Urban water environment whole-process intelligent monitoring and management and control system using Internet of Things

Abstract

The invention discloses an urban water environment whole-process intelligent monitoring and management and control system using the Internet of Things. According to the invention, through the cooperative work of the micro water turbine power generation unit and the flexible photovoltaic thin film assembly, the energy self-sufficiency of the system in a complex environment is improved, and the dependence on external power supply is reduced. And the intelligent energy management chip is matched with the super capacitor energy storage module, so that the stability of energy storage and distribution is enhanced, and the continuous operation time of equipment is prolonged. Through a dynamic routing protocol and a node health degree diagnosis function in the self-organizing sensing network module, the reliability of data transmission is improved, network interruption caused by a fault of a single node is reduced, meanwhile, the network coverage range and the communication efficiency are enhanced through a self-adaptive data forwarding mechanism, and the reliability of data transmission is improved. An electric adjusting gate assembly and an intelligent chemical dosing device in the execution unit module improve the automation degree of water environment treatment, the hysteresis of manual operation is reduced, and the risk of illegal operation and equipment failure is reduced.

Description

Technical Field

[0001] This invention belongs to the field of water environment management technology, specifically a smart monitoring and management system for the entire process of urban water environment using the Internet of Things. Background Technology

[0002] Urban water environment monitoring is a crucial means of understanding water quality, providing early warnings of pollution risks, and ensuring water ecological security by systematically detecting and evaluating the physical, chemical, and biological indicators of urban surface water, groundwater, drinking water, and wastewater treatment systems. Monitoring indicators include pH, dissolved oxygen, ammonia nitrogen, total phosphorus, heavy metal content, and microbial communities. Data collection and analysis are typically achieved through a combination of online monitoring equipment, remote sensing technology, and laboratory analysis. Modern monitoring systems rely on the Internet of Things, big data, and artificial intelligence technologies to construct real-time monitoring networks with functions such as pollution source tracing, trend prediction, and intelligent early warning. These systems provide a scientific basis for urban water resource management, water pollution control, and ecological protection, serving as a vital support for sustainable urban development and ecological civilization construction.

[0003] However, existing technologies rely on external power sources for energy supply, resulting in limited equipment deployment and high maintenance costs; energy storage and distribution are unstable, often leading to operational interruptions. Data transmission is susceptible to environmental interference, network coverage has blind spots, and a single node failure can cause overall communication paralysis. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent monitoring and control system for the entire process of urban water environment using the Internet of Things (IoT) in order to solve the problems mentioned above.

[0005] The technical solution adopted in this invention is as follows: an intelligent monitoring and control system for the entire process of urban water environment using the Internet of Things, comprising: an intelligent sensing layer module, a heterogeneous network transmission module, a cloud big data platform module, an intelligent decision support module, a linkage control execution module, and an energy and sensing collaborative optimization module; The energy and sensing collaborative optimization module internally includes: an energy harvesting module, an intelligent power consumption management module, and a self-organizing sensor network module. The linkage control execution module is internally equipped with an execution unit module and a remote control module; The output of the data preprocessing and calibration unit of the intelligent sensing layer module is connected to the input of the LoRa / NB-IoT dual-mode communication unit of the heterogeneous network transmission module. The output of the industrial Ethernet switch of the heterogeneous network transmission module is connected to the input of the distributed data storage unit of the cloud big data platform module, and the output of the machine learning analysis unit of the cloud big data platform module is connected to the input of the water quality anomaly diagnosis unit of the intelligent decision support module. The output of the decision execution scheduling unit of the intelligent decision support module is connected to the input of the PLC controller of the linkage control execution module. The output of the status feedback sensor array of the linkage control execution module is transmitted back to the real-time stream processing engine unit of the cloud big data platform module through the industrial Ethernet switch of the remote control module. The output of the energy acquisition module of the energy and perception collaborative optimization module is connected to the input of the intelligent power consumption management module. The output of the intelligent power management module is connected to the positioning and time synchronization unit of the intelligent sensing layer module, the power management unit of the heterogeneous network transmission module, and the power management unit of the linkage control execution module. The self-organizing sensor network module and the data forwarding and routing unit of the heterogeneous network transmission module communicate bidirectionally.

[0006] In a preferred embodiment, the intelligent sensing layer module internally includes a water quality multi-parameter sensing unit, an image acquisition and recognition unit, a data preprocessing and calibration unit, and a positioning and time synchronization unit. The water quality multi-parameter sensing unit integrates a pH electrode, a dissolved oxygen sensor, and a turbidity detector. The pH electrode employs a glass membrane-reference electrode composite structure, the dissolved oxygen sensor is based on the fluorescence quenching principle, and turbidity detection uses a 90-degree scattered light measurement method. The image acquisition and recognition unit is equipped with a 2-megapixel industrial camera with an 8mm lens focal length and a built-in infrared cutoff filter. A matching image recognition algorithm can label floating objects on the water surface in real time. The data preprocessing and calibration unit includes a signal amplification circuit and a temperature compensation module, employs the least squares method for linear calibration, and communicates with the main controller via an I2C bus. The positioning and time synchronization unit integrates a BeiDou / GPS dual-mode receiver and an RTC real-time clock chip, achieving a positioning accuracy of 1 meter and supporting second-level time synchronization.

[0007] In a preferred embodiment, the heterogeneous network transmission module internally includes a LoRa / NB-IoT dual-mode communication unit, an Ethernet access unit, a data forwarding and routing unit, and a transmission quality monitoring unit. The LoRa / NB-IoT dual-mode communication unit uses an SX1278 RF chip, supports an adjustable spreading factor of SF7-SF12, and has a maximum transmit power of 23dBm in NB-IoT mode. The Ethernet access unit is equipped with a gigabit Ethernet port, supports the IEEE 802.3 standard, and integrates a network transformer and ESD protection circuit. The data forwarding and routing unit runs the AODV routing protocol, maintains a dynamic routing table, supports packet fragmentation and reassembly, and has a forwarding delay ≤50ms. The transmission quality monitoring unit collects RSSI, signal-to-noise ratio, and packet loss rate in real time. When the link quality falls below a threshold, a switching mechanism is triggered, prioritizing channels with a signal-to-noise ratio higher than 15dB.

[0008] In a preferred embodiment, the cloud-based big data platform module internally includes a distributed data storage unit, a real-time stream processing engine unit, a machine learning analysis unit, and a visualization unit. The distributed data storage unit uses the HDFS file system with a block size of 128MB, 3 replicas, and supports petabyte-level data storage. The real-time stream processing engine unit is based on the Flink framework, with dynamically adjustable parallelism, data processing latency ≤200ms, and supports window sliding computation. The machine learning analysis unit deploys random forest and LSTM models for water quality trend prediction and anomaly detection, with a model training cycle of 24 hours / cycle. The visualization unit provides line charts, heatmaps, and dashboard components, supports data drill-down and time range filtering, and refreshes every minute.

[0009] In a preferred embodiment, the intelligent decision support module internally includes a water quality anomaly diagnosis unit, a multi-objective optimization decision unit, a knowledge base management unit, and a decision execution scheduling unit. The water quality anomaly diagnosis unit employs a Bayesian network model, taking five types of parameters (dissolved oxygen, pH, etc.) as input and outputting a fault probability distribution, achieving a diagnostic accuracy of ≥92%. The multi-objective optimization decision unit is based on the NSGA-III algorithm, with optimization objectives including treatment cost, energy consumption, and effectiveness, and decision variables including reagent dosage and equipment operating parameters. The knowledge base management unit stores a water quality treatment rule base, using production rule representation, and supports rule creation, deletion, modification, retrieval, and version control. The decision execution scheduling unit issues control commands via the MQTT protocol, supports priority queue management, and has an emergency command response time ≤1 second.

[0010] In a preferred embodiment, the energy harvesting module internally comprises a micro-turbine power generation unit, a flexible photovoltaic thin-film module, a supercapacitor energy storage module, and an intelligent energy management chip. The micro-turbine power generation unit adopts an axial-flow impeller structure with a 120mm impeller diameter, is equipped with a permanent magnet synchronous generator, uses a delta connection for the stator winding, has a rotor with built-in 24-pole neodymium iron boron magnets, and features a honeycomb rectifier grid at the inlet. The flexible photovoltaic thin-film module uses cadmium telluride thin-film material, covered with a polyimide protective layer, with 36 cells connected in series, and integrates a reverse-charging diode at the output. The supercapacitor energy storage module contains four 500F / 2.7V capacitors in a two-series, two-parallel topology, with 16AWG silicone wire for the positive and negative leads, and an aluminum heat dissipation substrate at the bottom of the module. The intelligent energy management chip integrates an MPPT controller, supports PWM dimming, integrates a voltage sampling circuit and overcharge / over-discharge protection module, and communicates with the main controller via an I2C interface.

[0011] In a preferred embodiment, the intelligent power management module achieves dynamic power regulation by constructing a three-dimensional decision model of priority-demand-energy. The system divides the node operating state into three basic tasks: data transmission (P1), sampling (P2), and standby (P3), with priority weights set to ω1=0.6, ω2=0.3, and ω3=0.1, respectively, satisfying ω1+ω2+ω3=1.

[0012] Real-time power allocation employs an adaptive coefficient adjustment mechanism: when the node's remaining energy E t ≥E th When E is at that time, power is allocated according to the basic weight; when E is at that time, power is allocated according to the basic weight. t <E th When this occurs, the power-saving mode is triggered, which optimizes energy consumption by dynamically compressing the power consumption of low-priority tasks.

[0013] Specifically, the sampling task power P2' = P2×(E t / E th )×u, where u is the urgency of the sampling task (determined by the water quality gradient change rate ΔC / Δt; u=1.2 when ΔC>th threshold, otherwise u=0.8); standby power P3' = P3×(1 -E t / E th It maintains sensory sensitivity by reducing the sleep-wake interval (from the default 5 minutes to 1 minute).

[0014] The formula for calculating the dynamic power allocation factor is: ; In the formula: αi represents the power allocation coefficient for the i-th type of task (i=1,2,3 correspond to transmission / sampling / standby). ωi represents the task priority weight (ω1=0.6, ω2=0.3, ω3=0.1); Et represents the node's current remaining energy (unit: J); Eth represents the energy threshold (default value is 20% of total energy). ΔCi / Δt: The rate of change of the parameter associated with the i-th task (unit: mg·L⁻¹·min⁻¹); τ represents the sensitivity adjustment factor (default 0.05 min·L·mg⁻¹, which can be calibrated remotely via the cloud). This formula achieves adaptive power allocation by integrating energy state, task priority, and dynamic environmental characteristics. When water quality parameters change abruptly (e.g., ΔC_i / Δt increases), αi automatically increases to ensure sufficient power for high-priority tasks (e.g., abnormal data transmission); when energy is insufficient, E... t / E thThis approach compresses the power consumption of low-priority tasks, resolving the "energy consumption-response" contradiction inherent in traditional static allocation.

[0015] In a preferred embodiment, the self-organizing sensor network module internally includes a dynamic routing protocol processing unit, a node health diagnosis unit, a distributed clock synchronization unit, and an adaptive data forwarding unit. The dynamic routing protocol processing unit stores a routing table optimized by the ant colony algorithm, with entries including the destination node address, hop count, link quality indicator, and remaining energy value, and updates the routing information using periodic flooding. The node health diagnosis unit monitors the power supply voltage, RF module temperature, and communication packet loss rate in real time, sets three-level warning thresholds, and determines the node's operating status using a state machine model. The distributed clock synchronization unit is based on the IEEE 1588 PTP protocol, with the master node sending synchronization messages at 2-second intervals, and slave nodes using phase-locked loops to calibrate their local crystal oscillator frequencies. The adaptive data forwarding unit dynamically adjusts the forwarding strategy based on link quality; when the received signal strength indicator is greater than -75dBm, it uses direct transmission mode; otherwise, it activates a multi-hop relay mechanism, prioritizing nodes with remaining energy above 30% as relays.

[0016] In a preferred embodiment, the execution unit module internally houses an electrically adjustable gate assembly, an intelligent dosing device, an emergency diversion pump set, and a status feedback sensor array. The electrically adjustable gate assembly comprises a DC brushless motor, a planetary gear reducer, and a stainless steel gate plate. The motor has a rated voltage of 24V, the reducer has a transmission ratio of 1:120, the gate plate is made of 304 stainless steel, and the seals are made of EPDM rubber. The intelligent dosing device consists of a drug storage tank, a diaphragm metering pump, a Y-type filter, and a static mixer. The storage tank has a volume of 50L, the metering pump has a flow rate adjustment range of 0-20L / h, and the mixer has built-in spiral guide vanes. The emergency diversion pump set is equipped with a horizontal centrifugal pump with a semi-open impeller, DN100 inlet and outlet pipe diameters, and a silicon carbide mechanical seal on the pump shaft, along with a soft-start control cabinet. The status feedback sensor array includes a gate position encoder, a flow sensor, and a pressure transmitter. The encoder has a resolution of 1024 lines, the flow sensor uses electromagnetic induction, and the pressure transmitter has a measurement range of 0-1.6MPa.

[0017] In a preferred embodiment, the remote control module internally includes a PLC controller, an industrial Ethernet switch, a power management unit, and safety protection components. The PLC controller adopts a modular structure, configured with a CPU module, digital I / O modules, and analog input modules, providing 32 digital output points and 16-bit analog input resolution. The industrial Ethernet switch offers 8 Gigabit Ethernet ports and 2 SFP optical ports, supports ring network redundancy protocols, and has a 1MB port buffer. The power management unit includes a switching power supply, a UPS battery, and a surge protector. The switching power supply outputs 24V / 5A, the battery capacity is 12V / 7Ah, and the surge protector has a nominal discharge current of 20kA. The safety protection components consist of an electromagnetic lock, an infrared beam detector, and a cabinet door status switch. The electromagnetic lock operates at 12V, the detector has a detection range of 5m, and the status switch is a normally closed contact.

[0018] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. In this invention, the collaborative operation of the micro-turbine power generation unit and the flexible photovoltaic thin-film module enhances the system's energy self-sufficiency in complex environments and reduces dependence on external power supply. The cooperation between the intelligent energy management chip and the supercapacitor energy storage module strengthens the stability of energy storage and distribution, extending the continuous operating time of the equipment. The dynamic routing protocol and node health diagnosis function in the self-organizing sensor network module improve the reliability of data transmission, reduce network interruptions caused by single node failures, and the adaptive data forwarding mechanism enhances network coverage and communication efficiency.

[0019] 2. In this invention, the electric regulating gate assembly and intelligent dosing device in the execution unit module improve the automation level of water environment management and reduce the lag of manual operation. The combination of the emergency diversion pump set and the status feedback sensor array enhances the rapid response capability in abnormal situations, ensuring that control measures are implemented in a timely manner. The PLC controller and industrial Ethernet switch in the remote control module improve the transmission speed and accuracy of control commands, while the safety protection components enhance the safety of system operation and reduce the risk of illegal operation and equipment failure. Attached Figure Description

[0020] Figure 1 This is an overall system block diagram of the present invention; Figure 2 This is a system block diagram of the energy and sensing collaborative optimization module in this invention; Figure 3 This is a system block diagram of the linkage control and execution module in this invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0022] Reference Figure 1-3 A smart monitoring and control system for the entire process of urban water environment using the Internet of Things includes: a smart sensing layer module, a heterogeneous network transmission module, a cloud big data platform module, a smart decision support module, a linkage control execution module, and an energy and sensing collaborative optimization module; The internal components of the energy and sensing collaborative optimization module include: an energy harvesting module, an intelligent power consumption management module, and a self-organizing sensor network module. The linkage control execution module internally includes an execution unit module and a remote control module; The output of the data preprocessing and calibration unit of the intelligent sensing layer module is connected to the input of the LoRa / NB-IoT dual-mode communication unit of the heterogeneous network transmission module. The output of the industrial Ethernet switch of the heterogeneous network transmission module is connected to the input of the distributed data storage unit of the cloud big data platform module, and the output of the machine learning analysis unit of the cloud big data platform module is connected to the input of the water quality anomaly diagnosis unit of the intelligent decision support module. The output of the decision execution scheduling unit of the intelligent decision support module is connected to the input of the PLC controller of the linkage control execution module. The output of the status feedback sensor array of the linkage control execution module is transmitted back to the real-time stream processing engine unit of the cloud big data platform module through the industrial Ethernet switch of the remote control module. The output of the energy acquisition module of the energy and perception collaborative optimization module is connected to the input of the intelligent power consumption management module. The output of the intelligent power management module is connected to the positioning and time synchronization unit of the intelligent sensing layer module, the power management unit of the heterogeneous network transmission module, and the power management unit of the linkage control execution module. The self-organizing sensor network module and the data forwarding and routing unit of the heterogeneous network transmission module communicate bidirectionally.

[0023] The intelligent sensing layer module internally includes a multi-parameter water quality sensing unit, an image acquisition and recognition unit, a data preprocessing and calibration unit, and a positioning and time synchronization unit. The multi-parameter water quality sensing unit integrates a pH electrode, a dissolved oxygen sensor, and a turbidity detector. The pH electrode uses a glass membrane-reference electrode composite structure, the dissolved oxygen sensor is based on the fluorescence quenching principle, and turbidity detection uses a 90-degree scattered light measurement method. The image acquisition and recognition unit is equipped with a 2-megapixel industrial camera with an 8mm lens focal length and a built-in infrared cutoff filter. Its accompanying image recognition algorithm can annotate floating objects on the water surface in real time. The data preprocessing and calibration unit includes a signal amplification circuit and a temperature compensation module, employing the least squares method for linear calibration and communicating with the main controller via an I2C bus. The positioning and time synchronization unit integrates a Beidou / GPS dual-mode receiver and an RTC real-time clock chip, achieving positioning accuracy up to 1 meter and supporting second-level time synchronization.

[0024] The heterogeneous network transmission module internally includes a LoRa / NB-IoT dual-mode communication unit, an Ethernet access unit, a data forwarding and routing unit, and a transmission quality monitoring unit. The LoRa / NB-IoT dual-mode communication unit uses the SX1278 RF chip, supports an adjustable spreading factor of SF7-SF12, and has a maximum transmit power of 23dBm in NB-IoT mode. The Ethernet access unit is equipped with a gigabit port, supports the IEEE 802.3 standard, and integrates a network transformer and ESD protection circuit. The data forwarding and routing unit runs the AODV routing protocol, maintains a dynamic routing table, supports packet fragmentation and reassembly, and has a forwarding latency of ≤50ms. The transmission quality monitoring unit collects RSSI, signal-to-noise ratio, and packet loss rate in real time. When the link quality falls below a threshold, a switching mechanism is triggered, prioritizing channels with a signal-to-noise ratio higher than 15dB.

[0025] The cloud-based big data platform module internally comprises a distributed data storage unit, a real-time stream processing engine unit, a machine learning analysis unit, and a visualization unit. The distributed data storage unit uses the HDFS file system with a block size of 128MB, 3 replicas, and supports petabyte-level data storage. The real-time stream processing engine unit is based on the Flink framework, with dynamically adjustable parallelism, data processing latency ≤200ms, and supports sliding window computation. The machine learning analysis unit deploys random forest and LSTM models for water quality trend prediction and anomaly detection, with a model training cycle of 24 hours per iteration. The visualization unit provides line charts, heatmaps, and dashboard components, supports data drill-down and time range filtering, and refreshes every minute.

[0026] The intelligent decision support module internally comprises a water quality anomaly diagnosis unit, a multi-objective optimization decision unit, a knowledge base management unit, and a decision execution scheduling unit. The water quality anomaly diagnosis unit employs a Bayesian network model, taking five types of parameters (dissolved oxygen, pH, etc.) as input and outputting a fault probability distribution, achieving a diagnostic accuracy of ≥92%. The multi-objective optimization decision unit is based on the NSGA-III algorithm, with optimization objectives including treatment cost, energy consumption, and effectiveness, and decision variables including reagent dosage and equipment operating parameters. The knowledge base management unit stores a water quality treatment rule base, using production rule representation, and supports rule creation, deletion, modification, retrieval, and version control. The decision execution scheduling unit issues control commands via the MQTT protocol, supports priority queue management, and has an emergency command response time of ≤1 second.

[0027] The energy harvesting module internally houses a micro-turbine power generation unit, a flexible photovoltaic thin-film module, a supercapacitor energy storage module, and an intelligent energy management chip. The micro-turbine power generation unit employs an axial-flow impeller structure with a 120mm diameter impeller, equipped with a permanent magnet synchronous generator. The stator winding uses a delta connection, and the rotor incorporates 24-pole neodymium iron boron magnets. A honeycomb rectifier grid is installed at the inlet. The flexible photovoltaic thin-film module uses cadmium telluride thin-film material, covered with a polyimide protective layer. It contains 36 cells connected in series, and integrates a reverse-charging diode at the output. The supercapacitor energy storage module comprises four 500F / 2.7V capacitors in a two-series, two-parallel topology. The positive and negative leads use 16AWG silicone wire, and an aluminum heat sink is located at the bottom of the module. The intelligent energy management chip integrates an MPPT controller, supports PWM dimming, and includes a voltage sampling circuit and overcharge / over-discharge protection module. It communicates with the main controller via an I2C interface.

[0028] The intelligent power management module achieves dynamic power regulation by constructing a three-dimensional decision model of priority, demand, and energy. The system divides the node operating state into three basic tasks: data transmission (P1), sampling (P2), and standby (P3), with priority weights set to ω1=0.6, ω2=0.3, and ω3=0.1, respectively, satisfying ω1+ω2+ω3=1.

[0029] Real-time power allocation employs an adaptive coefficient adjustment mechanism: when the node's remaining energy E t ≥E th When E is at that time, power is allocated according to the basic weight; when E is at that time, power is allocated according to the basic weight. t <E th When this occurs, the power-saving mode is triggered, which optimizes energy consumption by dynamically compressing the power consumption of low-priority tasks.

[0030] Specifically, the sampling task power P2' = P2×(E t / E th)×u, where u is the urgency of the sampling task (determined by the water quality gradient change rate ΔC / Δt; u=1.2 when ΔC>th threshold, otherwise u=0.8); standby power P3' = P3×(1 -E t / E th It maintains sensory sensitivity by reducing the sleep-wake interval (from the default 5 minutes to 1 minute).

[0031] The formula for calculating the dynamic power allocation factor is: ; In the formula: αi represents the power allocation coefficient for the i-th type of task (i=1,2,3 correspond to transmission / sampling / standby). ωi represents the task priority weight (ω1=0.6, ω2=0.3, ω3=0.1); Et represents the node's current remaining energy (unit: J); Eth represents the energy threshold (default value is 20% of total energy). ΔCi / Δt: The rate of change of the parameter associated with the i-th task (unit: mg·L⁻¹·min⁻¹); τ represents the sensitivity adjustment factor (default 0.05 min·L·mg⁻¹, which can be calibrated remotely via the cloud).

[0032] This formula achieves adaptive power allocation by integrating energy state, task priority, and dynamic environmental characteristics. When water quality parameters change abruptly (e.g., ΔC_i / Δt increases), αi automatically increases to ensure sufficient power for high-priority tasks (e.g., abnormal data transmission); when energy is insufficient, E... t / E th This approach compresses the power consumption of low-priority tasks, resolving the "energy consumption-response" contradiction inherent in traditional static allocation.

[0033] The self-organizing sensor network module internally comprises a dynamic routing protocol processing unit, a node health diagnosis unit, a distributed clock synchronization unit, and an adaptive data forwarding unit. The dynamic routing protocol processing unit stores a routing table optimized by the ant colony algorithm, with entries including the destination node address, hop count, link quality indicator, and remaining energy value. It updates routing information using periodic flooding broadcasts. The node health diagnosis unit monitors power supply voltage, RF module temperature, and communication packet loss rate in real time, sets three-level warning thresholds, and determines node operating status using a state machine model. The distributed clock synchronization unit is based on the IEEE 1588 PTP protocol. The master node sends synchronization messages at 2-second intervals, and slave nodes use phase-locked loops to calibrate their local crystal oscillator frequencies. The adaptive data forwarding unit dynamically adjusts its forwarding strategy based on link quality. When the received signal strength indicator is greater than -75dBm, it uses direct transmission mode; otherwise, it activates a multi-hop relay mechanism, prioritizing nodes with remaining energy above 30% as relays.

[0034] The execution unit module internally houses an electrically adjustable gate assembly, an intelligent dosing device, an emergency diversion pump set, and a status feedback sensor array. The electrically adjustable gate assembly includes a DC brushless motor, a planetary gear reducer, and a stainless steel gate plate. The motor has a rated voltage of 24V, the reducer has a transmission ratio of 1:120, the gate plate is made of 304 stainless steel, and the seals are made of EPDM rubber. The intelligent dosing device consists of a reagent storage tank, a diaphragm metering pump, a Y-type filter, and a static mixer. The storage tank has a volume of 50L, the metering pump has a flow rate adjustment range of 0-20L / h, and the mixer has built-in spiral guide vanes. The emergency diversion pump set is equipped with a horizontal centrifugal pump with a semi-open impeller, DN100 inlet and outlet pipe diameters, and a silicon carbide mechanical seal on the pump shaft, along with a soft-start control cabinet. The status feedback sensor array includes a gate position encoder, a flow sensor, and a pressure transmitter. The encoder has a resolution of 1024 lines, the flow sensor uses electromagnetic induction, and the pressure transmitter has a measurement range of 0-1.6MPa.

[0035] The remote control module internally houses a PLC controller, an industrial Ethernet switch, a power management unit, and safety protection components. The PLC controller employs a modular structure, configured with a CPU module, digital I / O modules, and analog input modules, offering 32 digital output points and 16-bit analog input resolution. The industrial Ethernet switch provides 8 Gigabit Ethernet ports and 2 SFP optical ports, supporting ring network redundancy protocols and featuring 1MB port buffer. The power management unit includes a switching power supply, a UPS battery, and surge protectors. The switching power supply outputs 24V / 5A, the battery capacity is 12V / 7Ah, and the surge protector has a nominal discharge current of 20kA. The safety protection components consist of an electromagnetic lock, an infrared beam detector, and a cabinet door status switch. The electromagnetic lock operates at 12V, the detector has a detection range of 5m, and the status switch is a normally closed contact.

[0036] From the above, we can conclude that: In this invention, the collaborative operation of a micro-turbine power generation unit and a flexible photovoltaic thin-film module enhances the system's energy self-sufficiency in complex environments and reduces dependence on external power supply. The cooperation between the intelligent energy management chip and the supercapacitor energy storage module strengthens the stability of energy storage and distribution, extending the continuous operating time of the equipment. The dynamic routing protocol and node health diagnosis function in the self-organizing sensor network module improve the reliability of data transmission, reduce network interruptions caused by single node failures, and the adaptive data forwarding mechanism enhances network coverage and communication efficiency.

[0037] In this invention, the electrically adjustable gate assembly and intelligent dosing device in the execution unit module enhance the automation level of water environment management and reduce the lag of manual operation. The combination of the emergency diversion pump set and the status feedback sensor array strengthens the rapid response capability in abnormal situations, ensuring timely implementation of control measures. The PLC controller and industrial Ethernet switch in the remote control module improve the transmission speed and accuracy of control commands, while the safety protection components enhance the safety of system operation and reduce the risk of illegal operation and equipment failure.

[0038] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the term "comprising" or any other variations thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0039] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A smart monitoring and control system for the entire process of urban water environment using the Internet of Things, characterized in that: include: The module consists of an intelligent sensing layer, a heterogeneous network transmission module, a cloud-based big data platform module, an intelligent decision support module, a coordinated control and execution module, and an energy and sensing collaborative optimization module. The energy and sensing collaborative optimization module internally includes: an energy harvesting module, an intelligent power consumption management module, and a self-organizing sensor network module. The linkage control execution module is internally equipped with an execution unit module and a remote control module; The output of the data preprocessing and calibration unit of the intelligent sensing layer module is connected to the input of the LoRa / NB-IoT dual-mode communication unit of the heterogeneous network transmission module. The output of the industrial Ethernet switch of the heterogeneous network transmission module is connected to the input of the distributed data storage unit of the cloud big data platform module, and the output of the machine learning analysis unit of the cloud big data platform module is connected to the input of the water quality anomaly diagnosis unit of the intelligent decision support module. The output of the decision execution scheduling unit of the intelligent decision support module is connected to the input of the PLC controller of the linkage control execution module. The output of the status feedback sensor array of the linkage control execution module is transmitted back to the real-time stream processing engine unit of the cloud big data platform module through the industrial Ethernet switch of the remote control module. The output of the energy acquisition module of the energy and perception collaborative optimization module is connected to the input of the intelligent power consumption management module. The output of the intelligent power management module is connected to the positioning and time synchronization unit of the intelligent sensing layer module, the power management unit of the heterogeneous network transmission module, and the power management unit of the linkage control execution module. The self-organizing sensor network module and the data forwarding and routing unit of the heterogeneous network transmission module communicate bidirectionally.

2. The intelligent monitoring and control system for the entire process of urban water environment using the Internet of Things as described in claim 1, characterized in that: The intelligent sensing layer module is internally equipped with a water quality multi-parameter sensing unit, an image acquisition and recognition unit, a data preprocessing and calibration unit, and a positioning and time synchronization unit.

3. The intelligent monitoring and control system for the entire urban water environment using the Internet of Things as described in claim 1, characterized in that: The heterogeneous network transmission module is internally equipped with a LoRa / NB-IoT dual-mode communication unit, an Ethernet access unit, a data forwarding and routing unit, and a transmission quality monitoring unit.

4. The intelligent monitoring and control system for the entire process of urban water environment using the Internet of Things as described in claim 1, characterized in that: The cloud-based big data platform module is internally equipped with a distributed data storage unit, a real-time stream processing engine unit, a machine learning analysis unit, and a visualization unit.

5. The intelligent monitoring and control system for the entire process of urban water environment using the Internet of Things as described in claim 1, characterized in that: The intelligent decision support module is internally equipped with a water quality anomaly diagnosis unit, a multi-objective optimization decision unit, a knowledge base management unit, and a decision execution scheduling unit.

6. The intelligent monitoring and control system for the entire process of urban water environment using the Internet of Things as described in claim 1, characterized in that: The energy harvesting module is internally equipped with a micro-turbine power generation unit, a flexible photovoltaic thin-film module, a supercapacitor energy storage module, and an intelligent energy management chip.

7. The intelligent monitoring and control system for the entire process of urban water environment using the Internet of Things as described in claim 1, characterized in that: The formula for calculating the dynamic power allocation coefficient of the intelligent power consumption management module is as follows: ； In the formula: αi represents the power allocation coefficient for the i-th type of task; ωi represents the task priority weight; Et represents the current remaining energy of the node; Eth represents the energy threshold; ΔCi / Δt: The rate of change of the parameter associated with the i-th type of task; τ represents the sensitivity adjustment factor.

8. The intelligent monitoring and control system for the entire process of urban water environment using the Internet of Things as described in claim 1, characterized in that: The self-organizing sensor network module is internally equipped with a dynamic routing protocol processing unit, a node health diagnosis unit, a distributed clock synchronization unit, and an adaptive data forwarding unit.

9. The intelligent monitoring and control system for the entire process of urban water environment using the Internet of Things as described in claim 1, characterized in that: The execution unit module is internally equipped with an electric regulating gate assembly, an intelligent dosing device, an emergency diversion pump group, and a status feedback sensor array.

10. The intelligent monitoring and control system for the entire process of urban water environment using the Internet of Things as described in claim 1, characterized in that: The remote control module is internally equipped with a PLC controller, an industrial Ethernet switch, a power management unit, and safety protection components.

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