time status with the preset water demand standard, water quality requirements or available water indicators, and dynamically generate intelligent optimization instructions for water resource scheduling based on the comparison results. The intelligent optimization instructions are one or more of multi-source water allocation instructions, water supply path optimization instructions or recycled water utilization priority adjustment instructions.

[0051] The functional relationship of the generation of intelligent optimization instructions is expressed as: ,in, Represents the intelligent optimization instruction vector, which contains all specific instructions for sewage treatment facilities and water resource scheduling; Represents the intelligent optimization decision function, which learns and optimizes based on historical operation data, system model and preset control strategy, and selects the sub-algorithm optimization strategy; A parameter vector representing the element data to be collected; Represents the preset target and threshold parameter vector; Represents input parameters and decision context information, including patterns learned from historical data, predicted values ​​of real-time conditions, or intermediate variables generated during algorithm execution.

[0052] For example, The function can be a deep reinforcement learning based model that learns by continuously interacting with the environment to minimize operating costs and energy consumption while meeting processing standards. May include real-time COD, water volume, pump current, etc.; This may include effluent COD target, pump maximum current threshold, etc. It may include historical water quality fluctuation patterns, rainfall forecasts for the next 24 hours, etc.

[0053] The sub-algorithm optimization strategy determines the type of algorithm to be executed based on the input parameter type and / or decision context of the wastewater treatment operation control algorithm or water resource scheduling optimization algorithm being executed. For example, if the decision context is responding to a sudden water pollution incident, a rule-based emergency scheduling algorithm may be triggered to quickly initiate emergency response measures; if the decision context is to optimize energy consumption during daily operations, a model-based predictive control algorithm may be selected to smoothly and efficiently adjust operating parameters.

[0054] like Figure 4 The figure shows an exemplary step of comprehensively allocating water resources in step S3 of this embodiment, including: Step S301: monitor the dust concentration in the port yard and surrounding areas in real time; intelligently calculate the amount of water required for dust suppression in the yard at the current moment based on the dust concentration, meteorological conditions, and yard operation conditions, and calculate the amount of water required for dust suppression based on the dust suppression water demand.

[0055] For example, a network of laser dust sensors can be deployed to monitor PM10 and PM2.5 concentrations at the yard boundary and work area in real time. Specifically, a calculation model can be based on empirical formulas or machine learning models, combining real-time dust concentration, wind speed, humidity, and work intensity to predict the optimal dust suppression water usage. For example, "When the wind speed reaches 5 meters per second and the PM10 concentration exceeds 100 micrograms per cubic meter, 10 cubic meters of dust suppression water is required per hour."

[0056] Step S302: Water demand includes water for production operations, domestic use, landscaping, road washing, and dust suppression; and the available water quantity and water quality of the treated recycled water are evaluated.

[0057] Specifically, production water may include ship tank washing water and equipment cooling water; domestic water refers to daily water use by port personnel; landscaping water is used for landscape plant irrigation; and road flushing water is used for road cleaning. Recycled water availability can be assessed by monitoring the recycle tank level and online monitoring of recycle water effluent quality (such as turbidity, COD, and total coliform bacteria) to ensure that it meets the appropriate reuse standards.

[0058] Step S303, based on water demand, recycled water supply capacity, meteorological conditions and the liquid level of water storage facilities, intelligently allocate municipal water, rainwater and recycled water through optimizing the scheduling algorithm.

[0059] For example, the optimal scheduling algorithm can use linear programming or multi-objective optimization algorithms, comprehensively considering factors such as water supply costs, water quality requirements, and environmental benefits, to intelligently adjust the proportion of municipal water, rainwater, and recycled water. Specific allocation strategies may include: during the rainy season, the system prioritizes the use of collected rainwater for landscaping irrigation, reducing municipal water consumption; when recycled water is sufficient and dust concentrations are high, it prioritizes recycled water for dust suppression; when recycled water is insufficient or water quality does not meet standards, it supplements with municipal tap water, taking into account the liquid level of water storage facilities to ensure water supply continuity.

[0060] In step S304, the calculated dust suppression water demand is sent to the water resource integrated scheduling optimization module in real time, and the intelligent scheduling of the recycled water supply is triggered.

[0061] The above contents are merely examples and explanations of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in similar ways. As long as they do not deviate from the scope defined by the invention, they should all fall within the scope of protection of the present invention.

Claims

1. A port sewage treatment intelligent control and water resource efficient utilization system, characterized by: It includes an environmental data sensing module, which is set in the port area and is used to collect water quality parameters, water volume data and operating status parameters of related equipment in the port sewage treatment process in real time, and simultaneously collect water pressure and flow data of the port water supply and drainage network; the water volume data includes sewage inlet flow, water outlet flow and liquid levels of various water tanks; the operating status parameters include the start and stop status of the lifting pump and the operating frequency of the dosing pump; A multi-source data fusion and analysis module, in communication with the environmental data perception module, is used to clean, standardize, store and analyze the real-time data collected by the environmental data perception module, including data statistics, trend prediction, anomaly detection and operating condition evaluation; Based on the analysis results, the sewage treatment operation control algorithm and water resource scheduling optimization algorithm are run to generate the basis for optimization decision-making; a process decision control module, in communication with the multi-source data fusion and analysis module, for generating intelligent control instructions for the port sewage treatment facility and water resource scheduling based on the optimization decision basis; wherein the port sewage treatment facility includes a lift pump and a dosing system; and the water resource scheduling includes a spray system and a water supply valve; The intelligent execution and feedback module is in communication with the process decision control module, and is used to receive and execute the intelligent control instructions, directly control the port sewage treatment facilities and related water-using equipment through the industrial control interface, and provide real-time feedback of the execution results to the multi-source data fusion and analysis module.

2. The intelligent control system for port sewage treatment and efficient water resource utilization according to claim 1 is characterized by: The environmental data perception module further includes: Water quality sensors, flow meters, liquid level meters, dust concentration sensors, meteorological sensors and operation status sensors of various equipment are set up at multiple monitoring points in the port area to collect the water quality parameters, the water volume data, the operation status parameters, as well as dust concentration and meteorological data.

3. The intelligent control system for port sewage treatment and efficient water resource utilization according to claim 1 is characterized by: The process decision control module further includes an intelligent start-stop control unit for the sewage lift pump, an intelligent coagulation and dosing control unit, and a water supply and drainage network leakage assessment and positioning unit, which are used to optimize the start-stop control of the lift pump based on real-time monitoring data, accurately adjust the dosing amount, and assess and locate the leakage points of the water supply and drainage network.

4. The intelligent control system for port sewage treatment and efficient water resource utilization according to claim 1 is characterized by: The intelligent execution and feedback module provides visualization data and supports user interaction functions. The visualization data and interactive functions include presenting real-time monitoring data, historical data query, report generation, abnormal alarm, system configuration to users, and allowing users to perform related operations.

5. A method for intelligent control of port sewage treatment and efficient utilization of water resources, characterized in that: The following steps are involved: Step S1: Sensors deployed at key monitoring points within the port area are used to obtain real-time data on water quality parameters, water volume, operating status parameters of related equipment, and water pressure and flow rate data of the port's water supply and drainage network during the port's sewage treatment process, and transmit the data to a data processing platform. Step S2: Cleaning, standardizing, storing, and deeply analyzing the port environmental factor data collected in real time. The deeply analyzing is based on big data processing and includes data statistics, trend prediction, anomaly detection, and operating condition evaluation. Based on the analysis results, intelligent optimization instructions for port sewage treatment facilities and water resource scheduling are dynamically generated by running a sewage treatment operation control algorithm and a water resource scheduling optimization algorithm. The generation of the instructions is adjusted based on comparing the real-time data with preset judgment criteria. Step S3, receiving and executing the intelligent optimization instructions, directly adjusting the operating status of the port sewage treatment facilities through the automated control system, the operating status including intelligent start and stop of the lifting pump and optimization of the coagulation and dosing process; allocating water resources through the intelligent optimization instructions, including the linkage between dust monitoring and recycled water supply.

6. The method for intelligent control of port sewage treatment and efficient utilization of water resources according to claim 5, characterized in that: The port environmental factor data collected in real time in step S1 include: water quality parameters, including turbidity, total dissolved solids concentration, pH value, ammonia nitrogen, chemical oxygen demand, total nitrogen, total phosphorus and total iron; water quantity data, including sewage inlet flow, outlet flow and liquid levels of various water tanks; equipment operating status parameters, including the start and stop status of the lifting pump and the operating frequency of the dosing pump; and water pressure and flow data of the port area water supply and drainage network.

7. The method for intelligent control of port sewage treatment and efficient utilization of water resources according to claim 5, characterized in that: The step S2 includes: Step S201, cleaning, standardizing and storing the collected port environmental factor data; Step S202: performing statistical analysis, trend prediction, and anomaly detection on the cleaned data to obtain the real-time status of various environmental factors; Step S203: Running the sewage treatment operation control algorithm to compare the real-time status with preset water quality targets, water quantity targets, or equipment operating condition thresholds. Based on the comparison results, intelligent optimization instructions for the sewage treatment facility are dynamically generated. The intelligent optimization instructions may be one or more of equipment start / stop instructions, operating frequency adjustment instructions, or process parameter adjustment instructions. Step S204, running the water resource scheduling optimization algorithm, by comparing the real-time status with the preset water demand standard, water quality requirements or available water indicators, dynamically generating intelligent optimization instructions for water resource scheduling based on the comparison results, the intelligent optimization instructions are one or more of multi-source water allocation instructions, water supply path optimization instructions or recycled water utilization priority adjustment instructions.

8. The method for intelligent control of port sewage treatment and efficient utilization of water resources according to claim 7, characterized in that: The functional relationship of the generation of the intelligent optimization instruction is expressed as: ,in, Represents the intelligent optimization instruction vector, which contains all specific instructions for sewage treatment facilities and water resource scheduling; Represents the intelligent optimization decision function, which learns and optimizes based on historical operation data, system model and preset control strategy, and selects the sub-algorithm optimization strategy; A parameter vector representing the element data to be collected; Represents the preset target and threshold parameter vector; Represents input parameters and decision context information, including patterns learned from historical data, predicted values ​​of real-time conditions, or intermediate variables generated during algorithm execution.

9. The method for intelligent control of port sewage treatment and efficient utilization of water resources according to claim 8, characterized in that: The sub-algorithm optimization strategy determines the type of algorithm actually executed according to the type of input parameters and / or decision context of the sewage treatment operation control algorithm or water resource scheduling optimization algorithm being executed.

10. The method for intelligent control of port sewage treatment and efficient utilization of water resources according to claim 5, characterized in that: The comprehensive allocation of water resources in step S3 includes Step S301: Real-time monitoring of dust concentration in the port yard and surrounding areas; intelligent calculation of the dust suppression water demand required at the yard at the current moment based on the dust concentration, meteorological conditions, and yard operation conditions; Step S302: The water demand includes water for production operations, domestic use, landscaping, road washing, and dust suppression; and the available water quantity and water quality of the treated recycled water are evaluated. Step S303, based on the water demand, the recycled water supply capacity, meteorological conditions and the liquid level of the water storage facility, intelligently allocate municipal water, rainwater and recycled water through an optimized scheduling algorithm; Step S304: Send the calculated dust suppression water demand to the water resources comprehensive scheduling optimization module in real time, and trigger the intelligent scheduling of recycled water supply.

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